Reviewer #1 (Public Review):

Luu et al. have developed a genome-edited African elite rice variety, Komboka. The work was initiated in response to the outbreak in Eastern Africa by Xanthomonas oryzae strains that are phylogenetically related to Asian strains and carry TALes, similar to strains from China, possessing an expanded repertoire of TALes compared to those in endemic strains. As these emerging strains contain TALe targeting SWEET11a, as well as that suppressing Xa1, pthXo1, and iTALes, the authors have generated edited lines targeting promoter regions of SWEET11a, 13 and 14 in African elite rice variety, Komboka. The same team has previously generated genome-edited lines targeting the promoter regions of SWEET11a, 13, and 14 in varieties Kitaake, IR64, and Ciherang-Sub1. Bacterial blight outbreaks and emerging pathogen lineages remain to be a threat to rice production. Thus, efforts in targeting pathogen weaknesses to generate genome-edited varieties possessing broad-spectrum resistance are required. The survey, collection of isolates, and strain characterization studies on >800 strains are commendable. This study has taken advantage of this ongoing collection to stay ahead in the arms race to deploy broad-spectrum resistance in an elite rice variety using TALe targets.

Overall conclusions presented here are supported to some extent; however, I have listed some of my comments and concerns below.

1. Data in supplementary table 2 suggests that Komboka is still a moderately resistant variety under field conditions in Africa, with a disease severity scale of 2 i.e. 4-6% disease severity, compared to other varieties having a disease severity scale of 5. Thus, I am not convinced that emerging strains are of concern on the Komboka variety under field conditions, thus, question the justification of Komboka being a choice for editing to tackle emerging strains.

2. Is Xa4 from Komboka related to Xa4_Teqing? The breakdown of Xa4T was due to the mutant allele of avrXa4 in virulent Xoo CR6. But this breakdown was accompanied by a fitness penalty and residual QTL had a significant residual effect on virulent strains. Would this be why Komboka carrying Xa1 (Xa45(t) and Xa4 under field conditions still showed moderate resistance? But Xoo strains showed susceptibility in leaf clipping assays.

3. I felt a bit of a disconnect in sections on phenotypic assays confirming the virulence profile of strains on Komboka and then understanding mechanisms underlying virulence since the same strains used in path data were not the ones mentioned in WGS and TALe analysis, leaving the readers with the only one strain to support the hypothesis of the basis for higher disease severity on Komboka due to new TALes, pthXo1, and iTALe. Do authors have pathogenicity data for African strains T19, Dak16, and Xoo3-1 that grouped with endemic African strains on Komboka? Authors present data on CIX4457, 4458, and 4462 being virulent on Komboka, and show that they cluster with Asian strains. However, in the tree, 4462 is the only one shown to be closely related to Chinese strains. Where are 4457 and 4458 placed? Do 4457 and 4458 also contain pthXo1 and iTALe? Authors could also provide path data for 4506/4509 that they included in TALe figure and in the phylogenetic tree.

4. The authors present pathogenicity data on EBE-edited T0, T1, and T2 lines of Komboka which are promising against the Tanzanian strains carrying new TALes. The cas9/cpf1 system developed here to target multiple EBEs will be a valuable contribution to the scientific community. What are the agronomic characteristics of these edited lines? As the edited lines have not been tested against a diversity panel of Asian and African strains, I would be skeptical of the choice of the term "broad-spectrum" yet.<br /> Regardless of my comment earlier on Komboka being moderately resistant under field conditions and thus a questionable choice for EBE-editing here, the genome-edited lines in any variety imparting resistance to bacterial blight remain to be a valuable contribution to managing disease outbreaks.

5. As this manuscript utilizes the diversity of African strains to generate edited lines, it would be good to make diagnostics and path data for 833 strains available to the scientific community (instead of select strains as indicated in the supplementary table), especially for the fact stated here in the manuscript about scarce data on Xoo in Africa and the goal of systematic comparison of the pathogen population.

Reviewer #1 (Public Review):

This study used intersectional genetic approaches to stimulate a specific brainstem region while recording swallow/laryngeal motor responses. These results, coupled with histology, demonstrate that the PiCo region of the IRt mediates swallow/laryngeal behaviors, and their coordination with breathing. The data were gathered using solid methods and difficult electrophysiological techniques. This study and its findings are interesting and relevant. The analysis (and/or the presentation of the analysis) is incomplete, as there are analyses that need to be added to the manuscript. The interpretation of the data is mostly valid, but there are claims that are too speculative and are not well-supported by the results. The introduction and discussion would benefit from more citations and a deeper exploration of how this study relates to other work - especially a thorough accounting of and comparison to other studies concerning putative swallow gates.

General/major concerns:

• The field of respiratory control is far from unified regarding the role of PiCo in breathing or any other laryngeal behaviors. If anything, the current consensus does not support the triple-oscillator hypothesis (in which PiCo is one of 3 essential respiratory oscillators). The name "PiCo", short for "post-inspiratory complex", suggests a function that has not been well-supported by data - it is a putative post-inspiratory complex, at best. I suggest putting this area in context with other discussions i.e. IRt (such as in Toor et al., 2019) or Dhingra et al. 2020 showed broad activation of many brainstem sites at the post-I period (including pons, BotC, NTS)

• Did you perform control experiments in which the opto stimulations were done on animals without the genetic channels (for example, WT or uncrossed ChAT-ires-cre, etc.), or in mice with the genetic channels that weren't crossed (uncrossed Ai32 mice)? If so, please include. If not, why?

• How do you know that your opto activations simulate physiological activation? First, the intensive optical activation at the stim site does not occur in those neurons naturally. Doing a natural (water) stim for comparison is good, but it cannot necessarily be directly compared to the opto stim. The water stim would activate many other brainstem regions in addition to PiCo. A caveat is that opto PiCo stim =/= water stim (in terms of underlying mechanisms) should be included. Second, in looking at the differences between water vs opto swallows in Table S2: it appears that the ChAT animals (S2A) have something weaker than a swallow with opto stim. For the Vglut2 and ChAT/Vglut2 (S2B&C), the opto swallows also aren't as "strong" as the water swallows (the X and EMG amplitudes are smaller). The interpretation/discussion attributes this to the lack of sensory input during opto stim, but does not mention the strong possibility that there is a difference in central mechanisms occurring. It also seems to be dismissed with the characterization of the swallow as "all-or-none" (see note on Fig 3 results).

• The writing needs extensive copy editing to improve clarity and precision, and to fix errors.

• Results/Fig 1: What proportion had no/other motor response (non-swallow, non-laryngeal) to the opto stim? I can extrapolate by subtraction, but it would be nice to see the "no/other response" on the plot.

• The explanation of genetics is too spread out and confusing. There needs to be more detail about all the genetic tools used, using the standard language for such tools, in one spot. Please also provide a clear explanation of what those tools accomplish. Include a figure if necessary. Pick a conventional designator/abbreviation for the different strains, define them in the methods and in the first paragraph of the results section, and use those abbreviations throughout. I think that using ChAT as an abbreviation for your ChAT-ires-cre x Ai32 mice is confusing because it makes it sound like you're talking about the enzyme rather than the specific strain/neurons. Saying "ChAT stimulated swallows... swallows evoked by water or ChAT" makes it sound like the enzyme choline acetyltransferase itself is stimulating swallow. As is convention, pick a more precise abbreviation like ChAT-cre/Ai32 or ChAT:Ai32 or ChAT-ChR2 or ChAT/EYFP. This goes for the other strains as well.

• For Fig S2C&D, why does it say mCherry? Isn't it tdTomato? Is it just an anti-ChAT antibody and then the tdTomato Ai65 is only labeling Vglut2? I don't see this in the methods section.

• I also don't see methods for all the staining in Fig S3. The photomicrograph says Vglut2-cre Ai6, but there's no mention of Ai6 anywhere else. Which mice are these? Did you cross Vglut2-cre with an Ai6 reporter mouse? How can you image an Ai6 mouse (which I assume expresses ZsGreen? and that you excited at 488?) and a 488 anti-goat in the same section (that's the only secondary listed in the methods that would work with your goat anti-ChAT)? Is there an error in listing the fluorophores in the methods? Please give more details on the microscopy including which filters were used for the triple staining.

• Regarding the staining: I would expect the staining/maps in for the 2 different ChAT/Vglut2 intersectional strains to be similar (Fig 5A/B and S2C/D). The photomicrographs look very different to me, while the heat maps (this goes for all the heat maps in the paper) have barely distinguishable differences. In Fig 5, the staining looks much stronger than in Fig S2C. Why does it look like there are so many more transfected neurons in Fig 5A2 than there are red neurons in the corresponding panel Fig S2C2? And for Fig 5A4 and Fig S2C44? The plot and results text for Fig 5 says the avg number of neurons was 123+¬11. The plot for Fig S2D says 112+¬15, but the results text says 242+¬12 (not sure which is the correct number).

• The results text for Fig S2C also says the staining is "similar to the previous ChAT staining...", which I assume refers to S2A/B. The plot and results text for Fig S2B reports 403+¬39 neurons, while S2D is either 112 or 242 (not sure?). The plots have different Y scales, which should be changed to be the same. But why do the photomicrographs and the heat maps look so similar? I would expect far fewer neurons to be stained in the intersectional mice (Fig 5 and Fig S2C/D) than in the ChAT staining (Fig S2A/B). I am having trouble reconciling the different presentations/quantifications and making sense of the data in these histology figures.

• How can you distinguish PiCo from non-PiCo in the histology, especially in the ChAT-only staining? It seems that you have arbitrarily defined the PiCo region, and only counted neurons within that very constrained area. I can see stained neurons in the area immediately outside of PiCo, and I'd like to see lower-magnification images that show the staining distribution in a broader region surrounding PiCo as well, especially in the rest of the reticular formation.

• Similarly, how can you be sure you're stereotaxically targeting PiCo precisely (600um in diameter?) with your opto fiber (200um in diameter). Wouldn't small variations in anatomy put the fiber outside the tiny PiCo area?

• Please put N's and stats results in Table S1 for both swallow and laryngeal activity. I took what I assume to be the Ns (10, 11, and 4) and did some stats like the ones you presented for the laryngeal duration. The differences between vagus duration for 40 and 200 ms pulse durations are all significant for each strain, by my calculations. Also, I think there must be an error in the orange swallow plot in Fig 3A. The orange dots don't correspond to the table values. I plotted all the Table S1 values for each strain. Each line looks similar to the blue laryngeal activation plot in Fig 3A. The slopes of the Vglut2 were less than the other strains, and the slopes for the swallow behavior were less than the laryngeal behavior for all strains. Otherwise, they all look similar. Please double-check your values/stats to address these discrepancies. If it is indeed true that the stim pulse duration affects swallow duration, revise the interpretations and manuscript accordingly.

• Please add more details on stats in general, including the specific tests that were performed, F values and degrees of freedom, etc.

• How do you know that you're not just activating motoneurons in the NA when you stimulate your ChAT animals, especially given the results in Fig 1B? In this case, the phase-specific results could be explained by inhibitory inputs (during inspiration) to motoneurons in the region of the opto stim.

• While the study from Toor et al is cited, there needs to be a much more thorough discussion of how their findings relate to the current study. They demonstrated that PiCo isn't necessary for the apneic portion of swallow. Inhibiting this region also didn't affect TI. PiCo cannot be the sole source of post-I timing, and the evidence overwhelmingly favors the major involvement of other regions such as the pons. They also showed that inhibition of all neurons (not just ChAT/Vglut) in the PiCo region suppresses post-I activity in eupnea. This suppression was overcome by the increased respiratory drive during hypoxia.

• This study has not demonstrated some of the things that are depicted in Fig 7 and included in the discussion. While swallow can inhibit inspiration, there are many mechanisms by which this can happen other than a direct inhibitory connection from the DGS to PreBotC. You cite Sun et al., 2011 findings of "a group of neurons that inhibits inspiration" during SLN stim, but don't mention that it is the BotC and that the paper shows that swallow apnea is dependent on BotC. That is also supported by the Toor study. I don't understand how post-I (aka E) can be discussed without discussion of the BotC - this is a glaring omission.

• Why is it necessary for PiCo to innervate the cNTS? That is true if the conjecture that PiCo gates swallowing is true, as the cNTS is the only known region for central swallow gating. However, PiCo could influence afferent input to the NTS less directly, and therefore not function as a gating hub per se. The experimental evidence does not warrant the claim that PiCo gates swallowing. The definition of a swallow gate(s) is a topic of much debate and no conclusive experimental evidence has emerged for swallow gating regions to exist anywhere except in the NTS. The current study's evidence also does not meet the criteria necessary to conclusively call PiCo a swallow gate. The authors should soften this claim and language throughout the manuscript.<br /> It is also unclear that PiCo acts directly on the swallow pattern generator to gate swallowing. It is not just "conceivable that the gating mechanism involves" the pons, but nearly certain. Swallow gating by respiratory activity may not be able to be ascribed to one particular location. At a minimum, it likely involves the NTS/DSG, pons, and possibly IRt (inclusive of PiCo). The authors are correct that "further studies are necessary to understand the interaction between PiCo and the pontine respiratory group on the gating swallow and other airway protective behaviors." This is why it shouldn't be stated that "this small brainstem microcircuits acts as a central gating mechanism for airway protective behaviors."<br /> PiCo is likely part of the VSG (and thus the swallow pattern generator). PiCo, as part of the IRt/VSG could indeed be surveilling afferent information and providing output that affects swallow or other laryngeal activation and the coordination of these behaviors with breathing. However, this is not the responsibility of PiCo alone. This role is likely shared by other parts of the SPG, and may require distributed SPG network participation to be functional. If one were to stim other regions of the distributed SPG, similar results might be expected. When Toor et al silenced the PiCo area (and locations that lie at least lightly beyond the borders of what the present study defines as PiCo), stim-evoked fictive swallows were greatly suppressed. However, swallow-related apnea was unaffected. This supports the role of PiCo as a premotor relay for swallow motor activation, but not as the site that terminates inspiration. Therefore, it cannot be called a gate.

• Similarly, Fig 7 does not accurately depict things that are already well-supported by evidence. PiCo should be included as part of the swallow pattern generator (VSG), not as a separate entity acting on it. The BotC and pons are glaring omissions. This study has not demonstrated the labeled inhibitory connection from DSG to PreBotC. The legend states speculations as fact and needs to be dialed way back to either include statements with solid experimental evidence or to clearly mark things as putative/speculative.

• The discussion of expiratory laryngeal motoneurons needs to be expanded and integrated better into the discussion of swallow, post-I, and other laryngeal motor activation. Why can't PiCo just be premotor to ELMs?

• Concerning the discussion of "PiCo's influence as a gate for airway protective behaviors is blurred...": The incomplete swallow motor sequence didn't seem super different in timing or duration compared to the fully transfected animals (comparing plots from Fig 6 to Fig S1, and Table S2 to Table S3. The values for swallow durations (XII and X) for each group for water and opto seem within similar ranges, as do the differences between water & opto-evoked swallows between strains. While the motor pattern is distinctive from the normal swallow, with laryngeal activity rather than submental activity leading, one might not even be able to call that a swallow. Is it evidence against a classic all-or-nothing swallow behavior any more than the graded swallow results from (fully transfected) Table S1?

• Please expand on this point and put it into context with others' results: "This brings into question whether this is the first evidence against the classic dogma of swallow as an "all or nothing" behavior, and/or whether this is an indication that activating the cholinergic/glutamatergic neurons in PiCo is not only gating the SPG, but is actually involved in assembling the swallow motor pattern itself."

Reviewer #1 (Public Review):

This paper explores the potential regulatory role of a previously unstudied phosphorylation site in the Src kinase SH3 domain. The data presented conclusively demonstrate that a phosphomimetic mutation of this site, src90E, causes an elevation in Src kinase activity, changes the structure of the Src catalytic domain as determined with a FRET sensor, disrupts certain SH3 domain interactions, causes changes in kinase intracellular dynamicity, and promotes cell invasiveness. Based on the behavior of the phosphomimetic mutant, the idea that constitutive phosphorylation of Y90 could have all of these effects is well-supported by the data. However, in wild-type cells or cells transformed by activated forms of Src, there is no constitutive phosphorylation of this site. Therefore, the question remains whether Y90 phosphorylation occurs to any significant extent in cells, and the data suggesting that it could do so is limited. It also remains to be conclusively established whether Y90 phosphorylation occurs via autophosphorylation.

Major comments:

1. Y90 was identified as a site of phosphorylation in Luo et al. It would be helpful if more information were provided about its significance relative to other sites identified in that study. Was it detected in non-transformed cells? Was it a major site? How does it relate to Y416 in abundance? The reference to the identification of the site in a different study from the White lab is made in the discussion but not in the introduction (this should be corrected). How abundant was it that study? A fuller description of its detection would strengthen the rationale for this study. Any additional phosphoproteomics studies that identified it should also be included.

2. Related to point 1, is there evidence from the literature indicating a significant site of phosphorylation in Src has been overlooked? Or, was this site only identified because of the recent advances in MS technology and increased sensitivity of this methodology? An introduction to these points would also enhance the rationale for the study.

3. The explanation of the MS experiment designed to show that Y90 phosphorylation happens in cells is insufficient in the text. It is not clear why the SYF cells were not used and not clear why the FRET sensor constructs were used. It is also not clear whether or how the proteins were purified before MS analysis. Also, rather than showing the MS data as a relative level, it would be preferable to provide the number of spectra obtained for each peptide/phosphopeptide and compare this also to Y416. A fuller comparison between the phosphorylation of Y90 to that of Y416 is necessary in order to place the potential Y90-mediated phosphoregulation in context.

4. I would like to see conclusive evidence that Y90 phosphorylation is due to autophosphorylation. This would involve relatively simple experiments. As one possibility, an IP kinase assay followed by immunoblotting with a site-specific antibody or MS or other types of phosphopeptide visualization/identification.

5. A few other mutations would be interesting to examine in both kinase and transformation assays for comparison to the mutants that were: Y527F Y416F; Y527F Y416F Y90E. The first is a low activity control and the second is for understanding whether Y90E could overcome the lack of Y416 phosphorylation.

5. I recommend that the results are discussed in a more circumspect manner. The results presented in Figure 7 on the double mutant, Y527F Y90F, suggest that phosphorylation of Y90 is not a very significant component of Src kinase regulation, at least in these biological contexts. That Y90 phosphorylation isn't a major regulatory factor does not diminish the value of the work describing Y90 phosphorylation. However, it does alter the interpretations. I encourage a more conservative discussion of its importance and a broader discussion of why it isn't a major site of Src phosphorylation, particularly if it is due to autophosphorylation.

Reviewer #1 (Public Review):

This manuscript reports the results of a clever chemogenetic manipulation study designed to probe how stimulation (excitation and inhibition) of D1-expressing spiny projection neurons in the striatum (direct pathway) influences hemodynamic characteristics of local and global regions in the brain in mice. Stimulating in the dorsal caudate, the authors found alteration of the local hemodynamic BOLD responses in adjacent areas of the striatum, regions of the thalamus known to project back to the striatum, and more unimodal cortical regions. The authors also observed a decrease in functional connectivity between several cortical regions and the striatum with direct pathway excitation, and the opposite effect with direct pathway inhibition. Put together the results begin to paint a picture of the macroscopic signatures of direct pathway stimulation (and inhibition) that could be used to help infer global BOLD patterns in task-related experiments.

Overall, this appears to be a timely study. The rise of papers using chemo/optogenetic methods to probe the underlying mechanisms of the BOLD response is crucial for the neuroimaging field to continue moving forward. The methods are, for the most part, rigorous and clear. There are, however, a few open issues that require addressing in order for readers to reach the same conclusion as the authors.

MAJOR CONCERNS

1. Classifier as an evaluation method.

The authors largely rely on a support vector machine (SVM) classifier to predict whether BOLD dynamics within atlas-defined regions reflect stimulation-on or stimulation-off windows. While in one way this is a conservative method for evaluating stimulation effects in the resting BOLD fluctuations, the authors largely report their findings as accuracies of the classifier. Figures 3-5 largely only report model accuracy effects, but we get no sense as to what exactly is happening to the BOLD dynamics in each region. The autocorrelation analysis (Fig. 6) somewhat tries to get at this, but only for a subset of regions and the results are largely unclear (see comment below). As a result, a key goal of the study is left largely unaddressed for the reader: i.e. how do intra-region BOLD dynamics change with direct pathway stimulation? The study needs more effort put into this descriptive level of analysis to complement the rigorous classifier analysis.

Also, the classifier method itself seems highly parameterized. The hctsa method returns 7702 features for each time series. It is unclear exactly how many were in the final set used to run the classification, but even if half of the features were removed, it would still make the classification problem highly overparameterized (e.g., 23 and 25 observations against thousands of features for the excitation and inhibition classifiers respectively). Assuming the authors used cross-validation correctly (which we need more information to support), the risk of inflated classification performance is mitigated. However, we need the details to be able to vet that the bias-variance tradeoff was resolved effectively in this model. In addition, it would be nice to know the features that loaded highly on the final model to resolve the questions about what changes in the local BOLD dynamics from excitation and inhibition of the direct pathway.

2. Local stimulation effects in the striatum

Figure 3 is quite confusing. The classifier is supposed to predict stimulation (excitation or inhibition) on vs. stimulation off (control) periods. This would predict a single number (balanced prediction accuracy) per striatal nuclei. Yet the heat maps shown in this figure show classification accuracies for both stimulation and control conditions. Where do the two numbers come from? Also, given the extremely limited short-range lateral connectivity in the striatum, why are the only stimulation effects observed not in the subnucleus being stimulated (Cpl,dm,cd), but for adjacent subnuclei (CPre, CPivmv) and *only* for excitation conditions? This lack of direct change in BOLD dynamics at the stimulated site seems important and largely ignored.

3. Autocorrelation findings.

The one attempt to characterize what happens in the intra-region BOLD dynamics is the autocorrelation analysis reported on page 11 and in Figure 6. However this analysis a) only focuses on the thalamic nuclei (not also the cortical and the single striatal site shown to exhibit stimulation effects) and b) only focuses on a few time series measures. Why this limited focus of both target (thalamic nuclei) and measure (first lag of the autocorrelation)? There are many measures for characterizing the temporal characteristics of autocorrelated series from the hctsa analysis. This selective focus seems both narrow and incomplete.

4. Connectivity results

The changes in functional connectivity as a result of direct pathway stimulation (excitation and inhibition) are both fascinating and limited. There is a clear excitation/inhibition difference in effects, as shown in Figure 7 B-C. However, Figure 7B suggests something different than the change results shown in Figure 7C. It appears that the application of clozapine increases functional connectivity in the control mice (black line Fig. 7B). This effect is exaggerated in the inhibition condition, but (most importantly) direct pathway excitation does not really reveal a significant change in the BOLD connectivity patterns. Now this does not change the authors' overall conclusions (connectivity is suppressed with direct pathway excitation relative to control mice), but the nuance of what is happening in the control mice is important for interpretation purposes: direct pathway excitation does not necessarily decrease functional connectivity but does not express the increase in connectivity observed from the application of clozapine. This needs to be elaborated more.

Along the same lines, there is an interesting disconnect between the intra-region results and the inter-region (connectivity) results. It is clear that resting BOLD dynamics in thalamic nuclei that project back to the striatum, as well as more unimodal cortical areas, change from direct pathway stimulation in the dorsal caudate. Yet, only one cortical region (MOp) with significant functional connectivity changes overlaps with the set of nuclei that exhibit intra-region BOLD changes. This suggests that local BOLD dynamics and global connectivity are largely disconnected effects. Yet this seems to be largely ignored in the current work. It would be nice to see more analysis, and discussion, of the intra-region and inter-region stimulation effects.

Reviewer #1 (Public Review):

The authors carried out a clever and systematic analysis. Most SNARE complex assembly reactions are thought to involve multi-subunit tethers, and it now seems possible that Uso1/p115 plays this role in the case of the ER-to-Golgi SNARE complex. In addition to documenting the main conclusions of the paper, they characterized Uso1 in various ways, including an analysis of how the different domains of Uso1 contribute to the higher-order structure of the protein and to interactions with other components.

I have only one significant comment about the presentation. The concept that the mutant Uso1 rescues the loss of Rab1 by binding more tightly to SNAREs is reasonable and likely but is not formally proven by the data. Instead, the data show that the mutant binds better to Golgi membranes in the absence of Rab1 and also binds better to Bos1. It should be acknowledged that this correlation is merely suggestive.

Reviewer #1 (Public Review):

This work by Stauber et al. is focused on understanding the signaling mechanisms that are associated with tendinopathy development, and by screening a panel of human tendinopathy samples, identified IL-6/JAK/STAT as a potential mediator of this pathology. Using an innovative explant model they delineated the requirement for IL-6 in the main body of the tendon to alter the dynamics of cells in the peritendinous synovial sheath space.

The use of a publicly available existing dataset is considered a strength since this dataset includes expression data from several different human tendons experiencing tendinopathy. This facilitates the identification of potentially conserved regulators of the tendinopathy phenotype.

The clear transcriptional shifts between WT and IL6-/- cores demonstrates the utility of the assembloid model, and supports the importance of IL6 in potentiating the cell response to this stimuli.

There are two main concerns with the manuscript in its current form:<br /> First, the experimental approach does not directly assess proliferation, as such the conclusions regarding proliferation are not well supported. In the ex-vivo model, the use of cell counting approaches is somewhat acceptable since the system is constrained by the absence of potential influx of new cells. However, given the nearly unlimited supply of extrinsically derived cells in vivo (vs. the explant model), assessment of actual proliferation (e.g. Edu, BrdU, Ki67) is critical to support this conclusion.

Second, the justification for the use of Scx-GFP+ cells as a progenitor population is not well supported. Indeed, in the discussion, Scx+ cells are treated as though they are uniformly a progenitor population, when the diversity of this population has been established by the cited studies, which do not suggest that these are progenitor populations. Additional definition/ delineation of these cells to identify the subset of these cells that may actually display other putative progenitor markers would support the conclusions. As it stands, the study currently provides important information on the impact of IL6 on Scx+ cells, but not tendon progenitors.

