Reviewer #3 (Public Review):

Summary:<br /> This paper describes transcriptomes from three tardigrade species with or without treatment with ionizing radiation (IR). The authors show that IR produces numerous single-strand and double-strand breaks as expected and that these are substantially repaired within 4-8 hours. Treatment with IR induces strong upregulation of transcripts from numerous DNA repair proteins including Dsup specific to the Hypsobioidea superfamily. Transcripts from the newly described protein TDR1 with homologs in both Hypsibioidea and Macrobiotoidea supefamilies are also strongly upregulated. They show that TDR1 transcription produces newly translated TDR1 protein, which can bind DNA and co-localizes with DNA in the nucleus. At higher concentrations, TDR appears to form aggregates with DNA, which might be relevant to a possible function in DNA damage repair. When introduced into human U2OS cells treated with bleomycin, TDR1 reduces the number of double-strand breaks as detected by gamma H2A spots. This paper will be of interest to the DNA repair field and to radiobiologists.

Strengths:<br /> The paper is well-written and provides solid evidence of the upregulation of DNA repair enzymes after irradiation of tardigrades, as well as upregulation of the TRD1 protein. The reduction of gamma-H2A.X spots in U2OS cells after expression of TRD1 supports a role in DNA damage.

Weaknesses:<br /> Genetic tools are still being developed in tardigrades, so there is no mutant phenotype to support a DNA repair function for TRD1, but this may be available soon.

Reviewer #2 (Public Review):

Summary:

A dominant hypothesis concerning the origin of life is that, before the appearance of the first enzymes, RNA replicated non-enzymatically by templating. However, this replication was probably not very efficient, due to the propensity of single strands to bind to each other, thus inhibiting template replication. This phenomenon, known as product inhibition, has been shown to lead to parabolic growth instead of exponential growth. Previous works have shown that this situation limits competition between alternative replicators and therefore promotes RNA population diversity. The present work examines this scenario in a model of RNA replication, taking into account finite population size, mutations, and differences in GC content. The main results are (1) confirmation that parabolic growth promotes diversity, but that when the population size is small enough, sequences least efficient at replicating may nevertheless go extinct; (2) the observation that fitness is not only controlled by the replicability of sequences, but also by their GC content ; (3) the observation that parabolic growth attenuates the impact of mutations and, in particular, that the error threshold to which exponentially growing sequences are subject can be exceeded, enabling sequence identity to be maintained at higher mutation rates.

Strengths:

The analyses are sound and the observations are intriguing. Indeed, it has been noted previously that parabolic growth promotes coexistence, its role in mitigating the error threshold catastrophe - which is often presented as a major obstacle to our understanding of the origin of life - had not been examined before.

Weaknesses:

Although all the conclusions are interesting, most are not very surprising for people familiar with the literature. As the authors point out, parabolic growth is well known to promote diversity (Szathmary-Gladkih 89) and it has also been noted previously that a form of Darwinian selection can be found at small population sizes (Davis 2000). Given that under parabolic growth, no sequence is ever excluded for infinite populations, it is also not surprising to find that mutations have a less dramatic exclusionary impact.

A general weakness is the presentation of models and parameters, whose choices often appear arbitrary. Modeling choices that would deserve to be further discussed include the association of the monomers with the strands and the ensuing polymerization, which are combined into a single association/polymerization reaction (see also below), or the choice to restrict to oligomers of length L = 10. Other models, similar to the one employed here, have been proposed that do not make these assumptions, e.g. Rosenberger et al. Self-Assembly of Informational Polymers by Templated Ligation, PRX 2021. To understand how such assumptions affect the results, it would be helpful to present the model from the perspective of existing models.

The values of the (many) parameters, often very specific, also very often lack justifications. For example, why is the "predefined error factor" ε = 0.2 and not lower or higher? How would that affect the results? Similarly, in equation (11), where does the factor 0.8 come from? Why is the kinetic constant for duplex decay reaction 1.15e10−8? Are those values related to experiments, or are they chosen because specific behaviors can happen only then?

The choice of the model and parameters potentially impact the two main results, the attenuation of the error threshold and the role of GC content:

Regarding the error threshold, it is also noted (lines 379-385) that it disappears when back mutations are taken into account. This suggests that overcoming the error threshold might not be as difficult as suggested, and can be achieved in several ways, which calls into question the importance of the particular role of parabolic growth. Besides, when the concentration of replicators is low, product inhibition may be negligible, such that a "parabolic replicator" is effectively growing exponentially and an error catastrophe may occur. Do the authors think that this consideration could affect their conclusion? Can simulations be performed?

Regarding the role of the GC content, GC-rich oligomers are found to perform the worst but no rationale is provided. One may assume that it happens because GC-rich sequences are comparatively longer to release the product. However, it is also conceivable that higher GC content may help in the polymerization of the monomers as the monomers attach longer on the template (as described in Eq.(9)). This is an instance where the choice to pull into a single step the association and polymerization reactions are pulled into a single step independent of GC content may be critical. It would be important to show that the result arises from the actual physics and not from this modeling choice.

Some more specific points that would deserve to be addressed:

- Line 53: it is said that p "reflects how easily the template-reaction product complex dissociates". This statement is not correct. A reaction order p<1 reflects product inhibition, the propensity of templates to bind to each other, not slow product release. Product release can be limiting, yet a reaction order of 1 can be achieved if substrate concentrations are sufficiently high relative to oligomer concentrations (von Kiedrowski et al., 1991).

- Population size is a key parameter, and a comparison is made between small (10^3) and large (10^5) populations, but without explaining what determines the scale (small/large relative to what?).

- In the same vein, we might expect size not to be the only important parameter, but also concentration.

- Lines 543-546: if understanding correctly, the quantitative result is that the error threshold rises from 0.1 in the exponential case to 0.196 in the parabolic. Are the authors suggesting that a factor of 2 is a significant difference?

- Figure 3C: this figure shows no statistically significant effect?

- line 542: "phase transition-like species extension (Figure 4B)": such a clear threshold is not apparent.

Reviewer #2 (Public Review):

Summary:<br /> This work describes the structure of Heparan-alpha-glucosaminide N-acetyltransferase (HGSNAT), a lysosomal membrane protein that catalyzes the acetylation reaction of the terminal alpha-D-glucosamine group required for the degradation of heparan sulfate (HS). HS degradation takes place during the degradation of the extracellular matrix, a process required for restructuring tissue architecture, regulation of cellular function, and differentiation. During this process, HS is degraded into monosaccharides and free sulfate in lysosomes.

HGSNAT catalyzes the transfer of the acetyl group from acetyl-CoA to the terminal non-reducing amino group of alpha-D-glucosamine. The molecular mechanism by which this process occurs has not been described so far. One of the main reasons to study the mechanism of HGSNAT is that multiple mutations spanning the entire sequence of the protein, such as nonsense mutations, splice-site variants, and missense mutations lead to dysfunction that causes abnormal accumulation of HS within the lysosomes. This accumulation is a cause of mucopolysaccharidosis IIIC (MPS IIIC), an autosomal recessive neurodegenerative lysosomal storage disorder, for which there are no approved drugs or treatment strategies.

This paper provides a 3.26A structure of HGSNAT, determined by single-particle cryo-EM. The structure reveals that HGSNAT is a dimer in detergent micelles and a density assigned to acetyl-CoA. The authors speculate about the molecular mechanism of the acetylation reaction, map the mutations known to cause MPS IIIC on the structure and speculate about the nature of the HGSNAT disfunction caused by such mutations.

Strengths:<br /> The description of the architecture of HGSNAT is the highlight of the paper since this corresponds to the first description of the structure of a member of the transmembrane acyl transferase (TmAT) superfamily. The high resolution of an HGSNAT bound to acetyl-CoA is an important leap in our understanding of the HGSNAT mechanism. The density map is of high quality, except for the luminal domain. The location of the acetyl-CoA allows speculation about the mechanistic role of multiple residues surrounding this molecule. The authors thoroughly describe the architecture of HGSNAT and map the mutations leading to MPS IIIC. The description of the dimeric interphase is a novel result, and future studies are left to confirm the importance of oligomerization for function.

Weaknesses:<br /> Apart from the cryo-EM structure, the article does not provide any other experimental evidence to support or explain a molecular mechanism. Due to the complete absence of functional assays, mutagenesis analysis, or other structures such as a ternary complex or an acetylated enzyme intermediate, the mechanistic model depicted in Figure 5 should be taken with caution.

The authors discuss that H269 is an essential residue that participates in the acetylation reaction, possibly becoming acetylated during the process. However, there is no solid experimental evidence, e.g. mutagenesis analysis or structural analysis, in this or previous articles, that demonstrates this to be the case.

In the discussion part, the authors mention previous studies in which it was postulated that the catalytic reaction can be described by a random order mechanistic model or a Ping Pong Bi Bi model. However, the authors leave open the question of which of these mechanisms best describes the acetylation reaction. The structure presented here does not provide evidence that could support one mechanism or the other.

Although the authors map the mutations leading to MPS IIIC on the structure and use FoldX software to predict the impact of these mutations on folding and fold stability, there is no experimental evidence to support FoldX's predictions.

Reviewer #2 (Public Review):

Summary:

The manuscript from Bekeova et al. entitled "Acyl-CoA thioesterase-2 facilitates P-oxidation in glycolytic skeletal muscle in a lipid supply dependent manner" examines whether loss of acyl-CoA thioesterase-2 (ACOT2) in the mitochondrial matrix of skeletal muscle alters mitochondrial fatty acid metabolism. The authors generate data demonstrating that under normal chow conditions, loss of ACOT2 increases mitochondrial respiration of long-chain fatty acid, but also increases susceptibility to the build-up of metabolic intermediates. However, during short-term high-fat feeding (7 days), mice with knockout of skeletal muscle ACOT2 had better glucose and insulin tolerance. Interestingly, skeletal muscle ACOT2 knockout mice on chow and high-fat diet utilized more glucose during the active (dark cycle) portion of the day. These data suggest that ACOT2 may be a potential therapeutic target to improve glucose homeostasis.

Strengths:

The use of creatine kinase cre recombinase to specifically target striated muscle localizes the genetic manipulation, thus increasing the rigor of these experiments by limiting potential off-site changes in ACOT2 expression. Also, the assessment of mitochondrial respiration and response to changes in energy change via the creatine kinase clamp technique is a strength. These data provide a measurement of isolated mitochondrial respiration at physiologically relevant concentrations of ATP and ADP, while also allowing for assessment of how these mitochondria respond to changes in free energy (Fisher-Wellman et al. 2018). The indirect calorimetry data provides systemic physiological context to the striated muscle-specific genetic manipulation, while also allowing for the examination of how this change in skeletal muscle ACOT2 impacts systemic responses to different energy challenges. Finally, the extensive metabolomics, transcriptomics, and lipidomics analysis, not only provides a wealth of data but is used to further the authors' investigation of skeletal muscle ACOT2 activity in mitochondrial fatty acid oxidation and glucose homeostasis.

Weaknesses:

Several general confounding factors exist in the experimental design that could potentially impact the interpretation of the observed outcomes. First, all mice were housed at housing temperatures (22C) below the thermoneutral zone, which has been well described by many investigators to result in dramatically increased energy expenditure. Changes in total and resting energy expenditure could alter the skeletal muscle and systemic utilization of lipids, response to high-fat diet, and glucose homeostasis. Second, no dietary control was observed in these experiments. While this did not impact outcomes when the diets were not compared, once the authors began to compare normal chow to high-fat diet, numerous differences in the composition of these diets could impact the outcomes. Third, the extended food withdrawal before the glucose- and insulin tolerance tests puts the mouse in a state of extreme energy stress more akin to starvation than fasting, which can negatively impact outcomes (Ayala et al. 2010, Virtue & Vidal-Puig 2021). Fourth, the use of the Seahorse platform for the assessment of respiration of isolated mitochondria is highly debatable (Schmidt et al. 2021), particularly when the investigators also used high-resolution respirometry specifically designed for the purpose of measuring isolated mitochondrial oxygen consumption. Importantly, the use of the Seahorse platform to assess cellular respiration in this investigation is quite appropriate. Finally, while the authors present data demonstrating that ACOT2 expression is highest in Type I fibers compared to the various Type II fiber types, a large number of the experiments are performed in a muscle that is primarily composed of Type II fibers. The authors briefly acknowledge this limitation. But, is important for the reader to keep this in mind when trying to consider how these findings would translate to humans.

Impact:

The authors have generated data that implicates skeletal muscle mitochondrial coenzyme A handling as a therapeutic target in the improvement of glucose homeostasis. While the exact role of increased tissue lipid burden on insulin action, glucose uptake, and substrate metabolism is still debated, the association between increased tissue lipid and impaired tissue- and systemic glucose handling is very strong. The data herein suggest that ACOT2 represents a pharmaceutical target to improve systemic glucose homeostasis in the population with obesity.

Reviewer #2 (Public Review):

Summary:<br /> In the present study, van Gerwen et al. perform deep phosphoproteomics on muscle from saline or insulin-injected mice from 5 distinct strains fed a chow or HF/HS diet. The authors follow these data by defining a variety of intriguing genetic, dietary or gene-by-diet phosphor-sites which respond to insulin accomplished through application of correlation analyses, linear mixed models and a module-based approach (WGCNA). These findings are supported by validation experiments by intersecting results with a previous profile of insulin-responsive sites (Humphrey et al, 2013) and importantly, mechanistic validation of Pfkfb3 where overexpression in L6 myotubes was sufficient to alter fatty acid-induced impairments in insulin-stimulated glucose uptake. To my knowledge, this resource provides the most comprehensive quantification of muscle phospho-proteins which occur as a result of diet in strains of mice where genetic and dietary effects can be quantifiably attributed in an accurate manner. Utilization of this resource is strongly supported by the analyses provided highlighting the complexity of insulin signaling in muscle, exemplified by contrasts to the "classically-used" C57BL6/J strain. As it stands, I view this exceptional resource as comprehensive with compelling strength of evidence behind the mechanism explored. I raised several comments in the last round of assessment but all of them have now been thoughtfully addressed.

Strengths: Generation of a novel resource to explore genetic and dietary interactions influencing the phospho-proteome in muscle. This is accompanied by elegant application of in silico tools to highlight the utility

Weaknesses: none noted

Reviewer #3 (Public Review):

The study by Jain et al. on recombinant adeno-associated viruses (rAAVs) represents a valuable contribution to the fields of virus genetics and gene therapy. As non-pathogenic vectors, rAAVs have become a popular choice for delivering gene therapies. The authors have previously investigated the effects of all possible single codon substitutions, deletions, and insertions in the AAV2 cap gene on AAV production. In this study, they extend their analysis to the AAV2 rep gene and rep genes in two additional capsid serotypes, establishing a genotype-phenotype landscape that enhances our understanding of Rep protein function and offers potential strategies for improving Rep function in gene therapy applications. The experimental design is rigorous, the analyses well-executed, and the interpretations of the data are convincing. While I have a few suggestions to further refine the study, I believe it is overall an excellent piece of research.

One aspect that may warrant further consideration is the assumption, as mentioned in Figure 2's legend, that synonymous mutations are neutral and can serve as controls for normalizing the production rate. However, Figures S5-6 and Figures S11-12 suggest that synonymous mutations are not necessarily neutral, as their distribution is similar to that of nonsynonymous mutations. Thus, it may be beneficial to more thoroughly examine the potential effects of synonymous mutations on the genotype-phenotype landscape.

Additionally, previous research by Jeff Collar and others has reported that synonymous mutations can affect mRNA levels through mRNA degradation rate. It would be interesting to determine if the 20-bp barcodes located at the 3' end are positioned within the untranslated regions and could thus be employed to quantify the mRNA levels of individual variants. This information could offer insight into another potential mechanism by which single codon mutations impact the production rate of rAAV.

The authors discovered several novel mutations that enhance AAV production yet are absent in natural occurrences. This intriguing finding could benefit from further elaboration, particularly with regard to the distribution of these mutations within the protein structure and the nature of the amino acid transitions involved. It would also be informative if the authors could provide a brief discussion as to why these mutations have not been observed in nature. For instance, could it be that optimal viral fitness necessitates an intermediate production rate rather than an excessively rapid one? Expanding on these points may further enrich the paper and offer valuable insights for readers.

The authors have taken commendable steps to address the concerns I raised in my previous evaluation. They have provided comprehensive clarifications, performed necessary revisions, and expanded upon certain key points in the manuscript.

Reviewer #3 (Public Review):

In the revised version the authors have addressed some of the reviewers' concerns, but, despite the new explanatory paragraph on page 16, the paper remains confusing because as shown in Figure 7 at the end of the Results the PARG KO 293A cells that were analyzed at the beginning of the Results are not true PARG knockouts. The authors stated that they did not rewrite the Results because they wanted to describe the experiments in the order in which they were carried out, but there is no imperative for the experiments to be described in the order in which they were done, and it would be much easier for the uninitiated reader to appreciate the significance of these studies if the true PARG KO cell data were presented at the beginning, as all three of the original reviewers proposed.

While the authors have to some extent clarified the nature of the PARG KO alleles, they have not been able to identify the source of the residual PARG activity in the PARG KO cells, in part because different commercial PARG antibodies give different and conflicting immunoblotting results. Additional sequence characterization of PARG mRNAs expressed in the PARG cKO cells, and also in-depth proteomic analysis of the different PARG bands could provide further insight into the origins and molecular identities of the various PARG proteins expressed from the different KO PARG alleles, and determine which of them might retain catalytic activity.

The authors have made no progress in identifying which are the key PARG substrates required for S phase progression, although they suggest that PARP1 itself may be an important target.

Reviewer #2 (Public Review):

In this manuscript, the authors have meticulously constructed a comprehensive atlas delineating hematopoietic stem/progenitor cell (HSPC) and immune-cell types within the zebrafish kidney, employing single-cell transcriptome profiling analysis. Notably, these cell populations exhibited distinctive responses to viral infection. Intriguingly, the investigation revealed that HSPCs manifest positive reactivities to viral infection, indicating the effective induction of trained immunity in select HSPCs. Furthermore, the study unveiled the capacity for the generation of antigen-stimulated adaptive immunity within the kidney, suggesting a role for the zebrafish kidney as a secondary lymphoid organ. This research elucidates the distinctive features of the fish immune system and underscores the multifaceted biology of the kidney in ancient vertebrates.

Reviewer #2 (Public Review):

Summary:<br /> The aim of this manuscript is to use molecular dynamics (MD) simulations to describe the conformational changes of the neurotransmitter binding site of a nicotinic receptor. The study uses a simplified model including the alpha-delta subunit interface of the extracellular domain of the channel and describes the binding of four agonists to observe conformational changes during the weak-to-strong affinity transition.

Strength:<br /> The 200 ns-long simulations of this model suggest that the agonist rotates about its centre in a 'flip' motion, while loop C 'flops' to restructure the site. The changes appear to be reproduced across simulations and different ligands and are thus a strong point of the study.

Weaknesses:<br /> After carrying out all-atom molecular dynamics, the authors revert to a model of binding using continuum Poisson-Boltzmann, surface area, and vibrational entropy. The motivations for and limitations associated with this approximate model for the thermodynamics of binding, rather than using modern atomistic MD free energy methods (that would fully incorporate configurational sampling of the protein, ligand, and solvent) could be provided. Despite this, the authors report a correlation between their free energy estimates and those inferred from the experiment. This did, however, reveal shortcomings for two of the agonists. The authors mention their trouble getting correlation to experiment for Ebt and Ebx and refer to up to 130% errors in free energy. But this is far worse than a simple proportional error, because -24 Vs -10 kcal/mol is a massive overestimation of free energy, as would be evident if the authors were to instead express results in terms of KD values (which would have an error exceeding a billion fold). The MD analysis could be improved with better measures of convergence, as well as a more careful discussion of free energy maps as a function of identified principal components, as described below. Overall, however, the study has provided useful observations and interpretations of agonist binding that will help understand pentameric ligand-gated ion channel activation.

Main points:<br /> Regarding the choice of model, some further justification of the reduced 2 subunit ECD-only model could be given. On page 5 the authors argue that, because binding free energies are independent of energy changes outside the binding pocket, they could remove the TMD and study only an ECD subunit dimer. While the assumption of distant interactions being small seems somewhat reasonable, provided conformational changes are limited and localised, how do we know the packing of TMD onto the ECD does not alter the ability of the alpha-delta interface to rearrange during weak or strong binding? They further write that "fluctuations observed at the base of the ECD were anticipated because the TMD that offers stability here was absent.". As the TMD-ECD interface is the "gating interface" that is reshaped by agonist binding, surely the TMD-ECD interface structure must affect binding. It seems a little dangerous to completely separate the agonist binding and gating infrastructure, based on some assumption of independence. Given the model was only the alpha and delta subunits and not the pentamer with TMD, I am surprised such a model was stable without some heavy restraints. The authors state that "as a further control we carried out MD simulation of a pentamer docked with ACh and found similar structural changes at the binding pocket compared to the dimer." Is this sufficient proof of the accuracy of the simplified model? How similar was the model itself with and without agonist in terms of overall RMSD and RMSD for the subunit interface and the agonist binding site, as well as the free energy of binding to each model to compare?

Although the authors repeatedly state that they have good convergence with their MD, I believe the analysis could be improved to convince us. On page 8 the authors write that the RMSD of the system converged in under 200 ns of MD. However, I note that the graph is of the entire ECD dimer, not a measure for the local binding site region. An additional RMSD of local binding site would be much more telling. You could have a structural isomerisation in the site and not even notice it in the existing graph. On page 9 the authors write that the RMSF in Figure S2 showed instability mainly in loops C and F around the pocket. Given this flexibility at the alpha-delta interface, this is why collecting those regions into one group for the calculation of RMSD convergence analysis would have been useful. They then state "the final MD configuration (with CCh) was well-aligned with the CCh-bound cryo-EM desensitized structure (7QL6)... further demonstrating that the simulation had converged." That may suggest a change occurred that is in common with the global minimum seen in cryo EM, which is good, but does not prove the MD has "converged". I would also rename Figure S3 accordingly.

The authors draw conclusions about the dominant states and pathways from their PCA component free energy projections that need clarification. It is important first to show data to demonstrate that the two PCA components chosen were dominant and accounted for most of the variance. Then when mapping free energy as a function of those two PCA components, to prove that those maps have sufficient convergence to be able to interpret them. Moreover, if the free energies themselves cannot be used to measure state stability (as seems to be the case), that the limitations are carefully explained. First, was PCA done on all MD trajectories combined to find a common PC1 & PC2, or were they done separately on each simulation? If so, how similar are they? The authors write "the first two principal components (PC-1 and PC-2) that capture the most pronounced C. displacements". How much of the total variance did these two components capture? The authors write the changes mostly concern loop C and loop F, but which data proves this? e.g. A plot of PC1 and PC2 over residue number might help.

The authors map the -kTln rho as a free energy for each simulation as a function of PC1 & PC2. It is important to reveal how well that PC1-2 space was sampled, and how those maps converged over time. The shapes of the maps and the relative depths of the wells look very different for each agonist. If the maps were sampled well and converged, the free energies themselves would tell us the stabilities of each state. Instead, the authors do not even mention this and instead talk about "variance" being the indicator of stability, stating that m3 is most stable in all cases. While I can believe 200ns could not converge a PC1-2 map and that meaningful delta G values might not be obtained from them, the issue of lack of sampling must be dealt with. On page 12 they write "Although the bottom of the well for 3 energy minima from PCA represent the most stable overall conformation of the protein, they do not convey direct information regarding agonist stability or orientation". The reasons why not must be explained; as they should do just that if the two order parameters PC1 and PC2 captured the slowest degrees of freedom for binding and sampling was sufficient. The authors write that "For all agonists and trajectories, m3 had the least variance (was most stable), again supporting convergence by 200 ns." Again the issue of actual free energy values in the maps needs to be dealt with. The probabilities expressed as -kTln rho in kcal/mol might suggest that m2 is the most stable. Instead, the authors base stability only on variance (I guess breadth of the well?), where m3 may be more localised in the chosen PC space, despite apparently having less preference during the MD (not the lowest free energy in the maps).

The motivations and justifications for the use of approximate PBSA energetics instead of atomistic MD free energies should be dealt with in the manuscript, with limitations more clearly discussed. Rather than using modern all-atom MD free energy methods for relative or absolute binding free energies, the author selects clusters from their identified states and does Poisson-Boltzmann estimates (electrostatic, vdW, surface area, vibrational entropy). I do believe the following sentence does not begin to deal with the limitations of that method: "there are limitations with regard to MM-PBSA accurately predicting absolute binding free energies (Genheden & Ryde, 2015; Hou et al., 2011) that depends on the parameterization of the ligand (Oostenbrink et al., 2004)." What are the assumptions and limitations in taking continuum electrostatics (presumably with parameters for dielectric constants and their assignments to regions after discarding solvent), surface area (with its assumptions and limitations), and of course assuming vibration of a normal mode can capture entropy. On page 30, regarding their vibrational entropy estimate, they write that the "entropy term provides insights into the disorder within the system, as well as how this disorder changes during the binding process". It is important that the extent of disorder captured by the vibrational estimate be discussed, as it is not obvious that it has captured entropy involving multiple minima on the system's true 3N-dimensional energy surface, and especially the contribution from solvent disorder in bound Vs dissociated states.

