Reviewer #2 (Public Review):

I will first state that I am an ecologist that studies wind dispersal, and so my expertise lies in evaluating the determination of their results, the dispersal model, and the ecological significance - I cannot evaluate the PIV methods, although their results seem very reasonable based on other work! I believe this is a very strong paper that will have a lot of interest. In my opinion, it is the perfect demonstration of how to understand wind dispersal from a fundamental and ecological perspective. They first explore the aerodynamics of dandelion diaspores and how they change with the environment. Then they use this information to scale up to how this might affect dispersal across the landscape under different environmental conditions. I think the experiments they have conducted and the models they have included are excellent! I also really enjoyed that this work is the culmination of their body of work, where they have taken a step-by-step approach to convincing readers how dandelion diaspores disperse under different humidity conditions (see Seale et al. 2020 and 2022).

The paper is very well written. The introduction lays out a very clear case as to why the environment should (and ultimately does) influence wind dispersal, and all of the relevant references are cited. It was nice to see them all in one place and is a great summary of the literature for those who are new to the field.

The authors also claim that the environment can have an impact on dandelion dispersal by altering the shape of the diaspore (the pappus closes). This influences the terminal velocity and drag coefficient, and the authors used the appropriate PIV tests to determine that this is the case. They then go on to show that while wet conditions can decrease the terminal velocity which ultimately decreases dispersal distance, under wet/stormy conditions there are often increased wind speeds and this can actually increase dispersal because of increased wind speed. However this last point is a bit confusing to me based on the way the data is laid out.

In all, I really enjoyed this paper! There is a lot to learn from this, and I look forward to reading it in print. I would encourage the authors to make a few updates to their text to make their conclusions crystal clear for readers!

Reviewer #2 (Public Review):

The data presented in this manuscript provide evidence that, in the ventral spinal cord of zebrafish embryos, "sister" V2a excitatory and V2b inhibitory neurons, which arise from common vsx1+ progenitors, extend descending, ipsilateral axons that, although differing in length, remain close to one another. Because of this alignment, V2a and V2b neurons could contribute to a common microcircuit, by receiving inputs from common synaptic circuits, forming synapses on one another, or projecting to common synaptic targets. However, a series of electrophysiological and optogenetic tests exclude these possibilities and indicate that, instead, they receive inputs from distinct sources, they do not engage in synaptic signaling with one another, and they have distinct, downstream synaptic targets. This differs from the mouse cortex, in which clonally related neurons appear to preferentially form connections within a shared microcircuit.

The chief strengths of this work include the imaging data, which nicely reveal the locations and morphologies of sister V2a/b neurons, and the electrophysiological experiments, which provide compelling evidence that sister V2a/b neurons do not function within a shared microcircuit.

The study does have some limitations. First, V2a/V2b sister pairs are not obligate. Instead, about 25% of V2b neurons arise from a vsx1+ progenitor division that also produces a V2s neuron instead of a V2a neuron. To distinguish between these outcomes, the authors use the transient expression of a vsx1:EGFP reporter to label clonal pairs combined with a chx10:Red reporter to label V2a neurons. For the optogenetic experiments, however, the authors were not able to use the V2a neuron marker because they were limited by the reporters available to them. Thus, there is a possibility that some sister neurons tested were V2b/s rather than V2a/b. Second, the electrophysiological data are not paired with examinations of synaptic contacts using light or electron microscopy. Third, the circuits in which V2a and V2b neurons function are incompletely understood, and so knowledge of the synaptic inputs and downstream targets is limited.

On balance, the limitations of the study are rather minor. The manuscript is nicely written, the figures are presented clearly and logically, and the data are sufficient to support the claims and conclusions made by the authors. The results extend our knowledge of developmental strategies used to form neural circuits.

Reviewer #2 (Public Review):

Miyakoshi et al. investigated the function of the small RNA GlnZ in E. coli and Salmonella. GlnZ originates from the 3'UTR of the glnA mRNA that encodes glutamine synthetase (GS), the central enzyme of nitrogen assimilation in bacteria. It has been confirmed that the processing of glnA and hence GlnZ formation involves Hfq and RNaseE. The authors also reveal that GlnZ regulates the sucA gene encoding the E1o component of 2-oxoglutarate dehydrogenase (OGDH) by complementary base pairing. As 2-OG is part of the TCA cycle as well as a precursor of the GS/GOGAT cycle, GlnZ appears to function as a major element to control carbon flow from the TCA cycle to this nitrogen assimilation pathway. This is an astonishing finding as the glnA gene and many other aspects of nitrogen assimilation via GS are rather well-investigated. Obviously central regulators can still be discovered, even on mRNAs that have been investigated for decades.

The authors present a nice piece of molecular biology work which justifies the major conclusion that GlnZ is formed by processing of glnA and regulates the sucA gene post-transcriptionally. In general, the manuscript is well-written and the data are clearly presented. The figures are great and allow the reader to easily follow the descriptions. Nevertheless, some aspects referring to the actual control of metabolism could be improved. For instance, the authors claim to have proven that "GlnZ represses the expression of SucA TO REDIRECT the carbon flow from the TCA cycle to the nitrogen assimilation pathway". However, the manuscript does not contain data, e.g. of metabolite profiling using glnZ mutants, that really confirm this statement. Even though GlnZ has a significant effect on SucA abundance it is rather weak, especially in E. coli (Fig. 4B). Of course, this is not uncommon for sRNA-dependent expression control and there is no doubt about the importance of the here presented finding. The authors should either include data that indeed show any effect on 2-OG levels and/or metabolic flux through OGDH or at least temper their conclusion and say that their findings only indicate this.

Reviewer #2 (Public Review):

Susswein et al. analyze a fine-scale, novel data stream of human mobility, openly available from Safegraph, based on the usage of mobile apps with GPS and sampled from over 45 million smartphone devices. They define a metric $\sigma_{it}$, properly normalized, that quantifies the propensity for visits to indoor locations relative to outdoor locations in a given county $i$ at week $t$. For each pair of counties $i$ and $j$, they compute the Pearson correlation coefficient $\rho_{ij}$ between the corresponding $\sigma$ metrics. This generates a correlation matrix that can be interpreted as the adjacency matrix of a network. They then perform community detection on this network/matrix, effectively clustering together time series that are correlated. This identifies three main clusters of counties, characterized geographically as either in the north of the country, in the south of the country, and possibly in tourism active areas. They then show, via a simple model, how including over-simplified models of seasonality may affect infectious disease models.

This work is very interesting for the infectious disease modeling community, as it addresses a complex problem introducing a new data stream.

This work builds on several strengths, among which:<br /> It is the first analysis of the Safegraph dataset to capture seasonality in indoor behavior.<br /> It provides a simple metric to quantify indoor activity, that thanks to the dataset can be computed with a high level of spatial detail.<br /> It aims at characterizing clusters of counties with a similar pattern of indoor activity.<br /> It aims at quantifying the impact of neglecting finer-scale patterns of seasonality, for example considering seasonality to be homogeneous at the US level.

At the same time, it presents several weaknesses that should be addressed to improve the methodology, its results, and the implication:<br /> There is no quantitative comparison of the newly introduced metric for indoor activity with other proxies of seasonality (e.g. temperature or relative humidity). The (dis)similarity with other proxies may help in assessing the importance of this metric, showing why it can not be exchanged with other data sources (like temperature data) that are widely available and are not affected by sampling issues (more on that later).<br /> A major flow of the analysis is to perform community detection on a network defined by the correlation between time series with an algorithm that is based on modularity optimization. As explained in Macmahon et al.[1], all modularity optimization methods rely on null assumptions that in the case of correlation between time series are violated. Therefore, there is a very strong potential bias in their results that is not accounted for. Possible solutions could be to proceed via the methodology presented in [1] or via a different type of algorithm (e.g. Infomap [2]). In both cases, as the network is thresholded (considering only a correlation larger than 0.9), a more quantitative assessment of the impact of the threshold value should be included.<br /> It is not clear what is the added value of the data on indoor activity, as no fitting to real data is performed. Although this may be considered beyond the scope of this paper, I think it would be crucial to quantify how much a data-informed model would better describe real epidemic data (for example in the case of COVID-19). For now, only the impact of neglecting heterogeneity in indoor activity is shown, comparing a model with region-average parameters vs a model with county-level average parameters. Given that the dataset comes with potential bias in sampling (more on this later) it would be good to assess its goodness in predicting real epidemic spread.<br /> When showing results from different models, no visible errors are shown on the plot. How have the errors been estimated?<br /> The dataset is presented as representative of the US population. However, this has not been assessed over time. As adherence to social distancing is influenced by several socio-economic determinants the lack of representativity in certain strata of the population at a given time may introduce an important bias in the dataset. Although this is an inherent limitation of the dataset, it should be discussed in the paper more thoroughly.

In conclusion, I think that the methodology should be revised to account for the fact that the analysis is performed on a correlation matrix. Capturing seasonal patterns of indoor activity can help in tackling the crucial problem of seasonality in human behavior. This could help in identifying effective strategies of disease containment able to curb disease spread at a lower societal cost than fully-fledged lockdowns.

References<br /> [1] Mel MacMahon and Diego Garlaschelli Phys. Rev. X 5, 021006 (2015).<br /> [2] Martin Rosvall and Carl T. Bergstrom PNAS 105, 1118 (2008).

Reviewer #2 (Public Review):

The developmental mechanisms underlying insect polyphenisms are understood for only a few species. Previous studies in pea aphids and planthoppers have shown that insulin signalling is important for differences in wing morphs across environmental conditions. In the pea aphid, mothers that are crowded produce a high proportion of offspring with wings, while mothers housed alone produce offspring without wings. The authors emphasise that in the pea aphid wing loss is a novel trait, and work to identify the developmental processes that lead to wing loss. They find that wing loss is induced by autophagy of the wing disc in the first instar nymphs. Using a transcriptomics approach, they identify a candidate gene, REPTOR2, whose expression is enriched in nymphs destined to become wingless. REPTOR2 is a novel gene that has arisen from duplication in REPTOR. They further demonstrate that reducing REPTOR2 expression by RNAi increases the proportion of winged nymphs. Similarly, reducing the target of rapamycin signalling or feeding mothers on a low-protein diet decreased the proportion of winged nymphs. The authors conclude from these studies that in crowded mothers, high TOR signalling represses REPTOR2 activity leading to reduced autophagy in the wing discs.

Strengths:<br /> 1. The authors have outlined very clear hypotheses and aims, which makes the arguments in the text very easy to follow.<br /> 2. This study is very carefully conducted, and the authors use multiple lines of approach to validate their claims (eg confocal imaging of wing discs to examine ATG8 expression and TUNEL staining to differentiate between autophagy or apoptosis respectively, followed up by qPCR for autophagy and apoptosis genes).<br /> 3. Experiments are appropriately quantified and are backed by statistical tests.<br /> 4. The results lend excellent support to the author's claims.

Weaknesses: The authors do not make a direct link between TOR and REPTOR2 signalling. This seems important since REPTOR2 is a novel gene that arose from the duplication of REPTOR.

Reviewer #2 (Public Review):

Microtubules are regarded as dynamic tracks for kinesin and dynein motors that generate force for moving cargoes through cells, but microtubules also act as motors themselves by generating force from outward splaying protofilaments at depolymerizing ends. Force from depolymerization has been demonstrated in vitro and is thought to contribute to chromosome movement and other contexts in cells. Although this model has been in the field for many years, key questions have remained unanswered, including the mechanism of force generation, how force generated might be regulated in cells, and how this system might be tuned across cellular contexts or organisms. The barrier is that we lack an understanding of experimental conditions that can be used to control protofilament shape and energetics. This study by Murray and colleagues makes an important advance towards overcoming that barrier.

This study builds on previous work from the authors where they developed a system to directly measure forces generated by outward curling protofilaments at depolymerizing microtubule ends. That study showed for the first time that protofilaments act like elastic springs and related the generated force to the estimated energy contained in the microtubule lattice. Furthermore, they showed that slowing polymerization rate did not diminish force generation. That study used recombinant yeast tubulin, including a 6x histidine tag on beta tubulin that created attachment points for the bead on the microtubule lattice. The current study extends that system to show that work output is related to the length of protofilament curls.

Murray and colleagues show this by manipulating curls in two ways - using bovine brain tubulin instead of yeast tubulin and altering magnesium concentration. Previous EM studies indicated that protofilaments on depolymerizing bovine microtubules have similar curvature but are shorter. The authors here use a blend of bovine brain tubulin and bead-linked recombinant yeast tubulin with the 6x histidine tag in their in vitro system and find smaller deflections of the laser-trapped bead than previously observed with pure yeast tubulin. A concern with comparing this heterogeneous bovine/yeast system to the previous work with homogeneous yeast tubulin is that density of 6x histidine-tagged tubulin subunits is likely to be different between the two systems. Also, the rate of incorporation of 6x histidine yeast tubulin into bovine microtubules in the current study may be different from the rate of incorporation into yeast microtubules in the previous study. These differences could lead to changes in the strength of bead attachment to the microtubule lattice and alter the compliance of the bead to deflection by curling protofilaments. These possibilities and lattice attachment strength are not explored in this study, raising concerns about comparing the two systems.

The authors go on to show that magnesium increases bead deflection and work output from the system. The use of magnesium was motivated by earlier studies which showed that increasing magnesium speeds up depolymerization and increases the lengths of protofilament curls. The use of magnesium here provides the first evidence that work output can be tuned biochemically. This is an important finding. The authors then go on to show that the effect of magnesium on bead deflection can be separated from its effect on depolymerization speed. They do this by proteolytically removing the beta tubulin tail domain, which previous studies had shown to be necessary to mediate the magnesium effect on depolymerization rate. The authors arrive at a conclusion that magnesium must promote protofilament work output by increasing their lengths. How magnesium might do this remains unanswered. The mechanistic insight from the magnesium experiments ends there, but the authors discuss possible roles for magnesium in strengthening longitudinal interactions within protofilaments or perhaps complexing with the GDP nucleotide at the exchangeable site, although that seems less likely at the concentrations in these experiments.

The major conclusion of the study is the finding that work output from curling protofilaments is a tunable system. The examples here demonstrate tuning by tubulin composition and by divalent cations. Whether these examples relate to tuning in biological systems will be an important next question and could expand our appreciation for the versatility of depolymerizing microtubules as a motor.

Reviewer #2 (Public Review):

This paper has collected an impressive data set of the visual response properties of neurons in the visual layers of the mouse superior colliculus. There are 3 main findings of the study. First, the authors identify 24 functional classes of neurons based on the clustering of each neuron's visual response properties. Second, unlike in the retina where each cell type is regularly spaced, functional classes in the superior colliculus appear to cluster near each other. Third, visual representation has a lower dimensionality in the superior colliculus compared to the retina. The dataset has the potential to support the conclusions of the paper, but further analysis is required to make the claims convincing.

Strengths:

The main strength of the paper is its impressive dataset of more than 5000 neurons from the visual layers of the superior colliculus. This data set includes recordings from both an interesting set of genetically labelled classes of cells and from a reasonably large portion of the superior colliculus. This dataset offers the opportunity to support the major claims of the paper. This includes i) the identification of 24 functional classes of neurons, ii) the intriguing possibility that functional classes form local patches within the superior colliculus and iii) that the representation of visual information in the superior colliculus has a lower dimensionality compared to the retina.

Weaknesses:

The weakness of the paper is that its main claims are not adequately supported by the presented data or analysis. First, support for the existence of 24 functional classes is not clear enough. Our major concern is that it is not clear that each class of neurons was distributed across different mice. Are certain cell types overrepresented in individual animals, or do you find examples of each cell type in most animals? In addition, it should be made explicit how the responses of each genetically labeled class of neurons are distributed among the 24 functional clusters. Second, the analysis of the spatial clustering of functional cell types is not complete. Do the same functional clusters sample the same retinotopic locations in different mice? How are clusters of the functional type distributed in visual space? Third, the lower dimensionality of representation in the superior colliculus may be the result of selective projections of retinal ganglion cells, not all retinal ganglion cell types project to the superior colliculus. Please estimate the dimensionality of the visual representation of those retinal ganglion cell types that projects to the superior colliculus.

Reviewer #2 (Public Review):

The data presented in this manuscript are sound but rather descriptive. The contribution - as presented - is mostly of a technical nature. The authors correctly state that anti-GFP nanobodies, while used extensively across many model organisms, have limited utility for in vivo applications when the GFP-tagged protein in question displays abnormal behavior or is non-functional. The creation of nanobodies that are uniquely specific for the protein(s) of interest is therefore a significant improvement, especially since the Sallimus and Projectin-specific reagents reported here react with PFA-fixed material. At least one of these nanobodies, when expressed in vivo, decorates the appropriate target. The source of antigens used for the construction of the nanobody library is Drosophila-derived. The extent of homology of Drosophila Sallimus and Projectin with related proteins in other species is not discussed. Whether the nanobodies reported here would be useful in other (closely related?) species, therefore, remains to be established. For those studying muscle biology in Drosophila, the nanobodies described here will be publicly available as cDNAs. Ease of production implies a readily shared and standardized resource for the field.

Further characterization of these nanobodies by biochemical methods such as immunoblotting would be challenging, given the size of the target proteins. In view of the technical nature of this manuscript, the authors should perhaps critically discuss the distinction between bulky GFP tags versus the much smaller epitope tags and the nanobodies that recognize them, although this was covered in a recent eLife paper from the Perrimon lab. Insertion of small tags, in conjunction with nanobodies that recognize them, would be less perturbing than the much bulkier GFP tag and lend itself to genome-wide applications. Creating nanobodies uniquely specific for each protein encoded in the Drosophila genome is not realistic, and the targeted approach deployed here is obviously valuable.

The authors apply two different approaches to characterize the newly generated Nanobodies: more or less conventional immunohistochemistry with fluorescently labeled nanobodies, and in vivo expression of nanobodies fused to the fluorescent neongreen protein. The superiority of nanobodies in terms of tissue penetration has been shown by others in a direct comparison of intact fluorescently labeled immunoglobulins versus nanobodies. The authors state that in vivo labeling with nanobody fusions "thus far was done only with nanobodies against GFP, mCherry or short epitope tags." There is no fundamental difference between these recognition events and what the authors report for their Sallimus and Projectin-specific reagents. The section that starts at line 304 is thus a little bit of a 'straw man'. There is no reason to assume that a nanobody that recognizes a muscle protein would behave differently than a nanobody that would recognize that same protein (or another) when epitope- or GFP-tagged. What might be interesting is to examine the behavior of these muscle-specific nanobodies in the course of muscle contraction/relaxation: are there conformational alterations that promote dissociation of bound nanobodies? Do different nanobodies display discrete behavior in this regard? The manuscript is silent on how muscles behave in live L3 larvae. The FRAP experiment seems to suggest that not much is happening, but the text refers to the contraction of larval sarcomeres from 8.5 µM to 4.5 µM. Does the in vivo expressed nanobody remain stably bound during this contraction/relaxation cycle? What about the other nanobodies reported in this manuscript? Since the larval motion was reduced by exposure to diethylether, have the authors considered imaging the contractive cycle in the absence of such exposure?

Reviewer #2 (Public Review):

This is a very interesting paper that described how multiple kinases regulate the phase separation of Cdc15 and thus impact its localization and function during cytokinesis. The authors build upon their prior research to show that Cdc15 is phosphorylated in its intrinsically disordered region at multiple sites by different kinases. Molecular simulations suggest that phosphorylation of Cdc15 impacts its F-BAR domain's ability to interact with the membrane. Indeed, the authors show that non-phosphorylatable Cdc15 mutants appear in larger dynamic clusters in the cells and also increase the recruitment of cytokinetic proteins to the actomyosin ring. Furthermore, the authors show that the purified Cdc15 intrinsically disordered region undergoes phase separation when treated with a phosphatase. Also, the phase-separated region can recruit cytokinetic proteins that are known interacting partners of Cdc15. Overall, this is a very well-designed study that provides a deep mechanistic insight into how the Cdc15 scaffold conformation is regulated so that it can bind other proteins and interact with the plasma membrane to facilitate cytokinesis. However, the authors do not show if the sites identified here are specifically involved in phase separation. The authors provide evidence that Cdc15 undergoes phase separation when dephosphorylated by a phosphatase. However, it is not shown if dephosphorylation at the sites identified is indeed responsible for the phase separation. It would be helpful to show whether the purified cdc15-31A mutant protein also undergoes phase separation and increased interaction with cytokinetic proteins even in the absence of phosphatase treatment. This would provide strong evidence that indeed the kinases phosphorylate the identified sites to prevent phase separation.

Reviewer #2 (Public Review):

This manuscript addresses the broad question of when humans use different learning and memory systems in the service of decision-making. Previous studies have shown that, even in tasks that can be performed well using incremental trial-and-error learning, choices can sometimes be based on memories of individual past episodes. This manuscript asks what determines the balance between incremental learning and episodic memory, and specifically tests the idea that the uncertainty associated with each alters the balance between them in a rational way. Using a task that can separate the influence of incremental learning and episodic memory on choice in two large online samples, several lines of evidence supporting this hypothesis are reported. People are more likely to rely on episodic memory in more volatile environments when incremental learning is more uncertain and during periods of increased uncertainty within a given environment. Individuals with more accurate episodic memories are also more likely to rely on episodic memory and less likely to rely on incremental learning. These data are compelling, even more so because all of the main findings are directly replicated in a second sample. These data extend the notion of uncertainty-based arbitration between different forms of learning/memory, which has been proposed and evaluated in other contexts, to the case of episodic memory versus incremental learning.

The weaknesses in the paper are mostly minor. One potential weakness is the nature of the online sample. Many participants apparently did not respond to the volatility manipulation, making it impossible to test whether this altered their choices. It is unclear whether this is a feature of online samples (where people can be distracted, unmotivated, etc.) or of human performance more generally.

Reviewer #2 (Public Review):

Humans learn about the world both directly, by interacting with it, and indirectly, by gathering information from others. There has been a longstanding debate about the extent to which social learning relies on specialized mechanisms that are distinct from those that support learning through direct interaction with the environment. In this work, the authors approach this question using an elegant within-subjects design that enables direct comparisons between how participants use information from social and non-social sources. Although the information presented in both conditions had the same underlying structure, participants tracked the performance of the social cue more accurately and changed their estimates less as a function of prediction error. Further, univariate activity in two regions-dmPFC and pTPJ-tracked participants' confidence judgments more closely in the social than in the non-social condition, and multivariate patterns of activation in these regions contained information about the identity of the social cues.

Overall, the experimental approach and model used in this paper are very promising. However, after reading the paper, I found myself wanting additional insight into what these condition differences mean, and how to place this work in the context of prior literature on this debate. In addition, some additional analyses would be useful to support the key claims of the paper.

(1) The framing should be reworked to place this work in the context of prior computational work on social learning. Some potentially relevant examples:

- Shafto, Goodman & Frank (2012) provide a computational account of the domain-specific inductive biases that support social learning. In brief, what makes social learning special is that we have an intuitive theory of how other people's unobservable mental states lead to their observable actions, and we use this intuitive theory to actively interpret social information. (There is also a wealth of behavioral evidence in children to support this account; for a review, see Gweon, 2021).<br /> - Heyes (2012) provides a leaner account, arguing that social and non-social learning are supported by a common associative learning mechanism, and what distinguishes social from non-social learning is the input mechanism. Social learning becomes distinctively "social" to the extent that organisms are biased or attuned to social information.

