Reviewer #2 (Public Review):

The authors convert the AHBA dataset into a dense cortical map and conduct an impressively large number of analyses demonstrating the value of having such data.

I only have comments on the methodology. First, the authors create dense maps by simply using nearest neighbour interpolation followed by smoothing. Since one of the main points of the paper is the use of a dense map, I find it quite light in assessing the validity of this dense map. The reproducibility values they calculate by taking subsets of subjects are hugely under-powered, given that there are only 6 brains, and they don't inform on local, vertex-wise uncertainties). I wonder if the authors would consider using Gaussian process interpolation. It is really tailored to this kind of problem and can give local estimates of uncertainty in the interpolated values. For hyperparameter tuning, they could use leave-one-brain-out for that.

I know it is a lot to ask to change the base method, as that means re-doing all the analyses. But I think it would strengthen the paper if the authors put as much effort in the dense mapping as they did in their downstream analyses of the data.

It is nice that the authors share some code and a notebook, but I think it is rather light. It would be good if the code was better documented, and if the user could have access to the non-smoothed data, in case they was to produce their own dense maps. I was only wondering why the authors didn't share the code that reproduces the many analyses/results in the paper.

Reviewer #2 (Public Review):

The manuscript by Jahncke and colleagues is centered on the CCK+ synaptic defects that are a consequence of Dystroglycanopathy and/or impaired dystroglycan-related protein function. The authors use conditional mouse models for Dag1 and Pomt2 to ablate their function in mouse forebrain neurons and demonstrate significant impairment of CCK+/CB1R+ interneuron (IN) development in addition to being prone to seizures. Mice lacking the intracellular domain of Dystroglycan have milder defects, but impaired CCK+/CB1R+ IN axon targeting. The authors conclude that the milder dystroglycanopathy is due to the partially reduced glycosylation that occurs in the milder mouse models as opposed to the more severe Pomt2 models. Additionally, the authors postulate that inhibitory synaptic defects and elevated seizure susceptibility are hallmarks of severe dystroglycanopathy and are required for the organization of functional inhibitory synapse assembly.

The manuscript is overall, fairly well-written and the description of the phenotypic impact of disruption of Dystroglycan forebrain neurons (and similar glycosyltransferase pathway proteins) demonstrate impairment in axon targeting and organization. There are some questions with regards to interpretation of some of the results from these conditional mouse models. The study is mostly descriptive, and some validation of subunits of the dystroglycan-glycoprotein complex and laminin interactions would go towards defining the impact of disruption of dystroglycan's function in the brain. The statistics and basic analysis of the manuscript appear to be appropriate and within parameters for a study of this nature. Some clarification between the discrepancies between the Walker Warburg Syndrome (WWS) patient phenotypes and those observed in these conditional mouse models is warranted. This manuscript has the potential to be impactful in the Dystroglycanopathy and general neurobiology fields.

Reviewer #2 (Public Review):

In this work, the authors elaborate on an analytically tractable, continuous-attractor model to study an idealized neural network with realistic spiking phase precession/procession. The key ingredient of this analysis is the inclusion of a mechanism for slow firing-rate adaptation in addition to the otherwise fast continuous-attractor dynamics. The latter which continuous-attractor dynamics classically arises from a combination of translation invariance and nonlinear rate normalization.

For strong adaptation/weak external input, the network naturally exhibits an internally generated, travelling-wave dynamics along the attractor with some characteristic speed. For small adaptation/strong external stimulus, the network recovers the classical externally driven continuous-attractor dynamics. Crucially, when both adaptation and external input are moderate, there is a competition with the internally generated and externally generated mechanism leading to oscillatory tracking regime. In this tracking regime, the population firing profile oscillates around the neural field tracking the position of the stimulus. The authors demonstrate by a combination of analytical and computational arguments that oscillatory tracking corresponds to realistic phase precession/procession. In particular the authors can account for the emergence of a unimodal and bimodal cells, as well as some other experimental observations with respect the dependence of phase precession/procession on the animal's locomotion.

The strengths of this work are at least three-fold: 1) Given its simplicity, the proposed model has a surprisingly large explanatory power of the various experimental observations. 2) The mechanism responsible for the emergence of precession/procession can be understood as a simple yet rather illuminating competition between internally driven and externally driven dynamical trends. 3) Amazingly, and under some adequate simplifying assumptions, a great deal of analysis can be treated exactly, which allows for a detailed understanding of all parametric dependencies. This exact treatment culminates with a full characterization of the phase space of the network dynamics, as well as the computation of various quantities of interest, including characteristic speeds and oscillating frequencies.

As mentioned by the authors themselves, the main limitation of this work is that it deals with a very idealized model and it remains to see how the proposed dynamical behaviors would persist in more realistic models. For example, the model is based on a continuous attractor model that assumes perfect translation-invariance of the network connectivity pattern. Would the oscillating tracking behavior persist in the presence of connection heterogeneities? Can the oscillating tracking behavior be observed in purely spiking models as opposed to rate models as considered in this work? Another important limitation is that the system needs to be tuned to exhibit oscillation within the theta range and that this tuning involves a priori variable parameters such as the external input strength. Is the oscillating-tracking behavior overtly sensitive to input strength variations? The author mentioned that an external pacemaker can serve to drive oscillation within the desired theta band but there is no evidence presented supporting this. A final and perhaps secondary limitation has to do with the choice of parameter, namely the time constant of neural firing which is chosen around 3ms. This seems rather short given that the fast time scale of rate models (excluding synaptic processes) is usually given by the membrane time constant, which is typically about 15ms. I suspect this latter point can easily be addressed.

Despite these limitations, it is my opinion that the authors convincingly achieved their aims in this work.

Reviewer #2 (Public Review):

It is increasingly recognized that the cerebellum is involved in a wide range of cognitive and behavioral processes beyond motor coordination and motor learning. This work contributes to the recent body of work showing functional connections between the cerebellum and many other brain regions. This study uses a combination of in vivo electrophysiology, viral tracing, and optogenetics to identify pathways from the deep cerebellar nuclei (DCN) to the nucleus accumbens (NA) core and medial shell running through "nodes" in the ventral tegmental area (VTA) and centromedial and parfascicular nuclei of the thalamus. The significance of this work is in providing function data and anatomical pathways that may underlie the role of the cerebellum in reward behavior.

This work makes two significant contributions to the field. First, the authors show that electrical stimulation in the DCN (the output of the cerebellar circuit) elicits (primarily excitatory) responses in neurons of the NA core and medial shell. Previous studies have shown that stimulation in the cerebellum increases dopamine in the NA, but this study is the first to use in vivo electrophysiology to measure changes in neuronal firing rates. Responses in NA neurons are primarily excitatory, with a small number of neurons showing inhibitory or mixed excitatory/inhibitory responses. The data here are clear and support the conclusions. The only caveat, acknowledged by the authors, is the use of ketamine/xylazine to anesthetize the mice may alter the firing properties of NA neurons and the balance of excitation and inhibition in neuronal responses. The specific mechanisms (neurotransmitters, synapses, or circuits) resulting in excitation or inhibition of NA neurons are not investigated here, though this may be an interesting avenue of future work.

The second significant contribution of this work is identifying anatomical pathways that connect DCN to the NA. The identification of these pathways is well supported by the viral injection data. The data using cre-expressing AAV in the DCN and floxed td-tomato AAV in the VTA or thalamus is particularly convincing. However, the inclusion of additional controls would strengthen the conclusions (see below).

In general, the conclusions are well-supported by the data. However, in a few places inadequate controls or description of the experiments weakens the conclusions.

1. In Figure 4, the authors injected a retrograde tracer in the NA and an anterograde tracer in DCN to find potential "nodes" of overlap. From this experiment, the authors identify the VTA and regions of the thalamus as potential areas of tracer overlap, but it is unclear how many other brain regions were examined. Did the authors jump straight to likely locations of overlap based on previous findings, or were large swaths of the brain examined systematically? If other brain regions were examined, which regions and how was this done? A table listing which brain regions were examined and the presence/intensity of ctb-Alexa568 and GFP fluorescence would be helpful.<br /> 2. In Figure 5, the authors inject AAV1-Cre in DCN and AAV-FLEX-tdTomato in VTA or thalamus. This is an interesting experiment, but controls are missing. An important control is to inject AAV-FLEX-tdTomato in the VTA or thalamus in the absence of AAV1-Cre injection in DCN. Cre-independent expression of tdTomato should be assessed in the VTA/thalamus and the NA.

Reviewer #2 (Public Review):

Place cells fire sequentially during hippocampal theta oscillations, forming a spatial representation of behavioral experiences in a temporally-compressed manner. The firing sequences during theta cycles are widely considered as essential assemblies for learning, memory, and planning. Many theoretical studies have investigated the mechanism of hippocampal theta firing sequences; however, they are either entirely extrinsic or intrinsic. In other words, they attribute the theta sequences to external sensorimotor drives or focus exclusively on the inherent firing patterns facilitated by the recurrent network architectures. Both types of theories are inadequate for explaining the complexity of the phenomena, particularly considering the observations in a previous paper by the authors: theta sequences independent of animal movement trajectories may occur simultaneously with sensorimotor inputs (Yiu et al., 2022).

In this manuscript, the authors concentrate on the CA3 area of the hippocampus and develop a model that accounts for both mechanisms. Specifically, the model generates extrinsic sequences through the short-term facilitation of CA3 cell activities, and intrinsic sequences via recurrent projections from the dentate gyrus. The model demonstrates how the phase precession of place cells in theta sequences is modulated by running direction and the recurrent DG-CA3 network architecture. To evaluate the extent to which firing sequences are induced by sensorimotor inputs and recurrent network architecture, the authors use the Pearson correlation coefficient to measure the "intrinsicity" and "extrinsicity" of spike pairs in their simulations.

I find this research topic to be both important and interesting, and I appreciate the clarity of the paper. The idea of combining intrinsic and extrinsic mechanisms for theta sequences is novel, and the model effectively incorporates two crucial phenomena: phase precession and directionality of theta sequences. I particularly commend the authors' efforts to integrate previous theories into their model and conduct a systematic comparison. This is exactly what our community needs: not only the development of new models, but also understanding the critical relationships between different models.

Reviewer #2 (Public Review):

The authors had two aims in this study. First, to develop a tool that lets them quantify the synaptic strength and sign of upstream neurons in a large network of cultured neurons. Second, they aimed at disentangling the contributions of excitatory and inhibitory inputs to spike generation.

For the quantification of synaptic currents, their methods allows them to quantify excitatory and inhibitory currents simultaneously, as the sign of the current is determined by the neuron identity in the high-density extracellular recording. They further made sure that their method works for nonstationary firing rates, and they did a simulation to characterize what kind of connections their analysis does not capture. They did not include the possibility of (dendritic) nonlinearities or gap junctions or any kind of homeostatic processes. I see a clear weakness in the way that they quantify their goodness of fit, as they only report the explained variance, while their data are quite nonstationary. It could help to partition the explained variance into frequency bands, to at least separate the effects of a bias in baseline, the (around 100 Hz) band of synaptic frequencies and whatever high-frequency observation noise there may be. Another weak point is their explanation of unexplained variance by potential activation of extrasynaptic receptors without providing evidence. Given that these cultures are not a tissue and diffusion should be really high, this idea could easily be tested by adding a tiny amount of glutamate to the culture media.

For the contributions of excitation and inhibition to neuronal spiking, the authors found a clear reduction of inhibitory inputs and increase of excitation associated with spiking when averaging across many spikes. And interestingly, the inhibition shows a reversal right after a spike and the timescale is faster during higher network activity. While these findings are great and provide further support that their method is working, they stop at this exciting point where I would really have liked to see more detail. A concern, of course is that the network bursts in cultures are quite stereotypical, and that might cause averages across many bursts to show strange behaviour. So what I am missing here is a reference or baseline or null hypothesis. How does it look when using inputs from neurons that are not connected? And then, it looks like the E/(E+I) curve has lots of peaks of similar amplitude (that could be quantified...), so why does the neuron spike where it does? If I would compare to the peak (of similar amplitude) right before or right after (as a reference) are there some systematic changes? Is maybe the inhibition merely defining some general scaffold where spikes can happen and the excitation causes the spike as spiking is more irregular?<br /> The averaged trace reveals a different timescale for high and low activity states. But does that reflect a superposition of EPSCs in a single trial or rather a different jittering of a single EPSC across trials? For answering this question, it would be good to know the variance (and whether/ how much it changes over time). Maybe not all spikes are preceded by a decrease in inhibition. Could you quantitify (correlate, scatterplot?) how exactly excitation and inhibition contributions relate for single postsynaptic spikes (or single postsynaptic non-spikes)? After all, this would be the kind of detail that requires the large amount of data that this study provides.

For the first part, the authors achieved their goal in developing a tool to study synaptic inputs driving subthreshold activity at the soma, and characterizing such connections. For the second part, they found an effect of EPSCs on firing, but they barely did any quantification of its relevance due to the lack of a reference.

With the availability of Neuropixels probes, there is certainly use for their tool in in vivo applications, and their statistical analysis provides a reference for future studies.<br /> The relevance of excitatory and inhibitory currents on spiking remains to be seen in an updated version of the manuscript.

I feel that specifically Figures 6 and 7 lack relevant detail and a consistent representation that would allow the reader to establish links between the different panels. The analysis shows very detailed examples, but then jumps into analyses that show population averages over averaged responses, losing or ignoring the variability across trials. In addition, while their results themselves pass a statistical test, it is crucial to establish some measure of how relevant these results are. For that, I would really want to know how much spiking would actually be restricted by the constraints that would be posed by these results, i.e. would this be reflected in tiny changes in spiking probabilities, or are there times when spiking probabilities are necessarily high, or do we see times when we would almost certainly get a spike, but neurons can fire during other times as well.<br /> I would agree that a detailed, quantitative analysis of this question is beyond the scope of this paper, but a qualitative analysis is feasible and should be done. In the following, I am detailing what I would consider necessary to be done about these two Figures:

Figure 6C is indeed great, though I don't see why the authors would characterize synchrony as low. When comparing with Figure 4B, I'd think that some of these values are quite high. And it wouldn't help me to imagine error bars in panel 6D.<br /> Figure 6B is useful, but could be done better: The autocovariance of a shotnoise process is a convolution of the autocovariance of underlying point process and the autocovariance of the EPSC kernel. So one would want to separate those to obtain a better temporal resolution. But a shotnoise process has well defined peaks, and the time of these local maxima can be estimated quite precisely. Now if I would do a peak triggered average instead of the full convolution, I would do half of the deconvolution and obtain a temporally asymmetric curve of what is expected to happen around an EPSC. Importantly, one could directly see expected excitation after inhibition or expected inhibition after excitation, and this visualization could be much better and more intuitively compared to panel 6E.<br /> Panel D needs some variability estimate (i.e. standard deviation or interquartile range or even a probability density) for those traces.<br /> Figure 6E: Please use more visible colors. A sensitivity analysis to see traces for 2E/(2E+I) and E/(E+2I) would be great.<br /> Figure 6F: with an updated panel B, we should be able to have a slope for average inhibition after excitation for each of these cells. A second panel / third column showing those slopes would be of interest. It would serve as a reference for what could be expected from E-I interactions alone.<br /> Figure 6G: Could the authors provide an interquartile range here?

Figure 7A: it may be hard to squeeze in variability estimates here, but the information on whether and how much variance might be explained is essential. Maybe add another panel to provide a variability estimate? The variability estimate in panel 7B and 7D only reflect variability across connections, and it would be useful to add panels for the timecourses of the variability of g (or E/(E+I) respectively).

As a suggestion for further analysis, though I am well aware that this is likely beyond the scope of this manuscript, I'd suggest the following analysis:<br /> I would split the data into the high and low activity states. Then I would compute the average of E/(E+I) values for spikes. Assuming that spikes tend to happen for local maxima of E/(E+I) I would find local maxima for periods without spike such that their average is equal to the value for actual spikes. Finally, I would test for a systematic difference in either excitation or inhibition.<br /> If there is no difference, you can make the claim that synaptic input does not guarantee a spike, and compare to a global average of E/(E+I).

Reviewer #2 (Public Review):

In this paper, the authors seek to identify genes that contribute to gut inflammation by capitalizing on deep phenotyping data in a mouse genetic reference population fed a high-fat or chow diet and then integrating it with human genetic data on gut inflammatory diseases, such as inflammatory bowel disease (IBD) and Ulcerative Colitis (UC). To achieve this the authors performed genome-wide gene expression in the colon of 52 BXD strains of mice fed either a high-fat or chow diet. From this analysis, they observed significant variation in gene expression related to inflammation among the 52 BXD strains and differential gene expression of inflammatory genes fed a high-fat diet. Overlaying this data with existing mouse and human data of inflammatory gut disease identified a significant enrichment. Using the 52 BXD strains the authors were able to identify specific subsets of strains that were susceptible and resistant to gut inflammation and analysis of gene expression within the colon of these strains was enriched with mouse and human IBD. Furthermore, analysis of cytokine levels of IL-10 and IL-15 were analyzed and found to be increased in resistant BXD strains and increased in susceptible BXD strains.

Using the colon genome-wide gene expression data from the 52 BXD strains, the authors performed gene co-expression analysis and were able to find distinct modules (clusters) of genes that correlated with mouse UC and human IBD datasets. Using the two modules, termed HFD_M28 and HFD_M9 that correlated with mouse UC and human IBD, the authors performed biological interrogation along with transcription factor binding motif analysis to identify possible transcriptional regulators of the module. Next, they performed module QTL analysis to identify potential genetic regulators of the two modules and identified a genome-wide significant QTL for the HFD_M28 on mouse chromosome 16. This QTL contained 552 protein-coding genes and through a deduction method, 27 genes were prioritized. These 27 genes were then integrated with human genetic data on IBD and two candidate genes, EPHA6 and MUC4 were prioritized.

Overall, this paper provides a framework and elegant use of data from a mouse genetic reference population coupled with human data to identify two strong candidate genes that contribute to human IBD and UC diseases. In the future, it will be interesting to perform targeted studies with EPHA6 and MUC4 and understand their role in gut inflammatory diseases.

Reviewer #2 (Public Review):

This is a novel and interesting study in which the authors aimed to gain a better understanding of whether there is an optimum number of close friends to gain good mental well-being/functioning and its underlying neural mechanisms. They thoroughly examined how the number of close friendships contributes to mental health, cognition, (social) brain structure, and neural molecular processes in adolescents. They conducted multiple analyses on two large datasets to answer their research question(s) and support the results with visually attractive figures. I believe this paper is of added value to the literature as the evidence presently robustly points to the optimum number of 5 close friends in relation to mental health and cognition and related neurobiological mechanisms. This greatly advances the knowledge in the field of social and neurocognitive psychology.

The authors use a variety of measures to assess mental health, cognition, and neural mechanisms, which is a strength of the study. However, the theoretical background of these constructs should be elaborated on or unpacked to a greater extent in the introduction. Relatedly, the discussion could benefit from clearer main messages conveyed by individual paragraphs. It is currently hard to follow how the authors interpret their results in the context of existing literature.

Reviewer #2 (Public Review):

Transporters cycle between several conformational states; however, developing a unifying cycle for a single transporter is often difficult, as different homologs are often used to experimentally determine the structures of different conformations. The manuscript of Mitrovic et al. is a clever and inspiring combination of computational methods to reconstruct the transport cycle and free-energy landscape of a single sugar transporter. Using co-evolution and machine learning, the authors extracted state-specific residue contacts, many of which were previously unobserved, and potentially describe subtle yet important structural features. Using these contacts, they bias AlphaFold2 structure determination and MD simulations to accurately predict any conformation. These structures combined with enhanced sampling methods facilitate the inference of free-energy landscapes of the transport cycle. Notably, this work continues to push the limits of using and interpreting AlphaFold2 past static snapshots of highly dynamic proteins. This combination of techniques represents the forefront of structural biology, clearly demonstrating how static protein structures can be leveraged using bioinformatic and computational techniques to understand the biophysical mechanisms of proteins. Though the methodology is technically and theoretically exciting, it is as of yet unclear if this represents a substantial enough improvement over existing techniques for wider adoption. Nevertheless, this work represents an innovative combination of existing approaches to create a cohesive framework of the sugar transport cycle, and the authors provide detailed methods and supplementary information to recreate these approaches in other transporter families.

Reviewer #2 (Public Review):

Summary:

Here, the authors show that neutral lipids play a role in spermatogenesis. Neutral lipids are components of lipid droplets, which are known to maintain lipid homeostasis, and to be involved in non-gonadal differentiation, survival, and energy. Lipid droplets are present in the testis in mice and Drosophila, but not much is known about the role of lipid droplets during spermatogenesis. The authors show that lipid droplets are present in early differentiating germ cells, and absent in spermatocytes. They further show a cell autonomous role for the lipase brummer in regulating lipid droplets and, in turn, spermatogenesis in the Drosophila testis. The data presented show that a relationship between lipid metabolism and spermatogenesis is congruous in mammals and flies, supporting Drosophila spermatogenesis as an effective model to uncover the role lipid droplets play in the testis.

Strengths and weaknesses:

The authors do a commendably thorough characterization of where lipid droplets are detected in normal testes: located in young somatic cells, and early differentiating germ cells. They use multiple control backgrounds in their analysis, including w[1118], Canton S, and Oregon R, which adds rigor to their interpretations. The authors employ markers that identify which lipid droplets are in somatic cells, and which are in germ cells. The authors use these markers to present measured distances of somatic and germ cell-derived lipid droplets from the hub. Because they can also measure the distance of somatic and germ cells with age-specific markers from the hub, these results allow the authors to correlate position of lipid droplets with the age of cells in which they are present. This analysis is clearly shown and well quantified.

The quantification of lipid droplet distance from the hub is applied well in comparing brummer mutant testes to wild type controls. The authors measure the number of lipid droplets of specific diameters, and the spatial distribution of lipid droplets as a function of distance from the hub. These measurements quantitatively support their findings that lipid droplets are present in an expanded population of cells further from the hub in brummer mutants. The authors further quantify lipid droplets in germline clones of specified ages; the quantitative analysis here is displayed clearly, and supports a cell autonomous role for brummer in regulating lipid droplets in spermatocytes.

Data examining testis size and number of spermatids in brummer mutants clearly indicates the importance of regulating lipid droplets to spermatogenesis. The authors show beautiful images supported by rigorous quantification supporting their findings that brummer mutants have both smaller testes with fewer spermatids at both 29 and 25C. There is also significant data supporting defects in testis size for 14-day-old brummer mutant animals compared to controls. The comparison of number of spermatids at this age is not significant, which does not detract from the the story but does not support sperm development defects specifically caused by brummer loss at 14 days. Their analysis clearly shows an expanded region beyond the testis apex that includes younger germ cells, supporting a role for lipid droplets influencing germ cell differentiation during spermatogenesis.

The authors present a series of data exploring a cell autonomous role for brummer in the germline, including clonal analysis and tissue specific manipulations. The clonal data indicating increased lipid droplets in spermatocyte clones, and a higher proportion of brummer mutant GSCs at the hub are convincing and supported by quantitation. The authors also show a tissue specific rescue of the brummer testis size phenotype by knocking down mdy specifically in germ cells, which is also supported by statistically significant quantitation. The authors present data examining the number of spermatocyte and post-meiotic clones 14 days after clonal induction. While data they present is significant with a 95% confidence interval and a p value of 0.0496, its significance is not as robust as other values reported in the study, and it is unclear how much information can be gained from that specific result.

The authors do a beautiful job of validating where they detect brummer-GFP by presenting their own pseudotime analysis of publicly available single cell RNA sequencing data. Their data is presented very clearly, and supports expression of brummer in older somatic and germline cells of the age when lipid droplets are normally not detected. The authors also present a thorough lipidomic analysis of animals lacking brummer to identify triglycerides as an important lipid droplet component regulating spermatogenesis.

Impact:

The authors present data supporting the broad significance of their findings across phyla. This data represents a key strength of this manuscript. The authors show that loss of a conserved triglyceride lipase impacts testis development and spermatogenesis, and that these impacts can be rescued by supplementing diet with medium-chain triglycerides. The authors point out that these findings represent a biological similarity between Drosophila and mice, supporting the relevance of the Drosophila testis as a model for understanding the role of lipid droplets in spermatogenesis. The connection buttresses the relevance of these findings and this model to a broad scientific community.

Reviewer #2 (Public Review):

The manuscript illuminates the biological function of the Cac-1 "KER" region within the CAF-1 chromatin assembly factor 1. (This region has a high density of lysine, glutamic acid and arginine residues). The authors present a comprehensive study including quantitative EMSA analyses, analysis of mutants in-vivo, CD, and X-ray crystallography to identify the KER domain as a single alpha-helix element (SAH) that is largely responsible for the ability of the yCAF-1 complex to selectively binding ~40 bp dsDNA fragments over shorter ds oligos, thought to be a 'measuring' function that determines there is sufficient space for assembling H3/H4 tetramers after passage of the DNA replication complex. Moreover, they find that deletions or modifications of the KER domain contribute to yeast phenotypes consistent with a deficiency in chromatin assembly. The data in the paper is compelling, supports the conclusions and adds critical new information regarding how CAF-1 functions accomplishes its 'spacing' function in cooperation with DNA replication machinery to deposit H3/H4 dimers onto replicated DNA.

Reviewer #2 (Public Review):

The authors demonstrated that noradrenaline regulates Cav1.2 through PKC, which phosphorylates and activates Pyk2. Pyk2, in turn, autophosphorylates itself at Y402, which serves as a binding site for Src SH2 domain. Src will then phosphorylate Pyk2 at Y579 for full activation. Src also autophosphorylates itself at Y416. In this way, these two proteins generate a self-activating complex where Pyk activate Src, which then activates Pyk. Overall, this leads to an an activation of Cav1.2 and mediates noradrenaline-mediated augmentation of LTCC-mediated LTP.

Reviewer #2 (Public Review):

One of the key questions in circuit neuroscience is how learned information guides behavior. Modi et al. investigated this question in Drosophila's mushroom bodies (MBs), where olfactory memory traces are formed during pavlovian olfactory conditioning. They have used optogenetics to restrict the formation of memory traces in selective output compartments of the Kenyon cell (KC) axon terminals, the principal intrinsic neurons of the MB, and tested how flies use these 'minimal memories' during learned olfactory discrimination. They found that memory traces formed in some compartments support discrimination between similar odor pairs, whereas others do not. They then investigated the neural basis of this difference by comparing the responses of relevant output neurons (MBONs) to similar and dissimilar odor pairs. They discovered that MBONs' responses could predict behavioral outcomes if odor presentation profiles during calcium imaging mimic olfactory experience during behavior. This paper and previous works support the idea that flies use olfactory memory templates flexibly to suit their behavioral needs. However, one key difference between this paper and the previous works is the site of discrimination. While previous studies using intensity discrimination have pointed towards spike-latency and on and off responses of the KCs as the main mechanism behind discrimination, Modi et al. have not detected any response difference for similar odor pairs among the KCs. Therefore, they concluded that a hitherto unknown mechanism creates these context-specific responses at the MBONs. The findings will advance our understanding of how memories are recalled during behavior. However, the authors need to bolster their data by including some critical controls that are currently missing.

Reviewer #2 (Public Review):

In Rey et al., the authors goal was to characterize the development of a myelin-like (lacunar) expansion of glial membrane in Drosophila. Although myelin is largely considered a vertebrate innovation, there are a handful of invertebrate models that have been described with glial-derived "myelin," though these systems are not amenable to the same genetic control as Drosophila. To that end, the authors first newly-developed genetics and antibodies to characterize the presence of an axon initial segment (AIS) for adult Drosophila motor neurons that is present at the border between the central and peripheral nervous systems. They show that both sodium (Para) and potassium (Shal) channels, which are typically enriched at the AIS in mammalian neurons, are enriched at this border specifically on motor neurons. They then used multiple types of transmission electron microscopy to visualize this region and found that along with clustering of channels, there is an expansion of membranes from wrapping glia that is reminiscent of myelin. At times, this expansion spirally wraps around larger axons. Finally, they show that genetic ablation of wrapping glia results in an upregulation and redistribution of Para.

Major strengths of this manuscript include the creation of new genetic tools for visualization of subcellular features (e.g. channels) by both light microscopy and electron microscopy.

While this manuscript provides an interesting set of data, but suffers from a lack of quantification and annotation to allow the reader to judge whether this is a robust phenomenon. To increase the reader's confidence in these studies, substantially more quantification of the data is required.

Furthermore, to improve the accessibility of this manuscript, I have the following suggestions:

1. Please label the panels throughout the figures with an abbreviated genotype and what the fluorophores signify. Similarly, the presence of scale bars in uneven across the figures.

2. For panels where only one channel is shown, please show these in black and white, which is easier for the visually-impaired.

Overall, the description of "myelin" in Drosophila would open up the field of myelin biology to a new model system to study the molecular mechanisms that facilitated the evolution of this important glial structure. Thus, further analysis of the data would be advantageous.

Reviewer #2 (Public Review):

In this manuscript, Clay et al. investigate the underlying effects of reduced mRNA translation beneficial on protein aggregation and aging. They aim to test two pre-existing hypotheses: The selective translation model proposes that downregulation of overall translation increases the capacity of ribosomes to translate selected factors that in turn increase stress resistance against toxicity. The reduced folding load model suggests that during high mRNA translation rates, newly synthesized peptides and proteins can overwhelm the protein folding capacity of the cell and therefore cause protein toxicity. By generally lowering mRNA translation, lower loads of newly synthesized proteins should cause less protein folding stress and hence protein toxicity.

To understand how reduced mRNA translation mediates its beneficial effects in the context of the proposed models, the authors use different drugs established previously in other in vitro and in vivo systems to inhibit selected steps of translation. The systemic effects of translation initiation versus elongation inhibition in C. elegans are compared during heat shock, specific protein aggregation stresses and aging. These phenotypes are further tested for dependence on hsf-1, as contradictory data on the effect of translation inhibition during thermal stress in the context of hsf-1 dependency exist.

The data show that inhibition of translation initiation protects from heat stress and age-associated protein aggregation but on the contrary further sensitizes animals to protein toxicity induced by a misfunctioning proteasome. Further, inhibition of translation initiation increases lifespan in WT animals. The survival phenotypes observed during heat shock and regular lifespan assays are dependent of HSF-1, supporting the selective translation model. As stated in the manuscript, these findings themselves are not new, given that similar observations were made before using genetic models. Interestingly, the inhibition of translation elongation protects from heat stress, but, unlike initiation inhibition, also proteasome-misfunction-induced protein toxicity. Both phenotypes were observed to be independent of hsf-1. The authors further find that inhibiting elongation does not reduce protein aggregation in aged worms and does not prolong lifespan in wildtype animals. It does increase lifespan in short-lived hsf-1 mutants, where protein homeostasis is compromised. To a degree, these findings support the reduced folding load model. Overall, from these observations the authors summarize that the systemic consequences of lowering translation depend on the step in which translation is inhibited as well as the environmental context. The authors conclude that different ways to inhibit translation can protect from different insults by independent mechanisms.

Impact, strengths and weaknesses:

mRNA translation and its regulation is one of the most studied mechanisms connected to lifespan extension. However, gaps behind the protective effects of translation inhibition are so far unresolved, as stated by the authors. Therefore, testing existing hypotheses explaining the beneficial effects of translation inhibition is of great interest, not only for C. elegans researchers but a broad community working on the effects of misregulated translation during aging and disease. Overall, the conclusions made by the authors are generally supported by the data shown in this manuscript. However, some major gaps remain and need to be clarified and extended.

Reviewer #2 (Public Review):

The authors explore the role of bicarbonate-regulated soluble adenylate cyclase in modulating cardiac mitochondrial energy supply. In isolated rat mitochondria, they show that cyclic AMP (but not the permeable cAMP analog 8-Br-cAMP) increases ATP production via a Ca-independent mechanism at a location in the intermembrane space of the mitochondria, rather than in the matrix, as previously reported. Moreover, they show that inhibition of EPAC, but not PKA, inhibits the response. The effect required supplementing the mitochondria with GTP and GDP to facilitate activation of the EPAC effector GTPase Rap1. The study provides interesting new information about how the heart might adapt to changes in energy supply and demand through complementary regulatory processes involving both Ca and cyclic AMP.

The authors nicely demonstrate that soluble adenylate cyclase is localized to mitochondria. They argue, based on the effects of cyclic AMP, which is accessible to the mitochondrial intermembrane space (IMS) but not the matrix, that the signalling pathway is located in the IMS. They also find that EPAC/Rap1 is the likely downstream effector of cyclic AMP, through yet unknown targets regulating oxidative phosphorylation.

A weakness is that the components of signaling (sAC, EPAC, and rap1) are not definitively localized to a specific mitochondrial compartment using the superresolution imaging methods employed.