As discussed above, errors in the free energy estimates need to be more faithfully represented, as fractional errors are not meaningful. On page 21 the authors write "The match improved when free energy ratios rather than absolute values were compared." But a ratio of free energies is not a typical or expected measure of error in delta G. They also write "For ACh and CCh, there is good agreement between.Gm1 and GLA and between.Gm3 and GHA. For these agonists, in silico values overestimated experimental ones only by ~8% and ~25%. The agreement was not as good for the other 2 agonists, as calculated values overestimated experimental ones by ~45%(Ebt) and ~130% (Ebt). However, the fractional overestimation was approximately the same for GLA and GHA." See the above comment on how this may misrepresent the error. On page 21 they write, in relation to their large fractional errors, that they "do not know the origin of this factor but speculate that it could be caused by errors in ligand parameterization". However the estimates from the PBSA approach are, by design, only approximate. Both errors in parameterisation (and their likely origin) and the approximate model used, need discussion.

Reviewer #4 (Public Review):

The manuscript entitled "Ebola Virus Sequesters IRF3 in Viral Inclusion Bodies to Evade Host Antiviral Immunity" mainly describes that the function of IBs formed by the viral proteins VP35 and NP in evading host antiviral immunity. They proved that Ebola virus VP35 protein can interact with STING, but not IRF3, to sequester IRF3 into inclusion bodies and thereby inhibit type-I interferon production. This work will be of some interest to readers in the Ebola Virus field, however, the current data do not clearly explain the relationship of VP35 protein and IRF3.

Reviewer #2 (Public Review):

Summary:

Bian et al. calculated Phenotypic Age Acceleration (PhenoAgeAccel) via a linear model regressing Phenotypic Age on chronological age. They examined the associations between PhenoAgeAccel and cancer incidence using 374,463 individuals from the UK Biobank and found that older PhenoAge was consistently related to an increased risk of incident cancer, even among each risk group defined by genetics.

Strengths:

The study is well-designed, and uses a large sample size from the UK biobank.

Weaknesses:

Since the UK biobank has a large sample size, it should have enough power to split the dataset into discovery and validation sets. Why did the authors use 10-fold cross-validation instead of splitting the dataset?

Reviewer #2 (Public Review):

Summary:<br /> Drosophila hematopoiesis has been shown to be governed by a number of signaling pathways such as JAK/STAT and Dpp. This important study shows the role of nutrient sensing and autophagy in determining blood cell differentiation. The authors show that General control non-derepressible 5 (Gcn5), a histone acetyltransferase affects blood cell differentiation. Gcn5 also negatively regulates autophagy through its effector TFEB which directly regulates autophagy genes. The authors also show that mTORC1 modulates Gcn5 levels and through it, TFEB activity thus acting as a fine-tuning mechanism that maintains optimal levels of autophagy.

Strengths:<br /> The main strength of the work lies in the interesting finding that cellular metabolic processes such as autophagy have a direct role in blood cell differentiation and has the potential to be of interest to those working on vertebrate haematopoiesis as well. The report has generated intriguing data, using promoters specific for sub-sections of the lymph gland, that different cellular subsets of the lymph gland contribute differently towards haematopoiesis, but this is not followed up in detail and the final conclusions are derived from a combination of whole lymph gland perturbations as well as those from specific promoters.

Weaknesses:<br /> 1. Gc5 seems to be expressed throughout the lymph gland but modulating it in the subsections does not have the same result. It is very striking that the knockdown of Gcn5 in the prohemocyte population does not have an effect on differentiation whereas overexpression does. The modulations of Gcn5 in PSC also have variable effects across hemocyte subpopulations which is not explored in the manuscript. Interestingly, also the domain deletion constructs show a differential effect on blood cell differentiation when altered solely in the prohemocytes which is not explained. While Gcn5 can be seen in all sections of the lymph gland in the first figure, under the HHLT-Gal4 and Hml-Gal4, Gcn5 looks cytoplasmic and almost completely excluded from the nucleus strikingly unlike Gcn5 expression under the Collier-Gal4 and Dome-Gal4. The rest of the experiments in the manuscript are done with multiple promoters, with autophagy flux measured by modulating Gcn5 with a pan hemocyte promoter, but the mTORC1-Gcn5 axis is explored using chemical modulators which affect the whole of the lymph gland (Fig7) or using two pro-hemocyte promoters (Fig8).

2. The knockdown of Gcn5 seems to affect the gland size (A compared to B and C). Since mTORC1 is a central regulator of cell size, it is possible that some of the effects seen in these knockdowns are potentially through mTORC1 affecting size suggesting that the signalling axis between mTORC1 and Gcn5 might not be a one-way axis as suggested in Figure 9. Also, this would mean that in experiments where absolute cell counts of crystal cells or niche cells are used to assess blood cell differentiation, further analysis to consider total cell numbers in the lymph gland would strengthen the manuscript.

3. A genetic manipulation of mTORC1 specifically in the pro hemocytes would strengthen the role of mTORC1 in the pathway rather than the chemical modulation which affects the whole of the lymph gland.

Reviewer #2 (Public Review):

Based on the corresponding author's response, the questions I raised were not addressed for various reasons. This is not necessarily a negative. The authors indicated that most of the points raised will be addressed in a separate manuscript. Specifically, the Cdu1 targeting of IkBa. They mentioned intriguing findings regarding IkBa in cells infected with a cdu1-null strain C. trachomatis in their response to reviewers. Similar to this, there appears to be a planned manuscript that will address the question of the timing of CTL0480's function in inclusion extrusion.

The lack of more direct infection-related evidence of Cdu1 interaction with various type III effectors was raised; and the authors attributed this to technical difficulties and low abundance of starting materials. It was not clear if they tried other approaches to demonstrate interaction.

Another suggestion was the quantitation of the three target effectors of Cdu1 in wild type and cdu1-null background. The authors provided western blot data and immunofluorescence images that revealed potential differences in stability/turnover kinetics. The authors might want to discuss the implications of the different kinetics of stability/turnover. For example, if all three proteins are necessary for optimal extrusion of inclusions, and concertedly act to mediate this process, all three would need to be present at the required levels. Could this be a temporal regulation strategy? Does acetylation also regulate function, interactions, etc.?

In short, the response to some of the questions is forthcoming in the form of follow-up manuscripts. New observations on the different stability profiles could be elaborated in the Discussion section, with a brief discussion on functional and/or regulatory implications.

Reviewer #2 (Public Review):

The authors succeed in generalizing the pre-alignment procedure for their cell identification method to allow it to work effectively on data with only small subsets of cells labeled. They convincingly show that their extension accurately identifies head angle, based on finding auto florescent tissue and looking for a symmetric l/r axis. They demonstrate method works to allow the identification of a particular subset of neurons. Their approach should be a useful one for researchers wishing to identify subsets of head neurons in C. elegans, and the ideas might be useful elsewhere.

The authors also assess the relative usefulness of several atlases for making identity predictions. They attempt to give some additional general insights on what makes a good atlas, but here insights seem less clear as available data does not allow for experiments that cleanly decouple: 1. the number of examples in the atlas 2. the completeness of the atlas. and 3. the match in strain and imaging modality discussed. In the presented experiments the custom atlas, besides the strain and imaging modality mismatches discussed is also the only complete atlas with more than one example. The neuroPAL atlas, is an imperfect stand in, since a significant fraction of cells could not be identified in these data sets, making it a 60/40 mix of Openworm and a hypothetical perfect neuroPAL comparison. This waters down general insights since it is unclear if the performance is driven by strain/imaging modality or these difficulties creating a complete neuroPal atlas. The experiments do usefully explore the volume of data needed. Though generalization remains to be shown the insight is useful for future atlas building that for the specific (small) set of cells labeled in the experiments 5-10 examples is sufficient to build a accurate atlas.

Reviewer #2 (Public Review):

The authors aimed to examine the role of a group of neurons expressing Foxb1 in behaviors through projections to the dlPAG. Standard chemogenetic activation or inhibition and optogentic terminal activation or inhibition at local PAG were used and results suggested that, while activation led to reduced locomotion and breathing, inhibition led to a small degree of increased locomotion.

The observed effects on breathing are evident and dramatic. However, due to the circumstance that does not permit to perform additional experiments, the conclusion is not as strong as it could be.

Reviewer #2 (Public Review):

Summary<br /> The study used eye tracking with a focus on pupillometry to examine how infants can learn to distinguish between informative and uninformative visual cues. Infants (n = 30, mean age = 8.2-months-old) viewed displays consisting of a sequence of stimuli: a fixation point, a central cue that predicted a subsequent informative or uninformative signal, the signal itself, and the target event (a cartoon animal, referred to as the reward). The key results are that: (1) pupil size differs depending on whether the infants anticipated an informative or uninformative signal, (2) this difference develops across trials, consistent with a slow learning process, and (3) there is rapid generalization when new shapes were introduced that shared features with the informative vs uninformative cues. The study complements a rich literature, including from this same group, showing that children are sensitive to information gains, and is interesting and important in revealing that pupil size is a physiological marker of information anticipation. We have several comments and concerns and believe that addressing them would substantially strengthen the manuscript.

Major points are related to interpretation, statistical robustness, and clarity

1. There is a tendency to overinterpret the findings.<br /> a. Throughout, the authors interpret the findings as meaning that pupil size tracks the "value" of information; however, the results do not demonstrate conclusively whether, or what kind of value information has in this task. A natural hypothesis is that infants are intrinsically motivated to predict - i.e., value the ability to predict the target event as early as possible. In a supplementary figure, the authors present evidence that infants indeed fixate on the target event sooner after seeing informative vs uninformative cues, consistent with the idea that they use the information for improving predictions. However, those results are not fully convincing, as we detail in point 2. Most importantly, the analysis is not integrated or even mentioned in the main analyses analysis. Making the link between the pupil reaction and the use of the information would greatly strengthen the paper (whether or not the supplementary findings hold up to more thorough scrutiny). Either this link should be made and discussed, or the authors should soften their conclusions about the utility of the informative cues.

b. On line 236, the text states that the evidence "...supports the growing body of evidence indicating that infants are proactive in shaping their learning environment by searching for and focusing on information-rich stimuli". The results do not show that the infants search for information, only that they have a pupil reaction that differentiates between informative and uninformative stimuli.

c. On lines 248-249, it seems a stretch to relate the changes in pupil dilation to a shift in information value onto the cue. Without some other measure (e.g., EEG), this remains speculative. While I believe the suggestion is plausible, the language should be softened to highlight this as a follow-up research question that the present research cannot directly speak to.

2. Several findings are statistically weak and several analyses are insufficiently controlled.

a. The analysis in Supplementary Figure 2, which shows that the latencies of target fixations are shorter after informative vs uninformative cues, raises several questions.<br /> i. We were unable to fully test these analyses as the OSF project seems to only contain latency data for 33 participants (including 22 of the 30 that remain in the final sample).<br /> ii. The results are described as revealing a significant difference, but the 89% confidence interval of the difference contains 0. How did the authors establish significance here?<br /> iii. How do the authors distinguish incidental fixations (which just happened to land near the target) from true predictive gaze shifts? Fixations were pooled if they occurred from 1.25 seconds before to 1 second after target onset. This is sufficient time for the eye to move in and out of the window several times. The authors should analyse the distributions of fixation durations to rule out various artifacts unrelated to target prediction.<br /> iv. Latencies to fixation were standardized, bringing the mean across each participant to 0, and yet the statistical model includes a random intercept; is there a justification for this?<br /> v. Standardizing removes information about whether fixations were proactive or reactive. It would be very interesting to see if/how information affects these two differently.<br /> vi. Since informativeness was learned across trials, it seems desirable that the model should include as random effects a trial number and an interaction between trial number and informativeness. This would allow a comparison between learning to predict and the pupil reaction. Are infants who have a stronger (or earlier) pupil reaction also more likely to show stronger learning to anticipate?

b. The main finding that pupil size differs between informative and uninformative cues is based on a 3-second analysis window. This long window most likely spans many saccades, which can affect pupil size on its own or by bringing the eye on or off visual stimuli. There is no analysis to show that the statistics of saccades or fixation locations are equivalent between the two trial types - but this is necessary to convincingly rule out a spurious artifact.

c. The second main finding that the effect of informativeness grows across trials seems statistically weak. The text (line 138) states that the interaction had a beta of 0.002, which was equal to the lower border of the 89%HDI ([0.002, 0.003]). For the second claim that pupil size decreased across informative trials, the beta is -0.002, and 89% HID is non-existent - i.e., [-0.002, -0.002]. (In general, the authors should check their numbers more carefully and make sure they are presented with a degree of precision that allows the reader to interpret them meaningfully.

d. The analyses do not indicate how well the TD model fits; we are shown only that it fits better than a linear model. On line 177 a correlation analysis is mentioned between the data and model, but the statistic cited for this test on line 179 is a mean beta coefficient, so it is impossible to know what this means. An analysis of goodness of fit or, at the very least, a figure superimposing the model and data, would be much more convincing.

3. The descriptions are very unclear in some key parts of the paper

a. The description of the TD model applied to pupil learning (starting on line 391) is very unclear. The model has to include some measure of informativeness - i.e., the match between the cued and true target location - but it is unclear how this was formalized. It is also very unclear how time within the trial is incorporated (the meaning of the TDE equation).

b. The description of the generalization analysis (Fig. 5) is also very unclear. Every single sentence in it evoked some confusion, so I will go through them one by one. "A Bayesian additive model showed that infants' pupil dilation was reduced for novel cues." Reduced relative to what? "This was specific to those novel cues that shared the features of the familiar informative cues (estimated mean difference = -0.05, 89%HDI = [-0.062, -0.038])." All the novel cues shared features with the informative cues; do the authors mean the novel cues that had the critical feature indicative of the informative cue? "The size of this effect approximated the difference between conditions that were observed for familiar stimuli (estimated mean difference = -0.067, 89% HDI = [-201 0.077, -0.057])." What is "this effect"? "Crucially, this difference was not observable at the start of the task, when the familiar stimuli were first introduced (estimated mean difference = -0.007, 89%HDI = [-0.015, 0.001])." At the start of the task, the stimuli were novel, and not familiar.

Reviewer #2 (Public Review):

Summary:<br /> The authors investigate sub-skin surface deformations to a number of different, relevant tactile stimuli, including pressure and moving stimuli. The results demonstrate and quantify the tension and compression applied from these types of touch to fingerprint ridges, where pressure flattens the ridges. Their study further revealed that on lateral movement, prominent vertical shearing occurred in ridge deformation, with somewhat inconsistent horizontal shear. This also shows how much the deeper skin layers are deformed in touch, meaning the activation of all cutaneous mechanoreceptors, as well as the possibility of other deeper non-cutaneous mechanoreceptors.

Strengths:<br /> The paper has many strengths. As well as being impactful scientifically, the methods are sound and innovative, producing interesting and detailed results. The results reveal the intricate workings of the skin layers to pressure touch, as well as sliding touch over different conditions. This makes it applicable to many touch situations and provides insights into the differential movements of the skin, and thus the encoding of touch in regards to the function of fingerprints. The work is very clearly written and presented, including how their work relates to the literature and previous hypotheses about the function of fingerprint ridges. The figures are very well-presented and show individual and group data well. The additional supplementary information is informative and the video of the skin tracking demonstrates the experiments well.

Weaknesses:<br /> There are very few weaknesses in the work, rather the authors detail well the limitations in the discussion. Therefore, this opens up lots of possibilities for future work.

Impact/significance:<br /> Overall, the work will likely have a large impact on our understanding of the mechanics of the skin. The detail shown in the study goes beyond current understanding, to add profound insights into how the skin actually deforms and moves on contact and sliding over a surface, respectively. The method could be potentially applied in many other different settings (e.g. to investigate more complex textures, and how skin deformation changes with factors like dryness and aging). This fundamental piece of work could therefore be applied to understand skin changes and how these impact touch perception. It can further be applied to understand skin mechanoreceptor function better and model these. Finally, the importance of fingertip ridges is well-detailed, demonstrating how these play a role in directly shaping our touch perception and how they can shape the interactions we have with surfaces.

Reviewer #3 (Public Review):

The authors utilize chimpanzee-human hybrid cell lines to assess cis-regulatory evolution. These hybrid cell lines offer a well-controlled environment, enabling clear differentiation between cis-regulatory effects and environmental or other trans effects.<br /> In their research, Wang et al. expand the range of chimpanzee-human hybrid cell lines to encompass six new developmental cell types derived from all three germ layers. This expansion allows them to discern cell type-specific cis-regulatory changes between species from more pleiotropic ones. Although the study investigates only two iPSC clones, the RNA- and ATAC-seq data produced for this paper is a valuable resource.

The authors begin their analysis by examining the relationship between allele-specific expression (ASE) as a measure of species divergence and cell type specificity. They find that cell-type-specific genes exhibit more divergent expression. By integrating this data with measures of constraint within human populations, the authors conclude that the increased divergence of tissue-specific genes is, at least in part, attributable to positive selection. A similar pattern emerges when assessing allele-specific chromatin accessibility (ASCA) as a measure of divergence of cis-regulatory elements (CREs) in the same cell lines.

By correlating these two measures, the authors identify 95 CRE-gene pairs where tissue-specific ASE aligns with tissue-specific ASCA. Among these pairs, the authors select two genes of interest for further investigation. Notably, the authors employ an intriguing machine learning approach in which they compare the inferred chromatin state of the human sequence with that of the chimpanzee sequence to pinpoint putatively causal variants.

Overall, this study delves into the examination of gene expression and chromatin accessibility within hybrid cell lines, showcasing how this data can be leveraged to identify potential causal sequence differences underlying between-species expression changes.

All in all most conclusions appear solid, with the exception of the interpretation of a cell type/state identification machine learning model to pinpoint putatively causal variants. The described variants lack any functional validation and there is no data that measure the certainty of the results.

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

Hong and collaborators investigated variations in the amount of synaptic proteins in plasma extracellular vesicles (EV) in Parkinson's Disease (PD) patients on one-year follow-up. Their findings suggest that plasma EV synaptic proteins may be used as clinical biomarkers of PD progression.

It is a preliminary study using semi-quantitative analysis of synaptic proteins.

The authors have a cohort of PD patients with clinical examination and a know-how on EV purification. Regarding this latter part, they may improve their description of EV purification. EV may be broken into smaller size EV after freezing. Does it explain the relatively small size in their EV preparation? Do the authors refer to the MISEV guidelines for EV purity? Regarding synaptic protein quantification, the choice of western blotting may not be the best one. ELISA and other multiplex arrays are available. How the authors do justify their choice? Do the authors try to sort plasma EV by membrane-associated neuronal EV markers using either vesicle sorting or immunoprecipitation?

Many technical aspects may be improved. Such technical questions weakened the authors' conclusions.

Reviewer #2 (Public Review):

The authors made an atlas of single-cell transcriptome of on a pure population of leukocytes isolated from the brain of adult Tg(cd45:DsRed) transgenic animals by flow cytometry. Seven major leukocyte populations were identified, comprising microglia, macrophages, dendritic-like cells, T cells, natural killer cells, innate lymphoid-like cells, and neutrophils. Each cluster was analyzed to characterize subclusters. Among lymphocytes, in addition to 2 subclusters expressing typical T cell markers, a group of il4+ il13+ gata3+ cells was identified as possible ILC2. This hypothesis is supported by the presence of this population in rag2KO fish, in which the frequency of lck and zap70+ cells is strongly reduced. The use of KO lines for such validations is a strength of this work (and the zebrafish model).

The subcluster analysis of mpeg1.1 + myeloid cells identified 4 groups of microglial cells, one novel group of macrophage-like cells (expressing s100a10b, sftpbb, icn, fthl27, anxa5b, f13a1b and spi1b), and several groups of DC like cells expressing the markers siglec15l, ccl19a.1, ccr7, id2a, xcr1a.1, batf3, flt3, chl1a and hepacam2. Combining these new markers and transgenic reporter fish lines, the authors then clarified the location of leukocyte subsets within the brain, showing for example that DC-like cells stand as a parenchymal population along with microglia. Reporter lines were also used to perform a detailed analysis of cell subsets, and cross with a batf3 mutant demonstrated that DC-like cells are batf3 dependent, which was similar to mouse and human cDC1. Finally, analysis of classical mononuclear phagocyte deficient zebrafish lines showed they have reduced numbers of microglia but exhibit distinct DC-like cell phenotypes. A weakness of this study is that it is mainly based on FACS sorting, which might modify the proportion of different subtypes.

This atlas of zebrafish brain leukocytes is an important new resource for scientists using the zebrafish models for neurology, immunology, and infectiology, and for those interested in the evolution of the brain and immune system.

Reviewer #2 (Public Review):

Summary<br /> This study deepens the former authors' investigations of the mechanisms involved in gating the long-term consolidation of an associative memory (LTM) in Drosophila melanogaster. After having previously found that LTM consolidation 1. costs energy (Plaçais and Préat, Science 2013) provided through pyruvate metabolism (Plaçais et al., Nature Comm 2017) and 2. is gated by the increased tonic activity in a type of dopaminergic neurons ('MP1 neurons') following only training protocol relevant for LTM, i.e. interspaced in time (Plaçais et al., Nature Neuro 2012), they here dig into the intra-cell signalling triggered by dopamine input and eventually responsible for the increased mitochondria activity in Kenyon Cells. They identify a particular PKC, PKCδ, as a major molecular interface in this process and describe its translocation to mitochondria to promote pyruvate metabolism, specifically after spaced training.

Methodological approach<br /> To that end, they use RNA interference against the isozyme PKCδ, in a time-controlled way and in the whole Kenyon cell populations or in the subpopulation forming the α/β lobe. This knock-down decreased the total PKCδ mRNA level in the brain by ca. 30%, and is enough to observe decreased in flies performances for LTM consolidation. Using Pyronic, a sensor for pyruvate for in vivo imaging, and pharmacological disruption of mitochondrial function, the authors then show that PKCδ knock-down prevents a high level of pyruvate from accumulating in the Kenyon cells at the time of LTM consolidation, pointing towards a role of PKCδ in promoting pyruvate metabolism. They further identify the PDH kinase PDK as a likely target for PKCδ since knocking down both PKCδ and PDK led to normal LTM performances, likely counterbalancing PKCδ knock-down alone.

To understand the timeline of PKCδ activation and to visualise its mitochondrial translocation in a subpart of Mushroom body lobes they imported in fruitfly the genetically-encoded FRET reporters of PKCδ, δCKAR, and mitochondria-δCKAR (Kajimoto et al 2010). They show that PKCδ is activated to the sensor's saturation only after spaced training, and not other types of training that are 'irrelevant' for LTM. Further, adding thermogenetic activation of dopaminergic neurons and RNA interference against Gq-coupled dopamine receptor to FRET imaging, they identify that a dopamine-triggered cascade is sufficient for the elevated PKCδ-activation.

Strengths and weaknesses<br /> The authors use a combination of new fluorescent sensors and behavioral, imaging, and pharmacological protocols they already established to successfully identify the molecular players that bridge the requirement for spaced training/dopaminergic neurons MP1 oscillatory activity and the increased metabolic activity observed during long-term memory consolidation.

The study is dense in new exciting findings and each methodological step is carefully designed. Almost all possible experiments one could think of to make this link have been done in this study, with a few exceptions that do not prevent the essential conclusions from being drawn.

The discussion is well conducted, with interesting parallels with mammals, where the possibility that this process takes place as well is yet unknown.

Impact<br /> Their findings should interest a large audience:<br /> They discover and investigate a new function for PKCδ in regulating memory processes in neurons in conjunction with other physiological functions, making this molecule a potentially valid target for neuropathological conditions. They also provide new tools in drosophila to measure PKCδ activation in cells. They identify the major players for lifting the energetic limitations preventing the formation of a long-term memory.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, Shore et al. investigate the consequent changes in excitability and synaptic efficacy of diverse neuronal populations in an animal model of juvenile epilepsy. Using electrophysiological patch-clamp recordings from dissociated neuronal cultures, the authors find diverging changes in two major populations of inhibitory cell types, namely somatostatin (SST)- and parvalbumin (PV)-positive interneurons, in mice expressing a variant of the KCNT1 potassium channel. They further suggest that the differential effects are due to a compensatory increase in the persistent sodium current in PV interneurons in pharmacological and in silico experiments.