Reviewer #1 (Public Review):

In this study, the authors examined the putative functions of hypothalamic groups identifiable through Foxb1 expression, namely the parvofox Foxb1 of the LHA and the PMd Foxb1, with emphasis on innate defensive responses. First, they reported that chemogenetic activation of Foxb1hypothalamic cell groups led to tachypnea. The authors tend to attribute this effect to the activation of hM3Dq expressed in the parvofox Foxb1 but did not rule out the participation of the PMd Foxb1 cell group which may as well have expressed hM3Dq, particularly considering the large volume (200 nl) of the viral construct injected. It is also noteworthy that the activation of the Foxb1hypothalamic cell groups in this experiment did not alter the gross locomotor activity, such as time spent immobile state. Thus, contrasts with the authors finding on the optogenetic activation of the Foxb1hypothalamic fibers projecting to the dorsolateral PAG. In the second experiment, the authors applied optogenetic ChR2-mediated excitation of the Foxb1+ cell bodies' axonal endings in the dlPAG leading to freezing and, in a few cases, bradycardia as well. The effective site to evoke freezing was the rostral PAGdl, and fibers positioned either ventral or caudal to this target had no response. Considering the pattern of Foxb1hypothalamic cell groups projection to the PAG, the fibers projecting to the rostral PAGdl are likely to arise from the PMd Foxb1 cell group, and not from the parvofox Foxb1 of the LHA. Here it is important to consider that optogenetic ChR2-mediated excitation of the axonal endings is likely to have activated the cell bodies originating these fibers, and one cannot ascertain whether the behavioral effects are related to the activation of the terminals in the PAGdl or the cell bodies originating the projection. Moreover, activation of PMd CCK cell group, which consists of around 90% of the PMd cells, evokes escape, and not freezing. According to the present findings, a specific population of PMd Foxb1 cells may be involved in producing freezing. In addition, only a small number of the animals with correct fiber placement presented sudden onset of bradycardia in response to the photostimulation. Considering the authors' findings, the Foxb1+ hypothalamic groups are likely to mediate behavioral responses related to innate defensive responses, where the parvofox Foxb1 of the LHA would be involved in promoting tachypnea and the PMd Foxb1group in mediating freezing and bradycardia. These findings are very interesting, and, at this point, they need to be tested in a scenario of real exposure to a natural predator.

Reviewer #1 (Public Review):

Somasundaram and colleagues explore the role of transcription factors in retinal ganglion cell (RGC) death and axonal regeneration after a disease relevant insult (mechanical axonal injury). The work significantly extends our knowledge of the role of MAPK and integrated stress response (ISR) in controlling RGC fate after injury. Specifically, the manuscript shows that after axonal injury PERK-activated ISR acts through Atf4 to drive a prodeath transcriptional response in RGCs, in part by crosstalk with the prodeath JUN transcriptional program. Also, and perhaps most interesting, the work shows that PERK-ATF4 pathway activation is pro-regenerative for RGC axons. A major plus of the manuscript is that many new RNA-seq datasets are generated that describe the major prodegenerative and proregenerative gene networks altered after axonal injury.

A limitation of the study is that it does not directly compare the effect of inhibiting the PERK-ATF4 pathway with inhibiting JUN and/or JUN-CHOP double deficient animals. It would also be useful, for the cell survival experiments shown in Figure 1, to examine a longer time point than 14 days to understand the long-term consequence of manipulating the PERK-ATF4 pathway.

Reviewer #1 (Public Review):

The study by Oikawa and colleagues demonstrates for the first time that a descending inhibitory pathway for nociception exists in non-mammalian organisms, such as Drosophila. This descending inhibitory pathway is mediated by a Drosophila neuropeptide called Drosulfakinin (DSK), which is homologous to mammalian cholecystokinin (CCK). The study creates and uses several Drosophila mutants to convincingly show that DSK negatively regulates nociception. They then use several sophisticated transgenic manipulations to demonstrate that a descending inhibitory pathway for nociception exists in Drosophila.

Strengths:

This study creates the possibility of using Drosophila to study descending nociceptive systems.

CRISPR/Cas9 is used to generate mutants of dsk, CCKLR-17D1, and CCKLR-17D3. The authors then use these mutants to clearly show that DSK negatively regulates nociception.

Several GAL4s are used to clearly show that these effects are likely mediated by two sets of neurons in the brain, MP1 and Sv.

RNAi and rescue experiments further show that CCKLR-17D1, a DSK receptor, functions in Goro neurons to negatively regulate nociception.

Thermogenetic experiments nicely show that activation of DSK neurons attenuates the nociceptive response.

Weaknesses:

Future studies should address how DSK negatively regulates nociception. An earlier study at the Drosophila nmj shows that loss of DSK signaling impairs neurotransmission and synaptic growth. In the current study, loss of CCKLR-17D1 in Goro neurons seems to increase intracellular calcium levels in the presence of noxious heat. An interesting future study would be the examination of the underlying mechanisms for this increase in intracellular calcium.

Reviewer #1 (Public Review):

stdpopsim is an existing, community-driven resource to support population genetics simulations across multiple species. This paper describes improvements and extensions to this resource and discusses various considerations of relevance to chromosome-scale evolutionary simulations. As such, the paper does not analyse data or present new results but rather serves as a general and useful guide for anyone interested in using the stdpopsim resource or in population genetics simulations in general.

Two new features in stdpopsim are described, which expand the types of evolutionary processes that can be simulated. First, the authors describe the addition of the ability to simulate non-crossover recombination events, i.e. gene conversion, in addition to standard crossover recombination. This will allow for simulations that come closer to the actual recombination processes occurring in many species. Second, the authors mention how genome annotations can now be incorporated into the simulations, to allow different processes to apply to different parts of the genome - however, the authors note that this addition will be further detailed in a separate, future publication. These additions to stdpopsim will certainly be useful to many users and represent a step forward in the degree of ambition for realistic population genetics simulations.

The paper also describes the expansion of the community-curated catalog of pre-defined, ready-to-use simulation set-ups for various species, from the previous 6 to 21 species (though not all new species have demographic models implemented, some have just population genetic parameters such as mutation rates and generation times). For each species, an attempt was made to implement parameters and simulations that are as realistic as possible with respect to what's known about the evolutionary history of that species, using only information that can be traced to the published literature. This process by which this was done appears quite rigorous and includes a quality-control process involving two people. Two examples are given, for Anopheles gambiae and Bos taurus. The detailed discussion of how various population genetic and demographic parameters were extracted from the literature for these two species usefully highlights the numerous non-trivial steps involved and showcases the great deal of care that underlies the stdpopsim resource.

The paper is clearly written and well-referenced, and I have no technical or conceptual concerns. The paper will be useful to anyone interested in population genetics simulations, and will hopefully serve as an inspiration for the broader effort of making simulations increasingly more realistic and flexible, while at the same time trying to make them accessible not just to a small number of experts.

Reviewer #1 (Public Review):

In this study, Dominici et. al. show that small molecule inhibition of Type I PRMTs in muscle stem cells (MSCs) can result in the expansion of this cell type in vitro, solving a major limitation in the field. Importantly, once the inhibitor is removed these stem cells differentiate "normally". This advance will likely facilitate CRISPR-based screening approaches and stem cell engraftment therapy. Furthermore, they show that when a mouse model of Duchenne muscular dystrophy is treated with these same inhibitors these mice rather rapidly gain grip strength, demonstrating the therapeutic value of these findings.

Strengths:

- Previous studies from the same group have shown that the conditional ablation of PRMT1 in MSCs results in the expansion of this cell type, but this expanded PRMT1-null MSC pool cannot terminate the myogenic differentiation program. This raises the question of whether PRMT1 small molecule inhibition of MSCs will also facilitate the expansion of these cells, and if the removal of the inhibitor after expansion will result in a large functional pool of MSCs, which could then be used for both in vitro and in vivo studies.

- Using a combination of muscle fiber culture, myoblast culture, and single-cell RNA-seq, this is indeed what they show.

- They also perform two types of in vivo experiments to validate their cell culture findings; 1) MSCs expanded under the treatment of MS023 were washed clean of the inhibitor and engrafted into the tibialis anterior muscle. These cells were marked with GFP to allow efficient tracking. Mice receiving the MS023-treated MSCs produced more than double the mature GFP+ muscle fibers than cells treated with DMSO. 2) A mouse model of Duchenne muscular dystrophy displayed grip strength improvement after just one treatment of MS023.

- MS023 is a Type I PRMT inhibitor and thus can also target CARM1. CARM1 has been implicated in MSC function by the Rudnicki group. Importantly, they exclude a role for CARM1 in the expansion of MSC cell numbers by treatment with a very specific CARM1 inhibitor, TP064. Thus, indicating that PRMT1 inhibition is likely the main driver of this expansion phenotype.

Public Review:

Barreat and Katzourakis analyze the evolutionary history of eukaryotic viruses (and related mobile elements) in the Bamfordvirae kingdom, and discuss potential scenarios regarding the origin of different viral taxa in this group. This version of their manuscript now includes a larger number of sequences to better represent diversity in these viral groups, and explored new evolutionary scenarios, including a "virophage-first" hypothesis now presented as the one best supported by phylogenetic analyses. The authors also present compelling analyses suggesting that the "nuclear escape" hypothesis in which these different viral groups separately "escaped" from nuclear (integrated) elements is not consistent with the current genomic and phylogenetic information available.

This work is thus an important step in our collective understanding of the ancient evolutionary history of eukaryotic viruses, and more generally of the constraints and main drivers of virus evolution.

Joint Public Review:

In this manuscript, Xie et al report the development of SCA-seq, a multiOME mapping method that can obtain chromatin accessibility, methylation, and 3D genome information at the same time. This method is highly relevant to a few previously reported long read sequencing technologies. Specifically, NanoNome, SMAC-seq, and Fiber-seq have been reported to use m6A or GpC methyltransferase accessibility to map open chromatin, or open chromatin together with CpG methylation; Pore-C and MC-3C have been reported to use long read sequencing to map multiplex chromatin interactions, or together with CpG methylation. Therefore, as a combination of NanoNome/SMAC-seq/Fiber-seq and Pore-C/MC-3C, SCA-seq is one step forward. The authors tested SCA-seq in 293T cells and performed benchmark analyses testing the performance of SCA-seq in generating each data module (open chromatin and 3D genome). The QC metrics appear to be good and the methods, data and analyses broadly support the claims. However, there are some concerns regarding data analysis and conclusions, and some important information seems to be missing.

1. The chromatin accessibility tracks from SCA-seq seem to be noisy, with higher background than DNase-seq and ATAC-seq (Fig. 2f, Fig. 4a and Fig. S5). Also, SCA-seq is much less sensitive than both DNase-seq and ATAC-seq (Figs. 2a and 2b). This and other limitations of SCA-seq (high background, high sequencing cost, requirement of specific equipment, etc) need to be carefully discussed.

2. In Fig. 2f, many smaller peaks are present besides the major peaks. Are they caused by baseline DNA methylation? How many of the small methylation signals are called peaks? In Fig. 4a, it seems that the authors define many more enhancers from SCA-seq data than what will be defined from ATAC-seq or DHS. Are those additional enhancers false positives? Also, it is difficult to distinguish the gray "inaccessible segments" from the light purple "accessible segments.

3. For 3D genome analysis, it is important to provide information about data yield from SCA-seq. With 30X sequencing depth, how many contacts are obtained (with long-read sequencing, this should be the number of ligation junctions)? How is the number compared to Hi-C.

4. Fig 3j. Because SCA-seq only do GpC methylation, the capability to detect the footprint at individual CTCF peaks depends on the density of GpC nearby. Have the authors taken GpC density into account when defining CTCF sites with or without footprint?<br /> 5. This study only performs higher resolution chromatin interaction analysis based on individual read concatenates. It is unclear to me if the data have enough depth to perform loop analysis with Hi-C pipelines.

6. It appears that SCA-seq is of low efficiency in detecting chromatin interactions. As shown in Fig. S7a, 65.4% of sequenced reads contained only one restriction enzyme (RE) fragment/segment (with no genomic contact), which is much higher than that reported in published PORE-C methods. In addition, Fig. S7g is very confusing and in conflict with Fig. S7a. For example, in Fig. S7g, 21.4% and 22.2% of CSA-seq concatemers contain one and two segments, whereas the numbers are 65.4% and 14.7% in Fig. S7a, respectively. Please explain.

7. I disagree with the rationale of the entire Fig. S9. Biologically there is no evidence that chromatin accessibility will change due to genome interactions (the opposite is more likely), therefore the definition of "expected chromatin accessibility" is hard to believe. If the authors truly believe this is possible, they will need to test their hypothesis by deleting cohesin and check if the chromatin accessibility driven by "power center" are truly abolished. The math in Fig. S9 is also confusing. Firstly, the dimension of the contact matrix in Fig. S9 appears to be wrong, it should have 8 rows. Secondly, I don't understand why the interaction matrix is not symmetric. Third, if I understand correctly the diagonal of the matrix should be all 1, it is also hard to understand why the matrix only has 1, 0 or -1. It appears that the authors assume that the observed accessibility is a simple sum of the expected accessibility of all its interacting regions; this is wrong. In my opinion, the whole Fig. S9 should be deleted unless the authors can make sense of it and ideally also provide more evidence.

Reviewer #1 (Public Review):

The paper is based around one very nice new marine reptile fossil from South China, but the authors make an excellent case in their Introduction that this can shed light on a wide range of fundamental phylogenetic problems around a whole array of Early and Middle Triassic marine reptiles. The description of the fossil is detailed and thorough and makes constant reference to comparative material of other taxa of saurosphargids. The phylogenetic analysis smartly adds some Triassic turtles and some other Early Triassic marine reptiles to a published cladistic data matrix and then can provide some really significant phylogenetic conclusions around Sauropterygia origins and Archelosauria.

Reviewer #1 (Public Review):

This manuscript focuses on a set of neurons from the border between the central and medial amygdala (AMGc/m-PAG ) that project to neurons in the periaqueductal gray (PAG) that gate ultrasonic vocalizations (USVs). These neurons suppress vocal production and are active in contexts where vocalizations would be inappropriate (e.g. in the presence of predator cues, or aggressive encounters with conspecifics). They then further characterized these neurons, demonstrating that like in males, these neurons are GABAergic in females and in both sexes, half of these neurons express estrogen receptor alpha (Esr1). To examine the inputs into these neurons, the authors performed monosynaptically-restricted transsynaptic rabies tracing and identified numerous cortical and subcortical projections. Of particular interest, neurons from the preoptic area of the hypothalamus (POA) in addition to terminating on PAG-USV neurons also project to AMGc/m-PAG neurons. Imaging the terminals of these neurons revealed elevated activity during vocalization-promoting contexts and optogenetically stimulating them resulted in evoking USVs. Together, these experiments further identify and quantify a circuit incorporating external factors (e.g. predatory factors, social interactions) in the drive to produce vocalizations.

The authors are commended for use of male and female mice, demonstrating that even though they produce USVs in different social contexts, AMGc/m-PAG neurons share a function in suppressing USV production in both sexes. They do this convincingly with a variety of methodologies while incorporating appropriate controls (e.g. light-only and GFP-control in optogenetic experiments). The experiments are performed in a logical order and the data generated is elaborate.

Reviewer #1 (Public Review):

This study presents a valuable finding for the incidence of bone avascular necrosis (AVN) in patients with Gaucher's Disease (GD) for twenty years. Furthermore, the evidence supporting the claims of the authors is solid.

The study's significant limitations relate to small numbers of patients, with only 155 GD patients analyzed. While the study period is excellent for incidence detection at 20 years, the overall number limits the strength of the analysis for cofactors. For example, there is an analysis for linkage to the type of therapy, the GBA1 genotype, spleen status, biomarkers, and other disease indicators. However, substantial numbers that would dictate changes to a preferential enzyme are not convincing. Moreover, the authors described 16 episodes of AVN in 14 patients, again making generalization difficult. Finally, there was a focus on Serum GlcSph levels, and the authors attempted to correlate levels according to probabilities for AVN occurrence while on treatment.

Overall, however, this is one of the best longitudinal studies for the incidence of AVN in GD patients, and the work will be of interest to medical biologists and professionals treating GD patients.

Reviewer #1 (Public Review):

The authors worked towards a better understanding of the functional diversification of flavodoxins among diatoms, and this represents a quantum contribution building on the initial findings of Whitney, Lins, Hughes, Wells, Chappelle, and Jenkins (2011), with the inclusion of metatranscriptomic and other data from field collections and on-deck incubation experiments, relatively new genomic and transcriptomic datasets, and the adoption of reverse genetics tools that are not yet widely used in T. pseudonana. They hypothesize that clade I flavodoxins play a role in mitigating oxidative stress, while additional clade II flavodoxins would respond according to canon, in response to low iron availability.

The authors embarked on several field campaigns across environmental gradients where iron-responsive and oxidative stress-responsive flavodoxins were expected to show differential expression. The use of metatranscriptomics allowed taxa-specific assignment of relative transcript expression levels, and the results of both measurements across the environmental gradient and manipulative incubation experiments show the widespread taxonomic distribution of iron-responsive clade II flavodoxin. The fieldwork was well thought out, and biogeochemical trends comported to expectations. It's worth noting that the concomitant inclusion of geochemical data such as dissolved iron further strengthened the work. The authors also found clade I flavodoxins were not iron-responsive (as expected), but rather exhibited diel patterns in transcript abundance that suggest responses to photo-oxidative stress. Taken together, these field data are stunning.

Lab experiments with five diatom species grown under varied iron and induced oxidative (H2O2) stress and transcript abundances for flavodoxin genes are reported. One reservation concerns the untoward and unknown effects of inducing outright iron starvation with the strong chelator, DFB (as opposed to achieving steady-state growth rate limitation from low iron by use of weak chelators such as EDTA). With DFB it is also difficult to predict sample timing (when cells have hit that "correct" and reproducible iron-limited space) when independent replicates are collected on different dates. Similarly, the use of DFB also makes it difficult to sample low and high iron cells at the same density or to maintain densities among replicate samples collected on different dates. pH and CO2 availability change with density unless special measures are taken.

A second set of lab experiments involved the (non-trivial) establishment and use of "knock out" clones of the clade I flavodoxin gene in the model diatom T. pseudonana to test the oxidative stress hypothesis. This is an exciting idea and the data suggest this flavodoxin may confer resistance to oxidative stress. The conclusion would be greatly strengthened if different phenotypes could be observed between WT and KO clones in response to environmentally relevant oxidative stress (such as supra-optimal irradiance), rather than exogenous H2O2 addition. The relationship between the experimental conditions and results in Figure 3C and Supplemental Figure 3H was not clear.

In the introduction, the authors suggest that Fe-S-containing proteins are particularly sensitive to damage via oxygen and ROS and that reliance on ferredoxin (Fd) for electron shuttling carries an enhanced sensitivity to the ROS generated during photosynthesis. References would be helpful here. Fe-S cluster-containing proteins are not monolithic regarding their behavior or susceptibility towards ROS. My limited understanding is that (i) several 4Fe-4S cluster proteins (such as aconitase, isopropylmalate isomerase) are particularly sensitive but that (ii) this is less so for canonical 2Fe-2S cluster ferredoxins; (iii) in some phototrophs Fd catalyzes the reduction of molecular oxygen to superoxide, as part of a mechanism that keeps the electron transport chain less reduced under extremely high light. Thus, ferredoxins may not necessarily be susceptible to in vivo ROS-mediated damage.

Reviewer #1 (Public Review):

In their study Mas Sandoval and colleagues estimate, from human genomic data, two important parameters that measure how intermarriages have been affected by social stratification in the Americas: sex-biased admixture (SB), which refers to sex differences in the chances to intermarry with another ethnic group, and ancestry-based assortative mating (AM), which refers to the higher probability of partners to intermarry when they carry similar genetic ancestries. To do so, the authors train a deep neural network (DNN) with simulations of admixture with non-random mating and use ancestry tract length distributions to infer the two parameters. They show that their approach estimates SB and AM parameters with a relatively good accuracy in a number of scenarios. When applying the DNN to empirical data, they find solid evidence that social stratification has constrained the admixture processes in the Americas for the last centuries.

In contrast with the vast majority of population genetic studies, which assume random mating, this study assesses if mating has been random or not in American populations. Furthermore, the study is very valuable because it leverages, for the first time, a deep learning approach and local ancestry inference to co-estimate the extent of SB and AM from genomic data. One limitation of the study, however, is that it assumes that (i) the admixture date in the simulations is known and equals 19 generations and (ii) admixture started at the same time in all admixed American populations. The authors also implicitly assume that the variance of the difference between male and female ancestry proportions only depends on AM, and not admixture timing. This may be problematic, as it has been shown that linkage disequilibrium between local ancestry tracts depends both on AM and admixture timing (Zaitlen et al., Genetics 2017). This is also suggested by the authors' results, showing that AM estimates are much lower in admixed Americans under the two-pulse model, relative to the one-pulse model, i.e., when admixture extends over time. Estimates of AM in admixed Americans may thus be biased, if admixture actually started less (or more) than 19 generations ago. Another potential limitation concerns local ancestry inference. The authors assume that RFMix makes no errors when inferring ancestry tracts. This can be a concern, as recent studies have shown that RFMix has reduced accuracy compared to other methods (Hilmarsson et al., bioRxiv 2022). In addition, the authors do not report a measure of uncertainty for the estimation of SB and AM, which is another important weakness. Interpretation of parameter estimates is limited if no measures of uncertainty are provided. Finally, the authors compare the likelihood of two competing models, assuming a single or two admixture pulses, but do not determine the accuracy of their model choice procedure. Overall, besides these methodological limitations, I expect that the study by Mas Sandoval and colleagues could be of great and broad interest for the scientific community studying population genetics, anthropology, sociology and history.

Reviewer #1 (Public Review):

This is a generally well-written manuscript that elegantly begins to explore the molecular basis of exosome release under conditions of sheer stress or calcium influx. The authors use a sensitive luciferase assay that enables them to monitor the release of exosomes from CD63-tag-expressing cells. Upon SLO pore formation or sheer stress, cells release exosomes in a calcium-dependent manner; MVBs are (indirectly) shown to undergo calcium-dependent plasma membrane fusion in a process that depends on a set of 4 proteins that were identified by an unbiased analysis of proteins that associate with MVBs. One of these is Annexin A6, a protein shown by several other groups to participate in membrane repair. Thus, calcium triggers the binding of 4 proteins to the surface of MVBs, and likely also to the plasma membrane, driving MVB fusion at the cell surface. The authors also present a semi-intact cell system that will permit functional analysis of the MVB fusion process.

Reviewer #1 (Public Review):

In this manuscript Radaelli et al investigate the effects of knocking out Parl, encoding a mitochondrial rhomboid protease, on spermatogenesis. Parl knockout has been used as a genetic model for Leigh syndrome, which in humans can be caused by mutations in several different components of the mitochondrial respiratory chain. This study describes the nature of the spermatogenesis defect found in Parl mutant mice, evaluates double mutants for Parl and other factors known to act with Parl in the context of neurodegeneration, and investigates the changes to mitochondrial function that occur in mutant testes. The authors conclude that Parl-/- males have a severe spermatogenesis defects with arrest at the spermatocyte stage, and that Parl function in spermatogenesis depends on different factors compared to neurons. Detailed characterization of mitochondrial function in mutant testis shows a variety of defects, including lower overall levels of coenzyme Q (coQ) and a higher ratio of reduced to oxidized coQ. They also conclude that ferroptosis is responsible for spermatocyte cell death in Parl mutants based on the presence of increased transferrin receptor, reduced GPX4 and increases in the ferroptosis end-product 4-hydroxynonenal (HNE).

The conclusions of this manuscript are well supported by (a) strong genetics including phenotype analysis in multiple double knockout mouse strains to show that Parl acts through different pathways in spermatogenic cells compared to neurons; (b) a clear spermatogenesis phenotype as shown by histology and immunostaining; (c) demonstration of mitochondrial defects during spermatogenesis using electron microscopy and respirometry of testis mitochondria; and (d) evidence for a mechanism of spermatocyte death by ferroptosis based on changes in transferrin receptor protein 1, coQ, GPX4, and HNE. Overall, this study advances understanding of the effects of mitochondrial dysfunction on spermatogenesis and may shed light on patient phenotypes in Leigh syndrome. The study will be useful in the fields of fertility and mitochondrial biology. There are a few places where the conclusions are not robustly supported by the data, especially inadequate quantification of some of the phenotype data and some cases where the data presented is not consistent with the model proposed:

1) In Figure 2, electron microscopy images represent n=1 cell, making it hard to know how generalizable the mitochondrial phenotypes are. It would be useful to see a quantitative summary of a larger dataset indicating how frequently the mitochondrial defects are seen.<br /> 2) In Figure 3, representative images are shown for a single field from n=1 animal. It is hard to decisively conclude that the phenotype of Pink1-/-;Pgam5-/- and Ttc19-/- testes is completely normal based on this limited data. There may be other tubules outside the field of view that are abnormal, or more subtle changes in cell ratios. This conclusion would be significantly strengthened by cell counting (e.g. # round spermatids per Sertoli cell per tubule and # spermatocytes per Sertoli cell per tubule) or other quantitation. Likewise, the similarities in phenotype between Parl-/-, Parl-/-;Pink2-/-, and Parl-/-;Pgam5-/- should be more thoroughly documented. At least some additional images should be shown.<br /> 3) In Figure 4, it looks like there is a significant decrease in CIV-driven respiration in Parl knockouts, but the text describes this as "did not significantly enhance" - that is, the absence of an increase. This result is difficult to interpret without further explanation.<br /> 4) In Figure 5B, there is some variation in band intensity between replicates. Quantifying the band intensity relative to the loading control would help to increase confidence in the conclusion that coQ levels are reduced.<br /> 5) GPX4 is not a Parl substrate, and no explanation is provided for why it might be reduced in Parl-/- testes. This makes the result and model difficult to interpret.<br /> 6) Since Parl knockout induces necrosis in the brain, necrosis could be a contributing factor to cell death in spermatocytes alongside ferroptosis. No data is presented that can exclude this possibility.<br /> 7) The severe spermatogenesis phenotype implies that Parl knockout males should be infertile, but the fertility status is not described in the manuscript. It may be difficult to test fertility in these animals due to the neurodegeneration phenotype; if so, this can be clarified. If it is feasible to test fertility, demonstration of a fertility phenotype would significantly strengthen the conclusion that loss of Parl leads to spermatogenic arrest.

