Reviewer #2 (Public Review):

Summary:

This manuscript from Liu et al. examines the role of Fat and Dachsous, two transmembrane proto-cadherins that function both in planar cell polarity and in tissue growth control mediated by the Hippo pathway. The authors developed a new method for measuring growth of the wing imaginal disc during late larval development and then used this approach to examine the effects of disruption of Fat/Dachsous function on disc growth. The authors show that during mid to late third instar the wing imaginal disc normally grows in a linear rather than exponential fashion and that this occurs due to slowing of the mitotic cell cycle as the disc grows during this period. Consistent with their known role in regulating Hippo pathway activity, this slowing of growth is disrupted by loss of Fat/Dachsous function. The authors also observed a previously unreported gradient of Fat protein across the wing blade. However, graded expression of Fat or Dachsous is not necessary for proper growth regulation in the late third instar because ectopic Dachsous expression, which affects gradients of both Dachsous and Fat, has no growth phenotype.

Strengths:

Although the role of the Hippo pathway in growth control has been extensively studied, our understanding of how the pathway controls growth during normal development remains relatively weak. This work addresses this question by examining normal growth of the wing imaginal disc during part of its development in the larva and characterizing the effects of Fat/Dachsous manipulation on that growth. The authors developed tools for measuring wing growth by measuring wing volume, an approach that could be useful in future studies of tissue growth.

Weaknesses:

1) Although the approach used to measure volume is new to this study, the basic finding that imaginal disc growth slows at the mid-third instar stage has been known for some time from studies that counted disc cell number during larval development (Fain and Stevens, 1982; Graves and Schubiger, 1982). Although these studies did not directly measure disc volume, because cell size in the disc is not known to change during larval development, cell number is an accurate measure of tissue volume. However, it is worth noting that the approach used here does potentially allow for differential growth of different regions of the disc.

2) Related to point 1, a main conclusion of this study, that cell cycle length scales with growth of the wing, is based on a developmentally limited analysis that is restricted to the mid-third instar larval stage and later (early third instar begins at 72 hr - the authors' analysis started at 84 hr). The previous studies cited above made measurements from the beginning of the 3rd instar and combined them with previous histological analyses of cell numbers starting at the beginning of the 2nd instar. Interestingly, both studies found that cell number increases exponentially from the start of the 2nd instar until mid-third instar, and only after that point does the cell cycle slow resulting in the linear growth reported here. The current study states that growth is linear due to scaling of cell cycle with disc size as though this is a general principle, but from the earlier studies, this is not the case earlier in disc development and instead applies only to the last day of larval life.

3) The analysis of the roles of Fat and Dachsous presented here has weaknesses that should be addressed. It is very curious that the authors found that depletion of Fat by RNAi in the wing blade had essentially no effect on growth while depletion of Dachsous did, given that the loss of function overgrowth phenotype of null mutations in fat is more severe than that of null mutations in dachsous (Matakatsu and Blair, 2006). An obvious possibility is that the Fat RNAi transgene employed in these experiments is not very efficient. The authors tried to address this by doubling the dose of the transgene, but it is not clear to me that this approach is known to be effective. The authors should test other RNAi transgenes and additionally include an analysis of growth of discs from animals homozygous for null alleles, which as they note survive to the late larval stages.

4) It is surprising that the authors detect a gradient of Fat expression that has not been seen previously given that this protein has been extensively studied. It is also surprising that they find that expression of Nubbin Gal4 is graded across the wing blade given that previous studies indicate that it is uniform (ie. Martín et al. 2004). These two surprising findings raise the possibility that the quantification of fluorescence could be inaccurate. The curvature of the wing blade makes it a challenging tissue to image, particularly for quantitative measurements.

5) Overall, in my view the impact of these findings is limited. The focus on growth solely at the end of larval development, when there are a number of potentially confounding variables (for example hormonal cues), makes the generality of the findings reported here difficult to judge. Additionally, the functional analysis of Fat/Dachsous function in this process is limited - for example does disruption of other Hippo pathway components have a similar effect?

Reviewer #2 (Public Review):

In the main text, the authors apply their metrics to a data set that was published by Mira et al. in 2015. The data consist of growth rate measurements for a combinatorially complete set of 16 genetic variants of the antibiotic resistance enzyme beta-lactamase across 10 drugs and drug combinations at 3 different drug concentrations, comprising a total of 30 different environmental conditions. In my previous report I had asked the authors to specify why they selected only 7 out of 30 environments for their analysis, with only one concentration for drug, but a clear explanation is still lacking. In the Data section of Material and Methods, the authors describe their criterion for data selection as follows: "we focus our analyses on drug treatments that had a significant negative effect on the growth of wildtype/TEM-1 strains". However, in Figure 2 it is seen that, even for the selected data sets, not all points are significant compared to wild type (grey points). So what criterion was actually applied?

In effect, for each chosen drug or drug combination, the authors choose the data set corresponding to the highest drug concentration. As a consequence, they cannot assess to what extent their metrics depend on drug concentration. This is a major concern, since Mira et al. concluded in their study that the differences between growth rate landscapes measured at different concentrations were comparable to the differences between drugs. I argued before that, if the new metrics display a significant dependence on drug concentration, this would considerably limit their usefulness. The authors challenge this, saying in their rebuttal that "no, that drug concentration would<br /> be a major actor in the value of the metrics does not limit the utility of the metric. It is simply another variable that one can consider when computing the metrics." While this is true in principle, I don't think any practicing scientist would disagree with the statement that the existence of additional confounding factors (in particular if they are unknown) reduces the usefulness<br /> of a quantitative metric.

As a consequence of the small number of variant-drug-combinations that are used, the conclusions that the authors draw from their analysis are mostly tentative. For example, on line 123 the authors write that the observation that<br /> the treatment of highest drug applicability is a combination of two drugs "fits intuition". In the Discussion this statement is partly retracted with reference to the piperacillin/tazobactam-combination which has low drug applicability. Being based on only a handful of data points, both observations are essentially anecdotal and it is unclear what the reader is supposed to learn.

To assess the environment-dependent epistasis among the genetic mutations comprising the variants under study, the authors decompose the data of Mira et al. into epistatic interactions of different orders. This part of the analysis is incomplete in two ways. First, in their study, Mira et al. pointed out that a fairly large fraction of the fitness differences between variants that they measured were not statistically significant. This information has been removed in the depiction of the Mira et al. fitness landscapes in Figure 1 of the present manuscript, and it does not seem to be reflected in the results of the interaction analysis in Figure 4. Second, the interpretation of the coefficients obtained from the epistatic decomposition depends strongly on the formalism that is being used. In a note added on page 15 of the revised manuscript, the authors write that they have used the LASSO regression for their analysis and refer the reader to a previous publication (Guerrero et al. 2019) which however (as far as I could see) also does not fully explain how the method works. To give an example of the difficulty of interpreting the data in Figure 4 without further information: The substitution C (G238S) is well known to have a strong positive effective in cefotaxime, but the corresponding coefficient is essentially zero. So whatever the LASSO regression does, it cannot simply measure the effect on growth.

Reviewer #2 (Public Review):

Summary:<br /> Gaikwad et al. investigated the role of eIF2A in translational response to stress in yeast. For this purpose, the authors conducted ribosome profiling under SM treatment in an eIF2A-depleted strain. Data analysis revealed that eIF2A did not influence translation from mRNAs bearing uORFs or cellular IRESes, in the stress condition, broadly. The authors found that only a small number of mRNAs were supported by eIF2A. The data should be helpful for researchers in the field.

Major points:<br /> 1. The weakness of this work is the lack of clarification on the function of eIF2A in general. The novelty of this study was limited.

2. Related to this, it would be worth investigating common features in mRNAs selectively regulated (surveyed in Figure 3A). Also, it would be worth analyzing the effect of eIF2A deletion on elongation (ribosome occupancy on each codon and/or global ribosome footprint distribution along CDS) and termination/recycling (footprint reads on stop codon and on 3′ UTR).

3. Regarding Figure 3D, the reporters were designed to include promoter and 5′ UTR of the target genes. Thus, it should be worth noting that reporter design was based on the assumption that eIF2A-dependency in translation regulation was not dependent on 3′ UTR or CDS region. The reason why the effects on ribosome profiling-supported mRNAs could not be recapitulated in reporter assay may originate from this design. This should be also discussed.

4. Related to the point above, the authors claimed that eIF2A affects "possibly only one" (HKR1) mRNA. However, this was due to the reporter assay which is technically variable and could not allow some of the constructs to pass the authors' threshold. Alternative wording for this point should be considered.

5. For Figure 3D, it would be worth considering testing the #-marked genes (in Figure 3C) in this set up.

6. In box plots, the authors should provide the statistical tests, at least where the authors explained in the main text.

Reviewer #2 (Public Review):

Summary:

The authors describe the discovery of a filovirus neutralizing antibody, AF03, by phage display, and its subsequent improvements to include NPC2 that resulted in a greater breadth of neutralization. Overall, the manuscript would benefit from considerable grammatical review, which would improve the communication of each point to the reader. The authors do not convincingly map the AF03 epitope, nor do they provide any strong support for their assumption that AF03 targets the NPC1 binding site. However, the authors do show that AF03 competes for MR78 binding to its epitope, and provides good support for the internalization of AF03-NL as the mechanism for improved breadth over the original AF03 antibody.

Strengths:

This study shows convincing binding to Marburgvirus GP and neutralization of Marburg viruses by AF03, as well as convincing neutralization of Ebolaviruses by AF03-NL. While there are no distinct populations of PE-stained cells shown by FACS in Figure 5A, the cell staining data in Figure 5C are compelling to a non-expert in endosomal staining like me. The control experiments in Figure 7 are compelling showing neutralization by AF03-NL but not AF03 or NPC2 alone or in combination. Altogether these data support the internalisation and stabilisation mechanism that is proposed for the gain in neutralization breadth observed for Ebolaviruses by AF03-NL over AF03 alone.

Weaknesses:

Overall, this reviewer is of the opinion that this paper is constructed haphazardly. For instance, the neutralization of mutant pseudoviruses is shown in Figure 2 before the concept of pseudovirus neutralization by AF03 is introduced in Figure 3. Similarly, the control experiments for AF03+NPC2 are described in Figure 7 after the data for breadth of neutralization are shown in Figure 6. GP quality controls are shown in Figure 2 after GP ELISAs / BLI experiments are done in Figure 1. This is disorienting for the reader.

Figure 1: The visualisation of AF03 modelling and docking endeavours is extremely difficult to interpret. Firstly, there is no effort to orient the non-specialist reader with respect to the Marburgvirus GP model. Secondly, from the figures presented it is impossible to tell if the Fv docks perfectly onto the GP surface, or if there are violent clashes between the deeply penetrating AF03 CDRs and GP. This information would be better presented on a white background, perhaps showing GP in surface view from multiple angles and slices. The authors attempt to label potential interactions, but these are impossible to read, and labels should be added separately to appropriately oriented zoomed-in views.

Figure 2: The neutralization of mutant pseudoviruses cannot be properly assessed using bar graphs. These data should be plotted as neutralization curves as they were done for the wild-type neutralization data in Figure 3. The authors conclude that Q128 & N129 are contact residues, but the neutralization data for this mutant appear odd as the lowest two concentrations of AF03 show higher neutralization than the second highest AF03 concentration. Neutralization of T204/Q205/T206 (green), Y218 (orange), K222 (blue), or C226 (purple) appears to be better than neutralization of the wild-type MARV. The authors do not discuss this oddity. What are the IC50's? The omission of antibody concentrations on the x-axis and missing IC50 values give a sense of obscuring the data, and the manuscript would benefit from greater transparency, and be much easier to interpret if these were included. I am intrigued that the Q128S/N129S mutant is reported as having little effect on the neutralization of MR78. The bar graph appears to show some effect (difficult to interpret without neutralization curves and IC50 data), and indeed PDB:5UQY seems to suggest that these amino acids form a central component of the MR78 epitope (Q128 forms potential hydrogen bonds with CDRH1 Y35 and CDRL3 Y91, while N129 packs against the MR78 CDRH3 and potentially makes additional polar contact with the backbone). Lastly, since neutralization was tested in both HEK293T cells and Huh7 cells in Figure 3, the authors should clarify which cells were used for neutralization in Figure 2.

Figure 3: The first two images in Figure 3C showing bioluminescent intensity from pseudovirus-injected mice pretreated with either 10mg/kg or 3mg/kg AF03 are identical images. This is apparent from the location, shape, and intensity of the bioluminescence, as well as the identical foot placement of each mouse in these two panels. Currently, this figure is incomplete and should be corrected to show the different mice treated with either 10mg/kg or 3mg/kg of AF03.

Figure 4 would benefit from a control experiment without antibodies comparing infection with GP-cleaved and GP-uncleaved pseudoviruses. The paragraph describing these data was also difficult to read and would benefit from additional grammatical review.

Figure 5: The authors should clarify in the methods section that the "mock" experiment included the PE anti-human IgG Fc antibody. Without this clarification, the lack of a distinct negative population in the FACS data could be interpreted as non-specific staining with PE. If the PE antibody was added at an equivalent concentration to all panels, what does the directionality of the arrowheads in Figure 5A (labelled PE) and 5B (labelled pHrodo Red) indicate?

Figure 6B: These data would benefit from the inclusion of IC50, transparency of antibody concentrations used, and consistency in the direction of antibody concentrations (increasing to the right or left of the x-axis) when compared to Figure 2.

Reviewer #2 (Public Review):

Summary:

Mandal et al. use WASP-deficient T cells to study the role of WASP in T cell signaling and activation and tying WASP to mechanosensing in T cells. Using both CD8 and CD4 T cells from WASP-deficient animals, the authors show defects in T cell signaling and function as well as defects in mechanosensing in activated CD8 T cells.

Strengths:

Confirming findings from many previous studies, Mandal et al. demonstrate that WASP-deficiency in T cells leads to defective T cell function (Figs 1, 2, 3, and 4). Fig 3 shows direct effects of mechanical stress on CD8 T cell signaling in the absence of WASP.

Weaknesses:

The title does not reflect the data presented as the only data demonstrating a role for WASP in mechanosensing in this manuscript doesn't directly connect WASP mechanosensing with tumors (Fig 3). The results shown in Fig 1 using an actin inhibitor doesn't directly connect WASP with mechanosensing. Fig 4 uses WASP-deficient animals in a tumor model, but doesn't demonstrate any role for mechanosensing in the WASP-deficient animals. The title should reflect the lack of data connecting WASP in mechanosensing to a tumor context.

One major oversight is the absence of discussion of a previous publication demonstrating a direct role of WASP in mechanosensing to the actin cytoskeleton in dendritic cells and naive CD4 and CD8 T cells (Gaertner et al. Dev Cell 2022). There should be a discussion of how the findings in Gaertner et al. shed light on the results from this manuscript.

The use of Myca to disrupt the actin cytoskeleton as a "modulator of stiffness" is problematic. While one of the potential effects of disrupting the actin cytoskeleton is changing stiffness, as shown in Figure 1, many other functions are simultaneously disturbed also. The use of B16 tumor cells is simply for antigen presentation, and not in a tumor context, so generalized statements about "stiffness" or "softness" and "tumor cells" in reference to Figure 1 should be changed to account for these alternative explanations.

Fig S2 shows Myca treatment of BMDCs leads to decreased functionality of OTII CD4s. Interpretation in the manuscript claims "This indicates that leaching of Myca from treated cells does not cause inhibition of bystander cells". This would not be my interpretation of the data. An alternative interpretation is that if Myca is remaining in the media, then effects on APCS (either BMDCs or B16s) could lead to decreased CD4 or CD8 T cell activation and thus be responsible for effects seen in Fig 1. This possibility should be considered.

Fig 4 claims that high rigidity leads to downstream effects of WASP-/- T cell function. But there is no demonstration of the role of mechanosensing in Figure 4. To make this claim, the authors would need to compare high and low rigidity conditions.

Fig 4 also shows that WASP-/- showed higher tumor growth in an implanted tumor model. For 4F, since WASP is deficient in all hematopoietic cells, the finding in 4G may not be due to T cells. In 4H-J, because implantation of tumors occurs within 1 day of lymphodepletion and assessing tumor growth prior to reconstitution of the hematopoietic compartment, there should be control experiments shown to demonstrate that other hematopoietic cell types that remain are not function and thus do not participate in the differences seen in tumor growth. Also, statistical tests need to be done to show the significance of the differences between groups in Fig 4I and 4J (also 4G).

Reviewer #2 (Public Review):

Summary:

The main purpose of this investigation was to 1) compare the effects of a single knockout (sKO) of Numb or a double knockout (dKO) of Numb and NumbL on ex-vivo physiological properties of the extensor digitorium longus (EDL) muscle in C57BL/6NCrl mice; and 2) analyze protein complexes isolated from C2C12 myotubes via immunoprecipitation and LC/MS/MS for potential Numb binding partners. The main findings are 1) the muscles from sKO and dKO were significantly weaker with little difference between the sKO and dKO lines, indicating the reduced force is mainly due to the inactivation of the Numb gene; and 2) there were 11 potential Numb binding proteins that were identified and cytoskeletal specific proteins including Septin 7.

Strengths:

Straight-forward yet elegant design to help determine the important role the Numb has in skeletal muscle.

Weaknesses:

There were a limited number of samples (3-6) that were used for the physiological experiments; however, there was a very large effect size in terms of differences in muscle tension development between the induced KO models and the controls.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors developed an open-top two-photon light sheet microscopy (OT-TP-LSM) that enables high-throughput and high-depth investigation of 3D cell structures. The data presented here shows that OT-T-LSM could be a complementary technique to traditional imaging workflows of human cancer cells.

Strengths:

High-speed and high-depth imaging of human cells in an open-top configuration is the main strength of the presented study. An extended depth of field of 180 µm in 0.9 µm thickness was achieved together with an acquisition of 0.24 mm2/s. This was confirmed by 3D visualization of human cancer cells in the skin, pancreas, and prostate.

Weaknesses:

The complementary aspect of the presented technique in human pathological samples is not convincingly presented. The traditional hematoxylin and eosin (H&E) staining is a well-established and widely used technique to detect human cancer cells. What would be the benefit of 3D cell visualization in an OT-TP-LSM microscope for cancer detection in addition to H&E staining?

Reviewer #2 (Public Review):

The authors provide convincing evidence that optogenetic stimulation of ChR2-expressing motor neurons implanted in muscles effectively restore innervation of severely affected skeletal muscles in the aggressive SOD1 mouse model of ALS, and concluded that this method can be applied to selectively control the function of implicated muscles, which was supported by convincing data presented in the paper.

Reviewer #2 (Public Review):

Summary:

The authors use single-cell "multi-comics" to study clonal heterogeneity in chronic myeloid leukemia (CML) and its impact on treatment response and resistance. Their main results suggest 1) Cell compartments and gene expression signatures both shared in CML cells (versus normal), yet 2) some heterogeneity of multiomic mapping correlated with ELN treatment response; 3) further definition of s unique combination of CD26 and CD35 surface markers associated with gene expression defined BCR::ABL1+ LSCs and BCR::ABL1- HSCs. The manuscript is well-written, and the method and figures are clear and informative. The results fit the expanding view of cancer and its therapy as a complex Darwinian exercise of clonal heterogeneity and the selective pressures of treatments.

Strengths:

Cutting-edge technology by one of the expert groups of single-cell 'comics.

Weaknesses:

Very small sample sizes, without a validation set.<br /> The obvious main problem with the study is that an enormous amount of results and conjecture arise from a very small data set: only nine cases for the treatment response section (three in each of the ELN categories), only two normal marrows, and only two patient cases for the division kinetic studies. Thus, it is very difficult to know the "noise" in the system - the stability of clusters and gene expression and the normal variation one might expect, versus patterns that may be reproducibly study artifact, effects of gene expression from freezing-thawing, time on the bench, antibody labeling, etc. This is not so much a criticism as a statement of reality: these elegant experiments are difficult, time-consuming, and very expensive. Thus in the Discussion, it would be helpful for the authors to just frankly lay out these limitations for the reader to consider. Also in the Discussion, it would be interesting for the authors to consider what's next: what type of validation would be needed to make these studies translatable to the clinic? Is there a clever way to use these data to design a faster/cheaper assay?

Reviewer #2 (Public Review):

Summary:

This manuscript provides important new findings regarding the connection between inflammation and metabolism. It also identifies a new type of post-translational modification and its connection to protein stability. This finding is expected to be generalizable to other protein targets. In vitro evidence is solid. In vivo evidence needs some additional controls.

Strengths:

A new connection between inflammation and metabolism.

A novel type of PTM was identified.

Findings would be of broad interest and the mechanisms are likely generalizable to related control systems.

In vitro data are well-supported.

The authors successfully demonstrated that treatment with 4-octyl Itaconate (4-OI), a prodrug form of itaconate, reduces neutral lipid accumulation in the AML12 cell line and primary hepatocytes. They show that 4-OI promotes fatty acid beta-oxidation through increased stability of CPT1a protein, the rate-limiting step in this process.

Weaknesses:

Some conclusions involving the Irg1 knockout mice require important controls and clarifications to be fully convincing and some controls are missing.

Reviewer #2 (Public Review):

Summary:

The goal of untargeted metabolomics is to identify differences between metabolomes of different biological samples. Untargeted metabolomics identifies features with specific mass-to-charge ratio (m/z) and retention time (RT). Matching those to specific metabolites based on the model compounds from databases is laborious and not always possible, which is why methods for comparing samples on the level of unmatched features are crucial.

The main purpose of the GromovMatcher method presented here is to merge and compare untargeted metabolomes from different experiments. These larger datasets could then be used to advance biological analyses, for example, for the identification of metabolic disease markers. The main problem that complicates merging different experiments is m/z and RT vary slightly for the same feature (metabolite).

The main idea behind the GromovMatcher is built on the assumption that if two features match between two datasets (that feature i from dataset 1 matches feature j from dataset 2, and feature k from dataset 1 matches feature l from dataset 2), then the correlations or distances between the two features within each of the datasets (i and k, and j and l) will be similar. The authors then use the Gromov-Wasserstein method to find the best matches matrix from these data.

The variation in m/z between the same features in different experiments is a user-defined value and it is initially set to 0.01 ppm. There is no clear limit for RT deviations, so the method estimates a non-linear deviation (drift) of RT between two studies. GromovMatcher estimates the drift between the two studies and then discards the matching pairs where the drift would deviate significantly from the estimate. It learns the drift from a weighted spline regression.

The authors validate the performance of their GromovMatcher method by a validation experiment using a dataset of cord blood. They use 20 different splits and compare the GromovMatcher (both its GM and GMT iterations, whereby the GMT version uses the deviation from estimated RT drift to filter the matching matrix) with two other matching methods: M2S and metabCombiner.

The second validation was done using a (scaled and centered) dataset of metabolics from cancer datasets from the EPIC cohort that was manually matched by an expert. This dataset was also used to show that using automatic methods can identify more features that are associated with a particular group of samples than what was found by manual matching. Specifically, the authors identify additional features connected to alcohol consumption.

Strengths:

I see the main strength of this work in its combination of all levels of information (m/z, RT, and higher-order information on correlations between features) and using each of the types of information in a way that is appropriate for the measure. The most innovative aspect is using the Gromov-Wasserstein method to match the features based on distance matrices.

The authors of the paper identify two main shortcomings with previously established methods that attempt to match features from different experiments: a) all other methods require fine-tuning of user-defined parameters, and, more importantly, b) do not consider correlations between features. The main strength of the GromovMatcher is that it incorporates the information on distances between the features (in addition to also using m/z and RT).

Weaknesses:

The first, minor, weakness I could identify is that there seem not to be plenty of manually curated datasets that could be used for validation. The second is also emphasized by the authors in the discussion. Namely, the method as it is set up now can be directly used only to compare two datasets.

Reviewer #2 (Public Review):

Summary:

The authors of this study have sought to better understand the timing and location of the attachment of the lpp lipoprotein to the peptidoglycan in E. coli, and to determine whether YafK is the hydrolase that cleaves lpp from the peptidoglycan.

Strengths:

The method is relatively straightforward. The authors are able to draw some clear conclusions from their results, that lpp molecules get cleaved from the peptidoglycan and then re-attached, and that YafK is important for that cleavage.

Weaknesses:

However, the authors make a few other conclusions from their data which are harder to understand the logic of, or to feel confident in based on the existing data. They claim that their 5-time point kinetic data indicates that new lpp is not substantially added to lipidII before it is added to the peptidoglycan, and that instead lpp is attached primarily to old peptidoglycan. I believe that this conclusion comes from the comparison of Fig.s 3A and 3C, where it appears that new lpp is added to old peptidoglycan a few minutes before new lpp is added to new peptidoglycan. However, the very small difference in the timing of this result, the minimal number of time points and the complete lack of any presentation of calculated error in any of the data make this conclusion very tenuous. In addition, the authors conclude that lpp is not significantly attached to septal peptidoglycan. The logic behind this conclusion appears to be based on the same data, but the authors do not provide a quantitative model to support this idea.

This work will have a moderate impact on the field of research in which the connections between the OM and peptidoglycan are being studied in E. coli. Since lpp is not widely conserved in gram negatives, the impact across species is not clear. The authors do not discuss the impact of their work in depth.

Reviewer #2 (Public Review):

Summary: CD8+ QFL T cells recognize a peptide, FYAEATPML (FL9), presented on Erap1-deficient cells. QFL T cells are present at a high frequency in the spleen of naïve mice. They express an antigen-experienced phenotype, and about 80% express an invariant TCRα chain Vα3.2Jα21.

Here, Guan and coll. report that QFL T cells are present not only in the spleen but also in the intestinal epithelium, where they display several phenotypic and functional peculiarities. The establishment of spleen and gut Vα3.2+ QFL T cells is TAP-dependent, and their phenotype is regulated by the presence/absence of Qa-1b and Erap1. Maintenance of gut Vα3.2+ QFL T cells depends on the gut microbiota and is associated with colonization by Pediococcus pentosaceus.

Strengths:

This article contains in-depth studies of a peculiar and interesting subset of unconventional CD8 T cells, based partly on generating two novel TCR-transgenic models.

The authors discovered a clear relation between the gut microbiome and the maintenance of gut QFL T cells. One notable observation is that monocolonization of the gut with Pediococcus pentosaceus is sufficient to sustain gut QFL T cells.

Weaknesses:

In the absence of immunopeptidomic analyses, the presence or absence of the FL9 peptide on various cell types is inferred based on indirect evidence. Hence, whether the FL9 peptide is presented by some cells that express Qa-1b but not Erap1 remains unknown.

Analyses of the homology between the FL9 and bacterial peptides were limited to two amino acid residues (P4 and P6). This limitation is mitigated in part by the justifications provided by the authors in the revised preprint.

The potential function of QFL T cells remains elusive. The present article should provide an incentive for further functional studies.

Reviewer #2 (Public Review):

Summary:

The paper examines the role L-cysteine metabolism plays in the biology of Mycobacterium tuberculosis. The authors have preliminary data showing that Mycobacterium tuberculosis has two unique pathways to synthesize cysteine. The data showing new compounds that act synergistically with INH is very interesting.

Strengths:

RNAseq data is interesting and important.

Weaknesses:

The paper would be strengthened if the authors were to add further detail to their genetic manipulations.

The authors provide evidence that they have successfully made a cysK2 mutant by recombineering. This data looks promising, but I do not see evidence for the cysM deletion. It is also important to state what sort of complementation was done (multicopy plasmid, integration proficient vector, or repair of the deletion). Since these mutants are the basis for most of the additional studies, these details are essential. It is important to include complementation in mouse studies as unexpected loss of PDIM could have occurred.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors have obtained an analytical expression that provides intuition about regimes of interfacial resistance that depend on droplet size. Additionally, through simulations, the authors provide microscopic insight into the arrangement of sticky and non-sticky functional groups at the interface. The authors introduce bouncing dynamics for rationalizing quantity recovery timescales.

I found several sections that felt incomplete or needed revision and additional data to support the central claim and make the paper self-contained and coherent.

First, the analytical theory operates with diffusion coefficients for dilute and dense phases. For the dilute phase, this is fine. For the dense phase, I have doubts that dynamics can be described as diffusive. Most likely, dynamics is highly subdiffusive due to crowded, entangled, and viscoelastic environments of densely packed interactive biomolecules. Some explanation and justification are in order here.

The second major issue is that I did not find a clean comparison of simulations with the derived analytical expression. Simulations test various microscopic properties on the value of k, which is important. But how do we know that it is the same quantity that appears in the expressions? Also, how can we be sure that analytical expressions can guide simulations and experiments as claimed? The authors should provide sound evidence of the predictive aspect of their derived expressions.

Are the plots in Figure 4 coming from experiment, theory, and simulation? I could not find any information either in the text or in the caption.

Reviewer #2 (Public Review):

Summary and Major Findings/Strengths:

Across diverse hosts, microbiota can influence viral infection and transmission. C. elegans is naturally infected by the Orsay virus, which infects intestinal cells and is transmitted via the fecal-oral route. Previous work has demonstrated that host immune defense pathways, such as antiviral RNAi and the intracellular pathogen response (IPR), can influence host susceptibility to virus infection. However, little is known about how bacteria modulate viral transmission and host susceptibility.

In this study, the authors investigate how diverse bacterial species influence Orsay virus transmission and host susceptibility in C. elegans. When C. elegans is grown in the presence of two Ochrobactrum species, the authors find that animals exhibit increased viral transmission, as measured by the increased proportion of newly infected worms (relative to growth on E. coli OP50). The presence of the two Ochrobactrum species also resulted in increased host susceptibility to the virus, which is reflected by the increased fraction of infected animals following exposure to the exogenous Orsay virus. In contrast, the presence of Pseudomonas lurida MYb11, as well as Pseudomonas PA01 or PA14, attenuates viral transmission and host susceptibility relative to E. coli OP50. For growth in the presence of P. aeruginosa PA01 and PA14, the attenuated transmission and susceptibility are suppressed by mutations in regulators of quorum sensing and the gacA two-component system. The authors also identify six virulence genes in P. aeruginosa PA14 that modulate host susceptibility to virus and viral transmission, albeit to a lesser extent. Based on the findings in P. aeruginosa, the authors further demonstrate that deletion of the gacA ortholog in P. lurida results in loss of the attenuation of viral transmission and host susceptibility.