Strengths:<br /> 1) Heterozygous KCNT1 gain of function variant was used which more accurately models the human disorder.<br /> 2) The manuscript is clearly written, and the flow is easy to follow. The authors explicitly state the similarities and differences between the current findings and the previously published results in the homozygous KCNT1 gain of function variant.<br /> 3) This study uses a variety of approaches including patch clamp recording, in silico modeling, and pharmacology that together make the claims stronger.<br /> 4) Pharmacological experiments are fraught with off-target effects and thus it bolsters the authors' claims when multiple channel blockers (TTX and VU170) are used to reconstruct the sodium-activated potassium current. Having said that, it would be helpful to see the two drug manipulations be used in the same experiment. Notably, does the more selective blocker VU170 mimic the results of TTX for NFS GABAergic cells in Figure 2? And does it unmask a genotype difference for FS GABAergic cells like the one seen in PV interneurons in Figure 5C3.

Weaknesses:<br /> 1) This study relies on recordings in dissociated cortical neurons. Although specific WT interneurons showed intrinsic membrane properties like those reported for acute brain slices, it is unclear whether the same will be true for those cells expressing KCNT1 variants. This reviewer highly recommends confirming some of the key findings using an ex vivo slice preparation. This is especially important given the discrepant result of reduced excitability of PV cells reported by Gertler et al., 2022 (cited here in the manuscript but not discussed in this context) in acute hippocampal slices for a different KCTN1 gain of function variant.<br /> 2) It is unclear how different pieces of results fit together to form a story about the disease pathophysiology. For example, hyperexcitability of PV cells would suggest more inhibition which would counter seizure propensity. However, spontaneous inhibitory postsynaptic currents show no change in pyramidal neurons. Moreover, how do the authors reconcile that the reductions in synaptic inputs onto interneurons in Figure 3B with the increases in Figure 8? This should be discussed.<br /> 3) Similarly, the results in this work are not entirely internally consistent. For example, given the good correspondence between FS and NFS GABAergic cells with PV and SST expression, why are FS GABAergic cells hyperexcitable in Figure 1? If anything, there is a tendency to show reduced excitability like the NFS GABAergic cells. Also, why do the WT I-V curves look so different between Figures 2 and 5? This reviewer suggests at least a brief explanation in the discussion.<br /> 4) Given the authors' claim that the KCNT1 activation curve is a major contributor to the observed excitability differences in specific GABA cell subtypes, it would be helpful to directly measure the activation curve in the variants experimentally as was done for WT KCNT1 in Figure 6A and use the derived kinetics in the compartmental model.

Reviewer #2 (Public Review):

Summary:

In this study, Zhao, Nern, et al. investigate a population of neurons in the optic lobe of Drosophila melanogaster that process optic flow, relative motion between the insect's eyes and its environment that is generated during flight and provides useful information to the fly about its own self-motion. Although a sample of these Lobula Plate Tangential (LPT) neurons has been studied across Diptera in prior work, the full population has not been exhaustively and thoroughly cataloged in a single species, limiting our understanding of how LPT tuning properties across the population convey features of optic flow fields relevant to downstream motor regions.

Through extensive manual reconstructions in a fly electron microscopy volume, the authors of this study identify 58 LPT neurons in the fruit fly encompassing previously studied Horizontal and Vertical cells and novel cells that have not been previously characterized. Using the detailed anatomy of each cell and knowledge of upstream T4/T5 selectivity, the authors derive the predicted motion pattern map (PMPM) of each neuron. To understand how optic flow field tunings of individual LPTs align with global optic flow patterns flies are expected to encounter during flight such as translation and rotation, the authors compute the average angular difference between each PMPM and idealized rotation and translation optic flow fields. The authors also map individual LPTs to their counterparts in a second fly brain to explore LPT-LPT connectivity and downstream connectivity to central brain neuropils. They find that distinct groupings of LPTs have diverse downstream connectivity patterns and that downstream neurons align more closely to global optic flow fields that are expected during flight. This study is a valuable resource to researchers studying motion vision in the insect brain and is of interest to researchers studying sensorimotor processing by providing hypotheses for how optic flow information is integrated downstream to guide fly behavior.

Strengths:

A key strength of this study is the thoroughness with which the authors comprehensively identify individual LPT neurons in the FAFB volume. They not only conduct an impressive number of careful manual reconstructions to recover individual LPTs, but they also attempt, and often succeed, to map each individual neuron to its counterpart in light microscopy, studies across Diptera, and available auto-segmented connectome datasets such as FlyWire, FAFB-FFN1, and Hemibrain. The authors are similarly thorough when surveying individual LPT properties such as neurotransmitter identities, in some cases using multiple datasets to reconcile ambiguous neurotransmitter predictions. The care with which the complete LPT population has been identified establishes this study as a useful resource for future studies of insect motion.

In addition to providing a comprehensive catalog of individual LPTs, the authors also contextualize their findings within broader sensorimotor circuitry by considering connectivity between LPTs and from LPTs to downstream regions. Exploration of structure in downstream connectivity suggests that optic flow information is directed to various central brain neuropils through specific groups of LPTs. With some additional analyses, these results broaden the scope of this study by providing useful hypotheses for sensorimotor circuit organization.

Weaknesses:

A novel method introduced in this study is the derivation of individual LPT-predicted motion pattern maps (PMPMs) using T4 preferred directions and LPT morphology. Although this method underlies core findings in this study, such as alignment to global optic flow fields and properties of downstream integration, aspects of the methods used to derive PMPMs are not explained sufficiently well, particularly in the main text. For example, in the Methods, the authors briefly describe the process of computing a weighted sum of T4 preferred directions to obtain the PMPM for each LPT, but a detailed understanding of these preferred directions combined is missing in Figure 2 or the associated descriptions in the main text. It is also not clear how PMPMs are derived in cases where LOP layer coverages are overlapping (for example VS 13-1 in Figure 3) to yield smooth PMPMs. In addition, it is not clear how the PMPMs of bilateral LPTs such as LPT-45 and LPT-50 in Figure 4 were integrated to compute downstream target composite PMPMs. Finally, all the PMPMs were derived from the T4 preferred direction that relies on the ommatidial viewing directions ("Eyemap") introduced in Zhao et al. 2022. It is also important for the current study to give an indication of how sensitive their results are to possible inaccuracies in this map and derived T4/T5 direction selectivities.

Although the authors explore some features of connectivity from LPT to downstream partners (Figure 6), there is a lack of reconciliation of these findings with individual LPT properties explored earlier in the study, such as those presented in Figures 2-4. In that sense, there is a disconnect between the two parts of the manuscript (and a missed opportunity). For instance, an important follow-up analysis would be to use knowledge about LPT-LPT connectivity to better predict effective PMPMs of LPTs taking into account network effects. This extension would lead to a better understanding of how LPT-LPT interactions shape optic flow responses in the LOP. In addition, in Figure 6 Supplement 2 (which I recommend to move to the main figures), the authors show that LPTs can be grouped together based on similarity of output connectivity (Panel B-D) and that this structure corresponds to output synapses located in different groups of central brain neuropils. However, they do not attempt to explicitly link these groupings with individual LPT PMPMs, alignment to global optic flow patterns, LPT layer enervation, cell morphologies, and input connectivity patterns. Such an analysis would be an important step to bring the manuscript together and to get a better understanding of the organization of the whole system.

Reviewer #2 (Public Review):

Summary:

The authors describe and quantify a phenomenon in the CA1 and CA3 of the hippocampus that they call aberrant Ca2+ micro-waves. Micro-waves are sometimes seen during 2-photon calcium imaging of populations of neurons under certain conditions. They are spatially confined slow calcium events that start in a few cells and slowly spread to neighboring groups of cells. This phenomenon has been uttered between researchers in the field at conferences, but no one has taken the time to carefully capture and quantify micro-waves and pin down the causes. The authors show that micro-waves are dependent on the viral titre of the genetically encoded calcium indicators (GECIs), the genetic promoter (synapsin), the neuronal subtype (granule cells in the dentate gyrus do not produce micro-waves and they are not seen in the neocortex), and the density of GECI expression. The authors should be commended for their work and raising awareness to all labs doing any form of calcium imaging in populations of neurons. The authors also come up with alternative approaches to avoid artifactual micro-waves such as reducing the transduction titre (1:2 dilution of virus) and a transduction method employing sparser and cre-dependent GECI expression in principal cells using a CaMKII promoter.

Strengths:

The micro-waves reported in the paper were robustly observed across 4 laboratories and 3 different countries with various experimenters and calcium imaging set-ups. This adds significant strength to the work.

The age of mice used covered a broad range (from 6 to 43 weeks). This is a strength because is covers most ages that are used in labs that regularly do calcium imaging.

Another strength is they used different GCaMP variants (GCaMP6m, GCaMP6s, GCaMP7f), as well a red indicator: RCaMP. This shows the micro-waves are not an issue with any particular GECI, as the authors suggest.

The authors include many movies of micro-waves. This is extremely useful for researchers in the field to view them in real-time so they can identify them in their own data.

They provide a useful table with specific details of the virus injected, titre, dilution, and other information along with the incidence of micro-waves. A nice look-up table for researchers to see if their viral strategy is associated with a high or low incidence of micro-waves.

Weaknesses:

Whether micro-waves are associated with the age of mice was not quantified. This would be good to know and the authors do have this data.

The effect of mico-waves on single cell function was not analyzed. It would be useful, for example, if we knew the influence of micro-waves on place fields. Can a place cell still express a place field in a hippocampus that produces micro-waves? What effect might a microwave passing over a cell have on its place field? Mice were not trained in these experiments, so the authors do not have the data.

The CaMKII-Cre approach for flexed-syn-GCaMP expression shows no micro-waves and is convincing, but it is only from 2 animals, even though both had no micro-waves.

The authors state in their Discussion that even without observable microwaves, a syn-Ca2+-indicator transduction strategy could still be problematic. This may be true, but they do not check this in their analysis, so it remains unknown.

Reviewer #2 (Public Review):

Summary:<br /> Non-canonical Wnt signaling plays an important role in morphogenesis, but how different components of the pathway are required to regulate different developmental events remains an open question. This paper focuses on elucidating the overlapping and distinct functions of dact1 and dact2, two Dishevelled-binding scaffold proteins, during zebrafish axis elongation and craniofacial development. By combining genetic studies, detailed phenotypic analysis, lineage tracing, and single-cell RNA-sequencing, the authors aimed to understand (1) the relative function of dact1/2 in promoting axis elongation, (2) their ability to modulate phenotypes caused by mutations in other non-canonical wnt components, and (3) pathways downstream of dact1/2.

Strong qualitative evidence was provided to support dact1/2's role in genetically modulating non-canonical wnt signaling to regulate body axis elongation and the morphology of the anterior neurocranium (ANC). However, there is currently insufficient evidence supporting the author's claim that suppression of calpain 8 by dact1/2 is important for craniofacial development and that "embryonic fields determined during gastrulation affect the CNCC ability to contribute to the craniofacial skeleton".

Strengths:<br /> (1) The generation of dact1/2 germline mutants and the use of genetic approaches to dissect their genetic interactions with wnt11f2 and gpc4 provide unambiguous and consistent results that inform the relative functions of dact1 and dact2, as well as their combined effects.

(2) Because the anterior neurocranium exhibits a spectrum of phenotypes in different genetic mutants, it is a useful system for studying how tissue morphology can be modulated by different components of the same pathway, as demonstrated in this study.

(3) The authors leveraged lineage tracing by photoconversion to dissect how dact1/2 differentially impacts the ability of different cranial neural crest populations to contribute to the anterior neurocranium. This revealed that distinct mechanisms can lead to similar phenotypes in different mutants.

Weaknesses:<br /> (1) While the qualitative data show altered morphologies in each mutant, quantifications of these phenotypes are lacking in several instances, making it difficult to gauge reproducibility and penetrance, as well as to assess the novel ANC forms described in certain mutants.

(2) Germline mutations limit the authors' ability to study a gene's spatiotemporal functional requirement. They therefore cannot concretely attribute nor separate early-stage phenotypes (during gastrulation) to/from late-stage phenotypes (ANC morphological changes).

(3) Given that dact1/2 can regulate both canonical and non-canonical wnt signaling, this study did not specifically test which of these pathways is altered in the dact1/2 mutants, and it is currently unclear whether disrupted canonical wnt signaling contributes to the craniofacial phenotypes, even though these phenotypes are typical non-canonical wnt phenotypes.

(4) The use of single-cell RNA sequencing unveiled genes and processes that are uniquely altered in the dact1/2 mutants, but not in the gpc4 mutants during gastrulation. However, how these changes lead to the manifested ANC phenotype later during craniofacial development remains unclear. The authors showed that calpain 8 is significantly upregulated in the mutant, but the fact that only 1 out of 142 calpain-overexpressing animals phenocopied dact1/2 mutants indicates the complexity of the system.

(5) Craniofacial phenotypes observed in this study are attributed to convergent extension defects but convergent extension cell movement itself was not directly examined, leaving open if changes in other cellular processes, such as cell differentiation, proliferation, or oriented division, could cause distinct phenotypes between different mutants.

Reviewer #2 (Public Review):

George and colleagues present a novel open-source toolbox to model rodent locomotor patterns and electrophysiological responses of spatially modulated neurons, such as hippocampal "place cells". The present manuscript describes a comprehensive Python package ("RatInABox") with powerful capabilities to simulate a variety of environments, exploratory behaviors and concurrent responses of a variety of cell types. In addition, they provide the tools to expand these basics functions and potentially multiple different model designs, new cell types or more complex neural network architectures. The manuscript also illustrated several simple application cases. The authors have also created a comprehensive GitHub repository with more detailed explanations, tutorials and example scripts. Overall, I found both the manuscript and associated repository very clear, well written and easy the scrips easy to follow and implement, to a superior level of many commercial software packages. RatInABox fills several existing gaps in the literature and features important improvements over previous approaches; for example, the implementation of continuous 2D environments instead of tabularized state spaces. I believe this toolbox will be of great interest for many researchers in the field of spatial navigation and beyond and provide them with a remarkably powerful and flexible tool. I don't have any major issues with the manuscript. However, the manuscript can be further improved by clarifying some aspects of the toolbox, discussing its limitations and biological plausibility.

Reviewer #2 (Public Review):

In this study, Lopes and colleagues provide convincing evidence to support the potential for gene therapy to restore expression of heparanase-2 (Hpse2) in mice mutant for this gene, as occurs in urofacial syndrome. Beyond symptomatic relief for the consequences of outlet obstruction that results from Hpse2 mutation, no treatments exist. Building on prior studies describing the nature of urinary tract dysfunction in Hpse2 mutant mice, the authors applied a gene therapy approach to determine whether gene replacement could be achieved, and if so, whether restoration of HPSE2 expression could mitigate the urinary tract dysfunction and present a potential cure. Using an AAV9 viral vector encoding HPSE2, the authors performed gene replacement in neonatal wild-type or Hpse2 mutant mice and determined gene and protein expression as well as the impact on bladder outflow tract and bladder body physiology in juvenile mice. In addition to dose-dependent transduction of liver and pelvic ganglia (that innervate the bladder) with HPSE2, and demonstration of increased HPSE2 protein in Hpse2 mutant mice, the authors showed restoration of nerve-evoked outflow tract relaxation and bladder body contraction, both of which were deficient in mutant mice. They also showed that the viral vector-based approach was not deleterious to weight gain or to liver morphology. Based on these findings the authors concluded that AAV9-based HPSE2 replacement is feasible and safe, mitigates the physiological deficits in outflow tract and bladder tissue from Hpse2 mutant mice, and provides a foundation for gene replacement approaches for other genes implicated in lower urinary tract disorders.

Strengths include a rigorous experimental design, solid data in support of the conclusions, and a discussion of the limitations of the approach.

Weaknesses include a lack of discussion of the basis for differences in carbachol sensitivity in Hpse2 mutant mice, limited discussion of bladder tissue morphology in Hpse2 mutant mice, some questions over the variability of the functional data, and a need for clarification on the presentation of statistical significance of functional data

Reviewer #3 (Public Review):

Summary: The authors used cross-linking of a known P-gp substrate in combination with single particle cyro-EM to investigate the translocation pathway of this important ABC transporter. Based on the results of this study, a new translocation mechanism is proposed that is supported by the data. While only one substrate was used, the data obtained are convincing. In addition, the proposed model will stimulate new experiments from other laboratories to proof or disproof the model.

Strengths: the combination of cross-linking and structural biology allowed novel insights in the translocation pathway of ABCB1

Weaknesses: While only one substrate was used, the data obtained are convincing. In addition, the proposed model will stimulate new experiments from other laboratories to proof or disproof the model.

Reviewer #2 (Public Review):

Summary:<br /> This important study by LaBerge and co-authors seeks to understand the causal drivers of faculty gender demographics by quantifying the relative importance of faculty hiring and attrition across fields. They leverage historical data to describe past trends and develop models that project future scenarios that test the efficacy of targeted interventions. Overall, I found this study to be a compelling and important analysis of gendered hiring and attrition in US institutions, and one that has wide-reaching policy implications for the academy. The authors have also suggested a number of fruitful future avenues for research that will allow for additional clarity in understanding the gendered, racial, and socioeconomic disparities present in US hiring and attrition, and potential strategies for mitigating or eliminating these disparities.

Strengths:<br /> In this study, LaBerge et al use data from over 268,000 tenured and tenure-track faculty from over 100 fields at more than 12,000 PhD-granting institutions in the US. The period they examine covers 2011-2020. Their analysis provides a large-scale overview of demographics across fields, a unique strength that allows the authors to find statistically significant effects for gendered attrition and hiring across broad areas (STEM, non-STEM, and topical domains).

LaBerge et al. find gendered disparities in attrition-using both empirical data and their counterfactual model-that account for the loss of 1378 women faculty across all fields between 2011 and 2020. It is true that "this number is both a small portion of academia... and a staggering number of individual careers," as ." - as this loss of women faculty is comparable to losing more than 70 entire departments. I appreciate the authors' discussion about these losses-they note that each of these is likely unnecessary, as women often report feeling that they were pushed out of academic jobs.

LaBerge et al. also find-by developing a number of model scenarios testing the impacts of hiring, attrition, or both-that hiring has a greater impact on women's representation in the majority of academic fields in spite of higher attrition rates for women faculty relative to men at every career stage. Unlike many other studies of historical trends in gender diversity, which have often been limited to institution-specific analyses, they provide an analysis that spans over 100 fields and includes nearly all US PhD-granting institutions. They are able to project the impacts of strategies focusing on hiring or retention using models that project the impact of altering attrition risk or hiring success for women. With this approach, they show that even relatively modest annual changes in hiring accumulate over time to help improve the diversity of a given field. They also demonstrate that, across the model scenarios they employ, changes to hiring drive the largest improvement in the long-term gender diversity of a field.

Future work will hopefully - as the authors point out - include intersectional analyses to determine whether a disproportionate share of lost gender diversity is due to the loss of women of color from the professoriate. I appreciate the author's discussion of the racial demographics of women in the professoriate, and their note that "the majority of women faculty in the US are white" and thus that the patterns observed in this study are predominately driven by this demographic. I also highly appreciate their final note that "equal representation is not equivalent to equal or fair treatment," and that diversifying hiring without mitigating the underlying cause of inequity will continue to contribute to higher losses of women faculty.

Weaknesses<br /> First, and perhaps most importantly, it would be beneficial to include a distinct methods section. While the authors have woven the methods into the results section, I found that I needed to dig to find the answers to my questions about methods. I would also have appreciated additional information within the main text on the source of the data, specifics about its collection, inclusion and exclusion criteria for the present study, and other information on how the final dataset was produced. This - and additional information as the authors and editor see fit - would be helpful to readers hoping to understand some of the nuance behind the collection, curation, and analysis of this important dataset.

I would also encourage the authors to include a note about binary gender classifications in the discussion section. In particular, I encourage them to include an explicit acknowledgement that the trends assessed in the present study are focused solely on two binary genders - and do not include an analysis of nonbinary, genderqueer, or other "third gender" individuals. While this is likely because of the limitations of the dataset utilized, the focus of this study on binary genders means that it does not reflect the true diversity of gender identities represented within the professoriate.

In a similar vein, additional context on how gender was assigned on the basis of names should be added to the methods section.

I do think that some care might be warranted regarding the statement that "eliminating gendered attrition leads to only modest changes in field-level diversity" (Page 6). while I do not think that this is untrue, I do think that the model scenarios where hiring is "radical" and attrition is unchanged from present (equal representation of women and men among hires (ER) + observed attrition (OA)) shows that a sole focus on hiring dampens the gains that can otherwise be addressed via even modest interventions (see, e.g., gender-neutral attrition (GNA) + increasing representation of women among hires (IR)). I am curious as to why the authors did not include an additional scenario where hiring rates are equal and attrition is equalized (i.e., GNA + ER). The importance of including this additional model is highlighted in the discussion, where, on Page 7, the authors write: "In our forecasting analysis, we find that eliminating the gendered attrition gap, in isolation, would not substantially increase representation of women faculty in academia. Rather, progress towards gender parity depends far more heavily on increasing women's representation among new faculty hires, with the greatest change occurring if hiring is close to gender parity." I believe that this statement would be greatly strengthened if the authors can also include a comparison to a scenario where both hiring and attrition are addressed with "radical" interventions.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Herneisen et al. examines the Toxoplasma SPARK kinase orthologous to mammalian PDK1 kinase. The extracellular signals trigger cascades of the second messengers and play a central role in the apicomplexan parasites' survival. In Toxoplasma, these cascades regulate active replication of the tachyzoites, which manifests as acute toxoplasmosis, or the development into drug-resilient bradyzoites characteristic of the chronic stage of the disease. This study focuses on the poorly understood signaling mechanisms acting upstream of such second messenger kinases as PKA and PKG. The authors showed that similar to PDK1, Toxoplasma SPARK appears to regulate several AGC kinases.

Strengths:<br /> The study demonstrated a strong association of the SPARK kinase with an elongin-like SPARKEL factor and an uncharacterized AGC kinase. Using a set of standard assays, the authors determined the SPARK/SPARKEL role in parasite egress and invasion. Finally, the study presented evidence of the SPARK/SPARKEL involvement in the bradyzoite differentiation.

Weaknesses:<br /> Although the study can potentially uncover essential sensing mechanisms operating in Toxoplasma, the evidence of the SPARK/SPARKEL mechanisms is weak. Specifically, due to incomplete data analysis, the SPARK/SPARKEL-dependent phosphoregulation of AGC kinases cannot be evaluated. The manuscript requires better organization and lacks guidance on the described experiments. Although the study is built on advanced genetics, at times, it is unnecessarily complicated, raising doubts rather than benefiting the study.

Reviewer #2 (Public Review):

Lentiviral infection of primate species has been linked to the rapid mutational evolution of numerous primate genes that interact with these viruses, including genes that inhibit lentiviruses as well as genes required for viral infection. In this manuscript, Warren et al. provide further support for the diversification of CD4, the lentiviral entry receptor, to resist lentiviral infection in great ape populations. This work builds on their prior publication (Warren et al. 2019, PMCID: PMC6561292 ) and that of other groups (e.g., Russell et al. 2021, PMCID: PMC8020793; Bibollet-Ruche et al. 2019, PMCID: PMC6386711) documenting both sequence and functional diversity in CD4, specifically within (1) the CD4 domain that binds to the lentiviral envelope and (2) great ape populations with endemic lentiviruses. Thus, the paper's finding that gorilla populations exhibit diverse CD4 alleles that differ in their susceptibility to lentiviral infection is well demonstrated both here and in a prior publication.

To bolster the argument that lentiviruses are indeed the causative driver of this diversification, which seems likely from a logical perspective but is difficult to prove, Warren et al. pursue two novel lines of evidence. First, the authors reconstruct ancestral CD4 genes that predate lentiviral infection of hominid populations. They then demonstrate that resistance to lentiviral infection is a derived trait in chimpanzees and gorillas, which have been co-evolving with endemic lentiviruses, but not in humans, which only recently acquired HIV. Nevertheless, the derived resistance could be stochastic or due to drift. This argument would be strengthened by demonstrating that bonobo and orangutan CD4, which also do not have endemic lentiviruses, resemble the ancestral and human susceptibility to great-ape-infecting lentiviruses.

Second, Warren et al. provide a population genetic argument that only endemically infected primates exhibit diversifying selection, again arguing for endemic lentiviruses being the evolutionary driver. The authors compare SNP occurrence in CD4 to neighboring genes, demonstrating that non-synonymous SNP frequency is only elevated in endemically infected species. Moreover, these amino-acid-coding changes are significantly concentrated in the CD4 domain that binds the lentiviral envelope. This is a creative analysis to overcome the problem of very small sample sizes, with very few great ape individuals sequenced. The additional small number of species compared (2-3 in each group) also limits the power of the analysis; the authors could consider expanding their analysis to Old World Monkey species that do or do not have endemic lentiviruses, as well as great apes.

Overall, this manuscript lends additional support to a well-documented example of a host-virus arms race: that of lentiviruses and the viral entry receptor.