Reviewer #2 (Public Review):

Jamge et al. set out to delineate the relationship between histone variants, histone modifications and chromatin states in Arabidopsis seedlings and leaves. A strength of the study is its use of multiple types of data: the authors present mass-spec, immunoblotting and ChIP-seq from histone variants and histone modifications. They confirm the association between certain marks and variants, in particular for H2A, and nicely describe the loss of constitutive heterochromatin in the ddm1 mutant.

The support for some of the conclusions is weak. The title of the discussion, "histone variants drive the overall organization of chromatin states" implies a causation which wasn't investigated, and overstates the finding that some broad chromatin states can be further subdivided when one considers histone variants (adding variables to the model).

Adding variables to a ChromHMM model naturally increases the complexity of the models that can be built, however it is difficult to objectively define which level of complexity is optimal. The differences between states may be subtle to the point that they may be considered redundant. The authors claim that the sub-states they define are biologically important, but provide little evidence to support this claim. It is not obvious whether the 26 states model is much more useful than a 9-states model. Removing variables naturally affects the definition of states that depend on these variables, but it is also hard to define the biological significance of that change. This sensitivity analysis is thus not very developed.

There are issues with the logical sequence of arguments in Fig1 and Fig3. Fig1A shows that nucleosomes often contain both H3.1 and H3.3. Therefore pulling-down H3.1-containing nucleosomes also pulls down H3.3 and whether specific H2A variants associated with H3.1 cannot be answered in this way (Fig1B). The same issue likely carries to the investigation of the association with H3 modifications if Fig1C and 1D, since the H3.1-HA pull-down also pulls down endogenous H3.1 (so presumably the rest of the nucleosome, with H3.3, as well).

In Fig3, the conclusion that it is the loss of H2A.Z -> H2A.W exchange in the ddm1 mutant that causes loss of constitutive heterochromatin is rushed. The fact that the h2a.w mutant does not recapitulate the loss of constitutive heterochromatin seen in ddm1 argues against this interpretation. It's also difficult to conclude about the importance of dynamic exchanges when the ddm1 mutation has been present for generations and the chromatin landscape has fully readapted. Further work is needed to support the authors' hypothesis.

The study also relies on a large number of custom (polyclonal) antibodies with no public validation data. Lack of specificity, a common issue with antibodies, would muddle the interpretation of the data.

Overall, this study nicely illustrates that, in Arabidopsis, histone variants (and H2A variants in particular) display specificity in modifications and genomic locations, and correlate with some chromatin sub-states. This encourages future work in epigenomics to consider histone variants with as much attention as histone modifications.

Reviewer #2 (Public Review):

This study investigates the drivers behind termite construction, with a particular focus on the environmental factors that drive pellet deposition. The authors performed experiments and computations in an attempt to disentangle the role of surface curvature, feature elevation, substrate evaporation, and a possible "cement" pheromone on the deposition of soil pellets.

In three different types of experiments, the authors present termites with pre-made, unmarked (pheromone-free) pellets, and they vary pre-existing topographic building cues: some experiments have two pillars, others have a wall, and a third type had no cues. In experiments with topographic cues, the authors find that deposition seems to occur preferentially at the locations of highest curvature (i.e., peaks of pillars and corners of the walls). Complementary experiments and simulations show that locations of highest curvature correspond to locations with highest evaporation rates, at least for pillars. Evaporation rates seem inconclusive in the wall geometry, yet the termites still deposit material at the high-curvature wall corners. The authors conclude that: (1) no "cement" pheromone is needed for construction, (2) that depositions preferentially occur at locations of high curvature (all experiments for pillars, 7 out of 11 experiments for walls), and (3) that evaporation (which is fastest at places of highest curvature, at least for pillars) drives deposition. The experiments and results seem sound and interesting, but some of the interpretations need more justification. For instance, why conclude that evaporation drives construction when there is not a measurable difference in evaporation rate across the wall geometry?

The authors also perform simulations (developed in a previous publication) that agree with their experimental observation that deposition occurs preferentially at locations of high curvature. However, there is not enough detail provided about the simulation to understand the degree to which simulation and experiment agree (e.g., is the agreement qualitative or quantitative?) as well as the significance of the agreement. The authors should provide additional details about the setup and mechanics of the simulation, the outputs and how they connect to experiments, and potential limitations of results/connections to the experimental system. Finally, more background about this termite species would be helpful in putting these results into context. For instance, what is known about the natural habitat and conditions, and natural nest locations and structures? What are (or might be, depending on what is known) the potential abilities/benefits for these animals to sense humidity gradients, and why building at these locations could benefit the animals?

Reviewer #2 (Public Review):

Neininger-Castro et al report on their original study entitled "Independent regulation of Z-lines and M-lines during sarcomere assembly in cardiac myocytes revealed by the automatic image analysis software sarcApp", In this study, the research team developed two software, yoU-Net and sarcApp, that provide new binarization and sarcomere quantification methods. The authors further utilized human induced pluripotent stem cell-derived cardiomyocytes (hiCMs) as their model to verify their software by staining multiple sarcomeric components with and without the treatment of Blebbistatin, a known myosin II activity inhibitor. With the treatment of different Blebbistatin concentrations, the morphology of sarcomeric proteins was disturbed. These disrupted sarcomeric structures were further quantified using sarcApp and the quantification data supported the phenotype. The authors further investigated the roles of muscle myosins in sarcomere assembly by knocking down MYH6, MYH7, or MYOM in hiCMs. The knockdown of these genes did not affect Z-line assembly yet the knockdown of MYOM affected M-line assembly. The authors demonstrated that different muscle myosins participate in sarcomere assembly in different manners.

Reviewer 2 (Public Review):

In this study, the authors aimed to evaluate the contribution of brain-age indices in capturing variance in cognitive decline and proposed an alternative index, brain-cognition, for consideration. The study employs suitable data and methods, albeit with some limitations, to address the research questions. A more detailed discussion of methodological limitations in relation to the study's aims is required. For instance, the current commonality analysis may not sufficiently address potential multicollinearity issues, which could confound the findings. Importantly, given that the study did not provide external validation for the indices, it is unclear how well the models would perform and generalize to other samples. This is particularly relevant to their novel index, brain-cognition, given that brain-age has been validated extensively elsewhere. In addition, the paper's rationale for using elastic net, which references previous fMRI studies, seemed somewhat unclear. The discussion could be more nuanced and certain conclusions appear speculative.

The authors aimed to evaluate how brain-age and brain-cognition indices capture cognitive decline (as mentioned in their title) but did not employ longitudinal data, essential for calculating 'decline'. As a result, 'cognition-fluid' should not be used interchangeably with 'cognitive decline,' which is inappropriate in this context.

In their first aim, the authors compared the contributions of brain-age and chronological age in explaining variance in cognition-fluid. Results revealed much smaller effect sizes for brain-age indices compared to the large effects for chronological age. While this comparison is noteworthy, it highlights a well-known fact: chronological age is a strong predictor of disease and mortality. Has the brain-age literature systematically overlooked this effect? If so, please provide relevant examples. They conclude that due to the smaller effect size, brain-age may lack clinical significance, for instance, in associations with neurodegenerative disorders. However, caution is required when speculating on what brain-age may fail to predict in the absence of direct empirical testing. This conclusion also overlooks extant brain-age literature: although effect sizes vary across psychiatric and neurological disorders, brain-age has demonstrated significant effects beyond those driven by chronological age, supporting its utility.

The second aim's results reveal a discrepancy between the accuracy of their brain-age models in estimating age and the brain-age's capacity to explain variance in cognition-fluid. The authors suggest that if the ultimate goal is to capture cognitive variance, brain-age predictive models should be optimized to predict this target variable rather than age. While this finding is important and noteworthy, additional analyses are needed to eliminate potential confounding factors, such as correlated noise between the data and cognitive outcome, overfitting, or the inclusion of non-healthy participants in the sample. Optimizing brain-age models to predict the target variable instead of age could ultimately shift the focus away from the brain-age paradigm, as it might optimize for a factor differing from age.

While a primary goal in biomarker research is to obtain indices that effectively explain variance in the outcome variable of interest, thus favouring models optimized for this purpose, the authors' conclusion overlooks the potential value of 'generic/indirect' models, despite sacrificing some additional explained variance provided by ad-hoc or 'specific/direct' models. In this context, we could consider brain-age as a 'generic' index due to its robust out-of-sample validity and significant associations across various health outcome variables reported in the literature. In contrast, the brain-cognition index proposed in this study is presumed to be 'specific' as, without out-of-sample performance metrics and testing with different outcome variables (e.g., neurodegenerative disease), it remains uncertain whether the reported effect would generalize beyond predicting cognition-fluid, the same variable used to condition the brain-cognition model in this study. A 'generic' index like brain-age enables comparability across different applications based on a common benchmark (rather than numerous specific models) and can support explanatory hypotheses (e.g., "accelerated ageing") since it is grounded in its own biological hypothesis. Generic and specific indices are not mutually exclusive; instead, they may offer complementary information. Their respective utility may depend heavily on the context and research or clinical question.

The study's third aim was to evaluate the authors' new index, brain-cognition. The results and conclusions drawn appear similar: compared to brain-age, brain-cognition captures more variance in the outcome variable, cognition-fluid. However, greater context and discussion of limitations is required here. Given the nature of the input variables (a large proportion of models in the study were based on fMRI data using cognitive tasks), it is perhaps unsurprising that optimizing these features for cognition-fluid generates an index better at explaining variance in cognition-fluid than the same features used to predict age. In other words, it is expected that brain-cognition would outperform brain-age in explaining variance in cognition-fluid since the former was optimized for the same variable in the same sample, while brain-age was optimized for age. Consequently, it is unclear if potential overfitting issues may inflate the brain-cognition's performance. This may be more evident when the model's input features are the ones closely related to cognition, e.g., fMRI tasks. When features were less directly related to cognitive tasks, e.g., structural MRI, the effect sizes for brain-cognition were notably smaller (see 'Total Brain Volume' and 'Subcortical Volume' models in Figure 6). This observation raises an important feasibility issue that the authors do not consider. Given the low likelihood of having task-based fMRI data available in clinical settings (such as hospitals), estimating a brain-cognition index that yields the large effects discussed in the study may be challenged by data scarcity.

This study is valuable and likely to be useful in two main ways. First, it can spur further research aimed at disentangling the lack of correspondence reported between the accuracy of the brain-age model and the brain-age's capacity to explain variance in fluid cognitive ability. Second, the study may serve, at least in part, as an illustration of the potential pros and cons of using indices that are specific and directly related to the outcome variable versus those that are generic and only indirectly related.

Overall, the authors effectively present a clear design and well-structured procedure; however, their work could have been enhanced by providing more context for both the brain-age and brain-cognition indices, including a discussion of key concepts in the brain-age paradigm, which acknowledges that chronological age strongly predicts negative health outcomes, but crucially, recognizes that ageing does not affect everyone uniformly. Capturing this deviation from a healthy norm of ageing is the key brain-age index. This lack of context was mirrored in the presentation of the four brain-age indices provided, as it does not refer to how these indices are used in practice. In fact, there is no mention of a more common way in which brain-age is implemented in statistical analyses, which involves the use of brain-age delta as the variable of interest, along with linear and non-linear terms of age as covariates. The latter is used to account for the regression-to-the-mean effect. The 'corrected brain-age delta' the authors use does not include a non-linear term, which perhaps is an additional reason (besides the one provided by the authors) as to why there may be small, but non-zero, common effects of both age and brain-age in the 'corrected brain-age delta' index commonality analysis. The context for brain-cognition was even more limited, with no reference to any existing literature that has explored direct brain-cognitive markers, such as brain-cognition.

While this paper delivers intriguing and thought-provoking results, it would benefit from recognizing the value that both approaches--brain-age indices and more direct, specific markers like brain-cognition--can contribute to the field.

Reviewer #2 (Public Review):

This study examines most monosomies in yeast in comparison to synthetic lethals resulting from combinations of heterozygous gene deletions that individually have a detrimental effect. The survival of monosomies, albeit with detrimental growth defects, is interpreted as positive epistasis for fitness. Gene expression was examined in monosomies in an attempt to gain insight into why monosomies can survive when multiple heterozygous deletions on the respective chromosome do not. In the RNAseq experiments, many genes were interpreted to be increased in expression and some were interpreted as reduced. Those with the apparent strongest increase were the subunits of the ribosome and those with the apparent strongest decreases were subunits of the proteasome.

The initiation and interpretation of the results were apparently performed in a vacuum of a century of work on genomic balance. Classical work in the flowering plant Datura and in Drosophila found that changes in chromosomal dosage would modulate phenotypes in a dosage sensitive manner (for references see Birchler and Veitia, 2021, Cytogenetics and Genome Research 161: 529-550). In terms of molecular studies, the most common modulation across the genome for monosomies is an upregulation (Guo and Birchler, Science 266: 1999-2002; Shi et al. 2021, The Plant Cell 33: 917-939).

In the present yeast study, not only are there apparent increases for ribosomal subunits but also for many genes in the GAAC pathway, the NCR pathway, and Msn2p. The word "apparent" is used because RNAseq studies can only determine relative changes in gene expression (Loven et al., 2012, Cell 151: 476-482). Because aneuploidy can change the transcriptome size in general (Yang et al., 2021, The Plant Cell 33: 1016-1041), it is possible and maybe probable that this occurs in yeast monosomies as well. If there is an increase in the general transcriptome size, then there might not be much reduction of the proteosome subunits as claimed and the increases might be somewhat less than indicated.

It should be noted that contrary to the claims of the cited paper of Torres et al 2007 (Science 317: 916-924), a reanalysis of the data indicated that yeast disomies have many modulated genes in trans with downregulated genes being more common (Hou et al, 2018, PNAS 115: E11321-E11330). The claim of Torres et al that there are no global modulations in trans is counter to the knowledge that transcription factors are typically dosage sensitive and have multiple targets across the genome. The inverse effect trend is also true of maize disomies (Yang et al., 2021, The Plant Cell 33: 1016-1041), maize trisomies (Shi et al., 2021), Arabidopsis trisomies (Hou et al. 2018) and Drosophila trisomies (Sun et al. 2013, PNAS 110: 7383-7388; Sun et al., 2013, PNAS 110: 16514-16519; Zhang et al., 2021, Scientific Reports 11: 19679; Zhang et al., genes 12: 1606). Taken as a whole it would seem to suggest that there are many inverse relationships of global gene expression with chromosomal dosage in both yeast disomies and monosomies.

To clarify the claims of this study, it would be informative to produce distributions of the various ratios of individual gene expression in monosomy versus diploid as performed by Hou et al. 2018. This will better express the trends of up and down regulation across the genome and whether there are any genes on the varied chromosome that are dosage compensated. The authors claim there are no genes that are compensated on the varied chromosome but considering how many genes are upregulated across the genome, it would seem that a subset are probably upregulated on the cis chromosome as well and approach the diploid level, i.e. are dosage compensated. A second experiment that would clarify the results would be to perform estimates of the general transcriptome size. If the general transcriptome size is actually increased, the claims of reduced expression of the proteosome might need to be revised (See Loven et al., 2012 for an explanation).

Reviewer #2 (Public Review):

The authors aimed to analyze different dermal compositions of various skin regions, focusing on fibroblast, endothelium and smooth muscle cells. They collect skin samples from six different skin regions of adult pig skin including the head, ear, shoulder, back, abdomen, and leg skins. After dissociating the tissues into single cells, they perform single-cell RNA analyses. A total of 215 thousand cells were analyzed. The authors identified distinct cell clusters, enriched molecules within each cell cluster, and the dynamic of cell cluster transition and interactions. Based on their findings, they conclude that tenascin N, collagen 11A1, and inhibin A are candidate genes for facilitating extracellular matrix accumulation.

Strength:

The methodology they used to prepare scRNA data is appropriate. Bioinformatic analyses are solid. The authors emphasize the heterogeneous phenotypes and composition ratios of smooth muscle cells, endothelial cells and fibroblasts in each skin region. They identify potential cell communication pathways among cell clusters. Expression of selective molecules on tissue sections were done.

Weakness:

While tenascin, collagen and inhibin are highlighted as genes important for ECM accumulation, there is no functional evaluation data. The discussion section is a compilation of comparisons, and is somewhat fragmentary. More significance from this dataset could have been extracted.

Summary:

The manuscript has the potential to be a useful cellular atlas. The direct impact of this paper on skin biology is limited because of the lack of evaluation data. But the database can be useful to many future studies using the pig skin model.

Reviewer #2 (Public Review):

Lazaro-Pena et al. investigated how a conserved kinase called homeodomain interacting protein kinase (HPK-1), helps to preserve neuronal function, motlity and stress resilience during aging in the metazoan, C. elegans. HPK-1 is a member of the HIPK kinases that, in mammalian systems, regulate the activity of transcription factors (TFs), chromatin modifiers, signaling molecules and scaffolding proteins in response to cellular stress. The group finds that in C. elegans, HPK-1 depletion causes a premature shortening of lifespan and decreases motility and stress resilience in the whole animal. Conversely, increasing active, but not enzymatically dead, HPK-1 levels in the nervous system alone is sufficient to extend lifespan and mitigate the accumulation of aging-associated protein aggregates. The authors then identify a subset of neurons and cell stress response pathways that could be responsible for the contribution of HPK-1 to lifespan and neuronal health. This leads the authors to propose a hypothesis whereby HPK-1 activity in specific neurons preserves protein homeostasis and neuronal integrity, and thus limits the aging-induced decline in organismal function.<br /> Overall, the authors test several functional readouts for neuronal activity to support their claim that HPK-1 activity limits functional decline during aging. These experiments are solid, and the use of a kinase dead HPK-1 in these experiments adds strong support to their claim that HPK-1 activity preserves organismal health. However, weaknesses in the experimental layout and rigor, and the statistical analyses of the publicly available data, limit the inferences that can be made, and further experimental evidence would be required to confirm the working model proposed by the authors.

Reviewer #2 (Public Review):

The authors dissected the effects of mycolacton on endothelial cell biology and vessel integrity. The study follows up on previous work by the same group, which highlighted alterations in vascular permeability and coagulation in patients with Buruli ulcer. It provides a mechanistic explanation for these clinical observations, and suggests that blockade of Sec61 in endothelial cells contributes to tissue necrosis and slow wound healing.

Overall, the generated data support their conclusions and I only have two major criticisms:

- Replicating the effects of mycolactone on endothelial parameters with Ipomoeassin F (or its derivative ZIF-80) does not demonstrate that these effects are due to Sec61 blockade. This would require genetic proof, using for example endothelial cells expressing Sec61A mutants that confer resistance to mycolactone blockade. The authors claimed in the Discussion that they could not express such mutants in primary endothelial cells, but did they try expressing mutants in HUVEC cell lines? Without such genetic evidence all statements claiming a causative link between the observed effects on endothelial parameters and Sec61 blockade should be removed or rephrased. The same applies to speculations on the role of Sec61 in epithelial migration defects in discussion. Data corresponding to Ipomoeassin F and ZIF-80 do not add important information, and may be removed or shown as supplemental information.<br /> - While statistical analysis is done and P values are provided, no information is given on the statistical tests used, neither in methods nor results. This must be corrected, to evaluate the repeatability and reproducibility of their data.

Reviewer #2 (Public Review):

During the breeding season, testosterone (T) levels rise in males, leading to seasonal song production. This behavioral plasticity is accompanied by changes in the size of brain nuclei that control song production, particularly the HVC, which expresses both androgen and estrogen receptors. To determine how testosterone controls song production, Ko et al performed a six point timecourse in female birds implanted with T capsules. The authors carefully document the onset of song production around day 4, and the subsequent progression from sub-songs to plastic songs with more complex syllables. They demonstrate a corresponding increase in HVC volume by 14 days. To identify the genes that direct these events, the authors compared gene expression in the HVC at each timepoint, ranging from 1 hr to 14 days. They report strong induction of gene expression at only 1 hr after T treatment. At subsequent time points, the number of induced genes varies markedly, with the greatest number of differential genes detected at day 14, when the HVC has increased in volume. Overall, a relatively small number of genes show consistent changes in expression across the duration of treatment, while the majority fall into a "transient" category of showing up- or -downregulation at one or a subset of timepoints. The authors put forward a model whereby T can rapidly induce the expression of transcription factors within the first 1-3 hours, followed by additional gene expression cascades directed by the induced TFs. These downstream pathways would then permit changes in HVC structure and connectivity to facilitate singing.

The bulk of the manuscript details WGCNA, GO terms, and promoter ARE/ERE motif abundance, using the initial pairwise comparisons for each timepoint as input lists. However, there are no p/adjp values provided for these pair-wise comparisons that form the basis of all subsequent analyses. Nor are there supplementary tables to indicate how consistent the replicates are within each group or how abundantly the genes-of-interest are expressed. With the statistical tests used here, and the lack of relevant information in the supplementary tables, I cannot determine if the data support the authors' conclusions. These omissions mar what is otherwise a conceptually intriguing line of investigation.

Reviewer #2 (Public Review):

A comparison of sea stars and sea urchins has been shown in the past to be a very fertile ground to understand the evolution of cell types. Among other reasons, this is due to the rich amount of information on the gene regulatory networks that control the establishment of cell types in the sea urchin embryo, the experimental amenability of both the sea urchin and sea star embryos, and the fact that embryos of these two animal groups show homologous cell types as well as morphological innovations. The study by Meyer et. al. takes full advantage of these features and takes the comparison of the sea urchin and the sea star to a new technological level by implementing single-cell technologies in the sea star embryo for the first time. The authors employ a single-nuclei RNA-sequencing protocol to profile the transcriptomes of all cell types in the sea star embryo at three stages of development and very convincingly show that the generated dataset is able to capture known cell types as well as previously undescribed cell types. In this context, the study significantly advances the molecular characterization of the previously known cell types and draws convincing conclusions about the biological significance of the newly discovered cell types. By using the newly generated sea star dataset, and a previously published sea urchin single-cell RNA-sequencing dataset at equivalent developmental stages, Meyer et. al. compare cell types between the two animals. Three important claims arise from this comparison: 1. The unanticipated discovery of a cell cluster in each species that has no counterpart in the clusters of the other species. 2. That the primary mesenchyme cells (PMCs) of the sea urchin, thought to be a novel cell type in the sea urchin, share significant transcriptomic profiles with the cells of the right coelom of the sea star; 3. That pigment cells of the sea urchin also thought to be a novelty in the sea urchin, shares transcriptomic signatures with immune and neural cells of the sea star.

The strength of the study by Meyer et. al. is the robustness of the newly generated sea star single-nuclei RNA-sequencing dataset, as well as the rigorous validation and biologically meaningful interpretation of the data. As a result, the conclusions of Meyer et. al. concerning the description of sea star cell types are convincing, robust, and biologically important. A potential weakness of the study is the method used for integrating this data with that of the sea urchin. The integration method employed is based on generating a list of genes with 1:1 orthology between the two species and then computing a common cell type atlas by using only the genes with 1:1 orthology. Given the relatively large evolutionary distance between sea urchins and sea stars, and the growing evidence suggesting that paralogs may be more functionally similar than orthologs across species, the method employed for integrating the two datasets might limit the depth and robustness of the comparison.

Reviewer #2 (Public Review):

Deep brain stimulation (DBS) is an important, relatively new approach for treating refractory psychiatric illnesses including depression, addiction, and obsessive-compulsive disorder. This study examines the structural and functional connections associated with symptom improvement following DBS in the posterior hypothalamus (pHyp-DBS) for severe and refractory aggressive behavior. Behavioral assessments, outcome data, electrode placements, and structural and functional (resting-state) imaging data were collected from 33 patients from 5 sites. The results show structural connections of the effective electrodes (91% of patients responded positively) were with sensorimotor regions, emotional regulation areas, and monoamine pathways. Functional connectivity between the target, periaqueductal gray, and amygdala was highly predictive of treatment outcome.

Strengths.<br /> This dataset is interesting and potentially valuable.

Weaknesses.<br /> The figures seem to indicate that electrodes and symptom improvement is located lateral to the hypothalamus, perhaps in the subthalamic nucleus (STN). This is might explain why the streamlines from the tractography are strongest in motor regions. The inclusion of the monoaminergic based on the tractography is not warranted, as the resolution is not sufficient to demonstrate the distinction between the MFB (a relatively small bundle) and others flowing through this region to the brainstem.

Reviewer #2 (Public Review):

A key aspect of the work is to use the simulations to explain differences between (i) dilute and dense phases and (ii) wild-type and mutant variants. Here, it would be important with a clearer analysis of convergence and errors to quantify which differences are significant.

It would also be useful with a clearer description of how the analytical model is predictive, of which properties, and how they have been/can be validated. Which measurable quantities does the model predict?

In addition to these overall questions, a number of more specific suggestions follow below.

Major:

p. 7, line 120 (Fig. S1B)<br /> The proteins do not appear particularly pure based on the presented SDS PAGE analysis. How pure is the protein estimated to be, and is the presence of the other bands expected to affect e.g. the data presented in Fig. 1?

p. 7 & 8, lines 138-159:<br /> Has the method and energy function used to calculate the interact potential been validated by comparison to experiments, including studying the effect of varying the solvent? I see the computed error bars are very small, but am more interested in the average error when comparing to experiments. The numbers in water appear different from those e.g. reported by Krainer et al (https://doi.org/10.1038/s41467-021-21181-9), though the latter are also not immediately compared to experiments. Thus, it would be useful to know how much to trust these numbers.

p. 8, lines 149-154:<br /> Following up on the above, the authors also write "Importantly, only in the latter case are the R-Y interactions slightly more favorable than the K-Y ones (Figure S1C). While this can potentially contribute to increasing of Csat for the R>K mutant as compared to WT, the estimated thermodynamic effect is not too strong, especially if one considers that these interactions take place in an environment with largely water-like polarity. Therefore, the effect of R>K substitution on LLPS should be further explored in the context of protein-protein interactions."<br /> In the absence of estimates of the accuracy of the predictions, these sentences are somewhat unclear. Also, it is unclear what the authors mean by that the effect of R>K should be studied; there are already several examples of this (https://doi.org/10.1016/j.cell.2018.06.006 [already cited], https://doi.org/10.1038/s41557-021-00840-w & https://doi.org/10.1073/pnas.2000223117 come to mind, but there are likely more).

p. 8, lines 161-162:<br /> The authors perform MD simulations of Lge1 and variants using 24 copies and a box that gives them protein concentrations "in the mM concentration range". I realize that there's a concern about what is computationally feasible, but it would be important with an argument for this choice. Why is 24 expected to be enough to represent a condensate (I expect that there could be substantial finite-size effects)? What is the exact protein concentration in the simulations of the 24 chains [and of the 1-chain simulations]? How does this protein concentration compare to that in the condensates? The authors performed simulations in the NPT ensemble; how stable were the box dimensions?

Also, did the authors include the Strep- and His-tags in the simulations? If not, why not?

Throughout:<br /> One of my major concerns about this work is the general lack of analysis of convergence of the simulations. The authors must present some solid analysis of which results are robust given the relatively short simulations and potential for bias from the chosen starting structures.

As an example, on p. 8 the authors discuss a potential asymmetry between the interactions found in the dilute (single-copy) and dense (24-mer) phases. These observations are somewhat in contrast to other observations in the field, namely that it is the same interactions that drive compaction of monomers as those that drive condensate formation.

Obviously, both the results in the literature and those presented here could be true. But in order to substantiate the statements made here, the authors should show some substantial statistical analyses to make it clear which differences are robust.

The above holds for all parts of the computational/simulation work (e.g. other aspects of Fig. 2)

Similarly, how were the errors of the radius of gyration for WT, R>K and Y>A mutants calculated? Is the Rg for WT significantly smaller than the values for the two mutants? And are the differences in Rg between single-copy and multi-copy simulations statistically significant? I am asking since converging the Rg of IDPs of this length in all-atom MD is not easy.

p. 12, line 251:<br /> Has the MIST formalism been validated for IDPs; if so please provide a reference.

p. 5, line 105, p. 16 line 334 and p. 18 line 283:<br /> It is not completely clear what the predictions are and what/which experiments they are compared to. On p. 16, exactly what does the analytical model predict? As far as I understand, the results from the MD simulations are input to the model, but I am probably missing something.<br /> Which concrete and testable predictions does the model enable?

p. 19, lines 408-411:<br /> The authors find that when building clusters of Y>A from the simulations they find filamentous structures that they suggest explain the aggregation of the Y>A variant at high concentrations. While that sounds like an intriguing suggestion, it would be useful with a bit more detail about the robustness of this observation. For example, the simulations of Y>A appear similar to that of R>K; are the differences in topology really significantly different?

Finally, I would suggest that the authors make their code and data available in electronic format.

Reviewer #2 (Public Review):

The study of Thiery et al. aims to elucidate how cells undergo fate decisions between neural crest and (pan-) placodal cells at the neural plate border (NPB). While several previous single-cell RNA-Seq studies in vertebrates have included neural plate border cells (e.g. Briggs et al., 2018; Wagner et al., 2018; Williams et al., 2022), these previous studies did not provide conclusive insights on cell fate decisions between neural crest and placodes, due to either the limited number of genes recovered, the limited number of cells sampled or the limited numbers of stages included. The present study overcomes these limitations by analyzing almost 18,000 cells at six stages of development ranging from gastrulation until after neural tube closure (8 somite-stage), with an average depth of almost 4000 genes/cell. Using this extensive and high-quality data set, the study first describes the timing of segregation of neural crest and placodal lineages at the NPB suggesting that at late neural fold stages (somite stage 4) most cells have decided between placodal and neural crest fates. It then identifies gene modules specific for neural crest and placodal lineages and characterizes their temporal and spatial expression. Focusing on an NPB-specific subset of cells, the study then shows that initially most of these cells co-express neural crest and placodal gene modules suggesting that these are undecided cells, which they term "border-located unstable progenitors" (BLUPs). The proportion of BLUPs decreases over time, while cells classified as placodal or neural crest cells increases, with few BLUPs remaining at late neural fold stages (and a few scattered BLUPs even at somite stage 8). Based on these findings, the authors propose a new model of cell fate decisions at the NPB (termed the "gradient border model"), according to which the NPB is not defined by a specific transcriptional state but is rather a region of undecided cells, which diminishes in size between gastrulation and neural fold stages due to more and more cells committing to a placodal or neural crest fate based on their mediolateral position (with medial cells becoming specified as neural crest and lateral cells as placodal cells).

The study of Thiery et al. provides an unprecedentedly detailed, methodologically careful, and well-argued analysis of cell fate decisions at the NPB. It provides novel insights into this process by clearly demonstrating that the NPB is an area of indecision, in which cells initially co-express gene modules for ectodermal fates (neural crest and placodes), which subsequently become segregated into mutually exclusive cell populations. The paper is very well written and largely succeeds in presenting the very complex strategy of data analysis in a clear way. By addressing the earliest cell fate decisions in the ectoderm and one of the earliest cell fate decisions in the developing vertebrate embryo, this study will have a significant impact and be of interest to a wide audience of developmental biologists. There are, two conceptual issues raised in the paper that require further discussion.

First, the authors suggest that their data resolve a conflict between two previously proposed models, the "binary competence model" and the "neural plate border model". The authors correctly describe, that the binary competence model proposed by Ahrens and Schlosser (2005) and Schlosser (2006) suggests that the ectoderm is first divided into two territories (neural and non-neural), which differ in competence, with the neural territory subsequently giving rise to the neural plate and neural crest and the non-neural territory giving rise to placodes and epidermis (sequence of cell-fate decisions: ([neural or neural crest]-[epidermal or placodal]). This model was proposed as an alternative to a "neural plate border state model", which instead suggests that initially the NPB is induced as a territory characterized by a specific transcriptional state, from which then neural crest and placodes are induced by different signals (sequence of cell fate decisions: neural-[placodal or neural crest]-epidermal) (see Schlosser, 2006, 2014). Instead in this paper, the authors contrast the binary competence model with a model they call the "neural plate border" model according to which the NPB can give rise to all four ectodermal fates with equal probability. However, I think this misses the main point of contention since all previously proposed models are in agreement that initially the neural plate border region is unspecified and can give rise to all four fates and that lineage restrictions only appear over time. "Binary competence" and "Neural plate border state" model, differ, however, in their predictions about the sequence, in which these fate restrictions occur.