Taken together, these findings provide important insights into the species-specific effects that bacteria can have on viral infection in C. elegans. The authors also describe a role for Pseudomonas quorum sensing and virulence genes in influencing viral transmission and host susceptibility.

Major weaknesses:

The manuscript has several issues that need to be addressed, such as insufficient rigor of the experiments performed and questions about the reproducibility of the data presented in some places. In addition, confounding variables complicate the interpretations that can be made from the authors' findings and weaken some of the conclusions that are stated in the manuscript.

1. The authors sometimes use pals-5p::GFP expression to indicate infection, however, this is not necessarily an accurate measure of the infection rate. Specifically, in Figures 4-6, the authors should include measurements of viral RNA, either by FISH staining or qRT-PCR, to support the claims related to differences in infection rate.

2. In several instances, the experimental setup and presentation of data lack sufficient rigor. For example, Fig 1D and Fig 2B only display data from one experimental replicate. The authors should include information from all 3 experimental replicates for more transparency. In Fig 3B, the authors should include a control that demonstrates how RNA1 levels change in the presence of E. coli OP50 for comparison with the results showing replication in the presence of PA14. In order to support the claim that "P. aeruginosa and P. lurida MYb11 do not eliminate Orsay virus infection", the authors should also measure RNA1 fold change in the presence of PA01 and P. lurida in the context of exogenous Orsay virus. Additionally, the authors should standardize the amount of bacteria added to the plate and specify how this was done in the Methods, as differing concentrations of bacteria could be the reason for species-specific effects on infection.

3. The authors should be more careful about conclusions that are made from experiments involving PA14, which is a P. aeruginosa strain (isolated from humans), that can rapidly kill C. elegans. To eliminate confounding factors that are introduced by the pathogenicity of PA14, the authors should address how PA14 affects the health of the worms in their assays. For example, the authors should perform bead-feeding assays to demonstrate that feeding rates are unaffected when worms are grown in the presence of PA14. Because Orsay virus infection occurs through feeding, a decrease in C. elegans feeding rates can influence the outcome of viral infection. The authors should also address whether or not the presence of PA14 affects the stability of viral particles because that could be another trivial reason for the attenuation of viral infection that occurs in the presence of PA14.

Ashby's law of requisite variety may also be at play for overloading our system 1 heuristic abilities with respect to misinformation (particularly in high velocity social media settings). Switching context from system 1 to system 2 on a constant basis to fact check everything in our (new digital) immediate environment can be very mentally and emotionally taxing. This can result in both mental exhaustion as well as anxiety.

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Reviewer #2 (Public Review):

Summary:<br /> This work analyses the historical spread and evolution, termed 'population dynamics', of a human bacterial pathogen, Neisseria gonorrhoeae, the cause of the sexually transmitted infection, gonorrhoea. N. gonorrhoeae is classified as a high priority pathogen by the World Health Organisation, due to infections numbering in the tens of millions annually, with high levels of antibiotic resistance and no vaccine available, meaning treating and preventing infections is becoming increasingly more difficult. To implement interventions effectively, important resistant lineages and their transmission routes must be identified on a national and international level.

In this work, Osnes et al. use genomic data, coupled with geographic, temporal and demographic metadata, to analyse the global population dynamics N. gonorrhoeae using 9,732 genomes. The study also includes a granular analysis of transmission between and within four regions of different sizes with high levels of data coverage: USA, Europe, Norway, and Victoria state in Australia.<br /> The authors built a phylogenetic tree including all genomes using a novel computationally efficient method for removing genome regions resulting from recombination, which would otherwise result in incorrect branch lengths and tree topology. Using the tree, the authors show that the effective population size of N. gonorrhoeae, describing population size and diversity, decreased in the period from 2010 to present day, and was not entirely an artefact of sampling bias. The authors then stratified the tree based on isolates that contained alleles that are associated with resistance to antibiotics commonly used to treat gonorrhoea. The authors found resistance was associated with particular lineages, of which most, but not all, underwent shrinking in effective population size in the last decade.<br /> Using the tree, the authors then inferred likely importation, exportation, and local transmission events, finding notable differences in the contribution of imports to local incidence between locations, as well as the likelihood of exportation. As inference of these events relies on sampling density, the authors used a novel method for identifying whether sampling was representative of the population diversity of a given location. Using this approach, they found that the densely sampled regions, Norway and Victoria, were likely representative of the local N. gonorrhoeae population diversity, whilst the larger, less densely sampled regions, Europe and USA, were not. Finally, they investigated the contribution of specific transmission networks to the spread gonorrhoea, finding that the frequency of males within a transmission network may play a role in the rate of N. gonorrhoeae transmission in Norway, but not Victoria.<br /> This work introduces several novel approaches to the analysis of pathogen population dynamics, and highlights notable differences in N. gonorrhoeae transmission between and within distinct geographic locations.

Strengths:<br /> • The authors have collated a large global collection of N. gonorrhoeae genomes with associated metadata, and in some cases generated assemblies themselves. A dataset of this size and detail is a valuable asset to the public health community, enabling analysis of both national and international population dynamics.<br /> • The stratification of the phylogenetic tree by antimicrobial resistance gene alleles enables the study of how antibiotic usage has shaped global and regional N. gonorrhoeae populations. Analysis of changes in the effective population size of clades harbouring resistance alleles is particularly impactful, as this can be used to show how changes in treatment patterns affect the growth or decline of drug-resistant pathogen populations. This analysis also enables the determination of the frequency of multiple resistance alleles being present in single isolates, important for determining the scale of multidrug resistance within the N. gonorrhoeae global population.<br /> • The use of ancestral trait reconstruction to quantify importation, exportation and local transmission is an important contribution to public health efforts tackling N. gonorrhoeae spread. Understanding the differences in transmission networks within and between different geographic locations provides public health researchers with crucial information to model and implement effective targeted interventions on regional and international scales.

Weaknesses:<br /> • The method used to generate the phylogenetic tree and mask regions of recombination is likely flawed. The authors repeatedly down-sampled the whole population to 500 genomes, using Gubbins to identify regions that have recombined and therefore would not follow the clonal history of the N. gonorrhoeae population. This small sample size will result in the same ancient internal nodes being sampled repeatedly, whilst more recent internal nodes will not. Therefore, more recent recombination events would not be identified by this method and were therefore likely included in the whole genome alignment used to build the tree. Furthermore, Gubbins was designed to identify recombination between closely related genomes, not across a whole species, where the background mutation rate will be too high to differentiate between recombined regions and the clonal frame. Both of these factors will mean that the amount of the genome predicted to have recombined will likely be underestimated, resulting in inflated branch lengths and incorrect tree topology. This effect is potentially the cause of the observed drop in N. gonorrhoeae effective population size between 2010-present day in Figure 2, which does not align with gonorrhoea incidence, and the elevated estimated mutation rate of 7.41x10-6 substitutions per site per year, which is higher than previous estimates based on N. gonorrhoeae global populations. The result of underestimation of recombined regions will be two-fold. Inclusion of recombined regions in the alignment will result in inflated branch lengths, which will impact all estimates of effective population size in the study. Furthermore, tree topology may be incorrect, which will impact ancestral trait reconstruction and result in incorrect inference of import, export and local transmission events in Figures 3, 4 and 5. Additionally, the clade-specific resistance gene analyses will be affected in Figure 2, as certain isolates may be incorrectly included or excluded within stratified clades. Therefore, the conclusions made about the changes in effective population size for the global population, and individual clades, as well as the differences in transmission dynamics between locations, are likely to be incorrect.<br /> • The method used to identify sampling bias, shown in Figure 4, is a novel and interesting take on the problem. However, it is not clear whether the effect being measured is the presence of sampling bias or an artefact of differences in N. gonorrhoeae diversity between locations. The results in Figure 4 do align with what is known about the population datasets; the data from Norway and Victoria is more comprehensive than that of the USA and Europe due to the difference in size of the respective human populations, meaning the likelihood of sampling bias will be lower in the smaller population. However, with increased human population size, we would also expect a greater amount of pathogen diversity, due to increased within-region transmission and greater numbers of importation events. Supporting this, we see in Figure 3 that the transmission lineages in the USA and Europe are estimated to have emerged earlier than Norway or Victoria, indicative of a greater amount of standing population diversity. Therefore, the reason why convergence is observed when up-sampling from smaller populations may be because a vast majority of isolates will sit within a small part of the tree, whilst from a larger, more diverse population, isolates will be placed all across the tree and so convergence will never be observed. In effect, it is unknown whether increasing the sample size of the USA and Europe to be truly representative of their respective N. gonorrhoeae populations would ever result in convergence between the two methods of up-sampling. Testing this method using simulations could be used to determine whether it is sensitive to sampling bias, or population diversity.<br /> • In Figure 5, a significant difference in transmission lineage size was only found between male-dominated and mixed lineages in Norway and not Victoria. Therefore, the conclusion that sex distribution within transmission networks affects the size of transmission lineages is not supported by the data, and could also be due to geographical and other demographic differences between the datasets which were not accounted for.

Reviewer #2 (Public Review):

Li et al present a method to extract "behaviorally relevant" signals from neural activity. The method is meant to solve a problem which likely has high utility for neuroscience researchers. There are numerous existing methods to achieve this goal some of which the authors compare their method to, though there are notable omissions. However, I do believe that d-VAE is a promising approach that has its own advantages.

That being said, there are issues with the paper as-is. This could have been a straightforward "methods" paper describing their approach and validating it on different ground truth and experimental datasets. Instead, the authors focus on the neuroscientific results and their implications for brain mechanisms. Unfortunately, while the underlying method seems sound and performs well relative to the assessed competition, the scientific results and presentation they put forward were not sufficiently strong to support these claims, especially given the small amount of data (recordings of one monkey per task, with considerable variability between them).

Specific comments<br /> - Is the apparently increased complexity of encoding vs decoding so unexpected given the entropy, sparseness, and high dimensionality of neural signals (the "encoding") compared to the smoothness and low dimensionality of typical behavioural signals (the "decoding") recorded in neuroscience experiments? This is the title of the paper so it seems to be the main result on which the authors expect readers to focus.

- I take issue with the premise that signals in the brain are "irrelevant" simply because they do not correlate with a fixed temporal lag with a particular behavioural feature hand-chosen by the experimenter. As an example, the presence of a reward signal in motor cortex [1] after the movement is likely to be of little use from the perspective of predicting kinematics from time-bin to time-bin using a fixed model across trials (the apparent definition of "relevant" for behaviour here), but an entire sub-field of neuroscience is dedicated to understanding the impact of these reward-related signals on future behaviour. Is there method sophisticated enough to see the behavioural "relevance" of this brief, transient, post-movement signal? This may just be an issue of semantics, and perhaps I read too much into the choice of words here. Perhaps the authors truly treat "irrelevant" and "without a fixed temporal correlation" as synonymous phrases and the issue is easily resolved with a clarifying parenthetical the first time the word "irrelevant" is used. But I remain troubled by some claims in the paper which lead me to believe that they read more deeply into the "irrelevancy" of these components.

- The authors claim the "irrelevant" responses underpin an unprecedented neuronal redundancy and reveal that movement behaviors are distributed in a higher-dimensional neural space than previously thought." Perhaps I just missed the logic, but I fail to see the evidence for this. The neural space is a fixed dimensionality based on the number of neurons. A more sparse and nonlinear distribution across this set of neurons may mean that linear methods such as PCA are not effective ways to approximate the dimensionality. But ultimately the behaviourally relevant signals seem quite low-dimensional in this paper even if they show some nonlinearity may help.

- Relatedly, I would like to note that the exercise of arbitrarily dividing a continuous distribution of a statistic (the "R2") based on an arbitrary threshold is a conceptually flawed exercise. The authors read too much into the fact that neurons which have a low R2 w.r.t. PDs have behavioural information w.r.t. other methods. To this reviewer, it speaks more about the irrelevance, so to speak, of the preferred direction metric than anything fundamental about the brain.

- there is an apparent logical fallacy that begins in the abstract and persists in the paper: "Surprisingly, when incorporating often-ignored neural dimensions, behavioral information can be decoded linearly as accurately as nonlinear decoding, suggesting linear readout is performed in motor cortex." Don't get me wrong: the equivalency of linear and nonlinear decoding approaches on this dataset is interesting, and useful for neuroscientists in a practical sense. However, the paper expends much effort trying to make fundamental scientific claims that do not feel very strongly supported. This reviewer fails to see what we can learn about a set of neurons in the brain which are presumed to "read out" from motor cortex. These neurons will not have access to the data analyzed here. That a linear model can be conceived by an experimenter does not imply that the brain must use a linear model. The claim may be true, and it may well be that a linear readout is implemented in the brain. Other work [2,3] has shown that linear readouts of nonlinear neural activity patterns can explain some behavioural features. The claim in this paper, however, is not given enough

- I am afraid I may be missing something, as I did not understand the fano factor analysis of Figure 3. In a sense the behaviourally relevant signals must have lower FF given they are in effect tied to the temporally smooth (and consistent on average across trials) behavioural covariates. The point of the original Churchland paper was to show that producing a behaviour squelches the variance; naturally these must appear in the behaviourally relevant components. A control distribution or reference of some type would possibly help here.

- The authors compare the method to LFADS. While this is a reasonable benchmark as a prominent method in the field, LFADS does not attempt to solve the same problem as d-VAE. A better and much more fair comparison would be TNDM [4], an extension of LFADS which is designed to identify behaviourally relevant dimensions.

[1] https://doi.org/10.1371/journal.pone.0160851<br /> [2] https://doi.org/10.1101/2022.03.31.486635<br /> [3] https://doi.org/10.1038/s41593-017-0028-6<br /> [4] Hurwitz et al, Targeted Neural Dynamical Modeling, NeurIPS 2021.

Reviewer #2 (Public Review):

In this manuscript, the authors propose a computational method based on deep convolutional neural networks (CNNs) to automatically detect cell divisions in two-dimensional fluorescence microscopy timelapse images. Three deep learning models are proposed to detect the timing of division, predict the division axis, and enhance cell boundary images to segment cells before and after division. Using this computational pipeline, the authors analyze the dynamics of cell divisions in the epithelium of the Drosophila pupal wing and find that a wound first induces a reduction in the frequency of division followed by a synchronised burst of cell divisions about 100 minutes after its induction.

In general, novelty over previous work does not seem particularly important. From a methodological point of view, the models are based on generic architectures of convolutional neural networks, with minimal changes, and on ideas already explored in general. The authors seem to have missed much (most?) of the literature on the specific topic of detecting mitotic events in 2D timelapse images, which has been published in more specialized journals or Proceedings. (TPMAI, CCVPR etc., see references below). Even though the image modality or biological structure may be different (non-fluorescent images sometimes), I don't believe it makes a big difference. How the authors' approach compares to this previously published work is not discussed, which prevents me from objectively assessing the true contribution of this article from a methodological perspective.

On the contrary, some competing works have proposed methods based on newer - and generally more efficient - architectures specifically designed to model temporal sequences (Phan 2018, Kitrungrotsakul 2019, 2021, Mao 2019, Shi 2020). These natural candidates (recurrent networks, long-short-term memory (LSTM), gated recurrent units (GRU), or even more recently transformers), coupled to CNNs are not even mentioned in the manuscript, although they have proved their generic superiority for inference tasks involving time series (Major point 2). Even though the original idea/trick of exploiting the different channels of RGB images to address the temporal aspect might seem smart in the first place - as it reduces the task of changing/testing a new architecture to a minimum - I guess that CNNs trained this way may not generalize very well to videos where the temporal resolution is changed slightly (Major point 1). This could be quite problematic as each new dataset acquired with a different temporal resolution or temperature may require manual relabeling and retraining of the network. In this perspective, recent alternatives (Phan 2018, Gilad 2019) have proposed unsupervised approaches, which could largely reduce the need for manual labeling of datasets.

Regarding the other convolutional neural networks described in the manuscript:

1) the one proposed to predict the orientation of mitosis performs a regression task, predicting a probability for the division angle. The architecture, which must be different from a simple Unet, is not detailed anywhere, so the way it was designed is difficult to assess. It is unclear if it also performs mitosis detection, or if it is instead used to infer orientation once the timing and location of the division have been inferred by the previous network.

2) the one proposed to improve the quality of cell boundary images before segmentation is nothing new, it has now become a classic step in segmentation, see for example Wolny et al. eLife 2020.

As a side note, I found it a bit frustrating to realise that all the analysis was done in 2D while the original images are 3D z-stacks, so a lot of the 3D information had to be compressed and has not been used. A novelty, in my opinion, could have resided in the generalisation to 3D of the deep-learning approaches previously proposed in that context, which are exclusively 2D, in particular, to predict the orientation of the division.

Concerning the biological application of the proposed methods, I found the results interesting, showing the potential of such a method to automatise mitosis quantification for a particular biological question of interest, here wound healing. However, the deep learning methods/applications that are put forward as the central point of the manuscript are not particularly original.

Major point 1: generalisation potential of the proposed method.

The neural network model proposed for mitosis detection relies on a 2D convolutional neural network (CNN), more specifically on the Unet architecture, which has become widespread for the analysis of biology and medical images. The strategy proposed here exploits the fact that the input of such an architecture is natively composed of several channels (originally 3 to handle the 3 RGB channels, which is actually a holdover from computer vision, since most medical/biological images are gray images with a single channel), to directly feed the network with 3 successive images of a timelapse at a time. This idea is, in itself, interesting because no modification of the original architecture had to be carried out. The latest 10-channel model (U-NetCellDivision10), which includes more channels for better performance, required minimal modification to the original U-Net architecture but also simultaneous imaging of cadherin in addition to histone markers, which may not be a generic solution.

Since CNN-based methods accept only fixed-size vectors (fixed image size and fixed channel number) as input (and output), the length or time resolution of the extracted sequences should not vary from one experience to another. As such, the method proposed here may lack generalization capabilities, as it would have to be retrained for each experiment with a slightly different temporal resolution. The paper should have compared results with slightly different temporal resolutions to assess its inference robustness toward fluctuations in division speed.

Another approach (not discussed) consists in directly convolving several temporal frames using a 3D CNN (2D+time) instead of a 2D, in order to detect a temporal event. Such an idea shares some similarities with the proposed approach, although in this previous work (Ji et al. TPAMI 2012 and for split detection Nie et al. CCVPR 2016) convolution is performed spatio-temporally, which may present advantages. How does the authors' method compare to such an (also very simple) approach?

Major point 2: innovatory nature of the proposed method.

The authors' idea of exploiting existing channels in the input vector to feed successive frames is interesting, but the natural choice in deep learning for manipulating time series is to use recurrent networks or their newer and more stable variants (LSTM, GRU, attention networks, or transformers). Several papers exploiting such approaches have been proposed for the mitotic division detection task, but they are not mentioned or discussed in this manuscript: Phan et al. 2018, Mao et al. 2019, Kitrungrotaskul et al. 2019, She et al 2020.

An obvious advantage of an LSTM architecture combined with CNN is that it is able to address variable length inputs, therefore time sequences of different lengths, whereas a CNN alone can only be fed with an input of fixed size.

Another advantage of some of these approaches is that they rely on unsupervised learning, which can avoid the tedious relabeling of data (Phan et al. 2018, Gilad et al. 2019).

References :<br /> Ji, S., Xu, W., Yang, M., & Yu, K. (2012). 3D convolutional neural networks for human action recognition. IEEE transactions on pattern analysis and machine intelligence, 35(1), 221-231. >6000 citations

Nie, W. Z., Li, W. H., Liu, A. A., Hao, T., & Su, Y. T. (2016). 3D convolutional networks-based mitotic event detection in time-lapse phase contrast microscopy image sequences of stem cell populations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 55-62).

Phan, H. T. H., Kumar, A., Feng, D., Fulham, M., & Kim, J. (2018). Unsupervised two-path neural network for cell event detection and classification using spatiotemporal patterns. IEEE Transactions on Medical Imaging, 38(6), 1477-1487.

Gilad, T., Reyes, J., Chen, J. Y., Lahav, G., & Riklin Raviv, T. (2019). Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy. Bioinformatics, 35(15), 2644-2653.

Mao, Y., Han, L., & Yin, Z. (2019). Cell mitosis event analysis in phase contrast microscopy images using deep learning. Medical image analysis, 57, 32-43.

Kitrungrotsakul, T., Han, X. H., Iwamoto, Y., Takemoto, S., Yokota, H., Ipponjima, S., ... & Chen, Y. W. (2019). A cascade of 2.5 D CNN and bidirectional CLSTM network for mitotic cell detection in 4D microscopy image. IEEE/ACM transactions on computational biology and bioinformatics, 18(2), 396-404.

Shi, J., Xin, Y., Xu, B., Lu, M., & Cong, J. (2020, November). A Deep Framework for Cell Mitosis Detection in Microscopy Images. In 2020 16th International Conference on Computational Intelligence and Security (CIS) (pp. 100-103). IEEE.

Wolny, A., Cerrone, L., Vijayan, A., Tofanelli, R., Barro, A. V., Louveaux, M., ... & Kreshuk, A. (2020). Accurate and versatile 3D segmentation of plant tissues at cellular resolution. Elife, 9, e57613.

Reviewer #2 (Public Review):

This manuscript by Port and colleagues describes rigorous experiments that provide a wealth of virologic, respiratory physiology, and particle aerodynamic data pertaining to aerosol transmission of SARS-CoV-2 between infected Syrian hamsters. The data is particularly significant because infection is compared between alpha and delta variants, and because viral load is assessed via numerous assays (gRNA, sgRNA, TCID) and in tissues as well as the ambient environment of the cage. The paper will be of interest to a broad range of scientists including infectious diseases physicians, virologists, immunologists and potentially epidemiologists. The strength of evidence is relatively high but limited by unclear presentation in certain parts of the paper.

Important conclusions are that infectious virus is only detectable in air samples during a narrow window of time relative to tissue samples, that airway constriction increases dynamically over time during infection limiting production of fine aerosol droplets, that variants do not appear to exclude one another during simultaneous exposures and that exposures to virus via the aerosol route lead to lower viral loads relative to direct inoculation suggesting an exposure dose response relationship.

While the paper is valuable, I found certain elements of the data presentation to be unclear and overly complex.

Reviewer #2 (Public Review):

Tian et al. performed a meta-analysis of 113 genome-wide origin profile datasets in humans to assess the reproducibility of experimental techniques and shared genomics features of origins. Techniques to map DNA replication sites have quickly evolved over the last decade, yet little is known about how these methods fare against each other (pros and cons), nor how consistent their maps are. The authors show that high-confidence origins recapitulate several known features of origins (e.g., correspondence with open chromatin, overlap with transcriptional promoters, CTCF binding sites). However, surprisingly, they find little overlap between ORC/MCM binding sites and origin locations.

Overall, this meta-analysis provides the field with a good assessment of the current state of experimental techniques and their reproducibility, but I am worried about: (a) whether we've learned any new biology from this analysis; (b) how binding sites and origin locations can be so mismatched, in light of numerous studies that suggest otherwise; and (c) some methodological details described below.

-- I understand better the inclusion/exclusion logic for the samples. But I'm still not sure about the fragments. As the authors wrote, there is both noise and stochasticity; the former is not important but the latter is essential to include. How can these two be differentiated, and what may be the expected overlap as a function of different stochasticity rates?

-- Many of the major genomic features analyzed have already been found to be associated with origin sites. For example, the correspondence with TSS has been reported before:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6320713/<br /> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6547456/

-- Line 250: The most surprising finding is that there is little overlap between ORC/MCM binding sites and origin locations. The authors speculate that the overlap between ORC1 and ORC2 could be low because they come from different cell types. Equally concerning is the lack of overlap with MCM. If true, these are potentially major discoveries that butts heads with numerous other studies that have suggested otherwise.

The key missing dataset is ORC1 and ORC2 CHiP-seq from the same cell type. This shouldn't be too expensive to perform, and I hope someone performs this test soon. Without this, I remain on the fence about how much existing datasets are "junk" vs how much the prevailing hypothesis about replication needs to be revisited. Nonetheless, the authors do perform a nice analysis showing that existing techniques should be carefully used and interpreted.

Reviewer #2 (Public Review):

Summary:<br /> In this study, Mu Qiao employs a bilinear modeling approach, commonly utilized in recommendation systems, to explore the intricate neural connections between different pre- and post-synaptic neuronal types. This approach involves projecting single-cell transcriptomic datasets of pre- and post-synaptic neuronal types into a latent space through transformation matrices. Subsequently, the cross-correlation between these projected latent spaces is employed to estimate neuronal connectivity. To facilitate the model training, connectomic data is used to estimate the ground-truth connectivity map. This work introduces a promising model for the exploration of neuronal connectivity and its associated molecular determinants. However, it is important to note that the current model has only been tested with Bipolar Cell and Retinal Ganglion Cell data, and its applicability in more general neuronal connectivity scenarios remains to be demonstrated.

Strengths:<br /> This study introduces a succinct yet promising computational model for investigating connections between neuronal types. The model, while straightforward, effectively integrates single-cell transcriptomic and connectomic data to produce a reasonably accurate connectivity map, particularly within the context of retinal connectivity. Furthermore, it successfully recapitulates connectivity patterns and helps uncover the genetic factors that underlie these connections.

Weaknesses:<br /> 1. The study lacks experimental validation of the model's prediction results.<br /> 2. The model's applicability in other neuronal connectivity settings has not been thoroughly explored.<br /> 3. The proposed method relies on the availability of neuronal connectomic data for model training, which may be limited or absent in certain brain connectivity settings.

Reviewer #2 (Public Review):

Summary:<br /> The authors investigate the effect of oscillatory activity on the chaotic dynamics of high-dimensional networks. The network oscillations are internally generated by synaptic delays which are known to produce oscillations. The authors demonstrate that the intensity of the chaos and the dimension of the chaotic attractor picks at a delay value. A similar effect is found when an external input drives the network. In this case, these quantities pick at the network's resonant frequency. This shows that the intensity of the chaotic dynamics can be boosted by internally or externally generated oscillations.

Strengths:<br /> The paper is technically solid. They introduce a novel method to perform calculations of the Lyapunov spectrum in networks with delays, which have infinite dimensions, effectively transforming it into a network of finite dimensions. The conclusions of the paper are supported by strong analytical calculations and novel and intensive numerical methods.

Weaknesses:<br /> The main weakness is that is difficult to find the relevance of the paper's findings to neuroscience. It is not clear to me that measures such as the rate of production of entropy of a chaotic attractor in spiking networks, its dimension, and its Lyapunov spectra are experimentally relevant. Moreover, the authors make little to no attempt to provide interpretations for these quantities nor put their work in a broader context in the field of systems neuroscience. The paper also is written in an overly technical way with sometimes the use of technical jargon which might be difficult to follow for a non-expert in mean field theories and statistical physics.

Reviewer #2 (Public Review):

This study by Adelus et al. profiled the transcriptome and chromatin accessibility in cultured human aortic endothelial cells (ECs) at single-cell resolution. They also stimulated these cells with EC-activating agents, such as IL1b, TGFB2, or si-EGR, to knock down this master transcription factor in ECs. The results show a subpopulation, EC3, with the highest plasticity and sensitivity to perturbations. The authors also reviewed and meta-analyzed three independent publicly available scRNA-seq datasets, identifying two distinct EC subpopulations. Additionally, they aligned CAD-related SNPs with open chromatin regions in EC subpopulations. This study provides fundamental evidence to enrich our understanding of vascular ECs and highlights potential subpopulations that may contribute to health and diseases. The work exhibits the potential impact in the field. While the manuscript is comprehensive, there are some concerns that should be addressed.

1. My major concern is whether EC4 is derived from ECs. It seems that EC4 showed a lesser reaction to those perturbations and had lower expression levels of EC marker genes. Did the authors evaluate the purity of their isolated HAECs? Please discuss the potential cell lineage mapping of EC4.

2. Although all the donors are de-identified, is there any information about the severity of their vascular impairment, particularly in the case of patient 5, who exhibits the unique EC5?

3. The meta-analysis of the published datasets is comprehensive. The identified EC heterogeneity corresponds to their in vitro data. I am wondering, in terms of transcriptome, is there any similarity between endo1 and EC1/EC2, and also endo2 and EC3/EC4?

4. The in vitro data indicates that EC3 shows the highest plasticity and sensitivity to perturbations, which may act as the major subtype of ECs responding to risk factors. It's very interesting that CAD-related SNPs do not seem to be enriched in EC3. Please discuss this discrepancy.

5. The last sentence in the legend of Figure 1 seems incomplete: 'Module scores are generated for each cell barcode with Seurat function AddModuleScore().'

Reviewer #2 (Public Review):

Summary:<br /> The authors present a theoretical study of the length dynamics of bundles of actin filaments. They first show a "balance point model" in which the bundle is described as an effective polymer. The corresponding assembly and disassembly rates can depend on bundle length. This model generates a steady-state bundle-length distribution with a variance that is proportional to the average bundle length. Numerical simulations confirm this analytic result. The authors then present an analysis of previously published length distributions of actin bundles in various contexts and argue that these distributions have variances that depend quadratically with the average length. They then consider a bundle of N-independent filaments that each grow in an unregulated way. Defining the bundle length to be that of the longest filament, the resulting length distribution has a variance that scales quadratically with the average bundle length.

Strengths:<br /> The manuscript is very well written, and the computations are nicely presented. The work gives fundamental insights into the length distribution of filamentous actin structures. The universal dependence of the variance on the mean length is of particular interest. It will be interesting to see in the future, how many universality classes there are, and which features of a growth process determine to which class it belongs.