Reviewer #2 (Public Review):

In this paper, Wang and collaborators characterize the rapid evolution of the X-linked miR-506 cluster in mammals and characterize the functional reference of depleting a few or most of the miRNAs in the cluster. The authors show that the cluster originated from the MER91C DNA transposon and provide some evidence that it might have expanded through the retrotransposition of adjacent LINE1s. Although the animals depleted of most miRNAs in the cluster show normal sperm parameters, the authors observed a small but significant reduction in litter size. The authors then speculate that the depletion of most miRNAs in the cluster could impair sperm competitiveness in polyandrous mating. Using a successive mating protocol, they show that, indeed, sperm lacking most X-linked miR-506 family members is outcompeted by wild-type sperm. The authors then analyze the evolution of the miR-506 cluster and its predicted targets. They conclude that the main difference between mice and humans is the expansion of the number of target sites per transcript in humans.

The conclusions of the paper are, in most cases, supported by the data; however, a more precise and in-depth analysis would have helped build a more convincing argument in most cases.

1) In the abstracts and throughout the manuscript, the authors claim that "... these X-linked miRNA-506 family miRNA [...] have gained more targets [...] " while comparing the human miRNA-506 family to the mouse. An alternative possibility is that the mouse has lost some targets. A proper analysis would entail determining the number of targets in the mouse and human common ancestor.

2) The authors claim that the miRNA cluster expanded through L1 retrotransposition. However, the possibility of an early expansion of the cluster before the divergence of the species while the MER91C DNA transposon was active was not evaluated. Although L1 likely contributed to the diversity within mammals, the generalization may not apply to all species. For example, SINEs are closer on average than L1s to the miRNAs in the SmiR subcluster in humans and dogs, and the horse SmiR subcluster seems to have expanded by a TE-independent mechanism.

3) Some results are difficult to reconcile and would have benefited from further discussion. The miR-465 sKO has over two thousand differentially expressed transcripts and no apparent phenotype. Also, the authors show a sharp downregulation of CRISP1 at the RNA and protein level in the mouse. However, most miRNAs of the cluster increase the expression of Crisp1 on a reporter assay. The only one with a negative impact has a very mild effect. miRNAs are typically associated with target repression; however, most of the miRNAs analyzed in this study activate transcript expression.

4) More information is required to interpret the results of the differential RNA targeting by the murine and human miRNA-506 family. The materials and methods section needs to explain how the authors select their putative targets. In the text, they mention the use of four different prediction programs. Are they considering all sites predicted by any method, all sites predicted simultaneously by all methods, or something in between? Also, what are they considering as a "shared target" between mice and humans? Is it a mRNA that any miR-506 family member is targeting? Is it a mRNA targeted by the same miRNA in both species? Does the targeting need to occur in the same position determined by aligning the different 3'UTRs?

5) The authors highlight the particular evolution of the cluster derived from a transposable element. Given the tendency of transposable elements to be expressed in germ cells, the family might have originated to repress the expression of the elements while still active but then remained to control the expression of the genes where the element had been inserted. The authors did not evaluate the expression of transcripts containing the transposable element or discuss this possibility. The authors proposed an expansion of the target sites in humans. However, whether this expansion was associated with the expansion of the TE in humans was not discussed either. Clarifying whether the transposable element was still active after the divergence of the mouse and human lineages would have been informative to address this outstanding issue.

Post-transcriptional regulation is exceptionally complex in male haploid cells, and the functional relevance of many regulatory pathways remains unclear. This manuscript, together with recent findings on the role of piRNA clusters, starts to clarify the nature of the selective pressure that shapes the evolution of small RNA pathways in the male germ line.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript "Cohesin still drives homologous recombination repair of DNA double-strand breaks in late mitosis" by Ayra-Plasencia et al. investigates regulations of HR repair in conditional cdc15 mutants, which arrests the cell cycle in late anaphase/telophase. Using a non-competitive MAT switching system of S. cerevisiae, they show that a DSB in telophase-arrested cells elicits a delayed DNA damage checkpoint response and resection. Using a degron allele of SMC3 they show that MATa-to-alpha switching requires cohesin in this context. The presence of a DSB in telophase-arrested cells leads to an increase in the kleisin subunit Scc1 and a partial rejoining of sister chromatids after they have separated in a subset of cells.

Strengths:<br /> The experiments presented are well-controlled. The induction systems are clean and well thought-out.

Weaknesses:<br /> The manuscript is very preliminary, and I have reservations about its physiological relevance. I also have reservations regarding the usage of MAT to make the point that inter-sister repair can occur in late mitosis.

Reviewer #2 (Public Review):

Summary:

The ability of cardiac cells to regenerate has been the object of intense (and sometimes controversial) research in biology. While lower organisms can robustly undergo cardiac regeneration by reactivation of the embryonic cardiogenic pathway, this ability is strongly reduced in mice, both temporally and qualitatively. Finding a way to derive precursor cells with regenerative ability from differentiated cells in mammals has been challenging.

Zhou, He, and colleagues hypothesized that ISL-1-positive cells would show regenerative capacity and developed a small molecules screen to dedifferentiate cardiomyocytes (CM) to ISL1-positive precursor cells. Using hESC-derived CM, the authors found that the combination of both, WNT activation (CHIR99021) and p300 acetyltransferase inhibition (A-485) (named 2C protocol) induces CM dedifferentiation to regenerative cardiac cells (RCCs). RCCs are proliferative and re-express embryonic cardiogenic genes while decreasing the expression of more mature cardiac genes, bringing them towards a more precursor-like state. RCCs were able to differentiate into CM, smooth muscle cells, and endothelial cells, highlighting their multipotent property. In vivo, administration of 2C in rats and mice had protective effects on myocardial infarction.

Mechanistically, the authors report that the 2C protocol drives CM-specific transcriptional and epigenetic changes.

Strengths:

The authors made a great effort to validate their data using orthogonal ways, and several hESC lines. The use of lineage tracing convincingly showed a dedifferentiation from CM. They translate their findings into an in vivo model of myocardial injury, and show functional cardiac regeneration post-injury. They also showed that 2C could surprisingly be used as a preventive treatment. Together their data may suggest a regenerative effect of 2C both in vitro and in vivo settings. If confirmed, this study might unlock therapeutic strategy for cardiac regeneration.

Weaknesses:

Several points remain puzzling to me and some aspects of this study need to be clarified and extended:

General comments:

* Experimental design & Interpretation*

1) The main hypothesis (line 50) that Isl1 cells have regenerative properties is not extremely novel (10.1172/jci.insight.80920, doi.org/10.1038/nature03215,10.1016/s1534-5807(03)00363-0).

2) Based on Table S1, concentration of A-485 used in the screen is 10uM but used throughout this study at 0.5um. Could the authors provide a rationale for this 20x reduction of concentration? It would be useful to get a titration of this compound for the effects tested.

3) It is confusing to clearly understand what proportion of CMs dedifferentiate to become RCCs. The lineage tracing data suggests only 0.6%-1.5% of cells undergo this transition. It is difficult to understand how such a small fraction can have wide effects in their different experimental settings. This is specifically true when the author quantified nuclear and cytosolic area on brightfield pictures - would the same effect on nuclear/cytosolic area be observed in Isl1 KO cells?

4) The authors totally disregard the effect of i-BET-762 that gives a very similar percentage of Isl1-positive cells when combined to 2C (Supp. 1E). What is the effect of CHIR + I-BET-762 alone?

5) It is really hard to understand the contradictory effects of A-485 on acetylation status. The authors mentioned that A-485 only has an effect on H3K27Ac and not on H3K9Ac (line 221) to later (line 226) contradict themselves by saying it also has an effect on H3K9Ac. To explain this discrepancy, the authors vaguely mentioned "further analyses" without giving any other details. It would be transparent to explain what led to this radical change in interpretation.

6) The difference in the ChIP peak height is rather minimal for the H3K9Ac data. Were the peaks normalized to the sequencing depth? What does the y-axis represent on these ChIP traces (number of counts?)

7) Would it be possible to test this 2C protocol on mESC and see if similar changes occur? How transcriptionally different would these mouse RCCs be to Isl1+ progenitors isolated from neonatal mice (P1-P5)?

Statistics & Data acquisition

1) The authors mentioned experiments were done at least 3 times and each dot plotted on a graph is an average of technical repeat for one biological repeat. My understanding would be that if I see 9 dots, it means the experiment has been done 9 times - What would be the rationale for such a high number of repeats? It is an "artificial" way to increase the power of a test and might lead to misinterpretation of the data. This becomes relevant for some figures where biological difference is minimal and they still show statistical differences (e.g. Supp 2E, Supp 3A, Supp 9C,...). This is also true for in vivo section (Fig. 4G).

It would help to have a precise clarification between technical and biological repeats in the figure legends (e.g. n=3 biological repeat (aka 3 dots on a graph) obtained from averaging XX technical repeats), as well as the specific test stated the legend in addition to the general paragraph in the methods. Providing raw numerical data so readers can re-test them independently would also be a transparent way to do it.

2) Does the author test for normality before applying a specific test? Please clarify and justify either way.

3) If each dot represents a biological repeat as stated in the method section, why do some datasets have different numbers of repeats between NC and 2C if obtained in parallel? Have repeats been excluded?

Reviewer #2 (Public Review):

Summary:<br /> Starting from an AlphaFold2 model of the outward-facing conformation of the GAT1 transporter, the authors primarily use state-of-the-art MD simulations to dissect the role of the two Na+ ions that are known to be co-transported with the substrate, GABA (and a co-transported Cl- ion). The simulations indicated that Na+ binding to OF GAT depends on the electrostatic environment. The authors identify an extracellular recruiting site including residues D281 and E283 which they hypothesized to increase transport by locally increasing the available Na+ concentration and thus increasing binding of Na+ to the canonical binding sites NA1 and NA2. The charge-neutralizing double mutant D281A-E283A showed decreased binding in simulations. The authors performed GABA uptake experiments and whole-cell patch clamp experiments that taken together validated the hypothesis that the Na+ staging site is important for transport due to its role in pulling in Na+.

Detailed analysis of the MD simulations indicated that Na+ binding to NA2 has multiple structural effects: The binding site becomes more compact (reminiscent of induced fit binding) and there is some evidence that it stabilizes the outward-facing conformation.

Binding to NA1 appears to require the presence of the substrate, GABA, whose carboxylate moiety participates in Na+ binding; thus the simulations predict cooperativity between binding of GABA and Na+ binding to NA1.

Strengths:<br /> - MD simulations were used to propose a hypothesis (the existence of the staging Na+ site) and then tested with a mutant in simulations AND in experiments. This is an excellent use of simulations in combination with experiments.

- A large number of repeat MD simulations are generally able to provide a consistent picture of Na+ binding. Simulations are performed according to current best practices and different analyses illuminate the details of the molecular process from different angles.

- The role of GABA in cooperatively stabilizing Na+ binding to the NA1 site looks convincing and intriguing.

Weaknesses:<br /> - Assessing the effects of Na+ binding on the large-scale motions of the transporter is more speculative because the PCA does not clearly cover all of the conformational space and the use of an AlphaFold2 model may have introduced structural inconsistencies. For example, it is not clear if movements of the inner gate are due to an AF2 model that's not well packed or really a feature of the open outward conformation.

- Quantitative analyses are difficult with the existing data; for example, the tICA "free energy" landscape is probably not converged because unbinding events haven't been observed.

Reviewer #2 (Public Review):

Summary:<br /> In this study by Bendzunas et al, the authors show that the formation of intra-molecular disulfide bonds involving a pair of Cys residues near the catalytic HRD motif and a highly conserved T-Loop Cys with a BRSK-specific Cys at an unusual CPE motif at the end of the activation segment function as repressive regulatory mechanisms in BSK1 and 2. They observed that mutation of the CPE-Cys only, contrary to the double mutation of the pair, increases catalytic activity in vitro and drives phosphorylation of the BRSK substrate Tau in cells. Molecular modeling and molecular dynamics simulations indicate that oxidation of the CPE-Cys destabilizes a conserved salt bridge network critical for allosteric activation. The occurrence of spatially proximal Cys amino acids in diverse Ser/Thr protein kinase families suggests that disulfide-mediated control of catalytic activity may be a prevalent mechanism for regulation within the broader AMPK family. Understanding the molecular mechanisms underlying kinase regulation by redox-active Cys residues is fundamental as it appears to be widespread in signaling proteins and provides new opportunities to develop specific covalent compounds for the targeted modulation of protein kinases.

The authors demonstrate that intramolecular cysteine disulfide bonding between conserved cysteines can function as a repressing mechanism as indicated by the effect of DTT and the consequent increase in activity by BSK-1 and -2 (WT). The cause-effect relationship of why mutation of the CPE-Cys only increases catalytic activity in vitro and drives phosphorylation of the BRSK substrate Tau in cells is not clear to me. The explanation given by the authors based on molecular modeling and molecular dynamics simulations is that oxidation of the CPE-Cys (that will favor disulfide bonding) destabilizes a conserved salt bridge network critical for allosteric activation. However, no functional evidence of the impact of the salt-bridge network is provided. If you mutated the two main Cys-pairs (aE-CHRD and A-loop T+2-CPE) you lose the effect of DTT, as the disulfide pairs cannot be formed, hence no repression mechanisms take place, however when looking at individual residues I do not understand why mutating the CPE only results in the opposite effect unless it is independent of its connection with the T+2residue on the A-loop.

Strengths:<br /> This is an important and interesting study providing new knowledge in the protein kinase field with important therapeutic implications for the rationale design and development of next-generation inhibitors.

Weaknesses:<br /> There are several issues with the figures that this reviewer considers should be addressed.

Reviewer #2 (Public Review):

Summary:<br /> In this paper, the authors investigated the admixture history of domestic cattle since they were introduced into Iberia, by studying genomic data from 24 ancient samples dated to ~2000-8000 years ago and comparing them to modern breeds. They aimed to (1) test for introgression from (local) wild aurochs into domestic cattle; (2) characterize the pattern of admixture (frequency, extent, sex bias, directionality) over time; (3) test for correlation between genetic ancestry and stable isotope levels (which are indicative of ecological niche); and (4) test for the hypothesized higher aurochs ancestry in a modern breed of fighting bulls.

Strengths:<br /> Overall, this study collects valuable new data that are useful for testing interesting hypotheses, such as admixture between domestic and wild populations, and correlation between genome-wide aurochs ancestry and aggressiveness.

Weaknesses:<br /> Most conclusions are partially supported by the data presented. The presence of admixed individuals in prehistorical periods supports the hypothesized introgression, although this conclusion needs to be strengthened with an analysis of potential contamination. The frequency, sex-bias, and directionality of admixture remain highly uncertain due to limitations of the data or issues with the analysis. There is considerable overlap in stable isotope values between domestic and wild groups, indicating a shared ecological niche, but variation in classification criteria for domestic vs wild groups and in skeletal elements sampled for measurements significantly weakens this claim. Lastly, the authors presented convincing evidence for relatively constant aurochs ancestry across all modern breeds, including the Lidia breed which has been bred for aggressiveness for centuries. My specific concerns are outlined below.

Contamination is a common concern for all ancient DNA studies. Contamination by modern samples is perhaps unlikely for this specific study of ancient cattle, but there is still the possibility of cross-sample contamination. The authors should estimate and report contamination estimates for each sample (based on coverage of autosomes and sex chromosomes, or heterozygosity of Y or MT DNA). Such contamination estimates are particularly important to support the presence of individuals with admixed ancestry, as a domestic sample contaminated with a wild sample (or vice versa) could appear as an admixed individual.

A major limitation of this study is uncertainty in the "population identity" for most sampled individuals (i.e., whether an individual belonged to the domesticated or wild herd when they were alive). Based on chronology, morphology, and genetic data, it is clear the Mesolithic samples from the Artusia and Mendandia sites are bona fide aurochs, but the identities of individuals from the other two sites are much less certain. Indeed, archeological and morphological evidence from El Portalon supports the presence of both domestic animals and wild aurochs, which is echoed by the inter-individual heterogeneity in genetic ancestry. Based on results shown in Fig 1C and Fig 2 it seems that individuals moo017, moo020, and possibly moo012a are likely wild aurochs that had been hunted and brought back to the site by humans. Although the presence of individuals (e.g., moo050, moo019) that can only be explained by two-source models strongly supports that interbreeding happened (if cross-contamination is ruled out), it is unclear whether these admixed individuals were raised in the domestic population or lived in the wild population and hunted.

Such uncertainty in "population identity" limits the authors' ability to make conclusions regarding the frequency, sex bias, and directionality of gene flow between domestic and wild populations. For instance, the wide range of ancestry estimates in Neolithic and Chalcolithic samples could be interpreted as evidence of (1) frequent recent gene flow or (2) mixed practices of herding and hunting and less frequent gene flow. Similarly, the statement about "bidirection introgression" (on pages 8 and 11) is not directly supported by data. As the genomic, morphological, and isotope data cannot confidently classify an individual as belonging to the domesticated or wild population, it seems impossible to conclude the direction of gene flow (if by "bidirection introgression" the authors mean something other than "bidirectional gene flow", they need to clearly explain this before reaching the conclusion.)

The f4 statistics shown in Fig 3B are insufficient to support the claim regarding sex-biased hybridization, as the f4 statistic values are not directly comparable between the X chromosome and autosomes. Because the effective population size is different for the X chromosome and autosomes (roughly 3:4 for populations with equal numbers of males and females), the expected amount of drift is different, hence the fraction of allele sharing (f4) is expected to be different. In fact, the observation that moo004 whose autosomal genome can be modeled as 100% domestic ancestry still shows a higher f4 value for the X chromosome than autosomes hints at this issue. A more robust metric to test for sex-biased admixture is the admixture proportion itself, which can be estimated by qpAdm or f4-ratio (see Patterson et al 2012). However, even with this method, criticism has been raised (e.g., Lazaridis and Reich 2017; Pfennig and Lachance, 2023). In general, detecting sex-bias admixture is a tough problem.

In general, the stable isotope analysis seems to be very underpowered, due to the issues of variation in classification criteria and skeletal sampling location discussed by the authors in supplementary material. The authors claimed a significant difference in stable nitrogen isotope between (inconsistently defined) domestic cattle and wild aurochs, but no figures or statistics are presented to support this claim. Please describe the statistical method used and the corresponding p-values. The authors can consider including a figure to better show the stable isotope results.

Reviewer #2 (Public Review):

Summary:<br /> Wang and collaborators have evaluated the impact of inflammation on bone loss induced by Doxorubicin, which is commonly used in chemotherapy to treat various cancers. In mice, they show that a single injection of Doxorubicin induces systemic inflammation, leukopenia, and significant bone loss associated with increased bone-resorbing osteoclast numbers. In vitro, the authors show that Doxorubicin activates the AIM2 and NLRP3 inflammasomes in macrophages and neutrophils. Importantly, they show that the full knockouts (germline deletions) of AIM2 (Aim2-/-) and NLRP3 (Nlrp3-/-) and Caspase 1 (Casp1-/-) limit (but do not completely abolish) bone loss induced 4 weeks after a single injection of Doxorubicin in mice. From these results, they conclude that Doxorubicin activates inflammasomes to cause inflammation-associated bone loss.

Strengths:<br /> While numerous studies have reported that Doxorubicin activates the inflammasome in myeloid cells and various other cell types, that Doxorubicin induces systemic inflammation, and that both the systemic inflammation and Doxorubicin treatment leads to bone loss, functional experiments demonstrating that NRLP3 and/or AIM2 loss-of-functions, and thus the systemic impairment of the inflammatory response, may prevent bone-loss induced by Doxorubicin were lacking. The strength of this manuscript is that it provides these missing data.

Weaknesses:<br /> However, one could argue that most of the conclusions drawn from the data presented here have been previously reported and that it was very much expected that reducing systemic inflammation in treated animals (in Aim2-/- and/or Nlrp3-/- mice) would preserve bone homeostasis to some extent, similarly to what has been reported in the context of cardiotoxicity induced by Doxorubicin.

Since the manuscript focuses on therapeutic considerations aiming to preserve bone homeostasis in animals treated with Doxorubicin, additional experiments evaluating and comparing various therapeutic options could improve the impact of the study. Drugs targeting the inflammasomes could be tested in addition to the genetic mouse models. Since increased osteoclast numbers (and likely bone resorption) are associated with Doxorubicin-induced bone loss, antiresorptive drugs such as Bisphosphonates or anti-RANKL antibodies could be tested and compared to anti-inflammatory drugs. Since autophagy and senescence have been shown to contribute to bone loss induced by Doxorubicin, it would be interesting to use the pharmacologic inhibitors (targeting autophagy or senescence) used in these previous studies to evaluate the relative impact of these different cellular mechanisms, on bone loss induced by Doxorubicin.

Moreover, the cellular and molecular mechanisms by which Doxorubicin induces bone loss in vivo could be further evaluated. Doxorubicin has been reported to directly affect bone-making osteoblasts and bone-resorbing osteoclasts. It would be important to determine the relative importance of the activation of the AIM2 and NLRP3 inflammasomes in these cells compared to macrophages and neutrophils. Floxed mouse lines exist for both Aim2 and Nlrp3, as well as relevant cell-specific Cre lines. Thus, cell-specific conditional knockouts could have been used in the current study, instead of using global knockout animals. Genetic tools also exist to induce the specific ablation of macrophages or neutrophils and could be used. Furthermore, it is unclear whether local inflammation is induced in the bone marrow of Doxorubicin-treated mice, and what is the relative impact of local versus systemic inflammation in bone loss in these mice. Markers of the inflammasomes, pyroptosis, and NETosis could be evaluated on bone sections, and on bone and bone marrow samples. The effect of Doxorubicin on osteoblast numbers in vivo and on bone resorption (not just osteoclast numbers) should be evaluated as well. These mechanistic aspects are important and needed to better understand the cytotoxic mechanisms triggered by Doxorubicin, and define the best therapeutic approaches to preserve bone integrity in chemotherapy.

Finally, it would be important to assess the bone mass of Doxorubicin-treated control, Aim2-/-, Nlrp3-/- and Cas1-/- mice at a later time point than 4 weeks post-injection. Nlrp3 knockout has been reported to increase the density of the cortical and trabecular bones. The bone mass of Aim2-/-, Nlrp3-/- and Cas1-/- mice at baseline may be higher than that of control mice, and it may take slightly longer for Doxorubicin to reduce bone mass to the same extent than in controls. It would be also interesting to do similar experiments using animals treated multiple times with Doxorubicin instead of using a single injection, since patients receive their chemotherapy multiple times.

Reviewer #2 (Public Review):

Summary and strengths:

Early career funding success has an immense impact on later funding success and faculty persistence, as evidenced by well-documented "rich-get-richer" or "Matthew effect" phenomena in science (e.g., Bol et al., 2018, PNAS). In this study the authors examined publicly available data on the distribution of the National Institutes of Health's K99/R00 awards - an early career postdoc-to-faculty transition funding mechanism - and showed that although 89% of K99 awardees successfully transitioned into faculty, disparities in subsequent R01 grant obtainment emerged along three characteristics: researcher mobility, gender, and institution. Men who moved to a top-25 NIH funded institution in their postdoc-to-faculty transition experienced the shortest median time to receiving a R01 award, 4.6 years, in contrast to the median 7.4 years for women working at less well-funded schools who remained at their postdoc institutions.

Amongst the three characteristics, the finding that researcher mobility has the largest effect on subsequent funding success is key and novel. Other data supplement this finding: for example, although the total number of R00 awards has increased, most of this increase is for awards to individuals moving to different institutions. In 2010, 60% of R00 awards were activated at different institutions compared to 80% in 2022. These findings enhance previous work on the relationship between mobility and ones' access to resources, collaborators, or research objects (e.g., Sugimoto and Larivière, 2023, Equity for Women in Science (Harvard University Press)).

These results empirically demonstrate that even after receiving a prestigious early career grant, researchers with less mobility belonging to disadvantaged groups at less-resourced institutions continue to experience barriers that delay them from receiving their next major grant. This result has important policy implications aimed at reducing funding disparities - mainly that interventions that focus solely on early career or early stage investigator funding alone will not achieve the desired outcome of improving faculty diversity.

The authors also highlight two incredible facts: No postdoc at a historically Black college or university (HBCU) has been awarded a K99 since the program's launch. And out of all 2,847 R00 awards given thus far, only two have been made to faculty at HBCUs. Given the track record of HBCUs for improving diversity in STEM contexts, this distribution of awards is a massive oversight that demands attention.

At no fault of the authors, the analysis is limited to only examining K99 awardees and not those who applied but did not receive the award. This limitation is solely due to the lack of data made publicly available by the NIH. If this data were available, this study would have been able to compare the trajectory of winners versus losers and therefore could potentially quantify the impact of the award itself on later funding success, much like the landmark paper by Bol et al. (PNAS; 2018) that followed the careers of an early career grant scheme in the Netherlands. Such an analysis would also provide new insights that would inform policy.