Second, the authors should be more careful when relating their data to the specification or commitment of cells. Questions of specification and commitment can only be tested by experimental manipulation and cannot be inferred from a transcriptome analysis of normal development. So the conclusion that the activation of placodal, neural and neural crest-specific modules in that sequence suggests a sequence of specification in the same temporal order (lines 706-709) is not justified. Studies from the authors' own lab previously showed that epiblast cells from pre-gastrula stages are specified to express a large number of NPB border markers including neural crest and panplacodal markers, when cultured in vitro (Trevers et al., 2018; see also Basch et al., 2006 for early specification of the neural crest), which is not easily reconciled with this interpretation. I am not aware of any experimental evidence that shows that a panplacodal regulatory state is specified prior to neural crest in the chick (although I may have missed this). In Xenopus, experimental studies have shown instead that neural crest is specified and committed during late gastrulation, while the panplacodal states are specified much later, at neural fold stages (Mancilla and Mayor, 2006; Ahrens and Schlosser, 2005). It may well be the case that the relative timing of neural crest and panplacodal specification is different between species (and such easy dissociability may even be expected from the perspective of the binary competence model).

Reviewer #2 (Public Review):

The goal of this paper is to use a model-based approach, developed by one of the authors and colleagues in 2021, to critically re-evaluate the claims made in a prior paper from 2018, written by the other author of this paper (and colleagues), concerning the role of perirhinal cortex in visual perception. The prior paper compared monkeys with and without lesions to the perirhinal cortex and found that their performance was indistinguishable on a difficult perceptual task (categorizing dog-cat morphs as dogs or cats). Because the performance was the same, the conclusion was that the perirhinal cortex is not needed for this task, and probably not needed for perception in general, since this task was chosen specifically to be a task that the perirhinal cortex *might* be important for. Well, the current work argues that in fact the task and stimuli were poorly chosen since the task can be accomplished by a model of the ventral visual cortex. More generally, the authors start with the logic that the perirhinal cortex gets input from the ventral visual processing stream and that if a task can be performed by the ventral visual processing stream alone, then the perirhinal cortex will add no benefit to that task. Hence to determine whether the perirhinal cortex plays a role in perception, one needs a task (and stimulus set) that cannot be done by the ventral visual cortex alone (or cannot be done at the level of monkeys or humans).

There are two important questions the authors then address. First, can their model of the ventral visual cortex perform as well as macaques (with no lesion) on this task? The answer is yes, based on the analysis of this paper. The second question is, are there any tasks that humans or monkeys can perform better than their ventral visual model? If not, then maybe the ventral visual model (and biological ventral visual processing stream) is sufficient for all recognition. The answer here too is yes, there are some tasks humans can perform better than the model. These then would be good tasks to test with a lesion approach to the perirhinal cortex. It is worth noting, though, that none of the analyses showing that humans can outperform the ventral visual model are included in this paper - the papers which showed this are cited but not discussed in detail.

Major strength:<br /> The computational and conceptual frameworks are very valuable. The authors make a compelling case that when patients (or animals) with perirhinal lesions perform equally to those without lesions, the interpretation is ambiguous: it could be that the perirhinal cortex doesn't matter for perception in general, or it could be that it doesn't matter for this stimulus set. They now have a way to distinguish these two possibilities, at least insofar as one trusts their ventral visual model (a standard convolutional neural network). While of course, the model cannot be perfectly accurate, it is nonetheless helpful to have a concrete tool to make a first-pass reasonable guess at how to disambiguate results. Here, the authors offer a potential way forward by trying to identify the kinds of stimuli that will vs won't rely on processing beyond the ventral visual stream. The re-interpretation of the 2018 paper is pretty compelling.

Major weakness:<br /> It is not clear that an off-the-shelf convolution neural network really is a great model of the ventral visual stream. Among other things, it lacks eccentricity-dependent scaling. It also lacks recurrence (as far as I could tell). To the authors' credit, they show detailed analysis on an image-by-image basis showing that in fine detail the model is not a good approximation of monkey choice behavior. This imposes limits on how much trust one should put in model performance as a predictor of whether the ventral visual cortex is sufficient to do a task or not. For example, suppose the authors had found that their model did more poorly than the monkeys (lesioned or not lesioned). According to their own logic, they would have, it seems, been led to the interpretation that some area outside of the ventral visual cortex (but not the perirhinal cortex) contributes to perception, when in fact it could have simply been that their model missed important aspects of ventral visual processing. That didn't happen in this paper, but it is a possible limitation of the method if one wanted to generalize it. There is work suggesting that recurrence in neural networks is essential for capturing the pattern of human behavior on some difficult perceptual judgments (e.g., Kietzmann et al 2019, PNAS). In other words, if the ventral model does not match human (or macaque) performance on some recognition task, it does not imply that an area outside the ventral stream is needed - it could just be that a better ventral model (eg with recurrence, or some other property not included in the model) is needed. This weakness pertains to the generalizability of the approach, not to the specific claims made in this paper, which appear sound.

A second issue is that the title of the paper, "Inconsistencies between human and macaque lesion data can be resolved with a stimulus-computable model of the ventral visual stream" does not seem to be supported by the paper. The paper challenges a conclusion about macaque lesion data. What inconsistency is reconciled, and how?

Reviewer #2 (Public Review):

It is certainly an interesting observation that lipid homeostasis influences proteostasis, although this need not be considered so surprising given that many fundamental cellular processes are interconnected. The paper is deserves to be read, but the level of general interest would be greatly enhanced if the authors were able to take the story further mechanistically. This might be too much of an ask, but they should go further in excluding one very attractive alternative model: effects on proteasome activity. This explanation should be addressed definitively because the transcription factor that regulates proteasome subunit gene expression (Nrf1/NFE2L1) is processed in the ER and is therefore well placed to be influenced by membrane conditions, and because it is shown here that proteasome inhibition increase ProteoStat puncta. Indeed, some years ago it was published that Nrf1/NFE2L1 is inhibited within the ER membrane by cholesterol, and a more recent paper showed that in C. elegans it is activated by oleic acid through effects on ER membrane homeostasis and lipid droplet formation. The authors address proteasome activity only by using a dye that is not referenced. Here a much more solid answer is needed. In general, most conclusions in the paper rely essentially solely on ProteoStat assays. The entire study would be greatly strengthened if the authors incorporated biochemical or other modalities to substantiate their results.

The presentation would be improved greatly if the authors provided diagrams illustrating the pathways implicated in their results, as well as their models. As it is the paper falls flat at the end of the results in the absence of a mechanism to explain their findings. Diagrams would be helpful for focusing the reader on what IS learned from the work, which is important.

Reviewer #2 (Public Review):

Pynapple and Pynacollada have the potential to become very valuable and foundational tools for the analysis of neurophysiological data. NWB still has a steep learning curve and Pynapple offers a user-friendly toolset that can also serve as a wrapper for NWB.

The scope of the manuscript is not clear to me, and the authors could help clarify if Pynacollada and other toolsets in the making become a future aspect of this paper (and Pynapple), or are the authors planning on building these as separate publications.

The author writes that Pynapple can be used without the I/O layer, but the author should clarify how or if Pynapple may work outside NWB.

This brings us to an important fundamental question. What are the advantages of the current approach, where data is imported into the Ts objects, compared to doing the data import into NWB files directly, and then making Pynapple secondary objects loaded from the NWB file? Does NWB natively have the ability to store the 5 object types or are they initialized on every load call?

Many of these functions and objects have a long history in MATLAB - which documents their usefulness, and I believe it would be fitting to put further stress on this aspect - what aspects already existed in MATLAB and what is completely novel. A widely used MATLAB toolset, the FMA toolbox (the Freely moving animal toolbox) has not been cited, which I believe is a mistake.

A limitation in using NWB files is its standardization with limited built-in options for derived data and additional metadata. How are derived data stored in the NWB files?

How is Pynapple handling an existing NWB dataset, where spikes, behavioral traces, and other data types have already been imported?

Reviewer #2 (Public Review):

In this study, Bashkirova et al. analyzed how the gene choice of olfactory receptors (ORs) is regulated in olfactory sensory neurons (OSNs) during development. In the mouse olfactory system, there are more than 1000 functional OR genes and several hundred pseudogenes. It is well-established that each individual OSN expresses only one functional OR gene in a mono-allelic manner. This is referred to as the one neuron - one receptor rule. It is also known that OR gene choice is not entirely stochastic but restricted to a particular area or zone in the olfactory epithelium (OE) along the dorsoventral axis. It is interesting to study how this stochastic but biased gene-choice is regulated during OSN development, narrowing down the number of OR genes to be chosen to eventually achieve the monogenic OR expression in OSNs.

In the present study, the authors cell-sorted OSNs into three groups; immediate neuronal precursors (INPs), immature OSNs (iOSNs), and mature OSNs (mOSNs). They found that OR gene choice is differentially regulated positively by transcription factors in INPs and negatively by heterochromatin-mediated OR gene silencing in iOSNs. The authors propose that by the combination of two opposing forces of polygenic transcription (positive) and genomic silencing (negative), each OSN finally expresses only one OR gene out of over 2000 alleles in a stochastic but stereotypic manner.

The authors' model of OR gene choice is supported by well-designed experiments and by large amounts of data. In general, the paper is clearly written and easy to follow. It will attract a wide variety of readers in the fields of neuroscience, developmental biology, and immunology. The present finding will give new insight into our understanding of gene choice in the multigene family in the mammalian brain and shed light on the long-standing question of monogenic expression of OR genes.

Reviewer #2 (Public Review):

The manuscript describes the detailed characterization of the C. trachomatis protein Cdu1. Previous work that laid the foundation identified two enzymatic activities associated with Cdu1 - deubiquitinase and transacetylase. This work advances current knowledge by identifying Cdu1 targets for stabilization, and establishing the relationship between the two activities of Cdu1. Furthermore, the authors determined that Cdu1 is subject to autostabilization. In addition to the novelty of the findings, the strength of this report is its scientific rigor, with several experimental evidence independently confirmed using a variety of approaches, including the creation of mutants that decoupled deubiquitination from transacetylase activity. Another strength is the direct demonstration of transacetylation of the targets, which increased the relevance of the reported colocalization and interaction of Cdu1 with the targets.

The authors also made a convincing case for the basis of Cdu1 modification of each of the effector targets by linking loss of acetylation with decreased stability. An unexpected result, at least to this reviewer is the requirement for the three effectors in chlamydial egress by extrusion of the inclusion. Cdu1 regulating all three effectors underscores the importance of the timing and efficiency of inclusion extrusion. Additional insights into how the three effectors interact functionally could be obtained by specifically monitoring the timing of extrusion. Data for CTL0480 points to a negative regulator of extrusion, which could be at the level of timing, in addition to efficiency.

Overall, the work is rigorous, and makes important contribution to our understanding of the significance of Cdu1 function in in vitro infection.

Reviewer #2 (Public Review):

Vangl2, a core planar cell polarity protein involved in Wnt/PCP signaling, mediates cell proliferation, differentiation, homeostasis, and cell migration. Vangl2 malfunctioning has been linked to various human ailments, including autoimmune and neoplastic disorders. Interestingly, Vangl2 was shown to interact with the autophagy regulator p62, and indeed, autophagic degradation limits the activity of inflammatory mediators such as p65/NF-κB. However, if Vangl2, per se, contributes to restraining aberrant p65/NF-kB activity remains unclear.

In this manuscript, Lu et al. describe that Vangl2 expression is upregulated in human sepsis-associated PBMCs and that Vangl2 mitigates experimental sepsis in mice by negatively regulating p65/NF-κB signaling in myeloid cells. Vangl2 recruits the E3 ubiquitin ligase PDLIM2 to promote K63-linked poly-ubiquitination of p65. Vangl2 also facilitates the recognition of ubiquitinated p65 by the cargo receptor NDP52. These molecular processes cause selective autophagic degradation of p65. Indeed, abrogation of PDLIM2 or NDP52 functions rescued p65 from autophagic degradation, leading to extended p65/NF-κB activity.

As such, the manuscript presents a substantial body of interesting work and a novel mechanism of NF-κB control. If found true, the proposed mechanism may expand therapeutic opportunities for inflammatory diseases. However, the current draft has significant weaknesses that need to be addressed.

Specific comments<br /> 1. Vangl2 deficiency did not cause a discernible increase in the cellular level of total endogenous p65 (Fig 2A and Fig 2B) but accumulated also phosphorylated IKK.<br /> Even Fig 4D reveals that Vangl2 exerts a rather modest effect on the total p65 level and the figure does not provide any standard error for the quantified data. Therefore, these results do not fully support the proposed model (Figure 7) - this is a significant draw back. Instead, these data provoke an alternate hypothesis that Vangl2 could be specifically mediating autophagic removal of phosphorylated IKK and phosphorylated IKK, leading to exacerbated inflammatory NF-κB response in Vangl2-deficient cells. One may need to use phosphorylation-defective mutants of p65, at least in the over-expression experiments, to dissect between these possibilities.<br /> 2. Fig 1A: The data indicates the presence of two subgroups within the sepsis cohort - one with high Vangl2 expressions and the other with relatively normal Vangle2 expression. Was there any difference with respect to NF-κB target inflammatory gene expressions between these subgroups?<br /> 3. The effect of Vangl2 deficiency was rather modest in the neutrophil. Could it be that Vangl2 mediates its effect mostly in macrophages?<br /> 4. Fig 1D and Figure 1E: Data for unstimulated Vangl2 cells should be provided. Also, the source of the IL-1β primary antibody has not been mentioned.<br /> 5. The relevance and the requirement of RNA-seq analysis are not clear in the present draft. Figure 1E already reveals upregulation of the signature NF-κB target inflammatory genes upon Vangl2 deficiency.<br /> 6. Fig 2A reveals an increased accumulation of phosphorylated p65 and IKK in Vangl2-deficient macrophages upon LPS stimulation within 30 minutes. However, Vangl2 accumulates at around 60 minutes post-stimulation in WT cells. Similar results were obtained for neutrophils (Fig 2B). There appears to be a temporal disconnect between Vangl2 and phosphorylated p65 accumulation - this must be clarified.<br /> 7. Figure 2E and 2F do not have untreated controls. Presentations in Fig 2E may be improved to more clearly depict IL6 and TNF data, preferably with separate Y-axes.<br /> 8. Line 219: "strongly with IKKα, p65 and MyD88, and weak" - should be revised.<br /> 9. It is not clear why IKKβ was excluded from interaction studies in Fig S3G.<br /> 10. Fig 3F- In the text, authors mentioned that Vangl2 strongly associates with p65 upon LPS stimulation in BMDM. However, no controls, including input or another p65-interacting protein, were used.<br /> 11. Figure 4D - Authors claim that Vangl2-deficient BMDMs stabilized the expression of endogenous p65 after LPS treatment. However, p65 levels were particularly constitutively elevated in knockout cells, and LPS signaling did not cause any further upregulation. This again indicates the role of Vangl2 in the basal state. The authors need to explain this and revise the test accordingly.

Reviewer #2 (Public Review):

In this study, the authors have developed methods that allow for repeatedly unfolding and refolding a membrane protein using a magnetic tweezers setup. The goal is to extend the lifespan of the single-molecule construct and gather more data from the same tether under force. This is achieved through the use of a metal-free DBCO-azide click reaction that covalently attaches a DNA handle to a superparamagnetic bead, a traptavdin-dual biotin linkage that provides a strong connection between another DNA handle and the coverslip surface, and SpyTag-SpyCatcher association for covalent connection of the membrane protein to the two DNA handles.

The method may offer a long lifetime for single-molecule linkage; however, it does not represent a significant technological advancement. These reactions are commonly used in the field of single-molecule manipulation studies. The use of multiple tags including biotin and digoxygenin to enhance the connection's mechanical stability has already been explored in previous DNA mechanics studies by multiple research labs. Additionally, conducting single-molecule manipulation experiments on a single DNA or protein tether for an extended period of time (hours or even days) has been documented by several research groups.

Reviewer #2 (Public Review):

Fulton et al. seek to understand the interplay between "morphogen exposure, intrinsic timers of differentiation, and cell rearrangement" that together regulate the differentiation process within the presomitic mesoderm tissue (PSM) in developing Zebrafish embryos. A combination of live-cell microscopy to measure cell movements, static measurements of gene expression, and computational and mathematical methods was used to develop a model that captures the observed differentiation profile in the PSM as a function of cell rearrangements and morphogen signaling.

The authors motivate their investigation into the link between cell rearrangements and differentiation by first comparing differentiation timing in vitro and in vivo. The authors report that a subset of cells differentiating in vitro do so synchronously while cells differentiating in vivo do so with a wide range of differentiation trajectories. By following a small group of photo-labeled cells, it is suggested that the variation of differentiation timing in vivo is related to variation in cell movements in the tissue. To explain these observations in terms of gene expression within single cells, a novel method to combine cell tracks with fixed measurements of gene expression is first used to estimate gene expression dynamics (AGET) in live cells within a tissue. A final ODE-based gene regulatory network (GRN) model is selected based on a combination of data fitting to AGETs and tissue level measurements, further in vitro experiments, and literature criteria. Importantly this model incorporates information from diverse experimental sources to generate a single unified model that can be potentially used in other contexts such as predicting how differentiation is perturbed by genetic mutations affecting cell rearrangement. The authors then use this GRN model to explain how cells starting from the same position in the PSM can have different fates due to differential movement along the A-P axis. Lastly, the model predicts and, the authors experimentally validate, that the expression of differentiation markers can be heterogeneously expressed between neighboring PSM cells.

The presented research addresses the important topic of patterning regulation accounting for individual cell motion. contributes to larger tissue patterns, this work may directly contribute to our understanding of how regulation across biological scales. Additionally, the methodology to estimate AGET is especially intriguing because of its potential applicability to a wide variety of developmental processes.

However several issues weigh down the strengths of this paper. First, some conclusions and interpretations in the paper do not obviously follow the data and require further clarification. Second, the authors should consider alternative explanations and models and include some discussion about instances where the final GRN model may not fit as well. Finally, the current manuscript lacks clarity in its presentation and this makes it difficult to follow and understand.

Major concerns:

1. A key conclusion made in this paper is that differentiation times show a high variability even when neighboring PSM cells are compared. This is based on the photoconversion experiment shown in Figure 2A-C, where a group of cells is labeled and over time, a trail of labeled cells is visible. It is crucial to understand which compartment is labeled, i.e. progenitor vs. maturation zone vs. PSM. If cells in the progenitor/marginal zone are labeled, the underlying reason for the trailing effect is not a difference in differentiation time, but rather, a difference in the timing of when cells exit the progenitor zone. This needs to be distinguished in my view. In other words, while the timing of progenitor zone exit varies (needs to), once cells are within the PSM, do they still show a difference in differentiation timing? From previous experimental evidence I would expect that in fact, PSM cells differ only very little in differentiation timing. My statement is based on previously published labeling experiments done in posterior PSM cells, not tail bud cells (in chick embryos), which showed that labeled neighboring PSM cells were incorporated into the same adjacent somites, without evidence of a 'trail' (see figure 4H in Dubrulle et al. 2001). In the case of single cell labeling, it was found that these are actually incorporated into the same somite (or adjacent one), even if labeled in the posterior PSM (Stern et al. 1988). The situation in zebrafish appears similar (see Griffin & Kimelman 2002 and Müller et al. 1996). Additionally, the scheme in Figure 2K suggests that the trailing effect reflects a sequential exit from the progenitor zone that is controlled and timed.

2. The data on cell movement needs to be presented more clearly. Currently, this data is mainly presented in Figure 3D, which does not provide a good description of the cell movements. Visualization of the single cell tracks and the different patterns that are in the tissue along with the characterization of the movement/timescales is needed to better communicate the data and to tie it to the main conclusions.

3. The conclusion "As a result of their different patterns of movement, and therefore different Wnt and FGF dynamics, the simulated T-box gene expression dynamics differ in both cells." (Line 249) is not convincing: what part of the data shows that it is not the other way around, i.e. the signaling activities control the movement? The way I understand the rationale of this analysis: the authors take the cell movement tracks as a given input into the problem, and then ask, what signaling environment is the cell exposed to? The challenge with this view is two-fold: first, the authors seem to assume that a cell moves into a new environment and is hence exposed to a different level of signal, while in reality, these signaling gradients act short-range and maybe even at a cellular scale and hence a moving cell would carry Wnt-ligands with it, essentially contributing to the signaling environment. This aspect of 'niche construction' seems to be missing. Second, it has been shown (in chick embryos) that cell movement is, in turn, controlled by signaling levels, how would this factor into this model?

4. On the comparison with the in vitro model:<br /> A. The interpretation of cells differentiating synchronously or coherently in vitro seems inconsistent with the data presented in figure 1. To me figure 1F/G does not seem compatible with the previous figure 1D/E since 1F seems to describe cells that upregulate tbx6 over a range of times, in a manner analogous to what is reported in vivo, i.e. figure 2.

B. The authors conclude that in vitro, single PSM cells differentiate 'synchronously' and hence differently to what is seen in vivo, where the authors conclude that there is a "range of time scales". As noted above, the situation in vivo can be explained by a timed exit from the progenitor zone, while PSM differentiation is proceeding similarly in all PSM cells. In this view, what is seen in vitro is that all those cells that undergo PSM differentiation, initiate this process in culture more synchronously but it is the exit from the progenitor state, not the dynamics of differentiation, that might be regulated differently in vivo vs. in vitro.

C. Another important point to clarify is that the overall timing of differentiation is entirely different in the in vitro experiment: as has been shown previously (Rohde et al. 2021, Figure S12) both the period of the clock and the overall time it takes to differentiate is very substantially increased, in fact, more than doubled. This aspect needs to be taken into account and hence the conclusion: "Our analysis revealed that cells undergo a range of temporal trajectories in gene expression, with the fastest cells transiting through to a newly formed somite in 3 hours; half the time taken for cells to fully upregulate tbx6 in vitro (Figure 2K-L).)" (line 142) appears misleading, as it seems to emphasize how fast some cells in vivo differentiate. However, given the overall slowing down seen in vitro, which more than doubles the time it takes for differentiation (see Rohde et al. 2021, Figure S12), this statement needs to be refined.

5. The GRN proposed in this work includes inhibition of ntl/brachyury by Fgf (Figure 3f). However, it has been shown that Fgf signaling activates, not inhibits, ntl (see for instance dnFgfr1 experiments in Griffin et al., 1995). This does not seem compatible with the presented GRN, can the authors clarify?

6. The authors use static mRNA in situ hybridization and antibody stainings to characterize Wnt and Fgf signaling activities. First, it should be clarified in Figure 3A that this is not based on any dynamic measurement (it now states Tcf::GFP, as if GFP is the readout, so the label should be GFP mRNA). Second, and more importantly, it is not clear how this quantification has been done. Figure 3C shows a single line, while the legend says n=6 and "all data plotted"..can this be clarified? Without seeing the data it is not possible to judge if the profiles shown (the mean) are convincing. As this experimental result is used to inform the model and the remainder of the paper, it is of critical importance to provide convincing evidence, in this case, based on static snapshots.

7. Although the AGET analysis and this specific GRN model development are of interest and warrant the explanation the authors have provided, I would be careful not to overstate the findings. In particular, I believe the word "predicted" is used too loosely throughout the manuscript to describe the agreement between model and experiments. For example, my understanding of Figure 4, and what is described in the supplemental diagram, is that the in vitro experiments are used to further refine the model selection process. Therefore, it should not be stated as a prediction of the selected model. This is not to say the final model is not predictive, but it's difficult to assess the predictive power of this model since it hasn't been tested in independent experimental conditions (e.g. by perturbing cell movement and using the model to predict the expected differentiation boundary).

Reviewer #2 (Public Review):

This work combines a model of two-dimensional dendritic growth with attraction and stabilisation by synaptic activity. The authors find that constraining growth models with competition for synaptic inputs produces artificial dendrites that match some key features of real neurons both over development and in terms of final structure. In particular, incorporating distance-dependent competition between synapses of the same dendrite naturally produces distinct phases of dendritic growth (overshoot, pruning, and stabilisation) that are observed biologically and leads to local synaptic organisation with functional relevance. The approach is elegant and well-explained, but makes some significant modelling assumptions that might impact the biological relevance of the results.

Strengths:<br /> The main strength of the work is the general concept of combining morphological models of growth with synaptic plasticity and stabilisation. This is an interesting way to bridge two distinct areas of neuroscience in a manner that leads to findings that could be significant for both. The modelling of both dendritic growth and distance-dependent synaptic competition is carefully done, constrained by reasonable biological mechanisms, and well-described in the text. The paper also links its findings, for example in terms of phases of dendritic growth or final morphological structure, to known data well.

Weaknesses:<br /> The major weaknesses of the paper are the simplifying modelling assumptions that are likely to have an impact on the results. These assumptions are not discussed in enough detail in the current version of the paper.

1) Axonal dynamics.<br /> A major, and lightly acknowledged, assumption of this paper is that potential synapses, which must come from axons, are fixed in space. This is not realistic for many neural systems, as multiple undifferentiated neurites typically grow from the soma before an axon is specified (Polleux & Snider, 2010). Further, axons are also dynamic structures in early development and, at least in some systems, undergo activity-dependent morphological changes too (O'Leary, 1987; Hall 2000). This paper does not consider the implications of joint pre- and post-synaptic growth and stabilisation.

2) Activity correlations<br /> On a related note, the synapses in the manuscript display correlated activity, but there is no relationship between the distance between synapses and their correlation. In reality, nearby synapses are far more likely to share the same axon and so display correlated activity. If the input activity is spatially correlated and synaptic plasticity displays distance-dependent competition in the dendrites, there is likely to be a non-trivial interaction between these two features with a major impact on the organisation of synaptic contacts onto each neuron.

3) BDNF dynamics<br /> The models are quite sensitive to the ratio of BDNF to proBDNF (eg Figure 5c). This ratio is also activity-dependent as synaptic activation converts proBDNF into BDNF. The models assume a fixed ratio that is not affected by synaptic activity. There should at least be more justification for this assumption, as there is likely to be a positive feedback relationship between levels of BDNF and synaptic activation.

A further weakness is in the discussion of how the final morphologies conform to principles of optimal wiring, which is quite imprecise. 'Optimal wiring' in the sense of dendrites and axons (Cajal, 1895; Chklovskii, 2004; Cuntz et al, 2007, Budd et al, 2010) is not usually synonymous with 'shortest wiring' as implied here. Instead, there is assumed to be a balance between minimising total dendritic length and minimising the tree distance (ie Figure 4c here) between synapses and the site of input integration, typically the soma. The level of this balance gives the deviation from the theoretical minimum length as direct paths to synapses typically require longer dendrites. In the model this is generated by the guidance of dendritic growth directly towards the synaptic targets. The interpretation of the deviation in this results section discussing optimal wiring, with hampered diffusion of signalling molecules, does not seem to be correct.

Reviewer #2 (Public Review):

MCM8 and MCM9 together form a hexameric DNA helicase that is involved in homologous recombination (HR) for repairing DNA double-strand breaks. The authors have previously reported on the winged-helix structure of the MCM8 (Zeng et al. BBRC, 2020) and the N-terminal structure of MCM8/9 hexametric complex (MCM8/9-NTD) (Li et al. Structure, 2021). This manuscript reports the structure of a near-complete MCM8/9 complex and the conformational change of MCM8/9-NTD in the presence of its binding protein, HROB, as well as the residues important for its helicase activity.

The presented data might potentially explain how MCM8/9 works as a helicase. However, additional studies are required to conclude this point because the presented MCM8/9 structure is not a DNA-bound form and HROB is not visible in the presented structural data. Taking into these accounts, this work will be of interest to biologists studying DNA transactions.

A strength of this paper is that the authors revealed the near-complete MCM8/9 structure with 3.66A and 5.21A for the NTD and CTD, respectively (Figure 1). Additionally, the authors discovered a conformational change in the MCM8/9-NTD when HROB was included (Figure 4) and a flexible nature of MCM8/9-CTD (Figure S6 and Movie 1).

The biochemical data that demonstrate the significance of the Ob-hp motif and the N-C linker for DNA helicase activity require careful interpretation (Figures 5 and 6). To support the conclusion, the authors should show that the mutant proteins form the hexamer without problems. Otherwise, it is conceivable that the mutant proteins are flawed in complex formation. If that is the case, the authors cannot conclude that these motifs are vital for the helicase function.

A weakness of this paper is that the authors have already reported the structure of MCM8/9-NTD utilizing human proteins (Li et al. Structure, 2021). Although they succeeded in revealing the high-resolution structure of MCM8/9-NTD with the chicken proteins in this study, the two structures are extremely comparable (Figure S2), and the interaction surfaces seem to be the same (Figure 2).

Another weakness of this paper is that the presented data cannot fully elucidate the mechanistic insights into how MCM8/9 functions as a helicase for two reasons. 1) The presented structures solely depict DNA unbound forms. It is critical to reveal the structure of a DNA-bound form. 2) The MCM8/9 activator, HROB, is not visible in the structural data. Even though HROB caused a conformational change in MCM8/9-NTD, it is critical to visualize the structure of an MCM8/9-HROB complex.

Reviewer #2 (Public Review):

Ehring et al. analyze contributions of Dispatched, Scube2, serum lipoproteins and Sonic Hedgehog lipid modifications to the generation of different Shh release forms. Hedgehog proteins are anchored in cellular membranes by N-terminal palmitate and C-terminal cholesterol modifications, yet spread through tissues and are released into the circulation. How Hedgehog proteins can be released, and in which form, remains unclear. The authors systematically dissect contributions of several previously identified factors, and present evidence that Disp, Scube2 and lipoproteins concertedly act to release a novel Shh variant that is cholesterol-modified but not palmitoylated. The systematic analysis of key factors that control Shh release is a commendable effort and helps to reconcile apparently disparate models. However, the results concerning the roles of lipoproteins and Shh lipid modifications are largely confirmatory of previous results, and molecular identity/physiological relevance of the newly identified Shh variant remain unclear.

The authors conclude that an important result of the study is the identification of HDL as a previously overlooked serum factor for secretion of lipid-linked Shh (p15, l24-25). This statement should be removed. A detailed analysis of Shh release on human lipoproteins was reported previously, including contributions of the major lipoprotein classes, in cells that endogenously express Shh, in human plasma and for Shh variants lacking palmitate and/or cholesterol modifications (PMID 23554573). The involvement of Disp is also not unexpected: the importance of Dips for release of cholesterol-modified Shh is well established, as is the essential function of Drosophila Disp for formation of lipoprotein-associated hemolymph Hh. A similar argument can be made for the sufficiency of sterol modification for lipoprotein association. The authors point out that GFP insertion at the C-terminus of the N-terminal Shh domain does not abrogate function. Perhaps more relevant, an mCherry-sterol that was generated using a similar strategy as in the present study associates with Drosophila lipoproteins (PMID 20685986).

A novel and surprising finding of the present study is the differential removal of Shh N- or C-terminal lipid anchors depending on the presence of HDL and/or Disp. In particular, the identification of a non-palmitoylated but cholesterol-modified Shh variant that associates with lipoproteins is potentially important. However, the significance of this result could be substantially improved in two ways: 1) The molecular properties of the processed Shh variants are unclear - incorporation of palmitate/cholesterol and removal of peptides were not directly demonstrated. This is particularly relevant for the N-terminus, as the signaling activity of non-palmitoylated Hedgehog proteins is controversial. A decrease in hydrophobicity is no proof for cleavage of palmitate, this could also be due to addition of a shorter acyl group. 2) All experiments rely on over-expression of Shh in a single cell line. The authors point out that co-overexpression of Hhat is important to ensure Shh palmitoylation, but the same argument could be made for any other protein that acts in Shh release, such as Disp or a plasma membrane sheddase. The authors detect Shh variants that are released independently of Disp and Scube2 in secretion assays, which however are excluded from interpretation as experimental artifacts. Thus, it would be important to demonstrate key findings in cells that secrete Shh endogenously.

The co-fractionation of Shh and ApoA1 in serum-containing media is not convincing (Fig. 4C), as the two proteins peak at different molecular weights. To support their conclusion, the authors could use an orthogonal approach, optimally a demonstration of physical interaction, or at least fractionation by a different parameter (density). On a technical note, all chromatography results are presented as stylized graphs. Please include individual data points.

Reviewer #2 (Public Review):

Here, Chitraju et al have studied the phenotype of mice with an adipocyte-specific deletion of the diglycerol acyltransferases DGAT1 and DGAT2, the two enzymes catalyzing the last step in triglyceride biosynthesis. These mice display reduced WAT TG stores but contrary to their expectations, the TG loss in WAT is not complete and the mice are resistant to a high-fat diet intervention and display a metabolically healthier profile compared to control littermates. The mechanisms underlying this are not entirely clear, but the double knockout (DKO) animals have increased EE and a lower RQ suggesting that enhanced FA oxidation and WAT "browning" may be involved. Moreover, both adiponectin and leptin are expressed in WAT and are detectable in circulation. The authors propose that "the capacity to store energy in adipocytes is somehow sensed and triggers thermogenesis in adipose tissue. This phenotype likely requires an intact adipocyte endocrine system...." Overall, I find this to be an interesting notion.