Weaknesses:<br /> 1) You present the data in Fig. 3 as arguments against the balance point model. Although I agree that the data is compatible with your description of a bundle of filaments, I think that the range of mean lengths you can explore is too limited to conclusively argue against the balance point model. In most cases, your data extend over half an order of magnitude only. Could you provide a measure to quantify how much your model of independent filaments fits better than the balance point model?

2) Concerning your bundled-filament model, why do you consider the polymerizing ends to be all aligned? Similarly to the opposite end, fluctuations should be present. Furthermore, it is not clear to me, where the presence of crosslinking proteins enters your description. Finally, linked to my first remark on this model, why is the longest filament determining the length of the bundle in all the biological examples you cite? I am thinking in particular about the actin cables in yeast.

Reviewer #2 (Public Review):

Summary:<br /> The replication of information-coding polymers and the emergence of catalytic ribozymes pose significant challenges, both experimentally and theoretically, in the study of the RNA world hypothesis. In this context, Tkachenko et al. put forth a novel hypothesis regarding a replication oligomer system based on a cleavage ribozyme. They initially highlighted that the breakage of oligomers could contribute to self-replication, provided that these fragments function as primers for subsequent replications. Next, they proposed a self-replicating system of oligomers founded on a hammerhead structure that catalyzes cleavage. By a simple dynamical model, they demonstrated that such a system is self-sustainable in certain parameter regimes. Furthermore, they delved into discussions regarding the potential emergence of such a system and the evolution toward further optimized ribozymes.

Strengths:<br /> Although the cleavage (hammerhead) ribozyme has been discussed in the context of the origins of life, the authors are the first to discuss how they could be selected using a mathematical model as far as I know. The idea is simple: ribozyme activity creates fragments by breakage of an oligomer, which works as a primer for the ribozyme itself, resulting in a positive feedback system (i.e., autocatalytic sets in a broader sense). This potentially enables us to resolve at the same time problems on the (i) supply of new primers (but note that there is a major concern on this as described in the 'weakness'), and (ii) the sustaining of the cleavage ribozyme.

Weaknesses:<br /> The major weakness of their theory is that the ends of the new primers, formed through the breakage/cleavage of polymers, must be chemically active (as the authors have already emphasized in the last paragraph of their discussion) to enable further elongation. Reactivating the ends of preexisting oligomers without enzymes, to the best of our current knowledge, could be a challenging task. Although their model heavily relies on this aspect, the authors do not elaborate on it.

Another weakness is in the setup of their discussion on evolutionary dynamics. While they claim that their model is robust against replication errors, their approach to evolutionary dynamics appears unconventional, and it remains unclear under what conditions their assumptions are founded. They treat a whole set of oligos as a subject of evolution, rather than each individual oligo. This may necessitate more complex assumptions, such as the encapsulation of sets of oligos inside a protocell, to be adequately rationalized. Thus, it remains uncertain whether the system is indeed robust against replication errors in a more natural context. For example, if a mutant oligo, denoted as b', arises due to an error in the replication of oligo b, and if b' has lower catalytic activity but replicates more rapidly than b, it may ultimately come to dominate the system.

Reviewer #2 (Public Review):

Overall: This paper describes new material of Acanthomeridion serratum that the authors claim supports its synonymy with Acanthomeridion anacanthus. The material is important and the description is acceptable after some modification. In addition, the paper offers thoughts and some exploration of the possibility of multiple origins of the dorsal facial suture among artiopods, at least once within Trilobita and also among other non-trilobite artiopods. Although this possibility is real and apparently correct, the suggestions presented in this paper are both surprising and, in my opinion, unlikely to be true because the potential homologies proposed with regard to Acanthomeridion and trilobite-free cheeks are unconventional and poorly supported.

What to do? I can see two possibilities. One, which I recommend, is to concentrate on improving the descriptive part of the paper and omit discussion and phylogenetic analysis of dorsal facial suture distribution, leaving that for more comprehensive consideration elsewhere. The other is to seek to improve both simultaneously. That may be possible but will require extensive effort.

Major concerns

Concern 1 - Ventral sclerites as free cheek homolog, marginal sutures, and the trilobite doublure

Firstly, a couple of observations that bear on the arguments presented - the eyes of A. serratum are almost marginal and it is not clear whether a) there is a circumocular suture in this animal and b) if there was, whether it merged with the marginal suture. These observations are important because this animal is not one in which an impressive dorsal facial suture has been demonstrated - with eyes that near marginal it simply cannot do so. Accordingly, the key argument of this paper is not quite what one would expect. That expectation would be that a non-trilobite artiopod, such as A. serratum, shows a clear dorsal facial suture. But that is not the case, at least with A. serratum, because of its marginal eyes. Rather, the argument made is that the ventral doublure of A. serratum is the homolog of the dorsal free cheeks of trilobites. This opens up a series of issues.

The paper's chief claim in this regard is that the "teardrop" shaped ventral, lateral cephalic plates in Acanthomeridion serratum are potential homologs of the "free cheeks" of those trilobites with a dorsal facial suture. There is no mention of the possibility that these ventral plates in A. serratum could be homologs of the lateral cephalic doublure of olenelloid trilobites, which is bound by an operative marginal suture or, in those trilobites with a dorsal facial suture, that it is a homolog of only the doublure portions of the free cheeks and not with their dorsal components.

The introduction to the paper does not inform the reader that all olenelloids had a marginal suture - a circumcephalic suture that was operative in their molting and that this is quite different from the situation in, say, "Cedaria" woosteri in which the only operative cephalic exoskeletal suture was circumocular. The conservative position would be that the olenelloid marginal suture is the homolog of the marginal suture in A. serratum: the ventral plates thus being homolog of the trilobite cephalic doublure, not only potential homolog to the entire or dorsal only part of the free cheeks of trilobites with a dorsal facial suture. As the authors of this paper decline to discuss the doublure of trilobites (there is a sole mention of the word in the MS, in a figure caption) and do not mention the olenelloid marginal suture, they give the reader no opportunity to assess support for this alternative.

At times the paper reads as if the authors are suggesting that olenelloids, which had a marginal cephalic suture broadly akin to that in Limulus, actually lacked a suture that permitted anterior egression during molting. The authors are right to stress the origin of the dorsal cephalic suture in more derived trilobites as a character seemingly of taxonomic significance but lines such as 56 and 67 may be taken by the non-specialist to imply that olenelloids lacked a forward egression-permiting suture. There is a notable difference between not knowing whether sutures existed (a condition apparently quite common among soft-bodied artiopods) and the well-known marginal suture of olenelloids, but as the MS currently reads most readers will not understand this because it remains unexplained in the MS.

With that in mind, it is also worth further stressing that the primary function of the dorsal sutures in those which have them is essentially similar to the olenelloid/limulid marginal suture mentioned above. It is notable that the course of this suture migrated dorsally up from the margin onto the dorsal shield and merged with the circumocular suture, but this innovation does not seem to have had an impact on its primary function - to permit molting by forward egression. Other trilobites completely surrendered the ability to molt by forward egression, and there are even examples of this occurring ontogenetically within species, suggesting a significant intraspecific shift in suture functionality and molting pattern. The authors mention some of this when questioning the unique origin of the dorsal facial suture of trilobites, although I don't understand their argument: why should the history of subsequent evolutionary modification of a character bear on whether its origin was unique in the group?

The bottom line here is that for the ventral plates of A. serratum to be strict homologs of only the dorsal portion of the dorsal free cheeks, there would be no homolog of the trilobite doublure in A. serratum. The conventional view, in contrast, would be that the ventral plates are a homolog of the ventral doublure in all trilobites and ventral plates in artiopods. I do not think that this paper provides a convincing basis for preferring their interpretation, nor do I feel that it does an adequate job of explaining issues that are central to the subject.

Concern 2. Varieties of dorsal sutures and the coexistence of dorsal and marginal sutures

The authors do not clarify or discuss connections between the circumocular sutures (a form of dorsal suture that separates the visual surface from the rest of the dorsal shield) and the marginal suture that facilitates forward egression upon molting. Both structures can exist independently in the same animal - in olenelloids for example. Olenelloids had both a suture that facilitated forward egression in molting (their marginal suture) and a dorsal suture (their circumocular suture). The condition in trilobites with a dorsal facial suture is that these two independent sutures merged - the formerly marginal suture migrating up the dorsal pleural surface to become confluent with the circumocular suture. (There are also interesting examples of the expansion of the circumocular suture across the pleural fixigena.) The form of the dorsal facial suture has long figured in attempts at higher-level trilobite taxonomy, with a number of character states that commonly relate to the proximity of the eye to the margin of the cephalic shield. The form of the dorsal facial suture that they illustrate in Xanderella, which is barely a strip crossing the dorsal pleural surface linking marginal and circumocular suture, is comparable to that in the trilobites Loganopeltoides and Entomapsis but that is a rare condition in that clade as a whole. The paper would benefit from a clear discussion of these issues at the beginning - the dorsal facial suture that they are referring to is a merged circumcephalic suture and circumocular suture - it is not simply the presence of a molt-related suture on the dorsal side of the cephalon.

Concern 3. Phylogenetics<br /> While I appreciate that the phylogenetic database is a little modified from those of other recent authors, still I was surprised not to find a character matrix in the supplementary information (unless it was included in some way I overlooked), which I would consider a basic requirement of any paper presenting phylogenetic trees - after all, there's no a space limit. It is not possible for a reviewer to understand the details of their arguments without seeing the character states and the matrix of state assignments.

The section "phylogenetic analyses" provides a description of how tree topology changes depending on whether sutures are considered homologous or not using the now standard application of both parsimony and maximum likelihood approaches but, considering that the broader implications of this paper rest of the phylogenetic interpretation, I also found the absence of detailed discussion of the meaning and implications of these trees to be surprising, because I anticipated that this was the main reason for conducting these analysis. The trees are presented and briefly described but not considered in detail. I am troubled by "Circles indicate presence of cephalic ecdysial sutures" because it seems that in "independent origin of sutures" trilobites are considered to have two origins (brown color dot) of cephalic ecdysial sutures - this may be further evidence that the team does not appreciate that olenelloids have cephalic ecdysial sutures, as the basal condition in all trilobites. Perhaps I'm misunderstanding their views, but from what's presented it's not possible to know that. Similarly, in the "sutures homologous" analyses why would there be two independent green dots for both Acanthomeridion and Trilobita, rather than at the base of the clade containing them both, as cephalic ecdysial sutures are basal to both of them? Here again, we appear to see evidence that the team considers dorsal facial sutures and cephalic ecdysial sutures to be synonymous - which is incorrect.

This point aside, and at a minimum, that team needs to do a more thorough job of characterizing and considering the variety of conditions of dorsal sutures among artiopods, their relationships to the marginal suture and to the circumocular suture, the number, and form of their branches, etc.

Reviewer #2 (Public Review):

In this work the authors describe the shape and interconnectedness of intracellular structures of malaria blood stage parasites by taking advantage of expansion microscopy. Compared to previous microscopy work with these parasites, the strength of this paper lies in the increased resolution and the fact that the NHE ester highlights protein densities. Together with the BodipyC membrane staining, this results in data that is somewhere in between EM and standard fluorescence microscopy: it has higher resolution than standard fluorescence microscopy and provides some points of reference of different cellular structures due to the NHE ester/BodipyC.

This study makes many interesting and useful observations and although it is somewhat "old school descriptory" in its presentation, researchers working in many different areas will find something of interest here. This ranges from mitosis, to organisation and distribution of major cellular structures, endocytosis and invasion, overall providing a rich and interesting resource. The results section is long but by taking the space to explain everything in detail, it has the advantage that it clearly transpires how things were done and on how many cells a conclusion is based on. Further the authors often also included a brief interpretation of their findings with a very open assessment what it does and what it does not show, highlighting interesting questions left by the data.

Overall this is a very nice and useful paper that will be of interest to many, particularly those working on nuclear division, cytokinesis, endocytosis or invasion in malaria parasites. The spatiotemporal arrangement and interconnection of subcellular structures will also give a framework for specific functional studies.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Liu et al. reports a task that is designed to examine the extent to which "past" and "future" information is encoded in working memory that combines a retro cue with rules that indicate the location of an upcoming test probe. An analysis of microsaccades on a fine temporal scale shows the extent to which shifts of attention track the location of the location of the encoded item (past) and the location of the future item (test probe). The location of the encoded grating of the test probe was always on orthogonal axes (horizontal, vertical) so that biases in microsaccades could be used to track shifts of attention to one or the other axis (or mixtures of the two). The overall goal here was then to (1) create a methodology that could tease apart memory for the past and future, respectively, (2) to look at the time-course attention to past/future, and (3) to test the extent to which microsaccades might jointly encode past and future memoranda. Finally, some remarks are made about the plausibility of various accounts of working memory encoding/maintenance based on the examination of these time courses.

Strengths:<br /> This research has several notable strengths. It has a clear statement of its aims, is lucidly presented, and uses a clever experimental design that neatly orthogonalizes "past" and "future" as operationalized by the authors. Figure 1b-d shows fairly clearly that saccade directions have an early peak (around 300ms) for the past and a "ramping" up of saccades moving in the forward direction. This seems to be a nice demonstration the method can measure shifts of attention at a fine temporal resolution and differentiate past from future-oriented saccades due to the orthogonal cue approach. The second analysis shown in Figure 2, reveals a dependency in saccade direction such that saccades toward the probe future were more likely also to be toward the encoded location than away from the encoded direction. This suggests saccades are jointly biased by both locations "in memory".

Weaknesses:<br /> 1. The "central contribution" (as the authors characterize it) is that "the brain simultaneously retains the copy of both past and future-relevant locations in working memory, and (re)activates each during mnemonic selection", and that: "... while it is not surprising that the future location is considered, it is far less trivial that both past and future attributes would be retained and (re)activated together. This is our central contribution." However, to succeed at the task, participants must retain the content (grating orientation, past) and probe location (future) in working memory during the delay period. It is true that the location of the grating is functionally irrelevant once the cue is shown, but if we assume that features of a visual object are bound in memory, it is not surprising that location information of the encoded object would bias processing as indicated by microsaccades. Here the authors claim that joint representation of past and future is "far less trivial", this needs to be evaluaed from the standpoint of prior empirical data on memory decay in such circumstances, or some reference to the time-course of the "unbinding" of features in an encoded object.

2. The authors refer to "future" and "past" information in working memory and this makes sense at a surface level. However, once the retrocue is revealed, the "rule" is retrieved from long-term memory, and the feature (e.g. right/left, top/bottom) is maintained in memory like any other item representation. Consider the classic test of digit span. The digits are presented and then recalled. Are the digits of the past or future? The authors might say that one cannot know, because past and future are perfectly confounded. An alternative view is that some information in working memory is relevant and some is irrelevant. In the digit span task, all the digits are relevant. Relevant information is relevant precisely because it is thought be necessary in the future. Irrelevant information is irrelevant precisely because it is not thought to be needed in the immediate future. In the current study, the orientation of the grating is relevant, but its location is irrelevant; and the location of the test probe is also relevant.

3. It is not clear how the authors interpret the "joint representation" of past and future. Put aside "future" and "past" for a moment. If there are two elements in memory, both of which are associated with spatial bindings, the attentional focus might be a spatial average of the associated spatial indices. One might also view this as an interference effect, such that the location of the encoded location attracts spatial attention since it has not been fully deleted/removed from working memory. Again, for the impact of the encoded location to be exactly zero after the retrieval cue, requires zero interference or instantaneous decay of the bound location information. It would be helpful for the authors to expand their discussion to further explain how the results fit within a broader theoretical framework and how it fits with empirical data on how quickly an irrelevant feature of an object can be deleted from working memory.

Reviewer #2 (Public Review):

This MEG study used co-registered eye-tracking and Rapid Invisible Frequency Tagging (RIFT) to track the effects of semantic parafoveal preview during natural sentence reading. Unpredictable target words could either be congruent or incongruent with sentence context. This modulated the RIFT response already while participants were fixating on the preceding word. This indicates that the semantic congruency of the upcoming word modulates visual attention demands already in parafoveal preview.<br /> The quest for semantic parafoveal preview in natural reading has attracted a lot of attention in recent years, especially with the development of co-registered EEG and MEG. Evidence from dynamic neuroimaging methods using innovative paradigms as in this study is important for this debate.

Major points:<br /> 1) The authors frame their study in terms of "congruency with sentence context". However, it is the congruency between adjective-noun pairs that determines congruency (e.g. "blue brother" vs "blue jacket", and examples p. 16 and appendix). This is confirmed by Suppl Figure 1, which shows a significantly larger likelihood of refixations to the pre-target word for incongruent sentences, probably because the pre-target word is most diagnostic for the congruency of the target word. The authors discuss some possibilities as to why there is variability in parafoveal preview effects in the literature. It is more likely to see effects for this simple and local congruency, rather than congruency that requires an integration and comprehension of the full sentence. I'm not sure whether the authors really needed to present their stimuli in a full-sentence context to obtain these effects. This should be explicitly discussed and also mentioned in the introduction (or even the abstract).

2) The authors used MEG and provided a source estimate for the tagging response (Figure 2), which unsurprisingly is in the visual cortex. The most important results are presented at the sensor level. This does not add information about the brain sources of the congruency effect, as the RIFT response probably reflects top-down effects on visual attention etc. Was it necessary to use MEG? Would EEG have produced the same results? In terms of sensitivity, EEG is better than MEG as it is more sensitive to radial and deeper sources. This should be mentioned in the discussion and/or methods section.

3) The earliest semantic preview effects occurred around 100ms after fixating the pre-target word (discussed around l. 323). This means that at this stage the brain must have processed the pre-target and the target word and integrated their meanings (at some level). Even in the single-word literature, semantic effects at 100 ms are provocatively early. Even studies that tried to determine the earliest semantic effects arrived at around 200 ms (e.g. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3382728/, https://psycnet.apa.org/record/2013-17451-002). The present results need to be discussed in a bit more detail in the context of the visual word recognition literature.

4) As in previous EEG/MEG studies, the authors found a neural but no behavioural preview effect. As before, this raises the question of whether the observed effect is really "critical" for sentence comprehension. The authors provide a correlation analysis with reading speed, but this does not allow causal conclusions: Some people may simply read slowly and therefore pay more attention and get a larger preview response. Some readers may hurry and therefore not pay attention and not get a preview response. In order to address this, one would have to control for reading speed and show an effect of RIFT response on comprehension performance (or vice versa, with a task that is not close to ceiling performance). The last sentence of the discussion is currently not justified by the results.

5) L. 577f.: ICA components were selected by visual inspection. I would strongly recommend including EOG in future recordings when the control of eye movements is critical.

6) The authors mention "saccade planning" a few times. I would suggest looking at the SWIFT model of eye movement control, which is less mechanistic than the dominant EZ-Reader model (https://psycnet.apa.org/record/2005-13637-003). It may be useful for the framing of the study and interpretation of the results (e.g. second paragraph of discussion).

Reviewer #2 (Public Review):

Summary:<br /> The authors combined inhibitory neurostimulation (continuous theta-burst stimulation, cTBS) with subsequent MRI measurements to investigate the impact of inhibition of the left anterior temporal lobe (ATL) on task-related activity and performance during a semantic task and link stimulation-induced changes to the neurochemical level by including MR spectroscopy (MRS). cTBS effects in the ATL were compared with a control site in the vertex. The authors found that relative to stimulation of the vertex, cTBS significantly increased the local GABA concentration in the ATL. cTBS also decreased task-related semantic activity in the ATL and potentially delayed semantic task performance by hindering a practice effect from pre to post. Finally, pooled data from their previous MRS study suggest an inverted U-shape between GABA concentration and behavioral performance. These results help to better understand the neuromodulatory effects of non-invasive brain stimulation on task performance.

Strengths:<br /> Multimodal assessment of neurostimulation effects on the behavioral, neurochemical, and neural levels. In particular, the link between GABA modulation and behavior is timely and potentially interesting.

Weaknesses:<br /> The analyses are not sound. Some of the effects are very weak and not all conclusions are supported by the data since some of the comparisons are not justified. There is some redundancy with a previous paper by the same authors, so the novelty and contribution to the field are overall limited. A network approach might help here.

Reviewer #2 (Public Review):

Summary:<br /> This study examined the longitudinal brain-behaviour link between attentional neural filtering and listening behaviour among a sample of aging individuals. The results based on the latent change score modeling showed that neither attentional neural filtering at T1 nor its T1-T2 change predicted individual two-year listening performance change. The findings suggest that neural filtering and listening behaviour may follow independent developmental trajectories. This study focuses on an interesting topic and has the potential to contribute a better understanding of the neurobiological mechanisms of successful communication across the lifespan.

Strengths:<br /> Although research suggests that speech comprehension is neurally supported by an attention-guided filter mechanism, the evidence of their causal association is limited. This study addresses this gap by testing the longitudinal stability of neural filtering as a neural mechanism upholding listening performance, potentially shedding light on translational efforts aiming at the preservation of speech comprehension abilities among aging individuals.

The latent change score modeling approach is appropriately used as a tool to examine key developmental questions and distinguish the complex processes underlying lifespan development in brain and behaviour with longitudinal data.

Weaknesses:<br /> Although the paper does have strengths in principle, the weaknesses of the paper are that the findings are merely based on a single listening task. Since both neural and behavioral indicators are derived from the same task, the results may be applicable only to this specific task, and it is difficult to extrapolate them to cognitive and listening abilities measured by the other tasks. Therefore, more listening tasks are required to comprehensively measure speech comprehension and neural markers.

The age span of the sample is relatively large. Although no longitudinal change from T1 to T2 was found at the group-level, from the cross-sectional and longitudinal change results (see Figure 3), individuals of different age groups showed different development patterns. Particularly, individuals over the age of 70 show a clear downward trend in both neural filtering index and accuracy. Therefore, different results may be found based on different age groups, especially older groups. However, due to sample limitations, this study was unable to examine whether age has a moderating effect on this brain-behaviour link.

In the Dichotic listening task, valid and invalid cues were manipulated. According to the task description, the former could invoke selective attention, whereas the latter could invoke divided attention. It is possible that under the two conditions, the neural filtering index may reflect different underlying cognitive processes, and thus may differ in its predictive effect on behavioral performance. The author could perform a more in-depth data analysis on indicators under different conditions.

Reviewer #2 (Public Review):

Summary: The authors tried to identify novel adult functions of the classical Drosophila juvenile-adult transition axis (i.e. ptth-ecdysone). Surprisingly, larval ptth-expressing neurons expressed the sex-specific doublesex gene, thus belonging to the sexual dimorphic circuit. Lack of ptth during late larval development caused enhanced female sexual receptivity, an effect rescued by supplying ecdysone in the food. Among many other cellular players, pC1 neurons control receptivity by encoding the mating status of females. Interestingly, during metamorphosis, a subtype of pC1 neurons required Ecdysone Receptor A in order to regulate such female receptivity. A transcriptomic analysis using pC1-specific Ecdyone signaling down-regulation gives some hints of possible downstream mechanisms.

Strengths: the manuscript showed solid genetic evidence that lack of ptth during development caused enhanced copulation rate in female flies, which includes ptth mutant rescue experiments by over-expressing ptth as well as by adding ecdysone-supplemented food. They also present elegant data dissecting the temporal requirements of ptth-expressing neurons by shifting animals from non-permissive to permissive temperatures, in order to inactivate neuronal function (although not exclusively ptth function). By combining different drivers together with a EcR-A RNAi line authors also identified the Ecdysone receptor requirements of a particular subtype of pC1 neurons during metamorphosis. Convincing live calcium imaging showed no apparent effect of EcR-A in neural activity, although some effect on morphology is uncovered. Finally, bulk RNAseq shows differential gene expression after EcR-A down-regulation.

Weaknesses: the paper has three main weaknesses. The first one refers to temporal requirements of ptth and ecdysone signaling. Whereas ptth is necessary during larval development, the ecdysone effect appears during pupal development. ptth induces ecdysone synthesis during larval development but there is no published evidence about a similar role for ptth during pupal stages. Furthermore, larval and pupal ecdysone functions are different (triggering metamorphosis vs tissue remodeling). The second caveat is the fact that ptth and ecdysone loss-of-function experiments render opposite effects (enhancing and decreasing copulation rates, respectively). The most plausible explanation is that both functions are independent of each other, also suggested by differential temporal requirements. Finally, in order to identify the effect in the transcriptional response of down-regulating EcR-A in a very small population of neurons, a scRNAseq study should have been performed instead of bulk RNAseq.

In summary, despite the authors providing convincing evidence that ptth and ecdysone signaling pathways are involved in female receptivity, the main claim that ptth regulates this process through ecdysone is not supported by results. More likely, they'd rather be independent processes.

Reviewer #2 (Public Review):

Summary:<br /> The sleep patterns of animals are adaptable, with shorter sleep durations in the winter and longer sleep durations in the summer. Chen and colleagues conducted a study using Drosophila (fruit flies) and discovered that a circadian photoreceptor called cryptochrome (cry) plays a role in reducing sleep duration during day/night cycles resembling winter conditions. They also found that cry functions in specific GABAergic circadian pacemaker cells known as s-LNvs inhibit these neurons, thereby promoting wakefulness in the animals in the winter. They also identified l-LNvs, known as arousal-promoting cells, as the downstream neurons.

Strengths:<br /> Detailed mapping of the neural circuits cry acts to mediate the shortened sleep in winter-like day/night cycles.

Weaknesses:<br /> The supporting evidence for s-LNvs being GABAergic neurons is not particularly strong. Additionally, there is a lack of direct evidence regarding changes in neural activity for s-LNvs and l-LNvs under varying day/night cycles, as well as in cry mutant flies.

Reviewer #2 (Public Review):

Summary:<br /> Klug et al. use monosynaptic rabies tracing of inputs to D1- vs D2-SPNs in the striatum to study how separate populations of cortical neurons project to D1- and D2-SPNs. They use rabies to express ChR2, then patch D1-or D2-SPNs to measure synaptic input. They report that cortical neurons labeled as D1-SPN-projecting preferentially project to D1-SPNs over D2-SPNs. In contrast, cortical neurons labeled as D2-SPN-projecting project equally to D1- and D2-SPNs. They go on to conduct pathway-specific behavioral stimulation experiments. They compare direct optogenetic stimulation of D1- or D2-SPNs to stimulation of MCC inputs to DMS and M1 inputs to DLS. In three different behavioral assays (open field, intra-cranial self-stimulation, and a fixed ratio 8 task), they show that stimulating MCC or M1 cortical inputs to D1-SPNs is similar to D1-SPN stimulation, but that stimulating MCC or M1 cortical inputs to D2-SPNs does not recapitulate the effects of D2-SPN stimulation (presumably because both D1- and D2-SPNs are being activated by these cortical inputs).

Strengths:<br /> Showing these same effects in three distinct behaviors is strong. Overall, the functional verification of the consequences of the anatomy is very nice to see. It is a good choice to patch only from mCherry-negative non-starter cells in the striatum.

Weaknesses:<br /> One limitation is that all inputs to SPNs are expressing ChR2, so they cannot distinguish between different cortical subregions during patching experiments. Their results could arise because the same innervation patterns are repeated in many cortical subregions or because some subregions have preferential D1-SPN input while others do not. There are also some caveats with respect to the efficacy of rabies tracing. Although they only patch non-starter cells in the striatum, only 63% of D1-SPNs receive input from D1-SPN-projecting cortical neurons. It's hard to say whether this is "high" or "low," but one question is how far from the starter cell region they are patching. Without this spatial indication of where the cells that are being patched are relative to the starter population, it is difficult to interpret if the cells being patched are receiving cortical inputs from the same neurons that are projecting to the starter population. Convergence of cortical inputs onto SPNs may vary with distance from the starter cell region quite dramatically, as other mapping studies of corticostriatal inputs have shown specialized local input regions can be defined based on cortical input patterns (Hintiryan et al., Nat Neurosci, 2016, Hunnicutt et al., eLife 2016, Peters et al., Nature, 2021). A caveat for the optogenetic behavioral experiments is that these optogenetic experiments did not include fluorophore-only controls. Another point of confusion is that other studies (Cui et al, J Neurosci, 2021) have reported that stimulation of D1-SPNs in DLS inhibits rather than promotes movement.

Reviewer #2 (Public Review):

Summary:<br /> The authors investigate replay (defined as sequential reactivation) and clustered reactivation during retrieval of an abstract cognitive map. Replay and clustered reactivation were analysed based on MEG recordings combined with a decoding approach. While the authors state to find evidence for both, replay and clustered reactivation during retrieval, replay was exclusively present in low performers. Further, the authors show that reactivation strength declined with an increasing graph distance.

Strengths:<br /> The paper raises interesting research questions, i.e., replay vs. clustered reactivation and how that supports retrieval of cognitive maps. The paper is well-written, well-structured, and easy to follow. The methodological approach is convincing and definitely suited to address the proposed research questions.

The paper is a great combination between replicating previous findings (Wimmer et al. 2020) with a new experimental approach but at the same time presenting novel findings (reactivation strength declines as a function of graph distance).<br /> What I also want to positively highlight is their transparency. They pre-registered this study but with a focus on a different part of the data and outlined this explicitly in the paper.

The paper has very interesting, individual findings but there are some shortcomings.

Weaknesses:<br /> Even though the individual findings are interesting, it is not easy to grasp how they are related. For example, the authors show that replay is present in low but not in high performers with the assumption that high performers tend to simultaneously reactivate items. But then, the authors do not investigate clustered reactivation (= simultaneous reactivation) as a function of performance (due to ceiling effects for most participants).

Unfortunately, the evidence for clustered reactivation is not well supported by the analysis approach and the observed evidence. The analysis approach still holds the possibility of replay driving the observed clustered reactivation effect.