Although data on applications versus awards for the K99/R00 mechanism are limited, there exists data for applicant race and ethnicity for the 2007-2017 period, which were made available by a Freedom of Information Act request through the now defunct Rescuing Biomedical Research Initiative (https://web.archive.org/web/20180723171128/http://rescuingbiomedicalresearch.org/blog/examining-distribution-k99r00-awards-race/). These results are highly relevant given the discussion of K99 award impacts on the sociodemographic composition of U.S. biomedical faculty. During the 2007-2017 period, the K99 award rate for white applicants was 31% compared to 26.7% for Asian applicants and 16.2% for Black applicants. In terms of award totals, these funding rates amount to 1,384 awards to white applicants, 610 to Asian applicants, and 25 to Black applicants. However, the work required to include these data may be beyond the scope of the study.

The conclusions are well-supported by the data, and limitations of the data and the name-gender matching algorithm are described satisfactorily.

Reviewer #2 (Public Review):

This paper makes important and novel advances that significantly enhance our understanding of the ClC-2 channel. The EM data are of high quality, and the most important argument, concerning the role of the N-terminus of the protein as an occluding inactivation gate, is very well supported by both structural, computational, and functional data (some of which is previously published). The proposal that the "run up" observed in patch clamp experiments represents relief of inactivation is interesting and compelling. The model predicts that mutations at the hairpin binding site should influence this "run up", which should be tested in the near future. Finally, the confirmation of the AK-42 binding site further solidifies evidence that this is a pore-blocking compound; the authors' argument about determinants of specificity is convincing.

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):

In this paper Sasani, Quinlan and Harris present a new method for identifying genetic factors affecting germline mutation, which is particularly applicable to genome sequence data from mutation accumulation experiments using recombinant inbred lines. These are experiments where laboratory organisms are crossed and repeatedly inbred for many generations, to build up a substantial number of identifiable germline mutations. The authors apply their method to such data from mice, and identify two genetic factors at two separate genetic loci. Clear evidence of such factors has been difficult to obtain, so this is an important finding. They further show evidence of an epistatic interaction between these factors (meaning that they do not act independently in their effects on the germline mutation process). This is exciting because such interactions are difficult to detect and few if any other examples have been studied.

The authors present a careful comparison of their method to another similar approach, quantitative trait locus (QTL) analysis, and demonstrate that in situations such as the one analysed it has greater power to detect genetic factors with a certain magnitude of effect. They also test the statistical properties of their method using simulated data and permutation tests. Overall the analysis is rigorous and well motivated, and the methods explained clearly.

The main limitation of the approach is that it is difficult to see how it might be applied beyond the context of mutation accumulation experiments using recombinant inbred lines. This is because the signal it detects, and hence its power, is based on the number of extra accumulated mutations linked to (i.e. on the same chromosome as) the mutator allele. In germline mutation studies of wild populations the number of generations involved (and hence the total number of mutations) is typically small, or else the mutator allele becomes unlinked from the mutations it has caused (due to recombination), or is lost from the population altogther (due to chance or perhaps selection against its deleterious consequences).

Nevertheless, accumulation lines are a common and well established experimental approach to studying mutation processes in many organisms, so the new method could have wide application and impact on our understanding of this fundamental biological process.

The evidence presented for an epistatic interaction is convincing, and the authors suggest some plausible potential mechanisms for how this interaction might arise, involving the DNA repair machinery and based on previous studies of the proteins implicated. However as with all such findings, given the higher degree of complexity of the proposed model it needs to be treated with greater caution, perhaps until replicated in a separate dataset or demonstrated in follow-up experiments exploring the pathway itself.

Reviewer #2 (Public Review):

Pinos et al present five atherosclerosis studies in mice to investigate the impact of dietary supplementation with b-carotene on plaque remodeling during resolution. The authors use either LDLR-ko mice or WT mice injected with ASO-LDLR to establish diet-induced hyperlipidemia and promote atherogenesis during 16 weeks, and then they promote resolution by switching the mice for 3 weeks to a regular chow, either deficient or supplemented with b-carotene. Supplementation was successful, as measured by hepatic accumulation of retinyl esters. As expected, chow diet led to reduced hyperlipidemia, and plaque remodeling (both reduced CD68+ macs and increased collagen contents) without actual changes in plaque size. But, b-carotene supplementation resulted in further increased collagen contents and, importantly, a large increase in plaque regulatory T-cells (TREG). This accumulation of TREG is specific to the plaque, as it was not observed in blood or spleen. The authors propose that the anti-inflammatory properties of these TREG explain the atheroprotective effect of b-carotene, and found that treatment with anti-CD25 antibodies (to induce systemic depletion of TREG) prevents b-carotene-stimulated increase in plaque collagen and TREG.

An obvious strength is the use of two different mouse models of atherogenesis, as well as genetic and interventional approaches. The analyses of aortic root plaque size and contents are rigorous and included both male and female mice (although the data was not segregated by sex). Unfortunately, the authors did not provide data on lesions in en face preparations of the whole aorta.

Overall, the conclusion that dietary supplementation with b-carotene may be atheroprotective via induction of TREG is reasonably supported by the evidence presented. Other conclusions put forth by the authors (e.g., that vitamin A production favors TREG production or that BCO1 deficiency reduces plasma cholesterol), however, will need further experimental evidence to be substantiated.

The authors claim that b-carotene reduces blood cholesterol, but data shown herein show no differences in plasma lipids between mice fed b-carotene-deficient and -supplemented diets (Figs. 1B, 2A, and S3A). Also, the authors present no experimental data to support the idea that BCO1 activity favors plaque TREG expansion (e.g., no TREG data in Fig 3 using Bco1-ko mice).

As the authors show, the treatment with anti-CD25 resulted in only partial suppression of TREG levels. Because CD25 is also expressed in some subpopulation of effector T-cells, this could potentially cloud the interpretation of the results. Data in Fig 4H showing loss of b-carotene-stimulated increase in numbers of FoxP3+GFP+ cells in the plaque should be taken cautiously, as they come from a small number of mice. Perhaps an orthogonal approach using FoxP3-DTR mice could have produced a more robust loss of TREG and further confirmation that the loss of plaque remodeling is indeed due to loss of TREG.

Reviewer #2 (Public Review):

Summary:

This work elaborates on a combined therapeutic approach comprising ionizing radiation and CCR5i/αPD1 immunotherapy as a promising strategy in pancreatic cancer. Previous research has established that NK cell-derived CCL5 and XCL1 play a crucial role in recruiting cDC1 cells to the tumor microenvironment, contributing to tumor control. In this study, by using a murine pancreatic cancer model, the authors propose that the addition of radiation therapy to CCR5i and αPD1 immunotherapy could upregulate CD8+ T cells and a subgroup of NK cells within the tumor and result in better tumor control. They further analyzed human single-cell sequencing data from pancreatic cancer patients and identified one subgroup of NK cells (NK C1) with tissue-resident features. Subsequent cell-cell contact analysis reveals the NK-cDC1-CD8 cell axis in pancreatic cancer. By analyzing TCGA data, they found that high NK C1 signature levels were associated with better survival in pancreatic cancer patients. Thus, radiotherapy could benefit the outcome of patients bearing low NK C1 signatures. Importantly, the positive correlation between NK C1 score with survival extends beyond pancreatic cancer, showing potential applicability across various solid cancers.

Strengths:

This study could add new insight into the clinical practice by introducing such novel combined therapy and shed light on the underlying immune cell dynamics. These findings hold potential for more effective and targeted treatment in the future. Mouse experiments nicely confirmed that such combined therapy could significantly reduce tumor volume. The elegant use of single-cell sequencing analysis and human database examination enriches the narrative and strengthens the study's foundation. Additionally, the notion that NK C1 signature correlates with patient survival in various solid cancers is of high interest and relevance.

Weaknesses:

1. The role of CCR5i requires further clarification. While the authors demonstrated its capacity to reduce Treg in murine tumors, its impact on other cell populations, including NK cells and CD8+ T cells, was not observed. Nevertheless, the effect of CCR5i on tumor growth in Figure 2B should be shown. If the combination of radiotherapy and αPD1 already can achieve good outcomes as shown in Figure 3A, the necessity to include CCR5i is questioned. Overall, a more comprehensive elucidation of the roles of CCL5 and CCR5i in this context would be good.

2. In line with this, spatial plots in Figure 4 did not include the group with only radiotherapy and αPD1. This inclusion would facilitate a clearer comparison and better highlight the essential role of CCR5i.

3. NK C1 cells should be also analyzed in the mouse model. The authors suggest that NKNKG2D-ve could be the cell population. Staining of inhibitory markers should be considered, for example, TIGIT and TIM3 as presented in Figure 5B.

4. While the cell-cell contact analysis generated from single-cell sequencing data is insightful, extending this analysis to the mouse model under therapy would be highly informative. NK and CD8 cells in the tumor increased upon the combined therapy. However, cDC1 was not characterized. Analysis regarding cDC1 would provide more information on the NK/cDC1/CD8 axis.

5. Human database analysis showed a positive correlation between NK C1 score and CCL5 in pancreatic cancer. Furthermore, radiotherapy could benefit the outcome of patients bearing low NK C1 scores. It would be interesting to test if radiotherapy could also benefit patients with low CCL5 levels in this cohort.

Reviewer #3 (Public Review):

Prior studies in humans and in chickens suggested that TMEM263 could play an important role in longitudinal bone growth, but a definitive assessment of the function and potential mechanism of action of this species-conserved plasma membrane protein has been lacking. Here, the authors create a TMEM263 null mouse model and convincingly show dramatic cessation of post-natal growth, which becomes apparent by day PND21. They report proportional dwarfism, highly significant bone and related phenotypes, as well as notable alterations of hepatic GH signaling to IGF1. A large body of prior work has established an essential role for GH and it's stimulation of IGF1 production in liver and other tissues in post-natal growth. On this basis, the authors conclude that the observed decrease in serum IGF1 seen in TMEM263-KO mice is causal for the growth phenotype, which seems likely. Moreover, they ascribe the low serum IGF1 to the observed decreases in hepatic GH receptor (GHR) expression and GHR/JAK2/STAT5 signaling to IGF1, which is plausible but not proven by the experiments presented.

The finding that TMEM263 is essential for normal hepatic GHR/IGF1 signaling is an important, and unexpected finding, one that is likely to stimulate further research into the underlying mechanisms of TMEM263 action, including the distinct possibility that these effects involve direct protein-protein interactions between GHR and TMEM263 on the plasma membrane of hepatocytes, and perhaps on other mouse cell types and tissues as well, where TMEM263 expression is up to 100-fold higher (Fig. 1C).

An intriguing finding of this study, which is under emphasized and should be noted in the Abstract, is the apparent feminization of liver gene expression in male TMEM263-KO mice, where many male-biased genes are downregulated, and many female-biased genes are upregulated. Further investigation of these liver gene responses by comparison to public datasets could be very useful, as it could help determine: (1) whether the TMEM263 liver phenotype is similar to that of hypophysectomized male mouse liver, where GH and GHR/STAT5/IGF1 signaling are both totally ablated; or alternatively, (2) whether the phenotype is more similar to that of a male mouse given GH as a continuous infusion, which induces widespread feminization of gene expression in the liver, and is perhaps similar to the gene responses seen in the TMEM263-KO mice. Answering this question could provide critical insight into the mechanistic basis for the hepatic effects of TMEM263 gene KO.

Comments on revised version:

The authors have addressed a majority of the concerns raised during the initial review. The evidence supporting the whole-body growth and skeletal phenotypes, as well as the disruption of GH/IGF1 signaling seen in TMEM263-KO mice, is convincing. However, there is insufficient evidence to definitively conclude that the observed alteration of hepatic GH/IGF1 signaling is causative of the body growth and skeletal phenotypes.

Reviewer #2 (Public Review):

Summary:<br /> Anil Verma et al. have performed prime-boost HIV vaccination to enhance HIV-1 Env antibodies in the rhesus macaques model. The authors used two different adjuvants, a cationic liposome-based adjuvant (CAF01) and a monophosphoryl lipid A (MPLA)+QS-21 adjuvant. They demonstrated that these two adjuvants promote different transcriptomes in the GC-TFH subsets. The MPLA+QS-21 adjuvant induces abundant GC TFH1 cells expressing CXCR3 at first priming, while the CAF01 adjuvant predominantly induced GC TFH1/17 cells co-expressing CXCR3 and CCR6. Both adjuvants initiate comparable Env antibody responses. However, MPLA+QS-21 shows more significant IgG1 antibodies binding to gp140 even after 30 weeks.<br /> The enhancement of memory responses by MPLA+QS-21 consistently associates with the emergence of GC TFH1 cells that preferentially produce IFN-γ.

Strengths:<br /> The strength of this manuscript is that all experiments have been done in the rhesus macaques model with great care. This manuscript beautifully indicated that MPLA+QS-21 would be a promising adjuvant to induce the memory B cell response in the HIV vaccine.

Weaknesses:<br /> The authors did not provide clear evidence to indicate the functional relevance of GC TFH1 in IgG1 class-switch and B cell memory responses.

Reviewer #2 (Public Review):

Summary:<br /> Planctomycetes encompass a group of bacteria with unique biological traits, the compartmentalized cells make them appear to be organisms in between prokaryotes and eukaryotes. However, only few of the Planctomycetes bacteria are cultured thus far, and hampers insight into the biological traits of this evolutionary important organisms.

This work reports the methodology details of how to isolate the deep-sea bacteria that could be recalcitrant to laboratory cultivation, and further reveals the distinct characteristics of the new species of a deep-sea Planctomycetes bacterium, such as the chronic phage release without breaking the host and promote the host and related bacteria in nitrogen utilization. Therefore, the finding of this work is of importance in extending our knowledge on bacteria.

Strengths:

Through combination of microscopic, physiological, genomics and molecular biological approaches, this reports isolation and comprehensively investigation of the first anaerobic representative of the deep-sea Planctomycetes bacterium, in particular in that of the budding division, and release phage without lysis the cells. Most of results and conclusions are supported by the experimental evidences.

Reviewer #2 (Public Review):

Theta-nested gamma oscillations (TNGO) play an important role in hippocampal memory and cognitive processes and are disrupted in pathology. Deep brain stimulation has been shown to affect memory encoding. To investigate the effect of pulsed CA1 neurostimulation on hippocampal TNGO the authors coupled a physiologically realistic model of the hippocampus comprising EC, DG, CA1, and CA3 subfields with an abstract theta oscillator model of the medial septum (MS). Pathology was modeled as weakened theta input from the MS to EC simulating MS neurodegeneration known to occur in Alzheimer's disease. The authors show that if the input from the MS to EC is strong (the healthy state) the model autonomously generates TNGO in all hippocampal subfields while a single neurostimulation pulse has the effect of resetting the TNGO phase. When the MS input strength is weaker the network is quiescent but the authors find that a single CA1 neurostimulation pulse can switch it into the persistent TNGO state, provided the neurostimulation pulse is applied at the peak of the EC theta. If the MS theta oscillator model is supplemented by an additional phase-reset mechanism a single CA1 neurostimulation pulse applied at the trough of EC theta also produces the same effect. If the MS input to EC is weaker still, only a short burst of TNGO is generated by a single neurostimulation pulse. The authors investigate the physiological origin of this burst and find it results from an interplay of CAN and M currents in the CA1 excitatory cells. In this case, the authors find that TNGO can only be rescued by a theta frequency train of CA1 pulses applied at the peak of the EC theta or again at either the peak or trough if the MS oscillator model is supplemented by the phase-reset mechanism.

The main strength of this model is its use of a fairly physiologically detailed model of the hippocampus. The cells are single-compartment models but do include multiple ion channels and are spatially arranged in accordance with the hippocampal structure. This allows the understanding of how ion channels (possibly modifiable by pharmacological agents) interact with system-level oscillations and neurostimulation. The model also includes all the main hippocampal subfields. The other strength is its attention to an important topic, which may be relevant for dementia treatment or prevention, which few modeling studies have addressed.

The work has several weaknesses. First, while investigations of hippocampal neurostimulation are important there are few experimental studies from which one could judge the validity of the model findings. All its findings are therefore predictions. It would be much more convincing to first show the model is able to reproduce some measured empirical neurostimulation effect before proceeding to make predictions. Second, the model is very specific. Or if its behavior is to be considered general it has not been explained why. For example, the model shows bistability between quiescence and TNGO, however what aspect of the model underlies this, be it some particular network structure or particular ion channel, for example, is not addressed. Similarly for the various phase reset behaviors that are found. We may wonder whether a different hippocampal model of TNGO, of which there are many published (for example [1-6]) would show the same effect under neurostimulation. This seems very unlikely and indeed the quiescent state itself shown by this model seems quite artificial. Some indication that particular ion channels, CAN and M are relevant is briefly provided and the work would be much improved by examining this aspect in more detail. In summary, the work would benefit from an intuitive analysis of the basic model ingredients underlying its neurostimulation response properties. Third, while the model is fairly realistic, considerable important factors are not included and in fact, there are much more detailed hippocampal models out there (for example [5,6]). In particular, it includes only excitatory cells and a single type of inhibitory cell. This is particularly important since there are many models and experimental studies where specific cell types, for example, OLM and VIP cells, are strongly implicated in TNGO. Other missing ingredients one may think might have a strong impact on model response to neurostimulation (in particular stimulation trains) include the well-known short-term plasticity between different hippocampal cell types and active dendritic properties. Fourth the MS model seems somewhat unsupported. It is modeled as a set of coupled oscillators that synchronize. However, there is also a phase reset mechanism included. This mechanism is important because it underlies several of the phase reset behaviors shown by the full model. However, it is not derived from experimental phase response curves of septal neurons of which there is no direct measurement. The work would benefit from the use of a more biologically validated MS model.

[1] Hyafil A, Giraud AL, Fontolan L, Gutkin B. Neural cross-frequency coupling: connecting architectures, mechanisms, and functions. Trends in neurosciences. 2015 Nov 1;38(11):725-40.

[2] Tort AB, Rotstein HG, Dugladze T, Gloveli T, Kopell NJ. On the formation of gamma-coherent cell assemblies by oriens lacunosum-moleculare interneurons in the hippocampus. Proceedings of the National Academy of Sciences. 2007 Aug 14;104(33):13490-5.

[3] Neymotin SA, Lazarewicz MT, Sherif M, Contreras D, Finkel LH, Lytton WW. Ketamine disrupts theta modulation of gamma in a computer model of hippocampus. Journal of Neuroscience. 2011 Aug 10;31(32):11733-43.

[4] Ponzi A, Dura-Bernal S, Migliore M. Theta-gamma phase-amplitude coupling in a hippocampal CA1 microcircuit. PLOS Computational Biology. 2023 Mar 23;19(3):e1010942.

[5] Bezaire MJ, Raikov I, Burk K, Vyas D, Soltesz I. Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. Elife. 2016 Dec 23;5:e18566.

[6] Chatzikalymniou AP, Gumus M, Skinner FK. Linking minimal and detailed models of CA1 microcircuits reveals how theta rhythms emerge and their frequencies controlled. Hippocampus. 2021 Sep;31(9):982-1002.

Reviewer #2 (Public Review):

In this study, Fleck and colleagues investigate the effects of auxin exposure on Drosophila melanogaster adults, focusing their analysis on feeding behavior, fatty acid metabolism, and oogenesis. The motivation for the study is that auxin-inducible transcription systems are now being used by Drosophila researchers to drive transcription using the Gal4-UAS system as a complement to Gal80ts versions of the system. I found the study to be carefully done. This study will be of interest for researchers using the Drosophila system, especially those focusing on fatty acid metabolism or physiology. The authors have adequately addressed all the minor points I raised in my review of the first submission.

Reviewer #2 (Public Review):

This study visualizes a specific localized form of cell-to-cell communication and conveys very well with what dynamics and sensitivity this biological phenomenon occurs.<br /> Using a FRET-based PKA biosensor, the authors observed that radial localized kinase activity changes spontaneously occur in adjacent cells of certain cell density. This phenomenon of radial propagation of PKA activity changes in groups of cells was further mechanistically elucidated and characterized. Interestingly, the authors found that individual cells in the cell groups form spontaneous Ca2+ transients, which at a certain strength can trigger the biosynthesis and release of prostaglandin E2 (PGE2). PGE2 then acts on the neighboring cells and triggers the increase of cAMP levels and the associated activation of the PKA via G-protein-coupled receptors (EP2 and EP4). In systematic, well-structured experiments, it was then found that the frequency of occurrence of such radial activations depends not only on the cell density but also on the activation state of the ERK MAP kinase pathway.

Strength<br /> In this study, the authors skillfully used various modern genetically encoded biosensors and other tools such as optogenetic tools to visualize and characterize an interesting biological phenomenon of cell-to-cell communication. The insights gained with these investigations produce a better understanding of the dynamics, sensitivity, and spatial extent with which such communications can occur in a cell network. It is also worth noting that the authors have not limited the studies to 2D cell culture in vitro, but were also able to confirm the findings in an animal model.

Weakness<br /> The work is hardly conclusive as to the actual biological significance of the phenomenon. It would be interesting to know more under which physiological and pathological conditions PGE2 triggers such radical PKA activity changes. It is not well explained in which tissues and organs and under what conditions this type of cell-to-cell communication could be particularly important.<br /> The authors also do not explain further why in certain cells of the cell clusters Ca2+ signals occur spontaneously and thus trigger the phenomenon. What triggers these Ca2+ changes? And why could this be linked to certain cell functions and functional changes?<br /> What explains the radius and the time span of the radial signal continuation? To what extent are these factors also related to the degradation of PGE2? The work could be stronger if such questions and their answers would be experimentally integrated and discussed.

Reviewer #2 (Public Review):

In this work, Boor and colleagues explored the role of microbial food cues in the regulation of neuroendocrine controlled foraging behavior. Consistent with previous reports, the authors find that C. elegans foraging behavior is regulated by the neuroendocrine TGFβ ligand encoded by daf-7. In addition to its known role in the neuroendocrine/sensory ASI neurons, Boor and colleagues show that daf-7 expression is dynamically regulated in the ASJ sensory neurons by microbial food cues - and that this regulation is important for exploration/exploitation balance during foraging. They identify at least two independent pathways by which microbial cues regulate daf-7 expression in ASJ: a gustatory pathway that promotes daf-7 expression and an opposing interoceptive pathway, also chemosensory in nature but which requires microbial ingestion to inhibit daf-7 expression via ASIC channels, encoded by del-3/del-7. In contrast, the authors show that the conserved PDF neuropeptide signaling pathway likely functions via the gustatory pathway to promote daf-7 expression. They further identify a novel role for the C. elegans ALK orthologue encoded by scd-2, which acts in interneurons to regulate daf-7 expression and foraging behavior. These results together imply that distinct cues from microbial food are used to regulate the balance between exploration and exploitation via conserved signaling pathways.

Strengths:<br /> The findings that gustatory and interoceptive inputs into foraging behavior are separable and opposing are novel and interesting, which they have shown most clearly in Figure 1 and Figure 3. These data clarify how these parallel chemosensory pathways can be integrated at the level of daf-7 expression.

It is also clear from their results that removal of the interoceptive cue (via transfer to non-digestible food) results in rapid induction of daf-7::gfp in ASJ - suggesting that this pathway is likely chemosensory and not simply nutritive in nature. They have also shown that daf-7 in ASJ plays an important role in the regulation of foraging behavior.

The role of the hen-1/scd-2 pathway in mediating the effects of ingested food is also compelling and well-interpreted, with a few small caveats, described below. This implies that important elements of this food sensing pathway may be conserved in mammals.

Weaknesses:<br /> Although not a weakness of this work per se, the roles of the 5-HT and hen-1/scd-2 pathway remain a bit unclear, likely reflecting their complex genetic contributions to foraging and daf-7 expression. Future work should clarify how these signals are integrated and whether the integration of these pathways improve exploration/exploitation balance to regulate animal fitness.

Reviewer #2 (Public Review):

Language skills are traditionally associated with a network of brain regions in the left hemisphere. In this intriguing study, Esteban Villar-Rodríguez and collaborators examined whether atypical hemispheric lateralization for language determines the functional and structural organisation of the network for inhibitory control as well as its relationship with schizotypy and autistic spectrum traits. The results suggest that individuals who have atypical lateralisation of the language function have also an atypical (mirrored) lateralisation of the inhibitory control network, compared to the typical group (individuals with left-lateralised language function). Furthermore, the atypical organization of language production is associated with a greater white matter volume of the corpus callosum, and atypical lateralization of inhibitory control is related to a higher interhemispheric functional coupling of the IFC, suggesting a link between atypical functional lateralisation (language and inhibitory control) and structural and functional changes in the brain.

This study also provides interesting evidence on how atypical language lateralisation impacts some aspects of language behaviour (reading), i.e., atypical lateralization predicts worse reading accuracy. Furthermore, the results suggest an association between atypical lateralization and increased schizotypy and autistic traits.