Reviewer #2 (Public Review):

The new work from Lemcke et al suggests that the infection with Influenza A virus causes such flu symptoms as sleepiness and loss of appetite through the direct action on the responsible brain region, the hypothalamus. To test this idea, the authors performed single-nucleus RNA sequencing of the mouse hypothalamus in controlled experimental conditions (0, 3, 7, and 23 days after intranasal infection) and analyzed changes in the gene expression in the specific cell populations. The key results are promising.

However, the analysis (cell type annotation, integration, group comparison) is not optimal and incomplete and, therefore should be significantly improved.

More specifically:

1) The current annotation of cell types (especially neuronal but also applicable to the group of heterogeneous "Unassigned cells") did not make a good link to existing cell heterogeneity in the hypothalamus identified with scRNA seq in about 20 recently published works. All information about different peptidergic groups can not be extracted from the current version (except for a few). There are also some mistakes or wrong interpretations (eg, authors assigned hypothalamic dopamine cells to the glutamatergic group, which is not true). This state is feasible to improve (and should be improved) with already existing data.

2) I am confused with the results shown in the label transfer (suppl fig 3 and 4; note, they do not have the references in the text) applied to some published datasets (authors used the Seurat functions 'FindTransferAnchors' and 'TransferData'). The final results don't make sense: while the dataset for the arcuate nucleus (Campbel et al) well covered the GABAergic neurons it is not the case for the whole hypothalamus datasets (Chen et al; Zeisel et al). Similarly, for glutamatergic neurons. Additionally, I could not see that the label transfer works well for PMCH cells which should be present in the dataset for the lateral hypothalamus (Mickelsen et al,2019).

3) There are newly developed approaches to check the shifts in the cell compositions and specific differential gene expression in the cell groups (e.g. Cacoa from Kharchenko lab, scCoda from Büttner et al; etc). Therefore, I did not fully understand why here the authors used the pseudo-bulk approaches for the data analysis (having such a valuable dataset with multiple hashed samples for each timepoint). Therefore it would be great to use at least one of those approaches, which were developed specifically for the scRNAseq data analysis. Or, if there are some reasons - the authors should argue why their approach is optimal

4) When the authors describe the DGE changes upon experimental conditions (Figures 5 and 6), my first comment is again relevant: it is difficult to use the current annotation and cell type description as the reference for testing virus effects and shifts in the DGE in distinct neuronal subtypes.

I have to note that the experimental design is well done and logical. Therefore I believe that to strengthen the conclusions, the already obtained datasets can be used for improved analysis.

Reviewer #2 (Public Review):

In this work, the authors use computational modeling and human neurophysiology (MEG) to uncover behavioral and neural signatures of choice history biases during sequential perceptual decision-making. In line with previous work, they see neural signatures reflecting choice planning during perceptual evidence accumulation in motor-related regions, and further show that the rate of accumulation responds to structured, predictable environments suggesting that statistical learning of environment structure in decision-making can adaptively bias the rate of perceptual evidence accumulation via neural signatures of action planning. The data and evidence show subtle but clear effects, and are consistent with a large body of work on decision-making and action planning.

Overall, the authors achieved what they set out to do in this nice study, and the results, while somewhat subtle in places, support the main conclusions. This work will have impact within the fields of decision-making and motor planning, linking statistical learning of structured sequential effects in sense data to evidence accumulation and action planning.

Strengths:<br /> - The study is elegantly designed, and the methods are clear and generally state-of-the-art<br /> - The background leading up to the study is well described, and the study itself conjoins two bodies of work - the dynamics of action-planning processes during perceptual evidence accumulation, and the statistical learning of sequential structure in incoming sense data<br /> - Careful analyses effectively deal with potential confounds (e.g., baseline beta biases)

Weaknesses:<br /> - Much of the study is primarily a verification of what was expected based on previous behavioral work, with the main difference (if I'm not mistaken) being that subjects learn actual latent structure rather than expressing sequential biases in uniform random environments. Whether this difference - between learning true structure or superstitiously applying it when it's not there - is significant at the behavioral or neural level is unclear. Did the authors have a hypothesis about this distinction? If the distinction is not relevant, is the main contribution here the neural effect?<br /> - The key effects (Figure 4) are among the more statistically on-the-cusp effects in the paper, and the Alternating group in 4C did not reliably go in the expected direction. This is not a huge problem per se, but does make the key result seem less reliable given the clear reliability of the behavioral results<br /> - The treatment of "awareness" of task structure in the study (via informal interviews in only a sub-sample of subjects) is wanting

Reviewer #2 (Public Review):

In this study, Yan et al. report that a cleaved form of METTL3 (termed METTL3a) plays an essential role in regulating the assembly of the METTL3-METTL14-WTAP complex. Depletion of METTL3a leads to reduced m6A level on TMEM127, an mTOR repressor, and subsequently decreased breast cancer cell proliferation. Mechanistically, METTL3a is generated via 26S proteasome in an mTOR-dependent manner.

The manuscript follows a smooth, logical flow from one result to the next, and most of the results are clearly presented. Specifically, the molecular interaction assays are well-designed. If true, this model represents a significant addition to the current understanding of m6A-methyltransferase complex formation.

A few minor issues detailed below should be addressed to make the paper even more robust. The specific comments are contained below.

1. The existence of METTL3a and METTL3b.<br /> In this study, the author found the cleaved form of METTL3 in breast cancer patient tissues and breast cancer cell lines. Is it a specific event that only occurs in breast cancer? The author may examine the METTL3a in other cell lines if it is a common rule.<br /> 2. Generation of METTL3a and METTL3b.<br /> 1) Figure 1 shows that METTL3a and METTL3b were generated from the C-terminal of full-length METTL3. Because the sequence of METTL3a is involved in the sequences of METTL3b, can METTL3b be further cleaved to produce METTL3a?<br /> 2) Based on current data, the generation of METTL3a and METTL3b are separated. Are there any factors that affect the cleavage ratio between METTL3a and METTL3b?<br /> 3. In Figure 2G, the author shows the result that incubation of the Δ198+Δ238 METTL3 protein with T47D cell lysates cannot produce the METTL3a and METTL3b variants. The author may also show the results that Δ198 METTL3 protein or Δ238 METTL3 protein incubates with T47D cell lysates, respectively.<br /> 4. As well as many results published in previous studies, the in vitro methylation assay shows that WT METTL3 is capable of methylating RNA probe (figure 2H). The main point of this study is that METTL3a is required for the METTL3-METTL14 assembly. However, the absence of METTL3a in the in vitro system did not inhibit METTL3-METTL14 methylation activity. Moreover, the presence of METTL3a even resulted in a weak m6A level.<br /> 5. In Figure 4A, the author suggests that WTAP cannot be immunoprecipitated with METTL3a and 3b because WTAP interacted with the N-terminal of METTL3. If this assay is performed in WT cells, the endogenous full-length METTL3 may help to form the complex. In this case, WTAP is supposed to be co-immunoprecipitated.

Reviewer #2 (Public Review):

In the present study, Masson et al. provide an elegant and profound demonstration of utilization of systems genetics data to fuel discovery of actionable therapeutics. The strengths of the study are many: generation of a novel skeletal muscle genetics proteomic dataset which is paired with measures of glucose metabolism in mice, systematic utilization of these data to yield potential therapeutic molecules which target insulin resistance, cross-referencing library screens from connectivity map with an independent validation platform for muscle glucose uptake and preclinical data supporting a new mechanism for thiostrepton in alleviating muscle insulin resistance. Future studies evaluating similar integrations of omics data from genetic diversity with compound screens, as well as detailed characterization of mechanisms such as thiostrepton on muscle fibers will further inform some remaining questions. In general, the thorough nature of this study not only provides strong support for the conclusions made, but additionally offers a new framework for analysis of systems-based data. As a result, my questions/comments below are mostly derived from interest and curiosity.

Line 105: The observation that variance in respiratory proteins is stable while lipid pathways is variable is quite interesting. Is this due to lower overall levels of lipid metabolism enzymes (ex. do these differ substantially from similar pathways ranked from high-low abundance?).

Line 154: the 664 associations are impressive and potentially informative. It would be valuable to know which of these co-map to the same locus - either to distinguish linkage in a 2mb window or identify any cis-proteins which directly exert effects in trans-

Line 194: Cross-platform validation of the CMAP fingerprint results is an admirable set of validations. It might be good to know general parameters like how many compounds were shared/unique for each platform. Also the concordance between ranking scores for significant and shared compounds.

Line 319: Another consideration in the molecular fingerprint is how unique these are for muscle. While studies evaluating gene expression have shown that many cis-eQTLs are shred across tissues, to my knowledge, this hasn't been performed systematically for pQTLs. Therefore, consider adding a point to the discussion pointing out that some of the proteins might be conserved pQTLs whereas others which would be more relevant here present unique druggable targets in muscle.

Line 332: These are fascinating observations. 1, that in general insulin signaling and ampk were not themselves shown as top-ranked enrichments with matsuda and that this was sufficient to alter glucose metabolism without changes in these pathways. While further characterization of this signaling emchanism is beyond the scope of this study, it would be good to speculate as to additional signaling pathways that are relevant beyond ROS (ex. CNYP2 and others)

Line: 314: Remove the statement: "While this approach is less powerful than QTL co-localisation for identifying causal drivers,", as I don't believe that this has been demonstrated. Clearly, the authors provide a sufficient framework to pinpoint causality and produce an actionable set of proteins.

Line 346: I would highlight one more appeal of the approach adopted by the authors. Given that these compound libraries were prioritized from patterns of diverse genetics, these observations are inherently more-likely to operate robustly across target backgrounds.

Line 434: I might have missed but can't seem to find where the muscle data are available to researchers. Given the importance and novelty of these studies, it will be important to provide some way to access the proteomic data.

Reviewer #2 (Public Review):

The authors Yang et al., examine the role of NR2F1/COUPTFI and NR2F2/COUPTF2 genes in hippocampus (HP) development, using two Cre lines, RxCre, and Emx1Cre. They report that loss of COUPTFI leads to a defective specification of dorsal CA1; loss of COUPTF2 leads to defects in the morphogenesis of the ventral HP with some ectopic CA field domains; loss of both results in a greatly shrunken hippocampus.

While the phenotypes are indeed interesting and important to examine carefully, there are major lacunae in (A) the authors' interpretation of the literature that sets up the problem (B) the data itself and the experimental design (C) the interpretation of the data. These are detailed below.

[A] Interpretation of the literature<br /> A1: The author's interpretation of the Lhx5 mutant phenotype (line 74-76) missed the fact that the hem appears to be missing or greatly reduced (Zhao et al., 1999; Figure 4D,I; Miquelajáuregui et al., 2010 Figure 5). If the hem is deficient, shrinkage/ agenesis of hippocampus is not surprising. It is incorrect to conclude that Lhx5 has a role in the hippocampal primordium, not only because of the above, but also because Lhx5 expression has been well characterized to be limited to the early hem and CR cells, but is not known to be expressed in the hippocampal primordium. The immunohistochemistry data in Figure 5B showing Lhx5 presence in the vz of the hippocampal and neocortical primordium is perplexing and not what other studies in the literature show for this gene. This is a major point because "regulation of the Lhx2-Lhx5 axis" is one of the main conclusions of the study.

A2: The Lhx2<->Lhx5 inhibition is pitched as a mechanism, but there's no evidence in the literature for this nor in this study. Lines 78-79 "Intriguingly, deficiency of either Lhx5 or Lhx2 results in agenesis of the hippocampus, and more particularly, these genes inhibit each other" are an incorrect interpretation of the literature. The "agenesis" of the hippocampus in the Lhx5 mutant (Zhao et al., 1999) is likely to be because the hem is deficient (point A1 above). The Lhx2 mutant lacks a hippocampus (and neocortex) because the entire dorsal telencephalon has transformed into hem and antihem (Mangale et al., 2008). To cite this as "agenesis of the hippocampus" as originally described by Porter et al (1997) misinterprets a complex stepwise process that was elucidated subsequently in the literature.

Finally, it has not been shown that Lhx2 and Lhx5 inhibit each other- the literature cited does not contain this information. The phenotype reported by the authors may actually have a basis in the effect of loss of COUPTFI/ II on the hem, and a rostro-caudal variation in this effect (or in the timing of action of the Cre lines used) may explain the phenotype.

Problems in the experimental design:<br /> B1: What is the expression domain and timing of RxCre? If it has a dorso-ventral bias in the early embryo, it could explain the regional difference in the COUPTF phenotypes. The authors must show the domain of Cre activation using an Ai9 reporter at E10.5-E11.5 and also at later embryonic stages to be able to interpret whether the shrunken hippocampal phenotype in the single and double mutants is a due to a defect in induction (from the hem), specification (in the early hippocampal primordium), or growth and maintenance (at later embryonic/ postnatal stages). A related point is whether COUPTFI expressed in the hem at E10.5-E11.5, since the earliest age shown is E14.5 which does show expression in the hem; likewise COUPTFII is shown to be expressed in the hem at E12.5. Emx1Cre acts in the hem and therefore the phenotypes could be partially explained by a deficit in the hem itself. Where RxCre acts is not shown and nor is it cited and the logic of shifting between RxCre and Emx1Cre is not clear. A comparison of the expression domains of these lines at relevant early and late embryonic ages is important.

B2:<br /> Line 187: "We would like to investigate the correlation of the CH and/or amygdala anlage with the duplicated ventral hippocampal domains in the COUP-TFII mutant in detail in our future study."<br /> This is inadequate, the effect of the mutation on the cortical hem may be central to the hippocampal phenotype and therefore is central to this study. Ectopic CA fields arising in unexpected places is a finding that needs an explanation, this is not a mere morphogenesis issue as implied in line 190.

B3: Questionable immunofluoresence data: Figure 5B panel h shows that Lhx2 expression extends into the region of the hem at E14.5, suggesting that the hem may in fact not have been specified in the first place. However, the choroid plexus appears to be LHX2 positive in the same image, which it isn't supposed to be, and this calls into question the quality and specificity of the immunofluoresence data. LHX5 staining in Figure 5B panel has been mentioned in point A1- it does not reflect the known expression pattern of this gene (Allen Brain atlas, Zhao et al., 2009). SOX2 also shouldn't be seen in the choroid plexus.

[C] Interpretation of the data<br /> C1: In the COUPTFII mutant, the ectopic presence of HuB+ve cells is intriguing, however it is a stretch to conclude that these cells are born at the expense of CTIP2+ve cells (line 179) without experiments that examine this point.

C2: Line 251: "Unexpectedly, an ectopic nucleus was observed in the region of the prospected temporal hippocampus, indicated by the arrowhead, in the double-mutant mice (Figure 3Ag, h)"<br /> These data are unclear and difficult to appreciate.

C3: The hippocampus is shrunken in the double mutants but the underlying cause has not been examined from the perspective of early cell cycle exit or cell death. How does the reduction of Tbr2+ and NeuroD1+ cells speak to the hippocampal defect? (Figure 5)

Reviewer #2 (Public Review):

Previous studies have extensively explored the rules by which patterned inputs from the two eyes are combined in the visual cortex. Here the authors explore these rules for un-patterned inputs (luminance flicker) at both the level of the cortex, using Steady-State Visual Evoked Potentials (SSVEPs) and at the sub-cortical level using pupillary responses. They find that the pattern of binocular combination differs between cortical and sub-cortical levels with the cortex showing less dichoptic masking and somewhat more binocular facilitation.

Importantly, the present results with flicker differ markedly from those with gratings (Hou et al., 2020, J Neurosci, Baker and Wade 2017 cerebral cortex, Norcia et al, 2000 Nuroreport, Brown et al., 1999, IOVS). When SSVEP responses are measured under dichoptic conditions where each eye is driven with a unique temporal frequency, in the case of grating stimuli, the magnitude of the response in the fixed contrast eye decreases as a function of contrast in the variable contrast eye. Here the response increases by varying (small) magnitudes. The authors favor a view that cortex and perception pool binocular flicker inputs approximately linearly using cells that are largely monocular. The lack of a decrease below the monocular level when modulation strength increase is taken to indicate that previously observed normalization mechanism in pattern vision does not play a substantial role in the processing of flicker. The authors present a computational model of binocular combination that captures features of the data when fit separately to each data set. Because the model has no frequency dependence and is based on scalar quantities, it cannot make joint predictions for the multiple experimental conditions which is one of its limitations.

A strength of the current work is the use of frequency-tagging of both pupil and EEG responses to measure responses for flicker stimuli at two anatomical levels of processing. Flicker responses are interesting but have been relatively neglected. The tagging approach allows one to access responses driven by each eye, even when the other eye is stimulated which is a great strength. The tagging approach can be applied at both levels of processing at the same time when stimulus frequencies are low, which is an advantage as they can be directly compared. The authors demonstrate the versatility of frequency tagging in a novel experimental design which may inspire other uses, both within the present context and others. A disadvantage of the tagging approach for studying sub-cortical dynamics via pupil responses is that it is restricted to low temporal frequencies given the temporal bandwidth of the pupil. The inclusion of a behavioral measure and a model is also a strength, but there are some limitations in the modeling (see below).

The authors suggest in the discussion that luminance flicker may preferentially drive cortical mechanisms that are largely monocular and in the results that they are approximately linear in the dichoptic cross condition (no effect of the fixed contrast stimulus in the other eye). By contrast, prior research using dichoptic dual frequency flickering stimuli has found robust intermodulation (IM) components in the VEP response spectrum (Baitch and Levi, 1988, Vision Res; Stevens et al., 1994 J Ped Ophthal Strab; France and Ver Hoeve, 1994, J Ped Ophthal Strab; Suter et al., 1996 Vis Neurosci). The presence of IM is a direct signature of binocular interaction and suggests that at least under some measurement conditions, binocular luminance combination is "essentially" non-linear, where essential implies a point-like non-linearity such as squaring of excitatory inputs. The two views are in striking contrast. It would thus be useful for the authors could show spectra for the dichoptic, two-frequency conditions to see if non-linear binocular IM components are present.

If the IM components are indeed absent, then there is a question of the generality of the conclusions, given that several previous studies have found them with dichoptic flicker. The previous studies differ from the authors' in terms of larger stimuli and in their use of higher temporal frequencies (e.g. 18/20 Hz, 17/21 Hz, 6/8 Hz). Either retinal area stimulated (periphery vs central field) or stimulus frequency (high vs low) could affect the results and thus the conclusions about the nature of dichoptic flicker processing in cortex. It would be interesting to sort this out as it may point the research in new directions.

Whether these components are present or absent is of interest in terms of the authors' computational model of binocular combination. It appears that the present model is based on scalar magnitudes, rather than vectors as in Baker and Wade (2017), so it would be silent on this point. The final summation of the separate eye inputs is linear in the model. In the first stage of the model, each eye's input is divided by a weighted input from the other eye. If we take this input as inhibitory, then IM would not emerge from this stage either.

Related to the model: One of the more striking results is the substantial difference between the dichoptic and dichoptic-cross conditions. They differ in that the latter has two different frequencies in the two eyes while the former has the same frequency in each eye. As it stands, if fit jointly on the two conditions, the model would make the same prediction for the dichoptic and dichoptic-cross conditions. It would also make the same prediction whether the two eyes were in-phase temporally or in anti-phase temporally. There is no frequency/phase-dependence in the model to explain differences in these cases or to potentially explain different patterns at the different VEP response harmonics. The model also fits independently to each data set which weakens its generality. An interpretation outside of the model framework would thus be helpful for the specific case of differences between the dichoptic and dichoptic-cross conditions.

Prior work has defined several regimes of binocular summation in the VEP (Apkarian et al.,1981 EEG Journal). It would be useful for the authors to relate the use of their terms "facilitation" and "suppression" to these regimes and to justify/clarify differences in usage, when present. Experiment 1, Fig. 3 shows cases where the binocular response is more than twice the monocular response. Here the interpretation is clear: the responses are super-additive and would be classed as involving facilitation in the Apkarian et al framework.

In the Apkarian et al framework, a ratio of 2 indicates independence/linearity. Ratios between 1 and 2 indicate sub-additivity and are diagnostic of the presence of binocular interaction but are noted by them to be difficult to interpret mechanistically. This should be discussed. A ratio of <1 indicates frank suppression which is not observed here with flicker.

Can the model explore the full range of binocular/monocular ratios in the Apkarian et al framework? I believe much of the data lies in the "partial summation" regime of Apkarian et al and that the model is mainly exploring this regime and is a way of quantifying varying degrees of partial summation.

Reviewer #2 (Public Review):

One of the major strengths in the current study is the implementation of the fully data-driven, gradient-based method for mapping connectopies of the LC. This approach is especially suited for brain structures that are difficult to localise because the resulted connectopic mapping is relatively robust to ROI definition (Fig. 7 in Haak et al., 2018). However, as a very inclusive definition of the LC (the "meta atlas") was adopted in the study, to what extent the gradient approach can tolerate changes of accuracy and specificity for LC ROI definition is unknown. Some comparative analyses would be helpful to provide assessments on the specificity and stability of the reported gradient pattern.

Haak et al. showed distinct reproducibility within and between subjects when comparing connectopic mappings between M1 and V1. M1 connectopic mapping showed very high consistency across subjects (ICCs > 0.9) compared with V1. This is very reasonable because the functional organisation within M1 is relatively homogeneous. Regarding the reliability of the LC rostro-caudal gradient, the authors only stated that "individual gradient estimation is often not consistent", but direct measurement on the consistency across subjects for the LC gradient was missing. This is important for future LC fMRI studies as more consistent pattern might warrant the application of an atlas-based method otherwise a more individualised pipeline is needed for investigating functional dissociation in LC subregions.

It puzzles me that why a dichotomous rostral vs caudal comparison was used to demonstrate the difference in connectivity patterns along the rostro-caudal gradient which might be an oversimplistic approach as described by the authors themselves? In fact, it might be more interesting to include the central "core" LC which is structurally organized in high density (Fernandes et al., 2012) and functionally distinguishable to the peri-LC "shell" region (Totah et al., 2018; Poe et al., 2022).

The composition of rostral vs caudal connectivity pattern changes over ageing, where the loss of rostral-like connectivity was consistent in bilateral LC whereas the gain of caudal-like connectivity in older subjects was only evident in the left LC. Do authors have any explanations on this left-lateralised ageing effect which is interestingly coincided with a lot of observations such as increased left LC contrast ratios was found during ageing (Betts et al., 2017) and in PD patients (Ye et al., 2022), reduced left LC-parahippocampal gyrus connectivity was reported in aMCI patients (Jacobs et al., 2015).

Reviewer #2 (Public Review):

Summary, general appraisal

This study examines the construct of "cognitive spaces" as they relate to neural coding schemes present in response conflict tasks. The authors utilize a novel paradigm, in which subjects must map the direction of a vertically oriented arrow to either a left or right response. Different types of conflict (spatial Stroop, Simon) are parametrically manipulated by varying the spatial location of the arrow (a task-irrelevant feature). The vertical eccentricity of the arrow either agrees or conflicts with the arrow's direction (spatial Stroop), while the horizontal eccentricity of the arrow agrees or conflicts with the side of the response (Simon). A neural coding model is postulated in which the stimuli are embedded in a cognitive space, organized by distances that depend only on the similarity of congruency types (i.e., where conditions with similar relative proportions of spatial-Stroop versus Simon congruency are represented with similar activity patterns). The authors conduct a behavioral and fMRI study to provide evidence for such a representational coding scheme. The behavioral findings replicate the authors' prior work in demonstrating that conflict-related cognitive control adjustments (the congruency sequence effect) shows strong modulation as a function of the similarity between conflict types. With the fMRI neural activity data, the authors report univariate analyses that identified activation in left prefrontal and dorsomedial frontal cortex modulated by the amount of Stroop or Simon conflict present, and multivariate representational similarity analyses (RSA) that identified right lateral prefrontal activity encoding conflict similarity and correlated with the behavioral effects of conflict similarity.<br /> This study tackles an important question regarding how distinct types of conflict, which have been previously shown to elicit independent forms of cognitive control adjustments, might be encoded in the brain within a computationally efficient representational format. The ideas postulated by the authors are interesting ones and the utilized methods are rigorous. However, the study has critical limitations that are due to a lack of clarity regarding theoretical hypotheses, serious confounds in the experimental design, and a highly non-standard (and problematic) approach to RSA. Without addressing these issues it is hard to evaluate the contribution of the authors findings to the computational cognitive neuroscience literature.

The primary theoretical question and its implications are unclear.

The paper would greatly benefit from more clearly specifying potential alternative hypotheses and discussing their implications. Consider, for example, the case of parallel conflict monitors. Say that these conflict monitors are separately tuned for Stroop and Simon conflict, and are located within adjacent patches of cortex that are both contained within a single cortical parcel (e.g., as defined by the Glasser atlas used by the authors for analyses). If RSA was conducted on the responses of such a parcel to this task, it seems highly likely that an activation similarity matrix would be observed that is quite similar (if not identical) to the hypothesized one displayed in Figure 1. Yet it would seem like the authors are arguing that the "cognitive space" representation is qualitatively and conceptually distinct from the "parallel monitor" coding scheme. Thus, it seems that the task and analytic approach is not sufficient to disambiguate these different types of coding schemes or neural architectures.

The authors also discuss a fully domain-general conflict monitor, in which different forms of conflict are encoded within a single dimension. Yet this alternative hypothesis is also not explicitly tested nor discussed in detail. It seems that the experiment was designed to orthogonalize the "domain-general" model from the "cognitive space" model, by attempting to keep the overall conflict uniform across the different stimuli (i.e., in the design, the level of Stroop congruency parametrically trades off with the level of Simon congruency). But in the behavioral results (Fig. S1), the interference effects were found to peak when both Stroop and Simon congruency are present (i.e., Conf 3 and 4), suggesting that the "domain-general" model may not be orthogonal to the "cognitive space" model. One of the key advantages of RSA is that it provides the ability to explicitly formulate, test and compare different coding models to determine which best accounts for the pattern of data. Thus, it would seem critical for the authors to set up the design and analyses so that an explicit model comparison analysis could be conducted, contrasting the domain-general, domain-specific, and cognitive space accounts.<br /> Relatedly, the reasoning for the use of the term "cognitive space" is unclear. The mere presence of graded coding for two types of conflict seems to be a low bar for referring to neural activity patterns as encoding a "cognitive space". It is discussed that cognitive spaces/maps allow for flexibility through inference and generalization. But no links were made between these cognitive abilities and the observed representational structure. Additionally, no explicit tests of generality (e.g., via cross-condition generalization) were provided. Finally, although the design elicits strong CSE effects, it seems somewhat awkward to consider CSE behavioral patterns as a reflection of the kind of abilities supported by a cognitive map (if this is indeed the implication that was intended). In fact, CSE effects are well-modeled by simpler "model-free" associative learning processes, that do not require elaborate representations of abstract structures.

More generally, it seems problematic that Stroop and Simon conflict in the paradigm parametrically trade-off against each other. A more powerful design would have de-confounded Stroop and Simon conflict so that each could be separately estimation via (potentially orthogonal) conflict axes. Additionally, incorporating more varied stimulus sets, locations, or responses might have enabled various tests of generality, as implied by a cognitive space account.

Serious confounds in the design render the results difficult to interpret.

As much prior neuroimaging and behavioral work has established, "conflict" per se is perniciously correlated with many conceptually different variables. Consequently, it is very difficult to distinguish these confounding variables within aggregate measures of neural activity like fMRI. For example, conflict is confounded with increased time-on-task with longer RT, as well as conflict-driven increases in coding of other task variables (e.g., task-set related coding; e.g., Ebitz et al. 2020 bioRxiv). Even when using much higher resolution invasive measures than fMRI (i.e., eCoG), researchers have rightly been wary of making strong conclusions about explicit encoding of conflict (Tang et al, 2019; eLife). As such, the researchers would do well to be quite cautious and conservative in their analytic approach and interpretation of results.

This issue is most critical in the interpretation of the fMRI results as reflecting encoding of conflict types. A key limitation of the design, that is acknowledged by the authors is that conflict is fully confounded within-subject by spatial orientation. Indeed, the limited set of stimulus-response mappings also cast doubt on the underlying factors that give rise to the CSE modulations observed by the authors in their behavioral results. The CSE modulations are so strong - going from a complete absence of current x previous trial-type interaction in the cos(90) case all the way to a complete elimination of any current trial conflict when the prior trial was incongruent in the cos(0) case - that they cause suspicion that they are actually driven by conflict-related control adjustments rather than sequential dependencies in the stimulus-response mappings that can be associatively learned.

To their credit, the authors recognize this confound, and attempt to address it analytically through the use of a between-subject RSA approach. Yet the solution is itself problematic, because it doesn't actually deconfound conflict from orientation. In particular, the RSA model assumes that whatever components of neural activity encode orientation produce this encoding within the same voxel-level patterns of activity in each subject. If they are not (which is of course likely), then orthogonalization of these variables will be incomplete. Similar issues underlie the interpretation target/response and distractor coding. Given these issues, perhaps zooming out to a larger spatial scale for the between-subject RSA might be warranted. Perhaps whole-brain at the voxel level with a high degree of smoothing, or even whole-brain at the parcel level (averaging per parcel). For this purpose, Schaefer atlas parcels might be more useful than Glasser, as they more strongly reflect functional divisions (e.g., motor strip is split into mouth/hand divisions; visual cortex is split into central/peripheral visual field divisions). Similarly, given the lateralization of stimuli, if a within-parcel RSA is going to be used, it seems quite sensible to pool voxels across hemispheres (so effectively using 180 parcels instead of 360).

The strength of the results is difficult to interpret due to the non-standard analysis method.

The use of a mixed-level modeling approach to summarize the empirical similarity matrix is an interesting idea, but nevertheless is highly non-standard within RSA neuroimaging methods. More importantly, the way in which it was implemented makes it potentially vulnerable to a high degree of inaccuracy or bias. In this case, this bias is likely to be overly optimistic (high false positive rate).

A key source of potential bias comes from the fact that the off-diagonal cells are not independent (e.g., the correlation between subject A and B is strongly dependent on the correlation between subject A and C). For appropriate degrees of freedom calculation, the model must take this into account somehow. As fitted, the current models do not seem to handle this appropriately. That being said, it may be possible to devise an appropriate test via mixed-level models. In fact, Chen et al. have a series of three recent Neuroimage articles that extensively explore this question (all entitled "Untangling the relatedness among correlations") - adopting one of the methods described in the papers, seems much safer, if possible.

Another potential source of bias is in treating the subject-level random effect coefficients (as predicted by the mixed-level model) as independent samples from a random variable (in the t-tests). The more standard method for inference would be to use test statistics derived from the mixed-model fixed effects, as those have degrees of freedom calculations that are calibrated based on statistical theory.

No numerical or formal defense was provided for this mixed-level model approach. As a result, the use of this method seems quite problematic, as it renders the strength of the observed results difficult to interpret. Instead, the authors are encouraged using a previously published method of conducting inference with between-subject RSA, such as the bootstrapping methods illustrated in Kragel et al. (2018; Nat Neurosci), or in potentially adopting one of the Chen et al. methods mentioned above, that have been extensively explored in terms of statistical properties.

Reviewer #2 (Public Review):

The manuscript describes an approach to monitor microglial structural dynamics and correlate it to ongoing changes in brain state during sleep-wake cycles. The main novelty here is the use of miniaturized 2p microscopy, which allows tracking microglia surveillance over long periods of hours, while the mice are allowed to freely behave. Accordingly, this experimental setup would permit to explore long-lasting changes in microglia in a more naturalistic environment, which were previously not possible to identify otherwise. The findings could provide key advances to the research of microglia during natural sleep and wakefulness, as opposed to anesthesia. The main findings of the paper are that microglia increase their process motility and surveillance during REM and NREM sleep as compared to the awake state. The authors further show that sleep deprivation induces opposite changes in microglia dynamics- limiting their surveillance and size. The authors then demonstrate potential causal role for norepinephrine secretion from the locus coeruleus (LC) which is driven by beta 2 adrenergic receptors (b2AR) on microglia. However, there are several methodological and experimental concerns which should be addressed.