Reviewer #1 (Public Review):

The authors tried to measure the accuracy of the decision-making of honey bees by carrying out behavioural experiments in which they trained the bees to forage on artificial flowers of 5 different colours that offered different levels of reward. Subsequently, the bees' decision-making behaviour was tested with flowers of the same or different colours, with no reward present. The authors found that bees tend to approach a flower only when they are highly certain of a reward, and these decisions are made quickly. The majority of flowers were rejected by the bees. Based on the results of the tests, the authors created a model to identify what circuit elements or connections would be necessary to mimic the bees' decisions. This model could be potentially used for robotics.

The study is well supported by the signal detection theory and the experiments are well designed which is a major strength. However, the methods are not completely clear, so would be better to make a clearer description. Another weakness is the lack of clear explanations of the importance and relevance of the model.

Given the experimental design was optimal, the authors could potentially achieve the aims of this study.

Reviewer #1 (Public Review):

Using in vitro assays that take advantage of thymic slices, with or without the ability to present pMHC antigens, the authors define an early period in which CCR4 expression is induced, which induces their migration to the medulla and likely encounter with cDC2 and other APCs. Notably, the timing for CCR4 expression precedes that of CCR7 and illustrates the potential role for this early expression to initiate the movement of post-positive selection thymocytes to the medulla. The evidence for supporting a role for CCR4, as well as CCR7, in sequential tolerance induction is provided using multiple approaches, and although the observed changes amount to small percent changes, the significance is clear and likely biologically relevant over the lifespan of a developing T cell repertoire. Overall, the model provides a holistic view of how tolerance to self-antigens is likely induced during T cell development, which makes this work highly topical and influential to the field.

Reviewer #1 (Public Review):

The sustainability of vaccination programs is subject to multiple threats, from a pandemic like COVID-19 to political changes. The present study assesses different strategies, including gender-neutral vaccination, to better respond to threats in HPV national immunization programs. The authors showed that vaccinating boys against HPV (compared to vaccinating girls alone), would not only prevent more cases of cervical cancer but also limit the impact of disruptions in the program. Moreover, it would help attain the goal set by the World Health Organization of eliminating cervical cancer as a public health problem sooner, even in the case of disruptions.

Strengths and weaknesses: I found the manuscript well-written and easy to read. Decision-makers may find the results helpful in policy development and other researchers may use the study as an example to investigate similar scenarios in their local contexts. Nevertheless, there are some limitations. First, it should be considered that the present study is only applicable to India and other countries with a similar HPV context. Second, because it is a study based on a mathematical model, errors might arise from the assumptions considered for its construction. It also relies on the quality of the data used to construct and calibrate the model.

Models are important tools for decision-making, they allow us to assess different scenarios when obtaining real-world data is not feasible. They also allow to carried-out multiple sensitivity analyses to test the strengths of the results. The study carries out a necessary assessment of different vaccination strategies to minimize the impact on cervical cancer prevention due to disruptions in the HPV immunization program. By using a mathematical model, the authors are able to assess different scenarios regarding vaccination coverage rates, disruption time, and cervical cancer incidence. Therefore, decision-makers can consider the scenario which best represents their current situation.

The present study is not only valuable for decision-making, but also from a methodological point of view as future research can be conducted exploring more in deep the impact of vaccination disruptions and prevention measures.

The conclusions of this paper are mostly well supported by data, but some aspects of the methodology need clarification; furthermore, some aspects of the calculations can be improved. It would be more informative, and better for comparisons between the four scenarios, to have relative measures instead of the absolute numbers of cases prevented.

Reviewer #1 (Public Review):

In this manuscript authors examined the effect of rif1 knockout on replication timing and transcription in early embryos of zebrafish. Contrary to the expectation, genome-wide replication timing domains did not significantly change upon Rif1 knockout, although the replication timing became less dynamic in the mutant, meaning the entire genomes are replicated toward the mid S. In contrast, transcriptional profiles change by rif1 mutation throughout the embryo stage. These effects were more predominantly observed after gastrulation at the early stages of zebrafish development.

The results presented in this manuscript provide new information on the effects of rif1 mutation on early zebrafish development, although the underlying mechanism has not been explored. The information is useful for researchers in the field of early development, with specific focus on replication and transcription regulation.

The genome wide analyses of replication timing has been conducted and analyzed properly. The transcriptional analyses are conducted by RNA-seq and SLAM-seq (determining the nascent mRNA), and the results convincingly show the overall transcriptional patterns at different developmental stages.

This work shows that Rif1 regulates replication timing and transcription in zebrafish embryos, while the extents of the effects vary during the developmental process. Although the data convincingly illustrate the whole picture of Rif1 KO on replication and transcription during zebrafish development, the mechanistic insight is missing. Especially, how Rif1 may or may not coordinately regulate replication and transcription during the zebrafish development has not been addressed.

Reviewer #1 (Public Review):

The manuscript by Mullen et al. investigated the gene expression changes in cancer cells treated with the DHODH inhibitor brequinar (BQ), to explore the therapeutic vulnerabilities induced by DHODH inhibition. The study found that BQ treatment causes upregulation of antigen presentation pathway (APP) genes and cell surface MHC class I expression, mechanistically which is mediated by the CDK9/PTEFb pathway triggered by pyrimidine nucleotide depletion. The combination of BQ and immune checkpoint therapy demonstrated a synergistic (or additive) anti-cancer effect against xenografted melanoma, suggesting the potential use of BQ and immune checkpoint blockade as a combination therapy in clinical therapeutics.

The interesting findings in the present study include demonstrating a novel cellular response in cancer cells induced by DHODH inhibition. However, whether the increased antigen presentation by DHODH inhibition actually contributed to the potentiation of the efficacy of immune-check blockade (ICB) is not directly examined is the limitation of the study. Moreover, the mechanism of the increased antigen presentation pathway by pyrimidine depletion mediated by CDK9/PTEFb was not validated by genetic KD or KO targeting by CDK9/PTEFb pathways. Finally, high concentrations of BQ have been reported to show off-target effects, sensitizing cancer cells to ferroptosis, and the authors should discuss whether the dose used in the in vivo study reached the ferroptotic sensitizing dose or not.

Reviewer #1 (Public Review):

Gap junctions, formed from connexins, are important in cell communication, allowing ions and small molecules to move directly between cells. While structures of connexins have previously reported, the structure of Connexin 43, which is the most widely expressed connexin and is important in many physiological processes was not known. Qi et al used cryo-EM to solve the structure of Connexin 43. They then compared this structure to structures of other connexins. Connexin gap junctions are built from two "hemichannels" consisting of hexamers of connexins. Hemichannels from two opposing cells dock together to form a complete channel that allows the movement of molecules between cells. N-terminal helices from each of the 6 subunits of each hemichannel allow control of whether the channels are open or closed. Previously solved structures of Cx26 and Cx46/50 have the N-termini pointing down into the pore of the protein leaving a central pore and so these channels have been considered to be open. The structure that Qi et al observed has the N-termini in a more raised position with a narrower pore through the centre. This led them to speculate whether this was the "closed" form of the protein. They also noted that, if only the protein was considered, there were gaps between the N-terminal helices, but these gaps were filled with lipid-like molecules. They therefore speculated that lipids were important in the closure mechanism. To address whether their structure was open or closed with respect to ions they carried out molecular dynamics studies, and demonstrated that under the conditions of the molecular dynamics ions did not traverse the channel when the lipids were present.

Strengths<br /> The high resolution cryo-EM density maps clearly show the structure of the protein with the N-termini in a lateral position and lipid density blocking the gaps between the neighbouring helices. The conformation that they observe when they have solved the structure from protein in detergent is also seen when they reconstitute the protein into nanodiscs, which is ostensibly a more membrane-like environment. They, therefore, would appear to have trapped the protein in a stable conformational state.<br /> The molecular dynamics simulations are consistent with the channel being closed when the lipid is present and raises the possibility of lipids being involved in regulation.<br /> A comparison of this structure with other structures of connexin channels and hemichannels gives another representation of how the N-terminal helix of connexins can variously be involved in the regulation of channel opening.

Weaknesses<br /> While the authors have trapped a relatively stable state of the protein and shown that, under the conditions of their molecular dynamics simulations, ions do not pass through, it is harder to understand whether this is physiologically relevant. Determining this would be beyond the scope of the article. To my knowledge there is no direct evidence that lipids are involved in regulation of connexins in this way, but this is also an interesting area for future exploration. It is also possible that lipids were trapped in the pore during the solubilisation process making it non-physiological. The authors acknowledge this and they describe the structure as a "putative" closed state.<br /> The positions of the mutations in disease shown in Figure 4 is interesting. However, the authors don't discuss/speculate how any of these mutations could affect the binding of the lipids or the conformational state of the protein.

It should also be noted that a structure of the same protein has recently been published. This shows a very similar conformation of the N-termini with lipids bound in the same way, despite solubilising in a different detergent.

Reviewer #1 (Public Review):

The authors examine signaling factors that differentiate parallel routes to activating phosphoinositide 3-kinase gamma (PI3Kγ). Dissecting the convergent pathways that control PI3Kγ activity is critical because PI3Kγ is a therapeutic target for treating inflammatory disease and cancer. Here, the authors employ a multipronged approach to reveal new aspects for how p84 and p101 pair with p110γ to activate the PI3Kγ heterodimer. The key instigator to this study is a previously reported inhibitory Nanobody, NB7. The hypothesized mechanism for NB7 allosteric inhibition of p84- p110γ was previously proposed to involve blockage of the Ras-binding domain. The authors revise the allosteric inhibition model based on meticulous profiling of various PI3Kγ complex interactions with NB7. In parallel, a cryo-EM-derived model of NB7 bound to the p110γ subunit convincingly reveals a Nanobody interaction pocket involving the helical domain and regulatory motifs of the kinase domain. This revelation shifts the focus to the helical domain, a known target of PKC phosphorylation. While the connections between NB7 interactions and the effects of PKC phosphorylation are sometimes tenuous, it could be argued that the Nanobody served as a tool to reveal the importance of the helical domain to p110γ regulation.

The sites of PKC-mediated p110γ helical domain phosphorylation were unexpectedly inaccessible in the available structural models. Nevertheless, mass spectrometry (MS)-based phosphorylation profiling indicates that PKC can phosphorylate the helical domain of p110γ and p84/p110γ (but not p101/p110γ) in vitro. The authors hypothesize that helical domain dynamics dictate susceptibility to PKC phosphorylation. To explore this notion, carefully executed, rigorous H/D exchange MS (HDX-MS) experiments were performed comparing phosphorylated vs. unphosphorylated p110γ. Notably, this design reveals more about the consequences of p110γ phosphorylation, rather than the mechanisms of p84/p101 promoting/resisting phosphorylation. Nevertheless, HDX-MS is very well suited to exploring secondary structure dynamics, and helical domain phosphorylation strikingly increases dynamics consistent with increased regional accessibility. The increased dynamics also nicely map to the pocket enveloped by the inhibitory NB7 Nanobody.

Ultimately, this study reveals an unexpected p110γ pocket that allows an engineered Nanobody to allosterically inhibit PI3Kγ complexes. The cryo-EM characterization of the interaction inspired an HDX-MS investigation of known sites of phosphorylation in the region. These insights could be linked to differences/convergences of p84 and p101 complex formation and activation of PI3Kγ, and future work may clarify these mechanisms further. The data presented herein will also be useful for broadening the target surface for future therapeutic developments. New allosteric connections between effector binding sites and post-translational modifications are always welcome.

Reviewer #1 (Public Review):

This paper consists in a comprehensive analysis of the malaria parasite Plasmodium falciparum during its development in erythrocytes, using expansion microscopy. The authors used general dyes to stain membranes or proteins and a set of specific markers to label diverse cellular structures of the parasite, with a particular focus on the microtubule organizing center (MTOC).

This is by nature a purely descriptive study, providing remarkable images with great details on subcellular structures such as the MTOC, the basal complex, the cytostome and rhoptries. The work is extremely well performed and the images are beautiful. It confirms a number of previous observations, but does not bring much novel biological insights. However, the study illustrates the strength of expansion microscopy, an affordable and adaptable sample preparation method that will undoubtedly become standard in the field.

While the narrative could be improved, this study provides a valuable resource that can serve as a reference dataset for analysis of P. falciparum and other apicomplexan parasites.

Joint Public Review:

Tilk and colleagues present a computational investigation of tumor transcriptomes to investigate the hypothesis that the large number of somatic mutations in some tumors is detrimental and that these detrimental effects are mitigated by an up-regulation by pathways and mechanisms that prevent protein misfolding.

The authors address this question by fitting a model that explains the log expression of a gene as a linear function of the log number of mutations in the tumor and additional effects for tumor homogeneity and type. This analysis identified a large number of genes (5000) that are more highly expressed at high mutational load at a FDR of 0.05. These genes are enriched in many core categories, most prominently in the proteasome, translation, and mitochondral translation. The authors then proceed to investigate specific categories of upregulated genes further.

The individual reviews, and the discussion among the reviewers, raised several issues that could potentially undermine or weaken some of the findings presented in this paper.

1) Systematic differences in expression of some genes from one tumor class to another might generate spurious associations with mutational load (ML), which would affect the results presented in Figs 1 and 3. The case of a causal link between ML and over-expression of genes that mitigate deleterious effects of misfolding would be stronger if these results were replicated within single cancer types with many samples with different ML (similar to how Fig S6 relates to Fig 3). A related concern might be an association between increased variance of expression and ML. The compositional nature of expression data could generate trends like the ones shown in Fig. 2 with changing variance.

2) Fig 4, Fig S5 and Fig S8 show results for the regression coefficient of expression on ML after leaving out one cancer at a time. All of us initially read this as results for 'one cancer at a time', rather than 'leave-one-out'. These figures are used to argue that the results are not driven by specific cancer types. However, this analysis would not reveal if the signal was driven by a (small) subset of cancer types. To justify claims like "significant negative relationship between mutational load and cell viability across almost all cancer types", one needs to analyze individual cancer types. Results for specific genes, rather than broad groups would also help interpret these results.

3) You use different model architecture for the TCGA and CCLE analysis because you suspect that the sample size imbalance in the latter might mean that a GLMM can not capture the different variance components accurately. Did you test this? Could you downsample to avoid this? Cancer type is likely a strong confounder of ML.

4) In the splicing analysis (Fig 2 and Fig S4), you report a 10% variation in splicing for a 100-fold variation in ML. This weak trend is replicated in very similar ways for many different types of alternative splicing events. It is not clear why different events (exon skipping, intron retention, etc) should respond in the same way to ML. A weak but homogeneous effect like the one shown here might result from some common confounder (see point 1). Similarly, it is not clear why with increasing intron retention PSI threshold the fraction of under-expressed transcripts would decrease and not increase.

Reviewer #1 (Public Review):

Wang and all present an interesting body of work focused on the effects of high altitude and hypoxia on erythropoiesis, resulting in erythrocytosis. This work is specifically focused on the spleen, identifying splenic macrophages as central cells in this effect. This is logical since these cells are involved in erythrophagocytosis and iron recycling. The results suggest that hypoxia induces splenomegaly with decreased number of splenic macrophages. There is also evidence that ferroptosis is induced in these macrophages, leading to cell destruction. Finally, the data suggest that ferroptosis in splenic red pulp macrophages causes the decrease in RBC clearance, resulting in erythrocytosis aka lengthening the RBC lifespan. However, there are many issues with the presented results, with somewhat superficial data, meaning the conclusions are overstated and there is decreased confidence that the hypotheses and observed results are directly causally related to hypoxia.

Major points:

1) The spleen is a relatively poorly understood organ but what is known about its role in erythropoiesis especially in mice is that it functions both to clear as well as to generate RBCs. The later process is termed extramedullary hematopoiesis and can occur in other bones beyond the pelvis, liver, and spleen. In mice, the spleen is the main organ of extramedullary erythropoiesis. The finding of transiently decreased spleen size prior to splenomegaly under hypoxic conditions is interesting but not well developed in the manuscript. This is a shortcoming as this is an opportunity to evaluate the immediate effect of hypoxia separately from its more chronic effect. Based just on spleen size, no conclusions can be drawn about what happens in the spleen in response to hypoxia.

2) Monocyte repopulation of tissue resident macrophages is a minor component of the process being described and it is surprising that monocytes in the bone marrow and spleen are also decreased. Can the authors conjecture why this is happening? Typically, the expectation would be that a decrease in tissue resident macrophages would be accompanied by an increase in monocyte migration into the organ in a compensatory manner.

3) Figure 3 does not definitively provide evidence that cell death is specifically occurring in splenic macrophages and the fraction of Cd11b+ cells is not changed in NN vs HH. Furthermore, the IHC of F4/80 in Fig 3U is not definitive as cells can express F4/80 more or less brightly and no negative/positive controls are shown for this panel.

4) The phagocytic function of splenic red pulp macrophages relative to infection cannot be used directly to understand erythrophagocytosis. The standard approach is to use opsonized RBCs in vitro. Furthermore, RBC survival is a standard method to assess erythrophagocytosis function. In this method, biotin is injected via tail vein directly and small blood samples are collected to measure the clearance of biotinilation by flow; kits are available to accomplish this. Because the method is standard, Fig 4D is not necessary and Fig 4E needs to be performed only in blood by sampling mice repeatedly and comparing the rate of biotin decline in HH with NN (not comparing 7 d with 14 d).

5) It is unclear whether Tuftsin has a specific effect on phagocytosis of RBCs without other potential confounding effects. Furthermore, quantifying iron in red pulp splenic macrophages requires alternative readily available more quantitative methods (e.g. sorted red pulp macrophages non-heme iron concentration).

6) In Fig 5, PBMCs are not thought to represent splenic macrophages and although of some interest, does not contribute significantly to the conclusions regarding splenic macrophages at the heart of the current work. The data is also in the wrong direction, namely providing evidence that PBMCs are relatively iron poor which is not consistent with ferroptosis which would increase cellular iron.

7) Tfr1 increase is typically correlated with cellular iron deficiency while ferroptosis consistent with iron loading. The direction of the changes in multiple elements relevant to iron trafficking is somewhat confusing and without additional evidence, there is little confidence that the authors have reached the correct conclusion. Furthermore, the results here are analyses of total spleen samples rather than specific cells in the spleen.

Joint Public Review:

The authors report the first use of the bacterial Tus-Ter replication block system in human cells. A single plasmid containing two divergently oriented five-fold TerB repeats was integrated on chromosome 12 of MCF7 cells. ChIP and PLA experiments convincingly demonstrate the occupancy of Tus at the Ter sites in cells. Using an elegant Single Molecule Analysis of Replicated DNA (SMARD) assay, convincing data demonstrate the replication block at Ter sites dependent on the presence of the protein. As an orthogonal method to demonstrate fork stalling, ChIP data show the accumulation of the replicative helicase component MCM3 and the repair protein FANCM around the Ter sites. It is unclear whether the Ter sites integrated by a single copy plasmid have any effect on the replication of this region but the data show that the observed effects are dependent on expression of the Tus protein. The SMARD data do not reveal what proportion of forks are arrested at Tus/Ter, or how long the fork delay is imposed. Fork stalling led to a highly localized gammaH2AX response, as monitored by ChIP using primer pairs spread along the integrated plasmid carrying the Ter sites. This response was shown to be dependent on ATR using the ATR inhibitor VE-822. This contrasts with a single Cas9-induced DSB between the two Ter sites, which causes a more spread gammaH2AX response. While this was monitored only at a single distal site, the difference between the DSB and the Tus-induced stall is very significant. Interestingly, despite evidence for ATR activation through the gammaH2AX response, no evidence for phosphorylation of ATR-T1989, CHK1-S345, or RPA2-S33 could be found under fork stalling conditions. The global replication inhibitor hydroxyurea (HU) elicited phosphorylation of ATR-T1989, CHK1-S345, or RPA2-S33. In this context, it would have been of interest to examine if a single DSB in the Ter region leads to phosphorylation of ATR-T1989, CHK1-S345, or RPA2-S33 and cell cycle arrest. It is not shown whether the replication inhibitor HU leads to the same widely spread gamma H2AX response. Overall, this is a well written manuscript, and the data provide convincing evidence that the Tus-Ter system poses a site-specific replication fork block in MCF7 cells leading to a localized ATR-dependent DNA damage checkpoint response that is distinct from the more global response to HU or DSBs.

Reviewer #1 (Public Review):

The authors sought to address the longstanding question of which cell types are infected during congenital or perinatal rubella virus infection. They used brain slice and organoid-microglia experimental models to demonstrate that the main cell types targeted by rubella virus are microglia. It does not appear that microglia support rubella virus production in this experimental system, though future studies would be needed to address this more thoroughly. The authors further show that infection results in augmented interferon responses in neighboring neuronal cells but not in the microglia themselves. The data support the conclusions, with major strengths being the sophisticated primary cell models and single-cell RNA-Seq used to pinpoint microglia as the main cellular targets of rubella virus, and neurons as the bystander targets of immune signaling. This study reveals a new cellular target that will have important implications for basic studies on rubella virus-host interactions and for the potential development of therapies or improved vaccines targeting this virus. As rubella virus is a pathogen of high concern during human pregnancy, this study also has important implications in the field of neonatal infectious diseases.

Reviewer #1 (Public Review):

This manuscript uses 3 large neuroimaging datasets - which together span childhood to late adulthood - to model the relationship between birthweight (BW) and cortical anatomy over time. The authors separately consider BW associations with the "height" of cortical anatomy trajectories (intercept effects) vs. BW associations with trajectory shape. The authors also distinguish between BW associations with cortical surface area (SA) and cortical thickness (CT), which together determine cortical volume (CV). Prior studies have firmly established robust positive associations between BW and cortical SA, but this study adds evidence for the protracted lifespan persistence of these associations, and the degree to which BW associations with cortical change over time are much weaker.

The study has several strengths including: clear motivation of this work in the Introduction and contextualization of the results in Discussion; use of three large neuroimaging datasets; inclusion of sensible sensitivity analyses; disambiguation of SA and CT findings; and use of formal spatial analysis to quantify the reproducibility of effects across cohorts.

The primary way in which this work seeks to extend beyond established findings is to determine if BW is associated with differences in cortical change over time. The results presented clearly establish that such BW-change associations are much more localized and less consistent across cohorts that BW-intercept associations. However, the evidential basis for this statement is partly limited by the nature of the neuroimaging cohorts used and the specific approaches taken to statistical modeling. Interpretation of findings for both BW-change and BW-intercept associations would also be assisted by greater clarity regarding the specification of statistical models, and the provision of effect-size maps.

Moreover, several factors complicate interpretation of the BW effects on cortical change - which are arguably the main way in which this work could extend on established knowledge of BW associations with brain anatomy. Under the study design presented, inferences regarding age-varying BW effects come from two main sources ... age effects which are quantitatively modeled within each sample, and qualitative differences in age effects between samples. Any inferences from the latter source of evidence are weakened by the fact that (i) no direct statistical comparisons are conducted between samples (beyond the spin tests), and (ii) the composition of samples with regard to age span covered (e..g 2 in ABCD vs longer in UKB and longest in LCBC) and density of longitudinal data makes it hard to know if between-samples differences in age*BW effects are about biology or methodology. Inferences about age*BW effects from models within each sample are also limited by the fact that (i) some samples (ABCD) have very narrow age ranges precluding detection of age-related effects, and (ii) the modeling strategy used does not allow for non-linear interactions between age and BW or linear interactions that occur in the context of e.g. non-linear BW effects. For this last concern, it would be helpful to know that there is no evidence in the data for such non-linear effects

The tests for spatial consistency between BW effects are a valuable aspect of the manuscript and provide a solid quantitative test for the main effects of BW. For the reasons detailed above however, I think that the more variable (and sometimes negative) correlations in age*BW maps are harder to interpret. One could argue for example that bivariate spline models of age*BW interactions on a lifespan dataset assembled from different COMBAT-aligned cohorts would provide a more solid basis for inference regarding the degree to which BW effects on cortical anatomy vary with age

Overall, this work provides a valuable new data point in our understanding of the profound and protracted influences that prenatal developmental features can have on postnatal outcomes.

Reviewer #1 (Public Review):

The study by Sianga-Mete et al revisits the effects of substitution model selection on phylogenetics by comparing reversible and non-reversible DNA substitution models. This topic is not new, previous works already showed that non-reversible, and also covarion, substitution models can fit the real data better than the reversible substitution models commonly used in phylogenetics. In this regard, the results of the present study are not surprising. Specific comments are shown below.

Major comments

It is well known that non-reversible models can fit the real data better than the commonly used reversible substitution models, see for example,<br /> https://academic.oup.com/sysbio/article/71/5/1110/6525257<br /> https://onlinelibrary.wiley.com/doi/10.1111/jeb.14147?af=R<br /> The manuscript indicates that the results (better fitting of non-reversible models compared to reversible models) are surprising but I do not think so, I think the results would be surprising if the reversible models provide a better fitting.<br /> I think the introduction of the manuscript should be increased with more information about non-reversible models and the diverse previous studies that already evaluated them. Also I think the manuscript should indicate that the results are not surprising, or more clearly justify why they are surprising.

In the introduction and/or discussion I missed a discussion about the recent works on the influence of substitution model selection on phylogenetic tree reconstruction. Some works indicated that substitution model selection is not necessary for phylogenetic tree reconstruction,<br /> https://academic.oup.com/mbe/article/37/7/2110/5810088<br /> https://www.nature.com/articles/s41467-019-08822-w<br /> https://academic.oup.com/mbe/article/35/9/2307/5040133<br /> While others indicated that substitution model selection is recommended for phylogenetic tree reconstruction,<br /> https://www.sciencedirect.com/science/article/pii/S0378111923001774<br /> https://academic.oup.com/sysbio/article/53/2/278/1690801<br /> https://academic.oup.com/mbe/article/33/1/255/2579471<br /> The results of the present study seem to support this second view. I think this study could be improved by providing a discussion about this aspect, including the specific contribution of this study to that.

The real data was downloaded from Los Alamos HIV database. I am wondering if there were any criterion for selecting the sequences or if just all the sequences of the database for every studied virus category were analysed. Also, was any quality filter applied? How gaps and ambiguous nucleotides were considered? Notice that these aspects could affect the fitting of the models with the data.