I highlight these papers because they go a step beyond asking whether there is any difference between mechanisms that support social and nonsocial learning-they also provide concrete proposals about what that difference might be, and what might be shared. I would like to see this work move in a similar direction.

(2) The results imply that dmPFC and pTPJ differentiate between learning from social and non-social sources. However, more work needs to be done to rule out simpler, deflationary accounts. In particular, the condition differences observed in dmPFC and pTPJ might reflect low-level differences between the two conditions. For example, the social task could simply have been more engaging to participants, or the social predictors may have been more visually distinct from one another than the fruits.

References<br /> (In the interest of transparency: I am not an author on these papers.)

Gweon, H. (2021). Inferential social learning: how humans learn from others and help others learn. PsyArXiv. https://doi.org/10.31234/osf.io/8n34t

Heyes, C. (2012). What's social about social learning?. Journal of Comparative Psychology, 126(2), 193.

Shafto, P., Goodman, N. D., & Frank, M. C. (2012). Learning from others: The consequences of psychological reasoning for human learning. Perspectives on Psychological Science, 7(4), 341-351.

Reviewer #2 (Public Review):

This study by Nguyen and Voeltz uses proximity biotinylation and advanced imaging to elucidate roles for a lipid-metabolizing enzyme in controlling sites of mitochondrial fusion and fission. Using proximity biotinylation, they identify ABHD16A, which they propose to rename Aphyd, as an ER-resident protein close to mitochondrial fusion and fission sites. They find that knockdown and overexpression of this protein affects mitochondrial morphology and rates of mitochondrial constriction and subsequent fusion and fission. They also find that mutation of two different catalytic domains within ABHD16A have similar but non-identical effects in rescue experiments of siRNA-induced phenotypes. Broadly speaking, the hydrolase domain, known to deacylate phospholipids to form lysophospholipids (primarily phosphatidylserine to lysoPS), was more important for the phenotypes, but roles were also required for the acyltransferase domain. Perturbation of lipid transfer protein activity implicated the PS/PI4P transporter ORP8 in this pathway, strongly suggesting a specific role for ABHD16A in modulating PS metabolism at these sites to promote the mitochondrial constriction, fission, and fusion machineries. This focused study builds a rigorous and compelling story centered around the role a lipid-modifying enzyme in an interesting and important cellular behavior, namely how sites of mitochondrial fission and fusion are defined. Overall, this important study presents a compelling new model for understanding how specific local lipid metabolism at ER-mitochondria contact sites could facilitate mitochondrial fission and fusion events.

Reviewer #2 (Public Review):

The data presented support the conclusions of the paper, and my concerns are largely conceptional in how we understand this data in the context of malaria infection in vaccination in endemic areas

1) The data is presented based on the idea that antigen uptake and presentation differ between particle and soluble antigens, and that during malaria infection particle uptake is more important due to circulating iRBCs. However, during parasite invasion of RBCs, the parasite sheds large amounts of antigen into the circulation, at least some of which would then be found in a soluble form in the circulation. Can the authors comment on this aspect of infection and if/how this may impact the interpretation of results? Do authors assume that any soluble antigen taken up and presented (via DCs?) during infection would be impacted as for GP66 soluble antigen? Or could an interaction on immune responses where the antigen is presented via both particle and soluble pathways?

2) Impact of particle antigen opsonisation on antigen uptake and presentation. The authors use parasites isolated from mice who have been infected for 6-7 days to investigate the ability of different subsets to update particle antigens. At this time point, have mice developed antibody responses that opsonise these parasites, or are antibody levels low and parasites opsonised? Would opsonised parasites, such as those coated with sera from children in a setting of chronic infection, have a different pattern/ability to be opsonised by different immune cell subsets? And/or would opsonisation change how the DC and other cell types are processing/presenting antigens? While these issues could be addressed experimentally either now or in the future, the manuscript should at least consider this issue because, during a human infection in areas of high exposure, individuals are likely to have reasonable levels of antibodies with opsonised parasites circulating.

3) While authors show that malaria infection disrupts the response to soluble antigens, the relevance directly to vaccination should be considered carefully, specifically because vaccines of soluble antigens are largely given alongside adjuvants which also will modulate DC function. Again, this could be addressed experimentally now or in the future, but definitely should be mentioned and considered when interpreting the results.

Reviewer #2 (Public Review):

In this manuscript, Najer et al., perform a comprehensive bioinformatic analysis of SARS-CoV-2 sequences available from public repositories. Through a comparison with the genome sequence of the original Wuhan 2020 strain, they identify the total accumulation of non-synonymous mutations as a predictor of the evolution of new strains. The manuscript provides data for three structural proteins - spike (S), membrane (M), and envelope (E) proteins, as well as data for the non-structural RNA-dependent RNA polymerase (RDRp) protein that serves as a negative control. However, the predictivity of this approach is most marked only for the Omicron variant, with considerable variation in the predictive power of SARS-CoV-2 proteins for other variants. Focusing on a spike, the method does not detect the alpha variant or delta variant surges, which were mostly driven by changes in spike protein, although the level of sequencing data available for the delta variant might have been less. Notably, although the authors conclude that other parameters such as the ratio of non-synonymous to synonymous mutations or the rate of accumulation of non-synonymous mutations are not predictive, they appear to have similar success in predicting the omicron surge.

Reviewer #2 (Public Review):

Goto and Miyamachi aimed to use fiber photometry to chronically record the activity of arcuate kisspeptin neurons, widely accepted as the GnRH pulse generator responsible for reproductive potential, to determine whether changes in their activity occur during the transition to reproductive senescence in female mice. The authors report that reduced estrous cycle regularity in aging mice is accompanied by changes in the amplitude, but not the frequency, of kisspeptin events. They conclude that the reduction in kisspeptin event amplitude may explain prior results showing reduced LH pulse amplitude in aged rats, potentially due to a reduction in kisspeptin expression. The following is a description of the strengths and weaknesses of this study.

Strengths: Fiber photometry recordings of kisspeptin cells in unanesthetized, freely moving mice at multiple time points over a period of months are technically impressive and a strong approach for interrogating changes in kisspeptin cell physiology that may control the reproductive lifespan of mice.

Weaknesses: Although the approach to use chronic imaging of kisspeptin cells from the same animal from 6 months to 15-18 months is impactful; this has only been conducted in two animals. The correlation between LH pulsatile secretion and kisspeptin activity has been well characterized in mice of reproductive ages in prior reports, however, no accompanying LH pulse measurements have been included in this study, and it is possible that the relationship between kisspeptin activity and LH secretion changes during the transition to acyclicity. The paper lacks a clear description of the parameters used to define acyclicity and to affirm that mice have reached reproductive senescence. Finally, the paper lacks histological analysis of the recorded kisspeptin cells, and although the viral vector used in this study has been well characterized, there is a potential for cytotoxicity from repeated imaging and long-term transfection of the vector.

Reviewer #2 (Public Review):

In this manuscript the authors build upon their previous work describing the structure of the iron efflux pump ferroportin. Here they examine ferroportin's capacity to bind and transport calcium ions. Previous studies indicated a binding site for calcium in Fpn and suggested that calcium binding was needed for iron efflux, while other studies found no requirement for calcium in iron efflux. Here the authors use cryo-EM to structurally characterize a calcium-bound form of Fpn and compare this form to iron- and hepcicin-bound Fpn structures. Using site directed mutagenesis, they functionally characterized the calcium binding site and kinetics of calcium transport and its effects on Fe(II) and Co(II) transport. They report that Ca2+ is transported by Fpn proteoliposomes and Fpn-overexpressing HEK cells, that Ca2+ uptake has little effect on Fe/Co transport, and that Fe/Co efflux inhibits Ca transport.

The data reported here appear to be of high quality and are convincing; the experimental design is excellent and the necessary controls are appropriately employed. A couple of issues need clarification in the text. FPN is clearly an iron efflux pump and these studies make clear that Fpn can also import Ca2+, although it does not appear to function as an Fe2+-Ca2+ antiporter. What is less clear is whether Fpn will transport calcium bi-directionally. A further question that needs explaining is why bind and transport calcium? Cells have a high capacity for calcium flux independent of Fpn. Is there a physiological importance to this activity?

Reviewer #2 (Public Review):

In this study, the authors used an audiobook listening paradigm and encoding analysis of MEG to examine the independent contributions to MEG responses of putative acoustic and phoneme-level linguistic features in speech and their modulation by higher-level sentence/discourse constraints and language proficiency. The results indicate that:

1) Acoustic and phoneme features do indeed make independent contributions to MEG responses in frontotemporal language regions (with a left-hemisphere bias for phoneme features).<br /> 2) Brain responses to acoustic and phoneme features are enhanced when sentence/discourse constraints are low (i.e. when word entropy is high).<br /> 3) While brain responses to phoneme features are enhanced when the language is comprehended (or word entropy is high), the opposite is observed for acoustic features.

These results are taken to support widely held views on the nature of information flow during language processing. On the one hand, processing is hierarchical, consistent with finding 1 above. On the other hand, information flow between lower and high-levels of language processing is also flexible and interactive (finding 2) and modulated by behavioural goals (finding 3).

This is a methodologically sophisticated study with useful findings that I think will be of interest to the burgeoning community investigating 'neural speech tracking' and also to the wider community interested in language processing and predictive coding. Moreover, the evidence appears convincing.

I thought the impact was somewhat limited by the results presentation, which I think missed some key details and made the study somewhat hard to follow (but this issue can be addressed).

Perhaps more major, I do wonder about the novelty of the study as each of the main findings has precedent in the literature. Finding 1 (e.g. Brodbeck, Simon et al.), Finding 2 (e.g. Broderick, Lalor et al.; Molinaro et al.), Finding 3 (e.g. Brodbeck, Simon et al. although here the manipulation of behavioural goals was through a cocktail party listening manipulation and there were was no opposing modulation of acoustic vs phoneme level representations). Thus, while the study appears well executed, overall I am unsure how significant the advance is. Related to this point, the study's findings and theoretical interpretations (e.g. the brain as a hierarchical 'filter') are consistent with widely held views of language processing (at least within cognitive neuroscience) and so again I question the potential advance of the study.

Reviewer #2 (Public Review):

This study contains a huge amount of data and the images are of high quality. However, the conclusions are not really well supported. The authors may have reached too far from their results. The roles of SHR, SCR and SCL23 in the shoot apex are not really clarified.

The manuscript by Bahafid et al., reports a study of the functions of SHORTROOT (SHR), a well-established root development regulator in the shoot apical meristem (SAM) development with focus on lateral organ initiation. A large amount of data is included in this paper. This study highly depends on imaging, and the images are in general of very good quality. The authors show reciprocal interactions between SHR and SCR with auxin/MP. There are also a large amount of genetic interactions among several genes, including WUS and CLV3. Although the study provides a vast amount of data, the conclusions are not so well supported. There seem to be many interactions, at the protein level, and at the transcriptional regulation level, but the conclusion is nevertheless ambiguous.

Reviewer #2 (Public Review):

Sørensen and colleagues performed a comprehensive analysis aiming to find how DNA repair genes shape mutational patterns. They take advantage of the Hartwig Medical Foundation (HMF) and TCGA/ICGC databases which have germline and somatic molecular data. These molecular data layers are used as input features for the predictive models of DNA damage response (DDR) gene deficiency.

Of note, the project is of interest to oncology in the sense of unveiling new genes to be further investigated as a therapeutic candidate target in cancer.

This paper brings statistical modelling based on LASSO regression coupled with appropriate metrics for unbalanced data sets. Their finds recapitulate known DDR-associate genes but novel genes that can be explored in animal models or functional assays with cell lines.

Reviewer #2 (Public Review):

The paper describes the participation in CRC screening in Denmark and compliance to colonoscopy in FIT positive screened people during pandemic.

There are interesting data, particularly in the breakdown by age socioeconomic status and immigrant status. Nevertheless, the study remains very descriptive. When a pandemic occurs and different strategies are put in place in different countries to afford this emergency, probably we also need simple descriptions of what happened, considering anything as a natural experiment to be reported. Furthermore, Denmark is one of the few (or the only) European countries that did not stop CRC screening even during the lockdown. Thus it is worth documenting what happened, with a scientific paper. The consequence is that the paper is not very gripping.

The paper is very well written and the report is rigorous, the methods well documented, tables and figure clear.

Reviewer #2 (Public Review):

Previous studies have shown that lhx1 progenitors proliferate upon AKI in adult zebrafish mesonephros. However, these studies have focused primarily on renal progenitor cells (RPCs). This study uses single-cell mRNA sequencing to identify a novel cell type (RICs) in the zebrafish mesonephros that is marked by fabp10a expression within a previously generated GFP transgenic zebrafish line. The authors show that RICs express cox2 to synthesize PGE2 upon gentamicin-mediated AKI, which correlates with PRC proliferation. They demonstrate that PGE2 stimulates RPC proliferation through EP4b receptor activation of PKA, which in turn stabilizes beta-catenin through phosphorylation of both beta-catenin and GSK3beta. They also indicate that beta-catenin stabilization and RPC proliferation is dependent upon wnt4 expression by the RPC itself. The topic of the paper is significant in that it identifies an interstitial in the zebrafish kidney and suggests several mechanisms by which it supports nephron regeneration.

Reviewer #2 (Public Review):

Tan et al. have used state-of-the-art methodology (mouse genetics, superresolution microscopy and synaptic electrophysiology) to further delineate the role of Munc13 proteins by investigating their function within a scenario in which the presynaptic active zone is deprived of major protein scaffolds. The authors have transduced Cre-expressing lentiviruses into hippocampal neuronal cultures from mice with floxed alleles to remove six fundamental components of the presynaptic active zone: RIM1, RIM2, ELKS1, ELKS2, Munc13-1, Munc13-2 and Munc13-3. The first part of the study comprises a comparison between neurons lacking RIM1, RIM2, ELKS1, ELKS2, on one side, and neurons lacking Munc13-1 and Munc13-2 on the other. Within the first group of neurons the levels of Munc13-1 at the nerve terminals are already reduced (assessed by confocal and STED microscopy and western blots) and the residual amount left is located far away from the active zone. Remarkably synapse formation occurs normally upon the hextuple knock-out of active zone proteins, however, vesicle docking is disrupted and single-action potential evoked and spontaneous release is reduced at glutamatergic and GABAergic synapses. A key finding is that the single-action potential evoked release still detected in the RIM/ELS cuadruple knock-out is almost completely abolished upon the additional knock-out of Munc13-1 and Munc13-2. This is a major observation of the study that support, as the authors concluded, that Munc13 promotes the fusogenicity of synaptic vesicles even when Munc13 is not properly located at the active zone. Careful electrophysiological measurements show that in the absence of Munc13 the size of the readily releasable pool (RRP) of synaptic vesicles is further reduced without specific changes in the vesicular release probability. Overall, the electrophysiological data support well the notion that Munc13 is specifically responsible for the remaining RRP and therefore reinforce the notion that Munc13 acts at the priming stage and can do it in part independently of RIMs and ELSs. Importantly, the results further support the notion that synapse formation is a remarkably resilient process that occurs even under strong perturbation of presynaptic function.

As a secondary conclusion, the authors point out that postsynaptic response is intact, this specific point should be further discussed and analyzed.

The study is very clearly written and presents very relevant findings of interest for readers in the field of the molecular mechanisms of synaptic operation.

Reviewer #2 (Public Review):

In this article, Iyer et al discuss the mechanics of reprogramming challenges encountered by supporting cells towards their potential pathway of dedifferentiating into hair cell types. Previous literature has shown the ability of ATOH-1, GFl-1, and POU4F3 to transform supporting cells into hair-like cell types. Here authors suggest that the combinatorial expression of these TFs can enhance the efficiency of the transcriptional remodeling of supporting cells to initiate the reprogramming toward hair-like cell lineage. It is a well-conducted study. Please see my comments/concerns below.

1. In the representative images, the effect of GFl-1 seems to be less efficient or has no effect on reprogramming the lineage of supporting cells to hair cell-like cells in comparison to two other groups ATOH-1 alone or ATOH-1, GFl-1, and Pou4F3 combined (Figure 1, 1- S2, 2B, 4A) and even the single-cell RNA seq can be interpreted similarly (Figure 3C, 6C. According to authors and previous literature, GFl1 is supposed to be acting in concert to enhance the efficiency of this lineage conversion at least in older animals. The representative images and single-cell UMAPs show that either GFl-1 is not efficient or less efficient than ATOH-1 alone or ATOH-1, GFl-1, and Pou4F3 combined. Hence, why authors chose not to explore ATOH-1 and Pou4F3 without GFl-1.

2. In Figure 3C, the authors find the most reduction in cell numbers in lateral GER during transcriptional reprogramming. Can authors comment on why the cells in this region are more susceptible to lineage reprogramming into hair cell-like cells?

3. In figure 5A, how can the existing hair cells be distinguished from newly formed hair cell-like cells.

4. Authors cited previous literature showing that existing hair cells can affect lineage reprogramming of supporting cells through Notch signaling. So would it not be a better experimental design when the hair cells were depleted prior to transcriptional reprogramming.

5. Genetic mutations that lead to functional disruptions in supporting cells are also linked to hearing loss. Can authors predict how feasible would be the idea of in vivo conversion of one important cell type to another important cell type?

6. Are reprogrammed hair cell-like cells transcriptionally similar to outer hair cells, inner hair cells, or none?

Reviewer #2 (Public Review):

The manuscript entitled "Fixation Can Change the Appearance of Phase Separation in Living Cells" discussed the different fixation artefacts that can change the appearance of LLPS. The manuscript points out a fundamental question in the field of phase separation which is rarely discussed. The authors found that PFA fixation can both enhance and diminish putative LLPS behaviors; in some cases, it can also create condensates that did not exist in living cells. Using a simple but elegant model, they found that protein localization in fixed cells depends on an intricate balance of protein-protein interaction dynamics, the overall rate of fixation, and notably, the difference between fixation rates of different proteins. They conclude that less dynamic interactions are better captured by PFA fixation. The text is clearly written, the experiments are well designed and the simulations give an interesting explanation of the different artefacts observed after fixation.

To describe LLPS or to distinguish between polymer-polymer phase separation and LLPS, recent studies have used single particle tracking, a technique allowing to follow the dynamics of individual proteins in living cells (https://doi.org/10.7554/eLife.60577; https://doi.org/10.7554/eLife.69181; https://doi.org/10.7554/eLife.47098). The authors should mention that such an approach can be a good alternative to avoid the artefact of fixation.<br /> Using techniques such as single particle tracking or FCS, it is possible to estimate the effective diffusion coefficient of protein-living cells. When a liquid phase separation is formed, it is also possible to estimate the diffusion coefficient of the protein of interest (POI) inside versus outside of the LLPS. The authors say that less dynamic interactions are better captured by PFA fixation. In the simulation part, would it be possible to predict from the diffusion coefficients of the POI inside a condensate the effect of the PAF fixation?

Finally, the authors propose that in the future, it will be important to design novel fixatives with significantly faster cross-linking rates than biomolecular interactions to eliminate fixation artifacts in the cell. It would be even more interesting if the authors could propose some ideas of potential novel fixatives. Did they test several concentrations of PFA, for example? Did they test different times of PFA incubation? Did they test cryofixation and do they know what would be their effect on LLPS? Do they have novel fixatives in mind?

Adding some precisions about these points in the simulation and in the fixation protocol would increase the impact of the manuscript. Otherwise, the study is interesting and thought-provoking.

Reviewer #2 (Public Review):

This paper reports the results of optical imaging experiments on areas V4, V2, and V1 in anesthetised macaque monkeys. The experiments were designed to reveal details of the representation of spatial frequency (SF), orientation (OR), and color in these areas. Evidence for gradients of SF selectivity across the areas is presented. It is also shown that SF and OR maps in V2 and V4 have iso-parameter contours that intersect at right angles, in agreement with, and extending, observations made in V1 and visual areas in cats and other species. Color domains tend to be located in low-SF domains and avoid the higher SF regions. Relationships between V2 stripes and SF preference are also established.

These findings are a potentially valuable contribution to understanding the maps that exist in V4, which have received less attention than those in areas V1 and V2. However, I have some serious concerns about the validity of the results.

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Reviewer #2 (Public Review):

Auwerx et al. present a framework for the integration of results from expression quantitative trait loci (eQTL), metabolite QTL (mQTL) and genome-wide association (GWA) studies based on the use of summary statistics and Mendelian Randomization (MR). The aim of their study is to provide the field with a method that allows for the detection of causal relationships between transcript levels and phenotypes by integrating information about the effect of transcripts on metabolites and the downstream effect of these metabolites on phenotypes reported by GWA studies. The method requires the mapping of identical SNPs in disconnected mQTL and eQTL studies, which allows MR-based inference of a causal effect from a transcript to a metabolite. The effect of both transcripts and metabolites on phenotypes is evaluated in the same MR-based manner by overlaying eQTL and mQTL SNPs with SNPs present in phenotypic GWA studies.

The aim of the presented approach is two-fold: (1) to allow identification of additional causal relationships between transcript levels and phenotypes as compared to an approach limited to the evaluation of transcript-to-phenotype associations (transcriptome-wide MR, TWMR) and (2) to provide information about the mechanism of effects originating from causally linked transcripts via the metabolite layer to a phenotype.

The study is presented in a very clear and concise way. In the part based on empirical study results, the approach leads to the identification of a set of potential causal triplets between transcripts, metabolites and phenotypes. Several examples of such causal links are presented, which are in agreement with literature but also contain testable hypotheses about novel functional relationships. The simulation study is well documented and addresses an important question pertaining to the approach taken: Does the integration of mQTL data at the level of a mediator allow for higher power to detect causal transcript to phenotype associations?

Major Concerns<br /> 1. Our most salient concern regarding the presented approach is the presence of multiple testing problems. In the analysis of empirical datasets (p. 4), the rational for setting FDR thresholds is not clearly stated. While this appears to be a Bonferroni-type correction (p-value threshold divided by number of transcripts or metabolites tested), the thresholds do not reflect the actual number of tests performed (7883 transcripts times 453 metabolites for transcript-metabolite associations, 87 metabolites or 10435 transcripts times 28 complex phenotypes). The correct and more stringent thresholds certainly decrease the overlap between causal relationships and thus reduce the identifiable number of causal triplets. Furthermore, we believe that multiple testing has to be considered for correct interpretation of the power analysis. The study compares the power of a TWMR-only approach to the power of mediation-based MR by comparing "power(TP)" against "power(TM) * power(MP)" (p. 12). This comparison is useful in a hypothetical situation given data on a single transcript affecting a single phenotype, and with potential mediation via a single metabolite. However, in an actual empirical situation, the number of non-causal transcript-metabolite-phenotype triplets will exceed the number of non-causal transcript-phenotype associations due to the multiplication with the number of metabolites that have to be evaluated. This creates a tremendous burden of multiple testing, which will very likely outweigh the increase in power afforded by the mediation-based approach in the hypothetical "single transcript-metabolite-phenotype" situation described here. Thus, for explorative detection of causal transcript-phenotype relationships, the TWMR-only method might even outperform the mediation-based method described by the authors, simply because the former requires a smaller number of hypotheses to be tested compared to the latter. The presented simulation would only hold in cases where a single path of causality with a known potential mediator is to be tested.