The major comments are summarized below:

1. The main technological advantage of the 2p miniaturized microscope is the ability to track single cells over sleep cycles. A main question that is unclear from the analysis and the way the data is presented is: are the structural changes in microglia reversible? Meaning, could the authors provide evidence that the same cell can dynamically change in sleep state and then return to similar size in wakefulness? The same question arises again with the data which is presented for anesthesia, is this change reversible?<br /> 2. The binary comparison between brain states is misleading, shouldn't the changes in structural dynamics compared to the baseline of the state onset? The authors method describes analysis of the last 5 minutes in each sleep/wake state. However, these transitions are directional- for instance, REM usually follows NREM, so the description of a decrease in length during REM sleep could be inaccurate.<br /> 3. Sleep deprivation- again, it is unclear whether these structural changes are reversible. This point is straightforward to address using this methodology by measuring sleep following SD. In addition, the authors chose a method to induce sleep deprivation that is rather harsh. It is unclear if the effect shown is the result of stress or perhaps an excess of motor activity.<br /> 4. The authors perform measurements of norepinephrine with a recently developed GRAB sensor. These experiments are performed to causally link microglia surveillance during sleep to norepinephrine secretion. They perform 2p imaging and collect data points which are single neurons, and it is unclear why the normalization and analysis is performed for bulk fluorescence similar to data obtained with photometry.<br /> 5. The experiments involving b2AR KO mice are difficult to interpret and do not provide substantial mechanistic insight. Since b2AR are expressed throughout numerous cell types in the brain and in the periphery, it is entirely not clear whether the effects on microglia dynamics are direct. The conclusion and the statement regarding the expression of b2AR in microglia is not supported by the references the authors present, which simply demonstrate the existence and function of b2AR in microglia. In addition, these mice show significant changes in sleep pattern and increased REM sleep. This could account for reasons for the changes in microglia structure rather than the interpretation that these are direct effects.<br /> To summarize, the main conclusions of the paper require further support with analysis of existing data and experimental validation.

Reviewer #2 (Public Review):

The authors decipher the signaling between vitamin D and proteins that are downstream of SIRT1. The importance of vitamin D in physiology is clear. However, the link between vitamin D and cancer is less clear. This study provides very interesting and solid information on the link between vitamin D and colorectal cancer. It is likely that this study will provide insight into the importance of vitamin D in other types of cancer. It may also lead to new therapeutic strategies for specific cases.

The authors focus on vitamin D-mediated signaling through VDR, SIRT1 and Ace H3K9. They highlight the importance of K610 in SIRT1 in this process. This article is convincing, although the authors can improve their study as outlined below:

* The authors should specify which cell line was used to perform the experiment in Figure 1E,F. What would be the result in the presence/absence of 1,25(OH)2D3? In Figure 1G, what is the meaning of # and ###?

* Figure 2C, it would have been ideal to show the VDR-SIRT1 interaction after a Sirt1 IP.

* I understand the authors' overall message for this figure, but it is far from clear. This section needs to be improved. For example, in Figure 3G, does this mean that the level of AceH3K9 is independent of the level of SIRT1? Is there a contradiction? The authors should indicate the color of the different stainings for Figure 3D. Do the authors mean that the secondary antibody marks in brown/red? If so, these results are inconsistent with the text considering that hematoxylin was used for non-tumor tissue. This part needs to be clarified. What about the level of FOXO3A in these tissues/tumors? What is the level of 1,25(OH)2D3 in these patients? In Figure 3D, the following information is missing: "A detailed amplification is shown in the lower left of each micrograph." In Figure 3E, it says p=0.325, in the legend p<0.01, and in the text there is a trend. Which is the correct version?

* Figure 4F. The quality of the presented blots is not optimal. It needs to be improved. In addition, the number of independent biological experiments is not indicated. In general, the authors should better indicate the number of independent biological experiments performed, at least for some of them. For example, see Figure 1G. Regarding Figure 2C, we understand that the WB was performed 3 times. Is this the case for the PI? etc...

Reviewer #2 (Public Review):

In this study, the authors validated a positive feedback loop between ZEB2 and ACSL4 in breast cancer, which regulates lipid metabolism to promote metastasis.

Overall, the study is original, well structured, and easy to read. Despite the reliability of the data discussed in this article, there are still some deficiencies that need to be addressed through further explanation.

Major issues:

1. The authors demonstrated that ACSL4 regulates ZEB2 not only via a post-transcriptional mechanism but also via a transcriptional mechanism. The authors have not provided a comprehensive explanation of the specific mechanism in this paper. Therefore, it is recommended that the author delve into the potential mechanisms in the discussion section. For example, related mechanisms affecting ZEB2 ubiquitination degradation, as well as factors affecting ZEB2 upstream transcriptional regulation, etc.

2. To further clarify the interaction of ZEB2 and ACSL4, it is best to perform in vitro glutathione-S-transferase (GST) pulldown assay and immunofluorescence assay.

3. In Figure 7B, the protein level of ZEB2 seems not to be altered in BT549 BCSC cell line after the depletion of ACSL4.

4. EMT is characterized by changes in cell morphology, so the staining of cytoskeletons with Phalloidin is needed.

5. Additional breast cancer cases or cohorts (such as TMA) should be used to validate the positive correlation between ACSL4 and ZEB2 expression through IHC analysis.

Reviewer #2 (Public Review):

Summary:

This work investigates the effects of various antipsychotic drugs on cortical responses during visuomotor integration. Using wide-field calcium imaging in a virtual reality setup, the researchers compare neuronal responses to self-generated movement during locomotion-congruent (closed loop) or locomotion-incongruent (open loop) visual stimulation. Moreover, they probe responses to unexpected visual events (halt of visual flow, sudden-onset drifting grating). The researchers find that, in contrast to a variety of excitatory and inhibitory cell types, genetically defined layer 5 excitatory neurons distinguish between the closed and the open loop condition and exhibit activity patterns in visual cortex in response to unexpected events, consistent with unsigned prediction error coding. Motivated by the idea that prediction error coding is aberrant in psychosis, the authors then inject the antipsychotic drug clozapine, and observe that this intervention specifically affects closed loop responses of layer 5 excitatory neurons, blunting the distinction between the open and closed loop conditions. Clozapine also leads to a decrease in long-range correlations between L5 activity in different brain regions, and similar effects are observed for two other antipsychotics, aripripazole and haloperidol, but not for the stimulant amphetamine. The authors suggest that altered prediction error coding in layer 5 excitatory neurons due to reduced long-range correlations in L5 neurons might be a major effect of antipsychotic drugs and speculate that this might serve as a new biomarker for drug development.

Strengths:

- Relevant and interesting research question:

The distinction between expected and unexpected stimuli is blunted in psychosis but the neural mechanisms remain unclear. Therefore, it is critical to understand whether and how antipsychotic drugs used to treat psychosis affect cortical responses to expected and unexpected stimuli. This study provides important insights into this question by identifying a specific cortical cell type and long-range interactions as potential targets. The authors identify layer 5 excitatory neurons as a site where functional effects of antipsychotic drugs manifest. This is particularly interesting as these deep layer neurons have been proposed to play a crucial role in computing the integration of predictions, which is thought to be disrupted in psychosis. This work therefore has the potential to guide future investigations on psychosis and predictive coding towards these layer 5 neurons, and ultimately improve our understanding of the neural basis of psychotic symptoms.

- Broad investigation of different cell types and cortical regions:

One of the major strengths of this study is quasi-systematic approach towards cell types and cortical regions. By analysing a wide range of genetically defined excitatory and inhibitory cell types, the authors were able to identify layer 5 excitatory neurons as exhibiting the strongest responses to unexpected vs. expected stimuli and being the most affected by antipsychotic drugs. Hence, this quasi-systematic approach provides valuable insights into the functional effects of antipsychotic drugs on the brain, and can guide future investigations towards the mechanisms by which these medications affect cortical neurons.

- Bridging theory with experiments:

Another strength of this study is its theoretical framework, which is grounded in the predictive coding theory. The authors use this theory as a guiding principle to motivate their experimental approach connecting visual responses in different layers with psychosis and antipsychotic drugs. This integration of theory and experimentation is a powerful approach to tie together the various findings the authors present and to contribute to the development of a coherent model of how the brain processes visual information both in health and in disease.

Weaknesses:

- Unclear relevance for psychosis research:

From the study, it remains unclear whether the findings might indeed be able to normalise altered predictive coding in psychosis. Psychosis is characterised by a blunted distinction between predicted and unpredicted stimuli. The results of this study indicate that antipsychotic drugs further blunt the distinction between predicted and unpredicted stimuli, which would suggest that antipsychotic drugs would deteriorate rather than ameliorate the predictive coding deficit found in psychosis. However, these findings were based on observations in wild-type mice at baseline. Given that antipsychotics are thought to have little effects in health but potent antipsychotic effects in psychosis, it seems possible that the presented results might be different in a condition modelling a psychotic state, for example after a dopamine-agonistic or a NMDA-antagonistic challenge. Therefore, future work in models of psychotic states is needed to further investigate the translational relevance of these findings.

- Incomplete testing of predictive coding interpretation:

While the investigation of neuronal responses to different visual flow stimuli Is interesting, it remains open whether these responses indeed reflect internal representations in the framework of predictive coding. While the responses are consistent with internal representation as defined by the researchers, i.e., unsigned prediction error signals, an alternative interpretation might be that responses simply reflect sensory bottom-up signals that are more related to some low-level stimulus characteristics than to prediction errors. Moreover, This interpretational uncertainty is compounded by the fact that the used experimental paradigms were not suited to test whether behaviour is impacted as a function of the visual stimulation which makes it difficult to assess what the internal representation of the animal actual was. For these reasons, the observed effects might reflect simple bottom-up sensory processing alterations and not necessarily have any functional consequences. While this potential alternative explanation does not detract from the value of the study, future work would be needed to explain the effect of antipsychotic drugs on responses to visual flow. For example, experimental designs that systematically vary the predictive strength of coupled events or that include a behavioural readout might be more suited to draw from conclusions about whether antipsychotic drugs indeed alter internal representations.

- Methodological constraints of experimental design:

While the study findings provide valuable insights into the potential effects of antipsychotic drugs, it is important to acknowledge that there may be some methodological constraints that could impact the interpretation of the results. More specifically, the experimental design does not include a negative control condition or different doses. These conditions would help to ensure that the observed effects are not due to unspecific effects related to injection-induced stress or time, and not confined to a narrow dose range that might or might not reflect therapeutic doses used in humans. Hence, future work is needed to confirm that the observed effects indeed represent specific drug effects that are relevant to antipsychotic action.

Conclusion:

Overall, the results support the idea that antipsychotic drugs affect neural responses to predicted and unpredicted stimuli in deep layers of cortex. Although some future work is required to establish whether this observation can indeed be explained by a drug-specific effect on predictive coding, the study provides important insights into the neural underpinnings of visual processing and antipsychotic drugs, which is expected to guide future investigations on the predictive coding hypothesis of psychosis. This will be of broad interest to neuroscientists working on predictive coding in health and in disease.

Reviewer #2 (Public Review):

The authors of the current study set out to improve the purity of extracellular vesicles obtained from plasma. A well-described problem is that various means of separating extracellular vesicles from other plasma constituents tend to leave residual impurities such as lipoproteins and free proteins in the final extracellular vesicles preparation. Van Deun and colleagues had previously improved on the size exclusion chromatography approach by adding a second form of chromatography using separation based on charge. The current authors have evaluated that method and another gold standard approach, iodixanol gradient ultracentrifugation, and they have extended the work with the addition of a third form of chromatography. They are building on their prior work on separating albumin from plasma extracellular vesicles.

A major strength of the paper is that the authors have used complementary methods including a digital immunoassay method and transmission electron microscopy to demonstrate the purity of their sample preparation method. In addition, they have used mass spectrometry to show that they are able to profile hundreds of proteins in their plasma extracellular vesicle sample preparations.

Another major strength of the work is that the authors have taken pains to aid others in reproducing and extending the work. The authors used commercially available human pooled plasma, which is a good decision in terms of reproducibility, compared with a single person's plasma. The authors have explained exactly how to make their new chromatography columns, and they've also explained how to make a manual or an automated apparatus to improve the parallel processing of samples. They explained exactly how to fabricate each apparatus, with computer-aided design files and Raspberry Pi software. I anticipate many others will be able to implement what the authors have done because they shared these resources.

Moreover, the authors have shared the essential data needed to understand and vet their work.

Meanwhile, they shared simple and practical information about the preparation of Sepharose columns to improve the yield of chromatography. They showed that in-column washing with PBS yielded more extracellular vesicles compared with washing Sepharose prior to making the column. This finding should help anyone using size-exclusion chromatography or the more sophisticated combinations of chromatography studied herein.

The major weakness of the method is that it remains unclear to what extent the results of proteomic profiling of these purer plasma extracellular vesicles continue to be confounded by free proteins. This is a problem that will take sustained efforts to resolve, but the authors have built the next piece of the road heading in that direction.

The authors have succeeded in their main aims, albeit without being able to completely rid the sample preparations of lipoproteins, which may or may not be possible.

The results support the authors' conclusions.

This work is going to be useful to the increasing number of researchers who find that circulating extracellular vesicles hold promise for the diagnosis of diseases. In order to find the "signal" within the noise of the complex admixture constituting human plasma, a suitable process for separating vesicles from what I would call impurities is essential. The ability to automate that process while also scaling it up are additional essential components for the extracellular vesicle biomarker field to develop into a clinically useful source of biomarkers. The authors have made progress in each of these areas.

Reviewer #2 (Public Review):

In this manuscript, authors had to circumvent some challenges in protein design that included the generation of peptide-receptive MHCI and a defined Man9GlcNAc2 glycan tree on the MHC I recognizable by UGGT1. Production of peptide-receptive MHCI was achieved by forming a fos/jun dimerized single-chain MHC1-fos with TAPBPR-jun in the presence of the α-mannosidase I inhibitor kifunensine. Glucozylation of MHCI by UGGT1 was monitored on protease-cleaved MHCI/TAPBPR, and liquid chromatography-mass spectrometry was used to monitor reglucosylation. Authors have provided convincing evidence that TAPBPR is sufficient and necessary for glucosylation of MHC 1, hence TAPBPR in addition to serving as an accessory protein in regulating peptide selection has a second function in quality control and fitness of newly synthesized MHC I during maturation.

The strength of the study lies in the generation of a complete in vitro system where different steps and direct interactions between different components of MHCI maturation can be monitored, hence leading to a better mechanistic understanding of MHC I maturation. However, some potential weakness might be that the major finding of the manuscript describing the critical role of TAPBPR as a chaperon in optimizing peptide selection and regulation of MHC I glucosylation and reglucosylation has been previously reported. Nonetheless, the current study further establishes and better defines some prior findings, thus quite valuable.

Reviewer #2 (Public Review):

This paper purports to unveil a mechanism controlling telomere length through SUMO modifications controlling interactions between PCNA unloader Elg1 and the CST complex that functions at telomeres. This is an extremely interesting mechanism to understand, and this paper indeed reveals some interesting genetic results, leading to a compelling model, with potential impact on the field. The conclusions are largely supported by experiments examining protein-protein interactions at low resolution and ambiguous regarding directness of interactions like co-IP and yeast two-hybrid (Y2H) combined with genetics. However, some results appear contradictory and there's a lack of rigor in the experimental data needed to support claims. There is significant room for improvement and this work could certainly attain the quality needed to support the claims. The current version needs substantial revision and lacks the necessary experimental detail. Stronger support for the claims would add detail to help distinguish competing models.

Reviewer #2 (Public Review):

The manuscript "Detecting and validating influential organisms for rice growth: An ecological network approach" explores the influence of biotic and abiotic entities that are often neglected on rice growth. The study has a straightforward experimental design, and well thought hypothesis for explorations. Monitoring data is collected to infer relationships between species and the environment empirically. It is analyzed with an up-to-date statistical method. This allowed the manuscript to hypothesize and test the effects most influential entities in a controlled experiment.

The manuscript is interesting and sets up a nice framework for future studies. In general, the manuscript can be improved significantly, when this workflow is smoothly connected and communicated how they follow each other more than the sequence and dates provided. It is valuable philosophical thinking, and the research community can benefit from this framework.

I understand the length and format of the manuscript make it difficult to add more details, but I am sure it can refer to/clear some concepts/methods that might be new for the audience. How/why variables are selected as important parts of the system, a tiny bit of information about the nonlinear time series analysis in the early manuscript, and the biological reasoning behind these statistically driven decisions are some examples.

Reviewer #2 (Public Review):

This is a well-written manuscript about a strong comparative study of diversity of facial movements in three macaque species to test arguments about social complexity influencing communicative complexity. My major criticism has to do with the lack of any reporting of inter-observer reliability statistics - see comment below. Reporting high levels of inter-observer reliability is crucial for making clear the authors have minimized chances of possible observer biases in a study like this, where it is not possible to code the data blind with regard to comparison group. My other comments and questions follow by line number:

38-40. Whereas I am an advocate of this hypothesis and have tested it myself, the authors should probably comment here, or later in the discussion, about the reverse argument - greater communicative complexity (driven by other selection pressures) could make more complicated social structures possible. This latter view was the one advocated by McComb & Semple in their foundational 2005 Biology Letters comparative study of relationships between vocal repertoire size and typical group size in non-human primate species.

72-84 and 95-96. In the paragraph here, the authors outline an argument about increasing uncertainty / entropy mapping on to increasing complexity in a system (social or communicative). In lines 95-96, though, they fall back on the standard argument about complex systems having intermediate levels of uncertainty (complete uncertainty roughly = random and complete certainty roughly = simple). Various authors have put forward what I think are useful ways of thinking about complexity in groups - from the perspective of an insider (i.e., a group member, where greater randomness is, in fact, greater complexity) vs from the perspective of an outside (i.e., a researcher trying to quantify the complexity of the system where is it relatively easy to explain a completely predictable or completely random system but harder to do so for an intermediately ordered or random system). This sort of argument (Andrew Whiten had an early paper that made this argument) might be worth raising here or later in the discussion? (I'm also curious where the authors sentiments lie for this question - they seem to touch on it in lines 285-287, but I think it's worth unpacking a little more here!)

115-129. See also:<br /> Maestripieri, D. (2005). "Gestural communication in three species of macaques (Macaca mulatta, M. nemestrina, M. arctoides): use of signals in relation to dominance and social context." Gesture 5: 57-73.<br /> Maestripieri, D. and K. Wallen (1997). "Affiliative and submissive communication in rhesus macaques." Primates 38(2): 127-138.<br /> On that note, it is probably worth discussing in this paragraph and probably later in the discussion exactly how this study differs from these earlier studies of Maestripieri. I think the fact that machine learning approaches had the most difficulty assigning crested data to context is an important methodological advance for addressing these sorts of questions - there are probably other important differences between the authors' study here and these older publications that are worth bringing up.

220-222. What is known about visual perception in these species? Recent arguments suggest that more socially complex species should have more sensitive perceptual processing abilities for other individuals' signals and cues (see Freeberg et al. 2019 Animal Behaviour). Are there any published empirical data to this effect, ideally from the visual domain but perhaps from any domain?

274-277. I am not sure I follow this - could not different social and non-social contexts produce variation in different affective states such that "emotion"-based signals could be as flexible / uncertain as seemingly volitional / information-based / referential-like signals? This issue is probably too far away from the main points of this paper, but I suspect the authors' argument in this sentence is too simplified or overstated with regard to more affect-based signals.

288 on. Given there are only three species in this study, the chances of one of the species being the 'most complex' in any measure is 0.33. Although I do not believe this argument I am making here, can the authors rule out the possibility that their findings related to crested macaques are all related to chance, statistically speaking?

329-330. The fact that only one male rhesus macaque was assessed here seems problematic, given the balance of sexes in the other two species. Can the authors comment more on this - are the gestures they are studying here identical across the sexes?

354-371. Inter-observer reliability statistics are required here - one of the authors who did not code the original data set, or a trained observer who is not an author, could easily code a subset of the video files to obtain inter-observer reliability data. This is important for ruling out potential unconscious observer biases in coding the data.

Reviewer #2 (Public Review):

This study presents a useful investigation of eccDNAs in spermatogenesis of mouse. It provides evidence about the biogenesis of eccDNAs and suggests that eccDNAs are derived from oligonucleosmal DNA fragmentation during apoptosis by MMEJ and may not be the direct products of germline deletions. However, the method of data analyses were not fully described and data analysis is incomplete. It provides additional observations about the eccDNA biogenesis and can be used as a starting point for functional studies of eccDNA in sperms. However, many aspects about data analyses and data interpretations need to be improved.

• Most of the conclusions made by the work are only based on the bioinformatics analyses, the validation of these foundlings using other method (biochemistry/molecular biology method) are missing. For example, no QC results presented for the eccDNA purification, which may show whether contaminates such as linear DNA or mitochondria DNA have been fully removed. Additionally, it is also helpful to use simple PCR to test the existence of identified eccDNAs in sperm or other samples to validate the specificity of the Circle-seq method.

• The reliability of the data analysis methods is uncertain, as the authors constructed and utilized their own pipeline to identify eccDNAs, despite the availability of established bioinformatics tools such as ECCsplorer, eccFinder, and Amplicon Architect. Moreover, the lack of validation of the pipeline using either ground truth datasets or simulation data raises concerns about its accuracy. Additionally, the methodology employed for identifying eccDNA that encompasses multiple gene loci remains unclear.

• Although the author stated that previous studies utilizing short-read sequencing technologies may have incorrectly annotated eccDNA breakpoints, this claim requires careful scrutiny and supporting evidence, which was not provided in the manuscript.

• The similarity between the eccDNA profiles of human and mouse sperm remains uncertain, and therefore, analyses of human eccDNA data and comparisons between the two are necessary if the authors claim that their findings of widespread eccDNA formation in mouse spermatogenesis extend to human sperms.

Reviewer #2 (Public Review):

Centrosomes are an integral part of cell division in most animal cells. There are notable exceptions, however, such as oocytes and plants. In addition, some animal cells can be engineered to lack centrosomes yet they can still manage to complete mitosis. This paper uses a couple methods (PLK4 inhibition and deletion of SASS6) to remove centrosomes from an immortalized cell line. Indeed, a strength of the paper is that similar results are obtained using both protocols to generate acentrosomal cells. The authors find acentrosomal cells take longer to divide, mostly due to a longer metaphase. The paper is based on the finding that inhibition of MPS1 results in a failure to divide, and the hypothesis that a SAC - dependent delay is required for these acentrosomal cells to divide.

The finding that MPS1 inhibition results in a failure to division is interesting. This is investigated by analyzing cells where AurB, APC or Eg5 (to generate monastral spindles) have been inhibited. My concerns are that the results are not conclusive that the effect of MPS1 is on cell cycle regulation. There is not enough data to make a conclusion as to why inhibition of MPS1 results in cell division failure.

1) An example is how to interpret the effect of Aurora B inhibition, which does not block acentrosomal cell division. If Aurora B is required for SAC activity, it suggests this effect of MPS1 may be a function other than SAC. Given the complexity of the SAC, it would be informative to test other SAC components. Instead, the authors conclude that the mitotic delay caused by MPS is required for acentrosomal cell division. I don't think they have ruled out, or even addressed other functions of MPS1.

2) The authors find that when both the APC and MPS1 are inhibited, the cells eventually divide. These results are intriguing, but hard to interpret. The authors suggest that the failure to divide in MPS1-inhibited cells is because they enter anaphase, and then must back out. This is hard to understand and there is not data supporting some kind of aborted anaphase. Is the division observed with double inhibition some sort of bypass of the block caused by MPS1 inhibition alone? It is not clear why inhibition of APC causes increased cell division when MPS1 is inhibited.

3) The authors characterize MTOC formation in these cells, which is also interesting. MTOCs are established after NEB in acentrosomal cells. Indeed, forming these MTOCs is probably a key mechanism for how these cells complete a division, like mouse oocytes.

Following this, the results with inhibiting Eg5 are interesting. The double inhibition of MPS1 and Eg5 results in division failure, like MPS1 inhibition in acentrosomal cells. Thus, there is a cell division block when the centrioles fail to divide. This result raises the possibility that failure to make a bipolar spindle, or the presence of a monopolar spindle, is the problem. In the absence of a bipolar spindle, a SAC induced delay is required for spindle assembly. This is a possibility but there are multiple interpretations of these results. Primarily, these results do not show the MPS1 effect on acentrosomal cells is SAC related. That a SAC mediated delay is required for acentrosmomal spindle assembly is not the only conclusion.

Reviewer #2 (Public Review):

This important work presents an example of a contextual computation in a navigation task through a comparison of task driven RNNs and mouse neuronal data. Authors perform convincing state of the art analyses demonstrating compositional computation with valuable properties for shared and distinct readouts. This work will be of interest to those studying contextual computation and navigation in biological and artificial systems.

This work advances intuitions about recent remapping results. Authors trained RNNs to output spatial position and context given velocity and 1-bit flip-flops. Both of these tasks have been trained separately, but this is the first time to my knowledge that one network was trained to output both context and spatial position. This work is also somewhat similar to previous work where RNNs were trained to perform a contextual variation on the Ready-Set-Go with various input configurations (Remington et al. 2018). Additionally findings in the context of recent motor and brain machine interface tasks are consistent with these findings (Marino et al in prep). In all cases contextual input shifts neural dynamics linearly in state space. This shift results in a compositional organization where spatial position can be consistently decoded across contexts. This organization allows for generalization in new contexts. These findings in conjunction with the present study make a consistent argument that remapping events are the result of some input (contextual or otherwise) that moves the neural state along the remapping dimension.

The strength of this paper is that it tightly links theoretical insights with experimental data, demonstrating the value of running simulations in artificial systems for interpreting emergent properties of biological neuronal networks. For those familiar with RNNs and previous work in this area, these findings may not significantly advance intuitions beyond those developed in previous work. It's still valuable to see this implementation and satisfying demonstration of state of the art methods. The analysis of fixed points in these networks should provide a model for how to reverse engineer and mechanistically understand computation in RNNs.

I'm curious how the results might change or look the same if the network doesn't need to output context information. One prediction might be that the two rings would collapse resulting in completely overlapping maps in either context. I think this has interesting implications about the outputs of the biological system. What information should be maintained for potential readout and what information should be discarded? This is relevant for considering the number of maps in the network. Additionally, I could imagine the authors might reproduce their current findings in another interesting scenario: Train a network on the spatial navigation task without a context output. Fix the weights. Then provide a new contextual input for the network. I'm curious whether the geometric organization would be similar in this case. This would be an interesting scenario because it would show that any random input could translate the ring attractor that maintains spatial position information without degradation. It might not work, but it could be interesting to try!

I was curious and interested in the authors choice to not use activity or weight regularization in their networks. My expectation is that regularization might smooth the ring attractor to remove coding irrelevant fluctuations in neural activity. This might make Supplementary Figure 1 look more similar across model and biological remapping events (Line 74). I think this might also change the way authors describe potential complex and high dimensional remapping events described in Figure 2A.

Overall this is a nice demonstration of state-of-the-art methods to reverse engineer artificial systems to develop insights about biological systems. This work brings together concepts for various tasks and model organisms to provide a satisfying analysis of this remapping data.

Reviewer #2 (Public Review):

Starting from the observation that difficulty estimation lies at the core of human cognition, the authors acknowledge that despite extensive work focusing on the computational mechanisms of decision-making, little is known about how subjective judgments of task difficulty are made. Instantiating the question with a perceptual decision-making task, the authors found that how humans pick the easiest of two stimuli, and how quickly these difficulty judgments are made, are best described by a simple evidence accumulation model. In this model, perceptual evidence of concurrent stimuli is accumulated and difficulty is determined by the difference between the absolute values of decision variables corresponding to each stimulus, combined with a threshold crossing mechanism. Altogether, these results strengthen the success of evidence accumulation models, and more broadly sequential sampling models, in describing human decision-making, now extending it to judgments of difficulty.

The manuscript addresses a timely question and is very well written, with its goals, methods and findings clearly explained and directly relating to each other. The authors are specialists in evidence accumulation tasks and models. Their modelling of human behaviour within this framework is state-of-the-art. In particular, their model comparison is guided by qualitative signatures which are diagnostic to tease apart the different models (e.g., the RT criss-cross pattern). Human behaviour is then inspected for these signatures, instead of relying exclusively on quantitative comparison of goodness-of-fit metrics. This work will likely have a wide impact in the field of decision-making, and this across species. It will echo in particular with many other studies relying on the similar theoretical account of behaviour (evidence accumulation).

A few points nevertheless came to my attention while reading the manuscript, which the authors might find useful to answer or address in a new version of their manuscript.

1. The authors acknowledge that difficulty estimation occurs notably before exploration (e.g., attempting a new recipe) or learning (e.g., learning a new musical piece) situations. Motivated by the fact that naturalistic tasks make difficult the identification of the inference process underlying difficulty judgments, the authors instead chose a simple perceptual decision-making task to address their question. While I generally agree with the authors's general diagnostic, I am nevertheless concerned so as to whether the task really captures the cognitive process of interest as described in the introduction. As coined by the authors themselves, the main function of prospective difficulty judgment is to select a task which will then ultimately be performed, or reject one which won't. However, in the task presented here, participants are asked to produce difficulty judgments without those judgements actually impacting the future in the task. A feature thus key to difficulty judgments thus seems lacking from the task. Furthermore, the trial-by-trial feedback provided to participants also likely differ from difficulty judgments made in real world. This comment is probably difficult to address but it might generally be useful to discuss the limitations of the task, in particular in probing the desired cognitive process as described in introduction. Currently, no limitations are discussed.

2. The authors take their findings as the general indication that humans rely on accumulation evidence mechanisms to probe the difficulty of perceptual decisions. I would probably have been slightly more cautious in excluding alternative explanations. First, only accumulation models are compared. It is thus simply not possible to reach a different conclusion. Second, even though it is particularly compelling to see untested predictions from the winning model in experiment #1 to be directly tested, and validated in a second experiment, that second experiment presents data from only 3 participants (1 of which has slightly different behaviour than the 2 others), thereby limiting the generality of the findings. Third, the winning model in experiment #1 (difference model) is the preferred model on 12 participants, out of the 20 tested ones. Fourth, the raw BIC values are compared against each other in absolute terms without relying on significance testing of the differences in model frequency within the sample of participants (e.g., using exceedance probabilities; see Stephan et al., 2009 and Rigoux et al., 2014). Based on these different observations, I would thus have interpreted the results of the study with a bit more caution and avoided concluding too widely about the generality of the findings.

3. Deriving and describing the optimal model of the task was particularly appreciated. It was however a bit disappointing not to see how well the optimal model explains participants behaviour and whether it does so better than the other considered models. Also, it would have been helpful to see how close each of the 4 models compared in Figures 2 & 3 get to the optimal solution. Note however that neither of these comments are needed to support the authors' claims.

4. The authors compared the difficulty vs. color judgment conditions to conclude that the accumulation process subtending difficulty judgements is partly distinct from the accumulation process leading to perceptual decisions themselves. To do so, they directly compared reaction times obtained in these two conditions (e.g. "in other cases, the two perceptual decisions are almost certainly completed before the difficulty decision"). However, I find it difficult to directly compare the 'color' and 'difficulty' conditions as the latter entails a single stimulus while the former comprises two stimuli. Any reaction-time difference between conditions could thus I believe only follow from asymmetric perceptual/cognitive load between conditions (at least in the sense RT_color < RT_difficulty). One alternative could have been to present two stimuli in the 'color' condition as well, and asking participants to judge both (or probe which to judge later in the trial). Implementing this now would however require to run a whole new experiment which is likely too demanding. Perhaps the authors could instead also acknowledge that this a critical difference between their conditions, which makes direct comparison difficult.

Reviewer #2 (Public Review):

The authors investigated the role of the Jak1-Stat1 signaling pathway in myogenic differentiation by screening the transcriptional targets of Jak1-Stat1 and identified Styxl2, a pseudophosphatase, as one of them. Styxl2 expression was induced in differentiating muscles. The authors used a zebrafish knockdown model and conditional knockout mouse models to show that Styxl2 is required for de novo sarcomere assembly but is dispensable for the maintenance of existing sarcomeres. Styxl2 interacts with the non-muscle myosin IIs, Myh9 and Myh10, and promotes the replacement of these non-muscle myosin IIs by muscle myosin IIs through inducing autophagic degradation of Myh9 and Myh10. This function is independent of its phosphatase domain.

A previous study using zebrafish found that Styxl2 (previously known as DUSP27) is expressed during embryonic muscle development and is crucial for sarcomere assembly, but its mechanism remains unknown. This paper provides important information on how Styxl2 mediates the replacement of non-muscle myosin with muscle myosin during differentiation. This study may also explain why autophagy deficiency in muscles and the heart causes sarcomere assembly defects in previous mouse models.

Reviewer #2 (Public Review):

The C. elegans embryo has been model system of study for more than 30 years because of the ease of doing forward and reverse genetics, coupled with its nearly invariant lineage which allows a description of development at high resolution. 4D time lapse imaging coupled with spatially resolved gene expression has enabled identification of transcriptional signatures of cells in space and time, and in the past decade this has been advanced with single-cell transcriptomics methods, using individually isolated embryonic cells (which can retain their identity) or by deconvolving complex mixtures of early cells. Recent work using these methods has resolved spatiotemporal expression patterns for many genes, defining cells up to gastrulation stage, but then changing to more tissue-specific patterns during morphogenesis. A key paradigm of specification in C. elegans and other systems is that early maternal factors initiate or restrict patterns of transcription factor expression from the zygotic genome. Combinatorial expression patterns and some symmetries broken by autonomous or extrinsic cell inductions ultimately program lineages towards their fates. To date, only simple networks have been elucidated, as the increasing complexity of these networks and the high level of redundancy has made functional dissection of such pathways difficult. Hence, almost all of the work in recent years has been descriptive.