A third shortcoming is that at least some analyses are underpowered (very low number of trials, n = ~10, and for some analyses, very low number of participants, n = 14). In both cases (low trial number and low participant number) the n could be increased by including the learning part in the analyses as well. It is not clear to me why the authors restricted their analyses to the retrieval period only (especially given that participants also have to retrieve during learning).

Reviewer #2 (Public Review):

This work provides a novel design of implantable and high-density EMG electrodes to study muscle physiology and neuromotor control at the level of individual motor units. Current methods of recording EMG using intramuscular fine-wire electrodes do not allow for isolation of motor units and are limited by the muscle size and the type of behavior used in the study. The authors of myomatrix arrays had set out to overcome these challenges in EMG recording and provided compelling evidence to support the usefulness of the new technology.

Strengths:<br /> • They presented convincing examples of EMG recordings with high signal quality using this new technology from a wide array of animal species, muscles, and behavior.<br /> • The design included suture holes and pull-on tabs that facilitate implantation and ensure stable recordings over months.<br /> • Clear presentation of specifics of the fabrication and implantation, recording methods used, and data analysis

I am satisfied with the authors' response to my previous concerns on the weaknesses of the study.

Reviewer #2 (Public Review):

Summary: The authors provide a nice summary on the possibility to study genetic heterogeneity and how to measure the dynamics of stem cells. By combining single cell and bulk sequencing analyses, they aim to use a stochastic process and inform on different aspects of genetic heterogeneity.

Strengths: Well designed study and strong methods.

Reviewer #2 (Public Review):

Summary:<br /> An article with lots of interesting ideas and questions regarding the evolution of timing of dormancy, emphasizing mammalian hibernation but also including ectotherms. The authors compare selective forces of constraints due to energy availability versus predator avoidance and requirements and consequences of reproduction in a review of between and within species (sex) differences in the seasonal timing of entry and exit from dormancy.

Strengths:<br /> The multispecies approach including endotherms and ectotherms is ambitious. This review is rich with ideas if not in convincing conclusions.

Weaknesses:<br /> The differences between physiological requirements for gameatogenesis between sexes that affect the timing of heterothermy and the need for euthermy during mammalian hibernator are significant issues that underlie but are under-discussed, in this contrast of selective pressures that determine seasonal timing of dormancy. Some additional discussion of the effects of rapid climate change on between and within species phenologies of dormancy would have been interesting.

Reviewer #2 (Public Review):

This study focuses on the eco-morphology, the feeding behaviors, and the co-evolution of feeding organs of longirostrine gomphotheres (Amebelodontidae, Choerolophodontidae, and Gomphotheriidae) which are characterised by their distinctive mandible and mandible tusk morphologies. They also have different evolutionary stages of food acquisition organs which may have co-evolve with extremely elongated mandibular symphysis and tusks. Although these three longirostrine gomphothere families were widely distributed in Northern China in the Early-Middle Miocene, the relative abundances and the distribution of these groups were different through time as a result of the climatic changes and ecosysytems.

These three groups have different feeding behaviors indicated by different mandibular symphysis and tusk morphologies. Additionally, they have different evolutionary stages of trunks which are reflected by the narial region morphology. To be able to construct the feeding behavior and the relation between the mandible and the trunk of early elephantiformes, the authors examined the crania and mandibles of these three groups from the Early and Middle Miocene of northern China from three different museums and also made different analyses.

The analyses made in the study are:<br /> 1. Finite Element (FE) analysis: They conducted two kinds of tests: the distal forces test, and the twig-cutting test. With the distal forces test, advantageous and disadvantageous mechanical performances under distal vertical and horizontal external forces of each group are established. With the twig-cutting test, a cylindrical twig model of orthotropic elastoplasity was posed in three directions to the distal end of the mandibular task to calculate the sum of the equivalent plastic strain (SEPS). It is indicated that all three groups have different mandible specializations for cutting plants.

2. Phylogenetic reconstruction: These groups have different narial region morphology, and in connection with this, have different stages of trunk evolution. The phylogenetic tree shows the degree of specialization of the narial morphology. And narial region evolutionary level is correlated with that of character-combine in relation to horizontal cutting. In the trilophodont longirostrine gomphotheres, co-evolution between the narial region and horizontal cutting behaviour is strongly suggested.

3. Enamel isotopes analysis: The results of stable isotope analysis indicate an open environment with a diverse range of habitats and that the niches of these groups overlapped without obvious differentiation.

The analysis shows that different eco-adaptations have led to the diverse mandibular morphology and open-land grazing has driven the development of trunk-specific functions and loss of the long mandible. This conclusion has been achieved with evidence on palaecological reconstruction, the reconstruction of feeding behaviors, and the examination of mandibular and narial region morphology from the detailed analysis during the study.

All of the analyses are explained in detail in the supplementary files. The 3D models and movies in the supplementary files are detailed and understandable and explain the conclusion. The conclusions of the study are well supported by data.

Reviewer #2 (Public Review):

This study addresses the catalytic activity of a Ras-like ROC GTPase domain of LRRK2 kinase, a Ser/Thr kinase linked to Parkinson's disease (PD). The enzyme is associated with gain-of-function variants that hyper-phosphorylate substrate Rab GTPases. However, the link between the regulatory ROC domain and activation of the kinase domain is not well understood.

It is within this context that the authors detail the kinetics of the ROC GTPase domain of pathogenic variants of LRRK2, in comparison to the WT enzyme. Their data suggest that LRRK2 kinase activity negatively regulates the ROC GTPase activity and that PD variants of LRRK2 have differential effects on the Km and catalytic efficiency of GTP hydrolysis.

Based on mutagenesis, kinetics, and biophysical experiments, the authors suggest a model in which autophosphorylation shifts the equilibrium toward monomeric LRRK2 (locked GTP state of ROC). The authors further conclude that T1343 is a crucial regulatory site, located in the P-loop of the ROC domain, which is necessary for the negative feedback mechanism. Unfortunately, the data do not support this hypothesis, and further experiments are required to confirm this model for the regulation of LRRK2 activity.

Specific comments are below:

- Although a couple of papers are cited, the rationale for focusing on the T1343 site is not evident to readers. It should be clarified that this locus, and perhaps other similar loci in the wider ROCO family, are likely important for direct interactions with the GTP molecule.

- Similar to the above, readers are kept in the dark about auto-phosphorylation and its effects on the monomer/dimer equilibrium. This is a critical aspect of this manuscript and a major conceptual finding that the authors are making from their data. However, the idea that auto-phosphorylation is (likely) to shift the monomer/dimer equilibrium toward monomer, thereby inactivating the enzyme, is not presented until page 6, AFTER describing much of their kinetics data. This is very confusing to readers, as it is difficult to understand the meaning of the data without a conceptual framework. If the model for the LRRK2 function is that dimerization is necessary for the phosphorylation of substrates, then this idea should be presented early in the introduction, and perhaps also in the abstract. If there are caveats, then they should be discussed before data are presented. A clear literature trail and the current accepted (or consensus) mechanism for LRRK2 activity is necessary to better understand the context for these data.

- Following on the above concepts, I find it interesting that the authors mention monomeric cyotosolic states, and kinase-active oligomers (dimers??), with citations. Again here, it would be useful to be more precise. Are dimers (oligomers?) only formed at the membrane? That would suggest mechanisms involving lipid or membrane-attached protein interactions. Also, what do the authors mean by oligomers? Are there more than dimers found localized to the membrane?

- Fig 5 is a key part of their findings, regarding the auto-phosphorylation induced monomer formation of LRRK2. From these two bar graphs, the authors state unequivocally that the 'monomer/dimer equilibrium is abolished', and therefore, that the underlying mechanism might be increased monomerization (through maintenance of a GTP-locked state). My view is that the authors should temper these conclusions with caveats. One is that there are still plenty of dimers in the auto-phosphorylated WT, and also in the T1343A mutant. Why is that the case? Can the authors explain why only perhaps a 10% shift is sufficient? Secondly, the T1343A mutant appears to have fewer overall dimers to begin with, so it appears to readers that 'abolition' is mainly due to different levels prior to ATP treatment at 30 deg. I feel these various issues need to be clarified in a revised manuscript, with additional supporting data. Finally, on a minor note, I presume that there are no statistically significant differences between the two sets of bar graphs on the right panel. It would be wise to place 'n.s.' above the graphs for readers, and in the figure legend, so readers are not confused.

- Figure 6B, Westerns of phosphorylation, the lanes are not identified and it is unclear what these data mean.

Reviewer #2 (Public Review):

Summary:<br /> HCN-4 isoform is found primarily in the sino-atrial node where it contributes to the pacemaking activity. LRMP is an accessory subunit that prevents cAMP-dependent potentiation of HCN4 isoform but does not have any effect on HCN2 regulation. In this study, the authors combine electrophysiology, FRET with standard molecular genetics to determine the molecular mechanism of LRMP action on HCN4 activity. Their study shows that parts of N- and C-termini along with specific residues in C-linker and S5 of HCN4 are crucial for mediating LRMP action on these channels. Furthermore, they show that the initial 224 residues of LRMP are sufficient to account for most of the activity. In my view, the highlight of this study is Fig. 7 which recapitulates LRMP modulation on HCN2-HCN4 chimera. Overall, this study is an excellent example of using time-tested methods to probe the molecular mechanisms of regulation of channel function by an accessory subunit.

Weaknesses:<br /> 1. Figure 5A- I am a bit confused with this figure and perhaps it needs better labeling. When it states Citrine, does it mean just free Citrine, and "LRMP 1-230" means LRMP fused to Citrine which is an "LF" construct? Why not simply call it "LF"? If there is no Citrine fused to "LRMP 1-230", this figure would not make sense to me.

2. Related to the above point- Why is there very little FRET between NF and LRMP 1-230? The FRET distance range is 2-8 nm which is quite large. To observe baseline FRET for this construct more explanation is required. Even if one assumes that about 100 amino are completely disordered (not extended) polymers, I think you would still expect significant FRET.

3. Unless I missed this, have all the Cerulean and Citrine constructs been tested for functional activity?

Reviewer #2 (Public Review):

Summary and strengths:

O'Brien et al. present a compelling strategy to both understand rare disease that could have a neuronal focus and discover drugs for repurposing that can affect rare disease phenotypes. Using C. elegans, they optimize the Brown lab worm tracker and Tierpsy analysis platform to look at the movement behaviors of 25 knockout strains. These gene knockouts were chosen based on a process to identify human orthologs that could underlie rare diseases. I found the manuscript interesting and a powerful approach to making genotype-phenotype connections using C. elegans. Given the rate at which rare Mendelian diseases are found and candidate genes suggested, human geneticists need to consider orthologous approaches to understand the disease and seek treatments on a rapid time scale. This approach is one such way. Overall, I have a few minor suggestions and some specific edits.

Weaknesses:<br /> (1) Throughout the text on figures, labels are nearly impossible to read. I had to zoom into the PDF to determine what the figure was showing. Please make text in all figures a minimum of 10-point font. Similarly, the Figure 2D point type is impossible to read. Points should be larger in all figures. Gene names should be in italics in all figures, following C. elegans convention.

(2) I have a strong bias against the second point in Figure 1A. Sequencing of trios, cohorts, or individuals NEVER identifies causal genes in the disease. This technique proposes a candidate gene. Future experiments (oftentimes in model organisms) are required to make those connections to causality. Please edit this figure and parts of the text.

(3) How were the high-confidence orthologs filtered from 767 to 543 (lines 128-131)? Also, the choice of the final list of 25 genes is not well justified. Please expand more about how these choices were made.

(4) Figures 3 and 4, why show all 8289 features? It might be easier to understand and read if only the 256 Tierpsy features were plotted in the heat maps.

(5) The unc-80 mutant screen is clever. In the feature space, it is likely better to focus on the 256 less-redundant Tierpsy features instead of just a number of features. It is unclear to me how many of these features are correlated and not providing more information. In other words, the "worsening" of less-redundant features is far more of a concern than the "worsening" of 1000 correlated features.

Reviewer #2 (Public Review):

Summary:

This manuscript builds from the interesting observation that local recruitment of the DHPH domain of the RhoGEF PRG can induce local retraction, protrusion, or neither. The authors convincingly show that these differential responses are tied to the level of expression of the PRG transgene. This response depends on the Rho-binding activity of the recruited PH domain and is associated with and requires (co?)-activation of Cdc42. This begs the question of why this switch in response occurs. They use a computational model to predict that the timing of protein recruitment can dictate the output of the response in cells expressing intermediate levels and found that, "While the majority of cells showed mixed phenotypes irrespectively of the activation pattern, in few cells (3 out of 90) we were able to alternate the phenotype between retraction and protrusion several times at different places of the cell by changing the frequency while keeping the same total integrated intensity (Figure 6F and Supp Movie)."

Strengths:

The experiments are well-performed and nicely documented. However, the molecular mechanism underlying the shift in response is not clear (or at least clearly described). In addition, it is not clear that a prediction that is observed in ~3% of cells should be interpreted as confirming a model, though the fit to the data in 6B is impressive.

Overall, the main general biological significance of this work is that RhoGEF can have "off target effects". This finding is significant in that an orthologous GEF is widely used in optogenetic experiments in drosophila. It's possible that these findings may likewise involve phenotypes that reflect the (co-)activation of other Rho family GTPases.

Weaknesses:

The manuscript makes a number of untested assumptions and the underlying mechanism for this phenotypic shift is not clearly defined.

This manuscript is missing a direct phenotypic comparison of control cells to complement that of cells expressing RhoGEF2-DHPH at "low levels" (the cells that would respond to optogenetic stimulation by retracting); and cells expressing RhoGEF2-DHPH at "high levels" (the cells that would respond to optogenetic stimulation by protruding). In other words, the authors should examine cell area, the distribution of actin and myosin, etc in all three groups of cells (akin to the time zero data from figures 3 and 5, with a negative control). For example, does the basal expression meaningfully affect the PRG low-expressing cells before activation e.g. ectopic stress fibers? This need not be an optogenetic experiment, the authors could express RhoGEF2DHPH without SspB (as in Fig 4G).

Relatedly, the authors seem to assume ("recruitment of the same DH-PH domain of PRG at the membrane, in the same cell line, which means in the same biochemical environment." supplement) that the only difference between the high and low expressors are the level of expression. Given the chronic overexpression and the fact that the capacity for this phenotypic shift is not recruitment-dependent, this is not necessarily a safe assumption. The expression of this GEF could well induce e.g. gene expression changes.

The third paragraph of the introduction, which begins with the sentence, "Yet, a large body of works on the regulation of GTPases has revealed a much more complex picture with numerous crosstalks and feedbacks allowing the fine spatiotemporal patterning of GTPase activities" is potentially confusing to readers. This paragraph suggests that an individual GTPase may have different functions whereas the evidence in this manuscript demonstrates, instead, that *a particular GEF* can have multiple activities because it can differentially activate two different GTPases depending on expression levels. It does not show that a particular GTPase has two distinct activities. The notion that a particular GEF can impact multiple GTPases is not particularly novel, though it is novel (to my knowledge) that the different activities depend on expression levels.

These descriptions are not precise. What is the nature of the competition between RhoA and Cdc42? Is this competition for activation by the GEFs? Is it a competition between the phenotypic output resulting from the effectors of the GEFs? Is it competition from the optogenetic probe and Rho effectors and the Rho biosensors? In all likelihood, all of these effects are involved, but the authors should more precisely explain the underlying nature of this phenotypic switch. Some of these points are clarified in the supplement, but should also be explicit in the main text.

Reviewer #2 (Public Review):

Although Trabid missense mutations are identified across a range of neurodevelopmental disorders, its role in neurodevelopment is not understood. Here the authors study two different patient mutations and implicate defects in its deubiquitylating activity and interactions with STRIPAK. Knockin mice for these mutations impaired trafficking of APC to microtubule plus ends, with consequent defects in neuronal growth cone and neurite outgrowth.

The authors focus on R438W and A451V, two missense mutations seen in patients. Recombinant fragments showed R438W is nearly completely DUB-dead whereas A451V showed normal activity but failed to efficiently precipitate STRIPAK. Knockin of these mutations showed a partially penetrant reduced cortical neuronal and glial cell numbers and reduced TH+ neurons and their neuronal processes. Cell culture demonstrated that both DUB and STRIPAK-binding activities of Trabid are required for efficient deubiquitylation of APC in cells, and alter APC transport along neurites. APC-tdTomato fluorescent reporter mice crossed with the Trabid mutants confirmed these results. The results suggest that Trabid's mechanism of action is to suppress APC ubiquitylation to regulate its intracellular trafficking and neurite formation.

Reviewer #2 (Public Review):

Summary:<br /> The present work addresses the mechanisms linking the sex-dependent temporal GH secretion patterns to the robust sex differences in chromatin accessibility and transcription factor binding that ultimately regulate sexually dimorphic liver gene expression. Using DNAseq analysis genomic sites hypersensitive to cleavage by DNase I, DNase hypersensitive sites [DHS] were studied in hepatocytes from male and female mice. DHS in the genome correspond to accessible chromatin regions and encompass key regulatory elements, including enhancers, promoters, insulators, and silencers, often flanked by specific histone modifications, and all of these players were described in different settings of GH action. Importantly, the dynamics of sex-dependent and independent chromatin accessibility linked to STAT5 binding were evaluated. For that purpose, hepatic samples from mice were divided into STAT high and STAT low binding by EMSA screening. With this information changes in DHS related to STAT binding were calculated in both sexes, giving an approximation of chromatin opening in response to STAT5, or alternatively to hypophsectomy, or a single GH pulse. More the 800 male-biased DHS (from a total of more than 70000 DHS) regions were identified in the STAT5 high groups, implying that the binding of a plasma GH pulse activates STAT5, and evokes a dynamic cycle of male liver chromatin opening and closing at sites that comprised 31% of all male-biased DHS. This proves that the pulsatility of plasma GH stimulation confers significant male bias in chromatin accessibility, and STAT5 binding at a fraction of the genomic sites linked to sex-biased liver gene expression and liver disease. As a proof of concept, authors show that a single physiological replacement dose or pulse of GH given to hypophysectomized mice recapitulate, within 30 min, the pulsatile re-opening of chromatin seen in pituitary-intact male mouse liver.

In another male-biased DHS set (69% of male-biased DHS), chromatin accessibility was static, that is unchanged across the peaks and valleys of GH-induced liver STAT5 activity and mapped to a set of target genes and processes distinct though sometimes overlapping those of the dynamic male-biased DHS.

In view of these distinct dynamic and static DHS in males, authors evaluated key epigenetic features distinguishing the dynamic STAT5-driven mechanism of chromatin opening from that of static male-biased DHS, which are constitutively open in the male liver but closed in the female liver. The analysis of histone marks enriched at each class of sex-biased DHS indicated exquisite differences in the epigenetic mechanisms that mediate sex-specific gene repression in each sex. For example, H3K27me3 and H3K9me3, two widely used repressive histone marks, are used in a unique way in each sex to enforce sex differences in chromatin states at sex-biased DHS.

Finally, the work recapitulates and explains the classifications of sex dimorphic genes made in previous works. Sex-biased and pituitary hormone-dependent DHS act as regulatory elements with a positive enhancer potential, to induce or maintain gene expression in the intact liver by sustaining an open chromatin in the case of class I male-biased DHS and class I male-biased genes in the male liver. Contrariwise DHS may participate in the inhibition of gene expression by maintaining a closed chromatin state, as in the case of class II male-biased DHS and class II female-biased genes in male liver.

These results as a whole present a complex mechanism by which GH regulates the sexual dimorphism of liver genes in order to cope with the metabolic needs of each sex. In a complete story, the information on chromatin accessibility, histone modification, and transcription factor binding was integrated to elucidate the complex patterns of transcriptional regulation, which is sexually dimorphic in the liver.

Strengths:<br /> The work presents a novel insight into the fundamental underlying epigenetic mechanisms of sex-biased gene regulation.<br /> Results are supported by numerous Tables, and Supplementary Tables with the raw data, which present the advantage that they may be reanalyzed in the future to prove new hypotheses.

Weaknesses<br /> It is a complicated work to analyze, even though the main messages are clearly conveyed.

Reviewer #2 (Public Review):

Summary:

The manuscript by Morel et al. aims to identify some potential mechano-regulators of transendothelial cell macro-aperture (TEM). Guided by the recognized role of caveolar invaginations in buffering the membrane tension of cells, the authors focused on caveolin-1 and associated regulator PTRF. They report a comprehensive in vitro work based on siRNA knockdown and optical imaging approach complemented with an in vivo work on mice, a biophysical assay allowing measurement of the mechanical properties of membranes, and a theoretical analysis inspired by soft matter physics.

Strengths:

The authors should be complimented for this multi-faceted and rigorous work. The accumulation of pieces of evidence collected from each type of approach makes the conclusion drawn by the authors very convincing, regarding the new role of cavolin-1 as an individual protein instead of the main molecular component of caveolae. On a personal note, I was very impressed by the quality of STORM images (Fig. 2) which are very illuminating and useful, in particular for validating some hypotheses of the theoretical analysis.

Weaknesses:

While this work pins down the key role of caveolin-, its mechanism remains to be further investigated. The hypotheses proposed by the authors in the discussions about the link between caveolin and lipids/cholesterol are very plausible though challenging. Even though we may feel slightly frustrated by the absence of data in this direction, the quality and merit of this paper remain.

- The analogy with dewetting processes drawn to derive the theoretical model is very attractive. However, although part of the model has already been published several times by the same group of authors, the definition of the effective membrane rigidity of a plasma membrane including the underlying actin cortex, was very vague and confusing. Here, for the first time, thanks to the STORM analysis, the authors show that HUVECs intoxicated by ExoC3 exhibit a loose and defective cortex with a significantly increased mesh size. This argues in favor of the validity of Helfrich formalism in this context. Nonetheless, there remains a puzzle. Experimentally, several TEMs are visible within one cell. Theoretically, the authors consider a simultaneous opening of several pores and treat them in an additive manner. However, when one pore opens, the tension relaxes and should prevent the opening of subsequent pores. Yet, experimentally, as seen from the beautiful supplementary videos, several pores open one after the other. This would suggest that the tension is not homogeneous within an intoxicated cell or that equilibration times are long. One possibility is that some undegraded actin pieces of the actin cortex may form a barrier that somehow isolates one TEM from a neighboring one. Could the authors look back at their STORM data and check whether intoxicated cells do not exhibit a bimodal population of mesh sizes and possibly provide a mapping of mesh size at the scale of a cell? In particular, it is quite striking that while bending rigidity of the lipid membrane is expected to set the maximal size of the aperture, most TEMs are well delimited with actin rings before closing. Is it because the surrounding loose actin is pushed back by the rim of the aperture? Could the authors better explain why they do not consider actin as a player in TEM opening?

- Instead of delegating to the discussion the possible link between caveolin and lipids as a mechanism for the enhanced bending rigidity provided by caveolin-1, it could be of interest for the readership to insert the attempted (and failed) experiments in the result section. For instance, did the authors try treatment with methyl-beta-cyclodextrin that extracts cholesterol (and disrupts caveolar and clathrin pits) but supposedly keeps the majority of the pool of individual caveolins at the membrane?

- Tether pulling experiments on Plasma membrane spheres (PMS) are real tours de force and the results are quite convincing: a clear difference in bending rigidity is observed in controlled and caveolin knock-out PMS. However, one recurrent concern in these tether-pulling experiments is to be sure that the membrane pulled in the tether has the same composition as the one in the PMS body. The presence of the highly curved neck may impede or slow down membrane proteins from reaching the tether by convective or diffusive motion. Could the authors propose an experiment to demonstrate that caveolin-1 proteins are not restricted to the body of the PMS and can access to the nanometric tether?

Reviewer #2 (Public Review):

Summary:

In the manuscript "Recall-Gated Consolidation: A Model for Learning and Memory in Neural Systems," the authors suggest a computational mechanism called recall-gated consolidation, which prioritizes the storage of previously experienced synaptic updates in memory. The authors investigate the mechanism with different types of learning problems including supervised learning, reinforcement learning, and unsupervised auto-associative memory. They rigorously analyse the general mechanism and provide valuable insights into its benefits.

Strengths:

The authors establish a general theoretical framework, which they translate into three concrete learning problems. For each, they define an individual mathematical formulation. Finally, they extensively analyse the suggested mechanism in terms of memory recall, consolidation dynamics, and learnable timescales.

The presented model of recall-gated consolidation covers various aspects of synaptic plasticity, memory recall, and the influence of gating functions on memory storage and retrieval. The model's predictions align with observed spaced learning effects.

The authors conduct simulations to validate the recall-gated consolidation model's predictions, and their simulated results align with theoretical predictions. These simulations demonstrate the model's advantages over consolidating any memory and showcase its potential application to various learning tasks.

The suggestion of a novel consolidation mechanism provides a good starting point to investigate memory consolidation in diverse neural systems and may inspire artificial learning algorithms.

Weaknesses:

I appreciate that the authors devoted a specific section to the model's predictions, and point out how the model connects to experimental findings in various model organisms. However, the connection is rather weak and the model needs to make more specific predictions to be distinguishable from other theories of memory consolidation (e.g. those that the authors discuss) and verifiable by experimental data.

While the article extensively discusses the strengths and advantages of the recall-gated consolidation model, it provides a limited discussion of potential limitations or shortcomings of the model, such as the missing feature of generalization, which is part of previous consolidation models. The model is not compared to other consolidation models in terms of performance and how much it increases the signal-to-noise ratio. It is only compared to a simple STM or a parallel LTM, which I understand to be essentially the same as the STM but with a different timescale (so not really an alternative consolidation model). It would be nice to compare the model to an actual or more sophisticated existing consolidation model to allow for a fairer comparison.

The article is lengthy and dense and it could be clearer. Some sections are highly technical and may be challenging to follow. It could benefit from more concise summaries and visual aids to help convey key points.

Reviewer #2 (Public Review):

Summary:<br /> The authors report the results of QM/MM simulations and kinetic measurements for the phosphoryl-transfer step in adenylate kinase. The main assertion of the paper is that a wide transition state ensemble is a key concept in enzyme catalysis as a strategy to circumvent entropic barriers. This assertion is based on the observation of a "structurally wide" set of energetically equivalent configurations that lie along the reaction coordinate in QM/MM simulations, together with kinetic measurements that suggest a decrease in the entropy of activation.

Strengths:<br /> The study combines theoretical calculations and supporting experiments.

Weaknesses:<br /> The role(s) of entropy in enzyme catalysis has been discussed extensively in the literature, from the Circe effect proposed by Jencks and many other works. The current paper hypothesizes a "wide" transition state ensemble as a catalytic strategy and key concept in enzyme catalysis. Overall, it is not clear the degree to which this hypothesis is supported by the data. The reasons are as follows:

1. Enzyme catalysis reflects a rate enhancement with respect to a baseline reaction in solution. In order to assert that something is part of a catalytic strategy of an enzyme, it would be necessary to demonstrate from simulations that the activation entropy for the baseline reaction is indeed greater and the transition state ensemble less "wide". Alternatively stated, when indicating there is a "wide transition state ensemble" for the enzyme system - one needs to indicate that is with respect to the non-enzymatic reaction. However, these simulations were not performed and the comparisons were not demonstrated.

2. The observation of a "wide conformational ensemble" is not a quantitative measure of entropy. In order to make a meaningful computational prediction of the entropic contribution to the activation of free energy, one would need to perform free energy simulations over a range of temperatures (for the enzymatic and non-enzymatic systems). Such simulations were not performed, and the entropy of activation was thus not quantified by the computational predictions.

3. The authors indicate that lid-opening, essential for product release, and not P-transfer is the rate-limiting step in the catalytic cycle and Mg2+ accelerates both steps. How is it certain that the kinetic measurements are reporting on the chemical steps of the reaction, and not other factors such as metal ion binding or conformational changes?

4. The authors explore different starting states for the chemical steps of the reaction (e.g., different metal ion binding and protonation states), and conclude that the most reactive enzyme configuration is the one with the more favorable reaction-free energy barrier. However, it is not clear what is the probability of observing the system in these different states as a function of pH and metal ion concentration without performing appropriate pKa and metal ion binding calculations. This was not done, and hence these results seem somewhat inconclusive.

Reviewer #2 (Public Review):

Summary:<br /> In this work, the authors sought to 1) establish a method for measuring muscle fiber subcellular structure (myofibrils) using common, non-specialized laboratory techniques and equipment, and 2) use this method to provide evidence on whether loading-induced muscle fiber growth was the result of myofibril growth (of existing myofibrils) or myofbrillogenesis (creation of new myofibrils) in mice and humans. The latter is a fundamental question in the muscle field. The authors succeeded in their aims and provided useful methods for the muscle field and detailed insight into muscle fiber hypertrophy; specifically, that loading-induced muscle fiber hypertrophy may be driven mostly by myofibrillogenesis.

Strengths:<br /> 1) The usage of murine and human samples to provide evidence on myofibril hypertrophy vs myofibrillogenesis.<br /> 2) A nice historical perspective on myofibrillogenesis in skeletal muscle.<br /> 3) The description of a useful and tractable IHC imaging method for the muscle biology field supported by extensive validation against electron microscopy.<br /> 4) Fundamental information on how myofiber hypertrophy ensues.

Weaknesses:<br /> 1) The usage of young growing mice (8-10 weeks) versus adult mice (>4 months) in the murine mechanical overload experiments, as well as no consideration for biological sex. The former point is partly curtailed by the adult human data that is provided (male only). Still, the usage of adult mice would be preferable for these experiments given that maturational growth may somehow affect the outcomes. For the latter point, it is not clear whether male or female mice were used.

2) Information on whether myofibrillogenesis is dependent on hypertrophy induced by loading, or just hypertrophy in general. To provide information on this, the authors could use, for instance, inducible Myostatin KO mice (a model where hypertrophy and force production are not always in lockstep) to see whether hypertrophy independent from load induces the same result as muscle loading regarding myofibrillogenesis.