2. A second concern regards the interpretation of the results based on the empirical datasets. For the identified 206 transcript-metabolite-phenotype causal triplets, the authors show a comparison between TWMR-based total effect of transcripts on phenotypes and the calculated direct effect based on a multivariable MR (MVMR) test (Figure 2B), which corrects for the indirect effect mediated by the metabolite in the causal triplet. The comparison shows a strong correlation between direct and total effect. A thorough discussion of the potential reasons for deviation (in both negative and positive directions) from the identity line is missing. Furthermore, no test of significance for potential cases of mediation is presented. Due to the issues of multiple testing discussed above, the significance of the inferred cases of mediation is drawn into question. The examples presented for causal triplets (involving the ANKH and SLC6A12 transcripts) feature transcripts with low total effects and a small ratio between direct and total effect, in line with the power analysis. However, in these examples, the total effects are also quite low. Its significance has to be tested with an appropriate statistical test, incorporating multiple testing correction. Furthermore, the analysis of the empirical data indicates that the ratio between direct and indirect effect of a transcript on a phenotype is in most cases close to identity, except for triplets with low total effects. This fact should be considered in the power analysis, which assigned the highest gain in power by the mediation analysis to cases of low direct to total effect ratio. The empirical data indicate that these cases might be rare or of minor relevance for the tested phenotypes.

3. Related to the interpretation of causal links: horizontal pleiotropy needs to be considered. The authors report the identification of causal links between TMEM258, FADS1 and FADS2, arachidonic acid-derived lipids and complex phenotypes. However, they also mention the high degree of pleiotropy due to linkage disequilibrium at the underlying eQTL and mQTL region as well as the network of over 50 complex lipids known to be associated with the expression of the above transcripts. Thus, it seems possible that the levels of undetected lipid species may be more important for the phenotypic effect of variation in these transcripts and that the reported "mediators" are rather covariates. Such horizontal pleiotropy would violate a basic assumption of the MR approach. While we think that this does not invalidate the approach altogether, it does affect the interpretation of specific metabolites as mediators. This is aggravated by the fact that metabolic networks are more tightly interconnected than macromolecular interaction networks (assortative nature of metabolic networks) and that single point-measurements of metabolites may not be generally informative about the flux through a specific metabolic pathway.

Reviewer #2 (Public Review):

In the manuscript, Mijnheer et al mainly exploited CyTOF Helios mass cytometer and TCRβ repertoire sequencing to investigate the T cell composition and distribution in peripheral blood and synovial fluid, and further explored the temporal and spatial dynamics of regulatory T cells (Tregs) and non-Tregs in the inflamed joints of Juvenile Idiopathic Arthritis (JIA) patients. Their results indicate that the activated effector T cells and hyper-expanded Treg TCRβ clones found at the inflamed joints are highly persistent in the circulation, and the dominant of high degree of sequence similarity of Treg clones could serve as the novel therapeutic targets for the JIA treatment. Overall, the research design is appropriate, and the methods are adequately described in the study. However, several issues are required to be addressed.

(1) The criteria for the JIA patient's recruitment should be clearly presented in the method section. For example, what is the specific included criteria and excluded criteria? Or did the patients take medicines for the treatment during the study?<br /> (2) As for the correlation analysis of the entire spectrum of node frequencies, the SFMCs and PBMCs isolated from 3 patients were conducted in the study. The sample size is too limited to obtain robust results and to make a convincing conclusion from the correlation analysis. And it is shown that a total of 9 JIA patients have been involved in the study. Could the author clarify it?<br /> (3) The results of the study indicate that the hyper-expanded T cell clones are shared between left and right knee joints. Since JIA may affect one or more joints, did the author check other joints to see if the same expanded T cell clones infiltrate multiple joints, such as hand or wrist?<br /> (4) For Fig.2B, the Treg CD25+FOXP3+ population was significantly enriched in synovial fluid (SF). Is it from the left knee joints or the right knee joints?<br /> And in the context of Line 144-148, it indicated the SF, however, the title of axis in Fig.2B indicated Synovial Fluid Mononuclear Cells (SFMCs). Please keep consistent.<br /> (5) For the longitudinal sampling timelines of JIA patients shown in Supplementary Fig.3, the interval of PB and SF sample collection is not consistent. And only 1 patient completed 4 visits and the sample collection. It is hard to make any conclusion from 1 patient.

Reviewer #2 (Public Review):

The current treatment for the radical cure of Plasmodium vivax malaria is primaquine, it was first made available in the 1950s and there is a need for better treatments. Recently a new drug was licensed, tafenoquine. Tafenoquine is a single-dose treatment due to the drug's long half-life. The expected increase in treatment adherence is an important advantage, however, the drug's slow elimination has also a drawback. Patients must be tested for a ubiquitous enzyme (glucose-6-phosphate dehydrogenase) deficiency prior to treatment, as the use of this drug in the G6DP deficient population could lead to life-threatening haemolysis. Implementing accurate quantitative testing in remote malaria-endemic areas is challenging. Providing point-of-care test equipment, supplies and training may not be cost-effective as the efficacy of tafenoquine has not been proven non-inferior to primaquine.

Thus strategies to increase tafenoquine efficacy are of paramount importance to raise the public health relevance of the first new drug developed for the radical cure of vivax malaria in the last 70 years. This paper polled together and analysed the clinical information of participants of the tafenoquine clinical trials, and using models they evaluated the influence of dose per weight on the recurrence rate of the disease. They predict that the tafenoquine efficacy would surpass the primaquine one if the tafenoquine dose was increased. They also correlated the levels of methaemoglobin production and the reduction of relapses, implying that methaemoglobin can be a surrogate marker of efficacy.

As with any model prediction, these results should be confirmed in clinical trials, especially the safety profile of the suggested regimen. The surrogate marker of oxidative drug activity is a very interesting indirect efficacy measurement, although it is limited to any indirect outcome. Even the vivax recurrence rate itself has limitations as vivax relapse and reinfection cannot be differentiated. Still, these results provide a solid support for future clinical trials that might reinforce the public health relevance of tafenoquine.

Reviewer #2 (Public Review):

In this manuscript, the authors use tandem mass spectrometry to identify peptides from the eggshell protein struthiocalcin-1 preserved in a fossilized eggshell ~6.5 Ma years old. They report multiple peptide spectral matches (peptide identifications) to a small section of the struthiocalcin-1 protein. The high-resolution, well-annotated spectral matches provided by the authors show that that region of struthiocalcin-1 may be preferentially preserved in ancient ostrich shell tissue.

These findings are important because Cenozoic tissues greater than 1 Ma in age are drastically unexplored in terms of protein preservation. Thus, these data represent a big step forward in the investigation of proteomic preservation throughout the Cenozoic.

Reviewer #2 (Public Review):

The authors report a series of genetic experiments that allow for the rigorous evaluation of the role of pre-synaptic contact and activity on dendrite morphogenesis and/or stabilization between a model pair of connected sensory and interneurons in the Drosophila larval CNS. Experiments to mis-position the presynaptic arbors of the DBD neuron reveal contact-dependent effects on post-synaptic dendrite growth. Ablation, silencing and activation experiments support the interesting model that neuronal activity in the sensory afferents act to globally constrain post-synaptic dendrite growth. Thus, coordination of presynaptic contact and activity act in opposition to sculpt post-synaptic dendrites. One weakness/limitation of the study is the inability of the authors to evaluate whether the observed effects are due to changes in the initiation of dendrite growth as opposed to maintenance or stabilization effects. The authors adequately acknowledge and discuss this limitation. Overall, this is a beautifully conducted study that adds new insights into synaptic partner matching.

Because of poor institutional development and excessive corruption, privatization causes serious problems

Claim 2: Water distribution is a business not a human right Evidence f/ Claim 2: The privatization of water sources is in the pursuit of profits and not distribution

Reviewer #2 (Public Review):

The study included all women aged 23-64 years invited for cervical cancer screening in Denmark in 2015-2021 (n=2,220,00). The Danish registries provide an ideal setting for the study. Classification of explanatory covariates followed Danish and international standards. The authors estimated the prevalence ratios using a generalised linear model with a log link for the Poisson family. Material and statistical methods are appropriate for the study's aims.

As the authors write, several studies have demonstrated lower participation among immigrants and women with lower socioeconomic status. However, the authors wanted to evaluate whether divergence may have been exacerbated during the pandemic. Unfortunately, they do not provide any justification for why they would hypothesize that to happen.

The authors write that women who unregistered from the screening programme within 1 year since invitation (n=56,920) were excluded. If those who are at higher risk of cancer and with lower participation rates unregister themselves, the compliance to screening could be overestimated.

The authors find that some age groups i.e. women aged 40-49 and those aged 60-64 years had a lower participation rate and conclude that it could indicate that the restrictions within a society affect different age groups disproportionally. The authors do not try to explain the finding and it should be scrutinized to rule out a chance. Comorbidity is strongly associated with age so if this is attributed to self-isolation, there should be a gradient. Why 50-59 years old would be different from 60-64 years?

In general, study results support the conclusions. The authors consider the inconsistent health messages as a reason for women not to participate. What about fear? In several countries, there was a clear decrease in emergency admissions to the hospital which suggest that people were avoiding hospital because they were nervous about catching COVID-19.

Reviewer #2 (Public Review):

The present manuscript provides a mechanistic explanation for an event in adrenal endocrinology: the resistance which develops during excessive inflammation relative to acute inflammation. The authors identify disturbances in adrenal mitochondria function that differentiate excessive inflammation. During severe inflammation the TCA in the adrenal is disrupted at the level of succinate production producing an accumulation of succinate in the adrenal cortex. The authors also provide a mechanistic explanation for the accumulation of succinate, they demonstrate that IL1b decreases expression of SDH the enzyme that degrades succinate through a methylation event in the SDH promoter. This work presents a solid explanation for an important phenomenon. Below are a few questions that should be resolved experimentally.

The authors should confirm through direct biochemical assays of enzymatic activity that steroidogenesis enzyme activity is not impaired. Many of these enzymes are located in the mitochondria and their activity may be diminished due to the disturbed, high succinate environment of the cortical cell as opposed to the low ATP production.

What is the effect of high ROS production. Is steroidogenesis resolved if ROS is pharmacologically decreased even if the reduction of ATP is not resolved?

Does increased intracellular succinate (through cell permeable succinate treatment) inhibit steroidogenesis even if there is not a blockage of OXPHOS?

It should be demonstrated the genetic loss of IL1 signaling in adrenal cortical cells results in a loss of the effect of LPS on reduced steroidogenesis and increased succinate accumulation.

It should be demonstrated the genetic loss of IL1 signaling in adrenal cortical cells results in a loss of the effect of LPS on SDH activity and ATP production and SDH promoter methylation

It should be shown that the silencing of DNMT eliminates or diminishes the effect of LPS on reduced steroidogenesis and increased succinate accumulation.

Does silencing of DNMT reduce OXPHOS in adrenal cortical cells?

The effects of LPS on reduced adrenal steroidogenesis are not elaborated at the physiological level. The manuscript should demonstrate the ramifications of the adrenal function decreasing after LPS. Does CORT release become less pronounced after subsequent challenges? Does baseline CORT decrease at some point? No physiological consequences are shown. Similarly, these physiological consequences of decreased adrenal function should be dependent on decreased SDH activity and OXPHOS in adrenal cells and this should be demonstrated experimentally.

Reviewer #2 (Public Review):

The authors use the phylogeny of SARS-CoV-2 to find signals of functional interactions among the evolving amino acids of the spike protein. They do this by looking for pairs of substitutions that either tend to appear consecutively on branches, indicating positive interactions, or to appear on separate branches, indicating negative interactions. Although a massive number of SARS-CoV-2 sequences have been collected, many of these sequences have errors in them or are similar to each other. This affects the accuracy of the reconstructed phylogeny and the placement of mutations on it, creating difficulties for this approach. Still, the authors are able to identify several sets of sites with clear signals of interaction, and where the interaction makes sense given the structure of the protein. Some of these sites are carried by the Omicron variant, indicating that positive epistasis likely played a role in its evolution.

Reviewer #2 (Public Review):

The authors examined the neural activity of the ventral hippocampus (vH) during exploration of anxiogenic environments. They first recorded vH neuronal activity when animals explored the elevated plus maze (EPM). Although they observed that peak firing activity increased when rats explored anxiogenic locations, this effect was difficult to quantify since rats did not often explore these locations. In order to resolve this issue, they developed a novel type of elevated linear maze (ELM). In the anxiogenic location of the ELM, they observed anxiety-related neuronal activity and demonstrated that the direction-dependent activity of vH neurons became homogenized. Additionally, the authors demonstrated that the activity of the vH neurons reflected and predicted, using a support vector machine (SVM), the exploration of an anxiogenic location, suggesting that vH neurons do not only code for anxiogenic environments, but also may reflect the intention to explore anxiogenic locations.

Strength:<br /> S1. In their study, the authors introduced a modified ELM task that can instantly reconfigure side walls in the anxiogenic environment while rats are being recorded on the maze. This method was intended to overcome the low-sampling issue observed in the anxiogenic environments where animals usually avoid entering. In fact, this modification allowed them to study between non-anxiogenic and anxiogenic conditions within the same maze and in a single recording session.

S2. Also, it is known that recording large number of cells from vH has been quite challenging in the field. The authors successfully examined more than 130 neurons from the vH area across six rats and determined remapping effect when animals were exposed to the anxiogenic environment.

S3. The authors tried to examine the neural population carefully to exclude any other factors to focus solely on the effect of anxiety, although it has been shown that abrupt changes in the environment can cause the hippocampus to remap.

Weakness:<br /> Despite the fact that the authors are trying to answer potentially important and intriguing questions in the anxiety field, some important details are missing from their description of the data.

W1. It is remarkable and impactful that the authors found that the vH neurons overrepresent, remap, and lose directionality under anxiogenic conditions. Conceptually, such dramatic changes as well as prospective biased memory 'replays' have been reported in the dorsal hippocampus under anxiogenic task settings, such as using electrical foot shocks, for example, Wu et al, Nat.Neuro, 2017. Also, another paper (Girardeau et al.., 2017, Nat Neuro) reported that an aversive trajectory is more reactivated in the dorsal hippocampus.

W2. Technically, they used tetrodes in vH and were able to collect more than 130 units, with histological data indicating that recording sites ranged from CA1 to CA3 of vH (Figure 1B). They used a semi-automated clustering method to isolate individual units but did not subdivide them into CA1, CA3 and/or pyramidal cells or interneurons. It appears that the representative examples in Figure 1C contain both pyramidal cells and interneurons, which are well characterized in terms of remapping in the dorsal area.

W3. Readers may find Figure 5 difficult to follow. They are not intuitive to understand how to read/interpret the figure panels.

Reviewer #2 (Public Review):

Differences between the infection environment and in vitro model systems likely contribute to disconnects between the antimicrobial susceptibility profile of bacterial isolates and the clinical response of patients. The authors of this paper focus on a specific aspect of the infection environment, the polymicrobial nature of some chronic infections like those in people with Cystic Fibrosis (CF), as a factor that could impact antibiotic tolerance. They first use published genomic datasets and computational techniques to identify a clinically relevant, four-member polymicrobial community composed of Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus spp., and Prevotella spp. They then develop a high throughput methodology in which this community grows and persists in a CF-like environment and in which antibiotic susceptibility can be tested. The authors determine that living as a member of this community decreases the antibiotic tolerance of some strains of biofilm-associated P. aeruginosa and increases the tolerance of most strains of planktonic and biofilm-associated S. aureus and planktonic and biofilm-associated Streptococcus. They focus on the decreased tolerance of P. aeruginosa and determine that a ΔlasR mutant of P. aeruginosa does not display increased tobramycin susceptibility in the mixed community. One of the phenotypes associated with a ΔlasR mutant is an overproduction of phenazines. The authors find that by deleting the phenazine biosynthesis genes from ΔlasR, they can restore community-acquired susceptibility. They further investigate this phenomenon by showing that a specific type of phenazine, PCA, is significantly increased in mixed communities with the ΔlasR mutant compared to WT. Finally, they demonstrate that adding a specific phenazine, pyocyanin, to mixed communities can restore the tolerance of WT P. aeruginosa.

Strengths:

With this study the authors address a very important problem in infectious disease microbiology - our in vitro drug susceptibility assays do a poor job of mimicking the infection environment and therefore do a poor job of predicting how effective particular drugs will be for a particular patient. By demonstrating how an infection-relevant community modifies tolerance to a clinically relevant drug, tobramycin, the authors identify specific interactions that could be targeted with therapeutics to improve our ability to treat the chronic infections associated with CF. In addition, this study provides a framework for how to effectively model polymicrobial infections in vitro.

The experiments in the paper are very rigorous and well-controlled. Statistical analysis is appropriate. The paper is very well-written and clear.

The authors do an admirable job of using in silico analysis to inform their in vitro studies. Specifically, they provide a comprehensive rationale for why they chose and studied the specific community they did.

The authors provide a very robust dataset which includes determining how strain differences of each of their four community members affect community dynamics and antibiotic tolerance. These types of analyses are laborious but very important for understanding how broadly applicable any given result is.

Weaknesses:

The authors very clearly and convincingly demonstrate that WT P. aeruginosa becomes more susceptible to tobramycin in their mixed community. Our ability to turn these types of observations into therapeutic development depends on mechanistic insight. That said, it is unclear if the authors can make any solid conclusions about what specific aspects of the polymicrobial environment cause WT P. aeruginosa to become more susceptible. The authors make a compelling case that increased phenazine production by the ΔlasR mutant restores tolerance in the mixed community and that exogenous phenazine addition increases the survival of WT P. aeruginosa in the mixed community. However, it remains a plausible explanation that the effects of phenazines on tobramycin susceptibility are independent of the initial observation that WT. P. aeruginosa becomes susceptible to tobramycin in the mixed community.

Some aspects of the methodology are unclear. Specifically, the authors note that they use a specific sealed container system to grow their strains in anoxic conditions, which mimic portions of CF sputum. However, it is unclear how the authors change medium over the course of their experiments, or how they test susceptibility to tobramycin, without exposing the cells to oxygen. It is well understood that oxygen exposure impacts the susceptibility of P. aeruginosa to tobramycin, so it is very important that the methodology involving oxygen deprivation and exposure is described in detail.

Reviewer #2 (Public Review):

Millar et al. have used multimodal brain magnetic resonance imaging (MRI) data from subjects with preclinical AD (cognitively normal with amyloid pathology), cognitive impairment, and matched cognitively normal (CN) subjects to predict the subject's age (i.e. brain age). To do so they have trained a Gaussian Process Regression (GPR) model using data from 3 datasets. The predicted age was then compared to the chronological age to calculate the brain age gap (BAG). Using resting-state functional MRI (rsfMRI) they calculated functional connectivity across 300 brain regions. Similarly, T1w MRI images have been used to calculate volumetric measures across 68 cortical and 33 subcortical regions. The results were then used as features in two separate models resulting in FC-BAG and Vol-BAG measures, respectively. A third model is then devised by "stacking" the previous model's predictions as features in a new model resulting in Vol+FC-BAG. The models were then applied to the test dataset and BAG measures were calculated for subjects in CN, preclinical AD, and cognitively impaired (CI) groups. All models show significantly higher BAGs for subjects with cognitive impairments. Finally, the authors have examined the relationship between BAGs measures and Amyloid Markers, Tau Markers, Neurodegeneration Markers, and Cognition.

Strengths:

The manuscript is very clearly written. The study is well designed and for the most parts the method section contains all the necessary information to replicate the steps. The sample size is comparable to similar studies investigating brain age in clinical populations and the inclusion and exclusion criteria are clearly stated. In order to avoid bias and overfitting in the predictive models the authors have (1) used a separate training (+validation) set and test set and (2) removed any subjects with potential pathology or impairment from the training set. Using data points on the plots as well as a combination of boxplot+violin plots makes the results clear and data distributions are provided when necessary. Furthermore, chronological age has been used as a covariate to correct the relationship between BAG and age, making the results more interpretable and reliable. Focusing on the stronger section of the results, the study shows a higher Vol-BAG (or Vol+FC-BAG) in subjects with CI, which is significantly related to higher Amyloid PET, higher PET and CSF-related Tau measures, and lower global cognition. In conclusion, the Vol-BAG results are clear and clinically relevant, based on a model with a reasonable prediction performance.

Weaknesses:

The manuscript follows authors' recently published work on FC-BAG in symptomatic and preclinical Alzheimer disease (Millar et al, Neuroimage 2022) by adding T1w volumetric measures from Freesurfer. Based on the results the additional value of rsfMRI connectivity is at best marginal. The FC-BAG model has a weak performance and is outperformed by Vol-BAG. The marginal benefit of adding FC-BAG to the Vol-BAG model is around 10% which comes with the additional cost of a new and more computationally demanding modality as well as making the biological relevance of the model almost untraceable. The preclinical findings reported as "Specifically, FC-BAG may capture a unique biphasic response to preclinical AD pathology" while potentially interesting are based on an unreliable model (FC-BAG) and can be a spurious finding. These results need further validation both robustness analysis within the current sample and in independent datasets. The other findings related to preclinical AD are based on the hippocampal volume which as the authors have mentioned in the discussion limitation is part of the features included in the Vol-BAG model and absent from FC-BAG.

In conclusion, the manuscript has clear findings based on the Vol-BAG model differentiating the cognitively impaired subjects from other groups and these results relate to the clinical severity of the disease as measured by Amyloid Markers, Tau Markers, and Cognition.

Reviewer #2 (Public Review):

Here the authors used viral expression and two-photon imaging, a very demanding approach, to explore the transport dynamics of three membrane markers (Neuropeptide Y-dense core vesicles, LAMP1-endolysosomes and RAB7-late endosomes) in vivo in the mouse brain. This allowed deciphering for the first time anterograde and retrograde velocities in vivo rather than in cultured neurons. The authors showed that the different vesicular compartments have different anterograde and retrograde velocities, pausing at synapses. They further used brain slices to explore the effect of increased calcium levels.

Major strengths reside in the novelty of the approach (in vivo!).

The main weakness relates to the lack of novel mechanisms and the difficulty of using such a sophisticated setup on a routine basis.

This is a technical 'tour-de-force', a clear reference article for future studies addressing vesicular transport in vivo.

Of course, one would be curious to see many more markers studied in this setup. Also, the same study in mouse mutants would be extremely interesting.

Reviewer #2 (Public Review):

Hebart et al., present a large-scale multi-model dataset consisting of fMRI, EEG, and behavioral similarity measures towards the study of object representation in the mind and brain. The effort is immense, the methods are rigorous, and the data are of reasonable quality, the demonstrative analyses are extensive and provocative. (One small note regarding one leg of this multi-modal dataset is that the fMRI design consisted of a single image presentation for 0.5s without repetitions for most of the images; this design choice has particular analysis implications, e.g. the dataset will have more power when leveraging a priori grouping of images. However, unlike other datasets of this kind, here the number of images and how they were selected does support this analysis mode, e.g. multiple exemplars per object concept, and rich accompanying meta-data and behavioral data.)

The manuscript is well-written, and the THINGs website that lets you explore the datasets is easy to navigate, delivering on the promise of making this an integrated, expanding worldwide initiative. Further, the datasets have clear complementary strengths to recent other large-scale datasets, in terms of the ways that the images were sampled (not to mention being multi-modal)-thus I suspect that the THINGs dataset will be heavily used by the cognitive/computational/neuroscience research community going forward.

Reviewer #2 (Public Review):

I found the study and findings important and largely convincing. While some of these observations might continue to refine with further sampling, the value of these data for what they already are, the novelty in the comparison, and the strength and importance of these results for our understanding of deep-sea marine ecosystems and variation thereof, are all exemplary.