In this work the authors fill a knowledge gap from the early embryo (~16 cells) to the ~100-cell stage and describe new patterns of gene expression. They reconcile their findings with that of others who have defined expression patterns using other methods, such as scRNA-Seq from complex mixtures of cells, and from transcription factor expression analyses. The resulting description of embryonic develop is the most precise to date, and offers a potentially useful resource for other researchers.<br /> The authors attempt to use their results to find patterns of gene expression that could hint at phylogenetic conservation of specification mechanisms. They find some supporting evidence that expression of homeobox genes occurs in anterior-posterior stripes, which recalls the elaborate A/P patterning system elucidated in the Drosophila embryo, which belongs to the sister phylum Arthropoda in the Ecdysozoan clade of molting animals. It felt as if the authors chose the Hox genes they need to support this conclusion.

Some caveats exist to the work. The expression patterns seem to be well-validated, and following prior work from the Yanai group are likely to be strongly correlated with expression in living embryos. When cells are separated, they could lose some expression patterns that require cell-cell interactions, so it is expected that there might be a small minority of expression patterns that are more complex than what has been documented here.

A major caveat is the idea of the stripes of Hox expression. I just did not find these arguments to be compelling. Seeing these 'stripes' requires organizing the data in a way that maximizes their appearance, for one. Since there is not a lot of movement of cells away from their birth in the early embryo, the AB descendants are anterior to those of MS, anterior to those of E, anterior to those of C, D, and P4. Lineage-specific expression will just naturally fall into 'stripes'. Second, the conservation of Hox expression patterns typically comes with collinearity of the genes along the length of a chromosome (i.e. the so-called Hox clusters) with expression along the body axis, as well as posterior-to-anterior fate transformations when Hox specification is disrupted.

A minor note is the detection of an enrichment of GATA factors in the early E lineage. This has now been found to be a derived condition even within the genus (see Broitman-Maduro et al. Development 149 (21): dev200984, as other species like C. angaria show only a simpler network of elt-3 -> elt-2. This suggests that many of the other patterns of gene expression, particularly in the early embryo, could be highly derived as well; some caution is warranted in generalizing the results as being conserved with arthropods as some of this could be convergent.

Given what the authors are proposing about Hox stripes, some omissions of prior work were surprising (or maybe I missed them). For example, a comprehensive study of Hox genes in C. elegans by Hench et al. (2015) (PLoS One 10(5): e0126947) evaluated all the homeobox genes and examined their genomic locations and expression patterns in the embryo at high spatiotemporal resolution. Work from the Hobert lab (Nature 2020, 584(7822):595-601) showed how homeobox codes specify classes of C. elegans neurons, and the Murray lab (PLoS Genet. 18(5):e1010187) examined Hox control of posterior lineage specification at high resolution, with functional assays.

The Discussion section of the paper is brief, consistent with the descriptive nature of the work overall, but it would have been nice to see the findings related to other published studies as indicated above.

Reviewer #2 (Public Review):

The study by Acosta et al. is very interesting as it presents a simple and easy method for identifying live and dead bacteria DNA in the skin - PMA labeling, verified by FISH. This study provides several meaningful conclusions that could inform future skin microbiome studies:

Firstly, the 16s rRNA gene sequencing of skin microbial samples collected by cotton swabs may include DNA from a large number of dead bacteria, leading to an over-representation of skin bacteria in the analysis.

Secondly, the study found that there were fewer live bacteria on the skin surface than the detected bacterial DNA predicted, with most skin bacteria harboring in the hair follicles. This conclusion aligns with the physiological properties of the skin, as the hair follicle epithelium creates a moist, nutrient-rich, low-UV, and immune-privileged environment, which is conducive to the growth, colonization, and development of microorganisms.

Finally, the authors propose that the bacteria on the skin surface originate from the proliferation and replenishment of hair follicle resident bacteria, which could be one reason for the short-term instability and long-term stability of the skin microbiome.

Overall, this study provides valuable insights into the composition and distribution of skin bacteria and highlights the importance of using appropriate methods to identify live bacteria in skin microbiome studies.

Reviewer #2 (Public Review):

The findings reported by Diaz-Vegas et al. extend those described in a previous paper from the same group establishing a role for mitochondrial CoQ depletion in the development of insulin resistance in muscle and adipose tissue (Fazakerley, 2018). In this new report, investigators sought to determine whether CoQ depletion contributes to insulin resistance caused by palmitate exposure and/or intracellular ceramide accumulation. To this end, researchers employed a widely used in vitro model of insulin resistance wherein L6 myocytes develop impaired Glut4 translocation upon exposure to palmitate (in this case, 150 uM for 16 hours). This model was combined with a variety of pharmacologic and genetic manipulations aimed at augmenting or inhibiting CoQ biosynthesis and/or ceramide biosynthesis, specifically in mitochondria. This series of experiments produced a valuable and provocative body of evidence positioning CoQ depletion downstream of mitochondrial ceramide accumulation and necessary for both palmitate- and ceramide-induced insulin resistance in L6 myocytes. Investigators concluded that mitochondrial ceramides, CoQ depletion and respiratory dysfunction are part of a core pathway leading to insulin resistance.

Strengths:

The study provides exciting, first-time evidence linking palmitate-induced insulin resistance to ceramide accumulation within the mitochondria and subsequent depletion of CoQ. Ceramide accumulation specifically in mitochondria was found to be necessary and sufficient to cause insulin resistance in cultured L6 myocytes.

The in vitro experiments featured a set of mitochondrial-targeted genetic manipulations that permitted up/down-regulation of ceramide levels specifically in the mitochondrial compartment. Genetically induced mitochondrial ceramide accumulation led to CoQ depletion, which was accompanied by increased ROS production and diminution of ETC proteins and OXPHOS capacity and impaired insulin action, thereby establishing cause/effect.

Analysis of mitochondria isolated from human muscle biopsies obtained from individuals with disparate metabolic phenotypes revealed a positive correlation between complex I proteins and insulin sensitivity and a negative correlation with mitochondrial ceramide content. While it is likely that many factors contribute to these correlations, the results support the possibility that the ceramide/CoQ mechanism might be relevant to glucose control in humans.

These important findings offer valuable new insights into mechanisms that connect ceramides to insulin resistance and mitochondrial dysfunction, and could inform new therapeutic approaches towards improved glucose control.

Weaknesses:

The mechanistic aspect of the work and conclusions put forth rely heavily on studies performed in cultured myocytes, which are highly glycolytic and generally viewed as a poor model for studying muscle metabolism and insulin action. Nonetheless, the findings provide a strong rationale for moving this line of investigation into mouse gain/loss of function models.

One caveat of the approach taken is that exposure of cells to palmitate alone is not reflective of in vivo physiology. It would be interesting to know if similar effects on CoQ are observed when cells are exposed to a more physiological mixture of fatty acids that includes a high ratio of palmitate, but better mimics in vivo nutrition.

While the utility of targeting SMPD5 to the mitochondria is appreciated, the results in Figure 5 suggest that this manoeuvre caused a rather severe form of mitochondrial dysfunction. This could be more representative of toxicity rather than pathophysiology. It would be helpful to know if these same effects are observed with other manipulations that lower CoQ to a similar degree. If not, the discrepancies should be discussed.

The conclusions could be strengthened by more extensive studies in mice to assess the interplay between mitochondrial ceramides, CoQ depletion and ETC/mitochondrial dysfunction in the context of a standard diet versus HF diet-induced insulin resistance. Does P053 affect mitochondrial ceramide, ETC protein abundance, mitochondrial function, and muscle insulin sensitivity in the predicted directions?

Reviewer #2 (Public Review):

This report describes a large-scale analysis of cell counts in mouse brains. The authors found that the Allen Mouse Connectivity project has a rich dataset for cell counting that is yet to be analyzed, and they developed methods to quantify cells in different nuclei. They go on to compare males vs females and two different strains. From this analysis, they found specific differences between male versus female brains, left versus right hemispheres, and C57BL/6 versus FVB.CD1 mice, especially with regard to cell counts and density.

Overall, the methodology is sound and the quality of the data seems high. In fact, this study uses >100 brains for the statistics, and this is one of the major strengths of this study. For researchers who are interested in interrogating the differences at the macroscopic level in brain structures, this study will be a great resource. For example, the manuscript contains an interesting finding that for most brain areas, females have larger volumes but fewer cell numbers.

Reviewer #2 (Public Review):

In this manuscript, the authors use generative adversarial networks (GANs) to manipulate neural data recorded from intracortical arrays in the context of intracortical BCIs so that these decoders are robust. Specifically, the authors deal with the hard problem where signals from an intracortical array change over time and decoders that are trained on day 0 do not work on day K. Either the decoder or the neural data needs to be updated to achieve the same performance as initially. GANs try to alter the neural data from day K to make it indistinguishable to day 0 and thus in principle the decoder should perform better. The authors compare their GAN approach to an older GAN approach (by an overlapping group of authors) and suggest that this new GAN approach is somewhat better.

Major Strengths are multiple datasets from behaving monkeys performing various tasks that involve motor function. Comparison between two different GAN approaches and a classical approach that uses factor analysis. The weakness is insufficient comparison to another state-of-the-art approach that has been applied on the same dataset (NoMAD, Karpowicz et al. BioRxiv 2022).

The results are very reasonable and they show their approach, Cycle GANs, does slightly better than the traditional GAN approach. However, the Cycle GANs have many more modules and also as I understand it performs a forward backward mapping of the day - 0 and day - k and thus theoretically better. But, it seems quite slow.

I think the results are interesting but as such, I am not sure this is such a fundamental advance compared to the Farashcian et al. paper, which introduced GANs to improve decoding in the face of changing neural data. There are other approaches that also use GANs and I think they all need to be compared against each other. Finally, these are all offline results and what happens online is anyone's real guess. Of course, this is not just a weakness of this study but many such studies of its ilk.

Reviewer #2 (Public Review):

In this interesting study, Wang et al. demonstrated a critical role of the key focal adhesion protein vinculin in the control of bone mass in mice. Specifically, the authors deleted vinculin expression by using the mouse 10-kb Dmp1-Cre transgenic mice that were reported to primarily target osteocytes and mature osteoblasts. The authors found that vinculin loss in these cells caused severe osteopenia in mice due to impairment of osteoblast and bone formation with minimal impact on osteoclast formation and bone resorption. Interestingly, the vinculin loss also reduced the mechanical loading induction of bone formation in mice. Mechanically, the authors found that vinculin knockdown increased, while vinculin overexpression decreased, sclerostin expression in osteocytes without affecting that of Mef2c, a major transcriptional regulator of the Sost gene, which encodes sclerostin. Mechanistically, the authors found that vinculin protein bound to Mef2c and vinculin loss increased Mef2c nuclear translocation and binding to the Sost enhancer ECR5. Deleting Sost expression largely reversed the osteopenic phenotypes caused by vinculin deletion. Finally, the authors demonstrated that estrogen promoted vinculin expression in osteocytes and that vinculin loss abolished the estrogen deficiency induction of bone loss in mice. In this study, with a tremendous amount of convincing in vitro and in vivo data, the authors have established a critical role of vinculin in bone and defined a novel mechanism that regulates bone mass. The findings from this study are important and interesting.

Reviewer #2 (Public Review):

This study used direct recording from the soma, the terminal and the postsynaptic cell in cerebellar inter-neuron- Purkinje cell synapses. The authors nicely showed that action potentials travel reliably from the soma to the axon. In addition, they showed that the postsynaptic responses elicited at the dendrites reliably traveled along the axon. Such sub-threshold potential could potentiate transmitter release in short-term (for tens of ms at most), by "priming" Ca channels and accelerating activation kinetics of Ca channels. Results are based on the technically demanding electrophysiological technique and are in general. The study directly solves the mechanism of short-term facilitation induced by sub-threshold depolarization.

Reviewer #2 (Public Review):

In this study, the authors aim to uncover the neuroanatomical and metabolite underpinnings of an intriguing phenomenon observed in some insects due to the infection of fungal pathogens. They very cleverly develop a high-throughput assay to examine and quantify this behaviour in a tractable model organism - Drosophila melanogaster which the authors have previously shown to also exhibit this phenomenon. They characterize the details of this behaviour and clearly show the temporal gating of this summiting-followed-by-death behavior to occur shortly before the dusk transition. They go on to examine using a candidate (over 200) screen approach potential neuronal circuits and genes based on the hypothesis that they may be related to 'arousal and gravitaxis'. They narrow down to a line that is restricted to the PI based on the fact that it has a significant effect on the summiting behaviour and that it is known to affect locomotion. They can demonstrate that flies when a subset of PI neurons (R19G10) are transiently activated, they will show summiting even without exposure to the pathogen. Based on Syt-eGFP staining they conclude that PI communicates with the carpora cardiaca (CA). They also show that CA itself is necessary for this behavior, but cannot demonstrate the role of Juvenile hormones using their pharmacological methods.

The authors then describe an automated classifier to identify an upcoming summiting behaviour. Further, they use this real-time classifier to stage different steps of the summiting and match it to the extent of pathology observed by microscopy. They also ask whether the constituents of the hemolymph differ between the summiting and not-yet summiting flies for which they conduct metabolome analysis of the hemolymphs. They are also able to show that cross-injection of uninfected or infected but not summiting flies can be induced to show summiting-like behaviour upon injection with the hemolymph.<br /> Finally, they propose the sequence by which the fungal pathogen may modulate the behaviours of the host fly so as to execute this highly gated act of increased locomotion prior to death.

Strengths<br /> • The detailed characterization of the behaviour in D melanogaster and development of the high-throughput behavioural arena.<br /> • Development of the automated classifier which appears to accurately predict this behaviour.<br /> • Narrowing down to a small group of PI neurons having a strong impact on this behaviour although sufficiency is not clearly demonstrated.

Weaknesses<br /> • The evidence of temporal (circadian) gating is weak despite the proposed DN1p - PI - CA connections.<br /> • The eventual modification of the behavior to enable enhanced locomotion and negative geotaxis to occur appears to be mediated by yet unknown factors<br /> • The metabolite analysis did not help to narrow down to candidates that can be speculated to cause this behaviour.

Reviewer #2 (Public Review):

In this manuscript, Gochman et al. studied the molecular mechanism by which cannabidiol (CBD) sensitizes the TRPV2 channel to activation by 2-APB. While CBD itself can activate TRPV2 with low efficacy, it can sensitize TRPV2 current activated by 2-APB by two orders of magnitude. The authors showed, via single-channel recording, that the CBD-dependent sensitization arises from an increase in Po when the channel binds to both CBD and 2-APB. The authors then used cryo-EM to investigate how CBD binds to TRPV2 and identified two CBD binding sites in each subunit, with one site being previously reported and the other being newly discovered.

TRPV1 and TRPV2 are two channels closely related to TRPV2. All three channels can be activated by CBD and 2-APB, but only TRPV2 and 3 are strongly sensitized by CBD. To understand the molecular basis of the different sensitivity to CBD, the authors compared the residues within the CBD binding sites and generated mutants by swapping non-conserved residues between TRPV1 and TRPV2. They then performed patch-clamp recordings on these mutants and found that mutations on non-conserved residues indeed influenced the CBD-dependent sensitization, thereby supporting the observed CBD binding sites.

Unexpectedly, the authors did not identify the binding site of 2-APB, despite its robust effect in electrophysiology recordings, especially when combined with CBD. Although previous structural studies of TRPV2 have reported 2-APB binding sites, the associated densities in these studies were not well-resolved. Therefore, the authors called on the field to re-examine published structural data with regard to the 2-APB binding sites.

Overall, this is an important study with well-designed and well-conducted experiments.

Reviewer #2 (Public Review):

The manuscript is well written, the data are based on well-performed experiments, and the conclusions are supported by the data. The authors study thoroughly the global phenotype of T and NK cells and also analyze antigen-specific T cell frequencies. The data confirm that individuals who had severe COVID-19 disease (required ventilation and/or ITU admission) have slightly more activated CD4 and CD8 T cells at 3 months post-infection and report more frequently long COVID symptoms, yet the novelty of this manuscript is to show that these two are not linked to each other. Moreover, the manuscript confirms that patients across all disease severities mount and maintain memory T cell and antibody responses to SARS-CoV-2.

In the introduction, the authors want to highlight the extent of patients who suffer from long COVID symptoms, yet it should be noted that these high frequencies (8-21%) are coming from unvaccinated and hospitalized patients (like those included in this study), while a large group of individuals experience asymptomatic SARS-CoV-2 infection, and these individuals are not integrated into these studies.

The authors find that patients who recovered from severe COVID-19 3 months ago have more activated CD4+ and CD8+ T cells than patients who recovered from the mild disease. Although the difference is significant, the frequency of CD4+ T cells with an activated phenotype is increased only by about 2-fold (~2% vs ~1%), while the frequency of activated CD8+ T cells is about 6% vs 4%, which should be added to the results to better describe the extent of the activation.

As the authors mention in the discussion, it cannot be excluded that the more activated T cell phenotype in patients who recovered from severe COVID-19 is not rather a consequence of the increased comorbidities associated with this group. However, their Luminex analysis of the serum shows that the levels of cytokines TNF-a, IL-4, IL-12, IL-15, and IL-17A decline by 8 and 12 months, suggesting that the immune activation by 3 months is most likely a consequence of the previous severe viral infection.<br /> To strengthen this point, PBMC is probably not available at a later time point, to see if the increased T cell activation decreases in line with the serum cytokines. Yet, the authors should at least try to repeat the experiments of coculturing CD3+ T cells from healthy volunteers with the serum of mild/severe patients at 8-12 months post-recovery (Fig. 3 D-E).

The authors tried to find if the activated T cell phenotype or increased serum cytokines at 3 months post-infection is linked with increased long COVID symptoms. The study does not find any direct association when the data are adjusted for age, sex, and severity. This is the only novelty of this study, yet it is an important piece of information in the attempt to broaden our understanding of the underlying causes of long COVID symptoms.

Overall, it would be important to understand if increased frequencies of T cell activation (~2-fold) and increased levels of serum cytokines at 3 months following severe COVID-19 that resulted in ventilation and/or ITU admission is specific to severe SARS-CoV-2 infection, or if similar consequences are resulting also from other severe acute viral infections. Addressing this question is beyond the scope of the manuscript, yet it should be discussed.

Reviewer #2 (Public Review):

This report uses massively parallel reporter assays to examine the impact on gene expression of >2000 uORFs found in yeast mRNAs with 5'UTR lengths <181nt, by comparing expression of two YFP reporters for each uORF, one containing the WT 5'UTR and the other with the uORF AUG codon mutated to a near-cognate AAG triplet. All of the mRNAs were expressed from the same promoter from the ENO2 gene, which is expected to produce the predicted 5' ends for all of the mRNAs being sampled. The results indicated that most AUG uORFs are repressive, while most nonAUG (near-cognate) uORFs have little effect on reporter expression; and a small fraction of AUG uORFs are stimulatory to YFP expression. They corroborated these results by sequencing the reporter library mRNAs in polysome vs monosome fractions and showing a good correlation (R=0.78) between the effects of the uORF AUG mutations on YFP expression versus fraction of the mRNA in polysomes. The reporter library was assayed in in both WT and upf1 mutants to evaluate the impact of NMD on uORF regulation of reporter expression and polysome association, which allowed them to determine that, on average, NMD accounts for ~35% of the uORF-mediated repression of reporter expression, ie. the magnitude of the repression is blunted in the upf1 mutant. Consistent with this, the reductions in YFP expression are frequently associated with reductions in reporter mRNA levels, measured by RNA-seq. Moreover, the repressive effects of the uORFs calculated from YFP expression versus polysome association of reporter mRNAs are more congruent in the upf1 mutant where NMD effects are absent versus the WT. Their bioinformatic analyses provide some evidence that NMD control is lessened by inefficient termination at uORFs with UGAC stop codons, for long vs. short uORFs, and by decreasing the distance of the uORF stop codon from the mRNA cap. Their large dataset allowed them to conduct machine learning to identify features of uORFs that are associated with their effects on YFP expression, finding that repression by the uORF is associated about equally with a good Kozak context for the start codon, a shorter distance of the uORF from the cap, and shorter distance of the uORF stop codon to the downstream CDS, with a somewhat weaker association with a longer uORF CDS. These findings for Kozak context were predictable from prior work, as were the associations with uORF length and distance to the YFP AUG in the context of known effects of these parameters on reinitiation. However, the association with distance of the uORF from the cap is more novel. They provide some additional support for the latter by analyzing the influence of different TSSs/5'UTR lengths on uORF repressive function for a subset of 333 uORFs, finding that the repressive effect can vary depending on the TSS, with several instances in which the uORF was less inhibitory when the TSS is located further upstream from the uORF AUG. Finally, they provide some evidence that uORFs conserved between closely related yeast species are generally less repressive and have poorer AUG contexts, leading to the conclusion that they are under purifying selection to make them less inhibitory.

This study is valuable in providing an unprecedented, comprehensive analysis of the regulatory effects of naturally occurring AUG and near-cognate uORFs on gene expression in a manner that distinguishes between repression of translation versus repression of mRNA stability via NMD. Owing to the large number of uORFs analyzed in a system that eliminates variations in transcription rate, it was possible to identify certain statistically significant associations between uORF features and the extent to which they repress translation or evoke NMD.

There are several areas in which the authors' claims or conclusions are not fully justified and require either additional statistical analysis or new experimentation to support the claims. In particular, additional experiments are needed to confirm that the reporter mRNAs initiate at the predicted TSS; to bolster the novel conclusion that moving a uORF farther from the cap reduces its inhibitory effect on translation initiation downstream, independently of the inclusion of other uORFs in the intervening interval; and to test their interpretations concerning the differences in uORF function between S. cerevisiae and S. paradoxus for particular mRNAs.

Reviewer #2 (Public Review):

Despite the fact that CTLA-4 is a critical molecule for inhibiting the immune response, surprisingly individuals with heterozygous CTLA-4 mutations exhibit immunodeficiency, presenting with antibody deficiency secondary to B cell loss. Why the loss of a molecule that regulates T cell activation should lead to B cell loss has remained unclear. In this study, Muthana and colleagues use an anti-CTLA-4 antibody drug conjugate (aCTLA-4 ADC) to delete cells expressing high levels of CTLA-4, and show that this leads to a reduction in B cells. The aCTLA-4 ADC is found to delete a subset of Tregs, leading to hyperactivation of T cells that is associated with B cell depletion. Using blocking antibodies, the authors implicate TNFa in the observed B cell loss.

The reciprocal regulation of T and B cell homeostasis is an important research area. While it has been shown that Treg defects are associated with B cell loss, the mechanisms at play are incompletely understood. CTLA-4 is not normally expressed in B cells so an indirect mechanism of action is assumed. The authors show that the decrease in Treg following aCTLA-4 ADC treatment is associated with activation of T cells, and that B cell loss is blunted if T cells are depleted. A role for both CD4 and CD8 T cells is identified by selective CD4/CD8 depletion. T cells appear to require CD28 costimulation in order to mediate B cell loss, since the response is partially inhibited in the presence of the costimulation blockade drug belatacept (CTLA-4-Ig). Finally, experiments using the anti-TNFa antibody adalimumab suggest a potential role for TNFa in the depletion of B cells.

While the manuscript makes a useful contribution, a number of questions remain. Perhaps most important is the extent to which this model mimics the natural situation in individuals with CTLA-4 mutations (or following CTLA-4-based clinical interventions). aCTLA-4 ADC treatment permits acute deletion of Treg expressing high levels of CTLA-4, whereas in patients the Treg population remains but is specifically impaired in CTLA-4 function. Secondly, although the requirement for T cells to mediate B cell loss is convincingly demonstrated, the incomplete reversal by TNFa blockade suggests additional unidentified factors contribute to this effect. Finally, although the manuscript favours peripheral killing of mature B cells over alterations to B cell lymphopoiesis, one concern is that this may simply reflect the model employed: the short-term (6 day) treatment used here may be too acute to alter B cell development, but this may nevertheless be a feature of prolonged immune dysregulation in humans.

Reviewer #2 (Public Review):

The manuscript by Kaneko set out to understand the mechanisms underlying cell proliferation in hepatocytes lacking Shp2 signals. To do this, the authors focused on CD133 as the proliferating clusters of cells in the Shp2 knockout (SKO) livers are CD133 expressing. After excluding the contribution of progenitors that are CD133 to this cell population, the authors focused on the intrinsic regulation of CD133 by Met/Shp2 regulated Ras/Erk parthway and showed upregulation of CD133 to be a compensatory signal to overcome loss of Ras/Erk signal and suggested Wnt10a in the regulation of CD133 signal. The study then focused on the observed filament localization of CD133 in the CD133+ cluster of cells. The study went on to identify the CD133+ vesicles that contain primarily mRNA vs. microRNA like other EVs. Specifically, the authors identified several mRNA species that encode IEGs, indicating a potential role for these CD133+ vesicles in cell proliferation signal transmission to neighboring cells via delivery of the IEG mRNAs as cargos. Finally, they showed that the induction of CD133 (and by derivative, the CD133+ vesicles) are necessary for maintaining cell proliferation in the cell cluster with high proliferation capacities in the SKO livers; and in intestinal crypt organoids treated with Met inhibitors to block Ras/ERk signal.

1) The identification of CD133+ vesicles is largely based on staining and costainings. Though the experiments are very well done with many controls and approaches, the authors may want to perform one or two key experiments with EM to definitively demonstrate the colocalization. For example, the mCherry experiment in Fig6H and the colocalization experiments for CD133 and HuR in Fig 7.

2) Since CD133+ marks the 50nM intracellsome defined by the authors, it is unclear what the CD133- vesicles used as controls are. Are they regular EVs that are larger in size? This needs better clarification as they are used as a control for many experiments such as Fig 7A.

Reviewer #2 (Public Review):

I agree that minor genetic variation could potentially be used to more accurately infer who-infected- whom in an outbreak scenario. Indeed, the use of minor genetic variation has proven very useful in reconstructing transmission chains for chronic infections such as HIV (e.g., see applications using Phyloscanner). To me, it seems that considering the full spectrum of viral genetic diversity within infected hosts would necessarily do the same if not better than considering only consensus-level viral sequence data. This is because there is a necessarily a loss of data and potentially a loss of information when going from considering the genetic composition of viral populations within a host to only considering the consensus sequences of those viral populations. As such, Ortiz et al.'s hypothesis stated on lines 66-70 is a reasonable one, and I was looking forward to seeing this hypothesis evaluated in detail in this manuscript.<br /> There are several parts of this manuscript I really like. In particular, encoding within-sample diversity as character states and using that alternative representation of sequence data for phylogenetic inference (as shown in Figure 3) is a very interesting idea, I think. There are some limitations that are not explicitly mentioned, however. For example, when using this 16-character state representation for phylogenetic inference, they assume independence between nucleotide sites. This is a major assumption that can be violated when considering longitudinal intrahost data and transmission dynamics in an outbreak setting, given genetic linkage between sites.

I have several major concerns about the work as it stands, particularly in the context of the SARS-CoV-2 application.

Concerns not related to the SARS-CoV-2 application:<br /> Concern #1: Figure 4 shows that a model using within-sample diversity can more accurately reconstruct evolutionary histories than a model that uses only consensus-level genetic data. This is really interesting. The Materials and Methods section (particularly lines 351-354) indicates that the sequence data were generated using certain specified substitution rates. The rates specified seem to be chosen in such a way to facilitate finding an improvement when using within-sample diversity. I don't know whether the relative rates of these 'substitutions' at all mirror "real-life". It would be very useful to have a broader set of analyses here to examine the effect of these 'substitution' rates on the utility of incorporating within-sample diversity into phylogenetic inference. (Also, 1, 100, 200 (line 353) inconsistent with 1, 20, 200 in Supp Table 3)

Concern #2: Figure 5 is very interesting, particularly the results at bottleneck sizes of 1-10. What are the 'substitution' rates that are inferred here from using this simulated dataset? The Material and Methods section also does not mention the within-host viral generation time anywhere, as far as I can see (~line 384 states the mutation rate per base per generation cycle but not the length of the generation cycle anywhere).

Concerns related to the SARS-CoV-2 application:<br /> Concern #3: I am very concerned about the testing of this hypothesis on the SARS-CoV-2 data presented. First, 1% is a very low variant calling threshold. Second, analysis of the 17 samples that were resequenced (out of 454) indicated that on average, 39% of iSNVS (intrahost single nucleotide variants) called between duplicate runs were only observed in one of the two runs (line 117). Their analysis in Figure 1 indicates that these discrepant (and seemingly spurious) variants occur at higher levels in high Ct samples (which makes sense; Figure 1b). They therefore decide to limit their analyses to samples with Ct values <= 30. This results in 249 samples. However, if we look at Figure 1b, only ~10% of iSNVs called across duplicate runs with Ct = 30 are shared! That means that 90% of iSNVs in the set appear to be spurious. If we assume that each duplicate run of a sample has approximately the same number of spurious iSNVs, then approximately 82% of iSNVs called in a sample with a Ct of 30 would be spurious. This fraction decreases with samples that have lower Ct values, but even at a Ct of 27, only ~60% of iSNVs called across duplicate runs are shared. All the downstream SARS-CoV-2 analyses based on within-host sample diversity therefore are based on samples where the large majority of considered sample diversity is not real. This leads to me necessarily discounting all of those downstream SARS-CoV-2 results.

Concern #4: Lines 153-167: I can't figure out how to square the quantitative results given in this paragraph with what is shown in Figure 2. To me, Figure 2 shows only that Technical Replicates have higher probabilities of sharing a variant than with 'No' relationship. What would also be helpful here so that the reader can get a better feel for the data would be to see the iSNV frequencies plotted over time for the longitudinal replicate samples in the supplement and, for the 'epidemiological' samples to show 'TV plots' in the supplement (as in Fig 3c in McCrone et al. eLife)

Concern #5: Figure 6 and associated text: (a) root-to-tip distance: what units is this distance in? (b) That the authors find a temporal signal in these transmission clusters (where all consensus sequences within a cluster are the same) is interesting but also a bit baffling to me. Given the inference of very small transmission bottlenecks in previous studies (e.g., Martin & Koelle - reanalysis of Popa et al.; Lythgoe et al.; Braun et al.), I don't understand where the temporal signal comes in. Do the samples become more genetically diverse over the outbreak (this seems to be indicated in lines 260-262 but never shown and unlikely given bottleneck sizes)? Additional analyses to help the reader understand WHY within-sample diversity allows for the identification of temporal signal is important. This could involve plotting genetic diversity of the samples by collection date or some other, similar analyses.

Concern #6: Paragraph consisting of lines 229-238 and Figure 7: This analysis stops abruptly. What are the conclusions here? Figure 7a (right) seems inconsistent to me with Figure 7b and 7C results. Also, the main hypothesis put forward in this paper is that within-sample sequence data can better resolve who-infected-whom in an outbreak setting. Figure 7b and 7c however are never compared against analogous panels that use just consensus sequences. (Even though the consensus sequences are the same, according to Figure 7a, the inferences shown in Figures 7b and 7c could use additional data such as collection times, etc. that would provide information even when using exclusively consensus-level data). Also, do the analyses in Figures 7b and 7c use the 16-character state model at all? I think Supp Figure 9 is relevant here but not sure how?)

Additional concerns:<br /> Concern #7: Some of the stated conclusions, particularly in the Discussion section and in the Abstract, do not seem to be supported by the presented results. For example, line 27: 'within-sample diversity is stable among repeated serial samples from the same host': Figure 2 does not show this conclusively. Line 28: 'within-sample diversity... is transmitted between those cases with known epidemiological links': Figure 2 also does not show this conclusively. Line 29: 'within-sample diversity... improves phylogenetic inference and our understanding of who infected whom': Figure 7b/c results using within-sample diversity is never compared against results that use only consensus, so improvement not demonstrated. Line 272-273: 'samples with shorter distance in the consensus phylogeny were more likely to share low frequency variants'. Line 287: 'We demonstrated that phylogenies... were heavily biased'.

Concern #8: The manuscript at times does not cite previous work that is highly relevant and thus overstates the novelty of the current work. For example: lines 21-23: '..conventional whole-genome sequencing phylogenetic approaches to reconstruct outbreaks exclusively use consensus sequences...' Phyloscanner uses within-sample diversity, for example, as does SCOTTI. These are finally cited in the discussion section (~line 310), but because this previous work is not acknowledged earlier in the manuscript, the novelty of the work presented here is somewhat overstated.