3) Limited information on Type 1 fiber hypertrophy. A "dual overload" model is used for the mouse where the soleus is also overloaded, but presumably, the soleus was too damaged to analyze. Exploring hypertrophy of murine Type 1 fibers using a different model (weight pulling, weighted wheel running, or forced treadmill running) would be a welcome addition.

Reviewer #2 (Public Review):

With the data presented in this manuscript, the authors help complete the set of high-resolution HER2-associated complex heterodimer structures as well as HER4 homodimer structures in the presence of NRG1b and BTC. Purification of HER2-HER4 heterodimers appears to be inherently challenging due to the propensity of HER4 to form homodimers. The authors have used an effective scheme to isolate these HER2-HER4 heterodimers and have employed graphene-oxide grid chemistry to presumably overcome the issues of low sample yield for solving cryo-EM structures of these complexes. The authors conclude HER2-HER4 heterodimers with either ligand are conformationally homogeneous relative to the HER4 homodimers. The HER2-HER4 heterodimers also appear to be better stabilized compared to other published HER2 heterodimers. The ability to model glycans in the context of HER4 homodimers is exciting to see and provides a strong rationale for the stability of these structures. Overall, the work is of great interest and the methods described in this work would benefit a wide variety of structural biology projects.

Major comments-<br /> 1. The HER2-HER4 heterodimer with BTC appears to be the lowest resolution of the reported structures. Although the authors claim the overall structure is similar to the HER2-HER4 heterodimer with NRG1b, it is therefore unclear whether the lower resolution of the BTC is due to challenging data collection conditions, sample preparation, or conformational dynamics not discernible due to the lower resolution. The authors should minimally clarify where they see the possible issues arising for the lower resolution as this is a key aspect of the work.

2. For all maps, authors should display Euler angle plots from their final refinements to assess the degree of preferred orientation. Judging by the sphericity, it appears all the structures, except HER2-HER4-BTC, have well-sampled projection distributions. However, a formal clarification would be useful to the reader.

3. The authors should also include map-model FSCs to ascertain the quality of the map with respect to model building, as this is currently missing in the submission.

Minor comments-<br /> 1. With respect to complex formation, is there a reason why HER2 expression is dramatically lower than HER4?

2. Figures S1e authors should clarify if HER2 substitutions are VR alone or do these include GD substitutions as well. These should be suitably clarified in the main text.

3. The validation reports for all 4 reported structures suggest the user-provided FSC-derived resolutions are different from those calculated by the deposition server. Are the masks deposited significantly different compared to the ones generated within cryoSPARC?

4. For interpretation regarding activation through phosphorylation in Figure 2e, have the authors considered HER4 could homodimerize as well? It appears from the data presented in Figure 4 and S12 that the propensity to form homodimers is greater for HER4 than to heterodimerize with HER2, despite the VR/IQ substitutions. This also appears to be supported by the reasonable amount of signal for pERK in lanes with HER4-IQ alone in the presence of NRG1b. It is recommended that the authors comment on this possibility.

5. In the following line, "NRG1b-induced phosphorylation of HER2, HER4, ERK and AKT was not notably affected by substitution of the HER4 dimerization arm to a GS-arm relative to wild type receptors", it is unclear what the authors mean by wild-type receptors? There is presently no wild-type HER2 and/or HER4 tested in this blot.

6. Considering the asparagine residues can potentially mediate stabilization of HER2-HER4 dimers through glycosylation, the authors should include western blot data for receptor-activation for mutants where glycosylation can be disrupted. This could minimally instruct the reader on how functionally relevant the identified interactions like N576-N358 are.

Reviewer #2 (Public Review):

In bacteria and mammals, metabolically generated aldehydes become toxic at high concentrations because they irreversibly modify the free amino group of various essential biological macromolecules. However, these aldehydes can be present in extremely high amounts in archaea and plants without causing major toxic side effects. This fact suggests that archaea and plants have evolved specialized mechanisms to prevent the harmful effects of aldehyde accumulation.

In this study, the authors show that the plant enzyme DTD2, originating from archaea, functions as a D-aminoacyl-tRNA deacylase. This enzyme effectively removes stable D-aminoacyl adducts from tRNAs, enabling these molecules to be recycled for translation. Furthermore, they demonstrate that DTD2 serves as a broad detoxifier for various aldehydes in vivo, extending its function beyond acetaldehyde, as previously believed. Notably, the absence of DTD2 makes plants more susceptible to reactive aldehydes, while its overexpression offers protection against them. These findings underscore the physiological significance of this enzyme.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript entitled "A 2-HB-mediated feedback loop regulates muscular fatigue" by the Johnson group reports interesting findings with implications for the health benefits of exercise. The authors use a combination of metabolic/biochemical in vivo and in vitro assays to delineate a metabolic route triggered by 2-HB (a relatively stable metabolite induced by exercise in humans and mice) that controls branched-chain amino transferase enzymes and mitochondrial oxidative capacity. Mechanistically, the author shows that 2-HB is a direct inhibitor of BCAT enzymes that in turn control levels of SIRT4 activity and ADp-ribosylation in the nucleus targeting C/EBP transcription factor, affecting BCAA oxidation genes (see Fig 4i in the paper). Overall, these are interesting and novel observations and findings with relevance to human exercise, with the potential implication of using these metabolites to mimic exercise benefits, or conditions or muscular fatigue that occurs in different human chronic diseases including rheumatic diseases or long COVID.

Weaknesses:<br /> There are several experiments/comments that will strengthen the manuscript-

1- A final model in Figure 6 integrating the exercise/mechanistic findings, expanding on Fig 4i) will clarify the findings.

2- In some of the graphs, statistics are missing (e.g Fig 6G).

3- The conclusions on SIRT4 dependency should be carefully written, as it is likely that this is only one potential mechanism, further validation with mouse models would be necessary.

4- One of the needed experiments to support the oxidative capacity effects that could be done in cultured cells, is the use of radiosotope metabolites including BCCAs to determine the ability to produce CO2. Alternatively or in combination metabolite flux using isotopes would be useful to strengthen the current results.

Reviewer #2 (Public Review):

Summary:<br /> Jojoa-Cruz et al. determined a high-resolution cryo-EM structure in the Arabidopsis thaliana (At) OSCA3.1 channel. Based on a structural comparison between OSCA3.1 and OSCA1.2 and the difference between these two paralogs in their mechanosensitivity to poking and membrane stretch, the authors performed structural-guided mutagenesis and tested the roles of three structural domains, including an amphipathic helix, a beam-like domain, and a lipid fenestration site at the pore domain, for mechanosensation of OSCA channels.

Strengths:<br /> The authors successfully determined a structure of the AtOSCA3.1 channel reconstituted in lipid nanodiscs by cryo-EM to a high resolution of 2.6 Å. The high-resolution EM map enabled the authors to observe putative lipid EM densities at various sites where lipid molecules are associated with the channel. Overall, the structural data provides the information for comparison with other OSCA paralogs.

In addition, the authors identified OSCA1.2 mutants that exhibit differential responses to mechanical stimulation by poking and membrane stretch (i.e., impaired response to poke assay but intact response to membrane stretch). This interesting behavior will be useful for further study on differentiating the mechanisms of OSCA activation by distinct mechanical stimuli.

Major weakness:<br /> 1. The major weaknesses of this study are the mutagenesis design and the functional characterization of the three structural domains - an amphipathic helix (AH), a beam-like domain (BLD), and the fenestration site at the pore, in OSCA mechanosensation.

1) First of all, it is confusing to the reviewer, whether the authors set out to test these structural domains as a direct sensor(s) of mechanical stimuli or as a coupling domain(s) for downstream channel opening and closing (gating). The data interpretations are vague in this regard as the authors tend to interpret the effects of mutations on the channel 'sensitivity' to different mechanical stimuli (poking or membrane stretch). The authors ought to dissect the molecular bases of sensing mechanical force and opening/closing (gating) the channel pore domain for the structural elements that they want to study.

Furthermore, the authors relied on the functional discrepancies between OSCA1.2 (sensitive to both membrane poking and stretch) and OSCA3.1 (little or weak sensitivity to poking but sensitive to membrane stretch). But the experimental data presented in the study are not clear to address the mechanisms of channel activation by poking vs. by stretch, and why the channels behave differently.

2) The reviewer questions if the "apparent threshold" of poke-induced membrane displacement and the threshold of membrane stretch are good measures of the change in the channel sensitivity to the different mechanical stimuli.

3) Overall, the mutagenesis design in the various structural domains lacks logical coherence and the interpretation of the functional data is not sufficient to support the authors' hypothesis. Essentially the authors mutated several residues on the hotspot domains, observed some effects on the channel response to poking and membrane stretch, then interpreted the mutated residues/regions are critical for OSCA mechanosensation. Examples are as follows.

In the section "Mutation of key residues in the amphipathic helix", the authors mutated W75 and L80, which are located on the N- and C-terminal of the AH in OSCA1.2, and mutated Pro in the OSCA1.2 AH to Arg at the equivalent position in OSCA3.1 AH. W75 and L80 are conserved between OSCA 1.2 and OSCA3.1. Mutations of W75 and/or L80 impaired OSCA1.2 activation by poking, but not by membrane stretch. In comparison, the wildtype OSCA3.1 which contains W and L at the equivalent position of its AH exhibits little or weak response to poking. The loss of response to poking in the OSCA1.2 W/L mutants does not indicate their roles in poking-induced activation.

Besides, the P2R mutation on OSCA1.2 AH showed no effect on the channel activation by poking, suggesting Arg in OSCA3.1 AH is not responsible for its weak response to poking. Together the mutagenesis of W75, L80, and P2R on OSCA1.2 AH does not support the hypothesis of the role of AH involved in OSCA mechanosensation.

In the section "Replacing the OSCA3.1 BLD in OSCA1.2", the authors replaced the BLD in OSCA 1.2 with that from OSCA3.1, and only observed slightly stronger displacement by poking stimuli. The authors still suggest that BLD "appears to play a role" in the channel sensitivity to poke despite the evidence not being strong.

OSCA1.2 has four Lys residues in TM4 and TM6b at the pore fenestration site, which were shown to interact with the lipid phosphate head group, whereas two of the equivalent residues in OSCA3.1 are Ile. In the section "Substitution of potential lipid-interacting lysine residues", the authors made K435I/K536I double mutant for OSCA1.2 to mimic OSCA3.1 and observed poor response to poking but an intact response to stretch. Did the authors mutate the Ile residues in OSCA3.1 to Lys, and did the mutation confer channel sensitivity to poking stimuli resembling OSCA1.2? The reviewer thinks it is necessary to perform such an experiment, to thoroughly suggest the importance of the four Lys residues in lipid interaction for channel mechanoactivation.

Reviewer #2 (Public Review):

Nucleosome structures inside cells remain unclear. Tan et al. tackled this problem using cryo-ET and 3-D classification analysis of yeast cells. The authors found that the fraction of canonical nucleosomes in the cell could be less than 10% of total nucleosomes. The finding is consistent with the unstable property of yeast nucleosomes and the high proportion of the actively transcribed yeast genome. The authors made an important point in understanding chromatin structure in situ. Overall, the paper is well-written and informative to the chromatin/chromosome field.

Reviewer #2 (Public Review):

Nucleosome structures inside cells remain unclear. Tan et al. tackled this problem using cryo-ET and 3-D classification analysis of yeast cells. The authors found that the fraction of canonical nucleosomes in the cell could be less than 10% of total nucleosomes. The finding is consistent with the unstable property of yeast nucleosomes and the high proportion of the actively transcribed yeast genome. The authors made an important point in understanding chromatin structure in situ. Overall, the paper is well-written and informative to the chromatin/chromosome field.

Reviewer #2 (Public Review):

Summary:

The authors describe five-year outcomes of an internship program for graduate students and postdoctoral fellows at their institution spurred by pilot funding from an NIH BEST grant. They hypothesized that such a program would be beneficial to interns, internship hosts, and research advisors. The mixed methods study used surveys and focus groups to gather qualitative and quantitative data from the stakeholder groups, and the authors acknowledge the limitation that the study subjects were self-selected and also had research advisors who agreed to allow them to participate. Thus the generally favorable outcomes may not be applicable to students such as those who are struggling in the lab and/or lack career focus or supportive research advisors. Nonetheless, the overall findings support the hypothesis and also suggest additional benefits, including in some cases positive impact for the lab, improved communication between the intern and their research advisor, and an advantage for recruitment of students to the institution. The data refute one of the principal concerns of research advisors: that by taking students out of the lab, internships reduce individual and overall lab productivity. Students who did internships were significantly less likely to pursue postdoctoral fellowships before entering the biomedical workforce and were more likely to have science-related careers versus research careers than control students who did not do internships, although the study design cannot determine whether this was due to selection bias or to the internship.

Strengths:

1. The sample size is good (123 internships).

2. The internship program is well described. Outcomes are clearly defined.

3. Methods and statistical analyses appear to be appropriate (although I am not an expert in mixed methods).

4. "Take-home" lessons for institutions considering implementing internship programs are clearly stated.

Weaknesses:

1. It is possible that interns, hosts, and research advisers with positive experiences were more likely to respond to surveys than those with negative experiences. The response rate and potential bias in responses should be discussed in the Results, not just given in a table legend in Methods.

2. With regard to the biased selection of participants, do the authors know many subjects requested but were not permitted to do internships?

3. While the authors mention internships in professional degree programs in fields such as law and business, some mention of internship practices in non-biomedical STEM PhD programs such as engineering or computer science would be helpful. Is biomedical science rediscovering lessons learned when it comes to internships?

4. Figure 1 k, l - internships did not appear to change career goals, but are the 76% who agreed pre-internship the same individuals as the 75% who agreed post-internship? What percentage gave discordant responses?

Appraisal:

Overall the authors achieve their aims of describing outcomes of an internship program for graduate career development and offering lessons learned for other institutions seeking to create their own internship programs.

Impact:

The paper will be very useful for other institutions to dispel some of the concerns of research advisers about internships for PhD students (although not necessarily for postdoctoral fellows). In the long run, wider adoption of internships as part of PhD training will depend not only on faculty buy-in but also on the availability of resources and changes to the graduate school funding model so that such programs are not viewed as another "unfunded mandate" in graduate education. Perhaps the industry will be motivated to support internships by the positive outcomes for hosts reported in this paper. Additionally, NIH could allow a certain amount of F, T, or even RPG funds to be used to support internships for purposes of career development.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors point out that the standard approach of estimating LD is inefficient for datasets with large numbers of SNPs, with a computational cost of O(nm^2), where n is the number of individuals and m is the number of SNPs. Using the known relationship between the LD matrix and the genomic-relatedness matrix, they can calculate the mean level of LD within the genome or across genomic segments with a computational cost of O(n^2m). Since in most datasets, n<<br /> Strengths:<br /> Generally, for computational papers like this, the proof is in the pudding, and the authors appear to have been successful at their aim of producing an efficient computational tool. The most compelling evidence of this in the paper is Figure 2 and Supplementary Figure S2. In Figure 2, they report how well their X-LD estimates of LD compare to estimates based on the standard approach using PLINK. They appear to have very good agreement. In Figure S2, they report the computational runtime of X-LD vs PLINK, and as expected X-LD is faster than PLINK as long as it is evaluating LD for more than 8000 SNPs.

Weakness:<br /> While the X-LD software appears to work well, I had a hard time following the manuscript enough to make a very good assessment of the work. This is partly because many parameters used are not defined clearly or at all in some cases. My best effort to intuit what the parameters meant often led me to find what appeared to be errors in their derivation. As a result, I am left worrying if the performance of X-LD is due to errors cancelling out in the particular setting they consider, making it potentially prone to errors when taken to different contexts.

Impact:<br /> I feel like there is value in the work that has been done here if there were more clarity in the writing. Currently, LD calculations are a costly step in tools like LD score regression and Bayesian prediction algorithms, so a more efficient way to conduct these calculations would be useful broadly. However, given the difficulty I had following the manuscript, I was not able to assess when the authors' approach would be appropriate for an extension such as that.

Reviewer #2 (Public Review):

In this manuscript, Xiaoxuan Lin and colleagues provide new insights into the dynamics of prestin using H/D exchange coupled with mass spectrometry. The authors aim to reveal how local changes in folding upon anion binding sustain the unique electro-transduction capabilities of prestin.

Prestin is an unusual member of the SLC26 family, that changes its cross-sectional area in the membrane upon binding of a chloride ion. In contrast to SLC26 homologs, prestin is not an anion transporter per se but requires an anion to sense voltage. Binding of Cl- at a conserved binding site located between the end of TM3 and TM10 drives the displacement of a conserved arginine (R399), that causes major conformational changes, transmitting the voltage sensing into a mechanical force exerted on the membrane.

Cryo-EM structures are available for the protein bound to various anions, including Cl-, but these structures do not explain how a conserved couple of positive (R399) and negative (the Cl- anion) charge pair transforms voltage sensitivity into mechanical changes in the membrane. To address this challenge, the authors explore local dynamics of the anion binding site and compare it with that of a "real" anion transporter SLC26A9. The authors make a convincing case that the differences in local dynamics they measure are the molecular basis for voltage sensing and its translation into electromotility.

Practically the authors make a thorough HDX-MS investigation of prestin in the presence of different anions Cl-, SO4-, salicylate as well as in the apo form, and provide insight mostly on local dynamics of the anion binding site. The experiments are well-designed and conducted and their quality and reproducibility allows for quantitative interpretation by deriving ΔΔG values of changes in dynamics at specific sites. Furthermore, the authors show by comparing the apo condition with Cl- bound condition that the absence of Cl- causes fraying of the TM3 and TM10 helices. They deduce that Cl- binding allows for directional helix structuration, leading to local structural changes that cause a rearrangement of the charge configuration at the anion binding site that lays the molecular basis for voltage sensitivity. They demonstrate based on a detailed analysis of their HDX data that such helix fraying is a specific feature of the binding site and differs from the cooperative unfolding happening elsewhere on the prestin.

However, the main question that the authors are addressing is how voltage sensitivity translates at the molecular level in the requirement for a negative-positive charge pair. The interpretation that the binding site instability observed only for prestin is a feature required for this voltage dependent is a bit speculative. Could other lines of evidence support the claim that the charge ion gap is reduced upon Cl- binding and that this leads to cross-section area expansion? An obvious option that comes to mind is MD simulations There are differences in time-scale between HDX and simulations, but the propensity for H-bond destabilization can be quantified even at short timescales. It might be that such data is already available out there but it should be explicit in the discussion. The discussion section itself is a bit narrow in scope at the moment. Discussing the data in the context of the available structures would help the non-specialist reader.

Reviewer #2 (Public Review):

In this manuscript, Xiaoxuan Lin and colleagues provide new insights into the dynamics of prestin using H/D exchange coupled with mass spectrometry. The authors aim to reveal how local changes in folding upon anion binding sustain the unique electro-transduction capabilities of prestin.

Prestin is an unusual member of the SLC26 family, that changes its cross-sectional area in the membrane upon binding of a chloride ion. In contrast to SLC26 homologs, prestin is not an anion transporter per se but requires an anion to sense voltage. Binding of Cl- at a conserved binding site located between the end of TM3 and TM10 drives the displacement of a conserved arginine (R399), that causes major conformational changes, transmitting the voltage sensing into a mechanical force exerted on the membrane.

Cryo-EM structures are available for the protein bound to various anions, including Cl-, but these structures do not explain how a conserved couple of positive (R399) and negative (the Cl- anion) charge pair transforms voltage sensitivity into mechanical changes in the membrane. To address this challenge, the authors explore local dynamics of the anion binding site and compare it with that of a "real" anion transporter SLC26A9. The authors make a convincing case that the differences in local dynamics they measure are the molecular basis for voltage sensing and its translation into electromotility.

Practically the authors make a thorough HDX-MS investigation of prestin in the presence of different anions Cl-, SO4-, salicylate as well as in the apo form, and provide insight mostly on local dynamics of the anion binding site. The experiments are well-designed and conducted and their quality and reproducibility allows for quantitative interpretation by deriving ΔΔG values of changes in dynamics at specific sites. Furthermore, the authors show by comparing the apo condition with Cl- bound condition that the absence of Cl- causes fraying of the TM3 and TM10 helices. They deduce that Cl- binding allows for directional helix structuration, leading to local structural changes that cause a rearrangement of the charge configuration at the anion binding site that lays the molecular basis for voltage sensitivity. They demonstrate based on a detailed analysis of their HDX data that such helix fraying is a specific feature of the binding site and differs from the cooperative unfolding happening elsewhere on the prestin.

However, the main question that the authors are addressing is how voltage sensitivity translates at the molecular level in the requirement for a negative-positive charge pair. The interpretation that the binding site instability observed only for prestin is a feature required for this voltage dependent is a bit speculative. Could other lines of evidence support the claim that the charge ion gap is reduced upon Cl- binding and that this leads to cross-section area expansion? An obvious option that comes to mind is MD simulations There are differences in time-scale between HDX and simulations, but the propensity for H-bond destabilization can be quantified even at short timescales. It might be that such data is already available out there but it should be explicit in the discussion. The discussion section itself is a bit narrow in scope at the moment. Discussing the data in the context of the available structures would help the non-specialist reader.

Reviewer #2 (Public Review):

Summary:<br /> Bian et al studied creatine (Cr) in the context of central nervous system (CNS) function. They detected Cr in synaptic vesicles purified from mouse brains with anti-Synaptophysin using capillary electrophoresis-mass spectrometry. Cr levels in the synaptic vesicle fraction were reduced in mice lacking the Cr synthetase AGAT, or the Cr transporter SLC6A8. They provide evidence for Cr release within several minutes after treating brain slices with KCl. This KCl-induced Cr release was partially calcium-dependent and was attenuated in slices obtained from AGAT and SLC6A8 mutant mice. Cr application also decreased the excitability of cortical pyramidal cells in one third of the cells tested. Finally, they provide evidence for SLC6A8-dependent Cr uptake into synaptosomes, and ATP-dependent Cr loading into synaptic vesicles. Based on these data, the authors propose that Cr may act as a neurotransmitter in the CNS.

Strengths:<br /> 1. A major strength of the paper is the broad spectrum of tools used to investigate Cr.<br /> 2. The study provides strong evidence that Cr is present in/loaded into synaptic vesicles.

Weaknesses:<br /> (in sequential order)<br /> 1. Are Cr levels indeed reduced in Agat-/-? The decrease in Cr IgG in Agat-/- (and Agat+/-) is similar to the corresponding decrease in Syp (Fig. 3B). What is the explanation for this? Is the decrease in Cr in Agat-/- significant when considering the drop in IgG? The data should be normalized to the respective IgG control.<br /> 2. The data supporting that depolarization-induced Cr release is SLC6A8 dependent is not convincing because the relative increase in KCl-induced Cr release is similar between SLC6A8-/Y and SLC6A8+/Y (Fig. 5D). The data should be also normalized to the respective controls.<br /> 3. The majority (almost 3/4) of depolarization-induced Cr release is Ca2+ independent (Fig. 5G). Furthermore, KCl-induced, Ca2+-independent release persists in SLC6A8-/Y (Fig. 5G). What is the model for Ca2+-independent Cr release? Why is there Ca2+-independent Cr release from SLC6A8 KO neurons?<br /> How does this relate to the prominent decrease in Ca2+-dependent Cr release in SLC6A8-/Y (Fig. 5G)? They show a prominent decrease in Cr control levels in SLC6A8-/Y in Fig. 5D. Were the data shown in Fig. 5D obtained in the presence or absence of Ca2+? Could the decrease in Ca2+-dependent Cr release in SLC6A8-/Y (Fig. 5G) be due to decreased Cr baseline levels in the presence of Ca2+ (Fig. 5D)?<br /> 4. Cr levels are strongly reduced in Agat-/- (Fig. 6B). However, KCl-induced Cr release persists after loss of AGAT (Fig. 6B). These data do not support that Cr release is Agat dependent.<br /> 5. The authors show that Cr application decreases excitability in ~1/3 of the tested neurons (Fig. 7). How were responders and non-responders defined? What justifies this classification? The data for all Cr-treated cells should be pooled. Are there indeed two distributions (responders/non-responders)? Running statistics on pre-selected groups (Fig. 7H-J) is meaningless. Given that the effects could be seen 2-8 minutes after Cr application - at what time points were the data shown in Fig. 7E-J collected? Is the Cr group shown in Fig. 7F significantly different from the control group/wash?<br /> 6. Indirect effects: The phenotypes could be partially caused by indirect effects of perturbing the Cr/PCr/CK system, which is known to play essential roles in ATP regeneration, Ca2+ homeostasis, neurotransmission, intracellular signaling systems, axonal and dendritic transport... Similarly, high GAMT levels were reported for astrocytes (e.g., Schmidt et al. 2004; doi: 10.1093/hmg/ddh112), and changes in astrocytic Cr may underlie the phenotypes. Cr has been also reported to be an osmolyte: a hyperosmotic shock of astrocytes induced an increase in Cr uptake, suggesting that Cr can work as a compensatory osmolyte (Alfieri et al. 2006; doi: 10.1113/jphysiol.2006.115006). Potential indirect effects are also consistent with a trend towards decreased KCl-induced GABA (and Glutamate) release in SLC6A8-/Y (Fig. 5C). These indirect effects may in part explain the phenotypes seen after perturbing Agat, SLC6A8, and should be thoroughly discussed.<br /> 7. As stated by the authors, there is some evidence that Cr may act as a co-transmitter for GABAA receptors (although only at high concentrations). Would a GABAA blocker decrease the fraction of cells with decreased excitability after Cr exposure?<br /> 8. The statement "Our results have also satisfied the criteria of Purves et al. 67,68, because the presence of postsynaptic receptors can be inferred by postsynaptic responses." (l.568) is not supported by the data and should be removed.

Reviewer #2 (Public Review):

Bian et al studied creatine (Cr) in the context of central nervous system (CNS) function. They detected Cr in synaptic vesicles purified from mouse brains with anti-Synaptophysin using capillary electrophoresis-mass spectrometry. Cr levels in the synaptic vesicle fraction was reduced in mice lacking the Cr synthetase AGAT, or the Cr transporter SLC6A8. They provide evidence for Cr release within several minutes after treating brain slices with KCl. This KCl-induced Cr release was partially calcium dependent and was attenuated in slices obtained from AGAT and SLC6A8 mutant mice. Cr application also decreased the excitability of cortical pyramidal cells in one third of the cells tested. Finally, they provide evidence for SLC6A8-dependent Cr uptake into synaptosomes, and ATP-dependent Cr loading into synaptic vesicles. Based on these data, the authors propose that Cr may act as neurotransmitter in the CNS.

Strengths:

1. A major strength of the paper is the broad spectrum of tools used to investigate Cr.<br /> 2. The study provides evidence that Cr is present in/loaded into synaptic vesicles.

Weaknesses (resubmission):

1. There is no significant decrease in Cr content pulled down by anti-Syp in AGAT-/- mice when normalized to IgG controls. Hence, blocking AGAT activity/Cr synthesis does not affect Cr levels in the synaptic vesicle fraction, arguing against a Cr enrichment.<br /> 2. There is no difference in KCl-induced Cr release between SLC6A8-/Y and SLC6A8+/Y when normalizing the data to the respective controls. Thus, the data are not consistent with the idea that depolarization-induced Cr release requires SLC6A8.<br /> 3. The rationale of grouping the excitability data into responders and non-responders is not convincing because the threshold of 10% decrease in AP rate is arbitrary. The data do therefore not support the conclusion that Cr reduces neuronal excitability.

Reviewer #2 (Public Review):

In this study the authors sought to understand the extent of similarity among species in intraspecific adaptation to environmental heterogeneity at the phenotypic and genetic levels. A particular focus was to evaluate if regions that were associated with adaptation within putative inversions in one species were also candidates for adaptation in another species that lacked those inversions. This study is timely for the field of evolutionary genomics, due to recent interest surrounding how inversions arise and become established in adaptation.

Major strengths

Their study system was well suited to addressing the aims, given that the different species of sunflower all had GWAS data on the same phenotypes from common garden experiments as well as landscape genomic data, and orthologous SNPs could be identified. Organizing a dataset of this magnitude is no small feat. The authors integrate many state-of-the-art statistical methods that they have developed in previous research into a framework for correlating genomic Windows of Repeated Association (WRA, also amalgamated into Clusters of Repeated Association based on LD among windows) with Similarity In Phenotype-Environment Correlation (SIPEC). The WRA/CRA methods are very useful and the authors do an excellent job at outlining the rationale for these methods.

Major weaknesses

The study results rely heavily on the SIPEC measure, but I found the values reported difficult to interpret biologically. For example, in Figure 4 there is a range of SIPEC from 0 to 0.03 for most species pairs, with some pairs only as high as ~0.01. This does not appear to be a high degree of similarity in phenotype-environment correlation. For example, given the equation on line 517 for a single phenotype, if one species has a phenotype-environment correlation of 1.0 and the other has a correlation of 0.02, I would postulate that these two species do not have similar evolutionary responses, but the equation would give a value of (1+0.02)*1*0.02/1 = 0.02 which is pretty typical "higher" value in Figure 4. I also question the logic behind using absolute values of the correlations for the SIPEC, because if a trait increases with an environment in one species but decreases with the environment in another species, I would not predict that the genetic basis of adaptation would be similar (as a side note, I would not question the logic behind using absolute correlations for associations with alleles, due to the arbitrary nature of signing alleles). I might be missing something here, so I look forward to reading the author's responses on these thoughts.

An additional potential problem with the analysis is that from the way the analysis is presented, it appears that the 33 environmental variables were essentially treated as independent data points (e.g. in Figure 4, Figure 5). It's not appropriate to treat the environmental variables independently because many of them are highly correlated. For example in Figure 4, many of the high similarity/CRA values tend to be categorized as temperature variables, which are likely to be highly correlated with each other. This seems like a type of pseudo replication and is a major weakness of the framework.

Below I highlight the main claims from the study and evaluate how well the results support the conclusions.