How the non-reversible model and the data are compared considering the non-reversible substitution process? In particular, given an input MSA, how to know if the nucleotide substitution goes from state x to state y or from state y to state x in the real data if there is not a reference (i.e., wild type) sequence? All the sequences are mutants and one may not have a reference to identify the direction of the mutation, which is required for the non-reversible model. Maybe one could consider that the most abundant state is the wild type state but that may not be the case in reality. I think this is a main problem for the practical application of non-reversible substitution models in phylogenetics.

Reviewer #1 (Public Review):

Tippett et al present whole cell and proteoliposome transport data showing unequivocally that purified recombinant SLC26A6 reconstituted in proteoliposomes mediates electroneutral chloride/bicarbonate exchange, as well as coupled chloride/oxalate exchange unassociated with detectable current. Both functions contrast with the uncoupled chloride conductance mediated by SLC26A9. The authors also present a novel cryo-EM structure of full-length human SLC26A6 chloride/anion exchanger. As part of the structure, they offer the first partial view of the STAS domain previously predicted to be unstructured. They further define a single Arg residue of the SLC26A6 transmembrane domain required for coupled exchange, mutation of which yields apparently uncoupled electrogenic chloride transport mechanistically resembling that of SLC26A9, although of lower magnitude. The authors further apply to proteoliposomes for the first time a still novel approach to the measurement of bicarbonate transport using a bicarbonate-selective Europium fluorophor. The evidence strongly supports the authors' claims and conclusions, with one exception.

The manuscript has numerous strengths:

As a structural biology contribution, the authors extend the range of SLC26 structures to SLC26A6, comparing it in considerable detail to the published SLC26A9 structure, and presenting for the first time the structure of a portion of the STAS IVS domain of SLC26A6 long considered unstructured.

The authors also apply a remarkably extensive range of creative technical approaches to assess the functional mechanisms of anion transport by SLC26A6, among them the first application of the novel, specific bicarbonate sensor Eu-L1+ to directly assess bicarbonate transport in reconstituted proteoliposomes. The authors also present the first (to this reviewer's knowledge) functional proteoliposome reconstitution of chloride-bicarbonate exchange mediated by an SLC26 protein. They define a residue in surrounding the anion binding pocket which explains part of the difference in anion exchange coupling between SLC26A6 and SLC26A9. In the setting of past conflicting results, the current work also contributes to the weight of previous evidence demonstrating that SLC26A6 mediates electroneutral rather than electrogenic Cl-/HCO3- exchange.

Each of these achievements constitutes a significant advance in our understanding.

The paper has only a few weaknesses:

One is an incomplete explanation of the mechanistic determinants of anion exchange coupling in SLC26A6 vs. uncoupled anion transport by SLC26A9.

A second weakness is the inconsistent, technique-dependent detection of SLC26A6- mediated electrogenic chloride/oxalate exchange. In particular whole cell currents attributable to SLC26A6 in SLC26A6-expressing HEK293 cells in an oxalate bath could not be detected, whereas robust, saturable Cl- efflux into oxalate solution from proteoliposomes reconstituted with recombinant SLC626A6 was detectable by AMCA fluorescence decay. This discrepancy was attributed to the relative sensitivities and/or signal-to-noise ratios of the assays.

Overall, the manuscript represents an important advance in our understanding of the SLC26 protein family and of coupled vs uncoupled carrier-mediated anion transport.

Reviewer #1 (Public Review):

Watanuki et al used metabolomic tracing strategies of U-13C6-labeled glucose and 13C-MFA to quantitatively identify the metabolic programs of HSCs during steady-state, cell-cycling, and OXPHOS inhibition. They found that 5-FU administration in mice increased anaerobic glycolytic flux and decreased ATP concentration in HSCs, suggesting that HSC differentiation and cell cycle progression are closely related to intracellular metabolism and can be monitored by measuring ATP concentration. Using the GO-ATeam2 system to analyze ATP levels in single hematopoietic cells, they found that PFKFB3 can accelerate glycolytic ATP production during HSC cell cycling by activating the rate-limiting enzyme PFK of glycolysis. Additionally, by using Pfkfb3 knockout or overexpressing strategies and conducting experiments with cytokine stimulation or transplantation stress, they found that PFKFB3 governs cell cycle progression and promotes the production of differentiated cells from HSCs in proliferative environments by activating glycolysis. Overall, in their study, Watanuki et al combined metabolomic tracing to quantitatively identify metabolic programs of HSCs and found that PFKFB3 confers glycolytic dependence onto HSCs to help coordinate their response to stress. Even so, several important questions need to be addressed as below:

1. Based on previous reports, the authors expanded the LSK gate to include as many HSCs as possible (Supplemental Figure 1B). However, while they showed the gating strategy on Day 6 after 5-FU treatment, results from other time-points should also be displayed to ensure the strict selection of time-points.

2. In Figure 1, the authors examined the metabolite changes on Day 6 after 5-FU treatment. However, it is important to consider whether there are any dynamic adjustments to metabolism during the early and late stages of 5-FU treatment in HSCs compared to PBS treatment, in order to coordinate cell homeostasis despite no significant changes in cell cycle progression at other time-points.

3. As is well known, ATP can be produced through various pathways, including glycolysis, the TCA cycle, the PPP, NAS, lipid metabolism, amino acid metabolism and so on. Therefore, it is important to investigate whether treatment with 5-FU or oligomycin affects these other metabolic pathways in HSCs.

4. In part 2, they showed that oligomycin treatment of HSCs exhibited activation of the glycolytic system, but what about the changes in ATP concentration under oligomycin treatment? Are other metabolic systems affected by oligomycin treatment?

5. In Figure 5M, it would be helpful to include a control group that was not treated with 2-DG. Additionally, if Figure 5L is used as the control, it is unclear why the level of ATP does not show significant downregulation after 2-DG treatment. Similarly, in Figure 5O, a control group with no glucose addition should be included.

6. In this study, their findings suggest that PFKFB3 is required for glycolysis of HSCs under stress, including transplantation. In Figure 7B, the results showed that donor-derived chimerism in PB cells decreased relative to that in the WT control group during the early phase (1 month post-transplant) but recovered thereafter. Although the transplantation cell number is equal in two groups of donor cells, it is unclear why the donor-derived cell count decreased in the 2-week post-transplantation period and recovered thereafter in the Pfkgb3 KO group. Therefore, they should provide an explanation for this. Additionally, they only detected the percentage of donor-derived cells in PB but not from BM, which makes it difficult to support the argument for increasing the HSPC pool.

7. In Figure 7E, they collected the BM reconstructed with Pfkfb3- or Rosa-KO HSPCs two months after transplantation, and then tested their resistance to 5-FU. However, the short duration of the reconstruction period makes it difficult to draw conclusions about the effects on steady-state blood cell production.

8. PFK is allosterically activated by PFKFB, and other members of the PFKFB family could also participate in the glycolytic program. Therefore, they should investigate their function in contributing to glycolytic plasticity in HSCs during proliferation. Additionally, they should also analyze the protein expression and modification levels of other members. Although PFKFB3 is the most favorable for PFK activation, the role of other members should also be explored in HSC cell cycling to provide sufficient reasoning for choosing PFKFB3.

9. In this study, the authors identified PRMT1 as the upstream regulator of PFKFB3 that is involved in the glycolysis activation of HSCs. However, PRMT1 is also known to participate in various transcriptional activations. Thus, it is important to determine whether PRMT1 affects glycolysis through transcriptional regulation or through its direct regulation of PFKFB3? Additionally, the authors should investigate whether PRMT1i inhibits ATP production in normal HSCs. Moreover, could we combine Figure 6I and 6J for analysis. Finally, the authors could conduct additional rescue experiments to demonstrate that the effect of PRMT1 inhibitors on ATP production can be rescued by overexpression of PFKFB3.

Reviewer #1 (Public Review):

The article "A randomized multiplex CRISPRi-Seq approach for the identification of critical combinations of genes" describes the development of a multiplex randomized CRISPRi screening method that they named MurCiS and applied it to study redundancy of L. pneumophila virulence factors. The authors used a L. pneumophila strain carrying dCas9 on the chromosome that they had constructed for a CRISPRi screen they had published recently and here combined it with self-assembly randomized multiplex CRISPR arrays that they developed. The strains carrying the dCas9 and the different CRISPRi arrays were used to infect U937 or Acanthamoeba castellanii cells and the intracellular growth phenotypes were recorded as readout. This allowed the authors to identify certain gene combinations that when knocked down induced a growth defect in either or both cells tested but not when they were knocked down alone. A particular gene combination caught their attention, as the genes lpg2888 and lpg3000 were inducing a growth defect only when both were knocked down in U937 cells but in A. castellanii cells lpg3000 alone was sufficient to cause a growth defect.

The concept of using CRISPRi to look at functional redundancy in effectors is a very useful one to the Legionella field and where biological redundancy limits studies. It has the potential to uncover virulence effectors of importance that have not been described before. However, my enthusiasm for the work was dampened when reading the article. The work presented here does not really flow and it seems to be more a method description than a research article but does not meet the requirements to be either.

The strength of the study is undermined by how it is set up. The set-up of the CRISPRi technology deployed by the authors may explain why the authors found only very few examples of redundant genes in this study.

Reviewer #1 (Public Review):

In this manuscript, the authors identified and characterized the five C-terminus repeats and a 14aa acidic tail of the mouse Dux protein. They found that repeat 3&5, but not other repeats, contribute to transcriptional activation when combined with the 14aa tail. Importantly, they were able to narrow done to a 6 aa region that can distinguish "active" repeats from "inactive" repeats. Using proximal labeling proteomics, the authors identified candidate proteins that are implicated in Dux-mediated gene activation. They were able to showcase that the C-terminal repeat 3 binds to some proteins, including Smarcc1, a component of SWI/SNF (BAF) complex. In addition, by overexpressing different Dux variants, the authors characterized how repeats in different combinations, with or without the 14aa tail, contribute to Dux binding, H3K9ac, chromatin accessibility, and transcription. In general, the data is of high quality and convincing. The identification of the functionally important two C-terminal repeats and the 6 aa tail is enlightening. The work shined light on the mechanism of Dux function.

A few major comments that the authors may want to address to further improve the work:

1) The summary table for the Dux domain construct characteristics in Fig. 6a could be more accurate. For example, C3+14 clearly showed moderate weaker Dux binding and H3K9ac enrichment in Fig 3c and 3e. However, this is not illustrated in Fig. 6a. The authors may consider applying statistical tests to more precisely determine how the different Dux constructs contribute to DNA binding (Fig. 3c), H3K9ac enrichment (Fig. 3e), Smarcc1 binding (Fig. 5e), and ATAC-seq signal (Fig. 5f).

2) Another concern is that exogenous overexpressed Dux was used throughout the experiments. The authors may consider validating some of the protein-protein interactions using spontaneous or induced 2CLCs (where Dux is expressed).

3) It could be technically challenging, but the authors may consider to validate Dux and Smarcc1 interaction in a biologically more relevant context such as mouse 2-cell embryos where both proteins are expressed. Whether Smarcc1 binding will be dramatically reduced at 4-cell embryos due to loss of Dux expression?

Reviewer #1 (Public Review):

Recent studies in plants and human cell lines argued for a central role of 1,5-InsP8 as the central nutrient messenger in eukaryotic cells, but previous studies concluded that this function is performed by 1-InsP7 in baker's yeast. Chabert et al now performed an elegant set of capillary electrophoresis coupled to mass spectrometry time course experiments to define the cellular concentrations of different inositol pyrophosphosphates (PP-InsPs) in wild-type yeast cells under normal and phosphate (Pi) starvation growth conditions. These experiments, in my opinion, form the center of the present study and clearly highlight that the levels of all major PP-InsPs drop under Pi starvation, with the 1,5-InsP8 isomer showing the most rapid changes.

The analysis of known mutants in the PP-InsP biosynthetic pathways furthermore demonstrate that loss-of-function of the PPIP5K enzymes Kcs1 and Vip1 result in a loss of 1,5-InsP8 and a hyperaccumulation of 5-InsP7, respectively. In line with this, loss-of-function of known PP-InsP phosphatases Ddp1 and Swi14 result in hyperaccumulation of either 1- or 5-InsP7, as anticipated from their in vitro substrate specificities. These experiments are of high technical quality and add to our understanding of the kinetics of PP-InsP metabolism/catabolism in yeast.

Next, the authors use changes in subcellular localisation of the central transcription factor Pho4 to assay at which time point after onset of Pi starvation the PHO pathway becomes activated. The early onset of the response, the behavior of the kcs1D mutant and of the ksc1D/vip1D all strongly argue for 1,5-InsP8 as the central nutrient messenger. I find this part of the manuscript well argued, nicely correlating PP-InsP levels, dynamics and the different mutant phenotypes.

The third part of the manuscript is a structure-function study of the CDK inhibitor Pho81, basically using a reverse genetics approach. This analysis demonstrates at the genetic level that the Pho81 SPX domain is required for activation of the PHO pathway. Next, the authors design point mutations that should block either interaction of Pho81-SPX with 1,5-InsP8 or interaction of Pho81 with the Pho80/Pho85 complex. In my opinion, these data can only provide limited insight into the molecular mechanism, as no complementary in vitro binding assays / in vivo co-IP experiments with the wild-type and mutant forms of Pho81 are presented.

The discussion section of the manuscript contains additional data such as PP-InsP levels for C. neoformans and complex structure predictions of Pho80 - Pho81. This, in my opinion, renders the discussion section of the work overly speculative. Perhaps, these results should be presented in the results section, and ideally (in the case of the complex structure predictions), be complemented by quantitative in vitro and/or qualitative in vivo binding assays.

Taken together, the work by Chabert et al, reinvestigates and clarifies the activation of the yeast PHO pathway by PP-InsP nutrient messengers and their cellular SPX receptors. From this work, a more unified eukaryotic mechanism emerges, in which 1,5-InsP8 represents the central signaling molecule in different species, with conserved SPX receptors sensing this signaling molecule.

Reviewer #1 (Public Review):

In this study, the authors investigate the biological function of the FK506-binding protein FKBP35 in the malaria-causing parasite Plasmodium falciparum. Like its homologs in other organisms, PfFKBP35 harbors peptidyl-prolyl isomerase (PPIase) and chaperoning activities, and has been considered a promising drug target due to its high affinity to the macrolide compound FK506. However, PfFKBP35 has not been validated as a drug target using reverse genetics, and the link between PfFKBP35-interacting drugs and their antimalarial activity remains elusive. The manuscript is structured in two parts addressing the biological function of PfFKBP35 and the antimalarial activity of FK506, respectively.

The first part combines conditional genome editing, proteomics and transcriptomics analysis to investigate the effects of FKBP35 depletion in P. falciparum. The work is very well performed and clearly described. The data provide definitive evidence that FKBP35 is essential for P. falciparum blood stage growth. Conditional knockout of PfFKBP35 leads to a delayed death phenotype, associated with defects in ribosome maturation as detected by quantitative proteomics and stalling of protein synthesis in the parasite. The authors propose that FKBP35 regulates ribosome homeostasis but an alternative explanation could be that changes in the ribosome proteome are downstream consequences of the abrogation of FKBP35 essential activities as chaperone and/or PPIase. It is unclear whether FKBP35 has a specific function in P. falciparum as compared to other organisms. The knockdown of PfFKBP35 has no phenotypic consequence, showing that very low amounts of FKBP35 are sufficient for parasite survival and growth. In the absence of quantification of the protein during the course of the experiments, it remains unclear whether the delayed death phenotype in the knockout is due to the delayed depletion of the protein or to a delayed consequence of early protein depletion. This limitation also impacts the interpretation of the drug assays.

In the second part, the authors investigate the activity of FK506 on P. falciparum, and conclude that FK506 exerts its antimalarial effects independently of FKBP35. This conclusion is based on the observation that FK506 has the same activity on FKBP35 wild type and knock-out parasites, suggesting that FK506 activity is independent of FKBP35 levels, and on the fact that FK506 kills the parasite rapidly whereas inducible gene knockout results in delayed death phenotype. However, there are alternative explanations for these observations. As mentioned above, the delayed death phenotype could be due to delayed depletion of the protein upon induction of gene knockout. FK506 could have a similar activity on WT and mutant parasites when added before sufficient depletion of FKBP35 protein. In some experiments, the authors exposed KO parasites to FK506 later, presumably when the KO is effective, and obtained similar results. However, in these conditions, the death induced by the knockout could be a confounding factor when measuring the effects of the drug. Furthermore, the authors show that FK506 binds to FKBP35, and propose that the FK506-FKBP35 complex interferes with ribosome maturation, which would point towards a role of FKBP35 in FK506 action. In summary, the study does not provide sufficient evidence to rule out that FK506 exerts its effects via FKBP35.

Reviewer #1 (Public Review):

The current paper tackles a central conundrum in transporter mechanism: how substrate recognition and conformational change are coupled to achieve substrate selectivity. The focus of this manuscript is the GLUT family of sugar importers, specifically GLUT5, a fructose importer. Using information from multiple GLUT structures in different conformational states, together with enhanced molecular dynamic simulations, the authors reconstruct a free energy landscape for the outward-open to inward-open GLUT5 conformational transition in the presence and absence of fructose. The authors are thorough in their approach, considering alternative approaches (for example, including vs. excluding a distantly related GLUT transporter).

These experiments provide insight into the energy barriers, fructose coordination in the occluded conformation, and the coupling between substrate binding, the motion of the extracellular gate, and conformational change. Uptake assays are used to test predictions about gating residues and residues predicted to bind fructose in the occluded state. Overall, this is a comprehensive study that provides broad insight into mechanistic diversity among GLUT sugar porters.

Reviewer #1 (Public Review):

This manuscript presents an inference technique for estimating causal dependence between pairs of neurons when the population is driven by optogenetic stimulation. The key issue is how to mitigate spurious correlations between unconnected neurons that can arise due to polysynaptic and other network-level effects during stimulation. The authors propose to leverage each neuron's refractory period (which begins at approximately random times, assuming Poisson-distributed spikes and conditional on network state) as an instrumental variable, allowing the authors to tease apart causal dependence by considering how the postsynaptic neuron fires when the presynaptic neuron must be muted (i.e., is in its refractory period). The idea is interesting and novel, and the authors show that their modified instrumental variable method outperforms similar approaches.

However, the scope of the technique is limited. The authors' results suggest that the proposed technique may not be practical because it requires considerable amounts of data (more than 10^6 trials for just 200 neurons, resulting in stimulation of more than 5000 times per neuron). Even with such data sizes, the method does not appear to converge to the true solution in simulations. The method is also not tested on any experimental data, making it difficult to judge how well the assumptions of the technique would be met in real use-cases. While the manuscript offers a unique solution to inferring causal dependence, its applicability for experimental data has not yet been convincingly demonstrated, and would therefore primarily be of interest to those looking to build on these theoretical results for further method development.

Reviewer #1 (Public Review):

HCN channels are atypically opened by the downward movement of gating charges during hyperpolarisation and have such weak coupling between the VSD and pore domain, and in the absence of an open state structure, extracting mechanistic information has been difficult. This manuscript is a continuation of a previous study on HCN channel gating that revealed how hyperpolarisation causes a downward movement of the VSD's S4, with breakage into two helices. The authors explore gating motions and the coupling between VSD and the pore domain using atomistic simulations. This includes microsecond MD with and without very strong -1V applied potentials to try to drive VSD-TMD changes to open the channel. In the end, however, the authors used a biased simulation approach (adiabatic bias) to enforce conformational change from resting to an open homology model of HCN based on hERG/rEAG. This microsecond simulation followed three interaction distances that were suggested to change between resting and open states based on free MD. This simulation caused pore opening and allowed a description of changes that may occur during gating, including a competition of S5-S6 and S6-S6 contacts and lipid binding locations, which may suggest lipid-dependent function and explain an unexpected closed structure at 0mV in micelles. While I feel the manuscript is written for the HCN expert audience, the mechanistic information in terms of hyperpolarisation-induced voltage gating makes it of much interest. The manuscript is presented at a high level, though there are a couple of points to address, including reproducibility of simulations and potential for more relation to experimental findings.

The authors carried out 1μs-MD simulations of the resting, activated, and a Y289D mutant at 0 mV, and then tried to drive the conformational change with a very large -1V voltage (double that studied previously). In 1 us MD, is the membrane stable with such a big voltage, as it would likely not be experimentally? Even with a volt applied, there was incomplete activation of the voltage sensors, despite timescales approaching that of activation. For the pulling/ driving simulations (adiabatic bias MD) to change suspected interaction distances (V390-I302, N300-W281, and D290-K412), it seems to be just 1 simulation, without reproducibility. One has to wonder, if the simulation was redone from a very different initial conformation, would the results be the same (in addition to the distances themselves that were enforced by the ABMD). Moreover, the authors had to model the open state, such that the results depend on a homology model based on other CNBD channels, hERG / rEAG. Although the model stayed open for a microsecond, what other measures of accuracy of the homology model are there, such as preserved distances according to mutants/double mutants?

The authors find that activation involves hydrophobic forces that strengthen the intra-subunit S4/S5/S6 interface, as well as lipid headgroups that make contact with hydrophilic residues at this interface, with lipid tails also contributing to hydrophobic contacts. The authors see bending and rotation of the lower S4 and a displacement of S1 away from S4 that exposes the VSD-pore interface to lipids, with increased lipid contacts at S4 and S5 during activation. This indicates lipid tails may play a role in coupling in HCN1 and may explain the closed state micelle structure at 0mV. Two sites of lipid contact are identified, one engaging VSD residues and the other polar or charged residues on S5 and S6. No experiments are presented or proposed to test the predicted lipid sites. e.g. Mutation of key residues, such as the arginine and histidine seen binding lipid headgroups could be tested as proof of their involvement, or perhaps experiments with varied phosphate moieties? In the absence of new experiments, is there existing data that could help validate the findings?

During free MD simulation, the authors see tilting of S5 caused by activation of the Y289D mutation that brings D290 and K412 positions into proximity. How do we know that the adjacent mutant of Y289 to aspartate has not caused this, or was this interaction also seen in wild-type simulation? Fig.3c might suggest the wt activated simulation may see such an interaction, but it is unclear given the large C_alpha distances, as opposed to H-bonding distances.

The authors predict that a D290-K412 salt bridge may be important for gating and sought to experimentally validate the interaction in the activated-open state using cysteine cross-bridging. As this is the only experimental backing in the paper, it is important to be able to judge its ability to report on the D290-K4512 salt bridge. A comparison experiment demonstrating other cross-links that do not favour the open state would have been helpful in this regard e.g. if cross-bridging at similar locations (but not predicted to change interaction during gating) had little effect on I/Imax, then the result may be bolstered. Are there existing mutagenesis experiments that may suggest the importance of these residues (as well as for other key interaction distances identified)?

Rotation of the V390 side chain from a position facing the pore lumen to a position facing I302 on S5 is coupled to an increase of the pore radius at V390, an increased hydration of the pore intracellular gate, and K+ ion movement. Perhaps 5 or 6 ions cross in that single simulation. As K channel ion permeation can depend critically on starting ion configs (as well as the model/force field), reproducibility of this finding is important but does not appear to have been tested. How can we be sure that periods of permeation or no permeation in individual simulations are reliable?

Reviewer #1 (Public Review):

The manuscript of Parab et al. reports a beautiful phenotype analysis of the vascular brain/meningeal anatomy in a variety of reporter lines and mutants for Wnt/β-catenin signaling and angiogenic cues (Vegfaa, Vegfab Vegfc, Vegfd) during zebrafish development.

The original finding is that a region-specific code of angiogenic cues controls fenestrated vessel formation. The authors show that fenestrated vessels form independently of Wnt/β-catenin signaling and BBB vascular development but require different combinations of Vegfa and Vegfc/d-dependent angiogenesis within and across brain regions. A previously unappreciated function of autocrine and paracrine Vegfc signaling is demonstrated in this brain region-specific regulation of fenestrated capillary development.

My only main concern is that no information is provided on the regional diversity of angiogenic receptor expression that may correlate with the regional angiogenic factor code. Without asking for a spatial transcriptomic study, the combination of Vegfr-reporter lines or in situ hybridization with a combination of receptor probes would allow for generating a comprehensive set of ligand/receptor data relative to the regional angiogenic signaling pattern involved in fenestrated vessel formation.

Reviewer #1 (Public Review):

The manuscript by Wagstyl et al. describes an extensive analysis of gene expression in the human cerebral cortex and the association with a large variety of maps capturing many of its microscopic and macroscopic properties. The core methodological contribution is the computation of continuous maps of gene expression for >20k genes, which are being shared with the community. The manuscript is a demonstration of several ways in which these maps can be used to relate gene expression with histological features of the human cortex, cytoarchitecture, folding, function, development and disease risk. The main scientific contribution is to provide data and tools to help substantiate the idea of the genetic regulation of multi-scale aspects of the organisation of the human brain. The manuscript is dense, but clearly written and beautifully illustrated.

# Main comments

The starting point for the manuscript is the construction of continuous maps of gene expression for most human genes. These maps are based on the microarray data from 6 left human brain hemispheres made available by the Allen Brain Institute. By technological necessity, the microarray data is very sparse: only 1304 samples to map all the cortex after all subjects were combined (a single individual's hemisphere has ~400 samples). Sampling is also inhomogeneous due to the coronal slicing of the tissue. To obtain continuous maps on a mesh, the authors filled the gaps using nearest-neighbour interpolation followed by strong smoothing. This may have two potentially important consequences that the authors may want to discuss further: (a) the intrinsic geometry of the mesh used for smoothing will introduce structure in the expression map, and (b) strong smoothing will produce substantial, spatially heterogeneous, autocorrelations in the signal, which are known to lead to a significant increase in the false positive rate (FPR) in the spin tests they used.

## a. Structured smoothing

A brain surface has intrinsic curvature (Gaussian curvature, which cannot be flattened away without tearing). The size of the neighbourhood around each surface vertex will be determined by this curvature. During surface smoothing, this will make that the weight of each vertex will be also modulated by the local curvature, i.e., by large geometric structures such as poles, fissures and folds. The article by Ciantar et al (2022, https://doi.org/10.1007/s00429-022-02536-4) provides a clear illustration of this effect: even the mapping of a volume of *pure noise* into a brain mesh will produce a pattern over the surface strikingly similar to that obtained by mapping resting state functional data or functional data related to a motor task.