In sum, I think that the 16 character-state model is a very interesting model. More analyses on simulated data would be helpful to expand on when below-the-consensus level genetic data would truly be informative of phylogenetic relationships and who-infected-whom in outbreak settings. The SARS-CoV-2 analyses are very worrisome to me, given the inclusion of samples where the majority of considered within-sample genetic diversity is very likely not real. Some of the stated conclusions appear to either be at odds with the results presented or not directly evaluated.

Reviewer #2 (Public Review):

Targeted genetic engineering with programmable nucleases and other targetable enzymes (aka "genome editing") has emerged as a technology with curative potential in hemoglobinopathies, sickle cell disease, and beta-thalassemia. Multiple ongoing clinical trials are evaluating such editing using distinct approaches: elevation of fetal hemoglobin (HbF), direct repair of the mutation causing SCD, and engineering of a Hb variant. The present work explores a different strategy: the targeted engineering of the promoter of a paralog of adult beta-globin known as HBD. This is a timely effort because there has emerged, over the past decade, a clear and charted path for advancing any such approach to human clinical trials. The study identifies three transcription factor binding sites as divergent in the HBD promoter vs the HBB one. A homology-directed repair (HDR)-based scheme using oligonucleotide repair templates in combination with a CRISPR-Cas9-induced double-strand break (DSB) is designed and used to generate pools of human immortalized cells bearing one, two, or all three such de novo introduced TF binding sites at the HBD promoter. Only the latter scheme is shown to measurably increase HBD (following erythroid differentiation) in pools of cells and single-cell-derived clones as gauged by qPCR and HPLC. A similar analysis is performed on pools of erythroid-like cells generated from genome-edited human hematopoietic stem and progenitor cells (HSPCs), as well as genetically clonal erythroid colonies bearing the edits of interest; trends in these data support the observations made on the immortalized cells. Overall the data support the notion that HBD promoter genome editing has the potential as a strategy to normalize hemoglobin synthesis in hemoglobinopathies. Further, the data support an advance of this approach down a well-established path of preclinical development in such cases: increasing the efficiency of genome editing in HSPCs to what would be deemed therapeutically useful, assessing the genotoxic burden from the editing, evaluating the potential negative impact on stemness, and determining whether this approach would normalize hemoglobin synthesis in the erythroid progeny of patient HSPCs.

The genome editing scheme for the "KDT" strategy in Fig 1B involves the introduction of three binding sites for transcription factors at progressively increasing distances from the site of the DSB induced by Cas9. It would be of interest to determine from the next-generation-sequencing data whether partial gene conversion tracks are observed at the edited locus (Elliott and Jasin MCB 18: 93), and if yes, whether these affect in some way the pool-level measurement by qPCR on HBD mRNA levels (Fig 1D).

The data in Fig 2A show an analysis of transcription factor and RNA pol II occupancy following genome editing at HBD. The figure legend refers to these data as having been obtained on single-cell-derived clones bearing the edits in homozygous or heterozygous form, but it is unclear from fig 2A, which clones were used for which analysis.

The data in Fig 3C present an analysis of HBD levels in erythroid colonies derived from genome-edited HSPCs. It would be helpful to clarify whether an individual dot represents a single such colony (this would seem to be the case from the cognate figure legend). If so, what number of such colonies would one need to obtain to gain a clearer sense of the effect on HBD levels from the various genome editing strategies used?

It would be helpful to comment, in the Discussion, on potential genome editing strategies to obtain high-efficiency pool-level uniform long-track gene conversion that is necessary to obtain high HBD levels in the progeny of edited CD34 cells. Would this be a good application of the AAV6 strategy developed by the Sangamo and Porteus groups? Would prime editing as developed by Liu be an option here?

It would be equally helpful, in the Discussion, to place the level of HbA2 obtained via the strategy shown in the manuscript in the context of other genome-editing-based approaches for normalizing Hb synthesis in the hemoglobinopathies (ie HbF elevation by editing the BCL11A enhancer, or the gamma-globin promoter; or direct repair of the SCD mutation; or engineering of Hb Makassar).

Reviewer #2 (Public Review):

This paper set out to understand the impact of early life stress on the behavior and individuality of animals, and how that impact might be amplified or masked by neuromodulation. To do so, the authors built on a previously established assay (Stern et al 2017) to measure the roaming fraction and speed of individuals. This technique allowed the authors to assess the effects of early life starvation on behavior across the entire developmental trajectory of the individual. By combining this with strains with mutant neuromodulatory systems, this enabled the authors to produce a rich dataset ripe for analysis to analyze the complicated interactions between behavior, starvation intensity, developmental time, individuality, and neuromodulatory systems.

The richness of this dataset - 2 behavioral measures continuous across 5 developmental stages, 3 different neuromodulatory conditions (with the dopamine system subject to decomposition by receptor types) and 4 different levels of starvation, with ~50-500 individuals in each condition-underlies the strength of this paper. This dataset enabled the authors to convincingly demonstrate that starvation triggers a behavioral effect in L1 and adult animals that is largely masked in intermediate stages, and that this effect becomes larger with increased severity of starvation. Furthermore, they convincingly show that the masking of the effect of starvation in L2-L4 animals depends on dopaminergic systems. The richness of the dataset also allowed a careful analysis of individuality, though only neuromodulatory mutants convincingly manipulated individuality, recapitulating earlier research. Nonetheless, a few caveats exist on some of their findings and conclusions:

1. Lack of quantitative analysis for effects within developmental stages. In making the argument for buffered effects of starvation on behavior during periods of larval development, the authors make claims regarding the temporal structure of behavior within specific stages. However, no formal analysis is performed and and the traces are provided without confidence intervals, making it difficult to judge the significance of potential deviations between starvation conditions.

2. Incorrect inferences from differences in significance demonstrating significant differences. The authors claim that there is an increase in PC1 inter-individual variation in tph-1 individuals, however the difference in significance is not evidence of a significant difference between conditions (see Nieuwenhuis et al. 2011). This undermines claims about an interaction of starvation, neuromodulators, and individuality.

3. Sensitivity of analysis to baseline effects and assumptions of additive/proportional effects. The neuromodulatory and stress conditions in this paper have a mixture of effects on baseline activity and differences from baseline. The authors normalize to the roaming fraction without starvation, making the reasonable assumption that the effect due to starvation is proportional to baseline, rather than an additive effect. This confound is most visible in the adult subpanel of figure 5d, where an ~2-3 fold difference in relative roaming due to starvation is clearly noted, however, this is from a baseline roaming fraction in tph-1 animals that are ~2 fold higher, suggesting that the effect could plausibly be comparable in absolute terms.

Unavoidably, any such assumptions on the expected interaction between multiple effects will be a gross simplification in complicated nonlinear systems, and the data are largely shown with sufficient clarity to allow the reader to make their own conclusions. However, some of the interpretations in the paper lean heavily on an assumption that the data support a direct interpretation (e.g. "neuronal mechanisms actively buffer behavioral alterations at specific development times") rather than an indirect interpretation (e.g. that serotonin reduces baseline roaming fraction which makes a fixed sized effect more noticeable). Parsing the differences requires either more detailed mechanistic study or careful characterization of the effect of different baselines on the sensitivity of behavior to perturbation-barring that it's worth noting that many of these interactions may be due to differences in biological and experimental sensitivity to change under different conditions, rather than a direct interaction of stress and neuromodulatory processes or evidence of differing neuromodulatory activity at different stages of development.

Reviewer #2 (Public Review):

Using an approach that combines synthetic genetic array (SGA) analysis with high-throughput microscopic analysis of the GFP-tagged yeast ORF collection in the budding yeast, Saccharomyces cerevisiae, this study has examined the contribution of the critical checkpoint kinases Mec1 and Rad53 to the subcellular relocalization of 322 candidate proteins in response to HU- and MMS-induced replication stress. Previous studies have established that Mec1 is required for Rad53 activation during replication stress and that Mec1 also serves checkpoint functions independent of Rad53. Unexpectedly, this study identifies groups of proteins whose stress-induced relocalization is dependent on Rad53 but not Mec1. This data indicates that Rad53 mediates some replication stress responses in a non-canonical manner that is independent of Mec1.

The authors confirm their initial observations from the screening approach by focusing on the Rad53-dependent and Mec1-independent focus formation of GFP-Rad54. Moreover, using mass-spec analysis the authors demonstrate that some Rad53 phosphorylation sites known to be critical for Rad53 activation, including a consensus Mec1 phosphorylation site, are phosphorylated after replication stress even in the absence of Mec1. Motivated by this finding the authors screen for potential kinase and phosphatase pathways that may regulate Rad53 function during MMS-induced replication stress. Top hits identified include members of the retrograde signaling pathway, which is confirmed by conventional genetic assays while mass spec analysis supports the involvement of Rtg3 in mediating Rad53 phosphorylation during replication stress in the absence of Mec1.

Overall this is a solid study reporting unexpected new findings that significantly advance our view of the global replication checkpoint response. The data are generally of high quality, well presented and quantified, and overall support the authors' claims. The mass spec approach used here to identify Rad53 phosphorylation sites offers an unbiased alternative to the simpler and more widely employed gel-shift method to monitor Rad53 activation. The hits identified in the various screens presented here provide a platform for potential follow-up studies by the community. The main drawback is that it remains unclear how Rtg3 promotes Rad53 activtation. However, this could be considered to be beyond the scope of this study.

Reviewer #2 (Public Review):

The relationship between measures of brain state, behavioral state, and performance has long been speculated to be relatively simple - with arousal and engagement reflecting EEG desynchronization and improved performance associated with increases in engagement and attention. The present study demonstrates that the outcome of the previous trial, specifically a miss, allows these associations to be seen - while a correct response appears less likely to do so. This is an interesting advance in our understanding of the relationship between brain state, behavioral state, and performance.

While the study is well done, the results are likely to be specific to their trial structure and states exhibited by the mice. To examine the full range of arousal states, it needs to be demonstrated that animals are varying between near-sleep (e.g. drowsiness) and high-alertness such as in rapid running. The fact that the trials occurred rapidly means that the physiological and neural variables associated with each trial will overlap with upcoming trials - it takes a mouse more than a few seconds to relax from a previous miss or hit, for example. Spreading the rapidity of the trials out would allow for a broader range of states to be examined, and perhaps less cross-talk between adjacent trials. The interpretation of the results, therefore, must be taken in light of the trial structure and the states exhibited by the mice.

Reviewer #2 (Public Review):

The manuscript by Mohebi et al. examines a critical open question regarding the interaction of cholinergic interneurons of the striatum and transmitter release from dopaminergic axons in behaving animals. Activation of cholinergic interneurons in the striatum can evoke dopamine release in brain slices and in vivo as measured with voltammetry. However, it remains an open question in what context and to what extent this acetylcholine-mediated dopamine occurs in behaving animals. Here, the authors argue that CIN activity triggers dopamine release in the nucleus accumbens which encodes the motivation to obtain a reward through increasing "ramps" of dopamine release. Their data suggest that the ramps are not reflected in the firing of dopaminergic neurons. Rather, they provide compelling evidence that the ramps of dopamine release correlate with ramps in cholinergic interneuron activity as measured with GCaMP6. What's more, the authors show that ACh-mediated dopamine release has no paired-pulse depression, a striking result that differs from all prior ex vivo brain slice data. The manuscript is extremely well written and the data are of very high quality. Overall, this study represents an important step forward in our understanding of how ACh-mediated dopamine release regulates behavior, and more broadly how axons can generate behaviors independently from somatic activity.

Major comments<br /> 1. The complete absence of any short-term plasticity in CIN-mediated dopamine release is a striking result that is important for the field. The authors should strengthen this result with additional quantitative analysis demonstrating the lack of STP. They have analyzed paired-pulse ratios, but they should analyze this for stimuli at the higher frequencies (4 Hz, etc) that are more physiologically relevant. For example, Fig 1e shows a CIN-evoked DA release at many optically-stimulated frequencies. The authors should quantify short-term plasticity by generating fits of the single stimulus signal and comparing the mathematical sum predicted from 4 stim DA signals at different frequencies to the recorded data. A similar analysis has been done with Ca signals (Koester and Sakmann, 2000).

2. The authors show that optical activation of CINs results in DA release as measured by dLight. To clearly establish that these signals are generated by DA release driven by nicotinic receptors (and not a partial effect of some unknown artifact), it would be useful to show that the optical CIN-evoked dLight signals shown in Fig. 1 are inhibited by nicotinic receptor antagonists such as DHbE. This control experiment would significantly strengthen the result shown here.

3. Similarly, the authors show clear correlations between CIN activity and DA release during behavior. The authors should consider determining whether CINs play a causal role in triggering DA release during behavior. For example, does infusion of DHbE in the NAc prevent the light-mediated DA release during behavior? As an alternative hypothesis, some groups have been suggesting that CIN activity has almost no direct influence over DA. Therefore, testing whether a causal relationship exists between CINs and DA release would be an important experiment in addressing these two opposing viewpoints.

4. The ramps that are described in this manuscript are an order of magnitude faster (increasing over 100s of milliseconds) than ramps described in other studies that occur over seconds. In fact, the two signals may be completely different functionally. Discussion of this topic would be helpful.

Reviewer #2 (Public Review):

In this study the authors investigate functional associations made by transcription factor ZMYM2 with chromatin regulators, and the impact of perturbing these complexes on the transcriptome of the U2OS cell line. They focus on validating two novel chromatin-templated interactions: with TRIM28/KAP1 and with ADNP, concluding that via these distinct chromatin regulators, ZMYM2 contributes to transcriptional control of LTR and SINE retrotransposons, respectively.

Strengths and weakness of the study:

- The co-localization of ZMYM2 with ADNP and TRIM28 is validated through RIME, ChIP-seq and co-IP. (Notably, since both RIME and ChIP-seq rely on crosslinking, and the co-IP with TRIM28 required crosslinking due to being SUMO-dependent, only the ZMYM2-ADNP co-IP experiment demonstrates an interaction in the absence of crosslinking).

- It is good that uniquely-mapped reads are used in the ChIP-seq analysis given the interest in repetitive elements. Likewise, though the RT-qPCR data in Fig5 should be complemented by analysis of the RNA-seq data that the authors already have, it seems that the primers are carefully designed such that a single retrotransposon copy is amplified.

- The top-scoring interactors are highly-abundant nuclear proteins: for example, data from the contaminant repository for affinity purification mass-spec data (https://reprint-apms.org/) show that TRIM28 is identified in 466 / 716 AP-MS experiments with a mean spectral count of 16. While this does not indicate that the ZMYM2-TRIM28 interaction is not 'true', it would have been helpful to further dissect the interaction to strengthen this conclusion. For example, it would be nice to see the co-IP (fig 3A) repeated from the cells expressing the ZMYM2 mutant that is no longer competent to bind SUMO (used in the ChIP-seq data of Fig 2). Alternatively - if the model is that ZMYM2 recruits SUMOylated TRIM28 - with well-characterized TRIM28 mutants that lack SUMOylation.

- The transcriptional response using bulk RNA-seq in ZMYM2-depleted cells is rather gene-centric despite the title of the paper being about TE transcription. In fact the only panels about TE transcription are the RT-qPCR data in Fig 5D,F. I may be missing something (and there aren't many details given about the RNA-seq experiments) but why not look at TE transcription in an unbiased way with the transcriptomic data at hand? I appreciate potential hazards of multi-mapping etc but it would be interesting to see at least some subfamily analysis (e.g. using the TEtranscripts tool). On a similar point, why not show some RNA-seq in the genome browser snapshots of the epigenomics - together with a RepeatMasker annotation track of TEs...

While the results broadly support the authors' conclusions, I have the overall impression that the central claim of TE transcriptional regulation by ZMYM2 could be strengthened a lot with some fairly straightforward additional experiments and analyses.

Reviewer #2 (Public Review):

In this manuscript, Chen et al. determined the structural basis for pre-RNA processing by Las1-Grc3 endoribonuclease and polynucleotide kinase complexes from S. cerevisiae (Sc) and C. jadinii (Cj). Using a robust set of biochemical assays, the authors identify that the sc- and CjLas1-Grc3 complexes can cleave the ITS2 sequence in two specific locations, including a novel C2' location. The authors then determined X-ray crystallography and cryo-EM structures of the ScLas1-Grc3 and CjLas1-Grc3 complexes, providing structural insight that is complimentary to previously reported Las1-Grc3 structures from C. thermophilum (Pillon et al., 2019, NSMB). The authors further explore the importance of multiple Las1 and Grc3 domains and interaction interfaces for RNA binding, RNA cleavage activity, and Las1-Grc3 complex formation. Finally, evidence is presented that suggests Las1 undergoes a conformational change upon Grc3 binding that stabilizes the Las1 HEPN active site, providing a possible rationale for the stimulation of Las1 cleavage by Grc3.

Several of the conclusions in this manuscript are supported by the data provided, particularly the identification and validation of the second cleavage site in the ITS2. However, several aspects of the structural analysis and complimentary biochemical assays would need to be addressed to fully support the conclusions drawn by the authors.

• There is a lack of clarity regarding the number of replicates performed for the biochemical experiments throughout the manuscript. This information is critical for establishing the rigor of these biochemical experiments.

• The authors conclude that Rat1-Rai1 can degrade the phosphorylated P1 and P2 products of ITS2 (lines 160-162, Figure 1H). However, the data in Fig. 1H shows complete degradation of 5'Phos-P2 and 5'Phos-P4 of ITS2, while the P1 and 5'Phos-P3 fragments remain in-tact. Additional clarification for this discrepancy should be provided.

• The authors determined X-ray crystal structures of the ScLas1-Grc3 (PDB:7Y18) and CjLas1-Grc3 (PDB:7Y17) complexes, which represents the bulk of the manuscript. However, there are major concerns with the structural models for ScLas1-Grc3 (PDB:7Y18) and CjLas1-Grc3 (PDB:7Y17). These structures have extremely high clashscores (>100) as well as a significant number of RSRZ outliers, sidechain rotamer outliers, bond angle outliers, and bond length outliers. Moreover, both structures have extensive regions that have been modeled without corresponding electron density, and other regions where the model clearly does not fit the experimental density. These concerns make it difficult to determine whether the structural data fully support several of the conclusions in the manuscript. A more careful and thorough reevaluation of the models is important for providing confidence in these structural conclusions.

• The presentation of the cryo-EM datasets is underdeveloped in the results section drawing and the contribution of these structures towards supporting the main conclusions of the manuscript are unclear. An in-depth comparison of the structures generated from X-ray crystallography and cryo-EM would have greatly strengthened the structural conclusions made for the ScLas1-Grc3 and CjLas1-Grc3 complexes.

• The authors conclude that truncation of the CC-domain contributes to Las1 IRS2 binding and cleavage (lines 220-222, Fig. 4C). However, these assays show that internal deletion of the CC-domain alone has minimal effect on cleavage (Fig 4C, sample 3). The loss in ITS2 cleavage activity is only seen when truncating the LCT and LCT+CC-domain (Fig 4C, sample 2 and 4, respectively). Consistently, the authors later show that Las1 is unable to interact with Grc3 when the LCT domain is deleted (Fig. 6 and Fig. 6-figure supplement 2). These data indicate the LCT plays a critical role in Las1-Grc3 complex formation and subsequent Las1 cleavage activity. However, it is unclear how this data supports the stated conclusion that the CC-domain is important for LasI cleavage.

• The authors conclude that the HEPN domains undergo a conformational change upon Grc3 binding, which is important for stabilization of the Las1 active site and Grc3-mediated activation of Las1. This conclusion is based on structural comparison of the HEPN domains from the CjLas1-Grc3 complex (PDB:7Y17) and the structure of the isolated HEPN domain dimer (PDB:7Y16). However, it is also possible that the conformational changes observed in the HEPN domain are due to truncation of the Las1 CC and CGT domains. A rationale for excluding this possibility would have strengthened this section of the manuscript.

Reviewer #2 (Public Review):

Caveney et al have overexpressed an engineered construct of the human membrane receptor guanyl cyclase GC-C in hamster cells and co-purified it with the endogenous HSP90 and CDC37. They have then determined the structure of the resultant complex by single particle cryoEM reconstruction at sufficient resolution to dock existing structures of HSP90 and CDC37, plus an AlphaFold model of the pseudo-kinase domain of the guanylyl cyclase. The novelty of the work stems from the observation that the pseudo-kinase domain of GC-C associates with CDC37 and HSP90 similarly to how the bona fide protein kinases CDK4, CRAF and BRAF have been previously shown to interact.

The experimentation is limited to the cryoEM analysis, and is lacking additional studies that would give deeper insight into the oligomeric nature - if any - of the GC-C when bound to HSP90-CDC37 as compared to the free protein. This is relevant, as the dimerization domain downstream of the pseudokinase, is evident in the maps - albeit not well resolved - and it is not clear whether it is still able to mediate dimerization with a second free or HSP90-CDC37-bound GC-C. It would also be good to see some experimentation that asks whether association with HSP90-CDC37 inhibits the guanyl cyclase activity. It is clear from previous work that HSP90-CDC37 silence the kinase activity of their bound client kinases, but in this case the catalytic guanyl cyclase is not directly associated with the chaperone complex and may still be able to function.

Although the sequence alignment presented in SuppFig 2 shows that GC-C conserves the classic DFG motif that plays a critical role in the regulation of most kinases, the numbering of the sequence is absent, making it very difficult to relate this to the structural detail shown in Fig 2B. This needs to be clarified, as the interaction of CDC37-Trp31 with the DFG motifs and downstream activation loops in CRAF and BRAF have been proposed as important features of the selectivity of these kinases for the HSP90-CDC37 system, and it would be good to be able to see clearly how much of this is also conserved in the GC-C pseudokinase domain interaction. For example, is the much shorter activation segment (DFG -> APE) ordered in the complex or disordered?

It was not easy to follow what was in the sample used for cryoEM. The cloning of the guanylyl cyclase (GC) component is described in the methods and they have shown some illustrations in fig 1 but a proper numbered figure of the domain organisation clearly showing domain boundaries and linker segments is really needed for a reader not familiar with the structure of GCs, especially since they have replaced the ECD with a leucine zipper in their construct. It is important to show a domain figure of what this construct looks like as well, as from the illustrations in fig 1 for examples its hard to see what's PK, DD, GC domains. It would also be helpful to see in the supplementary a gel of complex they put on the grids, to make it clearer what exactly the sample is and to reassure that the GC-C domains that are not resolved in the cryoEM are nonetheless present in the sample.

Overall there is only minimal proposal of mechanism or biological function based on the structure. The speculation in the Discussion of two fates - PP5 dephosphorylation or E3 ligase recruitment, is not supported by any experimentation, which is reasonable for speculation, but is also not underpinned by reference to any previously published work suggesting that these additional processes may be important. In the absence of any work by the authors can they put these speculations more in context with previously published work that supports the importance of these processes specifically for GC regulation?

Reviewer #2 (Public Review):

The study focuses on a mechanism of pest/pathogen resistance identified in Solanum commersonii, which appears to offer dominant resistance to Alternaria solani through the activity of specific glycosyltransferases which facilitate the production of tetraose glycoalkaloids in leaf tissue. The authors demonstrated that these glycoalkaloids are suppressive to the growth of multiple pathogenic ascomycetes and furthermore, that transgenic plants expressing these glycosyltransferases in susceptibleS. commersonii clones demonstrate improved resistance to a specific strain of A. solani and a genotype of Colorado Potato Beetle. The study design is straightforward, yet thorough, and does a good job demonstrating the importance of these genes in resistance. While the research findings are significant there are statements throughout the manuscript that overstate both the novelty and utility of the findings.

Title: While the protection is impressive, the title suggests that these glycoalkaloids provide protection against all fungi and insects, which is both unlikely and essentially impossible to prove. This should be changed to something more measured. This is especially true given that only a single fungus and insect were tested against transgenic plants, but would be an overstatement even with more robust evaluation.

Throughout the paper: A single isolate of A. solani and a single genotype of CPB were used in this study. While this is in line with the typical limitations of such a study, the authors need to be careful about claiming broad resistance to either of the species. Variability in fungicide tolerance and detoxification activity have been noted in both fungi and CPB, so more specific language should be used throughout (such as L213 and L221).

Reviewer #2 (Public Review):

Cacioppo et al describe a mechanism of translation regulation of Aurora A, which is dependent on alternative polyadenylation. They suggest that altered expression of the resulting isoforms in cancers is at least partly responsible for elevated Aurora A levels, which in turn is known to indicate poor prognosis.

The authors exploit publicly available databases and patient data to highlight the correlation of increased abundance of the SHORT isoform (relative to the LONG one) and poor patient survival in TNBC, as well as breast and lung cancer.

In their thorough mechanistic study they use a number of reporters to assess the impact of alternative polyadenylation on mRNA stability and translation efficiency and explore whether this process accounts for cell-cycle-regulated expression of Aurora A. These reporters are carefully controlled and well explained. I particularly commend the authors for the clear graphical presentations of the reporters (eg fig 2A, fig 3D, fig 4A). Rigorous control experiments are performed to make sure that the reporters work and "report" what they are meant to do, and to show that previous findings can be reproduced in experiments based on the reporters (eg higher protein expression from the short 3' UTR APA isoform of CDC6 mRNA, targeting of MZF1 3'UTR by hsa-let-7a).

They show that translation of the longer isoform is subject to suppression by hsa-let-7a, while the shorter isoform is not. They attribute cell-cycle regulated expression of Aurora A at least in part to the suppression of translation of the LONG isoform in G1 and S.<br /> In Figure 6 they address whether the APA-based regulatory mechanism alters Aurora A levels sufficiently to confer features associated with oncogenic transformation and overexpression of Aurora A. These data nicely tie together the observations in databases and the mechanistic part of the study.

The logic is clear and the conclusions are well supported by the data.

The authors state themselves that the impact of translation regulation on Aurora A levels in the cell cycle is an important but unanswered question. The evidence that suppression of translation of the LONG transcript contributes to the cell-cycle regulation of Aurora A is convincing, but the extent could be explored further. I wonder whether published genome-wide studies (eg PMCID 4548207, PMC3959127) have relevant data on the translation rate of Aurora A in the cell cycle.

In the paper this question is addressed in cells enriched in G1/S (Fig 6) and using the reporters (Fig 5). Having generated the ΔdPAS mutants, Aurora A levels could be easily assessed in each cell-cycle phase. The best way to do this would be sorting followed by immunoblotting.

The fact that Aurora A levels are reduced by a 6h treatment with 0.1 mg/ml CHX (Fig 6D) is interpreted as "AURKA expression in G1/S was reduced in the mutated cell lines when treated with CHX, indicating that translation of the short isoform is active in this phase" It is rather expected that using a translation inhibitor will stop the accumulation of a protein and so this experiment does not add much. A better approach to address the effect of the mutations on translation would be to add a proteasome inhibitor and follow accumulation of Aurora A, preferably not only in G1/S but also in other cell-cycle phases. Accumulation of the protein in this experiment would better reflect translation rates.

Reviewer #2 (Public Review):

Preserving and restoring the fertility of prepubertal patients undergoing gonadotoxic treatments involves freezing testicular fragments and waking them up in a culture in the context of medically assisted procreation. This implies that spermatogenesis must be fully reproduced ex vivo. The parameters of this type of culture must be validated using non-human models. In this article, the authors make an extensive study of the quality of the organotypic culture of neonatal mouse testes, paying particular attention to the differentiation and endocrine function of Leydig cells. They show that fetal Leydig cells present at the start of culture fail to complete the differentiation process into adult Leydig cells, which has an impact on the nature of the steroids produced and even on the signaling of these hormones.

The authors make an extensive study of the different populations of Leydig cells which are supposed to succeed each other during the first month of life of the mouse to end up with a population of adult and fully functional cells. The authors combine quantitative in situ studies with more global analyzes (RT-QtPCR Western blot, hormonal assays), which range from gene to hormone. This study is well written and illustrated, the description of the methods is honest, the analyses systematic, and are accompanied by multiple relevant control conditions.

Since the aim of the study was to study Leydig cell differentiation in neonatal mouse testis cultures, the study is well conceived, the results answer the initial question and are not over-interpreted.

My main concern is to understand why the authors have undertaken so much work when they mention RNA extractions and western blot, that the necrotic central part had to be carefully removed. There is no information on how this parameter was considered for immunohistochemistry and steroid measurements. The authors describe the initial material as a quarter testis, but they don't mention the resulting size of the fragment. A brief review of the literature shows that if often the culture medium is crucial for the quality of the culture (and in particular the supplementations as discussed by the authors here), the size of the fragments is also a determining factor, especially for long cultures. The main limitation of the study is therefore that the authors cannot exclude that central necrosis can have harmful effects on the survival and/or the growth and/or the differentiation of the testis in culture. In this sense, the general interpretation that the authors make of their work is correct, the culture conditions are not optimized.

Organotypic culture is currently trying to cross the doors of academic research laboratories to become a clinical tool, but it requires many adjustments and many quality controls. This study shows a perfect example of the pitfall often associated with this approach. The road is still long, but every piece of information is useful.

Reviewer #2 (Public Review):

This manuscript is an impressive "resurrection" of physiology regarding an enigmatic though unfortunately extinct species, and their potential adaptation to cold-water environments. I am largely convinced of their findings, which I feel are very straightforward and thorough.

One place where the authors perhaps fell a bit short was regarding some conclusions associated with maternal/fetal oxygen delivery. The sirenian versions of fetal & embryonic hemoglobin genes have been identified and assessed to some degree in previously published work the same research group. I feel the manuscript would have benefited from actual analysis of the fetal & embryonic hemoglobin (epsilon, gamma, zeta) to strengthen their assertions.

Reviewer #2 (Public Review):

Parasitic African trypanosomes are agents of devastating diseases in humans and animals. Currently, no vaccines exist, with control of human disease being realized thru vector suppression and elimination of infected hosts while animal diseases remain rampant on the continent. The molecular aspects of the multiple developmental stages the parasite undergoes thru its mammal and tsetse hosts, and the unique aspects of parasite gene expression regulation and host evasion mechanisms have been extensively investigated. Recent applications of single-cell transcriptomics (scRNA) to these approaches have expanded knowledge gained from total RNA and revealed new insights.

In this paper, Briggs et al., set out to determine the cell cycle-related genes (CCR) of T. brucei, which follows the typical eukaryotic progression through G1, S, G2, and M phases followed by cytokinesis, although trypanosomes are unusual in that both nuclear and mitochondrial genome replication and segregation are orchestrated during cell division. while many regulators remain unidentified, are absent, or have been replaced by trypanosomatid-specific factors. For these studies, they apply scRNA methodology using asynchronous mixed populations of cultured 'monomorphic' slender mammalian (BSF) and insect stage (PCF) cells and then determine their cell cycle phases computationally. Of interest, performing similar analysis with fresh and cryopreserved cells made minimal difference to the outcome, thus enabling future investigations with preserved cells.

The study identified 1,550 genes with dynamic transcript level changes reflective of the cell cycle, 1,151 of which had not been previously identified by bulk analysis. These revealed a common set of highly conserved CCR genes as well as unique gene transcript levels expressed thru the cell cycle for BSF and PCF cells. Expression patterns of the G1 and S phase genes are highly comparable between BSF and PCF forms, whereas, after the S phase, the timing of gene expression for the S-G2 transition is far less synchronized. Comparison between transcript expression patterns and previously published protein abundance changes identified a relative delay in peak levels for transcript and protein for at least 50% of the genes that could be compared. Collectively, this foundational analysis generates transcript atlases for BSF and PCF cell cycles, which can be further mined for downstream functional investigations.

Reviewer #2 (Public Review):

Nurr1 is a nuclear receptor and is important for mammalian brain development and homeostasis. Dysfunctional Nurr1 transcriptional activities are implicated in neurodegenerative diseases like Parkinson's. This exquisite ligand-dependent and specific transcriptional reprogramming make nuclear receptors ideal drug targets. However, the design of Nurr1-selective ligands has been confounded by the fact that Nurr1's ligand binding pocket appears to collapse in x-ray crystal structures. Interestingly, RXRalpha-targeted ligands, Nurr1's obligate heterodimer binding partner, show differential effects on Nurr1's transcriptional activities. In this study, the authors aimed to address how RXRalpha ligands lead to Nurr1 transcriptional activation. By combining biochemical approaches, NMR spectroscopy, and transcriptional reporter gene assays in neuronal cells, the authors convincingly show that these select RXRalpha ligands elicit an allosteric effect that reduces Nurr1 binding affinity. They further show that monomeric Nurr1 is a highly effective enhancer of the promoter that is repressed in the presence of RXRalpha. Overall, this is a well-presented and robust study as presented and the conclusions are supported by their evidence. This study should have a profound impact on the field as it provides a clear structural mechanism for ligand-dependent Nurr1 activation in neuronal cells.