My primary critique is the near-absence of statistical analyses in the current version of the manuscript that are necessary to support the many descriptive observations made with a more formal hypothesis testing framework, as well. Developing an appropriate framework for such analyses throughout the paper, including consideration of the multiple tests that will be performed. This is important for many reasons, including by providing a more formal sense of uncertainty in the conclusions to readers, given the understandable sampling limitations. Planning and conducting these analyses will require considerable work.

Reviewer #2 (Public Review):

This manuscript builds on previous work from the Pearson lab showing that one aspect of the trisomy 21 phenotype could be caused by an increase in the amount of pericentrin (PCNT), a component of the centrosome. The earlier work showed that the increase in PCNT is sufficient to reduce the frequency of ciliation in trisomy 21 cells and that the increased PCNT is often in the form of protein aggregates along microtubules proximal to the centrosome (in a preprint). Here they use several models with 3 or 4 copies of human chromosome 21 or the mouse equivalent to examine the defect in cilium formation at the level of specific proteins and the signaling function of the cilium. This is a substantial contribution that furthers the evidence for the authors' favored model of excess PCNT causing some form of pericentrosomal crowding that hinders the ability of other molecules, complexes, and/or vesicles to get to the right place at the right time. The work makes excellent use of cell lines and mice previously generated for the study of trisomy 21, making for well-controlled experiments in a situation where this is particularly important (only 1.5 x increased expression). One does wish that it were possible to do the PCNT depletion in more of the experiments than the single one shown, but that is understandable given the amount of work required and the uncertainty associated with RNAi depletion.

Reviewer #2 (Public Review):

Here, McKay, et al. describe a new automated system to feed killifish, and use it to explore dietary restriction effects on killifish lifespan and to develop an associative learning assay, two important goals in the KF/longevity field.

Fig. 1-2- The first figures focus on the design and evaluation of the feeding system. It appears that the feeding system works well and achieves what the authors set out to do.<br /> Fig. 3 explains the DR and overfeeding setup, and effects on growth and reproduction; demonstrates that the automated feeding system does achieve DR.<br /> Fig. 4 explains the DR setup and results on male and female KF, highlighting the fact that DR only extends the lifespan of males. This sex-specific effect seems somewhat surprising, and warrants further follow-up studies.<br /> Fig. 5 describes the associative learning assay, which is based on the ability of the fish to sense a red light and learn that it is associated with feeding. It is great that the authors have been able to develop a learning assay, which will no doubt become an important tool in the killifish researcher's arsenal, but additional experiments are necessary to increase the general impact of the work.

Overall, while the results seem sound, the current version of the manuscript may be pitched to a small audience (killifish researchers) who will benefit from the development of this methodology. Perhaps the paper could be re-structured to focus less on the methodology and more on the results, fleshing out the associative learning results even more (are there mutants that extend the length of associative learning? Does it require conserved genes? etc). Further exploration of the sex-specific effects of DR on lifespan (why does this only affect males) would also raise the general interest of the work, but both the DR and associative learning aspects of the paper would need to be studied quite a bit more to move this beyond a methods paper.

Reviewer #2 (Public Review):

In the current manuscript, Feng et al. investigate the mechanisms used by acute leukemia to get an advantage for the access to the hematopoietic niches at the expense of normal hematopoietic cells. They propose that B-ALLs hijack the niche by inducing the downmodulation of IL7 and CXCL12 by stimulating LepR+ MSCs through LTab/LTbR signaling. In order to prove the importance of LTab expression in B-ALL growth, they block LTab/LTbR signaling either through ligand/receptor inactivation or by using a LTbR-Ig decoy. They also show that CXCL12 and the DNA damage response induce LTab expression by B-ALL. They finally propose that similar mechanisms also favor the growth of acute myeloid leukemia.

Although the proposed mechanism is of particular interest, further experiments and controls are needed to strongly support the conclusions.

1/ Globally, statistics have to be revised. The authors have to include a "statistical analysis" section in the Material and Methods to explain how they proceeded and specify for each panel in the figure legend which tests they used according to the general rules of statistics.

2/ The setup of each experiment is confusing and needs to be detailed. Cell numbers are not coherent from one experiment to the other. As an example, there are discrepancies between Fig1 and Fig2. Based on the setup of the experiment in Fig.2 (Injection of B-ALL to mice followed by 2 injections of treatment every 5 days), mice have probably been sacrificed 12-14 days post leukemic cell injection. However, according to Fig.1, B cells and erythroid cells at this time point should be decreased >10 times while they are only decreased 2-4 times in Fig.2. This is also the case in Fig.4B-J or Fig.5D with even a lower decrease in B cells and erythroid cells despite a high number of leukemic cells. Please explain and give the end point for each experiment in each figure (main and supplemental).

3/ To formally prove that the observed effect is really due to LTab/LTbR signaling, the authors must perform further control experiments. LTbR signaling is better known for its positive role on lymphocyte migration. They cannot rule out by blocking LTbR signaling, that they inhibit homing of leukemic cells into the bone marrow through a systemic/peripheral effect, more than through an impaired crosstalk with BM LepR+ cells. They must confirm for inhibited/deficient LTbR signaling conditions, as compared to control, that similar B-ALL numbers home to the BM parenchyma at an early time point after injection. Furthermore, they cannot exclude that the effect on the expression of IL7 (and other genes), and consequently the effect on B cell numbers, is not simply due to the tumor burden. Indeed, B-ALL numbers/frequencies are different between control and inhibited/deficient signaling conditions at the time of analysis. The analyses should thus be performed at similar low and high tumor burden in the BM for both control and inhibited/deficient LTbR signaling conditions.

4/ LT/LTbR signaling is particularly known for its capacity to stimulate Cxcl12 expression. How do the authors explain that they see the opposite?

5/ The authors show that CXCL12 stimulates LTa expression in their cell line. They then propose that CXCR4 signaling in leukemic cells potentiates ALL lethality by showing that a CXCR4 antagonist reverses the decrease in IL7 and improves survival of the mice. This experiment is difficult to interpret. CXCL12 has been shown to be important for migration/retention of B-ALL in the BM and the decreased tumor burden is probably linked to a decreased migration more than an impaired crosstalk with LepR+ cells (see also point 3). If CXCL12 increases LTab expression, CXCR4 blockade should do the opposite. This result should be presented. The contradiction is that if B-ALLs induce a decrease in CXCL12 in the BM (in addition to IL7) and that CXCL12 regulates LTab levels, leukemic cells should be exhausted. Similarly, IL7 has been previously shown to stimulate LTab expression and B-ALL cells express the IL7R. Again, a decrease in IL7 should be unfavorable to B-ALL. How do they explain these discrepancies?

6/ In Supp 4A, RAG-/- mice are blocked at the pro-B cell stage and do not have pre-B cells. Please compare LTa and LTb expression by Artemis deficient pre-B cell to wt pre-B cells. In this experiment, the authors show that similarly to B-ALL artemis-/- pre-leukemic pre-B cells express high levels of LTab and induce IL7 downmodulation. Using mice deficient for LTbR in LepR+ cells, they show that IL7 expression is increased. However, in opposition to leukemic cells (see Figure 4F), pre-leukemic cells are increased in absence of LTab/LTbR signaling. Please explain this discrepancy. The authors use only one B-ALL model cell line for their demonstration (BCR-ABL expressing B-ALL). Another model should be used to confirm whether LTab/LTbR signaling does favor leukemic/pre-leukemic B cell growth.

7/ Pre-B cells are composed of large pre-B cells (pre-BCR+) and small pre-B cells (pre-BCR-). BCR-ABL B-ALL cells express the pre-BCR. What is the level of expression of LTa and LTb by each of these 2 subsets as compared to BCR-ABL B-ALL?

Reviewer #2 (Public Review):

This modeling paper looks at how single spikes in the cortex are able to evoke patterns of sequential neural response in the surrounding neural network, an effect observed in the visual cortex of turtles, rodents, and the middle temporal cortex of humans, and possibly generalizable across many other species and brain areas. The results are anchored by population recordings from the turtle cortex, recapitulating those data and exploring how single spikes might be able to have such an outsized effect on broad-scale neural activity. The authors aim to show which kinds of network connectivity support this kind of response.

The results reveal that sparse, but strong connections in a neural network are the necessary ingredient for the reliable triggering of network sequences by single spikes. Dense, but weaker networks can give rise to different sequences when triggered. One of the most intriguing results of the paper is the interaction of sequences triggered by different single spikes that are part of a strong, sparse sub-network. These concurrent sequences appear to be separable and potentially supported a wide repertoire of response states to very targeted and combinatorially expressive inputs.

The work is careful and well-executed and the work will be of interest to systems and computational neuroscientists. In particular, the work speaks to how to reliably trigger a wide array of broad-scale population sequence patterns. This could be important for signaling salient, complex external stimuli, especially in a dynamic environment. The work will also be of interest to the machine learning community working on recurrent neural networks and their computational capacity.

Reviewer #2 (Public Review):

Grasses develop morphologically unique stomata for efficient gas exchange. A key feature of stomata is the subsidiary cell (SC), which laterally flanks the guard cell (GC). Although it has been shown that the lateral SC contributes to rapid stomatal opening and closing, little is known about how the SC is generated from the subsidiary mother cell (SMC) and how the SMC acquires its intracellular polarity. The authors identified BdPOLAR as a polarity factor that forms a polarity domain in the SMC in a BdPAN1-dependent manner. They concluded that BdPAN1 and BdPOLAR exhibit mutually exclusive localization patterns within SMCs and that formative SC division requires both. Further mutant analysis showed that BdPAN1 and BdPOLAR act in SMC nuclear migration and the proper placement of the cortical division site marker BdTANGLED1, respectively. This study reveals a unique developmental process of grass stomata, where two opposing polarity factors form domains in the SMC and ensure asymmetric cell division and SC generation.

The findings of this study, if further validated, are novel and interesting. However, I feel that the data presented in the current manuscript do not fully support some crucial conclusions. The lack of dual-color images is the weakest point of this study. If it is technically impossible to add them, alternative analyses are needed to validate the main conclusions.

1. Is BdPOLAR-mVenus functional? Although the authors interpret that weak BdPOLAR-mVenus expression partially rescued the bdpolar mutant phenotype in Fig. S4D, the localization pattern visualized by BdPOLAR-mVenus may not be completely reliable with this partial rescue activity.<br /> 2. Regardless of the functionality of the tagged protein, the authors need to provide more information on their localization. For example, is there a difference in polarity pattern depending on expression level? Does overexpressed BdPOLAR-mVenus invade the BdPAN1 zone? In such cases, might the loss of BdPOLAR polarity in the bdpan1 mutant be a side effect of overexpression, not PAN1 exclusion? Does BdPOLAR expression (no tag) show a dose-dependent effect, similar to the mVenus-tagged protein?<br /> 3. A major conclusion of this study was that the polarity domains of BdPOLAR and BdPAN1 are mutually exclusive. However, not all the cells in the figures were consistent with this statement. For example, the BdPOLAR signals at the GMC/SMC interphase appear to match BdPAN1 localization (compare 0:03 s in Video 1 and 0:20 s in Video 2 [top cell]). The 3D rendered image in Fig. 2F shows that BdPOLAR is excluded near the GMC on the front side of the SMC, where BdPAN1 is not localized. Some cells did not exhibit polarization (Fig. 3A, bottom left; Fig. 3E, bottom left). The most convincing data are the dual-color images of these two proteins. Otherwise, a sophisticated image analysis is required to support this conclusion.<br /> 4. Another central conclusion was that BdPOLAR was excluded at the future SC division site, marked with BdTANGLED1. However, these data are also not very convincing, as such specific exclusion cannot be seen in some figure panels (e.g., Fig. 3A, bottom left; Fig. 3E, all three cells on the left). If dual-color imaging is not feasible, a quantitative image analysis is needed to support this conclusion.<br /> 5. I could not find detailed imaging conditions and data processing methods. Are Figs. 2B and 2E max-projection or single-plane images? If they are single-plane images, which planes of the SMC are observed? In addition, how were Figs. 2C and 2F rendered? (e.g., number of images, distance intervals, processing procedures). This information is important for data interpretations.<br /> 6. [Minor point] The authors should clearly describe where BdPAN1 is expressed and localized. Is it expressed in the GMC and localized at the GMC/SMC interface? Alternatively, is it expressed and localized in the SMC?

Reviewer #2 (Public Review):

In the paper by Chadwick et al., the authors identify the molecular determinants of CO2 tolerance in the human fungal pathogen Cryptococcus neoformans. The authors have screened a collection of deletion mutants to identify the genes that are sensitive at 37oC (host temperature) and elevated CO2 levels. The authors identified that the genes responsible for CO2 sensitivity are involved in the pathways responsible for thermotolerance mechanisms such as Calcineurin, Ras1-Cdc24, cell wall integrity, and the Regulator of Ace2 and Morphogenesis (RAM) pathways. Moreover, they identified that the mutants of the RAM pathway effector kinase Cbk1 were most sensitive to elevated temperature and CO2 levels. This study uncovers the previously unknown role of the RAM pathway in CO2 tolerance. Transcriptome data indicates that the deletion of CBK1 results in an alteration in the expression of CO2-related genes. To identify the potential downstream targets of Cbk1, the authors performed a suppressor screen and obtained the spontaneous suppressor mutants that rescued the sensitivity of cbk1 mutants to elevated temperature and CO2. Through this screen, the authors identified two suppressor groups that showed a modest improvement in growth at 37{degree sign}C and in presence of CO2.<br /> Interestingly, from the suppressor screen, the authors identified a previously known interactor of Cbk1 which is SSD1, and an uncharacterized gene containing a putative Poly(A)-specific ribonuclease (PARN) domain named PSC1 (Partial Suppressor of cbk1Δ) which acts downstream of Cbk1. Deletion of these two genes in cbk1 null mutants rescued the sensitivity to elevated CO2 levels and temperature but did not fully rescue the ability to cause disease in mice.

This study highlights the underappreciated role of the host CO2 tolerance and its importance in the ability of a fungal pathogen to survive and cause disease in host conditions. The authors claim to gain insight into the genetic components associated with carbon dioxide tolerance. The experimental results including the data presented, and conclusions drawn do justice to this claim. Overall, it is a well-written manuscript. However, some sections need improvement in terms of clarity and experimental design.

• One major drawback of the study is the virulence assay performed to test the ability of cbk1 mutants to cause the disease in the mouse model. The cbk1 null mutants are thermosensitive in nature. Using these mutants, establishing the virulence attributes in mice would undermine the mutants' ability to infect mice as they won't be able to survive at the host body temperature.

• The rationale for choosing the genes to test further is not clear in two instances in the study. a) From a list of 96 genes, how do the authors infer the pathways involved? Was any pathway analysis performed that helped them in shortlisting the pathways that they subsequently tested? A GO term analysis of the list of genes identified through the genetic screen would be more helpful to get an overview of the pathways involved in CO2 tolerance. b) The authors do not clearly mention why they chose only four genes to test for the CO2 sensitivity out of 16 downregulated genes identified from the nano string analysis.

• It would be more useful to the readers if the authors could also include a thorough analysis of the presence of the putative PARN domain-containing protein across various fungal species rather than mentioning that it is only observed in C. neoformans and S. pombe. Also, the authors may want to discuss the known role(s) of SSD1, if any, in pathogenic ascomycetous yeasts so that the proposed functional divergence is supported further.

Reviewer #2 (Public Review):

The authors re-analysed two previously published metagenomic datasets to test how diversity at the community level is associated with diversity at the strain level in the human gut microbiota. The overall idea was to test if the observed patterns would be in agreement with the "diversity begets diversity" (DBD) model, which states that more diversity creates more niches and thereby promotes further increase of diversity (here measured at the strain-level). The authors have previously shown evidence for DBD in microbiomes using a similar approach but focusing on 16S rRNA level diversity (which does not provide strain-level insights) and on microbiomes from diverse environments.

One of the datasets analysed here is a subset of a cross-sectional cohort from the Human Microbiome Project. The other dataset comes from a single individual sampled longitudinally over 18 months. This second dataset allowed the authors to not only assess the links between different levels of diversity at single timepoints, but test if high diversity at a given timepoint is associated with increased strain-level diversity at future timepoints.

Understanding eco-evolutionary dynamics of diversity in natural microbial communities is an important question that remains challenging to address. The paper is well-written and the detailed description of the methodological approaches and statistical analyses is exemplary. Most of the analyses carried out in this study seem to be technically sound.

The major limitation of this study comes with the fact that only correlations are presented, some of which are rather weak, contrast each other, or are based on a small number of data points. In addition, finding that diversity at a given taxonomic rank is associated with diversity within a given taxon is a pattern that can be explained by many different underlying processes, e.g. species-area relationships, nutrient (diet) diversity, stressor diversity, immigration rate, and niche creation by other microbes (i.e. DBD). Without experiments, it remains vague if DBD is the underlying process that acts in these communities based on the observed patterns.

Another limitation is that the total number of reads (5 mio for the longitudinal dataset and 20 mio for the cross-sectional dataset) is low for assessing strain-level diversity in complex communities such as the human gut microbiota. This is probably the reason why the authors only looked at one species with sufficient coverage in the longitudinal dataset.

Analyzing the effect of diversity at a given timepoint on strain-level diversity at a later timepoint adds an important new dimension to this study which was not assessed in the previous study about the DBD in microbiomes by some of the authors. However, only a single species was analysed in the longitudinal dataset and comparisons of diversity were only done between two consecutive timepoints. This dataset could be further exploited to provide more insights into the prevailing patterns of diversity.

Finally, the evidence that gene loss follows increase in diversity is weak, as very few genes were found to be lost between two consecutive timepoints, and the analysis is based on only a single species. Moreover, while positive correlation were found between overall community diversity and gene family diversity in single species, the opposite trend was observed when focusing on pathway diversity. A more detailed analysis (of e.g. the functions of the genes and pathways lost/gained) to explain these seemingly contrasting results and a more critical discussion of the limitations of this study would be desirable.

Reviewer #2 (Public Review):

The main analysis performed in the paper is to determine causal associations of 118 highly correlated lipid metabolites with coronary heart disease (CHD), using summary data from two genome-wide association studies, with 148 genetic variants identified for the exposures. A standard multivariable MR analysis is problematic in this case, as the genetic variants are not simultaneously relevant for all exposures, as clearly indicated by very low values of the conditional F-statistics. In order to reduce the multicollinearity problem, the use of (sparse) principal components techniques is proposed. For the summary data used here, this entails determining the (sparse) principal components from the matrix of the estimated univariate associations of the exposures and the genetic markers. This implicitly constructs linear combinations of the exposures. In a simulation study, this approach is shown to work well for determining whether an exposure has a causal association with the outcome. A conditional F-statistic is developed to evaluate the strength of relevance of the genetic markers for the principal components. In the application, these F-statistics show that instruments are jointly relevant for the transformed exposures. For the sparse methods, the transformed exposures are loaded on VLDL, LDL, and HDL traits, hence obtaining causal estimates for intervening on biologically meaningful pathways.

The dimension reduction techniques and the results obtained are very interesting. As the analysis is performed on summary statistics, the univariate associations are treated as data, on which to perform the principal components analysis. This could be explained more and contrasted with a standard PCA when one has all the individual-level data available.

Reviewer #2 (Public Review):

The authors addressed a timely and challenging topic, namely the role played by red blood cells (RBCs) and blood plasma in Covid-19 disease.

A remarkable feature reported here is that RBC from patients exhibits a notable morphological change, whereas when suspended in plasma control (healthy) exhibit normal shapes. Conversely, RBCs from healthy donors suspended in patients' plasma undergo similar morphological alteration as do RBCs of patients suspended in their plasma. Another important fact reported here is that RBCs affect plasma composition in a nontrivial way.

The data reported here cover a large panel of features, ranging from RBC morphological changes, plasma metabolites, and protein alteration, to collective RBC formation, in the form of clusters. They should constitute a precious enrichment of relevant information regarding the intricate response of organisms to the Covid-19 virus.

This work will be of the potential impact on the community aiming to decipher the multifactorial impacts of blood components on patients suffering Covid-19.

Reviewer #2 (Public Review):

This study models the fitness costs of loss-of-function mutations in a large cohort of a human database of 55,855 individuals. The modeling indicates different values for autosomal genes, X-linked genes, and those present in the pseudo-autosomal regions of the X and Y chromosomes. The study details the frequency of de novo mutations in zygotes and examined the relationship to a few specific genetic diseases. The authors have composed a well-written manuscript, have explicitly detailed their assumptions, and have noted caveats to interpretations. The results are a valuable documentation of the effects of loss-of-function mutations in humans.

Reviewer #2 (Public Review):

Interestingly, prior analysis of the 385A allele indicated a post-translational mechanism that led to instability of the protein and an ~50% reduction in protein concentrations and FAAH activity. In addition, FAAH degrades AEA, one of several known endocannabinoids, suggesting that FAAH is a significant part of the endocannabinoid signaling although there are several other endocannabinoids that are not affected by FAAH.

At the basal state, normal chow and home cage conditions, wild type mice were not different from homozygous mutant FAAH mice in terms of body weight and body composition. However, the FAAH mutants had reduced food intake that was compensated for by lower energy expenditure. This finding strongly suggests that compensatory mechanisms are in play during lifelong changes in strengths of AEA signaling.

The authors go on to perform increasingly shorter durations of manipulations of glucocorticoid manipulations (down to several hours) to examine the impact of the FAAH mutation. Thus, the authors are able to conclude that the FAAH mutation leads to acute changes of feeding.

Examination of the biochemical signaling pathway showed that AMPK activity is affected by GC/FAAH experimental manipulation although the relevance of the finding should be somewhat tempered by the later studies in hypothalamic AGRP neurons and FAAH since the measures were not neuron specific.

Finally, the authors examine the role of FAAH expression in hypothalamic AGRP neurons since their measures of AEA concentrations showed changes only in the hypothalamus after CORT treatments. Virally mediated knockdown of FAAH, using a AAV CRISPR/Cas9 single vector system, indicated that knockdown of FAAH in AGRP neurons is sufficient to recapitulate the authors' findings on GC-modulated feeding.

The data are convincing and settles the issue of variability in the evidence regarding the role of FAAH genetic variants in feeding.

Reviewer #2 (Public Review):

In this paper, the authors investigate the intriguing question of what orientation reference frame the visual selectivity of neurons in the IT cortex is expressed in - a world-centered gravitational one, or a retinal one? To address this, the authors physically rotate a monkey to dissociate a gravitational from a retinal reference frame. They find surprising and compelling evidence that many cells encode selectivity in a gravitational frame. The finding raises questions about whether the function of the IT cortex is solely object recognition, or whether it might play an important role in physical scene understanding.

In general, I found the paper clearly written, the analyses appropriate, and the results supportive of the conclusions. I think the work should spur new thinking about what the IT cortex is accomplishing. The notion that IT cells are receiving vestibular signals is likely to be unsettling for many who think of it as simply the endstage of a convolutional neural network.