1. It may be important to make the readers aware of this possible limitation, which is in large part a consequence of the sparsity of the microarray sampling and the necessity to map that to a mesh. This may confound the assessments of reproducibility (results, p4). Reproducibility was assessed by comparing pairs of subgroups split from the total 6. But if the mesh is introducing structure into the data, and if the same mesh was used for both groups, then what's being reproduced could be a combination of signal from the expression data and signal induced by the mesh structure.<br /> 2. It's also possible that mesh-induced structure is responsible in part for the "signal boost" observed when comparing raw expression data and interpolated data (fig S1a). How do you explain the signal boost of the smooth data compared with the raw data otherwise?<br /> 3. How do you explain that despite the difference in absolute value the combined expression maps of genes with and without cortical expression look similar? (fig S1e: in both cases there's high values in the dorsal part of the central sulcus, in the occipital pole, in the temporal pole, and low values in the precuneus and close to the angular gyrus). Could this also reflect mesh-smoothing-induced structure?<br /> 4. Could you provide more information about the way in which the nearest-neighbours were identified (results p4). Were they nearest in Euclidean space? Geodesic? If geodesic, geodesic over the native brain surface? over the spherically deformed brain? (Methods cite Moresi & Mather's Stripy toolbox, which seems to be meant to be used on spheres). If the distance was geodesic over the sphere, could the distortions introduced by mapping (due to brain anatomy) influence the geometry of the expression maps?<br /> 5. Could you provide more information about the smoothing algorithm? Volumetric, geodesic over the native mesh, geodesic over the sphere, averaging of values in neighbouring vertices, cotangent-weighted laplacian smoothing, something else?<br /> 6. Could you provide more information about the method used for computing the gradient of the expression maps (p6)? The gradient and the laplacian operator are related (the laplacian is the divergence of the gradient), which could also be responsible in part for the relationships observed between expression transitions and brain geometry.

## b. Potentially inflated FPR for spin tests on autocorrelated data

Spin tests are extensively used in this work and it would be useful to make the readers aware of their limitations, which may confound some of the results presented. Spin tests aim at establishing if two brain maps are similar by comparing a measure of their similarity over a spherical deformation of the brains against a distribution of similarities obtained by randomly spinning one of the spheres. It is not clear which specific variety of spin test was used, but the original spin test has well known limitations, such as the violation of the assumption of spatial stationarity of the covariance structure (not all positions of the spinning sphere are equivalent, some are contracted, some are expanded), or the treatment of the medial wall (a big hole with no data is introduced when hemispheres are isolated).

Another important limitation results from the comparison of maps showing autocorrelation. This problem has been extensively described by Markello & Misic (2021). The strong smoothing used to make a continuous map out of just ~1300 samples introduces large, geometry dependent autocorrelations. Indeed, the expression maps presented in the manuscript look similar to those with the highest degree of autocorrelation studied by Markello & Misic (alpha=3). In this case, naive permutations should lead to a false positive rate ~46% when comparing pairs of random maps, and even most sophisticated methods have FPR>10%.

7. There's currently several researchers working on testing spatial similarity, and the readers would benefit from being made aware of the problem of the spin test and potential solutions. There's also packages providing alternative implementations of spin tests, such as BrainSMASH and BrainSpace (Weinstein et al 2020, https://doi.org/10.1101/2020.09.10.285049), which could be mentioned.<br /> 8. Could it be possible to measure the degree of spatial autocorrelation?<br /> 9. Could you clarify which version of the spin test was used? Does the implementation come from a package or was it coded from scratch?<br /> 10. Cortex and non-cortex vertex-level gene rank predictability maps (fig S1e) are strikingly similar. Would the spin test come up statistically significant? What would be the meaning of that, if the cortical map of genes not expressed in the cortex appeared to be statistically significantly similar to that of genes expressed in the cortex?

Reviewer #1 (Public Review):

This important study from Jahncke et al. demonstrates inhibitory synaptic defects and elevated seizure susceptibility in multiple models of dystroglycanopathy. A strength of the paper is the use of a wide range of genetic models to disrupt different aspects of dystroglycan protein or glycosylation in forebrain neurons. The authors use a combination of immunohistochemistry and electrophysiology to identify cellular migration, lamination, axonal targeting, synapse formation/function, and seizure phenotypes in forebrain neurons. This is an elegant study with extensive data supporting the conclusions. The role of dystroglycan and the dystrophin glycoprotein complex (DGC) in cellular migration and synapse formation are of broad interest.

A strength of this paper is the use of several transgenic mouse lines with mutations in genes involved in glycosylation of dystroglycan. Knockout of POMT2 abolishes the majority of dystroglycan glycosylation, while point mutations in B4GAT and FKRP presumably produce more minor changes in glycosylation. This is a powerful approach to investigate the role of glycosylation in dystroglycan function. However, the authors do not address how mutations in these genes may affect glycosylation or expression of proteins other than dystroglycan. It is possible, even likely, that some of the phenotypes observed are due to changing glycosylation in any number of other proteins. The paper would be strengthened by addressing this possibility more directly.<br /> It would be helpful to have a more clear description of how dystroglycan glycosylation is altered in B4GAT1M155T or FKRPP448L mice. For example, Figure 1 makes it appear that the distal sugar moieties are missing, however, the IIH6 antibody, which binds to terminal matriglycan repeats on the glycan chain, recognizes dystroglycan in these mutants.

In Figure 1, the authors use the IIH6 antibody, which recognizes the terminal portion of the dystroglycan glycan chain, to label dystroglycan in the hippocampus. As expected, Emx1Cre,POMT2cKO mice, which lack glycosylation of dystroglycan, do not show any labelling. However, this experiment does not reveal anything about dystroglycan expression, only that the IIH6 antibody no longer recognizes dystroglycan. It would be very helpful in interpreting the later results to know whether the level and pattern of dystroglycan expression is normal or absent in the POMT2cKO mice, perhaps using another antibody that does not target the glycosylated region. For example, figure 3 shows reduced axon targeting to the cell body layer in POMT2cKO, however, it is unclear whether this is due to absence/mislocalization of dystroglycan at the cell surface, or if dystroglycan expression is normal, but glycosylation is directly required for axon targeting.

In Figures 3 and 5, the authors use CB1R labelling to measure axon targeting and synapses formation. However, it is not clear how the authors measure axon targeting and synapses number separately using the same CB1R antibody. In addition, figure 3 shows reduced CB1R labelling in Dag1cyto pyramidal cell layer, but Figure 5 shows no change in CB1R labelling in the same mice. These results would appear to be contradictory.

The authors measure spontaneous IPSCs (sIPSC) in CA1 pyramidal neurons to measure inhibitory synaptic function. This measure assesses inhibitory synaptic input from all sources, but dystroglycan mutations primarily impairs synapses arising from CCK+/CB1R interneurons, leaving synapses arising from PV or other interneurons relatively unchanged. To assess changes in CCK+/CB1R interneurons the authors apply the cholinergic receptor agonist Carbachol (which selectively activates CCK+/CB1R interneurons) and measure the change in sIPSC amplitude and frequency. While this is an interesting and reasonable experiment, the observed effects could be due to altered carbachol sensitivity in the transgenic mice. Control experiments showing that the effect of Carbachol on excitability of CCK+/CB1R interneurons is similar across mouse lines is missing.

Earlier work has shown that selective deletion of dystroglycan from pyramidal neurons produces near complete loss of CCK+/CB1R interneurons and synapse formation, a more severe deficit than observed here using a more widespread Cre-driver. This finding is surprising, as generally more wide-spread gene deletion results in more severe, not less severe, phenotypes. The authors make the reasonable claim that more wide-spread gene deletion better mimics human pathologies. However, possible speculation on why this is the case for dystroglycan could provide insight into the nature of CNS deficits in different forms of dystroglycanopathies.

Reviewer #1 (Public Review):

Continuous attractor networks endowed with some sort of adaptation in the dynamics, whether that be through synaptic depression or firing rate adaptation, are fast becoming the leading candidate models to explain many aspects of hippocampal place cell dynamics, from hippocampal replay during immobility to theta sequences during run. Here, the authors show that a continuous attractor network endowed with spike frequency adaptation and subject to feedforward external inputs is able to account for several previously unaccounted aspects of theta sequences, including (1) sequences that move both forwards and backwards, (2) sequences that alternate between two arms of a T-maze, (3) speed modulation of place cell firing frequency, and (4) the persistence of phase information across hippocampal inactivations.

I think the main result of the paper (findings (1) and (2)) are likely to be of interest to the hippocampal community, as well as to the wider community interested in mechanisms of neural sequences. In addition, the manuscript is generally well written and the analytics are impressive. However, several issues should be addressed, which I outline below.

Major comments:

In real data, population firing rate is strongly modulated by theta (i.e., cells collectively prefer a certain phase of theta - see review paper Buzsaki, 2002) and largely oscillates at theta frequency during run. With respect to this cyclical firing rate, theta sweeps resemble "Nike" check marks, with the sweep backwards preceding the sweep forwards within each cycle before the activity is quenched at the end of the cycle. I am concerned that (1) the summed population firing rate of the model does not oscillate at theta frequency, and (2) as the authors state, the oscillatory tracking state must begin with a forward sweep. With regards to (1), can the authors show theta phase spike preference plots for the population to see if they match data? With regards to (2), can the authors show what happens if the bump is made to sweep backwards first, as it appears to do within each cycle?

I could not find the width of the external input mentioned anywhere in the text or in the table of parameters. The implication is that it is unclear to me whether, during the oscillatory tracking state, the external input is large compared to the size of the bump, so that the bump lives within a window circumscribed by the external input and so bounces off the interior walls of the input during the oscillatory tracking phase, or whether the bump is continuously pulled back and forth by the external input, in which case it could be comparable to the size of the bump. My guess based on Fig 2c is that it is the latter. Please clarify and comment.

I would argue that the "constant cycling" of theta sweeps down the arms of a T-maze was roughly predicted by Romani & Tsodyks, 2015, Figure 7. While their cycling spans several theta cycles, it nonetheless alternates by a similar mechanism, in that adaptation (in this case synaptic depression) prevents the subsequent sweep of activity from taking the same arm as the previous sweep. I believe the authors should cite this model in this context and consider the fact that both synaptic depression and spike frequency adaptation are both possible mechanisms for this phenomenon. But I certainly give the authors credit for showing how this constant cycling can occur across individual theta cycles.

The authors make an unsubstantiated claim in the paragraph beginning with line 413 that the Tsodyks and Romani (2015) model could not account for forwards and backwards sweeps. Both the firing rate adaptation and synaptic depression are symmetry breaking models that should in theory be able to push sweeps of activity in both directions, so it is far from obvious to me that both forward and backward sweeps are not possible in the Tsodyks and Romani model. The authors should either prove that this is the case (with theory or simulation) or excise this statement from the manuscript.

The section on the speed dependence of theta (starting with line 327) was very hard to understand. Can the authors show a more graphical explanation of the phenomenon? Perhaps a version of Fig 2f for slow and fast speeds, and point out that cells in the latter case fire with higher frequency than in the former?

I had a hard time understanding how the Zugaro et al., (2005) hippocampal inactivation experiment was accounted for by the model. My intuition is that while the bump position is determined partially by the location of the external input, it is also determined by the immediate history of the bump dynamics as computed via the local dynamics within the hippocampus (recurrent dynamics and spike rate adaptation). So that if the hippocampus is inactivated for an arbitrary length of time, there is nothing to keep track of where the bump should be when the activity comes back on line. Can the authors please explain more how the model accounts for this?

Can the authors comment on why the sweep lengths oscillate in the bottom panel of Fig 5b during starting at time 0.5 seconds before crossing the choice point of the T-maze? Is this oscillation in sweep length another prediction of the model? If so, it should definitely be remarked upon and included in the discussion section.

Perhaps I missed this, but I'm curious whether the authors have considered what factors might modulate the adaptation strength. In particular, might rat speed modulate adaptation strength? If so, would have interesting predictions for theta sequences at low vs high speeds.

I think the paper has a number of predictions that would be especially interesting to experimentalists but are sort of scattered throughout the manuscript. It would be beneficial to have them listed more prominently in a separate section in the discussion. This should include (1) a prediction that the bump height in the forward direction should be higher than in the backward direction, (2) predictions about bimodal and unimodal cells starting with line 366, (3) prediction of another possible kind of theta cycling, this time in the form of sweep length (see comment above), etc.

Reviewer #1 (Public Review):

In the manuscript entitled "A theory of hippocampal theta correlations", the authors propose a new mechanism for phase precession and theta-time scale generation, as well as their interpretation in terms of navigation and neural coding. The authors propose the existence of extrinsic and intrinsic sequences during exploration, which may have complementary functions. These two types of sequences depend on external input and network interactions, but differ on the extent to which they depend on movement direction. Moreover, the authors propose a novel interpretation for intrinsic sequences, namely to signal a landmark cue that is independent of direction of traversal. Finally, a readout neuron can be trained to distinguish extrinsic from intrinsic sequences.

The manuscript has the potential to contribute to the way we interpret hippocampal temporal coding for navigation and memory. In its current form, however, there are some issues that affect the readability and intelligibility of the manuscript, that the authors may address in a revised version:

- The findings generally relate to network models of phase precession (reviewed in e.g., Maurer and McNaughton, 2007, Jaramillo and Kempter, 2017). An important drawback of these models with respect to explaining specific experimentally observed features of phase precession, is that they cannot straightforwardly explain phase precession upon first exposure onto a novel track. This is because, specific connectivity in network models may require experience-dependent plasticity, which would not be possible upon first exposure. This is essential, given that the manuscript addresses the possible origin of phase precession in terms of network models and at minimum, this weakness should be discussed.

- An important and perhaps essential component of the manuscript, is the distinction between extrinsic and intrinsic models. However, the main concepts on which this hinges, namely extrinsic and intrinsic sequences (and the related extrinsicity and intrinsicity) could be better explained and illustrated. Along these lines, the result suggested by the title, namely, hippocampal theta correlations, may be important yet incidental in light of the new concepts (e.g., extrinsicity, intrinsicity) and computational models (e.g., DG-CA3 recurrent loop) that are put forward.

- The study seems to put forward novel computational ideas related to neural coding. However, assessing novelty is challenging as this manuscript builds on previous work from the authors, including published (Leibold, 2020, Yiu et al., 2022) and unpublished (Ahmedi et al., 2022. bioRxiv) work. For example, the interpretation of intrinsic sequences in terms of landmarks had been introduced in Leibold, 2020.

- The significance of the readout tempotron neuron could be expanded on. In particular, there is room for interpretation of the output signal of that neuron (e.g., what is the significance of other neurons downstream? Why is the rationale for this output to being theta-modulated?)

Reviewer #1 (Public Review):

In this study the authors develop methods to interrogate cultured neuronal networks to learn about the contributions of multiple simultaneously active input neurons to postsynaptic activity. They then use these methods to ask how excitatory and inhibitory inputs combine to result in postsynaptic neuronal firing in a network context.

The study uses a compelling combination of high-density multi-electrode array recordings with patch recordings. They make ingenious use of physiology tricks such as shifting the reversal potential of inhibitory inputs, and identifying inhibitory vs. excitatory neurons through their influence on other neurons, to tease apart the key parameters of synaptic connections. The method doesn't have complete coverage of all neurons in the culture, and it appears to work on rather low-density cultures so the size of the networks in the current study is in the low tens.

1. It would be valuable to see the caveats associated with the small size of the networks examined here.<br /> 2. It would be also helpful if there were a section to discuss how this approach might scale up, and how better network coverage might be achieved.

The authors obtain a number of findings on the conditions in which the dynamics of excitatory and inhibitory inputs permit spiking, and the statistics of connectivity that result in this. This is of considerable interest, and clearly one would like to see how these findings map to larger networks, to non-cortical networks, and ideally to networks in-vivo. The suite of approaches discussed here could potentially serve as a basis for such further development.

3. It would be useful for the authors to suggest such approaches.<br /> 4. The authors report a range of synaptic conductance waveforms in time. Not surprisingly, E and I look broadly different. Could the authors comment on the implications of differences in time-course of conductance profiles even within E (or I) synapses? Is this functional or is it an outcome of analysis uncertainty?

One of the challenges in doing such studies in a dish is that the network is simply ticking away without any neural or sensory context to work on, nor any clear idea of what its outputs might mean. Nevertheless, at a single-neuron level one expects that this system might provide a reasonable subset of the kinds of activity an individual cell might have to work on.

5. Could the authors comment on what subsets of network activity is, and is not, likely to be seen in the culture?<br /> 6. Could they indicate what this would mean for the conclusions about E-I summation, if the in-vivo activity follows different dynamics?

Reviewer #1 (Public Review):

Using the colon transcriptomes of 52 BXD mouse strains fed either chow or a high-fat diet (HFD), Li et al. present their findings on gene-by-environment interactions underpinning inflammation and inflammatory bowel disease (IBD). They discovered modules that are enriched for IBD-dysregulated genes using co-expression gene networks. They determined Muc4 and Epha6 to be the leading candidates causing variations in HFD-driven intestinal inflammation by using systems genetics in the mouse and integration with external human datasets. In their analysis, they concluded that their strategy "enabled the prioritization of modulators of IBD susceptibility that were generalizable to the human situation and may have clinical value." This dataset is intriguing and generates hypotheses that will be investigated in the future. However, there were no mechanistic or causation-focused investigations; the results were primarily observational and correlative.

Reviewer #1 (Public Review):

The goal of this study was to examine the nature of the relationship between a number of close friends and mental health, cognition and brain structure. In particular, the authors were interested in any potential non-linear relationships between a number of close friends and various measures (neurocognition, brain structure).

Strengths<br /> The sample sizes are very large (total size > 23,000) across two datasets.<br /> There are a wide range of measures in the ABCD dataset -- mental health, cognition and brain data.<br /> There were two independent datasets and the results were broadly similar across datasets.<br /> The longitudinal aspect (2-year follow up) to the data is also a strength, as is the use of cross-lagged panel models.<br /> The use of the two-lines test -- formally testing a non-linear relationship among variables -- is a notable strength (many studies only test using a quadratic equation, which does not necessarily mean that any relationship is significantly non-linear).

Weaknesses<br /> The study is associational and causal relations cannot be determined (the authors' themselves are clear on this point).<br /> The measures in the two datasets were not identical, precluding a direct out-of-sample validation test.<br /> The depth of the information about friend relationships in the ABCD study was limited. The number of close friends was recorded, but not the quality of those relationships.

To the extent that the authors were attempting to show relations among variables - and not causal associations - the authors have achieved their aims. An impact of these results lies in the link between 'Dunbar's number' of *close* relationships and neurocognitive measures, supporting the link between social relationships and brain and cognition in humans. The brain data in ABCD were very rich and notably allowed the authors to investigate neurotransmitter density. This is not a weakness of the study per se but it is notable that the effect sizes are quite small (although highly significant given the large sample sizes).

Reviewer #1 (Public Review):

This manuscript confirms previous studies suggesting a great deal of heterogeneity of gene expression at the neural plate border in early vertebrate embryos, as neural, placodal, neural crest, and epidermal lineages gradually segregate. Using scRNA-seq, the study expands previous studies by using far larger numbers of genes as evidence of this heterogeneity. The evidence for this heterogeneity and the change in heterogeneity over time is compelling.

Many studies have suggested that there is considerable heterogeneity of gene expression in the developing neural plate border as the neural, neural crest, placodal and epidermal lineages segregate. Although the evidence for such heterogeneity was strong, until the advent of scRNA-seq, the extent of this heterogeneity was not appreciated. By using scRNA-seq at different stages of chick development, the authors sought to characterize how this heterogeneity develops and resolves over time.

The work is technically sound, and the level of analysis of gene expression, clustering, synexpression groups, and dynamic changes in gene modules over time is state-of-the-art. A weakness of the results as they stand now is that the conclusions of the analysis are not tested by the authors and thus, are over-interpreted. Such tests could be performed in future studies either by gain- and loss-of-function experiments or by using lineage tracing to demonstrate that the cell states the authors observe - especially the "unstable progenitors" they characterize - are biologically meaningful. The data will nevertheless be a useful resource to investigators interested in understanding the development of different cell lineages at the neural plate border.

Reviewer #1 (Public Review):

In this study, the authors investigate the role of triglycerides in spermatogenesis. This work is based on their previous study (PMID: 31961851) on triglyceride sex differences in which they showed that somatic testicular cells play a role in whole body triglyceride homeostasis. In the current study, they show that lipid droplets (LDs) are significantly higher in the stem and progenitor cell (pre-meiotic) zone of the adult testis than in the meiotic spermatocyte stages. The distribution of LDs anti-correlates with the expression of the triglyceride lipase Brummer (Bmm), which has higher expression in spermatocytes than early germline stages. Analysis of a bmm mutant (bmm[1]) - a P-element insertion that is likely a hypomorphic - and its revertant (bmm[rev]) as a control shows that bmm acts autonomously in the germline to regulate LDs. In particular, the number of LDs is significantly higher in spermatocytes from bmm[1] mutants than from bmm[rev] controls. Testes from males with global loss of bmm (bmm[1]) are shorter than controls and have fewer differentiated spermatids. The zone of bam expression, typically close to the niche/hub in WT, is now many cell diameters away from the hub in bmm[1] mutants. There is an increase in the number of GSCs in bmm[1] homozygotes, but this phenotype is probably due to the enlarged hub. However, clonal analyses of GSCs lacking bmm indicate that a greater percentage of the GSC pool is composed of bmm[1]-mutant clones than of bmm[rev]-clones. This suggests that loss of bmm could impart a competitive advantage to GSCs, but this is not explored in greater detail. Despite the increase in number of GSCs that are bmm[1]-mutant clones, there is a significant reduction in the number of bmm[1]-mutant spermatocyte and post-meiotic clones. This suggests that fewer bmm[1]-mutant germ cells differentiate than controls. To gain insights into triglyceride homeostasis in the absence of bmm, they perform mass spec-based lipidomic profiling. Analyses of these data support their model that triglycerides are the class of lipid most affected by loss of bmm, supporting their model that excess triglycerides are the cause of spermatogenetic defects in bmm[1]. Consistent with their model, a double mutant of bmm[1] and a diacylglycerol O-acyltransferase 1 called midway (mdy) reverts the bmm-mutant germline phenotypes.

There are numerous strengths of this paper. First, the authors report rigorous measurements and statistical analyses throughout the study. Second, the authors utilize robust genetic analyses with loss-of-function mutants and lineage-specific knockdown. Third, they demonstrate the appropriate use of controls and markers. Fourth, they show rigorous lipidomic profiling. Lastly, their conclusions are appropriate for the results. In other words, they don't overstate the results.

There are a few weaknesses. Although the results support the germline autonomous role of bmm in spermatogenesis, one potential caveat that the mdy rescue was global, i.e., in both somatic and germline lineages. The authors did not recover somatic bmm clones, suggesting that bmm may be required for somatic stem self-renewal and/or niche residency. While this is beyond the scope of this paper, it is possible that somatic bmm does impact germline differentiation in a global bmm mutant. Regarding data presentation, I have a minor point about Fig. 3L: why aren't all data shown as box plots (only Day 14 bmm[rev] does). Finally, the authors provide a detailed pseudotime analysis of snRNA-seq of the testis in Fig. S2A-D, but this analysis is not sufficiently discussed in the text.

Overall, the many strengths of this paper outweigh the relatively minor weaknesses. The rigorously quantified results support the major aim that appropriate regulation of triglycerides are needed in a germline cell-autonomous manner for spermatogenesis.

This paper should have a positive impact on the field. First and foremost, there is limited knowledge about the role of lipid metabolism in spermatogenesis. The lipidomic data will be useful to researchers in the field who study various lipid species. Going forward, it will be very interesting to determine what triglycerides regulate in germline biology. In other words, what functions/pathways/processes in germ cells are negatively impacted by elevated triglycerides. And as the authors point out in the discussion, it will be important to determine what regulates bmm expression such that bmm is higher in later stages of germline differentiation.

15 May homework - due by 22 May. Please download, complete, scan to PDF, and send to me via whatsapp (0768559400). Regards, John

Reviewer #1 (Public Review):

This study aimed at the identification additional region of Cac1 involved in DNA binding. Previously, it has been shown that Cac1, the large subunit of chromatin assembly factor 1 (CAF-1), contains DNA binding other regions in addition to the known WHD domain. This study shows that the KER region of Cac1 form a single alpha helix based on CD and crystal structure analysis. Furthermore, unlike the SAH motif in other proteins, the Cac1 SAH motif binds DNA. Further, this motif, along with WHD motif, is important for the function of Cac1 in heterochromatin silencing and in response to DNA damage agents in cells, suggesting that these two regions are important for nucleosome assembly. The majority of experiments are well controlled and the results support the confusions. The major concern is that the human KER region cannot complement the yeast KER region, likely due to multiple possibilities, which needed to be tested.

Reviewer #1 (Public Review):

This study shows that activation of α1-adrenergic receptors in hippocampal neurons in culture increases nPo of single L-type calcium channels. This pathway is dissected using a large number of activating agents and blockers to involve PKC, Pyk2 and src. The pathway is further examined using PC12 cells, where it is activated by bradykinin. Finally, a form of LTP which is dependent on L-type calcium channels is augmented in young mice by use of the α1-AR agonist, phenylephrine.

1) My main critique would be that the study, while very well executed and rigorous, is fragmented, consisting of three parts that each feel incomplete: i, hippocampal neuron studies, mainly single channel recordings; ii, biochemical studies mainly in PC12 cells, using a different agonist bradykinin, and iii, the examination of LTP in young mice.

Reviewer #1 (Public Review):

The paper addresses why and how odor discrimination ability achieved after learning occurs in select contexts. The finding is that two related odors trigger near identical Kenyon cell responses when tested in isolation, but trigger different responses to the second odor if these are experienced in sequence within a small temporal window. The authors argue that this template comparison requires some activity downstream of Kenyon cells, that is recruited by MBONs. Overall, the experiments provide very nice physiological evidence for a neural mechanism that underlies a contextual basis for the precision of memory recall.