Reviewer #2 (Public Review):

The manuscript presents a valuable investigation of genetic associations related to plant resistance against the turnip mosaic virus (TuMV) using Arabidopsis thaliana as a model. The study infects over 1,000 A. thaliana inbred lines with both ancestral and evolved TuMV and assesses four disease-related traits: infectivity, disease progress, symptom severity, and necrosis. The findings reveal that plants infected with the evolved TuMV strain generally exhibited more severe disease symptoms than those infected with the ancestral strain. However, there was considerable variation among plant lines, highlighting the complexity of plant-virus interactions.

A major genetic locus on chromosome 2 was identified, strongly associated with symptom severity and necrosis. This region contained several candidate genes involved in plant defense against viruses. The study also identified additional genetic loci associated with necrosis, some common to both viral isolates and others specific to individual isolates. Structural variations, including transposable element insertions, were observed in the genomic region linked to disease traits.

Surprisingly, the minor allele associated with increased disease symptoms was geographically widespread among the studied plant lines, contrary to typical expectations of natural selection limiting the spread of deleterious alleles. Overall, this research provides valuable insights into the genetic basis of plant responses to TuMV, highlighting the complexity of these interactions and suggesting potential avenues for improving crop resilience against viral infections.

Overall, the manuscript is well-written, and the data are generally high-quality. The study is generally well-executed and contributes to our understanding of plant-virus interactions.

Reviewer #2 (Public Review):

Murata et al have characterized a transcription activator previously identified in an earlier genetic screen by Russell et al (named Fd2; for female-defective 2), here named PFG. The authors show solid evidence that PFG is a partner of the previously described transcription factor AP2-FG and describe three sets of genes: genes activated by PFG or AP2-FG alone and genes activated by the complex. The authors also show differential binding to the target DNA sequences by AP2-FG to either a 10bp, if in a complex with PFG or a 5bp motif if alone. In all, this is a useful study which further elucidates the underlying regulatory network that drives development of sexual stages and ultimately transmission to mosquitoes. The data presented are clear and solid and the conclusions drawn are mostly supported by the results shown.

A few comments:

Given that the transcriptional programme is so dynamic, the timing of the ChIP-seq experiments is crucial. Could the authors clarify the timings of the different ChIP-seq experiments (AP2-FG, PFG, PFG in AP2-FG-, AP2-FG in PFG-, ...)

Fig 4c is an example of great overlap of peaks, but it would be helpful if the authors could quantify the overlaps between experiments (and describe the overlap parameters used).

It remains unclear if AP2-FG and PFG interact directly or if they bind sequentially in the transcriptional activation process. Perhaps they are part of a larger complex? Immunoprecipitation followed by mass spectrometry of the GFP-tagged version of PFG in the presence and absence of AP2-FG would be highly informative.

Reviewer #2 (Public Review):

The paper by Huan, Yong, et al. studies epithelial cell extrusion in MDCK monolayers grown on sinusoidally wavy surfaces in varying media osmolarities, finding that both curvature and osmolarity-mediated basal hydraulic stress spatially regulate extrusion events. The authors fabricated wavy substrates of varying periods and amplitude out of PDMS (and PA hydrogels) and monitored monolayer evolution and cell extrusion over time, by combining live-cell imaging with a convolutional network-based algorithm for automatic detection of extrusions.

In general, the study has been elegantly designed, starting with convincing evidence for enhanced extrusion rates in concave valleys with respect to convex hills. Next, the authors showed that hyper-osmotic medium reduced cell extrusion rate, which was demonstrated in a variety of different media compositions (e.g. with sucrose, DMSO, or NaCl), while hypo-osmotic medium increased cell extrusion rate. Additionally, the authors applied reflection interference contrast microscopy to reveal fluid spaces between the substrate and the basal side of the monolayer, which were found to grow when media composition was altered from hyper-osmotic to normal osmotic conditions. Using a 3D traction force microscopy approach, the authors demonstrated that cells on convex regions apply a downward pointing force on the substrate, opposite to cells on the concave regions. This was linked to a larger basal separation on the concave valleys as opposed to the convex hills. Finally, the authors focussed on the FAK-Akt pathway to explore the hypothesis that basal hydraulic stress interferes with focal adhesions, leading to differences in cell extrusion rates in media of different osmolarity and on convex or concave surfaces.

Comments on the revised version:

My previous comments were reasonably answered. In response to the comment that "experiments that are currently underway" for "Recommendation 5 - reviewer #1", I would also suggest the authors to either add the additional data or alter the emphasis on the FAK-AKT pathway in the manuscript accordingly if additional data is not presented.

Reviewer #2 (Public Review):

The authors convincingly show multiple inner and outer leaflet non-protein (lipid) densities in a cryo-EM closed state structure of GLIC, a prokaryotic homologue of canonical pentameric ligand-gated ion channels, and observe lipids in similar sites during extensive simulations at both resting and activating pH. The simulations not only corroborate structural observations but also suggest the existence of a state-dependent lipid intersubunit site only occupied in the open state. These important findings will be of considerable interest to the ion channel community and provide new hypotheses about lipid interactions in conjunction with channel gating.

Comments on the revised version:

The authors have addressed all of my comments.

Reviewer #2 (Public Review):

This work recorded neurons in the parahippocampal regions of the medial entorhinal cortex (MEC) and pre- and para-subiculum (PrS, PaS) during a visually guided navigation task on a 'tree maze'. They found that many of the neurons reflected in their firing the visual cue (or the associated correct behavioral choice of the animal) and also the absence of reward in inbound passes (with increased firing rate). Rate remapping explained best these firing rate changes in both conditions for those cells that exhibited place-related firing. This work used a novel task, and the increased firing rate at error trials in these regions is also novel.

The limitation is that cells in these regions were analyzed together.

Comments on the revised submission:

I accept the authors' response that histological differentiation of these regions was not possible.

Reviewer #2 (Public Review):

The authors investigated a central component of adaptive and flexible human behaviour: our ability to stop ongoing action plans. This ability is under prefrontal control, with an important contribution of the right inferior prefrontal gyrus (rIFG). This is a well-studied system, yet providing causal evidence, especially at an electrophysiological level, has proven challenging. In this study the authors use a novel non-invasive brain stimulation technique, transcranial ultrasonic stimulation (TUS), to selectively stimulate the rIFG and record behavioural and electrophysiological changes in the context of a stop-signal task.

The principal finding of this work is that following TUS over rIFG, participants are faster to respond to a stop signal when successfully inhibiting a planned action program. This faster stop-inhibition was reflected both in behaviour and evoked responses as measured with electroencephalography.

The spatial specificity of the TUS stimulation allows strong inferences on selective targeting. The inclusion of two control groups, one receiving stimulation over an active control site, and the other receiving a non-stimulating sham condition, makes the specificity of the observed effect convincing.

The EEG analyses are advanced, exploiting robust data-cleaning and selection approaches to allow strong inferences for analyses in sensor space. Through careful trial-matching and dynamic time-warping, the effects of primary interest - responses evoked by stopping behaviour - could be isolated from those evoked by the go-cue and go-response.

The manuscript focusses on the latency of the electrophysiological response (ERP). Indeed, an earlier P300 ERP is expected considering that TUS over rIFG led to an earlier stop-signal-reaction time (SSRT). However, as the SSRT is inferred from a model fit on the probability of go-responses as a function of the stop-signal delay (more often failing to inhibit go-responses when the stop-signal arrives late), the empirical observation of a latency shift in the closely related P300 ERP is valuable.

It is less clear how the P300 ERP itself relates to the TUS stimulation over rIFG, considering that this ERP has a well-established mid-frontal topology, while rIFG is in the lateral prefrontal cortex. The authors suggest that in the context of stopping control, rIFG is positioned upstream from the mid-frontal regions. However, previous work has revealed an inverse temporal and causal relationship, where rIFG contributions follow those of preSMA (e.g. Neubert et al., 2010, PNAS).

Behavioural changes, especially those dependent on attention and a speeded response, are commonly driven by non-specific cues, such as auditory, somatosensory, or multi-modal cues. This is a major confounding factor for all brain stimulation paradigms. TUS is no exception. Pulsed TUS protocols, such as the 1000 Hz pulsed protocol employed here, are very likely to be accompanied by an auditory confound. In the condition of interest in this experiment, TUS is delivered together with the visual stop-signal, creating a multimodal cue. In the main analyses (figures 3 and 4) this is only contrasted against conditions where the stop-signal is unimodal (visual) only, creating a multi-modal vs. uni-modal contrast.

Indeed, the critical comparison to allow the strongest inference is not between stop-TUS vs. go-TUS, nor between stop-TUS vs. no-TUS, but between the two TUS sites: rIFG-TUS vs rS1-TUS in the stop condition. The inclusion of the S1-TUS condition in this study is therefore highly valuable, although this contrast was implemented as a between-group design, and no assessment of confound matching between rIFG-TUS and S1-TUS is reported. Perhaps more importantly, the main analyses and figures (e.g. figure 3), do not include this comparison. In fact, the data from the TUS control-site group are not included in any analyses of evoked potentials (EEG) at all (e.g. figure 4), even though this is the main focus of the study.

The title of the study is "Transcranial focused ultrasound to rIFG improves response inhibition through modulation of the P300 onset latency". The discussion reads "P300 latency modulation occurred only in the rIFG group". It is not straightforward to see how this conclusion is supported without including a control site in the analyses. Further, the reported difference in onset latency is based on a visual inspection of the data, not on a quantified statistical analysis ("visually contrasting SS-US difference waveforms across tFUS conditions (Fig. 4B, upper right) revealed P300 onsets shifted earlier during Stop-tFUS"). Visual inspection of the same figure might also highlight a clear difference in ERP amplitude, in addition to latency. Lastly, the suggestion of a directional mediation effect ("improves response inhibition through modulation of the P300 onset latency") is only supported by a correlational analysis relating P300 onset latency with the estimated stop-signal-reaction-time.

In summary, by advancing transcranial ultrasonic stimulation to study prefrontal control, this work signifies a paradigm shift towards using interventional tools in cognitive neuroscience. The specificity and precision that ultrasound stimulation provides, with reduced discomfort as compared to TMS, are urgently needed to support a refined and causal understanding of the neural circuits underlying human cognition. The central claims of this study are partially supported by the data presented and might benefit from quantitatively comparing the effects of TUS over the region of interest and the control site.

Reviewer #2 (Public Review):

In this study, authors identified the complex TOR, HOG and CWI signaling networks-involved genes that relatively modulate the development, aflatoxin biosynthesis and pathogenicity of A. flavus by gene deletions combined with phenotypic observation.

They also analyzed the specific regulatory process and proposed that the TOR signaling pathway interacts with other signaling pathways (MAPK, CWI, calcineurin-CrzA pathway) to regulate the responses to various environmental stresses. Notably, they found that FKBP3 is involved in sclerotia and aflatoxin biosynthesis and rapamycin resistance in A. flavus, especially found that the conserved site K19 of FKBP3 plays a key role in regulating the aflatoxin biosynthesis. In general, there is heavy workload task carried in this study and the findings are interesting and important for understanding or controlling the aflatoxin biosynthesis. However, findings have not been deeply explored and conclusions are mostly are based on parallel phenotypic observations. In addition, there are some concerns for the conclusions.

Reviewer #3 (Public Review):

Summary:<br /> In this paper presented by Liu et al, native MS on the lipid A transporter MsbA was used to obtain thermodynamic insight into protein-lipid interactions. By performing the analyses at different lipid A concentrations and temperatures, dissociation constants for 2-3 lipid A binding sites were determined, as well as enthalpies were calculated using non-linear van't Hoff fitting.

Strengths:<br /> This is an extensive high quality native MS dataset that provides unique opportunities to gain insights into the thermodynamic parameters underlying lipid A binding. In addition, it provides coupling energies between mutations introduced into MsbA, that are implicated in lipid A binding.

Weaknesses:<br /> It remains elusive, which KD values belong to which of the possible lipid A binding sites.

Appraisal:<br /> The authors convincingly addressed the concerns raised by the reviewers.

Reviewer #2 (Public Review):

Summary:

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 molecules. However, there are some parts where explanations are insufficient, so supplementary explanations and experiments seem to be necessary.

Strengths:

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

Weaknesses:

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 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):

Summary:<br /> The authors described cell type mapping was conducted for both WT and fracture types. Through this, unique cell populations specific to fracture conditions were identified. To determine these, the most undifferentiated cells were initially targeted using stemness-related markers and CytoTrace scoring. This led to the identification of SSPC differentiating into fibroblasts. It was observed that the fibroblast cell type significantly increased under fracture conditions, followed by subsequent increases in chondrocytes and osteoblasts.

Strengths:<br /> This study presented the injury-induced fibrogenic cell (IIFC) as a characteristic cell type appearing in the bone regeneration process and proposed that the IIFC is a progenitor undergoing osteochondrogenic differentiation.

Weaknesses:<br /> This study endeavored to elucidate the role of IIFC through snRNAseq analysis and in vivo observation. However, such validation alone is insufficient to confirm that IIFC is an osteochondrogenic progenitor, and additional data presentation is required.

Reviewer #2 (Public Review):

In this study, Aso and Rubin generated new split-GAL4 lines to label Drosophila mushroom body output neurons (MBONs) that previously lacked specific GAL4 drivers. The MBONs represent the output channels for the mushroom body (MB), a computational center in the fly brain. Prior research identified 21 types of typical MBONs whose dendrites exclusively innervate the MB and 14 types of atypical MBONs whose dendrites also innervate brain regions outside the MB. These MBONs transmit information from the MB to other brain areas and form recurrent connections to dopaminergic neurons whose axonal terminals innervate the MB. Investigating the functions of the MBONs is crucial to understanding how the MB processes information and regulates behavior. The authors previously established a collection of split-GAL4 lines for most of the typical MBONs and one atypical MBON. That split-GAL4 collection has been an invaluable tool for researchers studying the MB. This work extends their previous effort by generating additional driver lines labeling the MBON types not covered by the previous split-GAL4 collection. Using these new driver lines, the authors also activated the labeled MBONs using optogenetics and assessed their role in learning, locomotion, and valence coding. The expression patterns of the new split-GAL4 lines and the behavioral analysis presented in this manuscript are convincing. I believe that these new lines will be a valuable resource for the fly community.

Reviewer #2 (Public Review):

It has been widely proposed that the neural circuit uses a copy of motor command, an efference copy, to cancel out self-generated sensory stimuli so that intended movement is not disturbed by the reafferent sensory inputs. However, how quantitatively such an efference copy suppresses sensory inputs is unknown. Here, Canelo et al. tried to demonstrate that an efference copy operates in an all-or-none manner and that its amplitude is independent of the amplitude of the sensory signal to be suppressed. Understanding the nature of such an efference copy is important because animals generally move during sensory processing, and the movement would devastatingly distort that without a proper correction. The manuscript is concise and written very clearly. However, experiments do not directly demonstrate if the animal indeed uses an efference copy in the presented visual paradigms and if such a signal is indeed non-scaled. As it is, it is not clear if the suppression of behavioral response to the visual background is due to the act of an efference copy (a copy of motor command) or due to an alternative, more global inhibitory mechanism, such as feedforward inhibition at the sensory level or attentional modulation. To directly uncover the nature of an efference copy, physiological experiments are necessary. If that is technically challenging, it requires finding a behavioral signature that unambiguously reports a (copy of) motor command and quantifying the nature of that behavior.

Reviewer #2 (Public Review):

This paper is an attempt to extend or augment muscle synergy and motor primitive analyses and ideas with addition of task-driven measures. The authors' idea is to use information metrics (mutual information, co-information) in 'synergy' constraint creation that includes task information directly. By using task related information and muscle information sources and then sparsification, the methods construct task relevant network communities among muscles, together with task redundant communities, and task irrelevant communities. This process of creating network communities may then constrain and help to guide subsequent synergy identification using the authors published sNM3F algorithm to detect spatial and temporal synergies. The revised paper is now much clearer and examples are helpful in various ways.

The impact of the information theoretic constraints developed as network communities on subsequent synergy separation are posited to be benign and to improve separation and identification of synergies over other methods (e.g., NNMF). However, not fully addressed are the possible impacts of the methods on the resulting compositionality and its links with physiological bases: the possibility remains that the methods here sometimes will instead lead to modules that represent more descriptive ML frameworks for task description, and resulting 'synergies' that may not support physiological work easily. Accordingly, there is a caveat for users of this framework. This is recognized and acknowledged by the authors in their rebuttal letters responding to prior reviews. It will remain for other work to explore this issue, likely through testing on detailed high degree of freedom artificial neuromechanical models and tasks. This possible issue and caveat with the strategy proposed by the authors likely should be more fully acknowledged in the paper.

The approach of the methods seeks to identify task relevant coordinative couplings. This identification is a meta problem for more classical synergy analyses. Classical/prior analyses seek compositional elements stable across tasks. These elements may then be explored in causal experiments and in generative simulations of coupling and control strategies. However, task-based understanding of synergy roles and functional uses as captured in the proposed methods are significant, and the field is clearly likely to be aided by methods in this study.<br /> Information based separation has been used in muscle synergy analyses previously, by using infomax ICA, to discover physiological primitives. Though linear mixing of sources is assumed in ICA, minimized mutual information among source (synergy) drives is the basis of the separation and can detect low variance synergy contributions (e.g., see Yang, Logan, Giszter, 2019). In the work in the current paper, instead, mutual information approaches are used to cluster muscles and task features into network communities preceding the SNM3F algorithm use for separation, rather than using minimized information in the separation process directly. This contrast of an accretive or agglomerative mutual information strategy in the paper here, which is used to cluster into networks, versus a minimizing mutual information source separation used in infomax ICA epitomizes a key difference in approach. Indeed, physiological causal testing of synergy ideas is neglected in the literature reviews presented in the paper. Although these are only in animal work (e.g., Hart and Giszter, 2010; Takei and Seki, 2017), the clear connection of muscle synergy analysis choices to physiology is important, and eventually these issues need to be better managed and understood in relation to the new methods proposed here, even if not in this paper. Analyses of synergies using the methods the paper has proposed will likely be very much dependent on the number and quality of task variables included and how these are chosen, and the impacts of these on the ensuing sparsification and network communities used prior to SNM3F has already been noted. The authors acknowledge this in their responses. It would be useful in the future to explore the approach described with a range of simulated data to better understand the caveats, and optimizations for best practices in applications of this approach.

A key component of the authors' arguments here is their 'emergentist' view presented in the work, but perhaps not made fully explicit. Through the reductionist lens, which was used in the other physiological work noted above, muscle groupings are the units (primitives or 'building blocks' with informational separations) of coordinated movement and thus the space of these intermuscular unit interactions and controls is of particular interest for understanding movement construction and underlying physiology. This may allow representation of a hierarchy or heterarchy of neural control elements with clear physiological bases at spinal, brainstem and cortical levels. On the other hand, the emergentist view utilized by the authors here suggests that muscle groupings emerge from interactions between many constituent parts in a more freeform fashion with potentially larger task synergy assemblies (also quantified here using information tools). Information methods are applied differently using the two different lenses. The emergentist lens may potentially obscure fundamental neural controls and make them harder to explore in the descriptions resulting. Nonetheless, the different approaches to muscle synergy research, seeking different sorts of explanation and description of 'synergy', can be complementary and beneficial for the field overall going forward, so long as the caveats and concerns noted here are employed by readers in the interpretation of this new method.

Reviewer #2 (Public Review):

Summary:<br /> The manuscript by Brotherton et al. describes a structural study of connexin-26 (Cx26) gap junction channel mutant K125E, which is designed to mimic the CO2-inhibited form of the channel. In the wild-type Cx26, exposure to CO2 is presumed to close the channel through carbamylation of the residue K125. The authors mutated K125 to a negatively charged residue to mimic this effect, and they observed by cryo-EM analysis of the mutated channel that the pore of the channel is constricted. The authors were able to observe conformations of the channel with resolved density for the cytoplasmic loop (in which K125 is located). Based on the observed conformations and on the position of the N-terminal helix, which is involved in channel gating and in controlling the size of the pore, the authors propose the mechanisms of Cx26 regulation.

Strengths:<br /> This is a very interesting and timely study, and the observations provide a lot of new information on connexin channel regulation. The authors use the state of the art cryo-EM analysis and 3D classification approaches to tease out the conformations of the channel that can be interpreted as "inhibited", with important implications for our understanding of how the conformations of the connexin channels controlled.

Weaknesses:<br /> My fundamental question to the premise of this study is: to what extent can K125 carbamylation by recapitulated by a simple K125E mutation? Lysine has a large side chain, and its carbamylation would make it even slightly larger. While the authors make a compelling case for E125-induced conformational changes focusing primarily on the negative charge, I wonder whether they considered the extent to which their observation with this mutant may translate to the carbamoylated lysine in the wild-type Cx26, considering not only the charge but also the size of the modified side-chain.

Reviewer #2 (Public Review):

In this manuscript, the authors set out to identify transporters that must exist in Stramenophiles due to the fact that the second half of glycolysis appears to be conducted in the mitochondria. They hypothesize that a Stramenophile-specific clade of transporters related to the dicarboxylate carriers is likely the relevant family and then go on to test two proteins from Blastocystis due to the infectious disease relevance of this organism. They show rather convincingly that these two proteins are expressed and are localized to the mitochondria in the native organism. The purified proteins bind to glycolytic intermediates and one of them, GIC-2, transports several glycolytic intermediates in vitro. This is a very solid and well-executed study that clearly demonstrates that bCIC-2 can transport glycolytic intermediates.

1. The major weakness is that the authors aren't able to show that this protein actually has this function in the native organism. This could be impossible due to the lack of genetic tools in Blastocystis, but it leaves us without absolute confidence that bGIC-2 is the important glycolytic intermediate mitochondrial transporter (or even that it has this function in vivo).

2. It's atypical that the figures and figure panels don't really follow the order of their citation in the text. It's not a big deal, but mildly annoying to have to skip around in the figures (e.g. Figure 3D-E are described in the same paragraph as Figure 5). In addition, to facilitate the flow and a proper understanding I would encourage a reordering between figures 5D and 6 since Figure 6 is needed to understand the results shown in panel 5D, which may lead to confusion.

3. My impression is that the authors under-emphasize the fact that the hDIC also binds (and is stabilized by) glycolytic intermediates (G3P and 3PG). In the opinion of this reviewer, this might change the interpretation about the uniqueness of the bGIC proteins. They act on additional glycolytic intermediates, but it's not unique.

Reviewer #2 (Public Review):

This manuscript reports the discovery and analysis of a large protein complex that controls mating type and sexual reproduction of the model ciliate Tetrahymena thermophila. In contrast to many organisms that have two mating types or two sexes, Tetrahymena is multi-sexual with 7 distinct mating types. Previous studies identified the mating type locus, which encodes two transmembrane proteins called MTA and MTB that determine the specificity of mating type interactions. In this study, mutants are generated in the MTA and MTB genes and mutant isolates are studied for mating properties. Cells missing either MTA or MTB failed to co-stimulate wild-type cells of different mating types. Moreover, a mixture of mutants lacking MTA or MTB also failed to stimulate. These observations support the conclusion that MTA and MTB may form a complex that directs mating-type identity. To address this, the proteins were epitope-tagged and subjected to IP-MS analysis. This revealed that MTA and MTB are in a physical complex, and also revealed a series of 6 other proteins (MRC1-6) that together with MTA/B form the mating type recognition complex (MTRC). All 8 proteins feature predicted transmembrane domains, three feature GFR domains, and two are predicted to function as calcium transporters. The authors went on to demonstrate that components of the MTRC are localized on the cell surface but not in the cilia. They also presented findings that support the conclusion that the mating type-specific region of the MTA and MTB genes can influence both self- and non-self-recognition in mating.

Taken together, the findings presented are interesting and extend our understanding of how organisms with more than two mating types/sexes may be specified. The identification of the six-protein MRC complex is quite intriguing. It would seem important that the function of at least one of these subunits be analyzed by gene deletion and phenotyping, similar to the findings presented here for the MTA and MTB mutants. A straightforward prediction might be that a deletion of any subunit of the MRC complex would result in a sterile phenotype. The manuscript was very well written and a pleasure to read.

Reviewer #2 (Public Review):

The current manuscript undoubtedly demonstrates that JAG1 can induce osteogenesis via non-canonical signaling. Using the mouse-calvarial critical defect model, the authors have clearly shown the anabolic regenerative effect of JAG1 via non-canonical pathways. Exploring the molecular mechanisms, the authors have shown that non-canonically JAG1 regulates multiple pathways including STAT5, AKT, P38, JNK, NF-ĸB, and p70 S6K, which together possibly culminate in the activation of p70 S6K. More analysis is required to strongly conclude the role of the JAG1-p70 S6K pathway in the process. In summary, these findings have significant implications for designing new approaches for bone regenerative research.