* "We find evidence of significant genome-wide repeatability in signatures of association to phenotypes and environments" (abstract)<br /> * Given the questions above about SIPEC, I did not find this conclusion well supported with the way the data are presented in the manuscript.

* "We find evidence of significant genome-wide repeatability in signatures of association to phenotypes and environments, which are particularly enriched within regions of the genome harbouring an  inversion in one species. " (Abstract) And "increased repeatability found in regions of the genome that harbour inversions" (Discussion)<br /> * These claims are supported by the data shown in Figure 4, which shows that haploblocks are enriched for WRAs. I want to clarify a point about the wording here, as my understanding of the analysis is that the authors test if *haploblocks* are enriched with *WRAs*, not whether *WRAs* are enriched for *haploblocks*. The wording of the abstract is claiming the latter, but I think what they tested was the former. Let me know if I'm missing something here.<br /> * Notwithstanding the concerns about highly correlated environments potentially inflating some of the patterns in the manuscript, to my knowledge this is the first attempt in the literature to try this kind of comparison, and the results does generally suggest that inversions are more likely capturing, rather than accumulating adaptive variation. However, I don't think the authors can claim that repeated signatures are enriched with haploblock regions, and the authors should take care to refrain from stating the relative importance of different regions of the genome to adaptation without an analysis.


* "While a large number of genomic regions show evidence of repeated adaptation, most of the strongest signatures of association still tend to be species-specific, indicating substantial genotypic redundancy for local adaptation in these species." (Abstract)<br /> * Figure 3B certainly makes it look like there is very little similarity among species in the genetic basis of adaptation, which leaves the question as to how important the repeated signatures really are for adaptation if there are very few of them. (Is 3B for the whole genome or only that region?). This result seems to be at odds with the large number of CRAs and the claims about the importance of haploblock regions to adaptation, which extend from my previous point.


* "we have shown evidence of significant repeatability in the basis of local adaptation (Figure 4, 5), but also an abundance of species-specific, non-repeated signatures (Figure 3)"<br /> * While the claim is a solid one, I am left wondering how much of these genomes show repeated vs. non-repeated signatures, how much of these genomes have haploblocks, and how much overlap there really is. Finding a way to intuitively represent these unknowns would greatly strengthen the manuscript.

Overall, I think the main claims from the study, the statistical framework, and the results could be revised to better support each other.

Although the current version of the manuscript has some potential shortcomings with regards to the statistical approaches, and the impact of this paper in its present form could be stifled because the biology tended to get lost in the statistics, these shortcomings may be addressed by the authors.

With some revisions, the framework and data could have a high impact and be of high utility to the community.

Reviewer #2 (Public Review):

In this study the authors sought to understand the extent of similarity among species in intraspecific adaptation to environmental heterogeneity at the phenotypic and genetic levels. A particular focus was to evaluate if regions that were associated with adaptation within putative inversions in one species were also candidates for adaptation in another species that lacked those inversions. This study is timely for the field of evolutionary genomics, due to recent interest surrounding how inversions arise and become established in adaptation.

Major strengths-

Their study system was well suited to addressing the aims, given that the different species of sunflower all had GWAS data on the same phenotypes from common garden experiments as well as landscape genomic data, and orthologous SNPs could be identified. Organizing a dataset of this magnitude is no small feat. The authors integrate many state-of-the-art statistical methods that they have developed in previous research into a framework for correlating genomic Windows of Repeated Association (WRA, also amalgamated into Clusters of Repeated Association based on LD among windows) with Similarity In Phenotype-Environment Correlation (SIPEC). The WRA/CRA methods are very useful and the authors do an excellent job at outlining the rationale for these methods.

Weaknesses-

The authors did an excellent job responding to the first set of reviews and overall I found the manuscript more streamlined and easier to read. The main weakness in the manuscript is that correlations among environmental variables were not controlled for in their results, and is a source of potential pseudoreplication. The authors are clear about the results that are affected by pseudoreplication.

The manuscript shows how to integrate many recent methods to study the repeatability of adaptation, and the methods and data are likely to be used in similar studies.

Reviewer #2 (Public Review):

Summary:

Reward and punishment learning have long been seen as emerging from separate networks of frontal and subcortical areas, often studied separately. Nevertheless, both systems are complimentary and distributed representations of rewards and punishments have been repeatedly observed within multiple areas. This raised the unsolved question of the possible mechanisms by which both systems might interact, which this manuscript went after. The authors skillfully leveraged intracranial recordings in epileptic patients performing a probabilistic learning task combined with model-based information theoretical analyses of gamma activities to reveal that information about reward and punishment was not only distributed across multiple prefrontal and insular regions, but that each system showed specific redundant interactions. The reward subsystem was characterized by redundant interactions between orbitofrontal and ventromedial prefrontal cortex, while the punishment subsystem relied on insular and dorsolateral redundant interactions. Finally, the authors revealed a way by which the two systems might interact, through synergistic interaction between ventromedial and dorsolateral prefrontal cortex.

Strengths:

Here, the authors performed an excellent reanalysis of a unique dataset using innovative approaches, pushing our understanding on the interaction at play between prefrontal and insular cortex regions during learning. Importantly, the description of the methods and results is truly made accessible, making it an excellent resource to the community.

This manuscript goes beyond what is classically performed using intracranial EEG dataset, by not only reporting where a given information, like reward and punishment prediction errors, is represented but also by characterizing the functional interactions that might underlie such representations. The authors highlight the distributed nature of frontal cortex representations and propose new ways by which the information specifically flows between nodes. This work is well placed to unify our understanding of the complementarity and specificity of the reward and punishment learning systems.

Weaknesses:

The conclusions of this paper are mostly supported by the data, but whether the findings are entirely generalizable would require further information/analyses.

First, the authors found that prediction errors very quickly converge toward 0 (less than 10 trials) while subjects performed the task for sets of 96 trials. Considering all trials, and therefore having a non-uniform distribution of prediction errors, could potentially bias the various estimates the authors are extracting. Separating trials between learning (at the start of a set) and exploiting periods could prove that the observed functional interactions are specific to the learning stages, which would strengthen the results.

Importantly, it is unclear whether the results described are a common feature observed across subjects or the results of a minority of them. The authors should report and assess the reliability of each result across subjects. For example, the authors found RPE-specific interactions between vmPFC and lOFC, even though less than 10% of sites represent RPE or both RPE/PPE in lOFC. It is questionable whether such a low proportion of sites might come from different subjects, and therefore whether the interactions observed are truly observed in multiple subjects. The nature of the dataset obviously precludes from requiring all subjects to show all effects (given the known limits inherent to intracerebral recording in patients), but it should be proven that the effects were reproducibly seen across multiple subjects.

Finally, the timings of the observed interactions between areas preclude one of the authors' main conclusions. Specifically, the authors repeatedly concluded that the encoding of RPE/PPE signals are "emerging" from redundancy-dominated prefrontal-insular interactions. However, the between-region information and transfer entropy between vmPFC and lOFC for example is observed almost 500ms after the encoding of RPE/PPE in these regions, questioning how it could possibly lead to the encoding of RPE/PPE. It is also noteworthy that the two information measures, interaction information and transfer entropy, between these areas happened at non overlapping time windows, questioning the underlying mechanism of the communication at play (see Figures 3/4). As an aside, when assessing the direction of information flow, the authors also found delays between pairs of signals peaking at 176ms, far beyond what would be expected for direct communication between nodes. Discussing this aspect might also be of importance as it raises the possibility of third-party involvement.

Reviewer #2 (Public Review):

Summary: This manuscript addresses what rapid molecular events underly the earliest responses after gravity-sensing via the sedimentation of starch-enriched amyloplasts in columella cells of the plant root cap. The LAZY or NEGATIVE GRAVITROPIC RESPONSE OF ROOTS (NGR) protein family is involved in this process and localizes to both the amyloplast and to the plasma membrane (PM) of columella cells.

The current manuscript complements and extends Nishimura et al., Science, 2023. Kulich and colleagues describe the role of the LZY2 protein, also called NGR1, during this process, imaging its fast relocation and addressing additional novel points such as molecular mechanisms underlying NGR1 plasma membrane association as well as revealing the requirement of NGR1/LZY2, 3,4 for the polar localization of the AGCVIII D6 protein kinase at the PM of columella cells, in which NGR1/LZY2 acts redundantly with LZY3 and LZY4.

The authors initially monitored relocalization of functional NGR1-GFP in columella cells of the ngr1 ngr2 ngr3 triple mutant after 180-degree reorientation of the roots. Within 10 -15 min NGR1-GFP signal disappeared from the upper PM after reorientation and reappeared at the lower PM of the reoriented cells in close proximity to the sedimented amyloplasts. Reorientation of NGR1-GFP occurred substantially faster than PIN3-GFP reorientation, at about the same time or slightly later than a rise in a calcium sensor (GCaMP3) just preceding a change in D2-Venus auxin sensor alterations. Reorientation of NGR1-GFP proved to be fast and not dependent on a brefeldin A-sensitive ARF GEF-mediated vesicle trafficking, unlike the trafficking of PIN proteins, like PIN3, or the AGCVIII D6 protein kinase. Strikingly, the PM association of NGR1-GFP was highly sensitive to pharmacological interference with sterol composition or concentration and phosphatidylinositol (4)kinase inhibition as well as dithiothreitol (DTT) treatment interfering with thioester bond formation e.g. during S-acylation. Indeed, combined mutation of a palmitoylation site and polybasic regions of NRG1 abolished its PM but not its amyloplast localization and rendered the protein non-functional during the gravitropic response, suggesting NRG1 PM localization is essential for the gravitropic response. Targeting the protein to the PM via an artificially introduced N-terminal myristoylation and an ROP2-derived polybasic region and geranylgeranylation site partially restored its functionality in the gravitropic response.

Strengths: This timely work should be of broad interest to plant, cell and developmental biologists across the field as gravity sensing and signaling may well be of general interest. The point that NGR1 is rapidly responsive to gravistimulation, polarizes at the PM in the vicinity to amyloplast and that this is required for repolarization of D6 protein kinase, prior to PIN relocation is really compelling. The manuscript is generally well-written and accessible to a general readership. The figures are clear and of high quality, and the methods are sufficiently explained for reproduction of the experiments.

Weaknesses: Statistical analysis has been performed for some figures but is lacking for most of the quantitative analyses in the figure legends.

The title claims a bit more than what is actually shown in the manuscript: While auxin response reporter alterations are monitored, "rapid redirection of auxin fluxes" are not really directly addressed and, while D6PK can activate PIN proteins in other contexts, it is not explicitly shown in the manuscript that PIN3 is a target in the context of columella cells in vivo. A title such as "Rapid redirection of D6 protein kinase during Arabidopsis root gravitropism relies on plasma membrane translocation of NGR proteins" would reflect the results better.

Fig. 4: The point that D6PK is transcytosed cannot be made here based on the data of these authors. They should have used a photoswitchable version of NGR1 to show that the same molecules observed at the upper PM are translocated to the lower PM. Nishimura and colleagues actually did that for NGR4. However, this is a lot of work and maybe for NGR1 that fusion would have too low fluorescence intensity (as it was the case for NGR3). So, I think a rewording would be sufficient such as NGR-dependent reorientation of D6PK plasma membrane localization" as this does not say, from where it comes to the lower PM. Theoretically, the signal could also be amyloplast-derived or newly synthesized (or just folded) NGR1-GFP.

The authors make a model in which D6PK AGCVIII kinase-dependent on NGRs activates PIN3 to drive auxin fluxes. However, alterations in auxin responses are observed prior to PIN3 reorientation. They should explain this discrepancy better and clearly describe that this is a working hypothesis for the future rather than explicitly proven, yet.

Reviewer #2 (Public Review):

Summary:<br /> This study presents data from a broad range of methods (biochemical, EPR, SAXS, microscopy, etc.) on the large disordered protein FRQ relevant to circadian clocks and its interaction partners FRH and CK1, providing novel and fundamental insight into oligomerization state, local dynamics, and overall structure as a function of phosphorylation and association. Liquid-liquid phase separation is observed. These findings have bearings on the mechanistic understanding of circadian clocks, and on functional aspects of disordered proteins in general.

Strengths:<br /> This is a thorough work that is well presented. The data are of overall high quality given the difficulty of working with an intrinsically disordered protein, and the conclusions are sufficiently circumspect and qualitative to not overinterpret the mostly low-resolution data.

Weaknesses:<br /> None

Reviewer #2 (Public Review):

Summary:<br /> The authors use a dual optical trap instrument combined with 2-color fluorescence imaging to analyze the diffusion of RSC and ISW2 on DNA, both in the presence and absence of nucleosomes, as well as long-range nucleosome sliding by these remodelers. This allowed them to demonstrate that both enzymes can participate in 1D diffusion along DNA for rather long ranges, with ISW2 predominantly tracking the DNA strand, while RSC diffusion involves hopping. In an elegant two-color assay, the authors were able to analyze interactions of diffusing remodeler molecules, both of the same or different types, observing their collisions, co-diffusion, and bypassing. The authors demonstrate that nucleosomes act as barriers for remodeler diffusion, either repelling or sequestering them upon collision. In the presence of ATP, they observed surprisingly processive unidirectional nucleosome sliding with a strong bias in the direction opposite to where the remodeler approached the nucleosome from for ISW2. These results have fundamentally important implications for the mechanism of nucleosome positioning at promoters in vivo, will be of great interest to the scientific community, and will undoubtedly spark exciting future research.

Strengths:<br /> The mechanism of target search for chromatin-interacting protein machines is a 'hot' topic, and this manuscript provides extremely important and timely new information about how RSC and ISW2 find the nucleosomes they slide. Intriguingly, although both remodelers analyzed in this study can diffuse along DNA, the diffusion mechanisms are substantially different, with extremely interesting mechanistic implications.<br /> The strong directional preference in nucleosome sliding by ISW2 dictated by the direction it approaches the nucleosomes from during 1D sliding on DNA is a very intriguing result with interesting implications for the regulation of nucleosome organization around promoters. It will be of great interest to the scientific community and will undoubtedly inspire future research.<br /> Relatively little is known about nucleosome sliding at longer ranges (>100bp), and this manuscript provides a unique view into such sliding and also establishes a versatile methodology for future studies.

Weaknesses:<br /> All measurements were conducted at 5pN tension, which induces unwrapping of the outer DNA gyre from nucleosomes. This could potentially represent a limitation for experiments involving nucleosomes, since partial nucleosome unwrapping could affect the behavior of remodelers, especially their sliding of nucleosomes.

Reviewer #2 (Public Review):

In the manuscript the authors conduct a series of in vitro experiments using N-terminal and C-terminal BRAF fragments (SPR, HDX-MS, pull-down assays) to interrogate BRAF domain-specific autoinhibitory interactions and engagement by H- and KRAS GTPases. Of the three RAF isoforms, BRAF contains an extended N-terminal domain that has yet to be detected in X-ray and cryoEM reconstructions but has been proposed to interact with the KRAS hypervariable region. The investigators probe binding interactions between 4 N-terminal (NT) BRAF fragments (containing one more NT domain (BRS, RBD, and CRD)), with full-length bacterial expressed HRAS, KRAS as well as two BRAF C-terminal kinase fragments to tease out the underlying contribution of domain-specific binding events. They find, consistent with previous studies, that the BRAF BSR domain may negatively regulate RAS binding and propose that the presence of the BSR domain in BRAF provides an additional layer of autoinhibitory constraints that mediate BRAF activity in a RAS-isoform-specific manner. One of the fragments studied contains an oncogenic mutation in the kinase domain (BRAF-KDD594G). The investigators find that this mutant shows reduced interactions with an N-terminal regulatory fragment and postulate that this oncogenic BRAF mutant may promote BRAF activation by weakening autoinhibitory interactions between the N- and C-terminus.

The manuscript is now significantly improved. The inclusion of additional controls and new experiments with KRAS strengthen the manuscript and aid in establishing RAS isoform-specific BRAF interactions.

Reviewer #2 (Public Review):

The manuscript by Huchede et al investigates the BMP pathway in H3K27M-mutant gliomas carrying or not activating mutations in ALK2 (ACVR1). Their results in cell lines and in datasets acquired from the literature on patient tumors indicate that the BMP signaling pathway is activated at similar levels between ACVR1 wild-type and mutant tumors. The group further identifies BMP2 and BMP7 as possibly the main activators of the pathway in cells. They then show that BMP2 and 7 crosstalk with the H3 mutation and synergize to induce transcriptomic rewiring leading to an invasive cell state.

The paper is well-written and easy to follow with a robust experimental plan and datasets supporting the claims. While previous work (acknowledged by the authors) indicated activation of BMP in H3K27M tumors, wild type for the ACVR1 mutation this paper is a nice addition and provides further mechanistic cues as to the importance of the BMP pathway and specific members in these deadly brain cancers. The effect of these BMPs in quiescence and invasion is of particular interest.

A few suggestions to clarify the message are provided below<br /> 1- In thalamic diffuse midline gliomas, the BMP pathway should not be activated as it is in the pons. The authors should identify thalamic tumors in the datasets they explored and patients-derived cell lines from thalamic tumors available to investigate whether this pathway is active across all H3.3K27M mutants in the brain midline or specifically in tumors from the pons.

2- There are ~20% H3.3K27M tumors that carry an ACVR1 mutation and similar numbers of H3.1K27M that are wild type for this gene. Can the authors identify these outliers in their datasets and assess the activation of BMP2 and 7 or other BMP pathway members in this context?

In all this is an interesting paper that provides meaningful data to pursue clinical targeting of the BMP pathway, which would be a nice addition to the field.

Reviewer #2 (Public Review):

Summary:<br /> The study demonstrated that GABRD was significantly overexpressed in breast cancer tissues and had correlations with disease progression and patient survival rates. When GABRD was downregulated in breast cancer cells, it resulted in reduced cell growth, increased apoptosis, and hindered cell migration and invasion. The study has identified CDK1 as a downstream target of GABRD in mediating its effects on breast cancer. These findings suggest that GABRD is a promising target for therapies related to cell cycle regulation in breast cancer, potentially enhancing the effectiveness of CDK1 inhibitors.

Strengths:<br /> The study identifies GABRD as a potential target in breast cancer and provides a new direction for developing breast cancer treatments. The study presents strong clinical correlations of GABARD and the functional studies show that CDK1 is a downstream target of GABARD. The in-vivo studies highlight its therapeutic potential for breast cancer.

Weaknesses:<br /> The data heavily relies on cell lines and the results lack the mechanistic details on GABARD/CDK1 regulation.

Reviewer #2 (Public Review):

Summary:<br /> Through this study, the authors combine a number of innovative technologies including scRNAseq to provide insight into Crohn's disease. Importantly samples from pediatric patients are included. The authors develop a principled and unbiased tiered clustering approach, termed ARBOL. Through high-resolution scRNAseq analysis the authors identify differences in cell subsets and states during pediCD relative to FGID. The authors provide histology data demonstrating T cell localisation within the epithelium. Importantly, the authors find anti-TNF treatment pushes the pediatric cellular ecosystem toward an adult state.

Strengths:<br /> This study is well presented. The introduction clearly explains the important knowledge gaps in the field, the importance of this research, the samples that are used, and study design.<br /> The results clearly explain the data, without overstating any findings. The data is well presented. The discussion expands on key findings and any limitations to the study are clearly explained.

I think the biological findings from, and bioinformatic approach used in this study, will be of interest to many and significantly add to the field.

Weaknesses:<br /> 1. The ARBOL approach for iterative tiered clustering on a specific disease condition was demonstrated to work very well on the datasets generated in this study where there were no obvious batch effects across patients. What if strong batch effects are present across donors where PCA fails to mitigate such effects? Are there any batch correction tools implemented in ARBOL for such cases?

2. The authors mentioned that the clustering tree from the recursive sub-clustering contained too much noise, and they therefore used another approach to build a hierarchical clustering tree for the bottom-level clusters based on unified gene space. But in general, how consistent are these two trees?

Reviewer #2 (Public Review):

This manuscript presents a well-designed study that combines multiple Mendelian randomization analyses to investigate the causal relationship between circulating immune cells and periodontitis. The main conclusions of the manuscript are appropriately supported by the statistics, and the methodologies used are comprehensive and rigorous.

These findings have significant implications for periodontal care and highlight the potential for systemic immunomodulation management on periodontitis, which is of interest to readers in the fields of periodontology, immunology, and epidemiology.

Reviewer #2 (Public Review):

Summary:<br /> Transmembrane signaling in plants is crucial for homeostasis. In this study, the authors set out to understand to what extent catalytic activity in the EFR tyrosine kinase is required in order to transmit a signal. This work was driven by mounting data that suggest many eukaryotic kinases do not rely on catalysis for signal transduction, relying instead on conformational switching to relay information. The crucial findings reported here involve the realisation that a kinase-inactive EFR can still activate (ie lead to downstream phosphorylation) its partner protein BAK1. Using a convincing set of biochemical, mass spectrometric (HD-exchange), and in vivo assays, the team suggests a model in which EFR is likely phosphorylated in the canonical activation segment (where two Ser residues are present), which is sufficient to generate a conformation that can activate BAK1 through dimerisation. A model is put forward involving C-helix positioning in BAK1, and the model is extended to other 'non-RD' kinases in Arabidopsis kinases that likely do not require activity for signaling.

Strengths:<br /> The work uses logical and well-controlled approaches throughout, and is clear and convincing in most areas, linking data from IPs, kinase assays (including clear 32P-based biochemistry), HD-MX data (from non-phosphorylated EFR) structural biology, oxidative burst data, and infectivity assays. Repetitions and statistical analysis all appear appropriate.

Overall, the work builds a convincing story and the discussion does a clear job of explaining the potential impact of these findings (and perhaps an explanation of why so many Arabidopsis kinases are 'pseudokinases', including XPS1 and XIIa6, where this is shown explicitly).

Weaknesses:<br /> No major weaknesses are noted from reviewing the data and the paper follows a logical course built on solid foundations; the use of Tables to explain various experimental data pertinent to the reported studies is appreciated.

1. The use of a, b,c, d in Figures 2C and 3C etc is confusing to this referee.

2. The debate about kinase v pseudokinases is well over a decade old. For non-experts, the kinase alignments/issues raised are in PMID: 23863165 and might prove useful if cited.

3. Early on in the paper, the concept of kinases and pseudokinases related to R-spine (and extended R-spine) stability and regulation really needs to be more adequately introduced to explain what comes next; e.g. some of the key work in this area for RAF and Tyr kinases where mutual F-helix Phe amino acid changes are evaluated (conceptually similar to this study of the E-helix Tyr to Phe changes in EFR) should be cited (PMID: 17095602, 24567368 and 26925779).

4. In my version, some of the experimental text is also currently in the wrong order (and no page numbers, so hard for me to state exactly where in the manuscript); However, I am certain that Figure 2C is mentioned in the text when the data are actually shown in Figure 3C for the EFR-SSAA protein.

5. Tyr 156 in PKA is not shown in Supplement 1, 2A as suggested in the text; for readers, it will be important to show the alignment of the Tyr residue in other kinases. Although it is clearly challenging to generate phosphorylated EFR (seemingly through Codon-expansion here?), it appears unlikely that a phosphorylated EFR protein, even semi-pure, couldn't have been assayed to test the idea that the phosphorylation drives/supports downstream signaling. What about a DD or EE mutation, as commonly used (perhaps over-used) in MEK-type studies?

Impact:<br /> The work is an important new step in the huge amount of follow-up work needed to examine how kinases and pseudokinases 'talk' to each other in (especially) the plant kingdom, where significant genetic expansions have occurred. The broader impact is that we might understand better how to manipulate signaling for the benefit of plants and mankind; as the authors suggest, their study is a natural progression both of their own work, and the kingdom-wide study of the Kannan group.

Reviewer #2 (Public Review):

The brown plant hopper (BPH) is a notorious crop pest and pesticides are the most widespread means of controlling its population. This manuscript shows that in response to sublethal doses of the pesticide (EB), BPH females show enhanced fecundity. This is in keeping with field reports of population resurgence post-pesticide treatment. The authors work out the mechanism behind this increase in fecundity. They show that in response to EB exposure, the expression of its target receptor, GluCl, increases. This, they show, results in an increase in the expression of genes that regulate the synthesis of juvenile hormone (JH) and JH itself, which, in turn, results in enhanced egg-production and egg-laying. Interestingly, these effects of EB exposure are species-specific, as the authors report that other species of plant hoppers either don't show enhanced fecundity or show reduced fecundity. As the authors point out, it is unclear how an increase in GluCl levels could result in increased JH regulatory genes.

Reviewer #2 (Public Review):

In this study, the investigators describe an unbiased phosphoproteomic analysis of cardiac-specific overexpression of adenylyl cyclase type 8 (TGAC8) mice that was then integrated with transcriptomic and proteomic data. The phosphoproteomic analysis was performed using tandem mass tag-labeling mass spectrometry of left ventricular (LV) tissue in TGAC8 and wild-type mice. The initial principal component analysis showed differences between the TGAC8 and WT groups. The integrated analysis demonstrated that many stress-response, immune, and metabolic signaling pathways were activated at transcriptional, translational, and/or post-translational levels.

The authors are to be commended for a well-conducted study with quality control steps described for the various analyses. The rationale for following up on prior transcriptomic and proteomic analyses is described. The analysis appears thorough and well-integrated with the group's prior work. Confirmational data using Western blot is provided to support their conclusions. Their findings have the potential of identifying novel pathways involved in cardiac performance and cardioprotection.

Reviewer #2 (Public Review):

Summary:<br /> The authors focused on delivering a comprehensive structural characterization of WbaP, a membrane-bound phosphoglycosyl transferase from Salmonella that is instrumental in bacterial glycoconjugate synthesis. Notably, the authors employed SMALP-200, an amphipathic copolymer, to extract WbaP in the form of native lipid bilayer nanodiscs. They then determined its oligomerization state through cross-linking and procured higher-resolution structural data via cryo-electron microscopy (cryo-EM). While the authors successfully characterized WbaP in a native-like lipid bilayer setting, and their findings support this, the paper's claim of introducing a novel methodology is not robust. The real contribution of this work lies in the newfound insights about WbaP's structure.

Strengths:<br /> The manuscript provides novel insights into WbaP's structure and oligomerization state, highlighting potentially significant interactions. The methodologies employed represent state-of-the-art practices in the field. Most of the drawn conclusions are well-supported by either experimental or computational data, with a few exceptions noted below.

Weaknesses:<br /> • Organization: The manuscript's organization lacks clarity. The authors seem to describe their processes in the sequence they occurred rather than a logical flow, leading to potential confusion. For instance, the authors delve into a series of inconclusive experiments to determine the oligomerization state of WbaP, utilizing techniques like SEC, SEC-MALS, mass photometry, and mass spectrometry. They then transition to cryo-EM but subsequently return to address the oligomerization issue, which they conclusively resolve using cross-linking experiments. Following this, they shift their focus to interpreting and discussing the structural features obtained from the cryo-EM data.


• Ambiguous and incorrect statements: There are instances of vague and at times inaccurate statements. Using more precise terminology like "native nanodiscs" or "lipid bilayer nanodiscs" would enhance clarity compared to the term "liponanoparticles." The claim on page 8 concerning the refractive index increment of SMA polymers needs rectification. The real reason why SEC-MALS cannot provide absolute particle masses in this case is that using two independent concentration detectors (typically, absorbance and refractive index), the decomposition of elution profiles is necessarily limited to two chemical species of a known molar or specific absorbance and refractive index. Thus, it is clear that nanodiscs containing a protein, a polymer, and a chemically undefined mixture of native lipids cannot be analyzed by this technique.

• Overstating of technical aspects: The technical aspects seem overstated. While the extraction of membrane proteins into native lipid bilayer nanodiscs and their characterization by cross-linking and cryo-EM are standard (and were published before by the same authors in ref. 29), the authors appear to promote them as groundbreaking. The statement that this study presents a novel, universal strategy and toolkit for examining small membrane proteins within liponanoparticles seems overstated, especially given the previous existence of similar methods.

Reviewer #2 (Public Review):

In this paper, Martinez-Gutierrez and colleagues present a dated, multidomain (= Archaea+Bacteria) phylogenetic tree, and use their analyses to directly compare the ages of various marine prokaryotic groups. They also perform ancestral gene content reconstruction using stochastic mapping to determine when particular types of genes evolved in marine groups.

Overall, there are not very many papers that attempt to infer a dated tree of all prokaryotes, and this is a distinctive and up-to-date new contribution to that oeuvre. There are several particularly novel and interesting aspects - for example, using the GOE as a (soft) maximum age for certain groups of strictly aerobic Bacteria, and using gene content enrichment to try to understand why and how particular marine groups radiated.

Comments:

One overall feature of the results is that marine groups tend to be quite young, and there don't seem to be any modern marine groups that were in the ocean prior to the GOE. It might be interesting to study the evolution of the marine phenotype itself over time; presumably some of the earlier branches were marine? What was the criterion for picking out the major groups being discussed in the paper? My (limited) understanding is that the earliest prokaryotes, potentially including LUCA, LBCA and LACA, was likely marine, in the sense that there would not yet have been any land above sea level at such times. This might merit discussion in the paper. Might there have been earlier exclusively marine groups that went extinct at some point?

What do the stochastic mapping analyses indicate about the respective ancestors of Gracilicutes and Terrabacteria? At least in the latter case, the original hypothesis for the group was that they possessed adaptations to life on land - which seems connected/relevant to the idea of radiating into the sea discussed here - so it might be interesting to discuss what your analyses say about that idea.

I very much appreciate that finding time calibrations for microbes is challenging, but I nonetheless have a couple of comments or concerns about the calibrations used here:

The minimum age for LBCA and LACA (Nodes 1 and 2 in Fig. 1) was calibrated with the earliest evidence of biogenic methane ~3.4Ga. In the case of LACA, I suppose this reflects the view that LACA was a methanogen, which is certainly plausible although perhaps not established with certainty. However, I'm less clear about the logic of calibrating the minimum age of Bacteria using this evidence, as I am not aware that there is much evidence that LBCA was a methanogen. Perhaps the line of reasoning here could be stated more explicitly. An alternative, slightly younger minimum age for Bacteria could perhaps be obtained from isotope data ~3.2Ga consistent with Cyanobacteria (e.g., see https://pubmed.ncbi.nlm.nih.gov/30127539/).