The experiments were well designed and done. The findings are interesting, but the pitch (e.g. the last paragraph of the discussion and the title of the paper) seems to both ignore the main finding of the paper and overstate the novelty of the idea that memory recall can be flexibly regulated by context. There should be more space dedicated to clearly articulated statements/descriptions of hypotheses and candidate mechanisms to explain the interesting phenomenon described here. For instance, explaining "enhanced template mismatch detection" by potential " real-time and delay line summation" of MBON activity is not super useful for the reader as seems to use one abstraction to explain another. The authors cite Lin et al, 2014 from Miesenbock's lab which shows a key role for GABAergic APL neurons in discrimination. Is there increased activation of APL neurons when similar odourants are being compared and discrimination is required? This seems like a simple physically embodied mechanism that could/ should be examined.

Overall, I think the idea that memories are recalled with high precision (less generalisation) only when increased precision is demanded, is a fact that sure is well appreciated by behavioral biologists even beyond the two papers cited here (Campbell et al., J Neurosci 2013; Xu and Südhof, Science 2013). The new findings fill in a physiological gap in this phenomenology. I think the paper would be greatly improved if the authors highlighted what and focused on the physiological correlate uncovered, and tried to communicate (or test) possible mechanistic origins for this in more physically accessible terms.

Reviewer #1 (Public Review):

This study characterizes the localization of the lone voltage-gated Na channel in Drosophila Para in motor and sensory neurons. Like previous studies, the authors identify an enrichment of Na (and importantly the K+ channel Shal) in axon initial segment-like (AIS-) areas in motor neuron axons, and show that this structure is not apparent in axons of sensory neurons. Upon ablation of wrapping glia in the periphery, the authors find this AIS-like organization of Para is lost. Finally, compelling EM analyses of peripheral nerves suggest an intriguing area devoid of glia around AIS-like structures, and some evidence for myelin-like structures along the distal axon. The author propose several interesting ideas for how these structures might be involved in AP signaling and as evolutionary precursors to conventional myelination and saltatory conductance in vertebrates.

Clearly, the evolution of myelination, and how glia contribute to neuronal firing in systems without classically accepted myelination and saltatory conductance are important questions. Although much of the Para clustering in AIS-like domains and regular densities along motor axons have been described in previous studies, the ultrastructural analyses and dependence on wrapping glia might be important advances in the field. In particular, major strengths of this study are the detailed analysis of AIS-like Para clusters, spanning molecular genetic, confocal and super resolution imaging, and ultrastructural approaches and clear writing. However, these strengths are somewhat tempered by a lack of functional approaches to test the idea of a lacunar structure that promotes ion exchange at putative AIS regions as well as little mechanistic insight into how glia may specifically coordinate the formation of Para clusters in AIS-like regions.

Reviewer #1 (Public Review):

Inhibition of translation has been found as a conserved intervention to extend lifespan across a number of species. In this work, the authors systematically investigate the similarities and differences between pharmacological inhibition of protein synthesis at the initiation or elongation steps on longevity and stress resistance. They find that translation elongation inhibition is beneficial during times when proteostasis collapse is the primary phenotype such as proteasome dysfunction, hsf-1 mutants, and heat shock, but this intervention does not extend the lifespan of wt worms. While translation initiation inhibition extends the lifespan of wt worms and heat shock, but in an HSF-1 dependent manner. This work shows that a simple explanation of just inhibiting total protein synthesis and reduced folding load cannot explain all of the phenotypes seen from protein synthesis inhibition, as initiation and elongation inhibition repress overall translation similarly, but have different effects depending on the experiment tested. Using multiple interventions that target both initiation and elongation lends further support to their findings. These experiments are important for conceptualizing how translation inhibition actually extends lifespan and promotes proteostasis.

Major Comment:

The authors acknowledge that lifespan extension must not necessarily arise just from reducing protein synthesis, as elongation inhibition reduced protein synthesis but did not extend lifespan. Yet for the converse effects from elongation inhibition they seem to suggest that it arises from reducing protein synthesis. For example, regarding how elongation inhibition extends lifespan in an hsf-1 mutant, the authors suggest that "inhibition of elongation lowers the production of newly synthesized proteins and thus reduces the folding load on the proteostasis machinery", even though initiation inhibitors do not extend lifespan in an hsf-1 background (while presumably lowering the production of newly synthesized proteins).

Reviewer #1 (Public Review):

The role of HCO3 (or possibly CO2) in regulating sACs is well established yet its physiological context is less clear. The heart is indeed an excellent choice of organ to study this. Isolated mitochondria offer a tractable model for studying the model, although are not without limitations. The quality of recordings is very high, as judged by the consistency of results (i.e. lack of clustering between biological repeats). My primary concern is about distinguishing the effect of pH and HCO3. A rise in HCO3 will also raise pH unless this had been compensated by CO2. It is unclear, from the legend or results, if the bicarbonate effect is due to HCO3 or pH. Was pH controlled by matching the rise in HCO3 with an appropriate level of CO2? The swings in pH are likely to be very large and, potentially, a confounding factor. Certainly, there will be an effect on the proton motive force. A more informative test would compare the effect of 0 CO2/0HCO3 at a pH set to say 7.2, 2.5% CO2/7.5 mM HCO3, and then 5% CO2/15 mM HCO3, etc. Control experiments would then repeat these observations over a range of pH (at zero CO2/HCO3) and over a range of CO2 (at constant HCO3). Data for zero bicarbonate are not informative, as this will never be a physiological setting (results claim 0-15 mM bicarb to represent physiology). Importantly, there seems to be no significant difference in 2A between 10 v 15 mM bicarb, i.e. the physiological range.

There is also a question on the validity of the model. A rise in respiratory rate will produce more CO2 in the matrix. This may raise matrix HCO3, and stimulate sACs therein, but the authors claim sACs are in the IMS, rather than the matrix. Since HCO3 is impermeable, it is unclear how sACs would detect HCO3 beyond the IMM. CO2 escaping the matrix will enter the continuum of the cytoplasmic space, which has finely controlled pH. Since membranes (including IMM) are highly permeable to CO2, the gradient between matrix and cytoplasm will be small (i.e. you only need a small gradient to drive a big flux, if the permeability is massive). Since CO2 can dissipate over a large volume, it is unlikely to accumulate to any degree. CO2 will be in equilibrium with HCO3 and pH (because there are carbonic anhydrases available). Since the cytoplasm has near-constant pH, [HCO3] must also be close to constancy. It is therefore difficult to imagine how HCO3 could change dramatically to meaningfully affect sACs and hence cAMP. Evidence for major changes in IMS pH in intact cells during swings of respiratory activity would be required to make this point. Indeed, for that reason, it would be more sensible to anchor sACS in the matrix, as there, HCO3 could rise to high levels, as it is impermeable, i.e. could be confined within the mitochondrion. I am therefore not convinced the numbers are favorable to the proposed mechanism to be meaningful physiologically.

Triad dentification exercises 1

Seventh chords identification 1

Crusell Menuetto

Schubert Piano Sonata No. 1 in D.137 in E II Andante

Binary form exercises - audio 1 (Schubert Acht Ecossiasen No 3 (starts at 1:07)

Solfège and scale degree identification 1

Reviewer #1 (Public Review):

Jamge et al. sought to identify the relationships between histone variants and histone modifications in Arabidopsis by systematic genomic profiling of 13 histone variants and 12 histone modifications to define a set of "chromatin states". They find that H2A variants are key factors defining the major chromatin types (euchromatin, facultative heterochromatin, and constitutive heterochromatin) and that loss of the DDM1 chromatin remodeler leads to loss of typical constitutive heterochromatin and replacement of this state with features common to genes in euchromatin and facultative heterochromatin. This study deepens our understanding of how histone variants shape the Arabidopsis epigenome and provides a wealth of data for other researchers to explore.

Strengths:<br /> 1. The manuscript provides convincing evidence supporting the claims that: A) Arabidopsis nucleosomes are homotypic for H2A variants and heterotypic for H3 variants, B) that H3 variants are not associated with specific H2A variants, and C) H2A variants are strongly associated with specific histone post-translational modifications (PTMs) while H3 variants show no such strong associations with specific PTMs. These are important findings that contrast with previous observations in animal systems and suggest differences in plant and animal chromatin dynamics.

2. The authors also performed comprehensive epigenomic profiling of all H2A, H2B, and H3 variants and 12 histone PTMs to produce a Hidden Markov Model-based chromatin state map. These studies revealed that histone H2A variants are as important as histone PTMs in defining the various chromatin states, which is unexpected and of high significance.

3. The authors show that in ddm1 mutants, normally heterochromatic transposable element (TE) genes lose H2A.W and gain H2A.Z, along with the facultative heterochromatin and euchromatin signatures associated with H2A.Z at silent and expressed genes, respectively.

Weaknesses:<br /> 1. Following up on the finding that H2A.Z replaces H2A.W at TE genes in ddm1 mutants, the authors provide in vitro evidence that DDM1 binds to H2A.Z-H2B dimers. These results are taken together to conclude that DDM1 normally removes H2A.Z-H2B dimers from nucleosomes at TE genes and replaces them with H2A.W-H2B dimers. However, the evidence for this model is circumstantial and such a model raises a variety of other questions that are not addressed by the authors. For example: if DDM1 does remove H2A.Z from TE genes, how does H2A.Z normally come to occupy these sites, given that they are highly DNA methylated and that H2A.Z is known to anticorrelate with DNA methylation in plants and animals? Given that H2A.Z does not accumulate in TEs in h2a.w mutants, how would H2A.X and H2A instead become enriched at these sites if DDM1 cannot bind these forms of H2A? Given that there are no apparent regions with common sequence between H2A.Z and H2A.W variants that are not also shared with other H2A classes, how would DDM1 selectively bind to H2A.W-H2B and H2A.Z-H2B dimers to the exclusion of H2A(.X)-H2B dimers?

Reviewer #1 (Public Review):

In this manuscript the authors performed experiments and simulations which showed that substrate evaporation is the main driver of early construction in termites. Additionally, these experiments and simulations were designed taking into account several different works, with independent (and sometimes conflicting) hypotheses, so that the current results shine a light on how substrate evaporation is a sufficient descriptor of most of the results seen previously.

The authors managed through simulations and ingenious experiments to show how curvature is extremely correlated with evaporation, and therefore, how results coming from these 2 environmental factors can most of the time be explained through evaporation alone. The authors have continued to use their expertise of numerical simulations and a previously developed model for termite construction, to highlight and verify their findings. On my first pass of the manuscript I felt the authors were missing an experiment: an array of humidity probes to measure evaporation in the three spatial dimensions and over time. Technologically such an experiment is not out of reach, but the author's alternative (a substrate made with a saline solution and later measuring the salt deposits on the surface) was a very ingenious low tech solution to the problem.

One possible missing experiment (and possibly the explanation of the only inconsistency of their results to previous literature) is to perform similar topographical experiments in high humidity chambers, where no humidity, or very low humidity gradients are present. Previous experiments done by Calovi and collaborators in 2019 showed that termite construction activity (without distinguishing digging from deposition) was focused on high curvature (concave) regions, where here the authors have seen higher depositions on convex structures. Despite the difference of "activity" by Calovi 2019 (clearly acknowledged by the authors), another main difference is that the experiments of the 2019 manuscript were performed in a closed chamber with very high humidity, and smooth transitions between regions of positive and negative curvature. Therefore, it stands to reason that the only missing component of the current article, would have been to perform similar experiments with curvature (positive and negative) but under an environment where gradients are reduced to a minimum.

The results presented here are so far the best attempt on characterizing multiple cues that induce termite construction activity, and that also possibly unifies the different hypothesis presented in the last 8 years into a single factor. More importantly, even if these results come from different species of termites than some of the previous works, they are relatable and seem to be mostly consistent, improving the strength of the author's claims.

Reviewer #1 (Public Review):

This manuscript by Neininger-Castro and colleagues presents a novel automatic image analysis method for assessing sarcomeres, the basic units of myofibrils and validates this tool in a couple of experimental approaches that interfere with sarcomere assembly in iPSC-cardiomyocytes (iPSC-CM).

Automatic quantification of sarcomeres is definitely something that is useful to the field. I am surprised that there is no reference in the manuscript to SarcTrack, published by Toepfer and colleagues in 2019 (PMID 30700234), which has exactly the same purpose. The advantage of the image analysis software presented in the current manuscript appears to me to be that it can cover both mature sarcomeres and nascent sarcomeres in premyofibrils effectively.

When going through the manuscript there were a few issues that should be addressed in a revised version of the manuscript:

1. I am a bit puzzled that they took 1.4 um length as a cutoff length for a mature A-band in their quantifications, since the consensus in the field for thick filament length seems to be 1.6 um?

2. When doing the knockdown for alpha and beta-myosin heavy chain, respectively, why did they not also do a Western blot for the "other" isoform as well (Figure 7)? We know that iPSC-CM express a mixture, so the relatively mild phenotype that they observe in single knockdown experiments may well be due to concomitant upregulation of the expression of the other isoform. In my point of view this should be checked.

3. There seems to be a disconnect between the images for myomesin knockdown shown in Figure 8H and the quantification shown in Figure 8I, which makes me wonder whether the image shown in H middle (MYOM1 (1) KD), where the beta-myosin doublets do not seem to be much affected is really representative?

Reviewer 1 (Public Review):

This is a reasonably good paper and the use of a commonality analysis is a nice contribution to understanding variance partitioning across different covariates. I have some comments that I believe the authors ought to address which mostly relate to clarity and interpretation.

First, from a conceptual point of view, the authors focus exclusively on cognition as a downstream outcome. I would suggest the authors nuance their discussion to provide broader considerations of the utility of their method and on the limits of interpretation of brain-age models more generally. Further, I think that since brain-age models by construction confound relevant biological variation with the accuracy of the regression models used to estimate them, there may be limits to the interpretation of (e.g.) the brain-age gap is as a dimensionless biomarker. This has also been discussed elsewhere (see e.g. https://academic.oup.com/brain/article/143/7/2312/5863667). I would suggest that the authors consider and comment on these issues.

Second, from a methods perspective, there is not a sufficient explanation of the methodological procedures in the current manuscript to fully understand how the stacked regression models were constructed. Stacked models can be prone to overfitting when combined with cross-validation. This is because the predictions from the first-level models (i.e. the features that are provided to the second level 'stacked' models) contain information about the training set *and* the test set. If cross-validation is not done very carefully (e.g. using multiple hold-out sets), information leakage can easily occur at the second level. Unfortunately, there is not a sufficient explanation of the methodological procedures in the current manuscript to fully understand what was actually done. Please provide more information to enable the reader to better understand the stacked regression models. If the authors are not using an approach that fully preserves training and test separability, they need to do so.

Please also provide an indication of the different regression strengths that were estimated across the different models and cross-validation splits. Also, how stable were the weights across splits?

Please provide more details about the task designs, MRI processing procedures that were employed on this sample in addition to the regression methods, and bias-correction methods used. For example, there are several different parameterisations of the elastic net, please provide equations to describe the method used here so that readers can easily determine how the regularisation parameters should be interpreted.

Reviewer #1 (Public Review):

The study by Korona and colleagues presents a rigorous experimental strategy for generating and maintaining a nearly complete set of monosomic yeast lines, thereby establishing a new standard for studying monosomes. Their careful approach in generating and handling monosome yeast lines, coupled with their use of high-throughput DNA sequencing and RNA sequencing, addresses concerns related to genomic instability and is commendable. However, I would like to express my concerns regarding the second part of the study, particularly the calculation of epistasis and the conclusion that vast positive epistatic effects have been observed. I believe that the conclusion of positive epistasis for fitness might be premature due to potential errors in estimating the expected fitness.

The method used to calculate fitness expectation (1 + sum(di), where di = rDRi - 1) may be inappropriate. By reading Figure 2a, it appears that the authors defined rDR as log(mutant growth rate)/log(wild-type growth rate), but I am unsure about the biological meaning of 1 + sum(di) here. In other words, what does it exactly mean when a negative y-axis value is observed in Figure 2b if it is a relative doubling rate? I would assume that the log transformation should be performed after (rather than before) dividing the mutant growth rate by the wild-type growth rate (i.e., log(mutant growth rate/wild-type growth rate)). I believe the expected growth rate for a monosome should be calculated as exp(sum(log(mutant growth rate i/wild-type growth rate))), which can then be compared with the wild-type (with a value equal to 1). Based on this calculation method, if gene A exhibits a 20% reduction in fitness when halved (A/-) and gene B exhibits a 30% reduction (B/-), the expected fitness of A/- B/- should be 56%. Therefore, it is unclear how exactly the expected fitness without epistasis was calculated and how that would affect the estimation of the sign and quantity of epistasis.

While widespread positive epistasis in yeast has been reported by other studies (e.g., doi: 10.1038/ng.524, but not to the extent reported in this study), the conclusion of the current study might not be sufficiently supported. I recommend that the authors revisit their calculation methods to provide a more convincing conclusion on the presence of positive epistasis for fitness in their dataset. Overall, I appreciate the authors' efforts in this study, but believe that addressing these concerns is essential for strengthening the validity of their findings.

Reviewer #1 (Public Review):

This manuscript is interesting because of the exploration of a novel model organisms utilizing next-generation sequencing approaches, such as single-cell-RNA-seq. Despite the authors' efforts the manuscript lacks a cohesive narrative and suffers from being extremely preliminary in nature. For example, most of the figures are cut and pasted directly from the computational programs with very little formatting or thought to creating new knowledge from the data generated. Essentially the manuscript consists of 2-3 experiments where the authors performed single-cell-RNA-seq on different anatomical locations in the pig and also on a couple of different pig types (The Chenghua and Large White). The authors used standard computational pipelines consisting of Seurat, Monocle, Cell Chat, and others to characterize differences in their data.

There is potential in this manuscript but the authors should improve upon the manuscript by mining the data better and generating a better understanding of anatomical positions of pig skin by evaluating the Hox genes.

Reviewer #1 (Public Review):

Wu et al. provide a powerful cross-species approach to better understand brain cell-type specific responses to mutant tau and aging. Therefore, they use scRNAseq of established Drosophila models that they had previously used for bulk RNAseq (Mangleburg et al., 2020) at 1, 10 and 20 days of age, which thus allows them to study the contribution of pathogenic tau (R406W-mutant) in isolation in an experimentally highly controllable manner. They find a large overlap between tau-induced and aging-induced deregulated genes, however different cell-types were primarily affected, suggesting that expression of tau does not simply induce accelerated aging. When assessing cell number abundance in response to tau expression the authors noted that certain excitatory neurons were preferentially lost. They then examined innate immune pathways downstream of NFkB, which they had already uncovered in their previous bulk studies to be associated with tau expression. Also at the scRNAseq level, they find these pathways to be deregulated after expression of tau. In addition, in control cell types that are lost when tau is expressed, they find an inverse correlation of the expression of these pathways and cellular loss, suggesting they might be predictors of neurodegeneration severity. Finally, they use this finding uncovered in Drosophila and reexamined human Alzheimer's disease snRNAseq datasets, were they also find the NFkB pathway to be deregulated.

This study has several strengths. It demonstrates the power of studying tau-effects in a tractable model and then using the obtained knowledge to pin-point relevant pathways in cross-sectional studies of human tauopathy, which are otherwise not easy to interpret given the overlayed effects of other disease triggers. By examining the single-cell level they uncover cell type specific effects, which would otherwise be hidden. This study also represents a valuable resource. Given that the authors have included multiple time points the dataset provides an opportunity to understand the evolution of cell-type specific tau effects over time. The authors have also included a replication dataset, which confirms the results of the primary analysis of neuronal loss. I also appreciate the efforts to understand the apparent increase in glia cell number after expression of tau. By combining computational and experimental methods the authors reach the well supported conclusion that in fact glial cell numbers remain constant but only appear increased due to the proportional nature of the scRNAseq data and profound loss of some neurons. Overall, it is interesting that the authors nominate the innate immunity and NFkB pathways in tauopathy, based on deregulated genes and also based on vulnerable neurons. Nevertheless, this is a correlative finding and as such does not proof that it is causal.

The authors correctly point out the importance of aging as a risk factor for Alzheimer's disease. However, it is unclear whether their models actually capture age-dependent neurodegeneration. Alternatively, they might represent neurodevelopmental tau toxicity. In Figure 1B it can be seen that all vulnerable cell types are already lost at day 1, most notably a'/b'-KC, a/b-KC and G-KC with a >4-fold decrease. This raises the question whether the lost cells might developmentally have not correctly formed, as suggested by a study that the authors cite (Kosmidis et al., 2010). This distinction is important in order to strengthen the translational value of the study to human tauopathies.

The analysis of tau expression levels relative to its impact across cell types in Figure S8 is interesting, however has caveats. The profound neuronal loss makes the interpretation of the correlation analysis of tau levels vs. neuronal vulnerability difficult - since it might be that the individual surviving a'/b'-KC, a/b-KC and G-KC cells are the ones that expressed little amounts of tau, while those that are missing used to express high tau. In addition, it is unclear from the methods whether the 3' UTR from the transformation vector to generate the models was included in the counting. The majority of reads would be expected to be there.

It would be relevant to know whether the animals were in the same genetic background. I.e. is UAS-TauR406W in the same background of the fly that was crossed to elav-Gal4 to serve as the control. This is not mentioned in the paper and also not in Mangleburg et al., 2020 which the authors refer to. There is a lot of tau-induced DEGs (~1/3 of the detected genes) and it would be relevant to know whether some of them might be due to genetic background.

The finding of the authors that NFkB pathways are higher in cell types that degenerate more is interesting. However, in Figure 4D it is also apparent that multiple cell types that do not degenerate have comparably high expression. Therefore, it is not a sufficient factor to explain why some neurons are vulnerable vs. others are not, but rather predicts amongst the vulnerable neurons how much they will be lost. It would be helpful to make this distinction clear in the text.

Reviewer #1 (Public Review):

The goal of the authors was to understand how the kinase, hpk-1, could regulate and interrogate different aspects of cellular stress resilience. To this end, the authors uncovered that hpk-1 is co-expressed with several transcription factors known to regulate different stress responses and this co-regulation only appears to occur in the nervous system. Taking a deeper dive, they convincingly find that hpk-1 overexpression in either serotonergic of GABAergic neurons can protect animals from heat stress or toxic protein aggregates. Interesting, it appears that hpk1 functions in serotonergic neurons differently from GABAergic neurons in the induction of the heat shock response and autophagy.

Overall, the experiments and results are solid and the conclusions drawn reflect the result. The model suggests that the receiving cell deciphers that either heat shock response or autophagy can be induced in the same cell, but the data suggest otherwise. perhaps the model should be reworked to reflect this point.

Reviewer #1 (Public Review):

Understanding how predators alter the behavior of their prey, a central question in neuroethology, has the potential to provide important insight into the neurobiological basis for behavioral flexibility. In this creative and intriguing work, the authors demonstrate that the predatory nematodes Pacificus pristionchus and P. uniformus can induce long-lasting changes in the behavioral patterns of C. elegans hermaphrodites. Exposure to these predators, probably sensed by the physical damaged caused by a bite, leads C. elegans to spend more time in food-poor environments and to increase their preference for laying eggs in these regions. Interestingly, this behavioral change appears to last for at least 24 hours, indicating that predator exposure induces a longer-term modulation of neural circuit function. The authors convincingly demonstrate that both dopamine and serotonin are required for this behavioral change. They identify specific neurons and receptors important for the effects of dopamine in this process, though whether dopamine signaling is itself modulated by predator exposure remains unclear. Some specific conclusions are not fully supported by the results, including the proposal that the CEM neurons are the key source of dopamine and that injury, rather than chemical cues, triggers the observed behavioral changes. Nevertheless, this paper reports a fascinating and robust behavioral finding, and provides some initial progress toward understanding its underlying neurobiological basis. As such, it will be of interest to those studying neuroethology, behavioral neurogenetics, and the modulation of behavior by monoamines.

Reviewer #1 (Public Review):

The authors have investigated the effect of the toxin mycolactone produced by mycobacterium ulcerans on the endothelium. Mycobacterium ulcerans is involved in Buruli ulcer classified as a neglected disease by WHO. This disease has dramatic consequences on the microcirculation causing important cutaneous lesions. The authors have previously demonstrated that endothelial cells are especially sensitive to mycolactone. The present study brings more insight into the mechanism involved in mycolactone-induced endothelial cells defect and thus in microcirculatory dysfunction. The authors showed that mycolactone directly affected the synthesis of proteoglycans at the level of the golgi with a major consequence on the quality of the glycocalyx and thus on the endothelial function and structure. Importantly, the authors show that blockade of the enzyme involve in this synthesis (galactosyltransferase II) phenocopied the effects of mycolactone. The effect of mycolactone on the endothelium was confirmed in vivo. Finally, the authors showed that exogenous laminin-511 reversed the effects of mycolactone, thus opening an important therapeutic perspective for the treatment of wound healing in patients suffering Buruli ulcer and presenting lesions.

Reviewer #1 (Public Review):

Testosterone modulates a range of adult behaviors, and its signaling contributes to behavioral plasticity. One of the more remarkable examples of this influence can be found in female canaries, who do not normally sing or have elevated levels of testosterone. However, introducing testosterone experimentally causes female canaries to begin singing within days and results in an enlargement of the neural circuitry responsible for song production. This work seeks to characterize the transcriptional responses in a key song brain region, HVC, to testosterone treatment in female canaries. They assay gene expression at a number of time points following testosterone administration and perform analyses characterizing patterns of differential expression using a broad range of approaches. This analysis in particular has a focus on understanding the putative gene regulatory networks that drive the observed testosterone-driven transcriptional responses, with the ultimate aim of understanding how these networks influence neural and behavioral properties.

Strengths

This work is well-focused on a specific question and has a number of excellent qualities. The experimental design of this study is strong, and the fine temporal resolution analysis of testosterone effects on gene expression in female songbirds is a novel and compelling approach to understanding the molecular basis of sex hormone-regulated neural plasticity. The authors have carefully assessed the influence of testosterone on a range of female song features, providing an excellent behavioral reference point for their transcriptional analysis. The gene expression analysis, from differential expression to correlation-based network analysis, appears generally sound and provides a good overview of the effects of testosterone on gene expression in HVC. Combined, the expression, neural, and behavioral data provide a rich resource to better understand the molecular mechanisms underlying testosterone-modulate neural and behavioral plasticity.