Reviewer #2 (Public Review):

In this report, the authors evaluate the possibility that LEC neurons send direct projection onto MEC cells, thus revising the current model of LEC and MEC sending independent inputs to the DG, whose role is to eventually combine both inputs. They demonstrate that L2a SCs in the LEC that receive neocortical inputs, send collaterals to L1 MEC, thus identifying a new indirect route by which MEC neurons can integrate cortical information. Vandrey et al., show that L2a SCs in the LEC contact directly with both inhibitory and excitatory cells in the MEC, but superficial principal cells with a higher probability. Therefore, L2 LEC neurons can exert control of the MEC activity, thus shaping its inputs to the hippocampus. By controlling the firing activity in superficial MEC, this newly identified LEC-MEC connection may participate in the combination of spatial inputs with sensory and high-order signals and thus "provide a substrate for the integration of 'what' and 'where' components of episodic memories".

The manuscript is well-written and the experimental design is well-suited to answer the question. The data presented here is a thorough, well-explained, and detailed work describing a new communication route between the LEC and MEC.

Reviewer #2 (Public Review):

The study aims to characterize the role of lncRNA H19 in senescence and proposes a mechanism involving CTCF and the activation of p53. The authors suggest that H19 loss induces let7b-mediated repression of EZH2, which is a critical component in the regulation of senescence-associated genes. Additionally, the authors state that H19 is required for inhibition of senescence by the mTOR inhibitor rapamycin.

The experiments appear to be performed to a high standard, and the individual observations, and conclusions about the importance of the individual players in senescence appear solid. For example, the authors convincingly show that H19 decreases in expression in aged cells/tissues and that its knockdown leads to entry into senescence. These results are consistent with recent studies in other systems (e.g., ref 38). Also, the knockdown of CTCF convincingly leads to senescence. However, these observations are largely not very surprising/novel. The premise of the manuscript is a connection between these components into a particular "axis" that regulates entry into senescence. This connection between the different regulators studied (H19, CTCF, EZH2, p53), and in particular, their specificity, which is key to the proposed "axis" remains insufficiently supported, and many of the results, unfortunately, appear to be over-interpreted.

Major comments

1. In Figure 1, the authors claim that H19 levels are reduced during aging in vitro and in vivo and that H19 levels are maintained by rapamycin treatment. To state the connection between H19 and rapamycin and its relation to aging, there is a need to show what happens in "young" cells treated with rapamycin.

Furthermore, the authors state that H19 "is essential for the inhibitory effect of rapamycin on cellular senescence". There doesn't appear to be sufficient evidence to support such a claim; additional data emphasizing the direct connection between H19 and rapamycin is needed - e.g., show that in H19-null cells rapamycin does not affect senescence.

2. CTCF is a general regulator involved in various cellular processes and supporting progression through the cell cycle; therefore, its perturbation can lead to global effects on cell health that are not necessarily related to H19. The data shown in figure 2 is insufficient to indicate a direct correlation between CTCF and H19. This will require showing that mutating specifically the CTCF binding sites near H19 affects senescence.

The same applies to the connection between H19 and let-7b shown in Figure 5. It is not very surprising that let-7b, a general antagonist of proliferation, positively regulates senescence. Here as well, the direct connection to H19 is weak. Can the authors rescue the cells that enter senescence following H19 depletion by H19 expression? If so - is this rescue capacity lost when let-7 sites are mutated? Is it possible to rescue by expressing an artificial let-7 sponge instead of H19? Otherwise, let-7b could very well be another factor related to senescence and/or regulated, but not the main mediator of the effects of H19, or part of an axis that includes H19, as proposed in the manuscript.

3. In figures 2d,3f,5i/j the authors present only representative tracks and regions from CUT&Tag-experiments, and its not clear to what extent these changes are significant when considering genome-wide data, replicates etc., and so these data are uninterpretable. This is important, as these panels are used as evidence for specific connections between members of the axis. The authors should provide a statistical test for all the regions in the genome, based on replicates, and show that these changes are significant to use these data to support their model. Otherwise, the specific connection between CTCF and H19 remains weak, and the specific change in p53 regulation of CTCF in the context of senescence is not convincing. In any case, the number of replicates and the QC of the data should be presented, and the data should be made available to the reviewers.

4. The authors state in the Discussion that the mechanism that lead to decreased H19 expression as part of the senescence program consists of two phases: an acute response driven by p53 activation and a prolonged response dictated by the loss of CTCF. There doesn't appear to be enough evidence to support this claim, as the individual experiments don't measure any such bi-phasic phenomena.

Reviewer #2 (Public Review):

In this manuscript, Eyndhoven and colleagues develop an experimental and analytical setup to test the role of cell-intrinsic factors in guiding fate decisions to viral infections. The study is motivated by the observations that early antiviral response mediated by type 1 interferon (IFN-1) is not fully penetrant in response to virus, and is initiated only in 1-3% of the cells. Using a combination of IFN-1 reporter system, automated image segmentation, DNMT inhibitors, and Luria-Delbrück fluctuation test in a murine cell line model, the authors state that cell intrinsic factors guide IFN-1 response in rare cells. This response (measured with IRF7 translocation) is predetermined and heritable over several generations. Lastly, the authors report cell density effects on IFN-1 response, a phenomena the authors refer to as "quorum sensing", and rationalize their observations with an ODE-based mathematical model.

Overall, this is a well-designed, well-controlled, and timely study, given the rapidly increasing reports documenting heritable cell states that can guide fate choices in single cells. The manuscript has elegant experiments and is generally clear to follow and the figures are easy to understand. While the authors largely state what they find, some of their claims and terminology are not supported by their experiments. Additionally, many figures lacked scale bars, axis, labels, and detailed captions. The authors are also encouraged to cite a wider set of seminal studies, acknowledging their contributions to transient cell states guiding fate choices.

Reviewer #2 (Public Review):

This manuscript by Lehman et al. details the structural characterization of human Ferroportin, which builds on the previous structural characterisation of this protein. Here, through the use of synthetic nanobodies, the authors capture the protein in the outward-facing state that has been obtained previously, and a new conformation in an occluded state, information which would advance understanding of the Ferroportin transport mechanism. In addition, the authors capture Ferroportin in complex with the first clinical-stage Ferroportin inhibitor, Vamifeport, which provides insight that could be used to improve inhibitor efficacy to treat human disease. The structural data is very well supported by clear, well-executed, and informative binding and transport studies. These data reveal that the purified protein is functionally active, able to interact with the peptide-based inhibitor hepcidin in addition to Vamifeport and that hepcidin and vamifeport bind competitively. Site-directed mutagenesis and binding assays were used to convincingly validate the Vamifeport binding site.

Overall, the conclusions in this manuscript are well supported by the data, in particular those relating to inhibitor binding. However, as the authors point out, the occluded state captured here contains an unexpectedly large aqueous cavity compared to the size of the transported substrate. With this peculiar observation in mind, the requirement for the presence of Sy3 nanobody to capture this state and the positioning of the nanobody in between the 2 lobes of the transporter, raises the question of whether this conformation is physiologically relevant, or whether its formation is merely a consequence of Sy3 binding.

Reviewer #2 (Public Review):

This paper from the Fyodorov lab reports the isolation of a native protein complex of SUUR, a Drosophila SNF2-related factor, in a complex with Mdg4, an established chromatin boundary protein. The discovery of this native complex, called SUMM4, was enabled by the development of a mass spec-linked proteomic analysis of fractions from an unbiased, conventional multi-step chromatographic purification of low-abundance protein complexes. The authors validate the native interactions by co-immunoprecipitation and show further with recombinant proteins that SUUR displays ATPase activity, a property not previously shown, and which is stimulated by Mdg4. From a functional perspective, authors demonstrate that both components SUUR and Mdg4 mediate activities of the Drosophila gypsy insulator that blocks enhancer-promoter interactions and acts as a heterochromatin-euchromatin barrier, and moreover, has a role in the under-replication of intercalary heterochromatin.

Overall, this work is a substantial contribution to the field in two respects. First, it provides a new approach to the identification of novel native complexes that are of low abundance and difficult to isolate and identify by conventional biochemistry and mass spectrometry. Second, the interaction between Mdg4 and SUUR is novel and offers an ATP-driven pathway to be further investigated for understanding the mechanism of insulator (gypsy) function. Together, these advances are supported by the compelling quality and quantity of data. However, the paper does not read smoothly and can benefit from rewriting for readers who are not familiar with mass-spec proteomics or Drosophila biology.

Reviewer #2 (Public Review):

Kintscher et al present a nice study on the responses of Adora2a and D1R expressing cells in the tail of the striatum/amygdala transition zone during auditory fear conditioning. Overall the conclusions are that (1) D1R cells show plasticity in activity patterns during the task, with the emergence of tone/movement co-modulated cells; (2) Adora2a cells show less of such changes; (3) gain of function of activity does little where (4) loss of function of activity in each cell class has moderate effects on the learned behavior (i.e. freezing to the CS). There is a nice section on rabies tracing which maps inputs to both cell types which then motivates an analysis of insular cortex inputs onto both cell types and reveals that (5) CS/US pairing alters insular inputs to both cell types.

Overall the paper is well done and the conclusions are believable. Furthermore, this brain area is understudied yet potentially very important.

The analysis of the fluorescence transients is heavy handed. This leads to potential for error and could obscure what appear to be large differences that could be extracted more easily. In some instances, the data are interpreted too optimistically, especially that the silencing experiments implicate plasticity of the neurons rather than the need for activity.

Reviewer #2 (Public Review):

The authors present an R/Bioconductor package, scatterHatch, aimed at providing a novel framework for the creation of color-vision deficiency (CVD) accessible plots. The authors lay out that in increasingly common dimensionality reduction plots, like UMAPs and tSNEs, color tends to be the primary factor for distinguishing points of distinct groups. Although color palettes created with accessibility to CVDs in mind are often helpful, none adequately cater to all forms of CVD. Further, when too many colors are needed, even viewers with full-color vision may struggle. The authors lay out the current primary alternative to color, using point shape, which only works for sparse plotting regions, but most data points in UMAP and tSNE plots are not in sparse regions of the plot. All very true, thus demonstrating the need for a tool like scatterHatch, which can overlay hatch patterns both over regions in dense portions of a scatter plot, and also over points within automatically detected sparsely populated regions. The primary function of scatterHatch produces such plots from a given data frame and the names of columns to use for x, y, and color. The authors go on to demonstrate, with example figures, how the hatch patterns are indeed helpful in cases where color is not enough on its own. They demonstrate that the user can delineate custom hatch patterns, which gives flexibility to the user over how much to rely on hatch patterns versus color. Of particular note, the authors show how scatterHatch can be helpful for readers with monochromatic vision, a population that other visualization tools designed with CVD-accessibility in mind often still fail to aid.

Reviewer #2 (Public Review):

Rava et al. by creating a series of deletion mutants of tRNAs, rRNAs, and tRNA modifying enzymes, have shown the importance of gene copy number redundancy in rich media. Moreover, they successfully showed that having too many tRNAs in poor media can be harmful (for a subset of the examined tRNAs). Below, please find my comments regarding some of the methodologies, conclusions, and controls needed to stratify this manuscript's findings.

Figure 2 presents Rrel as a relative measurement (GRmut/GRwt). Therefore, I'm confused as to how Rrel can be negative, as shown in supplemental file 3 (statistics).<br /> Does Figure 3 show the mean of 4 biological replicates or technical replicates? It should be stated clearly in the legend of figure 3.

Do all strains (datapoint on figure 3 left panel) significantly perform better than the WT in nutrient downshift? Looking at supplemental file 3 I see this is not the case. Please mark the statistically significant points. I suggest giving each set a different symbol/shape and coloring the significant ones in red.

Another issue is that in the statistics of figure 2 (in supplemental file 3), positive values reflect cases where the mutant performs poorly compared to the WT, while in figure 3 the negative values indicate this. Such discrepancy is not very clear. And again, how can Rrel be negative?

Both axes say glycerol. What about galactose?

Lines 414-419: The authors state that "all but one had a growth rate that was comparable to WT (16 strains) or higher than WT (10 strains) after transitioning from rich to poor media (i.e. during a nutrient downshift, note data distribution along the x-axis in Fig 3; Supplementary file 3). In contrast, after a nutrient upshift, 11 strains showed significantly slower growth in one or both pairs of media, and only 2 showed significantly faster growth than WT (note data distribution along the y-axis in Fig 3; Supplementary file 3)".

Looking at the Rrel values when transitioning from TB to Glycerol and vice versa suggests no direction in the effect of reducing redundancy. During downshift, four strains perform better, and three strains perform worse than the WT. During upshift, four stains perform better, and six strains perform worse. Only during downshift and upshift from TB to Gal and vice versa give a strong signal.

The authors should write it clearly in the text because the effect is specific to that transition/conditions and not of general meaning is written in the text (e.g., transition from every rich to every poor media and vice versa). I am convinced that the authors see an actual effect when downshifting or upshifting from TB to galactose and vice versa. In that case, the conclusion is that redundancy is good or bad depending on the conditions one used and not as a general theme.

Also, this is true just for some tRNAs, so I don't think the conclusion is general regarding the question of redundancy.

Figures are indicated differently along the text. Sometimes they are written "figure X", sometimes FigX. Referring to the supplemental figures are also not consistent.<br /> Line 443-444: "In fact, 10 tRNAs were significantly upregulated in the poor medium relative to the rich medium".

This result contradicts the author's hypothesis. If redundancy is bad in poor media because the cells have more tRNAs than they need, the tRNAs level will be downregulated, not upregulated. How do the authors explain this?

Line 445-447: "In contrast (and as expected), all tested tRNA deletion strains had lower expression of focal tRNA isotypes in the rich medium (Fig 4B, left panel), showing that the backup gene copies are not upregulated sufficiently to compensate for the loss of deleted tRNAs".

It is great that the authors validated the expression in their strains. However, for accuracy, please indicate that it was done in four strains to avoid the impression that they did it in all the strains.

Finally, across the manuscript, the authors reveal that deleting some tRNAs or modifying enzymes can be deleterious in rich media or advantageous in poor media. However, I think this result and the conclusions derived from it could be more convincing if the authors would show in a subset of their strains that expressing the deleted tRNAs or modifying enzymes from a plasmid can rescue the phenotype.

Reviewer #2 (Public Review):

In this manuscript, Geisberg et al. present profiles of poly(A) site usage in cells with RNA Polymerase II variants transcribing at different elongation rates. It was known that transcript termination sites in cell populations occur as clusters at the 3'UTR of genes but how the choice of poly(A) site may be influenced by transcription elongation speed was not known.

The strength of their study involves using 3' READ technologies and data analyses that they have previously developed. A weakness of the study is that since the speed of elongation of Pol II is central for the data obtained and conclusions drawn, it would be important to actually measure the speed of elongation by the slow, fast, and wt Pol II used in these studies within the genes analyzed. Although the findings presented in this manuscript are not surprising, they are novel and contribute a missing piece of how the transcription machinery determines which poly(A) site to utilize at the end of genes.

Reviewer #2 (Public Review):

As shown in this study, the focal adhesion protein, kindlin-2, plays an essential role in liver function in that its genetic inactivation leads to severe liver fibrosis and death in young mice. This lethality is attributed to activation of TNF-mediated inflammation and caspase-8-dependent cell death since effects of kindlin-2 (Fermt2 gene) knockout can be reversed by genetic inactivation of TNF or caspase signaling. Evidence is also presented that kindlin-2 overexpression can have a mildly protective effect on acute liver toxicity. Overall, this work successfully connects kindlin-2 with normal liver function and raises the possibility that modulation of kindlin-2 could have therapeutic potential for treating liver disease.

On the other hand, the underlying mechanism explaining why kindlin-2 loss stimulates TNF, caspase 8, inflammation, and fibrosis is not explored. As a major component of focal adhesions via its interaction with integrins, kindlin2 has primary functions in regulating cell-ECM signaling and mechanotransduction. However, this study does not connect these known functions with the liver fibrosis and inflammation observed. For example, only cursory analysis is provided concerning the effects of kindlin-2 loss on hepatocyte-ECM interactions, cytoskeletal structure, or focal adhesion distribution. Also, the slightly protective effects of Kindlin-2 overexpression on D-galactosamine/LPS-induced liver toxicity and death are not connected to the rest of the study. Also, one might question whether extending mouse survival by approx. 3-4 hrs with kindlin-2 overexpression is a potentially clinically relevant finding.

Reviewer #2 (Public Review):

In this manuscript, Ruesseler and colleagues use a continuous task to examine how neural correlates of decision-making change when subjects face conditions with different durations and frequencies of occurrence of signals embedded in noise. The authors develop a novel task where subjects must report the direction of relatively sustained (3 or 5 s) signal changes in average coherence of a random dot kinetogram that are intermittent among relatively transient noise fluctuations (<1 s) of motion coherence that is continuous. Subjects adjust their behavior to changes in the duration of signal events and the frequency of their occurrence. The authors estimate a decay time constant of leaky integration of evidence based on the average coherence leading up to decision responses. Interestingly, there is considerable inter-subject variability in decay time constants even under identical conditions. In addition, the average time constants are shorter when signal periods occur more frequently as opposed to when they are more rare. The authors use EEG to find that a component of the Centroparietal Positivity (CPP) regressed to the magnitude of changes in the noise coherence is larger in conditions when the signal periods occur less frequently. Using a control condition, the authors show that this component of the CPP is not simply based on surprise because it is smaller for changes in motion coherence in irrelevant directions with matched statistics as the changes in relevant directions. The authors also find that a different component of the CPP related to the magnitude of the motion coherence co-varies with the inter-subject variability in decay time constants estimated from behavior.

Overall, the authors use a clever experimental design and approach to tackle an important set of questions in the field of decision-making. The manuscript is easy to follow with clear writing. The analyses are well thought-out and generally appropriate for the questions at hand. From these analyses, the authors have a number of intriguing results. So, there is considerable potential and merit in this work. That said, I have a number of important questions and concerns that largely revolve around putting all the pieces together. I describe these below.

1) Quite sensibly, the authors hypothesize that "decay time constant" for past evidence and "decision threshold" would be altered between the different task conditions. They find clear and compelling evidence of behavioral alterations with the conditions. They also have a method to estimate the decay time constant. However, it is unclear to what extent the decision threshold is changing between subjects and conditions, how that might affect the empirical integration kernel, and how well these two factors can together explain the overall changes in behavior.

To be more specific, the authors state that the lower false alarm rates and slower reaction times for the LONG condition are consistent with a more cautious response threshold for LONG. The empirical integration kernels lead to the suggestion that the decay time constant is not changing between SHORT and LONG, while it is changing between FREQUENT and RARE. Does the lack of change in false alarm rate between FREQUENT and RARE imply no change in the decision threshold? Is this consistent with the behavior shown in Figure 2? I would expect that less decay in RARE would have led to more false alarms, higher detection rates, and faster RTs unless the decision threshold also increased (or there was some other additional change to the decision process). The CPP for motor preparatory activity reported in Fig. 5 is also potentially consistent with a change in the decision threshold between RARE and FREQUENT. If the decision threshold is changing, how would that affect the empirical integration kernel? These are important questions on their own and also for interpreting the EEG changes.

2) The authors find an interesting difference in the CPP for the FREQUENT vs RARE conditions where they also show differences in the decay time constant from the empirical integration kernel. As mentioned above, I'm wondering what else may be different between these conditions. Do the authors have any leverage in addressing whether the decision threshold differs? What about other factors that could be important for explaining the CPP difference between conditions? Big picture, the change in CPP becomes increasingly interesting the more tightly it can be tied to a particular change in the decision process.

I'll note that I'm also somewhat skeptical of the statements by the authors that large shifts in evidence are less frequent in the RARE compared to FREQUENT conditions (despite the names) - a central part of their interpretation of the associated CPP change. The FREQUENT condition obviously has more frequent deviations from the baseline, but this is countered to some extent by the experimental design that has reduced the standard deviation of the coherence for these response periods. I think a calculation of overall across-time standard deviation of motion coherence between the RARE and FREQUENT conditions is needed to support these statements, and I couldn't find that calculation reported. The authors could easily do this, so I encourage them to check and report it.

3) The wide range of decay time constants between subjects and the correlation of this with another component of the CPP is also interesting. However, in trying to interpret this change in CPP, I'm wondering what else might be changing in the inter-subject behavior. For instance, it looks like there could be up to 4 fold changes in false alarm rates. Are there other changes as well? Do these correlate with the CPP? Similar to my point above, the changes in CPP across subjects become increasingly interesting the more tightly it can be tied to a particular difference in subject behavior. So, I would encourage the authors to examine this in more depth.

Reviewer #2 (Public Review):

The authors set out to study whether there is altered liver regeneration under physiological homeostatic conditions depending on whether an experimental model is offered continuous feeding or intermittently fasted. They report, using a series of murine models in male mice, that hepatic adjustments to fasting/refeeding occur including hyperproliferation of pericentral hepatocytes during a period of relative liver enlargement. It is interesting to note that this occurs 1 week after daily fasting/feeding cycles and appears to occur very quickly following the reintroduction of food. During fasting, they show that the liver shrinks relative to body weight then, as demonstrated by a series of lineage tracing experiments, undergoes relative hyperproliferation, particularly by pericentral hepatocytes. This was shown using an Axin2-based reporter and additionally through zonal analysis or a confetti-multicolored reporter used to trace individual clones. This response appears stable then for upto 3 months. Ideally, additional data showing the liver and body weight individually would help to give an impression of whether the predominant effect is due to changes in body weight or liver weight but it appears implicit that there is an active contraction of liver and hepatocyte size and number during fasting. This is then followed by rapid growth upon refeeding, presumably without major changes in body weight.

It is not clear whether the length of fasting is critical and what the proliferative and metabolic state of the liver is immediately prior to refeeding. It is also unclear whether the relative expansion of pericentral hepatocytes results in an expansion of the pericentral zone or whether these hepatocytes then repopulate other zonal compartments of the liver. They do provide single-cell transcriptomic data which supports the expansion of pericentral transcripts, however, whether this represents a functionally advantageous liver metabolism and how this is achieved remains will be important questions for the future. The link changes to bile acids to altered expression of Cyp7A1, which suggests a role for altered bile acid metabolism in the fasting state. It would be interesting, in the future, to explore whether a liver-to-intestinal feedback loop exists utilising the altered hepatic bile acids occurring during fasting/refeeding to signal back to the intestine for example. This would also then potentially have implications for liver disease states including cholestatic liver diseases.

Mechanistically the authors use hepatocyte-targeted FGF receptor depletion (Klb) or Wnt/b-catenin transcription factor depletion (Tbx3), through efficient adeno-associated viral vector targeting to manipulate these axes combined with hepatocellular FGF overexpression. They demonstrate that the FGF receptor Klb is expressed throughout the lobule and that its global knockout results in the loss of the pericentral proliferative response in fasting/refeeding. It is interesting to note that with the loss of Klb particularly a senescence response occurs in the areas that previously underwent proliferation in response to IF. Similarly, the loss of Klb alters the metabolic rewiring which occurred during the IF response, unlike Tbx3 depletion. Tbx depletion was separately shown to result in a polyploidisation response within the normally diploid pericentral area, consistent with the previous report from this group.

Broadly the authors achieve their aim of both describing the effects resulting from fasting upon liver regenerative biology and also shedding significant insights mechanistically into this process. Overall, these results are highly provocative and raise important questions when interpreting murine studies. These include whether the experimental effect on liver pathophysiology might be explained by or influenced by altered dietary intake as a result of animal husbandry or animal pathology. It will also be interesting in the future what effect broader dietary modifications have on the liver, and other organs, physiologically. These would include but are not limited to a high-fat diet, altered microbiome, variable fasting, and background body habitus. It also has implications for what happens in response to fasting/refeeding during development and the longer-term adaptive responses to this.