Reviewer #2 (Public Review):

This study elucidates the toxic effects of the lipid aldehyde trans-2-hexadecenal (t-2-hex). The authors show convincingly that t-2-hex induces a strong transcriptional response, leads to proteotoxic stress, and causes the accumulation of mitochondrial precursor proteins in the cytosol.<br /> The data shown are of high quality and well controlled. The genetic screen for mutants that are hyper-and hypo-sensitive to t-2-hex is elegant and interesting, even if the mechanistic insights from the screen are rather limited. The last part of the study is less convincing. The authors show evidence that t-2-hex affects subunits of the TOM complex. However, they do not formally demonstrate that the lipidation of a TOM subunit is responsible for the toxic effect of t-2-hex. A t-2-hex-resistant TOM mutant was not identified. Moreover, it is not clear whether the concentrations of t-2-hex in this study are physiological. This is, however, a critical aspect. The literature is full of studies claiming the toxic effects of compounds such as H2O2; even if such studies are technically sound, they are misleading if non-physiological concentrations of such compounds were used.<br /> Nevertheless, this is an interesting study of high quality. A few specific aspects should be addressed.

Reviewer #2 (Public Review):

In the manuscript by Weber and colleagues, the authors investigated the role of a DEAD-box helicase DDX6 in regulating mRNA stability upon ribosome slowdown in human cells. The authors knocked out DDX6 KO in HEK293T cells and showed that the half-life of a reporter containing a rare codon repeat is elongated in the absence of DDX6. By analogy to the proposed function of fission yeast Dhh1p (DDX6 homolog) as a sensor for slow ribosomes, the authors demonstrated that recombinant DDX6 interacted with human ribosomes. The interaction with the ribosome was mediated by the FDF motif of DDX6 located in its RecA2 domain, and rescue experiments showed that DDX6 requires the FDF motif as well as its interaction with the CCR4-NOT deadenylase complex and ATPase activity for degrading a reporter mRNA with rare codons. To identify endogenous mRNAs regulated by DDX6, they performed RNA-Seq and ribosome footprint profiling. The authors focused on mRNAs whose stability is increased in DDX6 KO cells with high local ribosome density and validated that such mRNA sequences induced mRNA degradation in a DDX6-dependent manner.

The experiments were well-performed, and the results clearly demonstrated the requirement of DDX6 in mRNA degradation induced by slowed ribosomes. However, in some cases, the authors interpreted their data in a biased way, possibly influenced by the yeast study, and drew too strong conclusions. In addition, the authors should have cited important studies about codon optimality in mammalian cells. This lack of information hinders placing their important discoveries in a correct context.

1) Although the authors concluded that DDX6 acts as a sensor of the slowed ribosome, it is not clear if DDX6 indeed senses the ribosome speed. What the authors showed is a requirement of DDX6 for mRNA decay induced by rare codons, and DDX6 binds to the ribosome to exert this role. For example, DDX6 may bridge the sensor and decay machinery on the ribosome. Without structural or biochemical data on the recognition of the slowed ribosome by DDX6, the role of DDX6 as a sensor remains one of the possible models. It should be described in the discussion section.

2) It is not clear if DDX6 directly binds the ribosome. The authors used ribosomes purified by sucrose cushion, but ribosome-associating and FDF motif-interacting factors might remain on ribosomes, even after RNaseI treatment. Without structural or biochemical data of the direct interaction between the ribosome and DDX6, the authors should avoid description as if DDX6 directly binds to the ribosome.

3) Although the authors performed rigorous reporter assays recapitulating the effect of ribosome-retardation sequences on mRNA stability, this is not the first report showing that codon optimality determines mRNA stability in human cells. The authors did not cite important previous studies, such as Wu et al., 2019 (PMID: 31012849), Hia et al., 2019 (PMID: 31482640), Narula et al., 2019 (PMID: 31527111), and Forrest et al., 2020 (PMID: 32053646). These milestone papers should be cited in the Introduction, Results, and Discussion.

4) While both DDX6 and deadenylation by the CCR4-NOT were required for mRNA decay by the slowed ribosome, whether DDX6 is required for deadenylation was not investigated. Given that the CCR4-NOT deadenylate complex directly interacts with the empty ribosome E-site in yeast and humans (Buschauer et al., 2020 PMID: 32299921 and Absmeier et al., 2023 PMID: 37653243), whether the loss of DDX6 also affected the action of the CCR4-NOT complex is an important point to investigate, or at least should be discussed in this paper.

Reviewer #3 (Public Review):

Goetz, Akl and Dixit investigated the heterogeneity in the fidelity of sensing the environment by individual cells in a population using computational modeling and analysis of experimental data for two important and well-studied mammalian signaling pathways: (insulin-like growth factor) IGF/FoxO and (epidermal growth factor) EFG/EFGR mammalian pathways. They quantified this heterogeneity using the conditional mutual information between the input (eg. level of IGF) and output (eg. level of FoxO in the nucleus), conditioned on the "state" variables which characterize the signaling pathway (such as abundances of key proteins, reaction rates, etc.) First, using a toy stochastic model of a receptor-ligand system - which constitutes the first step of both signaling pathways - they constructed the population average of the mutual information conditioned on the number of receptors and maximized over the input distribution and showed that it is always greater than or equal to the usual or "cell state agnostic" channel capacity. They constructed the probability distribution of cell state dependent mutual information for the two pathways, demonstrating agreement with experimental data in the case of the IGF/FoxO pathway using previously published data. Finally, for the IGF/FoxO pathway, they found the joint distribution of the cell state dependent mutual information and two experimentally accessible state variables: the response range of FoxO and total nuclear FoxO level prior to IGF stimulation. In both cases, the data approximately follow the contour lines of the joint distribution. Interestingly, high nuclear FoxO levels, and therefore lower associated noise in the number of output readout molecules, is not correlated with higher cell state dependent mutual information, as one might expect. This paper contributes to the vibrant body of work on information theoretic characterization of biochemical signaling pathways, using the distribution of cell state dependent mutual information as a metric to highlight the importance of heterogeneity in cell populations. The authors suggest that this metric can be used to infer "bottlenecks" in information transfer in signaling networks, where certain cell state variables have a lower joint distribution with the cell state dependent mutual information.

The utility of a metric based on the conditional mutual information to quantify fidelity of sensing and its heterogeneity (distribution) in a cell population is supported in the comparison with data. Some aspects of the analysis and claims in the main body of the paper and SI need to be clarified and extended.

Remaining Comments:

- I think Review Figure 2 which is currently in the SI would improve the main body of the paper if moved there. In that case, the discussion of this figure in the main text would have to address more than it currently does, namely "the same cell's FoxO responses to the same input were found to have significantly less variation compared to the variation within the population".

Reviewer #2 (Public Review):

Summary: In this study, the authors performed a screening for PDXP inhibitors to identify compounds that could increase levels of pyridoxal 5'- phosphate (PLP), the co-enzymatically active form of vitamin B6. For the screening of inhibitors, they first evaluated a library of about 42,000 compounds for activators and inhibitors of PDXP and secondly, they validated the inhibitor compounds with a counter-screening against PGP, a close PDXP relative. The final narrowing down to 7,8-DHF was done using PLP as a substrate and confirmed the efficacy of this flavonoid as an inhibitor of PDXP function. Physiologically, the authors show that, by acutely treating isolated wild-type hippocampal neurons with 7,8-DHF they could detect an increase in the ratio of PLP/PL compared to control cultures. This effect was not seen in PDXP KO neurons.

Strengths: The screening and validation of the PDXP inhibitors have been done very well because the authors have performed crystallographic analysis, a counter screening, and mutation analysis. This is very important because such rigor has not been applied to the original report of 7,8 DHF as an agonist for TrkB. Which is why there is so much controversy on this finding.

Weaknesses: As mentioned in the summary report the study may benefit from some in vivo analysis of PLP levels following 7,8-DHF treatment, although I acknowledge that it may be challenging because of the working out of the dosage and timing of the procedure.

Reviewer #2 (Public Review):

Summary:

In this study, Kashio et al examined the role of TRPV4 in regulating perspiration in mice. They find coexpression of TRPV4 with the chloride channel ANO1 and aquaporin 5, which implies possible coupling of heat sensing through TRPV4 to ion and water excretion through the latter channels. Calcium imaging of eccrine gland cells revealed that the TRPV4 agonist GSK101 activates these cells in WT mice, but not in TRPV4 KO. This effect is reduced with cold-stimulating menthol treatment. Temperature-dependent perspiration in mouse skin, either with passive heating or with ACh stimulation, was reduced in TRPV4 KO mice. Functional studies in mice - correlating the ability to climb a slippery slope to properly regulate skin moisture levels - reveal potential dysregulation of foot pad perspiration in TRPV4 KO mice, which had fewer successful climbing attempts. Lastly, a correlation of TRPV4 to hypohydrosis in humans was shown, as anhidrotic skin showed reduced levels of TRPV4 expression compared to normohidrotic or control skin.

Strengths:

The functional studies of mice climbing slippery slopes is a novel method to determine mechanisms of functional perspiration in mice. Since mice do not perspire for thermoregulation, other functional readouts are needed to study perspiration in mice.

Weaknesses:

1. The coexpression data needs additional controls. In the TRPV4 KO mice, there appears to be staining with the TRPV4 Ab in TRPV4 KO mice below the epidermis. This pattern appears similar to that of the location of the secretory coils of the sweat glands (Fig 1A). Is the co-staining the authors note later in Figure 1 also seen in TRPV4 KOs? This control should be shown, since the KO staining is not convincing that the Ab doesn't have off-target binding.

2. Are there any other markers besides CGRP for dark cells in mice to support the conclusion that mouse secretory cells have clear cell and dark cell properties?

3. The authors utilize menthol (as a cooling stimulus) in several experiments. In the discussion, they interpret the effect of menthol as potentially disrupting TRPV4-ANO1 interactions independent of TRPM8. Yet, the role of TRPM8, such as in TRPM8 KO mice, is not evaluated in this study.

4. Along those lines, the authors suggest that menthol inhibits eccrine function, which might lead to a cooling sensation. But isn't the cooling sensation of sweating from evaporative cooling? In which case, inhibiting eccrine function may actually impair cooling sensations.

5. The climbing assay is interesting and compelling. The authors note performing this under certain temperature and humidity conditions. Presumably, there is an optimal level of skin moisture, where skin that is too dry has less traction, but skin that is too wet may also have less traction. It would bolster this section of the study to perform this assay under hot conditions (perhaps TRPV4 KO mice, with impaired perspiration, would outperform WT mice with too much sweating?), or with pharmacologic intervention using TRPV4 agonists or antagonists to more rigorously evaluate whether this model correlates to TRPV4 function in the setting of different levels of perspiration.

6. There are other studies (PMID 33085914, PMID 31216445) that have examined the role of TRPV4 in regulating perspiration. The presence of TRPV4 in eccrine glands is not a novel finding. Moreover, these studies noted that TRPV4 was not critical in regulating sweating in human subjects. These prior studies are in contradiction to the mouse data and the correlation to human anhidrotic skin in the present study. Neither of these studies is cited or discussed by the authors, but they should be.

Reviewer #2 (Public Review):

Summary:

In the paper, the authors use a cellular Potts model to investigate muscle regeneration. The model is an attempt to combine many contributors to muscle regeneration into one coherent framework. I believe the resulting model has the potential to be very useful in investigating the complex interplay of multiple actors contributing to muscle regeneration.

Strengths:

The manuscript identified relevant model parameters from a long list of biological studies. This collation of a large amount of literature into one framework has the potential to be very useful to other authors. The mathematical methods used for parameterization and validation are transparent.

Weaknesses:

I have a few concerns which I believe need to be addressed fully.

My main concerns are the following:

1) The model is compared to experimental data in multiple results figures. However, the actual experiments used in these figures are not described. To me as a reviewer, that makes it impossible to judge whether appropriate data was chosen, or whether the model is a suitable descriptor of the chosen experiments. Enough detail needs to be provided so that these judgements can be made.

2) Do I understand it correctly that all simulations are done using the same initial simulation geometry? Would it be possible to test the sensitivity of the paper results to this geometry? Perhaps another histological image could be chosen as the initial condition, or alternative initial conditions could be generated in silico? If changing initial conditions is an unreasonably large request, could the authors discuss this issue in the manuscript?

3) Cytokine knockdowns are simulated by 'adjusting the diffusion and decay parameters' (line 372). Is that the correct simulation of a knockdown? How are these knockdowns achieved experimentally? Wouldn't the correct implementation of a knockdown be that the production or secretion of the cytokine is reduced? I am not sure whether it's possible to design an experimental perturbation which affects both parameters.

4) The premise of the model is to identify optimal treatment strategies for muscle injury (as per the first sentence of the abstract). I am a bit surprised that the implemented experimental perturbations don't seem to address this aim. In Figure 7 of the manuscript, cytokine alterations are explored which affect muscle recovery after injury. This is great, but I don't believe the chosen alterations can be done in experimental or clinical settings. Are there drugs that affect cytokine diffusion? If not, wouldn't it be better to select perturbations that are clinically or experimentally feasible for this analysis? A strength of the model is its versatility, so it seems counterintuitive to me to not use that versatility in a way that has practical relevance. - I may well misunderstand this though, maybe the investigated parameters are indeed possible drug targets.

5) A similar comment applies to Figure 5 and 6: Should I think of these results as experimentally testable predictions? Are any of the results surprising or new, for example in the sense that one would not have expected other cytokines to be affected as described in Figure 6?

6) In figure 4, there were differences between the experiments and the model in two of the rows. Are these differences discussed anywhere in the manuscript?

7) The variation between experimental results is much higher than the variation of results in the model. For example, in Figure 3 the error bars around experimental results are an order of magnitude larger than the simulated confidence interval. Do the authors have any insights into why the model is less variable than the experimental data? Does this have to do with the chosen initial condition, i.e. do you think that the experimental variability is due to variation in the geometries of the measured samples?

8) Is figure 2B described anywhere in the text? I could not find its description.

Reviewer #2 (Public Review):

Summary:

Fertilization is a crucial event in sexual reproduction, but the molecular mechanisms underlying egg-sperm fusion remain elusive. Elofsson et al. used AlphaFold to explore possible synapse-like assemblies between sperm and egg membrane proteins during fertilization. Using a systematic search of protein-protein interactions, the authors proposed a pentameric complex of three sperm (IZUMO1, SPACA6, and TMEM81) and two egg (JUNO and CD9) proteins, providing a new structural model to be used in future structure-function studies.

Strengths:

1. The study uses the AlphaFold algorithm to predict higher-order assemblies. This approach could offer insights into a highly transient protein complex, which is challenging to detect experimentally.<br /> 2. The article predicts a pentameric complex between proteins involved in fertilization, shedding light on the architectural aspects of the egg-sperm fusion synapse.

Weaknesses:

1. The procedures and discriminator scores used to evaluate specific from non-specific complexes were developed previously by the same authors. Therefore, in this manuscript, they are not contributing a new method.<br /> 2. The proposed model, which is a prediction from a modeling algorithm, lacks experimental validation of the identity of the components and the predicted contacts.

It is noteworthy that in an independent study, Deneke et al. provide experimental evidence of the interaction between IZUMO1/SPACA6/TMEM81 in zebrafish. This is an important element that supports the findings presented in this manuscript.

Reviewer #2 (Public Review):

The setting of planar cell polarity in epithelial tissues involves a complex interplay of chemical interactions. While local interactions can spontaneously give rise to cell polarity, planar cell polarity also involves tissue scale gradients whose effects are not clear. To understand their role, the authors built a minimal mechanistic model in considering two atypical cadherins, Fat (Ft) and Dachsous (Ds) which can associate at cell-cell interfaces to form hetero-dimers in which monomers belong to adjacent cells. This association can be seen as a local interaction between cells and is also sensitive to overall concentration gradients. From their model which appears to capture diverse experimental observations, the authors conclude that tissue-scale gradients provide to planar cell polarity a directional cue and some robustness to cellular stochasticity. While this model comes after similar works reaching similar predictions, the quality of this model is in its simplicity, its convenience for experimental testing, and the diversity of experimental observations it recapitulates.

A strength of this work is to recapitulate many experimental observations made on planar cell polarity. It, for example, seems to capture the response of tissues to perturbations such as local downregulation of some important proteins, and the polarity patterns observed in the presence of noise in synthesis or cell-to-cell heterogeneity. It also gives a mechanistic description of planar cell polarity, making its experimental interpretation simple. Finally, the simplicity of the model facilitates its exploration and makes it easily testable because of the reduced amount of free model parameters.

A weakness of this work is that it comes after several models with similar hypotheses and similar predictions. Another weakness is that some conclusions of this work rely on visual appreciation rather than quantification. This is particularly true for what concerns 2D patterns. An argument of the authors is for example that their model reproduces a variety of known spatial patterns, but the comparison with experiments is only visual and would be more convincing in being more quantitative.

Reviewer #2 (Public Review):

In this study, Leiba et al. aim at establishing the developing zebrafish embryo as a suitable infection model to study Salmonella persistence in vivo. Under environmental stress (ex: macrophage phagosomes) a proportion of bacteria switch to a slow/arrested growth state confering increased resistance to antibiotic treatments. Persisters are getting increasingly linked to infection relapses. Understanding how persistent infections emerge and bacteria survive in an organism for long time without replicating before switching back to a replicative state is essential. Zebrafish represents an alternative model to mice offering the possibility to image the whole organism and capture persistency with an amazing spatio-temporal resolution.

In this paper, the authors demonstrate that persistent infections of Salmonella can be reproduced in the developing zebrafish. The kinetics of infection have been well characterized and shows a very nice heterogeneity between animals demonstrating the complex host-pathogen interactions (Fig 1). From the perspective of persistence, the presence of Salmonella survivors to host clearing is reported until 14dpi demonstrating the possibility to induce persistent infection in this model. Through the manuscript, the authors have used a variety of state-of-the-art technics illustrating the flexibility of this model including microscopy and imaging of specific immune populations, various transgenic animals and selective depletion of macrophages or neutrophils to assess their relative contributions. Overall, the conclusions of the authors are well supported by the presented data. This said, the authors should strengthen the conclusions of the paper by providing a better characterization of the infection.

Major comments:<br /> 1- Figure 1: What is the general life-spam of the fish?

2- Figure 2: It would be nice to clearly state what infection scenario we are looking at. Have the authors studied "high proliferation", "infected" or "cleared" zebrafish?

3- Figure 3 and 4: It would be very informative if the authors can tell us what proportion of Salmonella is associated with macrophages and neutrophils. From panel C and D (Figure 3) and Figure 4 C and D and Suppl Fig 1, it seems that a lot of bacteria are extracellular. Maybe an EM image of the tissue would help to understand if the bacteria is "all" intracellular or intracellular.

4- Figure 3 and 4: It would be very useful if the authors can tell us if the intracellular bacteria are mainly found individually (like in Figure 3C) or does host cells harbor many intracellular bacteria. Looking at figure 4G: it is not clear to me how many intracellular bacteria can be counted on this image.

5- Figure 3 and 4: The authors should also perform an experiment with a Salmonella strain harboring a growth reporter to quantify the amount of replicating and non-replicating bacteria. This experiment is not absolutely necessary for the story, but if possible, it would provide a very nice add-up to the story and impact to the paper.

6- Figure 6: The authors should provide in suppl. the flow cytometry scatter plots used to delineate the different subpopulations.

7- Figure 6: A specific characterization of macrophages harboring Salmonella persisters at 4dpi is missing. As shown by the authors in Figure 6, the tnfa- populations of macrophages at 4dpi are very similar for both infected and non-infected larvae. Persisters should indeed reside within tnfa- macrophages but they should also induce a specific signature through the actions of Salmonella effectors. Measuring this signature will allow a direct comparison with published data in mice and assess how accurately the zebrafish model recapitulates the manipulation of macrophages by Salmonella

Reviewer #2 (Public Review):

This paper uses two-photon imaging of mouse ganglion cells responding to chromatic natural scenes along with convolutional neural network (CNN) models fit to the responses of a large set of ganglion cells. The authors analyze CNN models to find the most effective input (MEI) for each ganglion cell as a novel approach to identifying ethological function. From these MEIs they identify chromatic opponent ganglion cells, and then further perform experiments with natural stimuli to interpret the ethological function of those cells. They conclude that a type of chromatic opponent ganglion cell is useful for the detection of the transition from the ground to the sky across the horizon. The experimental techniques, data, and fitting of CNN models are all high quality. However, there are conceptual difficulties with both the use of MEIs to draw conclusions about neural function and the ethological interpretations of experiments and data analyses, as well as a lack of comparison with standard approaches. These bear directly both on the primary conclusions of the paper and on the utility of the new approaches.

1. Claim of feature detection. The color opponent cells are cast as a "feature detector" and the term 'detector' is in the title. However insufficient evidence is given for this, and it seems likely a mischaracterization. An example of a ganglion cell that might qualify as a feature detector is the W3 ganglion cell (Zhang et al., 2012). These cells are mostly silent and only fire if there is differential motion on a mostly featureless background. Although this previous work does not conduct a ROC analysis, the combination of strong nonlinearity and strong selectivity are important here, giving good qualitative support for these cells as participating in the function of detecting differential motion against the sky. In the present case, the color opponent cells respond to many stimuli, not just transitions across the horizon. In addition, for the receiver operator characteristic (ROC) analysis as to whether these cells can discriminate transitions across the horizon, the area under the curve (AUC) is on average 0.68. Although there is not a particular AUC threshold for a detector or diagnostic test to have good discrimination, a value of 0.5 is chance, and values between 0.5 and 0.7 are considered poor discrimination, 'not much better than a coin toss' (Applied Logistic Regression, Hosmer et al., 2013, p. 177). The data in Fig. 6F is also more consistent with a general chromatic opponent cell that is not highly selective. These cells may contribute information to the problem of discriminating sky from ground, but also to many other ethologically relevant visual determinations. Characterizing them as feature detectors seems inappropriate and may distract from other functional roles, although they may participate in feature detection performed at a higher level in the brain.

2. Appropriateness of MEI analysis for interpretations of the neural code. There is a fundamental incompatibility between the need to characterize a system with a complex nonlinear CNN and then characterizing cells with a single MEI. MEIs represent the peak in a complex landscape of a nonlinear function, and that peak may or may not occur under natural conditions. For example, MEIs do not account for On-Off cells, On-Off direction selectivity, nonlinear subunits, object motion sensitivity, and many other nonlinear cell properties where multiple visual features are combined. MEIs may be a useful tool for clustering and distinguishing cells, but there is not a compelling reason to think that they are representative of cell function. This is an open question, and thus it should not be assumed as a foundation for the study. This paper potentially speaks to this issue, but there is more work to support the usefulness of the approach. Neural networks enable a large set of analyses to understand complex nonlinear effects in a neural code, and it is well understood that the single-feature approach is inadequate for a full understanding of sensory coding. A great concern is that the message that the MEI is the most important representative statistic directs the field away from the primary promise of the analysis of neural networks and takes us back to the days when only a single sensory feature is appreciated, now the MEI instead of the linear receptive field. It is appropriate to use MEI analyses to create hypotheses for further experimental testing, and the paper does this (and states as much) but it further takes the point of view that the MEI is generally informative as the single best summary of the neural code. The representation similarity analysis (Fig. 5) acts on the unfounded assumption that MEIs are generally representative and conveys this point of view, but it is not clear whether anything useful can be drawn from this analysis, and therefore this analysis does not support the conclusions about changes in the representational space. Overall this figure detracts from the paper and can safely be removed. In addition, in going from MEI analysis to testing ethological function, it should be made much more clear that MEIs may not generally be representative of the neural code, especially when nonlinearities are present that require the use of more complex models such as CNNs, and thus testing with other stimuli are required.

3. Usefulness of MEI approach over alternatives. It is claimed that analyzing the MEI is a useful approach to discovering novel neural coding properties, but to show the usefulness of a new tool, it is important to compare results to the traditional technique. The more standard approach would be to analyze the linear receptive field, which would usually come from the STA of white noise measurement, but here this could come from the linear (or linear-nonlinear) model fit to the natural scene response, or by computing an average linear filter from the natural scene model. It is important to assess whether the same conclusion about color opponency can come from this standard approach using the linear feature (average effective input), and whether the MEIs are qualitatively different from the linear feature. The linear feature should thus be compared to MEIs for Fig. 3 and 4, and the linear feature should be compared with the effects of natural stimuli in terms of chromatic contrast (Fig. 6b). With respect to the representation analysis (Fig. 5), although I don't believe this is meaningful for MEIs, if this analysis remains it should also be compared to a representation analysis using the linear feature. In fact, a representation analysis would be more meaningful when performed using the average linear feature as it summarizes a wider range of stimuli, although the most meaningful analysis would be directly on a broader range of responses, which is what is usually done.