Weaknesses

However, I do have several concerns about this work, and these concerns fall into three main areas:

1) At several points, the authors make claims that I believe extend beyond the data presented here. For instance, in the Abstract (line 27), the authors state "the development of adult songs requires restructuring the entire HVC, including most HVC cell types, rather than altering only neuronal subpopulations or cellular components." The gene ontology analyses performed do suggest that there is a progression from cellular transcriptional changes to organ-level changes, however caution should be taken in claiming that "most HVC cell types" exhibit transcriptional changes. In fact, according to Fig. 3D most of the transcriptional changes appear restricted to neurons. As the authors themselves note elsewhere, claims at this resolution are difficult without support from single-cell approaches. I do not suggest that the authors need to perform single-cell RNA-seq for this work, but strong claims like this should be avoided.

2) Similarly the Abstract states that parallel regulation "directly" by androgen and estrogen receptors, as well as the transcription factor SP8, "lead" to the transcriptional and neural changes observed after testosterone treatment of females. However, experiments that demonstrate such a causal role have not been performed. The authors do perform a set of bioinformatic analyses that point in this direction - enrichment of androgen and estrogen receptor binding sites in the promoters of differentially expressed genes, high coexpression of SP8 with other genes, and the enrichment of predicted SP8 binding sites in coexpressed genes. However, further support for direct regulation, at the level that the authors claim, would require some form of transcription factor binding assay, e.g. ChIP-seq or CUT&RUN. I am fully aware that these assays are enormously challenging to perform in this system (and again I don't suggest that these experiments need to be done for this work); however, statements of direct regulation should be tempered. This is especially true for the role of SP8. This does appear to be a compelling target, but without some manipulation of the activity of SP8 (e.g. through knockdowns) and subsequent analysis of gene expression, it is too much to claim that this transcription factor is a regulatory link in the testosterone-driven responses. SP8 does appear to be a highly connected hub gene in correlation network analysis, but this alone does not indicate that it acts as a hub transcription factor in a gene regulatory network.

Along these lines, the in situ hybridizations of ESR2 and SP8 presented in Figure 5 need significant improvement. The signals in the red and green channels, SP8 and ESR2, look suspiciously similar, showing almost identical subcellular colocalization. This signal pattern usually suggests bleed-through during image acquisition, as it's highly unlikely that the mRNA of both genes would show this degree of overlap. I would suggest that control ISHs be run with one probe left out, either SP8 or ESR2, and compare these ISHs with the dual label ISHs to determine if signal intensity and cellular distribution look similar. Furthermore, on lines 354-356 the authors write, "The fact that the two genes were expressed nearby in the same cell may indicate physical interactions between the gene pair and warrant further investigation into the nature of their relationship.". Yet, even if the overlap between ESR2 and SP8 shown in Figure 5 is confirmed, close localization of transcripts does not imply that the protein products physically interact. The STRING bioinformatic analysis is more convincing that there is a putative regulatory interaction between ESR2 and the SP8 locus, and this suggestion of protein-protein interaction is weak and should be omitted. In addition, the authors note that ESR2 has not been detected in the songbird HVC in a previous study. To further demonstrate the expression of ESR2 (and SP8) in HVC, it would be useful to plot their expression from the microarray data across the different testosterone conditions.

3) My final concern lies in the interpretation of these results as generalizable to other sex hormone-modualated behaviors. On lines 452-455, the authors write, "This suggests that the testosterone (or estrogen)-triggered induction of adult behaviors, such as parental behavior and courtship, requires a much more extensive reorganization of the transcriptome and the associated biological functions of the brain areas involved than previously thought.". The experiments and argument likely apply to other neural systems to undergo large seasonal fluctuations in sex hormones and similar morphological changes. However, the authors argue that the large number of transcriptional changes seen here may generalize broadly to sex hormone modulated adult behaviors. I think there are a couple of problems with this argument. First, as described here and in past work, testosterone drives major morphological changes the song system of adult canaries; such dramatic changes are not seen for instance in sex hormone-receptive areas underlying mating behavior in adult mammals. Similarly, the study introduced testosterone into female birds which drives a greater morphological change in HVC relative to similar manipulations in males, which again may account for the large number of differentially expressed genes. I would temper the generality of these results and note how the experimental and biological differences between this system and other sex hormone-responsive systems and behaviors may contribute to the observed transcriptional differences.

Reviewer #1 (Public Review):

The study by Meyer and collaborators is tackling the question of cell type evolution between sea urchins and sea stars. To address this question, they generated single nuclei RNA sequencing libraries originating from early developmental time points of the sea star Patiria miniata. The resulting cell type atlas recapitulated the cell types previously known to exist as indicated by traditional methods in the past and revealed hidden cell type complexity. The authors provide evidence for the existence of previously not described sea star neuronal types and provide a thorough characterization of their molecular signature. Once validating the sea star cell type atlas through means of WMISH they computationally compared the sea star cell types to the sea urchin ones by taking advantage of already available single-cell RNA sequencing data, carried out at equivalent stages of Strongylocentrotus purpuratus development. Using 1-1 orthologs they integrated the sea star and sea urchin datasets and provided evidence for the presence of novel cell types that are not shared between the two animals (at least novel for the specific developmental window analyzed) such as the left coelomic pouch in sea urchin. Moreover, their analysis suggests that sea urchin skeletal cells, a population known to not exist in sea stars, correlate transcriptionally to other mesodermal cell types of the sea star, while sea urchin pigment cells appear to be very similar to sea star immune cells and neurons. Overall, the data of this study demonstrate how single-cell RNA sequencing can be used as a tool to study cell type evolution and provide complete molecular evidence of cell type diversification between the two echinoderm species. Lastly, their P. miniata cell type atlas will be of great importance for the evo-devo field and contribute to a better understanding of the development and evolution of novelties.

Reviewer #1 (Public Review):

This manuscript harnesses recent advances in co-evolution based modeling and computational approaches to provide molecular details about the transport cycles and mechanisms of an entire family of transporters, the sugar porters. The authors evaluate the validity of their approach in a number of ways, including comparison to structurally characterized proteins/states excluded from the training set, comparison to the GLUT5 transport free energy landscape determine through conventional enhanced MD methods in a companion paper, and a global evaluation of RMSDs between models. Based on these structural models, the authors are able to generate a number of interesting insights into the networks of co-evolving contacts that form in different conformational states, and different why certain sugar porters are or are not proton-coupled.

Reviewer #1 (Public Review):

The authors have compiled and analysed a unique dataset of patients with treatment-resistant aggressive behaviours who received deep brain stimulation (DBS) of the posterior hypothalamic region. They used established analysis pipelines to identify local predictors of clinical outcomes and performed normative structural and functional connectivity analyses to derive networks associated with treatment response. Finally, Gouveia et al. perform spatial transcriptomics to determine the molecular substrates subserving the identified circuits. The inclusion of data from multiple centres is a notable strength of this retrospective study, but there are current limitations in the methodology and interpretation of findings that need to be addressed.

1) The validation of findings is heterogeneous and inconsistent across analysis pipelines. While the authors performed non-parametric permutation testing during sweet-spot mapping, structural and functional connectivity were validated using a 'four-fold consistency analysis'. The latter consists of a visual representation of streamlines and peak intensities after randomly dividing data into four groups, the findings were not validated quantitatively. If possible, the authors should apply permutation analysis in alignment with sweet-spot mapping and demonstrate the predictive ability of their identified networks in a LOO or k-fold cross-validation paradigm as carried out by similar studies. Given that the data has been derived from multiple centers, the prediction of left-out cohorts based on models generated by the remaining cohorts could be another means of validation. If validation is not possible, the authors should clearly state the limitations of their approach.

2) In addition to a 'four-fold consistency analysis', functional connectivity was evaluated using LOOCV in a priori identified ROIs. Their network analysis, however, revealed a far more extensive network encompassing cortical, subcortical, and cerebellar structures. To avoid selection bias the authors should incorporate identified structures into their analysis and apply appropriate means of validation.

3) Functional connectivity mapping: how were R-maps generated? The authors mention that patient-specific R-maps were p-thresholded and corrected for multiple comparisons, but it is not clear how group-level maps were generated. How did the authors perform regression on these maps? Were voxels that did not survive thresholding excluded?

4) The authors determined that age was a significant prédictor of the outcome, but it is unclear whether certain age groups presented with distinct etiologies underlying their aggressiveness. For example, aggression in epilepsy may show a better response to DBS as opposed to schizophrenia. How does patient outcome change when stratifying according to etiology? How does model performance change when controlling for etiology? The authors should include the etiology of aggressiveness in Table 1.

5) Stimulation parameters. The authors report average pulse widths of 219 µs and 142µs respectively, which is up to 4-fold higher as compared to DBS settings used conventionally in movement disorders and will significantly alter the volume of activated tissue. Did the authors account for the drastic increases in pulse width during VAT modeling?

6) Imaging transcriptomics. The methods described lack detail: How did the authors account for differences in expression across donors, samples, and regions during preprocessing of the Allen Human Brain Atlas? How was expression data collapsed into regions of interest? Did the authors apply any normalization? Recent publications have introduced reproducible workflows for processing and preparing the AHBA expression data for analysis that is publicly available.

7) 'genes with similar patterns of spatial distribution to the TFCE map were compiled in an extensive list'. It is unclear why authors used TFCE maps for spatial transcriptomics as opposed to the functional connectivity map featured in Figure 5. How was similarity measured between the TFCE map and the AHBA? How were candidate genes identified? Please provide a more comprehensive description of the analysis pipeline.

8) What do the bar plots in Figure 7 (left) represent? P-values? The authors should label the axes to make this clear to the reader.

9) Interprétation of imaging transcriptomics: The authors identify a therapeutic circuit associated with deep brain stimulation of the posterior hypothalamic area, however, it is unclear how to reconcile genes associated with hormones, inflammation, and plasticity in this context. The authors mention and discuss genes implicated in hormonal processing, specifically oxytocin. The results provided in Figure 7, however, do not support this finding and it is unclear how the authors identified genes linked to oxytocin. In addition, the authors identified reductions in the number of microglia and astrocytes, while oligodendrocytes were overexpressed relative to the expected distribution of genes per cell type. These findings were attributed to DBS effects, however, both connectomic and transcriptomic data are acquired from healthy subjects, which suggests a physiological deficit/enrichment in a therapeutic circuit. How do the authors interpret findings given that no electrode implantation and stimulation were performed?

10) Data availability. Code used for data processing should be made openly available or shared as source data along with the Figures that were generated using the code. Sweet-spot, structural, and functional connectivity maps should be shared openly.

Reviewer #1 (Public Review):

The strength of the manuscript is highlighted by the application of fractal formalism, which is commonly used in colloidal systems, in conjunction with MD simulation to study the phase separation of an IDP. The weakness lies in the fact that this study does not provide any discussion on how our understanding of the network structure and dynamical behavior of biomolecular condensates and their biological significance improves through this study. The experimental part remains weak, without any measurements of the dynamics of the condensates. Whether and how the formalism can distinguish between phase-separated condensates (WT) and classical protein aggregates (Y to A variant) remains unclear.

Reviewer #1 (Public Review):

This article describes the application of a computational model, previously published in 2021 in Neuron, to an empirical dataset from monkeys, previously published in 2018 in eLife. The 2021 modeling paper argued that the model can be used to determine whether a particular task depends on the perirhinal cortex as opposed to being soluble using ventral visual stream structures alone. The 2018 empirical paper used a series of visual discrimination tasks in monkeys that were designed to contain high levels of 'feature ambiguity' (in which the stimuli that must be discriminated share a large proportion of overlapping features), and yet animals with rhinal cortex lesions were unimpaired, leading the authors to conclude that perirhinal cortex is not involved in the visual perception of objects. The present article revisits and revises that conclusion: when the 2018 tasks are run through the 2021 computational model, the model suggests that they should not depend on perirhinal cortex function after all, because the model of VVS function achieves the same levels of performance as both controls and PRC-lesioned animals from the 2018 paper. This leads the authors of the present study to conclude that the 2018 data are simply "non-diagnostic" in terms of the involvement of the perirhinal cortex in object perception.

The authors have successfully applied the computational tool from 2021 to empirical data, in exactly the way the tool was designed to be used. To the extent that the model can be accepted as a veridical proxy for primate VVS function, its conclusions can be trusted and this study provides a useful piece of information in the interpretation of often contradictory literature. However, I found the contribution to be rather modest. The results of this computational study pertain to only a single empirical study from the literature on perirhinal function (Eldridge et al, 2018). Thus, it cannot be argued that by reinterpreting this study, the current contribution resolves all controversy or even most of the controversy in the foregoing literature. The Bonnen et al. 2021 paper provided a potentially useful computational tool for evaluating the empirical literature, but using that tool to evaluate (and ultimately rule out as non-diagnostic) a single study does not seem to warrant an entire manuscript: I would expect to see a reevaluation of a much larger sample of data in order to make a significant contribution to the literature, above and beyond the paper already published in 2021. In addition, the manuscript in its current form leaves the motivations for some analyses under-specified and the methods occasionally obscure.

Reviewer #1 (Public Review):

In this manuscript, Yong and colleagues link perturbations in lysosomal lipid metabolism with the generation of protein aggregates resulting from proteosome inhibition. The main tool used is the ProteoStat stain to assess protein aggregate burden in native cells (i.e. cells under no exogenous or endogenous stress). They initially use CRISPR-based genome-wide screens to identify several genes that affect this aggregate burden. Interestingly, knockdown of genes involved in lysosomal acidification was a major signature which led to identification of other culprit lysosome-associated genes that included ones involved in lipid metabolism. Subsequent CRISPR screen focused on lipidomic analysis led to identification of sphingolipid and cholesterol esters as lipid classes with effects on proteostasis. Despite using various tools of lysosomal function, acidity, permeability, etc, the authors couldn't identify the link between lysosomal lipid metabolism and protein aggregate formation. Nevertheless, the interrelationship of these two processes was the overall conclusion of this manuscript.

Although this work is interesting and thought-provoking, their approach to identify novel pathways involved in proteostasis is limited and this weakens the contribution of the paper in its current form.

Reviewer #1 (Public Review):

A typical path from preprocessed data to findings in systems neuroscience often includes a set of analyses that often share common components. For example, an investigator might want to generate plots that relate one time series (e.g., a set of spike times) to another (measurements of a behavioral parameter such as pupil diameter or running speed). In most cases, each individual scientist writes their own code to carry out these analyses, and thus the same basic analysis is coded repeatedly. This is problematic for several reasons, including the waste of time, the potential for errors, and the greater difficulty inherent in sharing highly customized code.

This paper presents Pynapple, a python package that aims to address those problems.

Strengths:

The authors have identified a key need in the community - well-written analysis routines that carry out a core set of functions and can import data from multiple formats. In addition, they recognized that there are some common elements of many analyses, particularly those involving timeseries, and their object-oriented architecture takes advantage of those commonalities to simplify the overall analysis process.

The package is separated into a core set of applications and another with more advanced applications, with the goal of both providing a streamlined base for analyses and allowing for implementations/inclusion of more experimental approaches.

Weaknesses:

There are two main weaknesses of the paper in its present form.

First, the claims relating to the value of the library in everyday use are not demonstrated clearly. There are no comparisons of, for example, the number of lines of code required to carry out a specific analysis with and without Pynapple or Pynacollada. Similarly, the paper does not give the reader a good sense of how analyses are carried out and how the object-oriented architecture provides a simplified user interaction experience. This contrasts with their GitHub page and associated notebooks which do a better job of showing the package in action.

Second, the paper makes several claims about the values of object-oriented programming and the overall design strategy that are not entirely accurate. For example, object-oriented programming does not inherently reduce coding errors, although it can be part of good software engineering. Similarly, there is a claim that the design strategy "ensures stability" when it would be much more accurate to say that these strategies make it easier to maintain the stability of the code. And the authors state that the package has no dependencies, which is not true in the codebase. These and other claims are made without a clear definition of the properties that good scientific analysis software should have (e.g., stability, extensibility, testing infrastructure, etc.).

There is also a minor issue - these packages address an important need for high-level analysis tools but do not provide associated tools for preprocessing (e.g., spike sorting) or for creating reproducible pipelines for these analyses. This is entirely reasonable, in that no one package can be expected to do everything, but a bit deeper account of the process that takes raw data and produces scientific results would be helpful. In addition, some discussion of how this package could be combined with other tools (e.g., DataJoint, Code Ocean) would help provide context for where Pynapple and Pynacollada could fit into a robust and reliable data analysis ecosystem.

Public Review:

This paper presents two new tools for investigating GLP-1 signaling. The genetically encoded sensor GLPLight1 follows the plan for other GPCR-based fluorescent sensors, inserting a circularly permuted GFP into an intracellular loop of the GPCR. The light-uncaged agonist peptide, photo-GLP1, has no detectable agonist activity (as judged by the GLPLight1 sensor) until it is activated by light. However, based on the current characterization, it is unclear how useful either of these tools will be for investigating native GLP-1 signaling.

The GLPLight1 sensor has a strong fluorescent response to GLP-1 with an EC50 of ~10 nM, and its specificity is high, as shown by lack of response to ligands of related class B GPCRs. However, the native GLP1R enables biological responses to concentrations that are ~1000-fold lower than this (as shown, for instance, in a supplemental figure of this paper). This makes it difficult to see how the sensor will be useful for in vivo detection of GLP-1 release, as claimed; although there may be biological situations where the concentration is adequate to stimulate the sensor, this is not established. Data using a GLP-1 secreting cell line suggest that the sensor has bound some of the released GLP-1, but it is difficult to have confidence without seeing an actual fluorescence response to stimulated release.

Alternatively, the sensor might be used for drug screening, but it is unclear that this would be an improvement over existing high-throughput methods using the cAMP response to GLP1R activation (since those are much more sensitive and also allow detection of signaling through different downstream pathways).

The utility of the caged agonist PhotoGLP1 is similarly unclear. The data demonstrate a substantial antagonism of GLP-1 binding by the still-caged compound, and it is therefore unclear whether the kinetics of the response to PhotoGLP1 itself would mimic the normal activation by GLP-1 in the absence of the caged compound. A further concern is that the light-dependence of the agonist effect of PhotoGLP1 was evaluated only with the GLPLight1 sensor and not with GLP1R signaling itself, which is 1000x more sensitive and which would be the presumed target of the tool. In addition, PhotoGLP1 is based upon native GLP-1, which is rapidly truncated and inactivated by the peptidase DPPIV, expressed in most cell types, and expressed at very high levels in the plasma. The utility of PhotoGLP1 is therefore limited to acute (minutes) in vitro experiments.

Reviewer #1 (Public Review):

Mature mammalian olfactory sensory neurons (OSN) express only one of the hundreds of possible odor receptors (ORs) encoded in the genome. The process of selecting this OR in each OSN is the consequence of both deterministic developmental processes involving transcription factors, and more stochastic processes. How this balance is implemented is a major problem in molecular neuroscience, one whose solution has significant systems-level implications for odor coding. In Bashkirova et al the authors substantially revise the canonical view of how this process works. By querying single cell transcriptomes and genetic architecture across OSN development, the authors demonstrate that OSN progenitors express ORs for their zone and for more dortsal zones, and that the degree of heterochromatinization of non-expressed ORs varies as a function of which zone a given OSN resides in. Through additional genetic experiments (including knockouts of transcription factors that seem to be associated with zonal identity, and the clever use of OR transgenes) they synthesize these findings into a model in which progenitors co-express many ORs - both ORs that are appropriate for their zone and ORs that are dorsal to their zone - and that this expression both facilitates heterochromatinzation of non-selected and extra-zonal ORs, and enables singular OR selection. The experiments are careful and the data are novel, and definitely revise our simplistic current view of how this process works; as such this work will have significant impact on the field. As presented the model requires additional experiments to fully flesh it out, and to definitively demonstrate that i.e., precocious expression leads to gene silencing, but with some additional clarifications in the discussion this paper both breaks new ground and sets the stage for future work exploring mechanisms of OSN development and OR selection.

Reviewer #1 (Public Review):

The initial goal of this work was to study how the activity of the C. trachomatis effector Cdu1 impacts on the number and nature of ubiquitinated proteins in infected host cells, and how this is related to a previously described function of Cdu1 in promoting Golgi distribution around the Chlamydia vacuole, known as inclusion.

The authors generated a cdu1-null mutant in C. trachomatis and used proteomics to analyse ubiquitinated proteins in cells infected with Cdu1-producing and Cdu1-deficient chlamydiae, by comparison to mock-infected cells. It was found that among the four proteins specifically ubiquitinated after infection with Cdu1-deficient chlamydiae there were three other C. trachomatis effectors (InaC, IpaM and CTL0480). These three proteins are part of a large family of Chlamydia effectors, known as Incs, that insert in the inclusion membrane.

Based on these observations, the authors then focused in understanding how Cdu1 protects InaC, IpaM and CTL0480 from ubiquitination, and what are the consequences of this protection for the protein levels of these Incs and for their functions during infection. It is shown that Cdu1 can bind InaC, IpaM and CTL0480, and protects these Incs and itself from ubiquitination and proteasomal degradation. This protective function of Cdu1 depends on its acetylation, but not on its deubiquitinating activity, and host cells infected by the cdu1 null mutant show defects that phenocopy those of cells infected by inaC, ipaM or ctl0480 null-mutants.

Finally, it was previously shown that CLT0480 controls/inhibits a pathway of chlamydial egress from host cells involving extrusion of the entire inclusion. The authors show that InaC and IpaM also control/promote extrusion of C. trachomatis inclusion and that the cdu1 null mutant also shows a defect in this process. This leads to the conclusion stated in the title that Cdu1 regulates chlamydial exit from host cells by protecting specific C. trachomatis effectors from degradation.

This is an excellent and impressive work, both from technical and conceptual perspectives, which accomplishes the goal of providing mechanistic insights on the mode of action of Cdu1. Overall, the data provides solid evidence for the proposed model by which the acetylation activity of Cdu1 protects itself and three Incs (InaC, IpaM and CTL0480) from degradation.

I agree that (all together) the data provides a solid support for the idea that the multiple phenotypes displayed by cells infected with the cdu1 null mutant are related to the decreased levels of InaC, IpaM and CTL0480. However, to some extent, these Incs can still be detected in cells infected with the cdu1 null mutant and it cannot be formally excluded that Cdu1 directly promotes assembly of F-actin and Golgi repositioning around the inclusion, MYPT1 recruitment to the inclusion, and extrusion of the inclusion.

Still, I think the major significance of this work comes from the combined use of proteomics and chlamydial genetics to disclose a unique a mechanism by which one effector controls the levels of other effectors. This further emphasizes that for a single bacterium injecting dozens of effectors into host cells, the function of one bacterial effector can control, and be controlled by other effectors.

Reviewer #1 (Public Review):

In the manuscript titled "Vangl2 suppresses NF-κB signaling and ameliorates sepsis by targeting p65 for NDP52-mediated autophagic degradation" by Lu et al, the authors show that Vangl2, a planner cell polarity component, plays a direct role in autophagic degradation of NFkB-p65 by facilitating its ubiquitination via PDLIM2 and subsequent recognition and autophagic targeting via the autophagy adaptor protein NDP52. Conceptually it is a wonderful study with excellent execution of experiments and controls. The concerns with the manuscript are mainly on two counts - First issue is the kinetics of p65 regulation reported here, which does not fit into the kinetics of the mechanism proposed here, i.e., Vangl2-mediated ubiquitination followed by autophagic degradation of p65. The second issue is more technical- an absolute lack of quantitative analyses. The authors rely mostly on visual qualitative interpretation to assess an increase or decrease in associations between partner molecules throughout the study. While the overall mechanism is interesting, the authors should address these concerns as highlighted below:

Major points:

1) Kinetics of p65 regulation by Vangl2: As mentioned above, authors report that LPS stimulation leads to higher IKK and p65 activation in the absence of Vangl2. The mechanism of action authors subsequently work out is that- Vangl2 helps recruit E3 ligase PDLIM to p65, which causes K63 ubiquitination, which is recognised by NDP52 for autophagic targeting. Curiously, peak p65 activation is achieved within 30 minutes of LPS stimulation. The time scale of all other assays is way longer. It is not clear that in WT cells, p65 could be targeted to autophagic degradation in Vangl2 dependent manner within 30 minutes. The HA-Myc-Flag-based overexpression and Co-IP studies do confirm the interactions as proposed. However, they do not prove that this mechanism was responsible for the Vangl2-mediated modulation of p65 activation upon LPS stimulation. Moreover, the Vangl2 KO line also shows increased IKK activation. The authors do not show the cause behind increased IKK activation, which in itself can trigger increased p65 phosphorylation.<br /> 2) The other major concern is regarding the lack of quantitative assessments. For Co-IP experiments, I can understand it is qualitative observation. However, when the authors infer that there is an increase or decrease in the association through co-IP immunoblots, it should also be quantified, especially since the differences are quite marginal and could be easily misinterpreted.<br /> 3) Figure 4E and F: It is evident that inhibiting Autolysosome (CQ or BafA1) or autophagy (3MA) led to the recovery of p65 levels and inducing autophagy by Rapamycin led to faster decay in p65 levels. Did the authors also note/explore the possibility that Vangl2 itself may be degraded via the autophagy pathway? IB of WCL upon CQ/BAF/3MA or upon Rapa treatment does indicate the same. If true, how would that impact the dynamics of p65 activation?<br /> 4) Autophagic targeting of p65 should also be shown through alternate evidence, like microscopy etc., in the LPS-stimulated WT cells.

Limitation: The mechanism behind enhanced activation of IKK in the absence of Vangl2 remains unclear. It is possible there is an autophagy-independent mechanism also involved in this regulation.

Summary: The study shows a new mechanism of NFkB-p65 regulation mediated by Vangl2-dependent autophagic targeting. Autophagic regulation of p65 has been reported earlier; this study brings an additional set of molecular players involved in this important regulatory event, which may have implications for chronic and acute inflammatory conditions.

Reviewer #1 (Public Review):

This manuscript features a key technical advance in single-molecular force spectroscopy. The critical advance is to employ a click chemistry (DBCO-cycloaddition) for making a stable covalent connection between a target biomacromolecule and solid support in place of conventional antigen-antibody binding. This tweak dramatically improves the mechanical stability of the pulling system such that the pulling/relaxation can be repeated up to a thousand times (the previous limit was a few hundred cycles at best). This improvement is broadly applicable to various molecular interactions and other types of single-molecule force spectroscopy allowing for more statistically reliable force measurements. Another strength of this method is that all conjugation steps are chemically orthogonal (except for Spy-catcher conjugation to the termini of a target molecule) such that the probability of side reactions could be reduced.