Reviewer #2 (Public Review):

The manuscript by Luan et. al. describes the role of EHD2 in promoting breast tumor growth. They showed that EHD2 cytoplasmic staining predicts poor patient outcome. Both EHD2 KO or knockdown cells showed decreased cell migration/invasion abilities and significant reduction of tumor growth and metastasis in mice. The authors further showed that the levels of EHD2 and Cav1/2 correlate with each other. EHD2 KO cells showed defects on Ca2+ trafficking. Overexpressing the SOCE factor STIM1 partially rescued SOCE defects in EHD2 KO cells. Treatment of the SOCE inhibitor SKF96365 inhibited tumor cell migration in vitro and tumor growth in vivo.

Major strengths:<br /> The authors showed that EHD2 cytoplasmic levels predict patient survival and provided strong evidence that EHD2 knockout or knockdown inhibits tumor cell migration in vitro and tumor growth in vivo. The authors also showed that SKF96365, which inhibits SOCE, suppresses tumor growth in vivo.

Major weaknesses:<br /> The connection between EHD2 and SOCE is weak.

Reviewer #2 (Public Review):

Van der Goes et al recorded HD cells in the retrosplenial cortex and anterodorsal nucleus of the mouse during the rotation of a prominent visual cue. They describe the temporal coordination of the HD representation between the two structures, also in the dark condition. They provide evidence for a near-simultaneous realignment of the HD representation in the two structures (no consistent temporal offset during the cue shift). This finding is interesting and quite surprising, in light of the existing literature postulating a role of the retrosplenial cortex in a binding visual landmark and HD information. I am not sure whether the authors' conclusions are convincingly supported by the data.

Reviewer #2 (Public Review):

Volume-regulated anion channels (VRACs), comprised of the LRRC8 family of proteins, play important roles in cell volume regulation. Physiological LRRC8 channels are heteromeric assemblies of LRRC8A and LRRC8B-LRRC8E subunits. Previous structural studies have focused on homomeric channels, which do not recapitulate functional properties of native heteromeric channels. Thus, the molecular basis of physiological VRAC assembly and function remains unknown. In this study, Takahashi and colleagues present the single-particle cryo-electron microscopy structure of a functional LRRC8 chimera, which is composed of LRRC8C and a swapped intracellular loop from LRRC8A. Surprisingly, the chimeric channel forms a heptamer, in sharp contrast to the previously reported hexamers of homomeric and heteromeric LRRC8 channels. The findings of the chimeric channel are interesting. However, the physiological implication of this chimera is unclear, and the proposal that native LRRC8 channels are heptamers is not well supported.

Reviewer #2 (Public Review):

In the present study, the authors have combined calcium imaging and electrophysiology to systematically replicate the previously reported finding that the mechanical activation ion channel Piezo1 might also serve as a gut RNA sensor. The authors have employed multiple cell lines, knockout of endogenous Piezo1, and heterologous overexpression of Piezo1, Yoda1 as a positive control for chemical activation of Piezo1, and similar dosage of ssRNA used in the previous study, but clearly did not replicate the finding that ssRNA can specifically activate Piezo1. The experiments have been well designed and data quality is high. The data support the conclusion that Piezo1 is not a receptor for ssRNA in the gut.

Reviewer #2 (Public Review):

Yang et al. produced a transgenic mouse line (Syt1-TDT) that could be used for labeling both excitatory and inhibitory synaptic sites in cultured neurons and in vivo neurons. The strength of the current study is to provide a series of thorough analyses to claim the applicability of this mouse line in the relevant neuroscience research field(s). The weakness is the potential impact/usefulness of this mouse line. To strengthen the merit of this mouse line, the authors should present evidence showing its advantage over other similar genetic approaches.

Reviewer #2 (Public Review):

Giorgi Rossi et al measured in their paper the impact of COVID-19 pandemic on the main indicators used to assess the performance of national screening programs for cancers. As expected, they highlighted a significant reduction that changed during the different waves and also across geographical areas. The results of the study might be considered valid and representative as the study is relied on current data flows to assess the performance of screening programs. The paper also reports a complementary analysis on the factor associated to the access to screening that gives some more insights on the reasons behind the access. This second part of the work also relied on data collected at national level that anyway have some intrinsic limitations. Nevertheless, on the whole, the paper gives a useful contribution to the assessment of the disruption due to the pandemic that can be also used in the light of preparedness actions.

Reviewer #2 (Public Review):

Wen et al. developed a useful tool for causal network inference based on scRNA-seq data. The authors comprehensively benchmarked 9 feature selection and 9 causal discovery algorithms using both synthetic data and real scRNA-seq data. Their conclusions regarding the performance of these algorithms on synthetic data are solid and valuable. I believe this tool or platform has the potential to help biologists discover novel cell type-specific signaling pathways or gene regulatory events since there is no prior knowledge (such as known pathway annotations) as inputs. However, several major concerns below need to be addressed to improve the paper.

(1) Current validation of the inferred causal networks using real scRNA-seq datasets seems quite simple and is not sufficient to support the accuracy and reliability of results. Annotations from the STRING database do not contain directions of edges among genes or proteins. However, the edge direction in the inferred network is a crucial aspect to explain the causal relationships. Besides using "spike-in" data, a systematic validation of the inferred network, especially the edge directions, should be provided.

(2) In order to illustrate the novel discovery, CausalCell should be further compared to existing gene network construction methods based on scRNA-seq data such as SCENIC (Aibar et al. Nature Methods, 2017).

(3) The authors should also claim what type of the inferred causal network represent from the biological perspective (e.g. signaling networks or gene regulatory networks?).

(4) Besides edge direction, an important feature of CausalCell is the determination of edge sign (i.e. activation or inhibition). The authors should describe its related procedures.

(5) The authors did not provide an example of constructing a causal network between cells or cell types, although they mentioned its importance in the Abstract. Such intercellular network examples can distinguish the utility of CausalCell in single-cell data analysis from bulk data analysis.

(6) If the control dataset is available, it is currently not clear whether batch effects of the query and control datasets will be removed in the data pre-processing step. Differentially expressed genes cannot be selected correctly if batch effects exist.

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

The Tp53 gene is deemed as one of the most critical tumor suppressors in humans. Not surprisingly, the latter is found inactivated or mutated in the majority (if not all) of human cancers. The present study by Q. Li et al describes an attempt to predict the functional status of p53 in those tumors where no mutations on the DNA sequencing level were identified. To this end, the authors employed SVM models to train the algorithm for the detection of the 'p53 inactivation' features using normal and tumor tissues, respectively. It turned out that the 'p53 loss of function' phenotype was associated not with DNA methylation but rather with yet unknown mechanisms. Based on the fact that the p53LoF-containing tumors are similar to the p53 mutant-expressing ones with respect to platinum-based therapy, they subsequently used their SVM model on the glioblastoma samples to predict their chemosensitivity.

Reviewer #2 (Public Review):

The structure was solved in its resting (i.e. non-activated) form and was stabilized by adding an antibody that recognizes an extracellular epitope. The protein - the complex of NOX2-p22 bound to the antibody- was reconstituted from proteins expressed in human cells through baculovirus transduction. The cryoEM gridswere obtained by using nanodisc-embedded complexes. The structure clarifies the topology of the p22 subunit, showing that it comprises four transmembrane helices. Moreover, it confirms that the oxygen-reacting center is conserved among NOXs implying a similar mechanism for ROS generation. Furthermore, the 3D structure explains the effect of the many known disease-causing mutations. They mostly affect the active sites or the NOX2-p22 subunit-subunit interface. The cytosolic dehydrogenase domain is not as ordered in the cryoEM maps. Clearly, NOX2 is a highly dynamic protein where the cytosolic and membrane domains can enjoy considerable flexibility. This feature very likely underpins the mechanism of activation, which is triggered by the cytosolic subunits and remains to be understood. The manuscript suggests that the cytosolic subunits might stabilize the enzyme in the conformation that is capable of conducting electrons from the NADP-flavin site to the inner heme, thereby enabling catalysis.

Overall, this is great experimental work: the structure of NOX2 has been awaited for a long time. The data reported in this manuscript should probably be seen as the beginning of the NOX2 structural era. Indeed, a lot remains to be clarified, especially with regard to NADPH binding and the mechanism of enzyme activation. Along this line, the manuscript reads more as a preliminary report rather than a full-story manuscript. Beside this general concern, I do not have any specific comment about the presentation style: the manuscript is clearly written and nicely illustrated.

Reviewer #2 (Public Review):

In this manuscript, Lin et al. reveal a novel and fundamental discovery regarding the role of the EZH2/SULF1/cMET signaling pathway in regulating the disease progression of chondrosarcoma, a malignant cartilaginous bone tumor.

The significant strengths of the manuscript include identifying the EZH2-targeted genes in chondrosarcoma using EZH2-chromatin immunoprecipitation sequencing (ChIP-seq) and cDNA microarray profiling, deciphering the role of the EZH2/SULF1/cMET signaling pathway in regulating the progression of chondrosarcoma, verifying the therapeutic significance of this pathway using clinically used specific EZH2 and cMET pharmacological inhibitors in vitro and in vivo (in mouse tumor models), and demonstrating the clinical significance of the SULF1/cMET pathway in chondrosarcoma.

The significant weaknesses of the manuscript appear not noted. A minor drawback seems associated with the manuscript presentation.

In summary, I believe this manuscript's data well justify the authors' claims and conclusions, and this paper will significantly impact the field.

Reviewer #2 (Public Review):

Casillas-Espinosa et al. present a well-designed study to evaluate the validity of sodium-selenate treatment in chronic epilepsy. Previous studies from the same group identified increased phospho-tau in models of seizures and epilepsy, which can be pharmacologically addressed through activation of protein phosphatase 2A with sodium-selenate. Here the authors tested the effect of delayed treatment with sodium selenate in the post-KA SE rat TLE model. Sodium selenate stopped the progression of seizures during and beyond a 4-week treatment phase compared to Levitiracetam and vehicle-treated animals. Sodium selenate further improved cognitive and sensorimotor impairments. It also persistently reduced phospho-tau and increased PP2A protein expression, and reversed TLE-associated telomere-shortening. Finally, proteome and metabolome data from the model is discussed and provides initial insights into sodium selenate treatment's molecular consequences.

This study validates the use of sodium selenate as a promising pharmacological treatment in experimental TLE that reduces seizure burden and restores cognitive deficits and pathomolecular changes. The specific strength of the study is a clinically relevant treatment paradigm, starting when recurrent seizures are fully established, and the antiepileptogenic effect with a sustainable reduction in seizure burden even after discontinuation of treatment.

The conclusions of this paper are mostly well supported by data, but some aspects of the proteome and metabolome data analysis need to be clarified and extended. The molecular data appears to be the weakest part of this study and would have benefited from adjusted sample sizes to account for interindividual variability between animals and the complex multi-dimensional nature of the data.

Reviewer #2 (Public Review):

Summary:

This work presents a new machine-learning method, RaSP, to predict changes in protein stability due to point mutations, measured by the change in folding free energy ΔΔG.

The model consists of two coupled neural networks, a 3D self-supervised convolutional neural network that produces a reduced-dimensionality representation of the structural environment of a given residue, and a downstream supervised fully-connected neural network that, using the former network's structural representation as input, predicts the ΔΔG of any given amino-acid mutation. The first network is trained on a large dataset of protein structures, and the second network is trained using a dataset of the ΔΔG values of all mutants of 35 proteins, predicted by the biophysics-based method Rosetta.

The paper shows that RaSP gives good approximations of Rosetta ΔΔG predictions while being several orders of magnitude faster. As compared to experimental data, judging by a comparison made for a few proteins, RaSP and Rosetta predictions perform similarly. In addition, it is shown that both RaSP and Rosetta are robust to variations of input structure, so good predictions are obtained using either structures predicted by homology or structures predicted using AlphaFold2.

Finally, the usefulness of a rapid approach such as RaSP is clearly demonstrated by applying it to calculate ΔΔG values for all mutations of a large dataset of human proteins, for which this method is shown to reproduce previous findings of the overall ΔΔG distribution and the relationship between ΔΔG and the pathological consequences of mutations. The RaSP tool and the dataset of mutations of human proteins are shared.

Strengths:

The single main strength of this work is that the model developed, RaSP, is much faster than Rosetta (5 to 6 dex), and still produces ΔΔG predictions of comparable accuracy (as compared with Rosetta, and with the experiment). The usefulness of such a rapid approach is convincingly demonstrated by its application to predicting the ΔΔG of all single-point mutations of a large dataset of human proteins, for which using this new method they reproduce previous findings on the relationship between stability and disease. Such a large-scale calculation would be prohibitive with Rosetta. Importantly, other researchers will be able to take advantage of the method because the code and data are shared, and a google colab site where RaSP can be easily run has been set up. An additional bonus is that the dataset of human proteins and their RaSP ΔΔG predictions, annotated as beneficial/pathological (according to the ClinVar database) and/or by their allele frequency (from the gnomAD database) are also made available, which may be very useful for further studies.

Weaknesses:

The paper presents a solid case in support of the speed, accuracy, and usefulness of RaSP. However, it does suffer from a few weaknesses.

The main weakness is, in my opinion, that it is not clear where RaSP is positioned in the accuracy-vs-speed landscape of current ΔΔG-prediction methods. The paper does show that RaSP is much faster than Rosetta, and provides evidence that supports that its accuracy is comparable with that of Rosetta, but RaSP is not compared to any other method. For instance, FoldX has been used in large-scale studies of similar size to the one used here to exemplify RaSP. How does RaSP compare with FoldX? Is it more accurate? Is it faster? Also, as the paper mentions in the introduction, several ML methods have been developed recently; how does RaSP compare with them regarding accuracy and CPU time? How RaSP fares in comparison with other fast approaches such as FoldX and/or ML methods will strongly affect the potential usefulness and impact of the present work.

Second, this work being about presenting a new model, a notable weakness is that the model is not sufficiently described. I had to read a previous paper of 2017 on which this work builds to understand the self-supervised CNN used to model the structure, and even so, I still don't know which of 3 different 3D grids used in that original paper is used in the present work.

A third weakness is, I think, that a stronger case needs to be made for fitting RaSP to Rosetta ΔΔG predictions rather than experimental ΔΔGs. The justification put forward by the authors is that the dataset of Rosetta predictions is large and unbiased while the dataset of experimental data is smaller and biased, which may result in overfitting. While I understand that this may be a problem and that, in general, it is better to have a large unbiased dataset in place of a small biassed one, it is not so obvious to me from reading the paper how much of a problem this is, and whether trying to fix it by fitting the model to the predictions of another model rather than to empirical data does not introduce other issues.

Finally, the method is claimed to be "accurate", but it is not clear to me what this means. Accuracy is quantified by the correlation coefficient between Rosetta and RaSP predictions, R = 0.82, and by the Mean Absolute Error, MAE = 0.73 kcal/mol. Also, both RaSP and Rosetta have R ~ 0.7 with experiment for the few cases where they were tested on experimental data. This seems to be a rather modest accuracy; I wouldn't claim that a method that produces this sort of fit is "accurate". I suppose the case is that this may be as accurate as one can hope it to be, given the limitations of current experimental data, Rosetta, RaSP, and other current methods, but if this is the case, it is not clearly discussed in the paper.

Reviewer #2 (Public Review):

The antennae of insects are excellent sensors and are able to distinguish chemicals/compounds using odorant receptor proteins. Though many are promiscuous, several ORs are extremely specific and respond to only one or few related chemicals. In this study, the authors focus on two ORs from southern house mosquito, Culex quinquefasciatus namely OR10 and OR2, which respond to (show high specificity) skatole and indole respectively. Notably, these two compounds differ only by a methyl group raising the question how this is achieved. To address this question, the authors have chosen CquiOR10 (as it is more sensitive) for swapping the transmembrane domains (TMDs) from CquiOR2 and by performing heroic work, arrive at one single residue in one of the TMDs to explain the specificity in these ORs.

The major strengths of the manuscript include the careful design of the many different chimeric receptors (36 in total) and dissecting the importance of each TMD and zeroing on TMD2 as the specificity determinant, followed by zooming to a single residue in TMD2 that can change responsiveness of CquiOR10 to CquiOR2 and vice versa. This residue in TM2 is an alanine in CquiOR10, which when mutated to bulky residue becomes responsive to indole but when mutated to glycine remains specific to skatole and shows higher sensitivity. Similarly, mutating the equivalent residue in CquiOR2, Leucine 74 to a smaller residue makes this receptor now more responsive to skatole and making it more like CquiOR10.

Using RoseTTAfold and AlphaFold, the authors build models of CquiOR10 and CquiOR2, which gives them a platform to observe how ligands can bind using Rosettaligand both in native structures as well as mutants. They further ask how larger ligands or the methyl group at different position in the indole ring affects the response of the receptor, which follow a consistent trend on the key residue of Alanine 73. All these analysis allow authors to propose that the odorants or chemicals are accommodated/restricted due to the volume constraints by residues lining the cavity derived from the TMDs.

Reviewer #2 (Public Review):

In this communication by Motta and colleagues, the authors address the emerging role of the gut microbiome in degrading and detoxifying plant metabolites, using bees as a study system. The experiments are elegantly controlled, spanning in vitro and in vivo work that leverages the increasing tractability of bees and their microbial symbionts. This is evident in the extensive screening of Bifidobacterium, Bombilactobacillus, Lactobacillus, and Gilliamella relative to their susceptibility to amygdalin. This provided a foundation to pinpoint which strains can degrade the cyanogenic glycoside, the potential pathways underlying that process, and the key enzymes involved. The strain Bifidobacterium wkB204 displayed elevated expression of GH3, correlating to the ability of this microbe to degrade amygdalin in vitro. Expression of the GH3 in E. coli corroborated its putative role in the transformation of amygdalin to prunasin, consistent with the single inoculation effects of Bifidobacterium wkB204 into microbiota-deprived bees. These experiments collectively point to the importance of the bee microbiota for the consistent degradation of amygdalin. The findings are nicely contextualized relative to prior work on the gut microbiome and the metabolism of the cyanogenic glycoside, including efforts on bees and rats.

Reviewer #2 (Public Review):

In recent years the activity of cortical VIP+ interneurons in relation to learning and sensory processing has raised great interest and has been intensely investigated. The ability of VIP+ interneurons in the auditory cortex to respond to both reward and punishment was already reported a few years ago by some of the authors (Pi et al., 2013, Nature). However, this work importantly adds to their previous study demonstrating a largely similar and synchronous response of a large fraction of these interneurons across the neocortex to salient stimuli of different valence during the performance of an auditory discrimination task.

An additional strength of this study is the analysis and identification of the general pattern of VIP+ interneuron responses associated to specific behaviors in the different layers of the neocortex depth.

Interestingly, the authors also identified using cluster analysis 5 different classes of VIP+ interneurons, based on the dynamic of their responses, that were unequally distributed in distinct cortical areas.

This is a well performed study that took advantage of a cutting-edge imaging approach with high recording speed and good signal-to-noise ratio. Experiments are well performed and the data are properly analyzed and nicely illustrated. However, one shortcoming of this paper, in my opinion, is the "case report" structure of the data. Essentially for each neocortical area the activity of VIP+ interneurons was analyzed only in one animal. This limits the assessment of the stability of the response/recruitment of these interneurons. I appreciate the high number of recorded VIP+ interneurons per area/animal and I do understand that it would be excessively laborious to perform 3D random-access two-photon microscopy in several mice for each cortical area. On the other hand, it would be important to have some knowledge of the general variability of the responses of these neurons among animals.

In conclusion, despite the findings described in this manuscript being generally sound, additional experiments are recommended to further substantiate the conclusions.

Reviewer #2 (Public Review):

General description:<br /> This study elucidates how Advanced Glycation End-products (AGE), found in processed food and endogenously, drive food intake and cause some of the pathophysiological defects associated with metabolic disease. In their previous C. elegans study, the authors found that glod-4 mutants, animals that lack glyoxalase activity and thus accumulate AGEs, eat more and share some of the pathophysiological effects seen in metabolic disease. In this study, they identify a specific AGE, hydroimidazolone (MG-H1), that is sufficient to increase feeding, similar to what was previously observed in the glod-4 mutants. Gene expression studies then show expression changes in several neurotransmitter and eating genes, including the tyramine decarboxylase gene tdc-1 and its receptor. Measuring eating behaviors in animals carrying mutations in tyramine signalling genes they show that tyramine signaling system is required for the behavioral and pathophysiological effects of MG-H1. Finally, they show that the transcription factor elt-3 controls the expression of tyramine signaling components and thus is also required for the response to MG-H1.

Strength: Strengths of the paper include the elegant approach to study how toxic metabolites affect physiology and behavior in vivo. The logic behind the study is easy to follow and the paper is clearly written.

Weakness: The main weakness is that the genetic studies were generally only carried out with a single mutation that was not rescued. To corroborate the requirement of tdc-1 and elt-3 for the response to MG-H1, the results should be repeated either in a rescue strain or using a different allele. Some of the effects are subtle and there is the danger of them being caused by background mutations.

Impact: The occurrence of metabolites like AGEs in either processed food or endogenously is a topic that is not well investigated despite its general importance. In this study the authors show the functional consequences of a non-enzymatically generated metabolite and how it exerts its toxicity.

Reviewer #2 (Public Review):

This is an important and timely characterization of a diversity of behaviors male and female rats exhibit during the acquisition of Pavlovian fear conditioning in a conditioned suppression procedure. Using hand-scored video analysis and ethogram of nine different behaviors, the authors report that auditory conditioned stimuli that predict shock with high certainty evoke not only freezing, but a variety of other behaviors including locomotion, jumping, and rearing (in addition to suppressing reward-seeking). Auditory stimuli that were followed by shock on only some trials (uncertainty condition), were less likely to evoke freezing and did not lead to a suppression of port/cup-directed behaviors (reward seeking). There were subtle sex differences in the temporal profile of freezing behavior, but not in the properties of the other behaviors under study.

Ultimately, these findings point to the importance of task variables (eg., reward seeking in a conditioned suppression procedure) and shock probability in shaping an animal's defense repertoire under threat.

An important factor that this work does not resolve is how the magnitude of the threat/shock (and presumably the state of fear that it engenders) influences an animal's defensive topography. This report used a modest/weak footshock intensity that supported very low levels of tone-elicited freezing (<20%) - a stark contrast to the extant fear conditioning literature that typically reports much higher levels of freezing behavior.

Reviewer #2 (Public Review):

This paper by Sherratt et al. evaluated the performance of real-time predictions for COVID-19 submitted to the European COVID-19 Forecast Hub between March 8 2021 and March 7 2022. This large-scale multi-team multi-county collaboration collected short-term forecasts for COVID-19 from 26 teams generated for 32 countries in Europe, making this dataset one of the largest archives of real-time COVID-19 forecasts. The results indicate that ensemble models combining forecasts from individual models generally performs better than each individual model, and ensemble methods based on medians outperform the ones based on means. The comparison also shows that incident death forecasts are more reliable than incident case forecasts beyond two weeks into the future. The paper further included detailed discussions on several practical considerations in the operational use of forecasting models. These findings provide practical guides for generating real-time forecasts for infectious diseases and novel insights into coordinating international forecasting efforts during a public health emergency.

The conclusions of this paper are well supported by the data and analyses. A few aspects could be further discussed in the manuscript.