I am also unclear about the rationale for setting the minimum age of the photosynthetic Cyanobacteria crown to the time of the GOE. Presumably, oxygen-generating photosynthesis evolved on the stem of (photosynthetic) Cyanobacteria, and it therefore seems possible that the GOE might have been initiated by these stem Cyanobacteria, with the crown radiating later? My confusion here might be a comprehension error on my part - it is possible that in fact one node "deeper" than the crown was being calibrated here, which was not entirely clear to me from Figure 1. Perhaps mapping the node numbers directly to the node, rather than a connected branch, would help? (I am assuming, based on nodes 1 and 2, that the labels are being placed on the branch directly antecedent to the node of interest)?

Reviewer #2 (Public Review):

In this paper, Martinez-Gutierrez and colleagues present a dated, multidomain (= Archaea+Bacteria) phylogenetic tree, and use their analyses to directly compare the ages of various marine prokaryotic groups. They also perform ancestral gene content reconstruction using stochastic mapping to determine when particular types of genes evolved in marine groups.

Overall, there are not very many papers that attempt to infer a dated tree of all prokaryotes, and this is a distinctive and up-to-date new contribution to that oeuvre. There are several particularly novel and interesting aspects - for example, using the GOE as a (soft) maximum age for certain groups of strictly aerobic Bacteria, and using gene content enrichment to try to understand why and how particular marine groups radiated.

One overall feature of the results is that marine groups tend to be quite young, and there don't seem to be any modern marine groups that were in the ocean prior to the GOE. This seems an interesting strand to pursue in future work. Presumably, the earliest branches of the bacterial tree were marine, so what happened in the intervening period? The authors' character mapping approach could also be used to infer the habitat of the Gracilicutes and Terrabacteria ancestors, and it might be interesting to revisit the question of the ancestral ecological differences between these groups, if any can be clearly distinguished.

Finally, some comments in which I disagree with a couple of the authors' methodological decisions. I don't think these disagreements are likely to have a major impact on the findings, but I feel it is worth mentioning them in any case, to stimulate future discussion and work. I very much appreciate that finding time calibrations for microbes is challenging, but I nonetheless have a couple of comments or concerns about the calibrations used here.

1. It is not clear that the earliest evidence for biogenic methane provides a minimum age for both Bacteria and Archaea. For Archaea, potentially --- if the methane is indeed biogenic, and if the last archaeal common ancestor was a methanogen. For Bacteria (and extant life as a whole), the link is harder to draw. The authors pointed out that there is other evidence from around this time for life, for example from the Strelley Pool at ~3.3Ga. This is a reasonable argument for a minimum on LUCA, but then the optimal approach would be to calibrate the root node with this minimum, rather than the two descendant clades.

2. I am also unclear about the rationale for setting the minimum age of the photosynthetic Cyanobacteria crown to the time of the GOE. Presumably, oxygen-generating photosynthesis evolved on the stem of (photosynthetic) Cyanobacteria - since the crown seems to have had it ancestrally - and it therefore seems possible that the GOE might have been initiated by these stem Cyanobacteria, with the crown radiating later. In their response to my comment, the authors confirm that they are calibrating the crown Cyanobacteria using the GOE as a minimum. I don't agree with the logic here: it seems a formal possibility that crown Cyanobacteriia are younger than the GOE. The authors argued that, although oxygenic photosynthesis likely evolved on the stem, due to extinction (or non-sampling) of intervening lineages there are no nodes on the tree that directly sample that event. I agree, but I would then suggest placing the minimum on the older, not the younger, end of the stem.

Reviewer #2 (Public Review):

Summary:<br /> The authors used a yeast model for analyzing Parkinson's disease-associated synphilin-1 inclusion bodies (SY1 IBs). In this model system, large SY1 IBs are efficiently formed from smaller potentially more toxic SY1 aggregates. Using a genome-wide approach (synthetic genetic array, SGA, combined with a high-content imaging approach), the authors identified the sphingolipid metabolic pathway as pivotal for SY1 IBs formation. Disturbances of this pathway increased SY1-triggered growth deficits, loss of mitochondrial membrane potential, increased production of reactive oxygen species (ROS), and decreased cellular ATP levels pointing to an increased energy crisis within affected cells. Notably, SY1 IBs were found to be surrounded by mitochondrial membranes using state-of-the-art super-resolution microscopy. Finally, the effects observed in the yeast for SY1 IBs turned out to be evolutionarily conserved in mammalian cells. Thus, sphingolipid metabolism might play an important role in the detoxification of misfolded proteins by large IBs formation at the mitochondrial membrane.

Strengths:<br /> • The SY1 IB yeast model is very suitable for the analysis of genes involved in IB formation.<br /> • The genome-wide approach combining a synthetic genetic array (SGA) with a high-content imaging approach is a compelling approach and enables the reliable identification of novel genes. The authors tightly checked the output of the screen.<br /> • The authors clearly showed, including a couple of control experiments, that the sphingolipid metabolic pathway is crucial for SY1 IB formation and cytotoxicity.<br /> • The localization of SY1 IBs at mitochondrial membranes has been clearly demonstrated with state-of-the-art super-resolution microscopy and biochemical methods.<br /> • Pharmacological manipulation of the sphingolipid pathway influenced mitochondrial function and cell survival.

Weaknesses:<br /> • It remains unclear how sphingolipids are involved in SY1 IB formation.<br /> • It remains undefined whether failure of sphingolipid-dependent SY1 IB formation from smaller potentially more toxic aggregates occurs at the mitochondrial membrane.<br /> • It remains open whether mitochondrial activity (e.g., respiratory activity) is needed for sphingolipid-dependent SY1 IB formation.

Reviewer #2 (Public Review):

Summary:<br /> The authors used existing mouse models to compare the effects of ablating the CD47 receptor and its signaling ligand Thrombospondin. The CD47-KO model used in this study was generated by Kim et al, 2018, where hemolytic anemia and splenomegaly was reported. This study analyzes the cell composition of the spleens from CD47-KO and Thsp-KO, focusing on early hematopoietic and erythroid populations. The data broadly shows that splenomegaly in the CD47-KO is largely due to an increase in committed erythroid progenitors as seen by Flow Cytometry and single-cell sequencing, whereas the Thsp-KO shows a slight depletion of committed erythroid progenitors but is otherwise similar to WT in splenic cell composition.

Strengths: The techniques used are appropriate for the study and the data support the main conclusions of the study. This study provides novel insights into a putative role of Thsp-CD47 signaling in triggering definitive erythropoiesis in the mouse spleen in response to anemic stress and constitutes a good resource for researchers seeking to understand extramedullary erythropoiesis.

Weaknesses: The Flow cytometry data alone supports the authors' main conclusion and single-cell sequencing confirms them but does not add further information, other than those already observed in the Flow data. The single-cell sequencing analysis and presentation could be improved by using alternate clustering methods as well as separating the data by genotype and displaying them in order for readers to fully grasp the nuanced differences in marker expression between the genotypes. Further, it is not clear from the authors' description of their results whether the increased splenic erythropoiesis is a direct consequence of CD47-KO or a response to the anemic stress in this mouse model. The enrichment of cKit+ Ter119+ Sca1- cells in CD47-KO indicates that these are likely stress erythroid progenitors. Another CD47-KO mouse model (Lindberg et al 1996) has no reported erythroid defects and was also not examined in this study.

Reviewer #2 (Public Review):

Summary:

In this paper, Banerjee & Banerjee argue that a solely autocatalytic assembly model of the centrosome leads to size inequality. The authors instead propose a catalytic growth model with a shared enzyme pool. Using this model, the authors predict that size control is enzyme-mediate and are able to reproduce various experimental results such as centrosome size scaling with cell size and centrosome growth curves in C. elegans.

The paper contains interesting results and is well-written and easy to follow/understand.

Suggestions:

● In the Introduction, when the authors mention that their "theory is based on recent experiments uncovering the interactions of the molecular components of centrosome assembly" it would be useful to mention what particular interactions these are.<br /> ● In the Results and Discussion sections, the authors note various similarities and differences between what is known regarding centrosome formation in C. elegan and Drosophila. It would have been helpful to already make such distinctions in the Introduction (where some phenomena that may be C. elegans specific are implied to hold centrosomes universally). It would also be helpful to include more comments for the possible implications for other systems in which centrosomes have been studied, such as human, Zebrafish, and Xenopus.<br /> ● For Fig 1.C, the two axes are very close to being the same but are not. It makes the graph a little bit more difficult to interpret than if they were actually the same or distinctly different. It would be more useful to have them on the same scale and just have a legend.<br /> ● The authors refer to Equation 1 as resulting from an "active liquid-liquid phase separation", but it is unclear what that means in this context because the rheology of the centrosome does not appear to be relevant.<br /> ● The authors reject the non-cooperative limit of Eq 1 because, even though it leads to size control, it does not give sigmoidal dynamics (Figure 2B). While I appreciate that this is just meant to be illustrative, I still find it to be a weak argument because I would guess a number of different minor tweaks to the model might keep size control while inducing sigmoidal dynamics, such as size-dependent addition of loss rates (which could be due to reactions happen on the surface of the centrosome instead of in its bulk, for example). Is my intuition incorrect? Is there an alternative reason to reject such possible modifications?<br /> ● While the inset of Figure 3D is visually convincing, it would be good to include a statistical test for completeness.<br /> ● The authors note that the pulse in active enzyme in their model is reminiscent of the Polo kinase pulse observed in Drosophila. Can the authors use these published experimental results to more tightly constrain what parameter regime in their model would be relevant for Drosophila? Can the authors make predictions of how this pulse might vary in other systems such as C. elegans?<br /> ● The authors mention that the shared enzyme pool is likely not diffusion-limited in C. elegans embryos, but this might change in larger embryos such as Drosophila or Xenopus. It would be interesting for the authors to include a more in-depth discussion of when diffusion will or will not matter, and what the consequence of being in a diffusion-limit regime might be.<br /> ● The authors state "Firstly, our model posits the sharing of the enzyme between both centrosomes. This hypothesis can potentially be experimentally tested through immunofluorescent staining of the kinase or by constructing FRET reporter of PLK1 activity." I don't understand how such experiments would be helpful for determining if enzymes are shared between the two centrosomes. It would be helpful for the authors to elaborate.

Reviewer #2 (Public Review):

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

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

The authors have made significant improvements to address my concerns in this resubmission and the previous critiques of the other reviewers since the prior submission. The work is comprehensive in scope and the statistics are appropriate where required. I believe the conclusions to be valid for this study and I don't have any additional recommendations. I believe this work to be of importance to the Dystroglycanopathy and neurobiology fields.

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

Overall, the study is original, well structured, and easy to read.

Reviewer #2 (Public Review):

In this manuscript, the authors have screened the ReFRAME library and identified candidate small molecules that can activate YAP. They found that SM04690, an inhibitor of the WNT signaling pathway, could efficiently activate YAP through CLK2 kinase which has been shown to phosphorylate SR proteins to alter gene alternative splicing. They further demonstrated that SM04690 mediated alternative splicing of AMOTL2 and rendered it unlocalized on the membrane. Alternatively spliced AMOTL2 prevented YAP from anchoring to the cell membrane which results in decreased YAP phosphorylation and activated YAP. Previous findings showed that WNT signaling more or less activates YAP. The authors revealed that an inhibitor of WNT signaling could activate YAP. Thus, these findings are potentially interesting and important. However, the present manuscript provided a lot of indirect data and lacked key experiments.

Major points:<br /> 1. In Figure S3, since inhibition of CLK2 resulted in extensive changes in alternative splicing, why did the authors choose AMOTL2? How to exclude other factors such as EEF1A1 and HSPA5, do they affect YAP activation? Angiomotin-related AMOTL1 and AMOTL2 were identified as negative regulators of YAP and TAZ by preventing their nuclear translocation. It has been reported that high cell density promoted assembly of the Crumbs complex, which recruited AMOTL2 to tight junctions. Ubiquitination of AMOTL2 K347 and K408 served as a docking site for LATS2, which phosphorylated YAP to promote its cytoplasmic retention and degradation. How to determine that alternative splicing rather than ubiquitination of AMOTL2 affects YAP activity? Does AMOTL2 Δ5 affect the ubiquitination of AMOTL2? Does overexpression of AMOTL2 Δ5Δ9 cause YAP and puncta to co-localize?<br /> 2. The author proposed that AMOTL2 splicing isoform formed biomolecular condensates,.However, there was no relevant experimental data to support this conclusion. AMOTL2 is located not only on the cell membrane but also on the circulating endosome of the cell, and the puncta formed after AMOTL2 dissociation from the membrane is likely to be the localization of the circulating endosome. The author should co-stain AMOTL2 with markers of circulating endosomes, or conduct experiments to prove the liquidity of puncta to verify the phase separation of AMOTL2 splicing isoform.<br /> 3. The localization of YAP in cells is regulated by cell density, and YAP usually translocates to the nucleus at low cell density. In Figure 2E, the cell densities of DMSO and SM04690-treated groups are inconsistent. In Figure 4A, the magnification of t DMSO and SM04690-treated groups is inconsistent, and the SM04690-treated group seems to have a higher magnification.<br /> 4. There have been many reports that the WNT signaling pathway and the Hippo signaling pathway can crosstalk with each other. The authors should exclude the influence of the WNT signaling pathway by using SM04690.

Reviewer #2 (Public Review):

This was an interesting study, and I enjoyed seeing different experimental approaches used to compare the properties of the different native proteins, the ancestral reconstructions, and the other mutants. In the original manuscript, I felt that the authors had over-simplified their explanations, as the differences between the ancestral proteins, and the changes induced by the two mutations, only partially explain the differences between IbpA proteins from the two different species. However, with their revised version, I think the presentation and discussion of their results are much better. Overall, I think this represents a valuable contribution to the field, providing convincing mechanistic evidence as to how these small heat shock proteins have evolved.

Reviewer #2 (Public Review):

Congenital cystic airway abnormalities (CPAM) are a common disorder in airway lung development whose etiology is poorly understood and can be fatal if not effectively treated at birth. This study by Luo and colleagues provides compelling new evidence in mice and cultured fetal mesenchymal cells that loss of mesenchymal Bmpr1a signaling disrupts branching morphogenesis, leading to the formation of numerous pulmonary cysts. Their airways were deficient in underlying smooth muscle and subepithelial elastin fibers along with perturbations in Sox2-Sox9 proximal-distal epithelial development. Interestingly, these changes were independent of canonical Smad1/5 signaling and suggestive of non-canonical signaling perhaps through p38 signaling. They were also independent of simply ablating non-vascular mesenchymal cells using Myocd-ko mice. Although the study does not define how loss of Bmpr1a causes cystic formation or whether this pathway is related to human CPAM disease, the findings are considered highly significant because they provide new evidence for BMP signaling in branching morphogenesis. The knowledge may pave the way for future studies designed to understand and prevent or treat newborn infants with CPAM. There are only a few weaknesses.

Major Weaknesses:<br /> 1. The authors may be aware that a recent paper (https://doi.org/10.1038/s41598-022-24858-3) reported on transcriptional changes seen in human CPAM. It would seem that some of the molecular changes seen in human CPAM move in the opposite direction of what is reported in mice lacking mesenchymal Bmrp1a. Perhaps the authors could comment on these differences in the discussion and whether they potentially explain the etiology of CPAM or branching morphogenesis in general.<br /> 2. Figure 4 shows that BMP4 increases SMADs, p38, and several muscle genes in mesenchymal cells. Figure 5 extends this finding with a clever strategy to label airway and vascular smooth muscle with different fluorescent molecules used to isolate different types of mesenchymal cells. It shows that non-vascular smooth muscle cells but not perivascular smooth muscles are responsive to BMP4 signaling as defined by increased expression of Myh11. Are there cell-restricted responses to the other genes shown in Figure 4? Given the lack of SMAD signaling and the increase seen in p38 signaling, would blocking p38 signaling influence the BMP responsiveness of these nonvascular smooth muscle cells?<br /> 3. Figure 6 shows that mesenchymal loss of Myocd causes a deficiency of airway smooth muscle cells, but this was not sufficient to create cysts. Did the authors ever check to see if it changed Sox2-Sox9 staining in the airway epithelium?<br /> 4. Figure 7 shows that mesenchymal loss of Bmpr1a proximalizes the distal airway as defined by loss of Sox2 and FoxJ1 (a ciliated marker) and gain in (Sox9 and SP-C) staining. But Club cells expressing Scgb1a1 and Cyp2F2 are the predominant epithelial cells in the distal airway. The transcriptomics data in panel B shows expression of these genes is less in the mutant mice. Does this mean they fail to generate Club cells or there is just less expression per cell? In other words, what are the primary epithelial cells present in the airways of mice with loss of mesenchymal Bmpr1a?

Reviewer #2 (Public Review):

The effectors of cellular aging in yeast have not been fully elucidated. To address this, the authors curated gene expression studies to link genes influenced by rapamycin - a well-known mediator of longevity across model systems - to genes known to affect chronological and replicative lifespan (RLS) in yeast. Through their analyses, they find one gene, ybr238c, whose deletion increases both CLS and RLS upon deletion and that is downregulated by rapamycin. Curiously, despite these selection criteria, the authors only use CLS as a proxy for cellular aging throughout their study and do not explore the effects of ybr238c deletion on RLS. This does not diminish their conclusions, but given the importance of this phenotype in their selection criteria, it is surprising that the authors did not choose to test both types of aging throughout their study.

Nonetheless, the authors demonstrate that deletion of ybr238c increases CLS across multiple yeast strains and through multiple assays. The authors also test the effects of YBR238C overexpression on lifespan and find the opposite effect, with overexpression yeast showing decreased survival relative to wild-type cells, consistent with "accelerated aging" as the authors propose. The authors also note that ybr238c has a paralog, rmd9, whose deletion decreases CLS and seems to be epistatic to ybr238c, as a double ybr238c/rmd9 mutant has decreased CLS relative to a wild-type strain.

Collectively, the data presented by the authors convincingly demonstrate that ybr238c influences lifespan in a manner that is distinct from (and likely opposite to) rmd9. However, the authors then link the increased CLS in Δybr238c yeast to mitochondrial function using only a handful of assays that do not directly test mitochondrial function. These include total cellular ATP levels, levels of reactive oxygen species, and the transcript levels of select nuclear-encoded mitochondrial genes. Yeast is well established to generate ATP through non-mitochondrial pathways such as glycolysis in fermentive conditions. While it is possible that the ATP levels assayed in the manuscript were tested in stationary phase, which would more likely reflect "mitochondrial function," the methods nor the figure legends contain these details, which are critical for the interpretation of these data. Similarly, ROS can be generated through non-mitochondrial pathways, and the transcription of nuclear-encoded mitochondrial genes is an indirect measure of mitochondrial function at best. Thus, the authors' proposed connection of ybr238c to mitochondrial function is correlative and should be substantiated with assays that more closely align with organellar function, such as respirometry or assaying the activity of oxidiative phosphorylation complexes. Finally, the authors attempt to tie the phenotypes of mitochondrial dysfunction caused by the deletion of ybr238c to TORC1 signaling, as the gene is influenced by rapamycin. However, the presentation of the data, such as reporting ATP levels as relative percentages or failing to perform appropriate statistical comparisons between conditions in which the authors derive conclusions, renders the data difficult to interpret. As such, this manuscript establishes that ybr238c is rapamycin responsive and influences CLS, but its influence on mitochondrial activity and ties to TORC1 signaling remain speculative.

Reviewer #2 (Public Review):

Significance<br /> Rubio et al. study the behavior of the transcription factor Hsf1 under ethanol stress, examining its distribution within the nucleus and the coalescence of heat shock response genes in budding yeast. In comparison to the heat shock response, the response to ethanol stress shows similar gene coalescence and Hsf1 binding. However, there is a notable delay in the transcriptional response to ethanol, and a disconnect between it and the appearance of irreversible Hsf1 condensates/puncta, highlighting important differences in how Hsf1 responds to these two related but distinct environmental stresses.

Overview and general concerns<br /> The authors studied how yeast responds to ethanol stress (8.5%) and compared it to the heat shock response (from 25{degree sign}C to 39{degree sign}C). They observed a more gradual increase in the expression of heat shock response (HSR) genes during ethanol stress compared to heat shock. Additionally, the recruitment of Hsf1 and Pol II to HSR genes, and the inter- and intrachromosomal interactions among these genes, showed slower kinetics under ethanol stress. They attribute the delay in transcriptional response to chromatin compaction induced by ethanol. Despite this delay, these interactions persisted longer. Hsf1 clusters, previously documented during the heat shock response, were also observed during ethanol stress and persisted for an extended period. The conditional degradation of Hsf1 and Rpb1 eliminated most inter- and intrachromosomal interactions for heat shock responsive genes in both ethanol stress and heat shock conditions, indicating the importance of these factors for long-distance interactions between HSR genes. Overall, this manuscript provides novel insights into the differential behavior of HSR genes under different stress conditions. This contributes to the broader understanding of how different stressors might elicit unique responses at the genomic and topographical level under the regulation of transcription factor Hsf1.

The central finding of the study highlights the different dynamics of Hsf1, Pol II, and gene organization in response to heat shock versus ethanol stress. However, one important limitation to consider is that the two chosen conditions may not be directly comparable. For a balanced assessment, the authors should ideally expose yeast to various ethanol concentrations and different heat shock temperatures, ensuring the observed differences stem from the nature of the stressor rather than suboptimal stress intensity. At the very least, an additional single ethanol concentration point on each side of 8.5% should be investigated to ensure that 8.5% is near the optimum. In fact, comparing the number of Hsp104 foci in the two conditions in Fig. 1E and F suggests that the yeast is likely experiencing different intensities of stress for the chosen heat shock condition and ethanol concentration used in this study.

A second significant concern is the use of the term "Hsf1 condensate". Chowdhary et al.'s 2022 Molecular Cell study highlighted an inhomogeneous distribution and rapid dynamics of Hsf1 clustering upon heat shock, with sensitivity to 1,6-hexandiol, which is interpreted as evidence for condensation by LLPS. However this interpretation has been criticized severely by McSwiggen et al. Genes Dev 2019 and Mussacchio EMBO J 2022. It is important to mention that 1,6-hexandiol is known to affect chromatin organization (Itoh et al. Life Science Alliance 2021). Describing such clusters as 'condensates' without further experimental evidence is premature. I encourage authors to settle on their neutral term 'puncta' which they use interchangeably with 'condensate' so as not to confuse the reader. The dynamic binding and unbinding of the low-abundance Hsf1 at coalescent chromatin target sites might explain the liquid-like properties of these clusters without the need for invoking the phase-separation hypothesis. While Hsf1 clusters exhibit features consistent with phase-separated condensates, other equally plausible alternative mechanisms, such as dynamic site-specific interactions (Musacchio, EMBO J, 2022), should also be considered. This is best left for the discussion where the underlying mechanism for puncta formation can be addressed.

Specific comments:

- Figure 1: Why does ethanol stress at 0 min display a larger number of Hsp104 foci per cell than heat shock at the same time? How are foci defined by the authors? In Fig. 1D, there are many smaller puncta. A comparative assessment of the number and size of foci for heat shock and ethanol stress would be beneficial.

- Figure 2: Selecting a housekeeping gene with consistent expression levels is crucial for meaningful qPCR analysis. Do SCR1 mRNA levels fluctuate during heat shock or ethanol stress? Additionally, certain genes, such as TMA10 and SSA4, lack visible bars at time 0. Are these levels undetectable? The varying y-axis scales are confusing; presenting data as relative fold changes could offer a clearer perspective.

- Line 239: The evidence for chromatin compaction is unconvincing. An increase in H3 occupancy by ChIP might indicate a reduction in histone exchange dynamics but may not relate to overall chromatin compaction. The authors use H2A-mCherry to suggest a decrease in chromatin volume, but this data is not persuasive. Did the authors observe any changes in nuclear size? Perhaps quantifying chromatin compaction more directly, using signal intensity per volume, would be informative.

- Line 340: The claim of a "strong spatiotemporal correlation" isn't evident from the data. Could correlation coefficients be provided? There is potential anti-correlation in Fig. 6 - Figure Supplement 1C.

- Figure 8: The WT data in Fig 8 seem inconsistent with Fig. 4 (e.g. the interaction frequency for HSP104 and SSA2). Are these fluctuations between experiments, or are they side effects of IAA treatment? The use of ethanol as an IAA solvent vehicle raises concerns. It would be beneficial if the authors could demonstrate that 1.7% ethanol in the control does not induce ethanol stress.

Reviewer #2 (Public Review):

Moutard, Laura, et al. investigated the gene expression and functional aspects of Leydig cells in a cryopreservation/long-term culture system. The authors found that critical genetic markers for Leydig cells were diminished when compared to the in-vivo testis. The testis also showed less androgen production and androgen responsiveness. Although they did not produce normal testosterone concentrations in basal media conditions, the cultured testis still remained highly responsive to gonadotrophin exposure, exhibiting a large increase in androgen production. Even after the hCG-dependent increase in testosterone, genetic markers of Leydig cells remained low, which means there is still a missing factor in the culture media that facilitates proper Leydig cell differentiation. Optimizing this testis culture protocol to help maintain proper Leydig cell differentiation could be useful for future human testis biopsy cultures, which will help preserve fertility and child cancer patients.

Overall, the authors addressed most comments and questions from the previous review. The additional data regarding the necrotic area is helpful for interpreting the quality of the cultures.

The authors did not conduct multiple comparison tests although there are multiple comparisons conducted for a single dependent variable (Fig 2J, Fig 3F, among many others), however, the addition of this multiple comparison is unlikely to change the conclusions of the paper or the figure and, thus is a minor technical detail in this case.

Reviewer #2 (Public Review):

The authors have studied in detail the embryogenesis of the ametabolan insect Thermobia domestica. They have also measured the levels of the two most important hormones in insect development: juvenile hormone (JH) and ecdysteroids. The work then focuses on JH, whose occurrence concentrates in the final part (between 70 and 100%) of embryo development. Then, the authors used a precocene compound (7-ethoxyprecocene, or 7EP) to destroy the JH producing tissues in the embryo of the firebrat T. domestica, which allowed to unveil that this hormone is critically involved in the last steps of embryogenesis. The 7EP-treated embryos failed to resorb the extraembryonic fluid and did not hatch. More detailed observations showed that processes like the maturational growth of the eye, the lengthening of the foregut and posterior displacement of the midgut, and the detachment of the E2 cuticle, were impaired after the 7EP treatment. Importantly, a treatment with a JH mimic subsequent to the 7EP treatment restored the correct maturation of both the eye and the gut. It is worth noting that the timing of JH mimic application was essential for correcting the defects triggered by the treatment with 7EP.

This is a relevant result in itself since the role of JH in insect embryogenesis is a controversial topic. It seems to have an important role in hemimetabolan embryogenesis, but not so much in holometabolans. Intriguingly, it appears important for hatching, an observation made in hemimetabolan and in holometabolan embryos. Knowing that this role was already present in ametabolans is relevant from an evolutionary point of view, and knowing exactly why embryos do not hatch in the absence of JH, is relevant from the point of view of developmental biology.

Then, the authors describe a series of experiments applying the JH mimic in early embryogenesis, before the natural peak of JH occurs, and its effects on embryo development. Observations were made under different doses of JHm, and under different temporal windows of treatment. Higher doses triggered more severe effects, as expected, and different windows of application produced different effects. The most used combination was 1 ng JHm applied 1.5 days AEL, checking the effects 3 days later. Of note, 1.5 days AEL is about 15% embryonic development, whereas the natural peak of JH occurs around 85% embryonic development. In general, the ectopic application of JHm triggered a diversity of effects, generally leading to an arrest of development. Intriguingly, however, a number of embryos treated with 1 ng of JHm at 1.5 days AEL showed a precocious formation of myofibrils in the longitudinal muscles. Also, a number of embryos treated in the same way showed enhanced chitin deposition in the E1 procuticle and showed an advancement of at least a day in the deposition of the E2 cuticle.

While the experiments and observations are done with great care and are very exhaustive, I am not sure that the results reveal genuine JH functions. The effects triggered by a significant pulse of ectopic JHm when the embryo is 15% of the development will depend on the context: the transcriptome existing at that time, especially the cocktail of transcription factors. This explains why different application times produce different effects. This also explains why the timing of JHm application was essential for correcting the effects of 7EP treatment. In this reasoning, we must consider that the context at 85% development, when the JH peaks in natural conditions and plays its genuine functions, must be very different from the context at 15% development, when the JHm was applied in most of the experiments. In summary, I believe that the observations after the application of JHm reveal effects of the ectopic JHm, but not necessarily functions of the JH. If so, then the subsequent inferences made from the premise that these ectopic treatments with JHm revealed JH functions are uncertain and should be interpreted with caution.

Those inferences affect not only the "JH and the progressive nature of embryonic molts" section, but also, the "Modifications in JH function during the evolution of hemimetabolous and holometabolous life histories" section, and the entire "Discussion". In addition to inferences built on uncertain functions, the sections mentioned, especially the Discussion, I think suffer from too many poorly justified speculations. I love speculation in science, it is necessary and fruitful. But it must be practiced within limits of reasonableness, especially when expressed in a formal journal.

Finally, In the section "Modifications in JH function during the evolution of hemimetabolous and holometabolous life", it is not clear the bridge that connects the observations on the embryo of Thermobia and the evolution of modified life cycles, hemimetabolan and holometabolan.