Reviewer #2 (Public Review):

The authors identified a novel TNFAIP3 variation Leu236Pro located in the A20 OUT domain and demonstrated its pathogenicity. Proinflammatory cytokines were substantially elevated in the patients. In vitro study showed decreased stability of the Leu236Pro A20 protein and Leu236Pro mutant failed to suppress TNF induced NF-κB activity. Review of previously reported TNFAIP3 missense variations revealed that only 3/7 are pathogenic. Truncating A20 mutations are easy to determine the pathogenicity, while missense TNFAIP3 variants require more functional studies to determine the pathogenicity. The results of this study can help interpretation of TNFAIP3 missense variations.

Reviewer #2 (Public Review):

Sachiko et al. study presents strong evidence that implicates environmental volatile odorants, particularly diacetyl, in an alternate role as an inhibitors HDAC proteins and gene expression. HDACs are histone deacetylases that generally have repressive role in gene expression. In this paper the authors test the hypothesis that diacetyl, which is a compound emitted by rotting food sources, can diffuse through blood-brain-barrier and cell membranes to directly modulate HDAC activity to alter gene expression in a neural activity independent manner. This work is significant because the authors also link modulation of HDAC activity by diacetyl exposure to transcriptional and cellular responses to present it as a potential therapeutic agent for neurological diseases, such as inhibition of neuroblastoma and neurodegeneration.

The authors first demonstrate that exposure to diacetyl, and some other odorants, inhibits deacetylation activity of specific HDAC proteins using in vitro assays, and increases acetylation of specific histones in cultured cells. Consistent with a role for diacetyl in HDAC inhibition, the authors find dose dependent alterations in gene expression in different fly and mice tissues in response to diacetyl exposure. In flies they first identify a decrease in the expression of chemosensory receptors in olfactory neurons after exposure to diacetyl. Subsequently, they also observe large gene expression changes in the lungs, brain, and airways in mice. In flies, some of the gene expression changes in response to diacetyl are partially reversable and show an overlap with genes that alter expression in response to treatment with other HDAC inhibitors. Given the use of HDAC inhibitors as chemotherapy agents and treatment methods for cancers and neurodegenerative diseases, the authors hypothesize that diacetyl as an HDAC inhibitor can also serve similar functions. Indeed, they find that exposure of mice to diacetyl leads to a decrease in the brain expression of many genes normally upregulated in neuroblastomas, and selectively inhibited proliferation of cell lines which are driven from neuroblastomas. To test the potential for diacetyl in treatment of neurodegenerative diseases, the authors use the fly Huntington's disease model, utilizing the overexpression of Huntingtin protein with expanded poly-Q repeats in the photoreceptor rhabdomeres which leads to their degeneration. Exposing these flies to diacetyl significantly decreases the loss of rhabdomeres, suggesting a potential for diacetyl as a therapeutic agent for neurodegeneration.

The findings are very intriguing and highlight environmental chemicals as potent agents which can alter gene expression independent of their action through chemosensory receptors.

Reviewer #2 (Public Review):

Granell et al. investigated genetic factors underlying wheezing from birth to young adulthood using a robust data-driven approach with the aim of understanding the genetic architecture of different wheezing phenotypes. The association of 8.1 million single nucleotide polymorphisms (SNPs) with wheeze phenotypes derived from birth to 18 years of age was evaluated in 9,568 subjects from five independent cohorts from the United Kingdom. This meta-genome-wide association study (GWAS) revealed the suggestive association of 134 independent SNPs with at least one wheezing subtype. Among these, 85 genetic variants were found to be potentially causative. Indeed, some of these were located nearby well-known asthma loci (e.g., the 17q21 chromosome band), although ANXA1 was revealed for the first time to play an important role in early-onset persistent wheezing. This was strongly supported by functional evidence. One of the top ANXA1 SNPs associated with wheezing was found to be potentially involved in the regulation of the transcription of this gene due to its location at the promoter region. This polymorphism (rs75260654) had been previously evidenced to regulate the ANXA1 expression in immune cells, as well as in pulmonary cells through its association as an eQTL. Protein-protein network analyses revealed the interaction of ANXA1 with proteins involved in asthma pathophysiology and regulation of the inflammatory response. Additionally, the authors conducted a murine model, finding increased anxa1 levels after a challenge with house dust mite allergens. Mice deficient in anxa1 showed decreased lung function, increased eosinophilia, and Th2 cell levels after allergen stimulation. These results suggest the dysregulation of the immune response in the lungs, eosinophilia, and Th2-driven exacerbations in response to allergens as a result of decreased levels of anxa1. This coincides with evidence of lower plasmatic ANXA1 levels in patients with uncontrolled asthma, suggesting this locus is a very promising candidate as a target of novel therapeutic strategies.

Limitations of this piece of work that need to be acknowledged: (1) the manual and visual inspection of Locus Zoom plots for the refinement of association signals and identification of functional elements does not seem to be objective enough; (2) the sample size is limited, although the statistical power was improved by the assessment of very accurate disease sub-phenotype; (3) association signals with moderate significance levels but with strong functional evidence were found; (4) no direct replication of the findings in independent populations including diverse ancestry groups was described. Nonetheless, the robustness and consistency of the findings supported by different analytical and experimental layers is the major strength of this study.

The authors successfully achieved the aims of the study, strongly supported by the results presented. This study not only provides an exciting novel locus for wheezing with potential implications in the development of alternative therapeutic strategies but also opens the path for better-powered research of asthma genetics, focused on accurate disease phenotypes derived by innovative data-driven approaches that might speed up the process to disentangle the missing heritability of asthma, making use of still useful GWAS approaches.

Reviewer #2 (Public Review):

This is an excellent study. It starts with the identification of two bactofilins in H. neptunium, a demonstration of their important role for the determination of cell shape and discovery of an associated endopeptidase to provide a convincing model for how these two classes of proteins interact to control cell shape. This model is backed up by a quantitative characterisation of their properties using high-resolution imaging and image analysis methods.

Overall, all evidence is very convincing and I do not have many recommendations on how to improve the manuscript.

In my opinion, there are only two issues that I have with the paper:

1. The single particle dynamics of BacA is presented as analysed and I would like to give some suggestions how to maybe extract even more information from the already acquired data:

1.1. Presentation: Figure 5A is only showing projections of single particle time-lapse movies. To convince the reader that it was indeed possible to detect single molecules it would be helpful if the authors present individual snapshots and intensity traces. In case of single molecules these will show step wise bleaching.

1.2. Analysis: Figure 5B and Supplement Figure 1 are showing the single particle tracking results, revealing that there are two populations of BacA-YFP in the cell. However, this data does not show if individual BacA particles transition between these two populations or not. A more detailed analysis of the existing data, where one can try to identify confinement events in single particle trajectories could be very revealing and help to understand the behaviour of BacA in more detail.

2. The title of Fig. 3 says that BacA and BacD copolymerise, however, the data presented to confirm this conclusion is actually rather weak. First, the Alphafold prediction does not show the co-polymer, and second, the in vitro polymerisation experiments were only done with BacA in the absence of BacD. Accordingly, the only evidence that supports this is their colocalization in fluorescence microscopy. I suggest either weakening the statement or changing the title adds more evidence.

Finally, did the authors think about biochemical experiments to study the interaction between the cytoplasmic part of LmdC and the bactofilins? These could further support their model.

Reviewer #2 (Public Review):

This work sheds new light on the growth trajectory of Bonobo and contributes heavily to the discussion of the exclusivity of certain aspects of growth in modern humans. These results are also interesting as long as they are based on the study of the largest sample ever considered in the study of the growth of this species by including morphometric measurements as well as endocrinological factors.

The authors approach the study of the presence of growth spurs (GS) in Bonobo on the basis that GS are exclusive to the growth in modern humans. This idea is fairly widespread, however studies on non-human primates have shown an acceleration of growth during adolescence in several species, these works are recalled, presented and discussed by the authors. The originality of this work lies in highlighting the importance of scaling in studies of growth trajectories. The absence of GS in Bonobo but also in other primate species may result from not considering the conjunction of weight and height in the analysis of growth, because the pronounced changes in the speed of the height are in relation to the speed of changes in weight and this is modified according to the size/age. The authors apply scaling corrections to their results and the GS become evident (or more obvious) in Bonobo. Thus, the exclusivity of GS in growth in modern humans may in fact result only by the application of analytical approach not very appropriate in non-human primates.

Reviewer #2 (Public Review):

In this manuscript, Castanera et al. investigated how transposable elements (TEs) altered gene expression in rice and how these changes were selected during the domestication of rice. Using GWAS, the authors found many TE polymorphisms in the proximity of genes to be correlated to distinct gene expression patterns between O. sativa ssp. japonica and O. sativa ssp. indica and between two different growing conditions (wet and drought). Thereby, the authors found some evidence of positive selection on some TE polymorphisms that could have contributed to the evolution of the different rice subspecies. These findings are underlined by some examples, which illustrate how changes in the expression of some specific genes could have been advantageous under different conditions. In this work, the authors manage to show that TEs should not be ignored when investigating the domestication of rise as they could have played an important role in contributing to the genetic diversity that was selected. However, this study stops short of identifying causations as the used method, GWAS, can only identify promising correlations. Nevertheless, this study contributes interesting insights into the role TEs played during the evolution of rice and will be of interest to a broader audience interested in the role TEs played during the evolution of plants in general.

Reviewer #2 (Public Review):

This paper extends prior work demonstrating the importance of K145 acetylation of TDP-43 as a post-translational modification that impacts its RNA-binding capacity and may contribute to pathology in FTLD-ALS. The main strengths of this paper are the generation of a novel mouse model, using CRISPR gene editing, in which an acetylation-mimetic mutation (K to Q) is introduced at position 145. Behavioral, biochemical, and genetic analyses indicate that these mice display phenotypes relevant to TDP-43-associated disease and will be a valuable contribution to the field. While most of the data are rigorous and clearly presented, several weaknesses should be addressed to strengthen the manuscript and further characterize the phenotype of mutant mice.

Reviewer #2 (Public Review):

This study reports a novel role of the natriuretic receptors Npr3 and Npr1 in the formation of neural crest (NC) and cranial placode (CP) progenitor populations in frog embryos. The authors discovered this receptor family in a screen for genes activated during NC development. They show the relevant expression of these receptors and the corresponding ligands in the NC and CP populations. Knockdown and rescue experiments combined with pharmacological drug treatment demonstrated that Npr3 clearance activity is required for NC progenitor formation. Surprisingly, adenylyl cyclase inhibition was required for cGMP production and the effect on CP development. The authors conclude that the two second messengers downstream participate in the segregation of the NC and CP progenitors in embryonic development.

The significance of this study is in the demonstration of two distinct developmental programs that are separately controlled by different activities of the same receptor. The study is well designed and executed with proper controls. Nevertheless, the data suggesting that Npr3 regulates NC and CP fates via different mechanisms are limited and need further support, such as the analysis of additional markers for CP progenitors, to be unambiguously interpreted. The work is likely to impact two different areas: early embryonic development and natriuretic peptide signaling.

Reviewer #2 (Public Review):

Rapan and colleagues did perform an impressive multi-modal parcellation of the macaque frontal cortex. In addition to qualitative cytoarchitectonic and resting-state functional fMRI data analyses, the authors based their parcellation on quantitative receptor density analysis of 14 receptors. Compared with the classic Walker map of the macaque frontal cortex, the authors produced a more refined map. Those results should be discussed in light of previous work on the same topic (Petrides et al. 2012 Cortex; Reveley et al. 2017 Cerebral Cortex; Saleem and Logothetis 2012).

Reviewer #2 (Public Review):

Immature lattice assembly remains an arcane topic, and these simulations provide high resolution data such as assembly kinetics and large-scale lattice rearrangement. Further, the authors extend their model to compare directly with experiments, e.g. SNAP-HALO dimerization, which provides a basis to interpret their conclusions. The manuscript is difficult to read, as it is a technical manuscript that overuses jargon; overall, it seems written for a specialized audience. Additionally, there are several aspects of the model design that remain opaque, such as the implicit lipid method and the suppression of multi-site nucleation. Further, analyses such as time auto-correlation and mean first passage time are not given much context by the authors. Altogether, it is the opinion of this reviewer that several revisions to the manuscript should be incorporated to improve clarity and strengthen the significance of the authors' efforts.

Reviewer #2 (Public Review):

This study by Yamaguchi and Peltier provides a detailed investigation of the brainstem CPG functional organization that rules vocal behaviors in several Xenopus species, from an evolutionary perspective. The main conclusion of the paper reveals that vocal CPGs, located in the brainstem, generating fast and slow clicks in Xenopus male courtship calls are conserved across various Xenopus species. But the development of the fast CPG depends on testosterone only in species producing fast-click courtship calls.

Reviewer #2 (Public Review):

ATM and Rad3-related (ATR) interact with ATRIP and plays a central role in DNA damage response. Previous studies have established the idea that ATR is recruited to RPA-covered ssDNA via ATRIP-RPA interaction. In this paper, the authors propose a new RPA-independent mechanism for ATR recruitment.

Reviewer #2 (Public Review):

The authors used single cell transcriptome analysis of zebrafish skin cells and characterized various types of cells that are involved in scale formation and stripe patterning. The methods employed in this study is highly powerful to provide mechanistic explanation of these fundamental biological issues and will be a good example for many researchers studying other biological issues. Furthermore, the results characterizing differences in gene expression patterns among various types of cells will be informative for other researchers in the field.

For scale formation, it is known that mineralized tissues may significantly differ in rayfins and lobefins since sox9, col2a1, and col10a1 are all expressed in osteoblasts, in addition to chondrocytes, in zebrafish and gar (Eames et al., 2012, BMC Evol. Biol.). Furthermore, in mammals, Col10 is expressed in chondrocytes in mature cartilage that undergoes ossification. Thus, unlike the authors argue, col10a1 expression is not apparently relevant to the elasticity of scales.

The authors also state that the expression of dlx4a, msx2a, and runx2b characterize cells homologous to mammalian ameloblasts. However, dlx4, runx2, and msx2 are all duplicated in zebrafish, and the function of duplicated genes in teleost fishes may differ from that of single ancestral gene. Moreover, none of Dlx4, Msx2, and Runx2 is expressed specifically by ameloblasts in mammals. Indeed, both Msx2 and Runx2 are expressed in osteoblasts, while the expression of Dlx4 in ameloblasts is not reported. These results, together with the expression of an enamel gene, enam, in dermal cells (SFC), do not appear to support the homology of the surface tissue of mammalian teeth and zebrafish scales.

Reviewer #2 (Public Review):

In a neonatal model of bacterial meningitis induced by s.c. injection of E. coli, transcriptional changes were found across all major cell types including endothelial cells, fibroblasts and macrophages. Among macrophages, they describe 2 resident subsets and 2 inflammatory subsets. By immunohistochemistry of arachnoid and dura flatmounts, they show vascular changes upon infection, including clustering of CLDN5 and PECAM1, and disorganized capillary morphology, which was dependent on Tlr4 signaling but independent of arachnoid macrophages.

The manuscript would benefit from rewriting, it is not written in a concise manner and the rationale for experiments, time points for analyses and their conclusions are not clear. The model of s.c. bacterial infection is not well introduced and overall changes in the periphery, survival curves or bacterial counts (in the KO models) in the meninges/brain are not mentioned.

Reviewer #2 (Public Review):

This study aimed to classify colorectal cancer (CRC) samples based on the expression of genes in selected gene lists, where the gene lists were chosen to represent aspects of the tumour microenvironment, tumour-associated immune cells, and tumour cells. The resulting clusters were then used to define a classifier, followed by a detailed description of molecular features of the tumours and tumour microenvironments assigned to each cluster. The authors claim this study is more "holistic" than previous work on CRC clustering/classifiers because they aimed to explicitly include additional components of the tumour microenvironment in both the clustering/classifier definition and in the subsequent description of molecular characteristics.

The CCCRC clustering and the resulting classifier presented in this paper are derived from published RNAseq studies. The multi-omics aspect of the work is restricted to smaller sample numbers for which both transcriptomic and another omics dataset were available in public resources and comprises a description or correlative analysis of each omics data type within each of the assigned CCCRC subtypes.

By applying solid computational methods to a compendium of published RNAseq datasets (n~1500 tumours), they found that tumour samples from colorectal cancers clustered into 4 subtypes ("CCCRC" subtypes) on the basis of 61 pre-defined gene expression signatures. These subtypes correlated with but did not correspond to, the previously described Consensus Molecular Subtypes (CMS) of colorectal tumours.

Other types of molecular data were available for some tumours, obtained from the same published resources: whole-slide images, mutations, tumour proteomics, and/or scRNAseq. The authors reanalysed these datasets using standard methods and drew correlations with the CCCRC subtypes they had assigned in this work. To (semi-)quantify immune infiltration characteristics from whole-slide images (WSI), they additionally performed automated segmentation in addition to review by pathologists, which in combination produced a convincing WSI-derived dataset.

In combination with existing CRC classifications, this study could facilitate future biomarker discoveries. This appears to be the authors' main claim, and the data and methods broadly support this claim.

Some aspects of the work need to be clarified:

This work relies on the definition of 4 clusters of CRC tumours based on consensus clustering of the 61 gene lists, which in turn depends on the choice of clustering method and the choice of gene lists. Sufficient detail is provided about the gene lists and resulting clusters, but this paper does not show how robust the 4 clusters are to these choices; for example, the "Energy" gene list appears to be a relatively strong component of clusters C2 and C3.

The authors examined whether their CCCRC classification showed differential disease progression in available retrospective cohorts of people treated with anti-PDL1 therapy. The authors presented this work as "significance of CCCRC in guiding the clinical treatment of colorectal cancer", but the data presented in this section cannot support clinical treatment decisions, which would require prospective studies and clinical trial designs. However, this section is potentially useful for generating hypotheses about potential biomarkers related to the CCCRC subtypes, and might, in the future with additional evidence, contribute to the design of a trial. The authors point out that additional experimental evidence would be required.

Other prognostic or predictive clinicopathological variables for colorectal cancer are not discussed in detail in the present work but are important for further work on the prognostic and predictive value of CRC molecular subtypes and biomarker derivation. Discrepancies in treatment response have previously been observed in separate CRC trials of biologically targeted agents with different chemotherapy backbones and other authors have suggested that treatment interactions with the tumour microenvironment might in part explain these discrepancies (e.g. Aderka (2019) PMID:31044725, and others).

Reviewer #2 (Public Review):

This paper provides an important and insightful investigation into patterns of activations that emerge in external task states. The authors use state-of-the-art methods and novel analytic approaches to establish that deactivations in the default mode network during external tasks are driven by activity in brain regions that are important in the current tasks (such as the visual or dorsal attention networks). It will be important in the future to understand whether this is a symmetrical phenomenon by studying this behaviour in states that maximize activity within the default mode network and also drive reductions in networks that are not relevant to these situations.

Reviewer #2 (Public Review):

Since this study is a long-term cohort study in children and adolescents, it is advisable to decide whether to highlight differences by age group or to show consistent effect after exposure. In particular, obesity and related diseases are closely related to socio-economic environmental factors, and its impact might be different according to age (group) at exposure.

The part described in comparison with previous studies is a good attempt. However, some results are consistent with those of previous studies and some are not. This may be related to the time difference in socio-economic environmental factors rather than simply the difference between the West and China (Hong Kong). According to modernization/urbanization, changes in living environment, changes in family relationships, and changes in the care environment can also be factors especially in children.

In studying the effect of environment on gene expression, it can be thought that the influence of genes and the degree of expression might be different depending on the age of the subject (newborn, infant, infant, adolescent, adult) duration of exposure and these still need to be elucidated.

Reviewer #2 (Public Review):

In the manuscript, Chen and colleagues reconstituted the minimal system that indicates the coupling of PSD condensates with actin polymerization. While the functional connection between the assembly and dynamics of PSD and actin was known, the molecular mechanism remained elusive. Using a series of elegant biochemical reconstitutions and in-vitro assays complemented with analysis in living cells and primary neurons, the authors characterized whether PSD condensates of Homer-1, Shank-3 and SAPAP/GKAP are sufficient to induce F-actin bundling. Furthermore, they dissected the positively-charged Arg patch within EVH1 domain of Homer to be crucial for the F-actin bundling. Postsynaptic CaMKII and a short isoform of Homer, Homer1a, can both attenuate this process, suggesting various mechanisms neurons can regulate this process. Overall, the topic is timely, the study is well-designed, and the assays are clearly executed. However, several aspects need to be experimentally addressed, including some important controls:

1. It is well established that molecular crowding plays a crucial role in F-actin bundling. For example, in the reconstitution assays in Fig.1, the authors use 10 µM of each component of PSD (total of 60 µM), to which 5 µM actin is added. Yet, in their control assays (Supp. Fig. 1), only 10 µM of each protein was checked with the same amount of actin. A control is missing where the total protein crowding would be preserved, for example, by adding BSA or protein to mimic non-specific protein crowding.<br /> 2. Is the F-bunding observed under these physiological ratios of PSD proteins and actin? For instance, a recent quantitative study (PMID: 34168338) suggests actin:Homer-1 is 200:1 or 100:1, which is in stark difference from the 1:2 molar ratio used in the study. The protein concentrations (molar ratios) need to match the physiological.<br /> 3. In the cell migration assays, it is somewhat unclear to what extent the interaction is direct. For instance, co-sedimentation at ultra-speed (100,000 g) was used to suggest a direct binding of EVH1-GNC4 fusions (Homer1, Enah) with F-actin. The control that needs to be included is a protein known not to bind to F-actin incubated under the same conditions (salt concentration, duration of incubation) and spun down at 100,000xg. This is important to exclude that the tested proteins non-specifically entangle into F-actin without specifically binding to it, particularly at such high speed.<br /> 4. The imaging assay in hippocampal neurons uses an increased spine head size as a proxy for F-actin bundling. However, one needs to be careful as the baseline includes soluble mCherry, which is both much smaller in size and does not specifically enrich the spines. The image of Homer 1 R3E shows overall lower localization at the spines. Thus, one cannot exclude that the spine enlargement upon overexpression of Homer 1 wt and R3E+EN is not primarily driven by their overall enrichment in the PSD phase. A suitable control for this assay would be mCherry-tagged PSD95, which would localize to the spines yet is not directly involved in F-actin bundling.

Reviewer #2 (Public Review):

This work is a cross-validation of an x-ray tomography technique (SAXS) and an optical microscopy technique (SLI) for imaging axonal orientations ex vivo. These innovative methods were introduced in recent papers by the authors, who have teamed up here to compare them side-by-side on the same tissue samples for the first time. The two methods are both label-free (do not require staining) and they are quite complementary. SAXS can provide full 3D orientation measurements on intact tissue, but it operates at a mesoscopic resolution and requires access to a synchrotron. SLI can measure the orientations of multiple fascicles per voxel at a microscopic resolution and relies on more widely accessible equipment, but its accuracy suffers for fiber orientations perpendicular to the imaging plane and it requires tissue to be sectioned before it is imaged. Therefore it makes a lot of sense to explore the complementary strengths of these two techniques, and to use one to "fill in the blanks" of the other. The paper also compares the orientation measurements obtained with SAXS and SLI to those obtained with diffusion MRI. The latter provides only indirect measurements based on water diffusion, at a mesoscopic resolution somewhat lower than that of SAXS, but has the benefit of being feasible in vivo.

A limitation of this study is that conclusions on the comparison between SAXS and SLI are drawn from only 2 sections of a partial monkey brain sample and 2 sections of a partial human brain sample. Conclusions on diffusion MRI are drawn only on the 2 human sample sections. This is particularly an issue for the comparison to diffusion MRI, as the diffusion MRI voxels are wider than the section thickness, hence one cannot preclude that any orientations detected with diffusion MRI but not with SAXS and SLI come from the portion of the voxel that is missing from the corresponding SAXS/SLI section.

The stated aim of the paper is to provide a framework for combining the complementary benefits of SAXS and SLI, rather than simply presenting the results of a cross-validation study. This is a significant and ambitious aim. However, in order for this to serve as a framework, there would have to be clear prescriptions for how researchers interested in obtaining ground-truth measurements of axonal orientations would do so by using these two methods in tandem. This is not adequately developed in the paper in its present form. For example, the results show reasonable agreement between SAXS and SLI orientations when fibers lie within the SLI imaging plane and decreasing agreement for fibers with increasing through-plane inclination. How would the two methods be combined in voxels where they disagree? Would one use SLI orientations in voxels with fewer through-plane fibers and SAXS orientations in voxels with more through-plane fibers? How would voxels be assigned to each category? How would the orientation vectors from the two modalities be composed and how would the resolution difference between the two be handled? When the through-plane measurement of SLI is unreliable, is its in-plane measurement still reliable? That is if there were one mainly in-plane and one mainly through-plane fiber population, would the orientation of the former still be measured correctly by SLI? There is also considerable agreement reported here between through-plane orientations obtained with SAXS and diffusion MRI. Would this mean that diffusion MRI itself could be used to supplement SLI with through-plane orientations? Any clear set of prescriptions along these lines would represent a framework for imaging orientations by combining modalities. This, however, would require detailed steps for how to perform the combination and use the multi- vs. uni-modal framework to reconstruct connectional anatomy.

A key advantage of SAXS is that it can be performed on intact samples, i.e., before any nonlinear distortions of the tissue are introduced by sectioning. Thus it can provide an undistorted reference, with contrast on axonal orientations that would be absent in, say, a structural MRI of comparable resolution. This contrast could be used to drive registration of the distorted SLI sections to an undistorted SAXS volume, and therefore is a key way in which the two techniques can complement each other. Here, however, this is not explored, as SAXS is performed after sectioning. It is not clear if this is the authors' prescription for how a combined SAXS/SLI framework would be implemented, or if it was done specifically for this study. First, it would seem that SAXS on the intact sample would be lower maintenance, requiring less setup time and hence potentially less overall beamtime than performing SAXS on each section separately. This would make it more practical for routine deployment beyond a few sections. Second, because the SAXS data are now nonlinearly distorted, they cannot be affinely aligned to the MRI volumes. While, in principle, performing both SAXS and SLI on the sections may facilitate the comparison between the two, having to unmount, rehydrate, and remount the sections in between may negate this advantage, as now there is no guarantee that SAXS and SLI can be affinely registered to each other. Here all these registration steps are performed affinely, so it is unclear to which extent the computed errors between modalities are characterizing the inherent limitations of the respective contrasts, or limitations of the registration technique. Some of the alignment is performed manually, for example, specific regions of the images are realigned by hand, and the slice of the diffusion MRI volume that is aligned to the SAXS/SLI sections is chosen by hand. Again, for this to serve as a framework that can be deployed on whole samples, there would have to be clear prescriptions for how to perform these steps robustly, how to ensure that the MRI can be acquired in a coordinate frame parallel to the sections, etc.

Finally, the paper puts forth a general conclusion that diffusion MRI overestimates the number of fiber populations per voxel, on the basis of small ODF peaks appearing perpendicular to the main ODF peaks. Of all conclusions in the paper, this is the least convincingly supported by evidence. First, these small perpendicular peaks are a known artifact, which would be typically eliminated by ignoring ODF peaks below a certain amplitude, a common practice in diffusion tractography algorithms. The authors refrain from using an amplitude threshold, with the rationale that it may also remove true diffusion orientations. However, they apply a threshold when they detect SLI peaks (a rather stringent 8% of the maximum). Second, the explanation that these artifactual peaks may appear due to vessel walls is not convincing. Vasculature is sparse. A single vessel wall will not impact the diffusion signal in the same way as a bundle of parallel axons. In an axon bundle, water molecule displacements are restricted in all directions except parallel to the axons. A single vessel wall in a voxel will not have the same effect on displacements (which are much smaller than the size of the voxel). From Figure 5, it looks like there would be at most 1-2 of these vessels in a diffusion MRI voxel, and they would not be in all voxels. This cannot explain the widespread appearance of these small artifactual peaks. Third, many ODF reconstruction methods have parameters that can be adjusted to make these artifactual peaks more or less prominent. The default parameters may be optimal for in vivo but not ex vivo data, due to the effects of fixation. In light of these concerns, I would caution against making such a general statement about all diffusion MRI in the human brain, especially on the basis of a single diffusion reconstruction method applied to a single location in one brain.

Reviewer #2 (Public Review):

Londoño-Nieto et al. investigated the influence of temperature on the form and intensity of sexual conflict in Drosophila melanogaster. They aimed to test the effect of naturally occurring temperature fluctuations on a wild population of Drosophila while disentangling pre- and post-copulatory episodes of sexual conflict. To this end, they exposed females to males under monogamy or polyandry, hence manipulating the degree of male harm experienced by females. The effect of temperature was explored by exposing these groups to 20, 24, or 28{degree sign}C. They found that female fitness suffered from male harm most at 24{degree sign}C and less at the other two temperatures. Interestingly, pre- and postcopulatory episodes of sexual conflict were affected differently by temperature. Overall, these data suggest that the relationship between sexual conflict and temperature can be strong and complex. Hence, these results can have important implications for the impact of sexual conflict on population viability, especially in light of the climate crisis.

This paper tackles a highly relevant question using an established model organism for sexual conflict and contains a rich dataset obtained using a series of carefully planned experiments and analysed in an appropriate way. Importantly, the authors used biologically meaningful temperatures and mating treatments, which increases the relevance of the data. The main conclusions are well supported by the data. Nevertheless, the devil is in the detail, and given the way the authors frame their study (i.e. testing a natural population under naturally occurring temperature fluctuations) and their results (i.e. sexual conflict is buffered by temperature effects in the wild) there are some limitations to be considered:

1) The authors frame their study as addressing the question of how sexual conflict reacts to naturally occurring temperature fluctuations in the wild. Nevertheless, the population used in this experiment had been kept for nearly 3 years in the laboratory prior to the experiment. Importantly, the authors ensured that the laboratory population maintained genetic diversity, by regularly crossing wild lines into it. Nevertheless, this population remained for some time in the laboratory under standardized conditions. The applied temperature fluctuations are in a biologically meaningful range (though only during the reproductive season), but it remains unclear if the applied fluctuations were in a standardized way (i.e. pre-programmed) or included random fluctuations (i.e. a more natural setting). This laboratory setup has certainly clear advantages, for example, it enables the exclusion of any effects other than the temperature on sexual conflict. Nevertheless, how these will then ultimately play out in the wild could be a different story.

2) The authors highlight clearly that temperature fluctuations in the wild might play an important part in how sexual conflict plays out in natural populations. This very interesting and highly relevant point might lead the reader to assume that this is what was actually tested in the experiment. Nevertheless, in the experiments, different constant temperatures were applied to the flies, while only the stock population was kept at a fluctuating temperature regime. Hence, the influence of fluctuations during episodes of sexual conflict remains untested. While the present data show that sexual conflict can be modulated by temperature, the effect of naturally occurring fluctuations on the net cost of sexual conflict to a population remains unclear.

3) The authors conclude that the effect of sexual conflict can be buffered by temperature in the wild. In general, I agree with this, although a more conservative way of framing this would be to say that temperature modulates or moderates sexual conflict instead of buffers it. If there really is a buffering effect of temperature in the wild remains to be tested, I believe. This will depend on how actual changes in temperature affect this dynamic (see point 2). In addition, I think another interesting open question is what the mechanism behind the observed differences might be. Are male and female interests really more aligned at different temperatures (i.e. males plastically reduce harm)? This would really buffer the harm of sexual conflict at those temperatures. Nevertheless, alternatively, males might not be perfectly adapted to manipulate the female optimally at lower or higher temperatures. This would mean that if the temperatures change, males might evolve to increase the manipulation of females, and hence the scope for sexual conflict might not change in the end under this scenario. Nevertheless, as the authors themselves state: 'An intriguing possibility is thus that SFPs are more effective at lowering female re-mating rates at warm temperatures, thereby buffering these costs.' Therefore, a temperature-dependent increase in the effectiveness of male manipulation might counterintuitively reduce sexual conflict in this species.

4) In the end the authors argue that the climate crisis might have 'unexpected positive consequences via its effect on male harm'. Sexual conflict is indeed widespread, but it takes many different forms (as has been nicely described in the introduction of this paper). Because the studied system seems to be quite a specific example, it is questionable how far spread this phenomenon is in nature. In addition, it remains unclear how male harm will evolve in response to the climate crisis (see point 3). Finally, the relative fitness of females increased in the present experiment, as the tested range was within the reproductive optimum of the species. Nevertheless, the relative importance of the positive effect of sexual conflict on fitness outside of optimal temperatures seems questionable.

Nonetheless, I believe these results to be of exceeding interest to the scientific community and of importance to the field. It opens up many potential research directions and adds further data to the fascinating field of sexual conflict, SFPs, and male harm in Drosophila.