1. A parallel effort of real-time COVID-19 forecasting in the US (i.e., the US COVID-19 Forecast Hub) reported similar findings on the use of ensemble models. This study from Europe provides independent validation that shows the robustness of these findings. While both studies followed similar guidelines and used the same evaluation metrics (coverage and WIS), I believe there should be unique challenges associated with forecasting for multiple countries (as opposed to forecasting in a single country). As a result, it might be worthwhile to discuss those challenges and potential solutions to inform similar efforts in the future.

2. WIS is a strictly proper score for evaluating forecast performance; however, it must rely on a reference forecast model. This may create difficulties in interpreting forecast accuracy for the general public who may not understand the concept of WIS. For instance, what is a WIS score good enough to trust? The authors may want to include a simple metric (e.g., mean absolute error) as a supplement even though these metrics have some caveats. I presume the performance should be highly correlated using different evaluation metrics.

3. It might be helpful to elaborate more on the assumptions for near-term predictions in participating models (e.g., status quo, reactive change of transmission, etc.). Essentially all real-time predictions were generated based on assumptions, although sometimes those assumptions were not stated explicitly. For behavior-induced changing points (peaks or troughs), it might be challenging to predict using the status quo without considering a change in model states.

4. Data in the tables and figures were used to compare forecasts. It would be great to have a formal statistical test for comparing model performance, if possible.

Reviewer #2 (Public Review):

It is known that bacterial outer membrane proteins must interact with a variety of cellular factors to reach their final destination safely. There is considerable biochemical evidence in the literature (primarily from crosslinking studies) that these factors interact to promote the movement of client proteins and to prevent their aggregation or misfolding, but the details of the interactions are unknown. The authors showed that they could use a novel virtual screening method together with known crystal structures of individual factors to predict the three-dimensional structures of several pairs or groups of interacting factors (supercomplexes). The predicted supercomplex structures are both fascinating and compelling because they are consistent with the published results and they help to explain the mechanism by which the cellular factors promote outer membrane protein biogenesis. I think that this study will be of interest to a wide audience because it serves as a proof-of-concept that although Alpha Fold is incredibly useful for predicting the structures of protein monomers, more sophisticated applications can be used to successfully predict the structures of protein complexes which are often the workhorses of the cell. I have only two significant concerns. First, the authors focused on high confidence supercomplexes that have known biological significance. Their method also identified other high confidence supercomplexes, but they need to explain how they can distinguish predicted supercomplexes that have potential biological significance from those that are simply "false positives". Second, one of the proposed functional models does not seem to be consistent with the results of a previous study.

Reviewer #2 (Public Review):

Luongo et al. investigated the behavioural ability of 4 different species (macaque, mouse lemur, tree shrew and mouse) to segment figures defined by opponent motion, as well as different visual features from the background. With carefully designed experiments they convincingly make the point that figures that are not defined by textural elements (orientation or phase offsets, thus visible in a still frame) but purely by motion contrast, could not be detected by non-primate species. Interestingly it appears to be particularly motion contrast, since pure motion - figures moving on a static background - could be discriminated better, at least by mice.

This is highly interesting and surprising -- especially for a tree shrew, a diurnal, arboreal mammal, very closely related to primates and with a highly evolved visual system. It is also an important difference to take into account considering the multitude of studies on the mouse visual system in recent years.

The authors additionally present neuronal activity in mice, from three different visual cortical areas recorded with both electrophysiology and imaging. Their conclusions are mostly supported by the data, but some aspects of the recordings and data analysis need to be clarified and extended.

The main issues are outlined below roughly in order of importance:

1. The most worrying aspect is that, if I interpret their figures correctly, their recordings seem not very stable and this may account for many of the differences across the visual conditions. The authors do not report in which order the different stimuli were shown, their supplemental movie, however, makes it seem as though they were not recorded fully interleaved, but potentially in a block design with all cross1 positions recorded first, before switching to cross2 positions and then on to iso... If I interpret Figure 6a correctly, each line is the same neuron and the gray scale shows the average response rate for each condition. Many of these neurons, however, show a large change in activity between the cross1 and the cross2 block. Much larger than the variability within each block that should be due to figure location and orientation tuning. If this interpretation is correct, this would mean that either there were significant brain state changes (they do have the mice on a ball but don't report whether and how much the animals were moving) between the blocks or their recordings could be unstable in time. It would be good to know whether similar dramatic changes in overall activity level occur between the blocks also in their imaging data.

The same might be true for differences in the maps between conditions in figure 4. If indeed the recordings were in blocks and some cells stopped responding, this could explain the low map similarities. For example Cell 1 for the cross stimuli seems to be a simple ON cell, almost like their idealized cell in 3d. However, even though the exact texture in the RF and large parts of the surround for a large part of the locations is exactly identical for Cross1 and Iso2, as well as Cross2 and Iso1, the cells responses for both iso conditions appear to only be noise, or at least extremely noise dominated. Why would the cell not respond in a phase or luminance dependent manner here?

This could either be due to very high surround suppression in the iso condition (which cannot be judged within condition normalization) or because the cell simply responded much weaker due to recording instability or brain state changes. Without any evidence of significant visual responses, enough spikes in each condition and a stable recording across all blocks, this data is not really interpretable. Instability or generally lower firing rates could easily also explain differences in their decoding accuracy.

Similarly, it is very hard to judge the quality of their imaging data. They show no example field of views or calcium response traces and never directly compare this data to their electrophysiology data. It is mentioned that the imaging data is noisy and qualitatively similar, but some quantification could help convince the reader. Even if noisy, it is puzzling that the decoding accuracy should be so much worse with the imaging data: Even with ten times more included neurons, accuracy still does not even reach 30% of that of the ephys data. This could point to very poor data quality.

2. There is no information on the recorded units given. Were they spike sorted? Did they try to distinguish fast spiking and regular spiking units? What layers were they recorded from? It is well known that there are large laminar differences in the strength of figure ground modulation, as well as orientation tuned surround suppression. If most of their data would be from layer 5, perhaps a lack of clear figure modulation might not be that surprising. This could perhaps also be seen when comparing their electrophysiology data to the imaging data which is reportedly from layer 2/3, where most neurons show larger figure modulation/tuned surround suppression effects. There is, however, no report or discussion of differences in modulation between recording modalities.

3. There is an apparent discrepancy between Figure 5d and i. How can their modulation index be around -0.1 for cross (Figure 5d) - which would correspond to on average ~20% weaker responses to a figure than to background, when their PSTH (5i) shows an almost 50% increase of figure over ground. This positive figure modulation has also been widely reported in the literature (Schnabel, Kirchberger, Keller). Are there different populations of cells going into these analyses?

4. In a similar vein, it is not immediately clear why the average map correlation would be bigger for random cell pairs (~0.2, Fig 3g) than for the different conditions of the same cell (~0, Fig 5b). Could this be due to differences in recording modality (imaging in 3g and ephys in 5b)?

5. The maps in Figure 4 should show the location of the RF, because they cannot be interpreted without knowledge of the RF center and size. For example cell 4 in the iso 1 condition could be a border cell, or could respond to the center of the figure. It is impossible to deduce without knowledge of the location of the RF.

6. It could help the reader to discuss the interpretation of the map correlations in Fig 5 a and b in more detail. My guess is that negatively correlated maps (within cross or iso condition) could come from highly orientation tuned neurons, whereas higher correlation values point to more generally figure/contextually modulated cells (within this condition). While the distribution is far from bimodal, this does not rule out a population of nicely figured modulated cells at the high end of the distribution. It might not be necessary at the level of V1 that the figure modulation be consistent across all textures. It would not be surprising, if orientation contrast-defined, phase contrast-defined and motion contrast-defined figures could be signalled to higher areas by discrete populations of V1 or even LM cells.

7. Some of the behavioural results warrant a little more explanation or discussion, as well. In Figure 2h, the mice seem significantly better on the static version of the iso task, than on the moving one. If statistically significant, this should be discussed. Is this because the static frame was maximally phase offset? Then the figure would indeed be better visible better (bigger phase contrast in more frames) than in the moving condition.

Figure 2 and extended Figure 1c: why is the mouse lemur performing so poorly on average? It also appears to have biggest problems with the cross stimulus early on in training.

Tree shrews seem not to be able to memorize the textures as well as the mice do. Is this because of less deprivation/motivation? Or because of the bigger set of textures in training? This would make memorization harder and could thus lower their overall performance. The comparative aspects are very interesting but the absolute differences in performance could be discussed in more detail or explained better.

8. In Figure 7b, why wouldn't the explanation for the linear decodability in cross also hold for iso? There are phase offsets at the borders that simple cells should readily be able to resolve, just as in the case of orientation discontinuities. Could they make a surround phase model, similar to their surround orientation model, that could more readily capture the iso discontinuities?

Reviewer #2 (Public Review):

The authors trained two monkeys to perform a task that involved sequential (blocked) but unsignalled rules for discriminating the colour and shape of visual stimulus, by responding with a saccade to one of four locations. In rules 1 and 3, the monkeys made shape (rule 1) or colour (rule 3) discriminations using the same response targets (upper left / lower right). In rule 2, the monkeys made colour judgments using a unique response axis (lower left/upper right). The authors report behaviour, with a focus on time to relearn the rules after an (unsignalled) switch for each rule, discrimination sensitivity for partially ambiguous stimuli, and the effect of congruency. They compare the ability of models based on Q-learning, Bayesian inference, and a hybrid to capture the results.

The two major behavioural observations are (1) that monkeys re-learn faster following a switch to rule 2 (which occurs on 50% of blocks and involves a unique response axis), and (2) that monkeys are more sensitive to partially ambiguous stimuli when the response axis is unique, even for a matched feature (colour). These data are presented clearly and convincingly and, as far as I can tell, they are analysed appropriately. The former finding is not very surprising as rule 2 occurs most frequently and follows each instance of rule 1 or 3 (which is why the ideal observer model successfully predicts that the monkeys will switch by default to rule 2 following an error on rules 1 or 3) but it is nevertheless reassuring that this behaviour is observed in the animals. It additionally clearly confirms that monkeys track the latent state that denotes an uncued rule.

The latter finding is more interesting and seems to have two potential explanations: (i) sensitivity is enhanced on rule 2 because it is occurs more frequently; (ii) sensitivity is enhanced on rule 2 because it has a unique response axis (and thus involves less resource sharing/conflict in the output pathway).

The authors do not directly distinguish between these hypotheses per se but their modelling exercise shows that both results (and some additional constraints) can be captured by a hybrid model that combines Bayesian inference and Q learning, but not by models based on either principle alone. A Q-learning model fails to capture the latent state inference and/or the rule 2 advantage. The Bayesian inference model captures the rapid switches to rule 2 (which are more probable following errors on rule 1 and rule 3) but predicts matched discrimination performance for partially ambiguous stimuli on colour rules 2 and 3. This is because although knowing the most likely rule increases the probability of a correct response overall it does not increase discriminability and thus boosts the more ambiguous stimuli. I wondered whether it might be possible to explain this result with the addition of an attention-like mechanism that depends on the top-down inference about the rule. For example, greater certainty about the rule might increase the gain of discrimination (psychometric slope) in a more general way.

The authors propose a hybrid model in which there is an implicit assumption that the response axis defines the rule. The model infers the latent state like an ideal observer but learns the stimulus-response mappings by trial and error. This means that the monkeys are obliged to constantly re-learn the response mappings along the shared response axis (for rules 1/3) but they remain fixed for rule 2 because it has a unique response axis. This model can capture the two major effects, and for free captures the relative performance on congruent and incongruent trials (those trials where the required action is the same, or different, for given stimuli across rules) on different blocks.

I found the author's account to be plausible but it seemed like there might be other possible explanations for the findings. In particular, having read the paper I remained unclear as to whether it was the sharing of response axis per se that drove the cost on rule 3 relative to 2, or whether it was only because of the assumption that response axis = rule that was built into the authors' hybrid model. It would have been interesting to know, for example, whether a similar advantage for ambiguous stimuli on rule 2 occurred under circumstances where the rule blocks occured randomly and with equal frequency (i.e. where there was response axis sharing but no higher probability); or even whether, if the rule was explicitly signalled from trial to trial, the rule 2 advantage would persist in the absence of any latent state inference at all (this seems plausible; one pointer for theories of resource sharing is this recent review: https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(21)00148-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1364661321001480%3Fshowall%3Dtrue). No doubt these questions are beyond the scope of the current project but nevertheless it felt to me that the authors' model remained a bit tentative for the moment.

Reviewer #2 (Public Review):

In this manuscript, the authors investigated the mechanism by which glycine prevents cell membrane rupture. They found that deficiency of NINJ1 (the key executioner of plasma membrane rupture by forming oligomers) phenocopies the cytoprotection of glycine during lytic cell death, and glycine treatment inhibits the oligomerization of NINJ1. Based on these observations, they claimed that glycine executes its inhibitory effect on cell lysis by targeting and inactivating NINJ1. This study addresses an important subject, because how glycine prevents cell membrane rupture is not understood and the literature is full of the implausible conclusion that it works as an osmoprotectant and that pyroptotic cell rupture is secondary to osmotic changes in cells undergoing pyroptosis, even though the gasdermin pore is very large and should allow the free passage of ions and many small proteins.

Reviewer #2 (Public Review):

This manuscript provides additional data about how smell is encoded by insects. The study includes both new experimental measurements and simulations. At present, there are questions about whether simulations are appropriately performed to support experimental measurements.

The main experimental finding reported here is that the same olfactory receptor neurons (ORN) can respond with different temporal dynamics to different odorants. This finding is of interest. However, it is very important to discuss whether the differences in temporal dynamics can be explained by differences in how this odorant is carried by air, as has been described here: https://pubmed.ncbi.nlm.nih.gov/23575828/.

There are several questions that need to be addressed regarding the simulations part of the manuscript.

1) There is a mismatch between the number of ORNs used in the model and in the insect system studied.

2) The demonstration in Figure 5 that motif switching improves odor classification includes motif switching for a given odorant, which is not observed experimentally.

3) The methodology for estimating neural temporal dynamics needs to be corrected to apply to the natural stimuli used here.

Reviewer #2 (Public Review):

This fascinating study describes a possible effect of cancer-generated microvesicles on fibroblasts. Microvesicles from a particularly metastatic line promote more contractile and proliferative fibroblasts, and there is a key role for at least one microvesicle factor - the crosslinking enzyme Transglutaminase-2. A wide range of studies help identify and elucidate these effects, but a few aspects remain unclear.

1. MV- has more crosslinking TGM2 but also less MMP14 degradation, and so ECM is more stable either way. The authors should describe any other factors that would give a similar effect as these. The authors should address: do other genes change with TGM2 knockdown; does MMP14 change? If the latter changes, does it have a more important role than TGM2?

2. Perhaps the cleanest and important study of MV effects is in Fig.6j,k, but it shows in vivo differences that are barely significant or not significant, and compares to 'SF' serum free media as a control. Are serum components detected in Mass Spec? If so, wouldn't this suggest a serum supplemented media is a better control? The serum is usually from another species, which is a further (xenogenic) concern that motivates care and discussion about dose -- especially given the high frequency of injection. Also, is there a survival difference for the mice?

Reviewer #2 (Public Review):

The goal of the study by Rutherford and colleagues was to characterize functional, structural, and molecular changes at the highly specialized cochlear inner hair cell (IHC) - spiral ganglion neuron (SGN) ribbon synapse in GluA3 AMPA receptor subunit knockout mice (GluA3KO). Previous work by the authors demonstrated that 2-month-old GluA3KO mice experienced impaired auditory processing and changes in synaptic ultrastructure at the SGN - bushy cell synapse, the next synapse in the auditory pathway.

In the present study, the authors investigated whether GluA3 is required for ribbon synapse formation and physiology in 5-week-old mice using a series of functional and light- and electron microscopy imaging approaches. While deletion of GluA3 AMPAR subunit did not affect hearing sensitivity at this age, the authors reported that cochlear ribbon synapses exhibited changes in the molecular composition of AMPARs and pre- and postsynaptic ultrastructural alterations. Specifically, the authors demonstrated that GluA3KO ribbon synapses exhibit i) a global reduction in postsynaptic AMPARs, which is also reflected by smaller AMPAR arrays, ii) a reduction in GluA2 and an increase in GluA4 protein expression at individual postsynaptic sites, and iii) changes in the dimensions and morphology of the presynaptic specialization ("ribbon") and in the size of synaptic vesicles. These reported structural changes are linked to the side of innervation with respect to the IHC modiolar-pillar axis.

The results presented by the authors are conceptually very interesting as the data support the notion that potentially detrimental changes in the molecular composition of a sensory synapse can be compensated to sustain synaptic function to a certain extent during development. The conclusions of the study are mostly well supported by the data, but some experimental details or control experiments are missing or need to be clarified to allow a full assessment.

1. The authors tested which GluA isoforms are expressed in SGNs of GluA3KO mice and reported that only GluA2 and GluA4, and not GluA1, receptor subunits are present in the cochlear. It is, however, a bit difficult to understand why immunolabelling for GluA1 was only performed on brainstem sections (Fig. 1B right) and not in the cochlear to probe for postsynaptic localization at ribbon synapses as it was done for the other isoforms (Fig. 2 and 6) given that GluA3KO IHCs exhibited a larger number of ribbons that lacked GluA2 and 3 (lone or 'orphaned' ribbons; Fig. 6B). It is also not clear why immunolabelling for GluA2 and 4 was performed to probe for expression of these receptor subunits on SGN cell bodies in the cochlear spiral ganglion. Which neurons are expected to synapse onto these somata?

2. The authors state in the text that GluA3 expression is completely abolished in GluA3KO IHCs, however, there appears to still be a faint punctate immunofluorescence signal visible when an antibody directed against GluA3 was used (Fig. 2C). Providing additional information on the specificity of this (and the other) antibodies used in the study would be helpful.

3. The authors reported changes in the volume of the presynaptic ribbon and postsynaptic density surface area in GluA3KO KO animals. The EM data as presented are however not sufficiently convincing.<br /> i) There appears to be a mismatch between the EM data shown in Fig. 3 and 4 and the information in the text with respect to the number of data points in the plots and the reported number of reconstructed synapses. This raises several questions with respect to the analysis. For instance, it is unclear whether certain synapses were reconstructed but excluded from the analysis. If so, what were the exclusion criteria?<br /> ii) The authors compare PSD surface areas in reconstructions from 3D serial sections, but for some of the shown reconstructions (i.e. Fig. 3A' and B' and 4B'), it appears as if PSDs were only incompletely reconstructed.

4. The immunolabelling experiments shown in Fig. 2 and 6 are of very high quality and the quantitative analysis of the light microscopy data (Fig. 6-9) is clearly very detailed, but slightly difficult to interpret the way it is presented. Specifically, it is unclear how the number of synapses per IHC (Fig. 6B) and the separation into modiolar and pillar side (Fig. 8) was achieved based on the shown images without the outlines of individual cells being visible.

5. Adding more detailed information about important parameters (mean, N/n, SD/SEM) and the statistical tests used for the individual comparisons presented in the Figures would help strengthen the confidence in the presented data.

6. In general, the authors report a series of molecular and structural changes in IHCs and reach the conclusion that GluA3 subunits may have a role in "trans-synaptically" determining or organizing the architecture of both the pre- and post-synapse. However, some of the arguments are very speculative and many of the claims are not supported by experimental data presented in the paper. The authors should consider to also compare their findings to studies that investigated ultrastructural changes of AMPAR subunit knockouts in other synapse types, and discuss alternative interpretations (e.g. homeostatic changes).

Reviewer #2 (Public Review):

The manuscript by Lefebvre et al. investigates how the tissue-scale spatial organization of protein evolves during germ band extension. The key question is whether changes in the localization of important features such as pair-rule gene (PRG) stripes and apical myosin orientation can be explained purely via passive advection without the need for additional regulatory mechanisms. In the case of the PRG, as well as TLRs, their data strongly suggests the answer is yes: the authors show that the deformation of the characteristic stripe pattern closely matches that predicted by advecting the initial pattern in a velocity field extracted from the observed tissue flow. By contrast, the authors find that anisotropic myosin orientation cannot be explained purely in terms of the local velocity field, in particular the fact that myosin remains robustly oriented with the DV axis. This leads the authors to postulate that myosin orientation is continually re-established via a static source aligned with said axis, which dominates over re-orientation due to advection. A simple model of myosin reorientation is developed from this hypothesis, which produces qualitatively similar relationships between orientation and local vorticity to that seen both in WT and in several mutants.

The strongest feature of this paper is illustrated by the results in Figure 2. The result it presents, which the authors summarize as "PRGs flow with tissue while myosin does not," is a very nice application of recent advances in using toto microscopy for embryonic systems to extract and quantify whole embryo expression patterns and flow fields, which are needed information for this kind of result. Tissue flow is a complicated, active process, and identifying which parts of the dynamics can be sufficiently explained by passive transport can tremendously simplify the conceptual challenges of germ band extension and related tissue movements found during neurulation or organogenesis. The resistance the authors found that myosin exhibits to re-orientation is likewise very interesting because it implies that information about global geometry (the direction of the DV axis) is somehow maintained at the cellular level throughout the convergent extension.

The principle weakness in this manuscript is the vagueness of the proposed static source mechanism and the lack of direct evidence for it in experiments. The FRAP experiments performed here suggest that binding/unbinding happens on the right timescale to play a role in anisotropy maintenance, but if the principle question is 'how does myosin remain oriented along the DV axis' then the static source hypothesis just kicks the can down the road to ask 'how does the static source remain oriented along the DV axis'? The minimal model the authors employ has the benefit that it lets them relate angular deviation to vorticity, at the cost that it is agnostic to the form and nature of the source term, so it cannot be used to extract useful constraints. This said the evidence provided regarding the connection between vorticity and binding rates to myosin deflection is sufficient indirect evidence of the hypothesized mechanism that I suspect it will be of interest to a good number of people interested in epithelial morphogenesis.

Reviewer #2 (Public Review):

This manuscript by Winter et al represents an analysis of the function of the ATAD1 gene in cancer. At present, the manuscript makes a number of interesting observations, with strong experimental support. First, the authors show that tumors with PTEN deletions frequently have additional mutations in ATAD1, and that prostate tumors with both mutations are associated with a shorter period of survival. Second, tumors lacking ATAD1 are more sensitive to proteotoxic stress, based in part on an increased tendency to apoptosis. Third, the ATAD1 protein interacts with BIM, and interactions with BIM contribute in part to an increased tendency to apoptosis. Fourth, ATAD1 and MARCH5 have at least moderate synthetic sick/lethal interactions; together with other data, this suggests they control the release of BIM from the OMM, contributing to its degradation. Overall, the data suggest that tumors with ATAD1 deletions may be particularly vulnerable to drugs that induce proteotoxic stress, suggesting new potential therapeutic regimens, which would be a valuable contribution to the field. The level of data presented here is already substantial; however, some additional experiments to support the authors' contentions would strengthen the work. Some claims about the mechanism are overstated given the current body of data and should be qualified.

Reviewer #2 (Public Review):

In this work, the authors were trying to prove the model that the fungal pathogen Fusarium oxysporum f. sp. lycopersici (Fol) utilizes the acetyltransferase FolArd1 to induce the acetylation of the K167 residue of the effector protein FolSvp1. This acetylation prevents the K152, K258 and K284 ubiquitination-mediated degradation of FolSvp1 in Fol, and meanwhile inhibits the K167 ubiquitination-mediated degradation of FolSvp1 in tomato plants. In the host plants, FolSvp1 interacts specifically with the apoplastic defense protein SlPR1 and translocates it to the nucleus, which suppresses the SlPR1-derived CAPE1 peptide-induced fungal resistance. Overall, the experiments were well designed and the large amount of data justified most of their conclusions. The work sheds novel insight into the virulence mechanisms of fungal effectors by showing that acetylation modification can stabilize a fungal effector, which is able to mis-localize a key defense protein to dampen the host immunity.