Reviewer #2 (Public Review):

Summary:

The authors compared the cortical folding of human brains with folding in macaque monkey brains to reveal shared and unique locations of gyral peaks. The shared gyral peaks were located in cortical regions that are functionally similar and less changed in humans from those in macaques, while the locations of unique peaks in humans are in regions that have changed or expanded functions. These findings are important in that they suggest where human brains have changed more than macaque brains in their subsequent evolution from a common ancestor. The massive analysis of comparative results provides evidence of where humans and macaques are similar or different in cortical markers, as well as noting some of the variations within each of the two primates.

Strengths:

The study includes massive detail.

Weaknesses:

The manuscript is too long and there is not enough focus on the main points. A brief listing of previous views on why fissures form and what factors are important would be helpful.

Reviewer #2 (Public Review):

Summary:<br /> In the current work, authors deploy a set of behavioral tasks to explore individual differences in the preferred perceptual and motor rhythms. They found a consistent individual preference for a given perceptual and motor frequency across tasks and, while these were correlated, the latter is slower than the former one. Additionally, they show that the accuracy of adaptation to rate changes is proportional to the amount of rate variation and, crucially, the amount of adaptation decreases with age.

Strengths:<br /> Authors carefully designed several experiments to measure individual preferred motor and perceptual tempo. Furthermore, before completing the main experiment they validated the experimental design by testing the consistency across tasks and test-retest. Additionally, to the value of the reported findings, the introduced paradigm represents a useful tool for future research.<br /> The obtained data is rigorously analyzed using a diverse set of tools, each adapted to the specificities across the different research questions and tasks.<br /> This study identifies several relevant behavioral features: (i) each individual shows a preferred and reliable motor and perceptual tempo and, while both are related, the motor is consistently slower than the pure perceptual one; (ii) the existence of hysteresis in the adaptation to rate variations; and (iii) the decrement of this adaptation with age. All these observations are valuable for the auditory-motor integration field of research, and they could potentially inform existing biophysical models to increase their descriptive power.

Weaknesses:<br /> The current study is presented in the framework of the ongoing debate of oscillator vs. timekeeper mechanisms underlying perceptual and motor timing, and authors claim that the observed results support the former mechanism. In this line, every obtained result is related by the authors to a specific ambiguous (i.e., not clearly related to a biophysical parameter) feature of an internal oscillator. As pointed out by an essay on the topic (1), claiming that a pattern of results is compatible with an "oscillator" could be misleading, since some features typically used to validate or refute such mechanisms are not well grounded on real biophysical models. Relatedly, a recent study (2) shows that two quantitatively different computational algorithms (i.e., absolute vs relative timing) can be explained by the same biophysical model. This demonstrates that what could be interpreted as a timekeeper, or an oscillator can represent the same biophysical model working under different conditions. For this reason, if authors would like to argue for a given mechanism underlying their observations, they should include a specific biophysical model, and test its predictions against the observed behavior. For example, it's not clear why authors interpret the observation of the trial's response being modulated by the rate of the previous one, as an oscillator-like mechanism underlying behavior. As shown in (1) a simple oscillator returns to its natural frequency as soon as the stimulus disappears, which will not predict the long-lasting effect of the previous trial. Furthermore, a timekeeper-like mechanism with a long enough integration window is compatible with this observation.<br /> Still, authors can choose to disregard this suggestion, and not testing a specific model, but if so, they should restrict this paper to a descriptive study of the timing phenomena.

1. Doelling, K. B., & Assaneo, M. F. (2021). Neural oscillations are a start toward understanding brain activity rather than the end. PLoS biology, 19(5), e3001234.<br /> 2. Doelling, K. B., Arnal, L. H., & Assaneo, M. F. (2022). Adaptive oscillators provide a hard-coded Bayesian mechanism for rhythmic inference. bioRxiv, 2022-06.

Reviewer #2 (Public Review):

When people help others is an important psychological and neuroscientific question. It has received much attention from the psychological side, but comparatively less from neuroscience. The paper translates some ideas from a social Psychology domain to neuroscience using a neuroeconomically oriented computational approach. In particular, the paper is concerned with the idea that people help others based on perceptions of merit/deservingness, but also because they require/need help. To this end, the authors conduct two experiments with an overlapping participant pool:

1) A social perception task in which people see images of people that have previously been rated on merit and need scales by other participants. In a blockwise fashion, people decide whether the depicted person a) deserves help, b) needs help, and c) whether the person uses both hands (== control condition).

2) In an altruism task, people make costly helping decisions by deciding between giving a certain amount of money to themselves or another person. How much the other person needs and deserves the money is manipulated.

The authors use a sound and robust computational modelling approach for both tasks using evidence accumulation models. They analyse behavioural data for both tasks, showing that the behaviour is indeed influenced, as expected, by the deservingness and the need of the shown people. Neurally, the authors use a block-wise analysis approach to find differences in activity levels across conditions of the social perception task (there is no fMRI data for the other task). The authors do find large activation clusters in areas related to the theory of mind. Interestingly, they also find that activity in TPJ that relates to the deservingness condition correlates with people's deservingness ratings while they do the task, but also with computational parameters related to helping others in the second task, the one that was conducted many months later. Also, some behavioural parameters correlate across the two tasks, suggesting that how deserving of help others are perceived reflects a relatively stable feature that translates into concrete helping decisions later-on.

The conclusions of the paper are overall well supported by the data.

1) I found that the modelling was done very thoroughly for both tasks. Overall, I had the impression that the methods are very solid with many supplementary analyses. The computational modelling is done very well.

2) A slight caveat, however, regarding this aspect, is that, in my view, the tasks are relatively simplistic, so even the complex computational models do not do as much as they can in the case of more complex paradigms. For example, the bias term in the model seems to correspond to the mean response rate in a very direct way (please correct me if I am wrong).

3) Related to the simple tasks: The fMRI data is analysed in a simple block-fashion. This is in my view not appropriate to discern the more subtle neural substrates of merit/need-based decision-making or person perception. Correspondingly, the neural activation patterns (merit > control, need > control) are relatively broad and unspecific. They do not seem to differ in the classic theory of mind regions, which are the focus of the analyses.

4) However, the relationship between neural signal and behavioural merit sensitivity in TPJ is noteworthy.

5) The latter is even more the case, as the neural signal and aspects of the behaviour are correlated across subjects with the second task that is conducted much later. Such a correlation is very impressive and suggests that the tasks are sensitive for important individual differences in helping perception/behaviour.

6) That being said, the number of participants in the latter analyses are at the lower end of the number of participants that are these days used for across-participant correlations.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Shen and collaborators described the generation of cDKO mice lacking LRRK1 and LRRK2 selectively in DAT-positive DAergic neurons. The Authors asked whether selective deletion of both LRRK isoforms could lead to a Parkinsonian phenotype, as previously reported by the same group in germline double LRRK1 and LRRK2 knockout mice (PMID: 29056298). Indeed, cDKO mice developed a late reduction of TH+ neurons in SNpc that partially correlated with the reduction of NeuN+ cells. This was associated with increased apoptotic cell and microglial cell numbers in SNpc. Unlike the constitutive DKO mice described earlier, however, cDKO mice did not replicate the dramatic increase in the number of autophagic vacuoles. The study supports the authors' hypothesis that loss of function rather than gain of function of LRRK2 leads to PD.

Strengths:

The study described for the first time a model where both the PD-associated gene LRRK2 and its homolog LRRK1 are deleted selectively in DAergic neurons, offering a new tool to understand the physiopathological role of LRRK2 and the compensating role of LRRK1 in modulating DAergic cell function.

Weaknesses:

The model has no construct validity since loss of function mutations of LRRK2 are well-tolerated in humans and do not lead to PD. The evidence of a Parkinsonian phenotype in these cDKO mice is limited and should be considered preliminary.

Reviewer #2 (Public Review):

Summary:<br /> The authors tried to identify the genetic factors for asthenoteratozoospermia. Using whole-exome sequencing, they analyzed a family with an infertile male and identified CFAP52 variants. They further knockout mouse Cfap52 gene and the homozygous mice phenocopied the patient. CFAP52 interacts with several other sperm proteins to maintain normal sperm morphology. Finally, CFAP52-associated male infertility in humans and mice could be overcome by using intracytoplasmic sperm injections (ICSI).

Strengths:<br /> The major strength of this study is to identify genetic factors contributing to asthenoteratozoospermia, and to generate a mouse knockout model to validate the factor.

Weaknesses:<br /> The authors did not use the OMICS to dissect the potential mechanisms. Instead, they took the advantage of direct co-IP experiment to fish the binding partners. They also did not discuss in detail why other motile cilia have different behavior.

Reviewer #2 (Public Review):

Summary and Strengths:<br /> In this manuscript, Chotiner and colleagues demonstrated the localization of TRIP13 and clarified the phenotypes of Trip13-null mice in mouse meiosis. The meiotic phenotypes of Trip13 have been well characterized using the hypomorph alleles in the literature. However, the null phenotypes have not been examined, and the localization of TRIP13 was not clearly demonstrated. The study fills these important knowledge gaps in the field. The demonstration of TRIP13 localization to SC in mice provides an explanation of how HOMRA domain proteins are evicted from SC in diverse organisms. This conclusion was confirmed in both IF and TRIP13-tagged Tg mice. Further, the phenotypes of Trip13-null mice are very clear. The manuscript is well crafted, and the discussion section is well organized and comprehends the topic in the field. All in all, the manuscript will provide important knowledge in the field of meiosis.

Weaknesses:<br /> The heterozygous phenotypes demonstrate that TRIP13 is a dosage-sensitive regulator of meiosis. In relation to this conclusion, as summarized in the discussion section, other mutants defective in meiotic recombination showed dosage-sensitive phenotypes. However, the authors did not examine meiotic recombination in the Trip13-null mice.

Reviewer #2 (Public Review):

The objective of this study from Lazar-Contes et al. is to examine chromatin accessibility changes in "spermatogonial cells" (SCs) across testis development. Exactly what SCs are, however, remains a mystery. The authors mention in the abstract that SCs are undifferentiated male germ cells and have self-renewal and differentiation activity, which would be true for Spermatogonial STEM Cells (SSCs), a very small subset of total spermatogonia, but then the methods they use to retrieve such cells using antibodies that enrich for undifferentiated spermatogonia encompass both undifferentiated and differentiating spermatogonia. Data in Fig. 1B prove that most (85-95%) are PLZF+, but PLZF is known to be expressed both by undifferentiated and differentiating (KIT+) spermatogonia (Niedenberger et al., 2015; PMID: 25737569). Thus, the bulk RNA-seq and ATAC-seq data arising from these cells constitute the aggregate results comprising the phenotype of a highly heterogeneous mixture of spermatogonia (plus contaminating somatic cells), NOT SSCs. Indeed, Fig. 1C demonstrates this by showing the detection of Kit mRNA (a well-known marker of differentiating spermatogonia - which the authors claim on line 89 is a marker of SCs!), along with the detection of markers of various somatic cell populations (albeit at lower levels). This admixture problem influences the results - the authors show ATAC-seq accessibility traces for several genes in Fig. 2E (exhibiting differences between P15 and Adult), including Ihh, which is not expressed by spermatogenic cells, and Col6a1, which is expressed by peritubular myoid cells. Thus, the methods in this paper are fundamentally flawed, which precludes drawing any firm conclusions from the data about changes in chromatin accessibility among spermatogonia (SCs?) across postnatal testis development. In addition, there already are numerous scRNA-seq datasets from mouse spermatogenic cells at the same developmental stages in question. Moreover, several groups have used bulk ATAC-seq to profile enriched populations of spermatogonia, including from synchronized spermatogenesis which reflects a high degree of purity (see Maezawa et al., 2018 PMID: 29126117 and Schlief et al., 2023 PMID: 36983846 and in cultured spermatogonia - Suen et al., 2022 PMID: 36509798) - so this topic has already begun to be examined. None of these papers was cited, so it appears the authors were unaware of this work. The authors' methodological choice is even more surprising given the wealth of single-cell evidence in the literature since 2018 demonstrating the exceptional heterogeneity among spermatogonia at these developmental stages (the authors DID cite some of these papers, so they are aware). Indeed, it is currently possible to perform concurrent scATAC-seq and scRNA-seq (10x Genomics Multiome), which would have made these data quite useful and robust. As it stands, given the lack of novelty and critical methodological flaws, readers should be cautioned that there is little new information to be learned about spermatogenesis from this study, and in fact, the data in Figures 2-5 may lead readers astray because they do not reflect the biology of any one type of male germ cell. Indeed, not only do these data not add to our understanding of spermatogonial development, but they are damaging to the field if their source and identity are properly understood. Here are some specific examples of the problems with these data:

1. Fig. 2D - Gata4 and Lhcgr are not expressed by germ cells in the testis.

2. Fig. 3A - WT1 is expressed by Sertoli cells, so the change in accessibility of regions containing a WT1 motif suggests differential contamination with Sertoli cells. Since Wt1 mRNA was differentially high in P15 (Fig. 3B) - this seems to be the most likely explanation for the results. How was this excluded?

3. Fig. 3D - Since Dmrt1 is expressed by Sertoli cells, the "downregulation" likely represents a reduction in Sertoli cell contamination in the adult, like the point above. Did the authors consider this?

Reviewer #2 (Public Review):

Summary:

Liao and colleagues generated tagged SMAD1 and SMAD5 mouse models and identified genome occupancy of these two factors in the uterus of these mice using the CUT&RUN assay. The authors used integrative bioinformatic approaches to identify putative SMAD1/5 direct downstream target genes and to catalog the SMAD1/5 and PGR genome co-localization pattern. The role of SMAD1/5 on stromal decidualization was assayed in vitro on primary human endometrial stromal cells. The new mouse models offer opportunities to further dissect SMAD1 and SMAD5 functions without the limitation from SMAD antibodies, which is significant. The CUT&RUN data further support the usefulness of these mouse models for this purpose.

Strengths:<br /> The strength of this study is the novelty of new mouse models and the valuable cistromic data derived from these mice.

Weaknesses:<br /> The weakness of the present version of the manuscript includes the self-limited data analysis approaches such as the proximal promoter based bioinformatic filter and a missed opportunity to investigate the role of SMAD1/5 on determining the genome occupancy of major uterine transcription regulators.

Reviewer #2 (Public Review):

Summary:<br /> The authors investigated the molecular evolution of members of the gasdermin (GSDM) family. By adding the evolutionary time axis of animals, they created a new molecular phylogenetic tree different from previous ones. The analyzed result verified that non-mammalian GSDMAs and mammalian GSDMAs have diverged into completely different and separate clades. Furthermore, by biochemical analyses, the authors demonstrated non-mammalian GSDMA proteins are cleaved by the host-encoded caspase-1. They also showed mammalian GSDMAs have lost the cleavage site recognized by caspase-1. Instead, the authors proposed that the newly appeared GSDMD is now cleaved by caspase-1.

Through this study, we have been able to understand the changes in the molecular evolution of GSDMs, and by presenting the cleavage of GSDMAs through biochemical experiments, we have become able to grasp the comprehensive picture of this family of molecules. However, there are some parts where explanations are insufficient, so supplementary explanations and experiments seem to be necessary.

Strengths:<br /> It has a strong impact in advancing ideas into the study of pyroptotic cell death and even inflammatory responses involving caspase-1.

Weaknesses:<br /> Based on the position of mammalian GSDMA shown in the molecular phylogenetic tree (Figure 1), it may be difficult to completely agree with the authors' explanation of the evolution of GSDMA.

1) Focusing on mammalian GSDMA, this group, and mammalian GSDMD diverged into two clades, and before that, GSDMA/D groups and mammalian GSDMC separated into two, more before that, GSDMB, and further before that, non-mammalian GSDMA, when we checked Figure 1. In the molecular phylogenetic tree, it is impossible that GSDMA appears during evolution again. Mammalian GSDMAs are clearly paralogous molecules to non-mammalian GSDMAs in the figure. If they are bona fide orthologous, the mammalian GSDMA group should show a sub-clade in the non-mammalian GSDMA clade. It is better to describe the plausibility of the divergence in the molecular evolution of mammalian GSDMA in the Discussion section.

2) Regarding (1), it is recommended that the authors reconsider the validity of estimates of divergence dates by focusing on mammalian species divergence. Because the validity of this estimation requires a recheck of the molecular phylogenetic tree, including alignment.

3) If GSDMB and/or GSDMC between non-mammalian GSDMA and mammalian GSDMD as shown in the molecular phylogenetic tree would be cleaved by caspase-1, the story of this study becomes clearer. The authors should try that possibility.

Reviewer #2 (Public Review):

In this work, Chang-Gonzalez and co-workers investigate the role of force in peptide recognition by T-cells using a model T-cell/peptide recognition complex. By applying forces through a harmonic restraint on distances, the authors probe the role of mechanical pulling on peptide binding specificity. They point to a role for force in distinguishing the different roles played by agonist and antagonist peptides for which the bound configuration is not clearly distinguishable. Overall, I would consider this work to be extensive and carefully done, and noteworthy for the number of mutant peptides and conditions probed. From the text, I'm not sure how specific these conclusions are to this particular complex, but I do not think this diminishes the specific studies.

I have a couple of specific comments on the methodology and analysis that the authors could consider:<br /> 1) It is not explained what is the origin of force on the peptide-MHC complex. Although I do know a bit about this, it's not clear to me how the force ends up applied across the complex (e.g. is it directional in any way, on what subdomains/residues do we expect it to be applied), and is it constant or stochastic. I think it would be important to add some discussion of this and how it translates into the way the force is applied here (on terminal residues of the complex).

2) In terms of application of the force, I find the use of a harmonic restraint and then determining a distance at which the force has a certain value to be indirect and a bit unphysical. As just mentioned, since the origin of the force is not a harmonic trap, it would be more straightforward to apply a pulling force which has the form -F*d, which would correspond to a constant force (see for example comment articles 10.1021/acs.jpcb.1c10715, 10.1021/acs.jpcb.1c06330). While application of a constant force will result in a new average distance, for small forces it does so in a way that does not change the variance of the distance whereas a harmonic force pollutes the variance (see e.g. 10.1021/ct300112v in a different context). A constant force could also shift the system into a different state not commensurate with the original distance, so by applying a harmonic trap, one could be keeping ones' self from exploring this, which could be important, as in the case of certain catch bond mechanisms. While I certainly wouldn't expect the authors to redo these extensive simulations, I think they could at least acknowledge this caveat, and they may be interested in considering a comparison of the two ways of applying a force in the future.

3) For the PCA analysis, I believe the authors learn separate PC vectors from different simulations and then take the dot product of those two vectors. Although this might be justified based on the simplified coordinate upon which the PCA is applied, in general, I am not a big fan of running PCA on separate data sets and then comparing the outputs, as the meaning seems opaque to me. To compare the biggest differences between many simulations, it would make more sense to me to perform PCA on all of the data combined, and see if there are certain combinations of quantities that distinguish the different simulations. Alternatively and probably better, one could perform linear discriminant analysis, which is appropriate in this case because one already knows that different simulations are in different states, and hence the LDA will directly give the linear coordinate that best distinguishes classes.

Reviewer #2 (Public Review):

In HD patients, immune pathways alteration leads to higher susceptibility to infection and lower response to the COVID-19 mRNA vaccine. Therefore, it is important to understand the immune response to vaccines in ESRD patients against the COVID-19 pandemic. In this MS, the authors recruited 20 HD patients and cohort-matched controls to perform multiple experimental studies (including transcriptomic analysis, RNAseq, and Anti-Spike (trimer) IgG Titer Quantification) to investigate how immune pathways alteration in HD patients after COVID-19 mRNA vaccine injection. They demonstrate differing expression of BTMs and differing time courses of immune responses to the BTN162b2 mRNA COVID-19 vaccination in maintenance hemodialysis subjects (HD) compared to controls, which warrants further characterization of the immune dysregulation of ESRD and immune biomarkers. Overall, the study is well designed, and the result has potential clinical value and will interest nephrologists. The major concern of this study is the cohort set up. The sample sizes of recruited candidates are relatively small, and no validation cohort was designed. More importantly, between the two groups, the race distribution is uneven. For example, 10 black and 2 white HD patients were included, but accordingly, 3 black and 8 white people were recruited as controls. In such a small size of the clinical study, this kind of unevenness might cause potential issues in concluding. In addition, the control cohort also included 1 diabetes and 4 hypertension, patients. Will these existing primary diseases in controls cause noise in the data analysis because these metabolic diseases also can directly cause immune system dysfunction? In addition, there were 8 HD patients and 5 HC with a positive test of SARS-CoV-2 from 8 months to four weeks preceding vaccination. How long does the immune response last after being infected with COVID-19? Several studies have found that people infected with COVID-19 continue to produce antibodies to the virus for seven or eight months after recovery. Therefore, people with a COVID-19 history might not be suitable for this trial.

Reviewer #2 (Public Review):

This study proposes to evaluate and compare different replay methods in the absence of "ground truth" using data from hippocampal recordings of rodents that were exposed to two different tracks on the same day. The study proposes to leverage the potential of Bayesian methods to decode replay and reactivation in the same events. They find that events that pass a higher threshold for replay typically yield a higher measure of reactivation. On the other hand, events from the shuffled data that pass thresholds for replay typically don't show any reactivation. While well-intentioned, I think the result is highly problematic and poorly conceived.

The work presents a lot of confusion about the nature of null hypothesis testing and the meaning of p-values. The prescription arrived at, to correct p-values by putting animals on two separate tracks and calculating a "sequence-less" measure of reactivation are impractical from an experimental point of view, and unsupportable from a statistical point of view. Much of the observations are presented as solutions for the field, but are in fact highly dependent on distinct features of the dataset at hand. The most interesting observation is that despite the existence of apparent sequences in the PRE-RUN data, no reactivation is detectable in those events, suggesting that in fact they represent spurious events. I would recommend the authors focus on this important observation and abandon the rest of the work, as it has the potential to further befuddle and promote poor statistical practices in the field.

The major issue is that the manuscript conveys much confusion about the nature of hypothesis testing and the meaning of p-values. It's worth stating here the definition of a p-value: the conditional probability of rejecting the null hypothesis given that the null hypothesis is true. Unfortunately, in places, this study appears to confound the meaning of the p-value with the probability of rejecting the null hypothesis given that the null hypothesis is NOT true-i.e. in their recordings from awake replay on different mazes. Most of their analysis is based on the observation that events that have higher reactivation scores, as reflected in the mean log odds differences, have lower p-values resulting from their replay analyses. Shuffled data, in contrast, does not show any reactivation but can still show spurious replays depending on the shuffle procedure used to create the surrogate dataset. The authors suggest using this to test different practices in replay detection. However, another important point that seems lost in this study is that the surrogate dataset that is contrasted with the actual data depends very specifically on the null hypothesis that is being tested. That is to say, each different shuffle procedure is in fact testing a different null hypothesis. Unfortunately, most studies, including this one, are not very explicit about which null hypothesis is being tested with a given resampling method, but the p-value obtained is only meaningful insofar as the null that is being tested and related assumptions are clearly understood. From a statistical point of view, it makes no sense to adjust the p-value obtained by one shuffle procedure according to the p-value obtained by a different shuffle procedure, which is what this study inappropriately proposes. Other prescriptions offered by the study are highly dataset and method dependent and discuss minutiae of event detection, such as whether or not to require power in the ripple frequency band.

Reviewer #2 (Public Review):

This study proposes to evaluate and compare different replay methods in the absence of "ground truth" using data from hippocampal recordings of rodents that were exposed to two different tracks on the same day. The study proposes to leverage the potential of Bayesian methods to decode replay and reactivation in the same events. They find that events that pass a higher threshold for replay typically yield a higher measure of reactivation. On the other hand, events from the shuffled data that pass thresholds for replay typically don't show any reactivation. While well-intentioned, I think the result is highly problematic and poorly conceived.

The work presents a lot of confusion about the nature of null hypothesis testing and the meaning of p-values. The prescription arrived at, to correct p-values by putting animals on two separate tracks and calculating a "sequence-less" measure of reactivation are impractical from an experimental point of view, and unsupportable from a statistical point of view. Much of the observations are presented as solutions for the field, but are in fact highly dependent on distinct features of the dataset at hand. The most interesting observation is that despite the existence of apparent sequences in the PRE-RUN data, no reactivation is detectable in those events, suggesting that in fact they represent spurious events. I would recommend the authors focus on this important observation and abandon the rest of the work, as it has the potential to further befuddle and promote poor statistical practices in the field.

The major issue is that the manuscript conveys much confusion about the nature of hypothesis testing and the meaning of p-values. It's worth stating here the definition of a p-value: the conditional probability of rejecting the null hypothesis given that the null hypothesis is true. Unfortunately, in places, this study appears to confound the meaning of the p-value with the probability of rejecting the null hypothesis given that the null hypothesis is NOT true-i.e. in their recordings from awake replay on different mazes. Most of their analysis is based on the observation that events that have higher reactivation scores, as reflected in the mean log odds differences, have lower p-values resulting from their replay analyses. Shuffled data, in contrast, does not show any reactivation but can still show spurious replays depending on the shuffle procedure used to create the surrogate dataset. The authors suggest using this to test different practices in replay detection. However, another important point that seems lost in this study is that the surrogate dataset that is contrasted with the actual data depends very specifically on the null hypothesis that is being tested. That is to say, each different shuffle procedure is in fact testing a different null hypothesis. Unfortunately, most studies, including this one, are not very explicit about which null hypothesis is being tested with a given resampling method, but the p-value obtained is only meaningful insofar as the null that is being tested and related assumptions are clearly understood. From a statistical point of view, it makes no sense to adjust the p-value obtained by one shuffle procedure according to the p-value obtained by a different shuffle procedure, which is what this study inappropriately proposes. Other prescriptions offered by the study are highly dataset and method dependent and discuss minutiae of event detection, such as whether or not to require power in the ripple frequency band.

replying to Will at https://forum.zettelkasten.de/discussion/comment/18885/#Comment_18885

I've generally found that this is much easier to do when it's an area you tend to specialize in and want to delve ever deeper (or on which you have larger areas within your zettelkasten) versus those subjects which you care less about or don't tend to have as much patience for.

Perhaps it's related to the System 1/System 2 thinking of Kahneman/Tversky? There are only some things that seem worth System 2 thinking/clarifying/citing and for all the rest one relies on System 1 heuristics. I find that the general ease of use of my zettelkasten (with lots of practice) allows me to do a lot more System 2 thinking than I had previously done, even for areas which I don't care as much about.

syndication link: https://forum.zettelkasten.de/discussion/comment/18888/#Comment_18888

DOI: 10.7554/eLife.85930

Resource: ZFIN_ZDB-ALT-080910-2

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-080910-2

What is this?

DOI: 10.7554/eLife.85930

Resource: (ZFIN Cat# ZDB-GENO-080714-2,RRID:ZFIN_ZDB-GENO-080714-2)

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-080714-2

What is this?

Reviewer #2 (Public Review):

Summary:

The work demonstrates that high-intensity ultrasound produces a release of extracellular vesicles from murine myotubes that is dependent on ultrasound intensity. It shows that this increase in extracellular vesicles is abolished in a nominally zero Ca2+ solution. It then shows that these vesicles reduce the mRNA levels of IL-1b and IL-6 in murine bone marrow-derived macrophages and uses a dilution technique to demonstrate that the number, but not the type of vesicles is responsible for this change in mRNA expression. It also compares the miRNA levels in constitutively-released vesicles with those released by high-intensity ultrasound.

Strengths:

The experiments are logically sequenced. It is very helpful to see assessment of the viability of the preparations.<br /> The results are presented fairly clearly and the statistical approach is described. The findings are reasonably clear and the writing is succinct.

Weaknesses:

The work is quite limited in scope and of limited novelty, largely recapitulating work from the first-named author's own recent publications.<br /> Thus, perhaps the most significant weakness of this study is that it makes claims of mechanisms, or of clinical or therapeutic relevance, that are not supported or even addressed by the study.

The aspects of the current work which are novel are hard to identify because the statement of aims is too broad and therefore encapsulates previous work. In addition, the introduction and discussion are vague and fail, for example, to mention the cell types used in the previous studies that are quoted. This means that it is not obvious from the Introduction whether the present study is at all novel.

The size of the study is quite small, with most experiments employing n = 4. This inevitably means that, for example, there is no significant effect of the lower power levels of ultrasound despite shifts in the mean values that might be of interest. Thus, the study appears underpowered. This problem is compounded by a failure to use appropriate analysis methods in the studies looking at dose-responses, where a regression analysis might be more appropriate than multiple individual t-tests / ANOVA.

The assessment of the role of Ca2+ is important but incomplete. Measurement of whole-cell Ca2+ levels is not really a substitute for measuring cytosolic Ca2+ as cell volume changes and sarcoplasmic reticulum Ca2+ changes would greatly influence the possible meaning of the findings. Furthermore, a statement that Ca2+ increase causes the vesicle release could only be supported by experiments that increase intracellular Ca2+, such as the use of a Ca2+ ionophore.

mRNA expression levels of IL-1b and IL-6 are reported. There should also be a report of a non-inflammatory mRNA to act as a control.

Reviewer #2 (Public Review):

The flowering plant Capsella bursa-pastoris is an allotetraploid formed from the genomes of Capsella orientalis and Capsella grandiflora. An outstanding question in the evolution of allotetraploids is the relative contribution of immediate consequences of allopolyploidization vs. long-term evolution after the event. The authors address this question by re-synthesizing the allotetraploid in the lab using the two progenitor species, and comparing its phenotypic and gene expression variation to naturally occurring C. bursa-pastoris. They find evidence primarily for long-term phenotypic evolution towards a selfing syndrome in C. bursa-pastoris, and a combination of short and long-term changes to gene expression.

The manuscript is thorough and provides lots of new insights into the mechanisms driving evolution in allopolyploids. I especially appreciated the detailed examination of different mechanisms driving gene expression variation. There are some important limitations of the experimental design related to independent evolution of the progenitor species and effects of the colchicine treatment used to induce polyploidy, but these are well-addressed in the Discussion.