The reliability of kinetic and thermodynamic parameters obtained from single-molecule force spectroscopy depends on statistics, that is, the number of pulling measurements and their distribution. By extending the number of measurements, this robust method enables fundamental/critical statistical assessment of those parameters. That is, it is an important and interesting lesson from this study that ~200 repeats can yield statistically reasonable parameters.

The authors carried out carefully designed optimization steps and inform readers of the critical aspects of each. The merit, quality, and rigor as a method-oriented manuscript are impressive. Overall, this is an excellent study.

I think Google search page is doing a good job. People can search by voice and photo search. The whole page is very clean and easy to understand. There are no extra buttons on the page, which makes even first-time users understand the usage quickly.

Reviewer #1 (Public Review):

In the manuscript " Cell Rearrangement Generates Pattern Emergence as a Function of Temporal Morphogen Exposure" by Fulton et al., the authors set out to link cell dynamics and single-cell gene expression states, in order to understand the dynamics of cell differentiation. This important challenge is tackled by studying somitogenesis in the zebrafish embryo and combining reverse-engineering gene regulatory networks (GRNs) with cell tracking data. The differentiation of the presomitic cells is evaluated by the differential tbx marker expression through in situ HCR and antibody staining, and live imaging of reporters. Through mathematical modelling taking into consideration the HCR tbx data, live reporter data of the morphogen activity, and the 3D tracking data at different stages, the authors find a candidate model of a gene regulatory network that recapitulates both in vivo and in vitro patterns of the dynamics of cell differentiation. Using this live-modelling approach, the authors move on to question the impact of cell movement on gene expression and conclude that pattern emerges as a function of cell rearrangements tuning the temporal exposure of the cells to the morphogen gradients.

The major strength of the manuscript is the development of a unique method for addressing cell differentiation dynamics by combining static gene expression data with live cell dynamics. Bridging spatiotemporal information is key to understanding tissue and embryo development and this work provides a great basis for it. A potential weakness is how one selects which of the GRNs predicted from the live-modelling is physiologically relevant to the system of interest, since it requires fitting techniques.

The major goal of the paper is mostly achieved. This is evident by the proposed model predicting well the dynamics of differentiation both in vivo and in vitro. To fully support the conclusion that cell rearrangements are necessary for patterning, the addition of functional experiments targeted in this direction might be beneficial.

Overall, this live-modelling approach has the potential of being relevant to various model systems where gene expression and migration are changing simultaneously (e.g. organoids and embryos) and it is thus important to a wide audience including the fields of developmental, stem cell, and quantitative biology.

Reviewer #1 (Public Review):

In this study, authors examine immune signatures from patients that experienced mild, moderate, or severe COVID-19 symptoms and followed them for months to evaluate whether there was a correlation between their immune activation phenotypes, disease severity, and long COVID. Authors observed higher T cell activation/proliferation marker expression in blood samples of patients with severe disease whereas other cell types were more or less unchanged. The authors also examined the cytokine profile of the patient's serum samples to determine the potential drivers of T cell activation phenotypes. Authors then perform T-cell responses to viral peptides to determine the differences in activation phenotypes with disease severity.

The major strengths of the paper appear in the evaluation of the appropriate cohort of human samples and following them over a period of months. Additionally, the authors perform detailed T-cell analysis in an unbiased way to determine any possible activation correlations with disease severity. The authors also perform antigen-specific T-cell analysis via peptide stimulation which adds to the overall findings. However, there are a number of drawbacks that need to be mentioned. Firstly, the phenotypes of T cells prior to the 3-month time-point are not known. Hence, there is no information on baseline or during the early phase of infection. Secondly, the response is largely obtained from blood. How much information about T cells in blood correlate with lung disease is a matter of concern. Analysis of lungs, where actual disease manifestation is ideal, however close to impossible in the human cohort. Alternatively, analysis of local lymph node aspirate or nasal swabs could be useful. Thirdly, the claim that bystander T cell activation plays a role seems loose, specifically the IL-15 in vitro data. Moreover, the analysis of T cells seems very focused on activation/proliferation phenotypes. Alternative T cell phenotypes such as regulatory, IL-10 producing, or FoxP3 expression are not extensively analyzed.

Major points

1) In Figure 1, the CD4 T cell activation phenotypes do not seem consistent across the groups. Why does moderate vs. severe show increases in CXCR3 expression but not mild vs. severe? The same goes for other markers. Performing T cell stimulation with class II peptides specific for CoV-2 and looking at IFN etc. to determine antigen-specific T cells and then gating on these activation/proliferation markers may be a better way to observe differences.

2) One major drawback is the control patients. It would have helped to include a batch of samples from uninfected patients. Or to have the plasma/blood from patients before COVID-19 symptoms. This way there is a baseline for each group that could be compared. It is difficult to draw broad conclusions across the group at 3 months if we do not know their baseline phenotypes.

3) Although the authors focused on activating/proliferating markers to correlate with disease severity, this analysis does not consider alternate T cell phenotypes such as the ones with regulatory or anti-inflammatory phenotypes. Did authors detect differences in T cells with regulatory profiles such as expression of IL-10, FoxP3, etc. in their unsupervised UMAP analysis or otherwise flow experiments?

Reviewer #1 (Public Review):

The authors introduce a computational model that simulates the dendrites of developing neurons in a 2D plane, subject to constraints inspired by known biological mechanisms such as diffusing trophic factors, trafficked resources, and an activity-dependent pruning rule. The resulting arbors are analyzed in terms of their structure, dynamics, and responses to certain manipulations. The authors conclude that 1) their model recapitulates a stereotyped timecourse of neuronal development: outgrowth, overshoot, and pruning 2) Neurons achieve near-optimal wiring lengths, and Such models can be useful to test proposed biological mechanisms- for example, to ask whether a given set of growth rules can explain a given observed phenomenon - as developmental neuroscientists are working to understand the factors that give rise to the intricate structures and functions of the many cell types of our nervous system.

Overall, my reaction to this work is that this is just one instantiation of many models that the author could have built, given their stated goals. Would other models behave similarly? This question is not well explored, and as a result, claims about interpreting these models and using them to make experimental predictions should be taken warily. I give more detailed and specific comments below.

Line 109. After reading the rest of the manuscript, I worry about the conclusion voiced here, which implies that the model will extrapolate well to manipulations of all the model components. How were the values of model parameters selected? The text implies that these were selected to be biologically plausible, but many seem far off. The density of potential synapses, for example, seems very low in the simulations compared to the density of axons/boutons in the cortex; what constitutes a potential synapse? The perfect correlations between synapses in the activity groups is flawed, even for synapses belonging to the same presynaptic cell. The density of postsynaptic cells is also orders of magnitude of, etc. Ideally, every claim made about the model's output should be supported by a parameter sensitivity study. The authors performed few explorations of parameter sensitivity and many of the choices made seem ad hoc.

Many potentially important phenomena seem to be excluded. I realize that no model can be complete, but the choice of which phenomena to include or exclude from this model could bias studies that make use of it and is worth serious discussion. The development of axons is concurrent with dendrite outgrowth, is highly dynamic, and perhaps better understood mechanistically. In this model, the inputs are essentially static. Growing dendrites acquire and lose growth cones that are associated with rapid extension, but these do not seem to be modeled. Postsynaptic firing does not appear to be modeled, which may be critical to activity-dependent plasticity. For example, changes in firing are a potential explanation for the global changes in dendritic pruning that occur following the outgrowth phase.

Line 167. There are many ways to include activity -independent and -dependent components into a model and not every such model shows stability. A key feature seems to be that larger arbors result in reduced growth and/or increased retraction, but this could be achieved in many ways (whether activity dependent or not). It's not clear that this result is due to the combination of activity-dependent and independent components in the model, or conceptually why that should be the case.

Line 183. The explanation of overshoot in terms of the different timescales of synaptic additions versus activity-dependent retractions was not something I had previously encountered and is an interesting proposal. Have these timescales been measured experimentally? To what extent is this a result of fine-tuning of simulation parameters?

Line 203. This result seems at odds with results that show only a very weak bias in the tuning distribution of inputs to strongly tuned cortical neurons (e.g. work by Arthur Konnerth's group). This discrepancy should be discussed.

Line 268. How does the large variability in the size of the simulated arbors relate to the relatively consistent size of arbors of cortical cells of a given cell type? This variability suggests to me that these simulations could be sensitive to small changes in parameters (e.g. to the density or layout of presynapses).

The modeling of dendrites as two-dimensional will likely limit the usefulness of this model. Many phenomena- such as diffusion, random walks, topological properties, etc - fundamentally differ between two and three dimensions.

The description of wiring lengths as 'approximately optimal' in this text is problematic. The plotted data show that the wiring lengths are several deviations away from optimal, and the random model is not a valid instantiation of the 2D non-overlapping constraints the authors imposed. A more appropriate null should be considered.

It's not clear to me what the authors are trying to convey by repeatedly labeling this model as 'mechanistic'. The mechanisms implemented in the model are inspired by biological phenomena, but the implementations have little resemblance to the underlying biophysical mechanisms. Overall my impression is that this is a phenomenological model intended to show under what conditions particular patterns are possible. Line 363, describing another model as computational but not mechanistic, was especially unclear to me in this context.

Reviewer #1 (Public Review):

MCM8 and MCM9 are paralogues of the eukaryotic MCM2-7 proteins. MCM2-7 form a heterohexameric complex to function as a replicative helicase while MCM8-9 form another hexameric helicase complex that may function in homologous recombination-mediated long-tract gene conversion and/or break-induced replication. MCM2-7 complex is loaded during the low Cdk period by ORC, CDC6, and Cdt1, when the origin DNA may intrude into the central channel via the MCM2-MCM5 entry "gate". In the S phase, MCM2-7 complex is activated as CMG helicase with the help of CDC45 and GINS complex. On the other hand, it still remains unclear how MCM8-9 complex is loaded onto DNA and then activated.

In this study, the authors first investigated the cryo-EM structure of chicken MCM8-9 (gMCM8-9) complex. Based on the data obtained, they suggest that the observed gMCM8-9 structure might represent the structure of a loading state with possible DNA entry "gate". The authors further investigated the cryo-EM structure of human MCM8-9 (hMCM8-9) complex in the presence of the activator protein, HROB, and compared the structure with that obtained without HROB1, which the authors published previously. As a result, they suggest that MCM8-9 complex may change the conformation upon HROB binding, leading to helicase activation. Furthermore, based on the structural analyses, they identified some important residues and motifs in MCM8-9 complex, mutations of which actually impaired the MCM8-9 activity in vitro and in vivo.

Overall, the data presented would support the authors' conclusions and would be of wide interest for those working in the fields of DNA replication and repair. One caveat is that most of the structural data are shown only as ribbon model without showing the density map data obtained by cryo-EM, which makes accurate evaluation of the data somewhat difficult.

Reviewer #1 (Public Review):

This manuscript presents a model in which combined action of the transporter-like protein DISP and the sheddases ADAM10/17 promote shedding of a mono-cholesteroylated Sonic Hedgehog (SHH) species following cleavage of palmitate from the dually lipidated precursor ligand. The authors propose that this leads to transfer of the cholesterol-modified SHH to HDL for solubilization. The minimal requirement for SHH release by this mechanism is proposed to be the covalently linked cholesterol modification because DISP could promote transfer of a cholesteroylated mCherry reporter protein to serum HDL. The authors used an in vitro system to demonstrate dependency on DISP/SCUBE2 for release of the cholesterol modified ligand. These results confirm previously published results from other groups (PMC3387659 and PMC3682496). In vivo support for these activities is provided by data from previously published studies from this group. It is unclear whether new in vivo experiments were conducted for this study.

A strength of the work is the use of a bicistronic SHH-Hhat system to consistently generate dually-lipidated ligands to determine the quantity and lipidation status of SHH released into cell culture media.

A critical shortcoming of the study is that the experiments showing SHH secretion/export by western blot of media fractions do not include a SHH(-) control condition. This is an essential control because SHH media blots can be dirty. Without demonstration that the bands being analyzed are specific for SHH(+) conditions, these experiments cannot be appropriately evaluated. Further, it appears that SHH is transiently transfected/expressed for each experimental condition. A stably expressing SHH/HHAT cell line would reduce condition to condition and experiment to experiment variability. Unusual normalization strategies are used for many experiments, and quantification/statistical analyses are missing for several experiments. Due to these shortcomings, the data do not justify the conclusions. The significance of the data provided is overstated because many of the presented experiments confirm/support previously published work. The study provides a modest advance in the understanding of the complex issue of SHH membrane extraction.

Reviewer #1 (Public Review):

Park et al demonstrate that cells on either side of a BM-BM linkage strengthen their adhesion to that matrix using a positive feedback mechanism involving a discoidin domain receptor (DDR-2) and integrin (INA-1 + PAT-3). In response to its extracellular ligand (Collagen IV/EMB-9), DDR-2 is endocytosed and initiates signaling that in turn stabilizes integrin at the membrane. DDR-2 signaling operates via Ras/LET-60. This work's strength lies in its excellent in vivo imaging, especially of endogenously tagged proteins. For example, tagged DDR-2:mNG could be seen relocating from seam cell membranes to endosomes. I also think a second strength of this system is the ability to chart the development of BM-BM linkage over time based on the stages of worm larval development. This allows the authors to show DDR signaling is needed to establish linkage, rather than maintain it. It likely is relevant to many types of cells that use integrin to adhere to BM and left me pondering a number of interesting questions. For example: (1) Does DDR-2 activation require integrin? Perhaps integrin gets the process started and DDR-2 positively reinforces that (conversely is DDR-2 at the top of a linear pathway)? (2) In ddr-2(qy64) mutants, projections seem to form from the central portion of the utse cell. Does this reveal a second function for DDR-2, regulating perhaps the cytoskeleton? And (3) can you use the forward genetic tools available in C. elegans to find new genes connecting DDR-2 and integrin?

I do see two areas where the manuscript could be improved. First, the authors rely on imprecise genetic methods to reach their conclusions (i.e. systemic RNAi, or expression of dominant negative constructs.) I think their conclusion would be stronger if they used tissue specific degradation to block ddr-2 function specifically in the utse or seam cells. Methods to do this are now regularly used in C. elegans and the authors have already developed the necessary tissue-specific promoters. Second, the manuscript is presented in the introduction as a study on formation and function of BM-BM linkage. The authors start the discussion in a similar manner. But their results are about adhesion between cells and BM. In fact they show the BM-BM linkage forms normally in ddr-2 mutants. Thus it seems like what they have really uncovered is an adhesion mechanism that works in parallel to the BM-BM linkage. Since ddr-2 appears to function equally in both utse + seam cells (based on their dominant negative data), there are likely three layers of adhesion (utse-BM, BM-BM, BM-seam) and if any of those break down, you get a partially penetrant rupture phenotype.

These concerns do not undercut the significance of this work, which identifies an interesting mechanism cells use to strengthen adhesion during BM linkage formation. In fact, I am excited to read future papers detailing the connection between DDR-2 and integrin. But before undertaking those experiments the authors should be certain which cells require DDR-2 activity, and that should not be determined based solely on mis expression of a dominant negative.

Reviewer #1 (Public Review):

The present study examined the physiological mechanisms through which impaired TG storage capacity in adipose tissues affects systemic energy homeostasis in mice. To accomplish this, the authors deleted DGAT1 and DGAT2, crucial enzymes for TG synthesis, in an adipocyte-specific manner. The authors found that ADGAT DKO mice substantially lost the adipose tissues and developed hypothermia when fasted; however, surprisingly, ADGAT KO mice were metabolically healthy on a high-fat diet. The authors found that it was accompanied by elevated energy expenditure, enhanced glucose uptake by the BAT, and enhanced browning of white adipose tissues. This unique animal model provided exciting opportunities to identify new mechanisms to maintain systemic energy homeostasis even in a compromised energy storage capacity. Overall, the data are compelling and well support the conclusion of this paper. The manuscript is clearly written.

Reviewer #1 (Public Review):

This study uses single-cell genomics and gene pathway analysis to characterize the transcriptional effects of influenza H1N1 infection on cell types of the lateral hypothalamus and dorsomedial hypothalamus. The authors use droplet-based single-nuclei RNA-seq to profile single-cell gene expression at 3, 7, and 23 days post intranasal infection with H1N1 influenza virus. Through state-of-the-art and rigorous computational methods, the authors find that many hypothalamic cell types, including glia and neurons, are transcriptionally altered by respiratory infection with a non-neurotropic influenza virus, and that these alterations can persist for weeks and potentially affect cell type interactions that disrupt function. Their thorough discussion of the findings raises interesting questions and hypotheses about the functional implications of the molecular changes they observed, including the physiological changes that can persist long after acute viral infection. Given the role of the hypothalamus in homeostasis, this work sheds light on potential mechanisms by which the H1N1 virus can disrupt cell function and organismal homeostasis beyond the cells that it directly infects.

Despite its strengths, there are several points in the manuscript lacking sufficient evidence or clarity, which need to be addressed through revision. For instance, the conclusion that neurons but not non-neurons show persistent changes in gene expression may be alternatively explained by differences in the number of neuron and non-neuronal cells and transcripts. Also, the authors highlight the connection between influenza infection and loss of appetite and sleepiness but do not explore whether the influenza infection affected the cell types in their dataset previously associated with appetite and sleepiness, or whether differences in weight loss among the influenza-infected subjects correspond to any differences in gene expression.

Joint Public Review:

In the current paper, Jones et al. describe a new framework, named coccinella, for real-time high-throughput behavioral analysis aimed at reducing the cost of analyzing behavior. In the setup used here each fly is confined to a small circular arena and able to walk around on an agar bed spiked with nutrients or pharmacological agent. The new framework, built on the researchers' previously developed platform Ethoscope, relies on relatively low-cost Raspberry Pi video cameras to acquire images at ~0.5 Hz and pull out, in real time, the maximal velocity (parameter extraction) during 10 second windows from each video. Thus, the program produces a text file, and not voluminous videos requiring storage facilities for large amounts of video data, a prohibitive step for many behavioral analyses. The maximal velocity time-series is then fed to an algorithm called Highly Comparative Time-Series Classification (HCTSA)(which itself is based on a large number of feature extraction algorithms) developed by other researchers. HCTSA identifies statistically salient features in the time-series which are then passed on to a type of linear classifier algorithm called support vector machines (SVM). In cases where such analyses are sufficient for characterizing the behaviors of interest this system performs as well as other state-of-the-art systems used in behavioral analysis (e.g., DeepLabCut).

In a pharmacobehavior paradigm testing different chemicals, the authors show that coccinella can identify specific compounds as effectively as other more time-consuming and resource-consuming systems.<br /> The new paradigm should be of interest to researchers involved in drug screens, and more generally, in high-throughput analysis focused on gross locomotor defects in fruit flies such as identification of sleep phenotypes. By extracting/saving only the maximal velocity from video clips, the method is fast. However, the rapidity of the platform comes at a cost--loss of information on subtle but important behavioral alterations. When seeking subtle modifications in animal behavior, solutions like DeepLabCut, which are admittedly slower but far superior in terms of the level of details they yield, would be more appropriate.

The manuscript reads well, and it is scientifically solid.

1- The fact that Coccinella runs on Ethoscopes, an open source hardware platform described by the same group, is very useful because the relevant publication describes Ethoscope in detail. However, the current version of the paper does not offer details or alternatives for users that would like to test the framework, but do not have an Ethoscope. Would it be possible to overcome this barrier and have coccinella run with any video data (and, thus, potentially be used to analyze data obtained from other animal models)?

2- Readers who want background on the analytical approaches that the platform relies on following maximal velocity extraction, will have to consult the original publications. In particular, the current manuscript does not provide much information on Highly Comparative Time-Series Classification (HCTSA) or SVM; this may be reasonable because the methods were developed earlier by others. While some readers may find that the lack of details increases the manuscript's readability, others may be left wanting to see more discussion on these not-so-trivial approaches. In addition, it is worth noting that the same authors who published the HCTSA method also described a shorter version named catch22, that runs faster with a similar output. Thus, explaining in more detail how HCTSA operates, considering that it is a relatively new method, will make the method more convincing.

Reviewer #1 (Public Review):

This paper aims to study the effects of choice history on action-selective beta band signals in human MEG data during a sensory evidence accumulation task. It does so by placing participants in three different stochastic environments, where the outcome of each trial is either random, likely to repeat, or likely to alternate across trials. The authors provide good behavioural evidence that subjects have learnt these statistics (even though they are not explicitly told about them) and that they influence their decision-making, especially on the most difficult trials (low motion coherence). They then show that the primary effect of choice history on lateralised beta-band activity, which is well-established to be linked to evidence accumulation processes in decision-making, is on the slope of evidence accumulation rather than on the baseline level of lateralised beta.

The strengths of the paper are that it is: (i) very well analysed, with compelling evidence in support of its primary conclusions; (ii) a well-designed study, allowing the authors to investigate the effects of choice history in different stochastic environments.

There are no major weaknesses to the study. On the other hand, investigating the effects of choice/outcome history on evidence integration is a fairly well-established problem in the field. As such, I think that this provides a valuable contribution to the field, rather than being a landmark study that will transform our understanding of the problem.

The authors have achieved their primary aims and I think that the results support their main conclusions. One outstanding question in the analysis is the extent to which the source-reconstructed patches in Figure 2 are truly independent of one another (as often there is 'leakage' from one source location into another, and many of the different ROIs have quite similar overall patterns of synchronisation/desynchronisation.). A possible way to investigate this further would be to explore the correlation structure of the LCMV beamformer weights for these different patches, to ask how similar/dissimilar the spatial filters are for the different reconstructed patches.

Reviewer #1 (Public Review):

This study presents an important finding on human m6A methyltransferase complex (including METTL3, METTL14 and WTAP). The evidence supporting the claims of the authors is convincing, although the model and assays need to be further modified. The work will be of interest to biologists working on RNA epigenetics and cancer biology.

In mammals, a large methyltransferase complex (including METTL3, METTL14 and WTAP) deposits m6A across the transcriptome, and METTL3 serves as its catalytic core component. In this manuscript, the authors identified two cleaved forms of METTL3 and described the function of METTL3a (residues 239-580) in breast tumorigenesis. METTL3a mediates the assembly of METTL3-METTL14-WTAP complex, the global m6A deposition and breast cancer progression. Furthermore, the METTL3a-mTOR axis was uncovered to mediate the METTL3 cleavage, providing potential therapeutic target for breast cancer. This study is properly performed and the findings are very interesting; however, some problems with the model and assays need to be modified. It is widely known that METTL3 and METTL14 form a stable heterodimer with the stoichiometric ratio of 1:1 (Wang X et al. Nature 534, 575-578 (2016), Su S et al. Cell Res 32(11), 982-994 (2022), Yan X et al. Cell Res 32(12), 1124-1127 (2022)), the numbers of METTL3 and METTL14 in the model of Fig 7P are not equivalent and need to be modified.

Reviewer #1 (Public Review):

Masson et al. leveraged the natural genetic diversity presented in a large cohort of the Diversity Outbred in Australia (DOz) mice (n=215) to determine skeletal muscle proteins that were associated with insulin sensitivity. The hits were further filtered by pQTL analysis to construct a proteome fingerprint for insulin resistance. These proteins were then searched against Connectivity Map (CMAP) to identify compounds that could modulate insulin sensitivity. In parallel, many of these compounds were screened experimentally alongside other compounds in the Prestwick library to independently validate some of the compound hits. These two analyses were combined to score for compounds that would potentially reverse insulin resistance. Thiostrepton was identified as the top candidate, and its ability to reverse insulin resistance was validated using assays in L6 myotubes. The mechanism of action was also partially investigated. The concept of this work is certainly interesting, and the reviewer appreciates the amount of work the authors put into this study.

(1) What's the rationale of trypsinizing the tissue prior to mitochondrial isolation? This is not standard for subsequent proteomics analysis. This step will inevitably cause protein loss, especially for the post mitochondrial fractions (PMF). Treating samples with 0.01ug/uL trypsin for 37oC 30 min is sufficient to partially digest a substantial portion of the proteome. If samples from different subjects were not of the same weight, then this partial digestion step may introduce artificial variability as variable proportions of proteins from different subjects would be lost during this step. In addition, the mitochondrial protein enrichment in the mito fraction, despite statistically significant, does not look striking (Figure 1E, ~30% mitochondrial proteins in the mito fraction). As a comparison, Williams et al., MCP 2018 seem to have obtained high mitochondrial protein content in the mito fraction without trpsinizing the frozen quadriceps using a similar SWATH-MS-based approach.

(2) The authors mentioned that the proteomics data were Log2 transformed and median-normalized. Would it be possible to provide a bit more details on this? Were the subjects randomized?

(3) In Figure 1D, what were the numbers of mice the authors used for the CV comparisons in each group? Were they of similar age and sex? Were the differences in CV values statistically significant?

(4) The authors stated in lines 155-157 that proteins negatively associated with the Matsuda index were further filtered by presence of their cis-pQTLs. Perhaps more explanations would be needed to justify this filtering criterion? Having a cis-pQTL would mean the protein abundance variation is explained by the variation in its coding gene, this however conceptually would not be relevant to its association with the Matsuda index. With the data that the authors have in hand, would it not be natural to align the Matsuda index QTL with the pQTLs (cis and trans if available), and/or to perform mediation analysis to examine causal relationships with statistical significance?

(5) It seems a bit odd that the first half of the paper focused extensively on the authors' discoveries in the mitochondrial proteome, and how proteins involved in mitochondrial processes (such as complex I) were associated with Matsuda Index, but the final fingerprint list of insulin resistance, which contained 76 proteins, only had 7 mitochondrial proteins. Was this because many mitochondrial proteins were filtered out due to no cis-pQTL presenting?

(6) The authors found that thiostrepton-induced insulin resistance reversal effects were not through insulin signalling. It activated glycolysis but the mechanism of action was not clear. What are the proteins in the fingerprint list that led to identification of thiostrepton on CMAP? Is thiostrepton able to bind or change the expression of these proteins? Since thiostrepton was identified by searching the insulin resistance fingerprint protein list against CMAP, it would be rational to think that it exerts the biological effects by directly or indirectly acting on these protein targets.