There are two issues that need to be addressed.

1. As far as I know, the apoplastic PR1 proteins may have a fungicide activity. When the authors tested the interaction between FolSvp1 and SlPR1 in Nicotiana benthamiana by BiFC, both apoplastic and nuclear interactions could be detected. Therefore, the authors should discuss the possibilities whether the binding of FolSvp1 to SlPR1 remained in the apoplast can inhibit (i) its anti-Fol activity and (ii) the cleavage of SlPR1 to produce the CAPE1 peptide. In other words, although translocating SlPR1 to the nucleus by FolSvp1 is effective for suppressing CAPE1 production, this may not be the only way.

2. The FolSvp1 produced in N. benthamiana was using the SlPR1 signal peptide and lacked the acetylation modification. It is possible that the acetylation of FolSvp1 can affect the interaction affinity or localization between FolSvp1 and SlPR1. The K167Q mutation of FolSvp1 might not be able to faithfully mimic the K167 acetylation.

Reviewer #2 (Public Review):

This study seeks to determine how neuronal glycolysis is coupled to electrical activity. Previous studies had found that glycolytic enzymes cluster within nerve terminals (in C. elegans) during activity. Furthermore, the glucose transporter GLUT4 is recruited to synaptic surface during activity. The authors previously showed that Ca2+ does not stimulate glycolysis in active neurons. Here, the authors show that the cytosolic Na+, not Ca2+, and the activity of the Na/K pump drive glycolysis. However, it is important to note that in this study, glycolysis was examined in the soma, not nerve terminals, where some of the previous studies were conducted. A few other caveats in the interpretation of the findings are listed below:

1. The NADH/NAD ratio is used throughout as the only measurement reflecting glycolytic flux.<br /> 2. It has been hypothesized that the close association of glycolytic enzymes with ion transporters (such as the Na+/K+ pump) is meant to provide localized ATP to power these pumps. How does bulk glycolysis (monitored with NADH/NAD ratio) relate to localized/compartmentalized glycolysis?<br /> 3. Related to point 2, most of the peredox measurements in the paper have been made at baseline, in the absence of electrical activity. Therefore, it is not clear how the findings relate to activity-driven glycolysis.<br /> 4. The finding that inhibition of SERCA during stimulation actually elevates cytosolic NADH level argues against Na+ being the only ion that regulates glycolysis.<br /> 5. The finding that "SBFI ΔF/F transients were longer in duration than the RCaMP LT transient" does not necessarily mean that Na+ elevation lasts longer than Ca2+ in the cell. This could be an artefact of the SBFI on/off rate relative to RCaMP. In fact, prolonged elevation of cytosolic Na+ would make neurons refractive to depolarization in AP trains.

Reviewer #2 (Public Review):

The authors applied an innovative and very interesting approach based on different -omics platforms to study the biological post-mortem transformations of human bone. Despite the study being a proof-of-concept, because of the small number of collected samples and the lack of external validation, the methodology is promising. The study will have a strong impact on the field and present the state-of-art of -omics sciences.

Reviewer #2 (Public Review):

The goal of this study was to understand the molecular mechanism of how transcription factor DUX4, which has a role in cancer, inhibits the induction of genes stimulated by interferon-gamma. The authors achieved this goal, and their results mostly support their conclusions. They found that DUX4, in their experimental model, interacts with STAT1, thereby decreasing STAT1 and Pol-II recruitment to sites of gene transcription.

The present study has many strengths: The topic is of broad interest, the findings are novel and intriguing, the experiments are well-designed and controlled, the data, with one exception, is carefully interpreted, and the manuscript is very well-written.

Two major weaknesses were identified. One is that all experiments, except Figure 6, rely on one experimental setup, which is a human skeletal muscle cell line with an integrated doxycycline-inducible transgene. The concern is that both the treatment of cells with the drug doxycycline and the fact that signaling pathways could be disrupted in this (immortalized?) cell line could lead to artifacts that skew results. Indeed, results in Figure 4C indicate that total STAT1 is completely localized in the nucleus even prior to interferon stimulation when it should be in the cytoplasm. The other weakness is the use of the DUX4-C-terminal-domain (DUX4-CTD) mutant for the majority of the mechanistic experiments. The concern here is that although the phenotype of ISG repression is observed in this truncated mutant, important regulatory domains could be missing that modulate the interaction with STAT1 or other proteins. Is the NLS added after the flag tag identical to the endogenous NLS? Related, I disagree with the interpretation of Figure 4C that "this interaction happens within the nuclei of DUX4-CTC expressing cells". The interaction could happen prior to STAT1 shuttling to the nucleus.

Reviewer #2 (Public Review):

To test how oxytocin impacts the brain and the psychological, neural, and hormonal response to touch, the authors tested human females during two counterbalanced fMRI sessions wherein females were stroked on the arm or the palm, by a real-world romantic partner or a stranger, while blood levels of oxytocin and cortisol were collected at multiple time points.

This combination of measures, and the number of hypotheses that could be tested with them, is remarkable - virtually unheard of. This impressive, difficult, and more ecological design than is typical for the field is a major strength of the study, which allowed the authors to test many important hypotheses concurrently and to show contextual effects that could not otherwise be observed. The only potential drawback perhaps is that with such a large design, including many measures, the authors produced so many significant interactions and results that it could be hard for the casual reader to appreciate the importance of each.

The authors supported their hypothesis that oxytocin effects are context-sensitive, as they found a key interaction wherein experiencing the partner first increased oxytocin for the partner relative to when they came first the OT levels were low but then increased if they were preceded by the partner (excepting one timepoint). Cortisol responses (which reflect hormonal stress) were also higher when the stranger came first than when he was preceded by the partner). In addition, touch was experienced more positively on the arm than on the palm, supporting the role of c-fibers in conveying specifically felt responses to warm, tender touch.

These data indicate significant context sensitivity with real-world implications. For example, experiencing warm touch on the arm can make us more receptive to other people in subsequent encounters. Conversely, when strangers try to approach and get close to us "out of the blue" people experience this as stressful, which reduces the pleasantness of the interaction and may reduce trust in the moment...perhaps even subsequently.

This research is critical to the basic science of neurohormonal modulation, given that most of this research occurs in rodents or in simplified studies in humans, usually through intranasal oxytocin administration with unclear impacts on circulating levels in the brain and blood. Oxytocin in particular has suffered from oversimplification as the "love drug" - wherein people assume that it always renders people more loving and trusting. The reality is more complex, as they showed, and these demonstrations are needed to clarify for the field and the public that neurohormones adaptively shift with the context, location, and identity of the social partner in an adaptive way. These results also help us understand the many null effects of oxytocin on trusting strangers in human neuroeconomic studies. In a modern world that is characterized by significant loneliness, interactions with strangers and outsiders, and touch-free digital interactions, our ability to understand the human need for genuine social contact and how it impacts our response to outsiders (welcomed in versus a source of stress) is critical to human health and the wellbeing of individuals and society.

Reviewer #2 (Public Review):

In their manuscript, Wiest and colleagues focus on testing two primary hypotheses. The first is that the aperiodic exponent from the intracranial EEG / LFP reflects to population EI balance, and the second is that Parkinson's disease is specifically associated with reduced inhibition-concomitant excessive excitation-in the STN.

To accomplish this, they make use of data from 24 patients with Parkinson's disease who have undergone surgery to implant a deep brain stimulator as part of the treatment of their disease. These patients provide a rare opportunity to record high signal-to-noise EEG/LFP data directly from the human brain. These data are complemented by an additional dataset collected from eight 6-OHDA-lesioned rats, which provide a model of Parkinson's disease. The rat data includes both single-unit spiking activity, which allows Wiest and colleagues to examine periods of relatively high- or low-firing as a proxy for excitatory tone, as well as LFP data which allows them to bridge to the human data and more directly test their first hypothesis that the aperiodic exponent reflects EI balance.

Overall this is a very strong paper. The cross-species approach is especially convincing, and the methods are well-implemented and sound. The authors use appropriate analysis tools and statistical methods, and their inferences are clear, but measured. Their results are convincing, and the potential for aperiodic activity to serve as a potential physiologically interpretable index of Parkinsonian state.

Reviewer #2 (Public Review):

This work presents valuable evidence of the connection between Huntingtin's (HTT) phosphorylation state and the recruitment of Kif1A in the axonal anterograde trafficking of synaptic vesicles precursors (SVPs). In brief, the authors describe how phosphorylation of HTT in Serine 421 determines the recruitment of the anterograde molecular motor Kif1A to SVPs, increasing their rate of transport along the axons to the synapse. This conclusion is substantiated by the measured impact of HTT phosphorylation on motor skills learning ability.

The study presents a variety of investigative angles, combining both ex vitro and in vivo approaches. The use of custom microfluidics chamber to recreate neuronal circuits is a point of strength as it allows for in depth analysis of the transport phenotype. This tool could be a very useful tool for the community to explore for a variety of similar studies. The use of mouse models also adds credibility to the physiological importance of the findings.<br /> The evidence presented supports the claims, though more emphasis could be added to the explanation and mechanisms behind how an increased transport dynamic of SVPs due to HTT phosphorylation, results in a detrimental effect on motor skill learning. This finding is perhaps the most critical as it reiterates the importance of balance in SVPs transport and highlights how the system is finely regulated and sensitive to both down and upregulation. This fine tuning might ensure the presence of the proper quantity of SVs at synapses to guarantee an effective synaptic function.

This works adds an important angle to role of HTT phosphorylation, which could open new avenues of treatment for HTT disease based on the manipulation of HTT phosphorylation state.

Reviewer #2 (Public Review):

Ines Lago-Baldaia et al. investigate the connection between transcriptional and morphological diversity of glial cells. This is an important question to answer in the glial biology field and has been amplified by recent advances in single-cell sequencing. It remains unclear if transcriptional diversity that is often reported in scRNA analysis equates to morphologically distinct glia. To explore the correlation between transcriptome and morphology, the authors utilize the strength of the Drosophila model system to demonstrate that although morphotypes of glia can be identified in the nervous system, the morphotypes do not correlate with a distinct transcriptional profile. Overall, the paper is well written and the conclusion matches the results that are presented. This work will be an important contribution to the glial biology field.

Reviewer #2 (Public Review):

What the authors were trying to explore is very interesting with translational potential toward glaucoma treatment. They used a topical dexamethasone (dex) induced mouse model showing ocular hypertension and a culture model using human TM cells treated with tBHP to induce TM oxidative stress. Their results suggested that metformin protected TM cells from cytoskeletal destruction by enhancing the integrin/ROCK pathway and alleviated elevated IOP in the mouse model. However, the provided simulative results were vague and the research needs extra experimental data to support its conclusion.

Reviewer #2 (Public Review):

The work of Iyer et al. uses a computational approach to investigate how cells using multiple tiers of processing and multiple parallel receptor types allow more accurate reading of position from a noisy signal. Authors find that combining signaling and non-signaling types of receptors together with additional feedback increases the accuracy of positional readout against extrinsic noise that is conveyed in the morphogen signal. Further, extending the number of layers of signal processing counteracts the intrinsic stochasticity of the signal reading and processing steps. The mathematical formulation of the model is general but comprehensive in the way it handles the difference between branches and tiers for the processing of channels with feedbacks. The results of the model are presented from simple one-branch and one-tier architecture to two-branch and two-tier architecture with feedbacks. Interestingly authors find that adding more tiers results in only very small improvements in the accuracy of positional readout. The model is tested against a perturbation experiment that impairs one of the signaling branches in the Drosophila wing disc, but the comparison is only qualitative as further experiment-oriented work is planned in a separate paper.

Strengths

There is a clear statement of objectives, model, and how the model is evaluated. In particular, the objective is to find what number of receptor types and their concentrations for a given number of tiers and feedback types is resulting in the most accurate positional readout. The employed optimization procedure is capable to find signalling architectures that result in one cell diameter positional precision for most of the tissue with 3-4 cells at the tissue end that is most distant to the morphogen source. This demonstrates that employing additional complexity in signal processing results in a very accurate positional readout, which is comparable with estimates of positional precision obtained in other developmental systems (Petkova et al., Cell 2019, Zagorski et al., Science 2017).

The optimal signalling architectures indicate that both signalling (specific) and non-signalling (non-specific) receptors affect the precision of positional readout, but the contributions of each type of these receptors are qualitatively different. Even slight perturbation of signalling receptors drives the system out of optimum, resulting in a decrease in positional precision. In contrast, the non-signalling receptors could accommodate much larger perturbations. This observation could provide a biophysical explanation for how cross-talk between different morphogen species could be realized in a way that positional precision is kept at the optimum when morphogen signaling undergoes extrinsic and intrinsic perturbations.

Last, the model formulation allows to specifically address perturbations of signalling and feedbacks, that could be explored to validate model predictions experimentally in Drosophila wing disc, but also in other developmental tissues. The authors present a proof-of-concept by obtaining consistent results of variation of output profiles in two-tier two-branch architectures with non-signaling branch removed and intensity profiles of Wg in wing disc where the CLIC/GEEC endocytic pathway was perturbed.

Weaknesses

The list of model parameters is long including more than 20 entries for two-tier two-branch architectures. This is expected, as the aim of the model is to describe the sophisticated signalling architecture mimicking the biological system. However, this also makes it very challenging or impossible to provide guiding principles or understanding of the system behaviour for the complete space of signalling architectures that optimize positional readout. Although, the employed optimization procedure finds solutions that exhibit very high positional accuracy, there is only very limited notion how these solutions depend on variation of different parameters. The authors do not address the following question, whether these solutions correspond to broad global optima in the space of all solutions, or were rather fine-tuned by the optimization procedure and are quite rare.

It is unclear how contributions from the intrinsic noise affect the system behaviour compared to contributions from extrinsic noise. In principle, the two-branch one-tier architecture results in an already very accurate positional readout across the tissue. The adding of another tier seems to provide only a very weak improvement over a one-tier solution. It is possible that contributions from intrinsic noise for the investigated signalling architectures are only mildly affecting the system compared with contributions from extrinsic noise. Hence, it is difficult to assess whether the claim of reducing intrinsic noise by adding another tier is supported by the presented data, as the contributions from intrinsic noise could overall very weakly affect the positional readout.

Reviewer #2 (Public Review):

Hwang et al take an unconventional approach to address a longstanding problem in the field of Wnt signaling and cancer: the mechanism of beta-catenin nuclear import. The authors introduce expression of Xenopus beta-catenin in budding yeast, a heterologous model system that does not harbor any known Wnt signaling components but carries highly conserved nuclear transport machinery. They find that GFP-tagged beta-catenin is actively transported to the yeast nucleus in a Ran-GEF-dependent process, indicating NTR-dependent transport. An elegant rapamycin treatment-dependent Anchor-Away method is applied to systematically inhibit 10 budding yeast NTRs, for which orthologues exist in human cells. Significant and specific inhibition of beta-catenin nuclear import is identified when Kap104 (orthologue of Kapbeta2/Transportin-1 (TNPO1) was anchored to the plasma membrane. Furthermore, nuclear import depends on a PY-like NLS sequence in the beta-catenin C-terminus, which was shown to mediate a direct interaction with TNPO1. A role of the vertebrate paralogs tnpo1/2 and the PY-like NLS was confirmed in Xenopus, using double axis formation assays, and in mouse and human cell lines, combining tnpo1/2 depletion with nuclear localization and reporters for beta-catenin dependent transcription. Finally, the authors provide proof that responses of MEF cells to Wnt3a or human beta-catenin overexpression can be inhibited by treatment with a TNPO1/2 blocking peptide (M9M).

Overall, the results of this study provide a valuable addition to the longstanding and ongoing discussions on the mechanisms of beta-catenin nuclear import. The conclusions are based on a well-focused and solid set of experiments and are confirmed across species in a diverse set of model systems, and findings are discussed against the state of the field. Although the identified TNPO1/2-dependent beta-catenin nuclear import pathway was shown to be a target for peptide-based inhibitory strategies, these findings remain to be confirmed in relevant (colorectal) cancer model systems in which levels of beta-catenin are inappropriately enhanced and inhibition of its nuclear entry is most warranted.

Reviewer #2 (Public Review):

This manuscript puts forward a new idea that topography in neural networks helps to remove noise from inputs. The neural network consists of multiple stages. At each stage, the network is structured to be balanced in terms of the strength of inhibitory and excitatory signals. Because of topography, the networks become "dis-balanced" and receive more recurrent excitatory signals locally for those regions that receive strong initial inputs. This leads to error correction. The main weakness in the manuscript is that the approach will only work for inputs that are constant-in-time. It is important to acknowledge this limitation in both the title and throughout the manuscript.

Reviewer #2 (Public Review):

Overall, I think that the screen or mutants in the Arabidopsis flowering pathway and its outcome are biologically interesting and important. The authors show that FIO1 methylates U6 snRNA and not (or rarely) mRNA. However, subsequent to this, the results are entirely from bioinformatics of RNAseq data from the derived mutants; there are no further experiments performed, either to confirm or test newly-derived hypotheses. Furthermore, the main hypothesis, that 5'SS pos.+4 identity is critical for sensitivity to U6 N6-methylation, was already described in yeast S. pombe, based on data from mutants in the pombe ortholog Mtl16. Minimally, the conclusions based on bioinformatics should be confirmed with experimental data. In addition, there are examples throughout the manuscript where the authors state results or conclusions without providing any data; this is not acceptable and data supporting these assertions must be included.

Reviewer #2 (Public Review):

Pulmonary neuroendocrine cells (PNECs) are known to monitor oxygen levels in the airway and can serve as stem cells that repair the lung epithelium after injury. Due to their rarity, however, their functions are still poorly understood. To identify potential sensory functions of PNECs, the authors have used single-cell RNA-sequencing (scRNA-seq) to profile hundreds of mouse and human PNECs. They report that PNECs express over 40 distinct peptidergic genes, and over 150 distinct combinations of these genes can be detected. Receptors for these neuropeptides and peptide hormones are expressed in a wide range of lung cell types, suggesting that PNECs may have mechanical, thermal, acid, and oxygen sensory roles, among others. However, since some of these cognate receptors are not expressed in the lung, PNECs may also have systemic endocrine functions. Although these data are largely descriptive, the results represent a significant resource for understanding the potential roles of PNECs in normal biology as well as in pulmonary diseases and cancer and are likely to be relevant for understanding neuroendocrine cells in other tissue contexts.

However, there are several aspects of the data analysis that are unclear and require clarification, most notably the definition of a neuroendocrine cell (points #1 and #2 below).

1. Figure S1 shows the sorting strategy used for isolation of putative PNECs from Ascl1CreER/+; Rosa26ZsGreen/+ mice, and distinguishes neuroendocrine cells defined as ZsGreen+ EpCAM+ and "neural" cells defined as ZsGreen+ EpCAM-; the figure legend also refers to the ZsGreen+ EpCAM- cells as "control" cells. However, the table shown in panel D indicates that the NE population combines 112 ZsGreen+ EpCAM+ cells together with 64 ZsGreen+ EpCAM- cells to generate the 176 cells used for subsequent analyses. Why are these ZsGreen+ EpCAM- cells initially labeled as neural or control, but are then defined as neuroendocrine? If these do not express an epithelial marker, can they be rigorously considered as neuroendocrine?

2. Similarly, in the human scRNA-seq analysis, how were PNECs defined? The methods description states that these cells were identified by their expression of CALCA and ASCL1, but does not indicate whether they also expressed epithelial markers.

3. The presentation of sensitivity and specificity in Figure 1 is confusing and potentially misleading. According to Figure 1B, Psck1 and Nov are two of the top-ranked differentially expressed genes in PNECs with respect to both sensitivity and specificity. However, the specificity of these two genes appears to be lower than that of Scg5, Chgb, and several other genes, as suggested in Figure 1C and Figure S1E. In contrast, Chgb appears to have higher specificity and sensitivity than Psck1 in Figures 1C and E but is not shown in the list of markers in Figure 1B.

4. The expression of serotonin biosynthetic genes in mouse versus human PNECs deserves some comment. The authors fail to detect the expression of Tph1 and Tph2 in any of the mouse PNECs analyzed, but TPH1 is expressed in 76% of the human PNECs (Table S8). Is it possible that Tph1 and Tph2 are not detected in the mouse scRNA-seq data due to gene drop-out? If serotonin signaling by mouse PNECs is due to protein reuptake, as implied on p. 5, is there a discrepancy between serotonin expression as detected by smFISH versus immunostaining?

5. The smFISH and immunostaining analyses are often presented without any indication of the number of independent replicate samples analyzed (e.g., Figure 2B, Figure 3F, G).

6. It would be helpful to provide a statistical analysis of the similarities and differences shown in the graphs in Figures 1E and G.

Reviewer #2 (Public Review):

The authors introduce a model based on textual data for predicting odor properties of a mixture of chemicals. Modelling approach is relevant to olfactory scientists and experimental neuro-scientists.

Work is relevant because it unifies and studies multiple mixture odor datasets, achieving satisfactory results. Work is novel because modelling for mixture datasets is scarce, this work introduces a grounded approach for modeling such data. Model is directly interpretable since it relies on a linear model (lasso) to build mapping between features (metric learning).

The authors's evidence supports most of the conclusions of the work with some room for improvement.

This work can be of the many in the future trying to further modelling approaches for mixture data.

Reviewer #2 (Public Review):

The study by Sommer et al. applies alphafold to the CHESS selection of transcripts with the goal of generating predicted 3D protein structures and a quality measure of folding, the pLDDT score. From these data, the authors build up a database for result exploration. In addition, they provide examples to underline this approach. Examples include proteins, where the authors propose the pLDDT score as a measure of presumed superior biological functionality over other isoforms. The authors also use the generated data to propose novel functionally relevant isoforms, e.g. in the mouse.

The study is based on the elegant idea to aid genome annotation through 3D structure prediction. This is a very powerful approach that allows large-scale data generation for functional interpretation. This approach appears technically sound and well executed (although I may miss details not being a protein expert). However, in my opinion, the authors could make more use of the potential of their approach. From the big-data start, they seem to directly restrict themselves to interesting examples. I am missing a global analysis that shows the bigger picture of their results. Given that they have generated structures from 90,415 isoforms, each associated with a pLDDT score, conservation scores, length, expression levels and other quantifiable data listed on page 18. I would wish for a comprehensive analysis of these data and their potential before applying the focus on a few (admittedly very nice) examples.

Furthermore, one of the weak spots of such an analysis is the relationship between foldability and functional relevance. Disordered regions would imply reduced relevance due to poor pLDDT scores, which may be a misleading conclusion. While this may be a problem difficult to solve with their approach, I think this still needs to be addressed and discussed throughout the paper and particularly as part of the global analysis, not just in the context of examples.

As a minor point, I would like to motivate the authors to be more explicit with some quantifications. For example, when focusing on proteins < 500 aa long, what does this mean in relation to what they are not representing in their analysis? How many isoforms will they miss? Is there going to be a bias (e.g. against scaffolding proteins, kinases like ATM, etc.)?

Overall, I consider the idea of the paper very elegant and well executed, yet focusing too much on trees, while I, as a reader, would like to know more about the forest.