4. Definition of ethological problem. The ethological problem posed here is the detection of the horizon. The stimuli used do not appear to relate to this problem as they do not include the horizon and only include transitions across the horizon. It is not clear whether these stimuli would ever occur with reasonable frequency, as they would only occur with large vertical saccades, which are less common in mice. More common would be smooth transitions across the horizon, or smaller movements with the horizon present in the image. In this case, cells which have a spatial chromatic opponency (which the authors claim are distinct from the ones studied here) would likely be more important for use in chromatic edge detection or discrimination. Therefore the ethological relevance of any of these analyses remains in question.

It is further not clear if detection is even the correct problem to consider. The horizon is always present, but the problem is to determine its location, a conclusion that will likely come from a population of cells. This is a distinct problem from detecting a small object, such as a small object against the background of the sky, which may be a more relevant problem to consider.

5. Difference in cell type from those previously described. It is claimed that the chromatic opponent cells are different from those previously described based on the MEI analysis, but we cannot conclude this because previous work did not perform an MEI analysis. An analysis should be used that is comparable to previous work, the linear spatiotemporal receptive field should be sufficient. However, there is a concern that because linear features can change with stimulus statistics (Hosoya et al., 2005), a linear feature fit to natural scenes may be different than those from previous studies even for the same cell type. The best approach would likely be presenting a white noise stimulus to the natural scenes model to compute a linear feature, which still carries the assumption that this linear feature from the model fit to a natural stimulus would be comparable to previous studies. If the previous cells have spatial chromatic opponency and the current cells only have chromatic opponency in the center, there should be both types of cells in the current data set. One technical aspect relating to this is that MEIs were space-time separable. Because the center and surround have a different time course, enforcing this separability may suppress sensitivity in the surround. Therefore, it would likely be better if this separability were not enforced in determining whether the current cells are different than previously described cells. As to whether these cells are actually different than those previously described, the authors should consider the following uncited work; (Ekesten Gouras, 2005), which identified chromatic opponent cells in mice in approximate numbers to those here (~ 2%). In addition, (Yin et al., 2009) in guinea pigs and (Michael, 1968) in ground squirrels found color-opponent ganglion cells without effects of a spatial surround as described in the current study.

Reviewer #3 (Public Review):

This study succeeds to highlight and address important gaps in our understanding of plant-insect interactions mediating pest control in a widely known agro-ecological system for sustainable intensification, push-pull agriculture. In particular, the authors present a large amount of data on plant volatile emission, thought to be critical for the functioning of these systems, in reasonable and relevant contexts, as well as on other traits of the plants in the system relevant for pest control. These data come from plants grown both in controlled and field environments, which is unusual. The arguments on mechanism are further supported by insect behavioral assays, which seem to be thoughtfully designed, but also use some non-standard approaches that could be better explained. While most or all of the authors' results pre-date some relevant recent publications in this field, they do incorporate comparisons to current literature in order to better place their findings in the current state of the art.

Reviewer #2 (Public Review):

This study by Du et. al addressed the role and regulation of proline-serine-threonine phosphatase interacting protein 2 (PSTPIP2) and neutrophil extracellular traps (NETs) in Aristolochic acid Nephropthathy (AAN) and immune defense. PSTPIP2 expression is downregulated in AAN. Conditional knock-in of PSTPIP2 in mouse kidneys inhibited cell apoptosis, reduced neutrophil infiltration, suppressed the production of inflammatory factors and NETs, and ameliorated renal dysfunction. Reducing the expression of PSTPIP2 to normal levels in knock-in mouse using shRNA promoted kidney injury. Using in vivo model, the role of PSTPIP2 in AAN injury and renal function, apoptosis, neutrophil infiltration and NET formation is established. Using in vitro models, a PSTPIP2/NFkB-mediated NET formation via IL-19-IL20-beta Receptor pathway is shown to induce inflammation and apoptosis in AAN. The studies are well presented.

Reviewer #2 (Public Review):

For about four decades it has been known that RNA molecules can increase the rate of chemical reactions, just like the much more prevalent protein enzymes. Some have suggested that RNA enzymes, also called "ribozymes" were very important at the beginning of life, but that the importance was mostly erased when ribosomal protein synthesis emerged through evolution. The ribosome and spliceosome are two important examples of modern biological functions known to be catalyzed by RNA. In addition to these large RNA machines, the genomes of humans, and all domains of life, also contain a class of small ribozymes that catalyze self-cleavage of the RNA backbone. However, unlike RNA cleaving proteins that are well studied, there exists little evidence that the self-cleaving of RNA by ribozymes has important downstream consequences. This new paper provides evidence that a ribozyme found in all mammals has an important role in memory formation. The authors found a way to block the ribozyme activity and then observe the effect on memory formation in mice, and in the expression of genes in neurons that are known to underly this memory formation process. The authors found that blocking the ribozyme activity in mouse brains actually improved their performance in a memory task. In addition, they found that blocking the ribozyme changed the expression of the gene in which the ribozyme is found (a gene called CPEB3), suggesting that the way the ribozyme effects memory is through controlling the expression of the gene where it is found. The paper confirms the biological importance of this ribozyme, and encourages further investigation into self-cleaving ribozymes in general. Interestingly, the ribozyme found in humans is in fact slower cleaving than most mammals, similar to the blocked ribozyme in these experiments, which brings up the intriguing possibility that the CPEB3 ribozyme is a part of what makes us human!

Reviewer #2 (Public Review):

The authors examine the use of metformin in the treatment of hepatic ischemia/reperfusion injury (HIRI) and suggest the mechanism of action is mediated in part by the gut microbiota and changes in hepatic ferroptosis. While the concept is intriguing, the experimental approaches are inadequate to support these conclusions.

The histological and imaging studies were considered a strength and reveal a significant impact of metformin post-HIRI.

Weaknesses largely stem from the experimental design. The impact of metformin on the microbiota is profound resulting in changes in bile acid, lipid, and glucose homeostasis. Throughout the manuscript no comparisons are made with metformin alone which would better capture the metformin-specific effects. With the pathology and metabolic disturbances resulting from HIRI, it is important to understand if metformin is providing beneficial effects from reported mechanisms such as changes in bile acid, glucose, and/or lipid metabolism, or are these changes the result of a new unappreciated mechanism. A comparison of the reported and the new pathways is not included.

Overall, while the concept is interesting and has potential to better understand the pleiotropic functions of metformin, the limitations with the experimental design and lack of key controls make it challenging to support the conclusions.

Reviewer #2 (Public Review):

Summary:

This study uses transcriptome sequence from a dioecious plant to compare evolutionary rates between genes with male- and female-biased expression and distinguish between relaxed selection and positive selection as causes for more rapid evolution. These questions have been explored in animals and algae, but few studies have investigated this in dioecious angiosperms, and none have so far identified faster rates of evolution in male-biased genes (though see Hough et al. 2014 https://doi.org/10.1073/pnas.1319227111).

Strengths:

The methods are appropriate to the questions asked. Both the sample size and the depth of sequencing are sufficient, and the methods used to estimate evolutionary rates and the strength of selection are appropriate. The data presented are consistent with faster evolution of genes with male-biased expression, due to both positive and relaxed selection.

This is a useful contribution to understanding the effect of sex-biased expression in genetic evolution in plants. It demonstrates the range of variation in evolutionary rates and selective mechanisms, and provides further context to connect these patterns to potential explanatory factors in plant diversity such as the age of sex chromosomes and the developmental trajectories of male and female flowers.

Weaknesses:

The presence of sex chromosomes is a potential confounding factor, since there are different evolutionary expectations for X-linked, Y-linked, and autosomal genes. Attempting to distinguish transcripts on the sex chromosomes from autosomal transcripts could provide additional insight into the relative contributions of positive and relaxed selection.

Reviewer #2 (Public Review):

In this study, Kajtor et al investigated the use of a single-animal trial-based behavioral assay for the assessment of subtle changes in the locomotor behavior of different genetic models of Parkinson's disease of Drosophila. Different genotypes used in this study were Ddc-GAL4>UAS-Parkin-275W and UAS- α-Syn-A53T. The authors measured Drosophila's response to predator-mimicking passing shadow as a threatening stimulus. Along with these, various dopamine (DA) receptor mutants, Dop1R1, Dop1R2 and DopEcR were also tested.<br /> The behavior was measured in a custom-designed apparatus that allows simultaneous testing of 13 individual flies in a plexiglass arena. The inter-trial intervals were randomized for 40 trials within 40 minutes duration and fly responses were defined into freezing, slowing down, and running by hierarchical clustering. Most of the mutant flies showed decreased reactivity to threatening stimuli, but the speed-response behavior was genotype invariant.<br /> These data nicely show that measuring responses to the predator-mimicking passing shadows could be used to assess the subtle differences in the locomotion parameters in various genetic models of Drosophila.

The understanding of the manifestation of various neuronal disorders is a topic of active research. Many of the neuronal disorders start by presenting subtle changes in neuronal circuits and quantification and measurement of these subtle behavior responses could help one delineate the mechanisms involved. The data from the present study nicely uses the behavioral response to predator-mimicking passing shadows to measure subtle changes in behavior. However, there are a few important points that would help establish the robustness of this study.<br /> 1) The visual threat stimulus for measuring response behavior in Drosophila is previously established for both single and multiple flies in an arena. A comparative analysis of data and the pros and cons of the previously established techniques (for example, Gibson et al., 2015) with the technique presented in this study would be important to establish the current assay as an important advancement.<br /> 2) Parkinson's disease mutants should be validated with other GAL-4 drivers along with Ddc-GAL4, such as NP6510-Gal4 (Riemensperger et al., 2013). This would be important to delineate the behavioral differences due to dopaminergic neurons and serotonergic neurons and establish the Parkinson's disease phenotype robustly.<br /> 3) The DopEcR mutant genotype used for behavior analysis is w1118; PBac{PB}DopEcRc02142/TM6B, Tb1. Balancer chromosomes, such as TM6B,Tb can have undesirable and uncharacterised behavioral effects. This could be addressed by removing the balancer and testing the DopEcR mutant in homozygous (if viable) or heterozygous conditions.<br /> 4) The height of the arena is restricted to 1mm. However, for the wild-type flies (Canton-S) and many other mutants, the height is usually more than 1mm. Also, a 1 mm height could restrict the fly movement. For example, it might not allow the flies to flip upside down in the arena easily. This could introduce some unwanted behavioral changes. A simple experiment with an arena of height at least 2.5mm could be used to verify the effect of 1mm height.<br /> 5) The detailed model for Monte Carlo simulation for speed-response simulation is not described. The simulation model and its hyperparameters need to be described in more depth and with proper justification.<br /> 6) The statistical analysis in different experiments needs revisiting. It wasn't clear to me if the authors checked if the data is normally distributed. A simple remedy to this would be to check the normality of data using the Shapiro-Wilk test or Kolmogorov-Smirnov test. Based on the normality check, data should be further analyzed using either parametric or non-parametric statistical tests. Further, the statistical test for the age-dependent behavior response needs revisiting as well. Using two-way ANOVA is not justified given the complexity of the experimental design. Again, after checking for the normality of data, a more rigorous statistical test, such as split-plot ANOVA or a generalized linear model could be used.<br /> 7) The dopamine receptor mutants used in this study are well characterized for learning and memory deficits. In the Parkinson's disease model of Drosophila, there is a loss of DA neurons in specific pockets in the central brain. Hence, it would be apt to use whole animal DA receptor mutants as general DA mutants rather than the Parkinson's disease model. The authors may want to rework the title to reflect the same.

Reviewer #2 (Public Review):

Summary:

The generation of functional extranumerary inner hair cells (IHCs) in postnatal mice, particularly with virus-mediated knockdown of Cldn9 mRNA expression in the neonatal cochlear duct, is an important observation. It is significant because not many studies exist that report molecular manipulations of the neonatal organ of Corti that result in the generation of new hair cells that remain functional and appear to be intact for an extended time, here more than one year. Overall, this is a carefully conducted study; the observations are clear, and the methods are solid. Two independent methods for reducing the expression of Cldn9 mRNA were used: a conditional transgenic model and AAV-mediated knockdown with shRNA. The lack of a functional explanation of how the reduced expression of Cldn9 specifically leads to the formation of extranumerary IHCs leaves open questions. For example, it is not clear whether there is indeed a fate change happening and whether Cldn9 reduction affects developmental processes. The discussion of how Cldn9 reduction potentially affects Notch signaling, without hard evidence, is handwaving.

Strengths:

It is a very interesting observation and somewhat unexpected in its specificity for inner hair cells. Using two different approaches to manipulate Cldn9 expression provides a strong experimental foundation. The study is conducted quantitatively and with care.

Weaknesses:

The lack of mechanistic insight results in an open-ended story where at least the potential interaction of Cldn9 reduction with known and well-characterized signaling pathway components should have been investigated. This missed opportunity limits the scope of the study and should be addressed: How does Cldn9 downregulation affect the expression levels of other known genes linked to hair cell production and cell fate decisions? Quantitative RT-PCR is working well for the authors, and a comparison of the expression of Notch or other known pathway components could provide mechanistic insight.

It is unclear how P21 inner hair cells were identified for the patch clamp experiments shown in Fig 4E-H. This is a challenging endeavor without the possibility of using specific markers.

Please also address the numerous minor points outlined below; it will improve the paper's readability.

Please include page numbers and line numbers in a revised manuscript.

Reviewer #2 (Public Review):

Summary,<br /> The paper aimed to examine the effect of co-ablating Substance P and CGRPα peptides on pain using Tac1 and Calca double knockout (DKO) mice. The authors observed no significant changes in acute, inflammatory, and neuropathic pain. These results suggest that Substance P and CGRPα peptides do not play a major role in mediating pain in mice. Moreover, they reveal that the lack of behavioral phenotype cannot be explained by the redundancy between the two peptides, which are often co-expressed in the same neuron

Strengths,<br /> The paper uses a straightforward approach to address a significant question in the field. The authors confirm the absence of Substance P and CGRPα peptides at the levels of DRG, spinal cord, and midbrain. Subsequently, they employ a comprehensive battery of behavioral tests to examine pain phenotypes, including acute, inflammatory, and neuropathic pain. Additionally, they evaluate neurogenic inflammation by measuring edema and extravasation, revealing no changes in DKO mice. The data are compelling, and the study's conclusions are well-supported by the results. The manuscript is succinct and well-presented.

Reviewer #2 (Public Review):

Summary<br /> This study examines the construct of "cognitive spaces" as they relate to neural coding schemes present in response conflict tasks. The authors use a novel experimental design in which different types of response conflict (spatial Stroop, Simon) are parametrically manipulated. These conflict types are hypothesized to be encoded jointly, within an abstract "cognitive space", in which distances between task conditions depend only on the similarity of conflict types (i.e., where conditions with similar relative proportions of spatial-Stroop versus Simon conflicts are represented with similar activity patterns). Authors contrast such a representational scheme for conflict with several other conceptually distinct schemes, including a domain-general, domain-specific, and two task-specific schemes. The authors conduct a behavioral and fMRI study to test whether prefrontal cortex activity is correlated to one of these coding schemes. Replicating the authors' prior work, this study demonstrates that sequential behavioral adjustments (the congruency sequence effect) are modulated as a function of the similarity between conflict types. In fMRI data, univariate analyses identified activation in left prefrontal and dorsomedial frontal cortex that was modulated by the amount of Stroop or Simon conflict present, and representational similarity analyses that identified coding of conflict similarity, as predicted under the cognitive space model, in right lateral prefrontal cortex.

Strengths

This study addresses an important question regarding how conflict or difficulty might be encoded in the brain within a computationally efficient representational format. Relative to the other models reported in the paper, the evidence in support of the cognitive space model is solid. The ideas postulated by the authors are interesting and valuable ones, worthy of follow-up work that provides additional necessary scrutiny of the cognitive-space account.

Weaknesses

Future, within-subject experiments will be necessary to disentangle the cognitive space model from confounded task variables. A between-subjects manipulation of stimulus orientation/location renders the results difficult to interpret, as the source and spatial scale of the conflict encoding on cortex may differ from more rigorous (and more typical) within-subject manipulations.

Results are also difficult to interpret because Stroop and Simon conflict are confounded with each other. For interpretability, these two sources of conflict need to be manipulated orthogonally, so that each source of conflict (as well as their interaction) could be separately estimated and compared in terms of neural encoding. For example, it is therefore not clear whether the RSA results are due to encoding of only one type of conflict (Stroop or Simon), to a combination of both, and/or to interactive effects.

Finally, the motivation for the use of the term "cognitive space" to describe results is unclear. Evidence for the mere presence of a graded/parametric neural encoding (i.e., the reported conflict RSA effects) would not seem to be sufficient. Indeed, it is discussed in the manuscript that cognitive spaces/maps allow for flexibility through inference and generalization. Future work should therefore focus on linking neural conflict encoding to inference and generalization more directly.

Reviewer #2 (Public Review):

Summary:<br /> Sullivan and Bashaw delve into the mechanisms that drive neural circuit assembly, and specifically, into the regulation of cell surface proteins that mediate axon pathfinding. During nervous system development, axons must traverse a molecularly and physically complex extracellular milieu to reach their synaptic targets. A fundamental, conserved repulsive signaling pathway is initiated by the Slit-Robo ligand-receptor pair. Robo, expressed on axon growth cones, binds Slit, secreted by midline cells, to prevent "pre-crossing" and "re-crossing" of axons at the midline. To control this repulsion, Robo surface levels are tightly regulated. In Drosophila, Commissureless (Comm) downregulates Robo surface levels and is required for axon crossing at the midline. Several studies suggest that PY motifs in Comm are required to localize Robo to endosomes. PY motifs have been shown to bind WW-domain containing proteins including the ubiquitin ligase Nedd4 family, so the authors propose that Comm may regulate Robo through Nedd4 interactions. Previous studies have hinted at a role for Nedd4-mediated ubiquitination of Comm in the regulation of Robo localization, but there have also been conflicting data. For example, Comm mutants that are unable to be ubiquitinated mimic wild-type Comm, suggesting that ubiquitination of Comm is not required for regulation of Robo. The current study utilizes a suite of genetic analyses in Drosophila to resolve discrepancies pertaining to the mode of Comm-dependent regulation of Robo1 and proposes that Comm acts as an adapter for the Nedd4 ubiquitin ligase to recognize Robo1 as a substrate. The authors also demonstrate that Nedd4 is indeed required for midline crossing.

Strengths:<br /> While this work is more incremental rather than field-shifting, it is nonetheless an excellent example of a rigorous, thorough analysis that culminates in enriching our mechanistic understanding of how neurons regulate cell-surface receptors in a spatiotemporal manner to control fundamental steps of circuit wiring. The experimental approach is thorough, and the manuscript is extremely well-written.

Weaknesses:<br /> Some key experiments (eg. complex formation) were performed in cell culture in an overexpression background. Also, there was a missed opportunity to bolster the model proposed by using Comm PY mutants in several experiments. Finally, Comm PY domains are required for proper Comm localization in neurons, but corresponding Robo localization was not analyzed.

Reviewer #2 (Public Review):

Summary:<br /> In this manuscript, L&S investigates the important general question of how humans achieve invariant behavior over stimuli belonging to one category given the widely varying input representation of those stimuli and more specifically, how they do that in arbitrary abstract domains. The authors start with the hypothesis that this is achieved by invariance transformations that observers use for interpreting different entries and furthermore, that these transformations in an arbitrary domain emerge with the help of the transformations (e.g. translation, rotation) within the spatial domain by using those as "scaffolding" during transformation learning. To provide the missing evidence for this hypothesis, L&S used behavioral category learning studies within and across the spatial, auditory, and visual domains, where rotated and translated 4-element token sequences had to be learned to categorize and then the learned transformation had to be applied in new feature dimensions within the given domain. Through single- and multiple-day supervised training and unsupervised tests, L&S demonstrated by standard computational analyses that in such setups, space and spatial transformations can, indeed, help with developing and using appropriate rotational mapping whereas the visual domain cannot fulfill such a scaffolding role.

Strengths:<br /> The overall problem definition and the context of spatial mapping-driven solution to the problem is timely. The general design of testing the scaffolding effect across different domains is more advanced than any previous attempts clarifying the relevance of spatial coding to any other type of representational codes. Once the formulation of the general problem in a specific scientific framework is done, the following steps are clearly and logically defined and executed. The obtained results are well interpretable, and they could serve as a good stepping stone for deeper investigations. The analytical tools used for the interpretations are adequate. The paper is relatively clearly written.

Weaknesses:<br /> Some additional effort to clarify the exact contribution of the paper, the link between analyses and the claims of the paper, and its link to previous proposals would be necessary to better assess the significance of the results and the true nature of the proposed mechanism of abstract generalization.

1) Insufficient conceptual setup: The original theoretical proposal (the Tolman-Eichenbaum-Machine, Whittington et al., Cell 2020) that L&S relate their work to proposes that just as in the case of memory for spatial navigation, humans and animals create their flexible relational memory system of any abstract representation by a conjunction code that combines on the one hand, sensory representation and on the other hand, a general structural representation or relational transformation. The TEM also suggests that the structural representation could contain any graph-interpretable spatial relations, albeit in their demonstration 2D neighbor relations were used. The goal of L&S's paper is to provide behavioral evidence for this suggestion by showing that humans use representational codes that are invariant to relational transformations of non-spatial abstract stimuli and moreover, that humans obtain these invariances by developing invariance transformers with the help of available spatial transformers. To obtain such evidence, L&S use the rotational transformation. However, the actual procedure they use actually solved an alternative task: instead of interrogating how humans develop generalizations in abstract spaces, they demonstrated that if one defines rotation in an abstract feature space embedded in a visual or auditory modality that is similar to the 2D space (i.e. has two independent dimensions that are clearly segregable and continuous), humans cannot learn to apply rotation of 4-piece temporal sequences in those spaces while they can do it in 2D space, and with co-associating a one-to-one mapping between locations in those feature spaces with locations in the 2D space an appropriate shaping mapping training will lead to the successful application of rotation in the given task (and in some other feature spaces in the given domain). While this is an interesting and challenging demonstration, it does not shed light on how humans learn and generalize, only that humans CAN do learning and generalization in this, highly constrained scenario. This result is a demonstration of how a stepwise learning regiment can make use of one structure for mapping a complex input into a desired output. The results neither clarify how generalizations would develop in abstract spaces nor the question of whether this generalization uses transformations developed in the abstract space. The specific training procedure ensures success in the presented experiments but the availability and feasibility of an equivalent procedure in a natural setting is a crucial part of validating the original claim and that has not been done in the paper.

2) Missing controls: The asymptotic performance in experiment 1 after training in the three tasks was quite different in the three tasks (intercepts 2.9, 1.9, 1.6 for spatial, visual, and auditory, respectively; p. 5. para. 1, Fig 2BFJ). It seems that the statement "However, our main question was how participants would generalise learning to novel, rotated exemplars of the same concept." assumes that learning and generalization are independent. Wouldn't it be possible, though, that the level of generalization depends on the level of acquiring a good representation of the "concept" and after obtaining an adequate level of this knowledge, generalization would kick in without scaffolding? If so, a missing control is to equate the levels of asymptotic learning and see whether there is a significant difference in generalization. A related issue is that we have no information on what kind of learning in the three different domains was performed, albeit we probably suspect that in space the 2D representation was dominant while in the auditory and visual domains not so much. Thus, a second missing piece of evidence is the model-fitting results of the ⦰ condition that would show which way the original sequences were encoded (similar to Fig 2 CGK and DHL). If the reason for lower performance is not individual stimulus difficulty but the natural tendency to encode the given stimulus type by a combo of random + 1D strategy that would clarify that the result of the cross-training is, indeed, transferring the 2D-mapping strategy.

Reviewer #2 (Public Review):

Summary:<br /> This is an unusual, but interesting approach to link the "taste" of plants and plant extracts to their therapeutic use in ancient Graeco-Roman culture. The authors used a panel of 11 trained tasters to test ~700 different medicinal plants and describe them in terms of 22 "taste" descriptors. They correlated these descriptors with the plant's medical use as reported in the De Materia Medica (DMM 1st Century, CE). Correcting for some of the plants' evolutionary phylogenetic relationships, the authors found that taste descriptors along with intensity measures were correlated with the "versatility" and/or a specific therapeutic use of the medicine. For example, simple but intense tastes were correlated with versatility of a medicine. Specific intense tastes were linked to versatility while others were not; intense bitter, starchy, musky, sweet, cooling and soapy were associated with versatility, but sour and woody were negatively associated. Also some specific tastes could be associated with specific uses - both positive and negative associations. Some of these findings make sense immediately, but others are somewhat surprising, and the authors propose some links between taste and medicinal use (both historical and modern use) in the discussion. The authors state that this study allows for a re-evaluation of pre-scientific knowledge, pointing toward a central role for taste in medicine.

Strengths:<br /> The real strength of this study is the novelty of this approach - using modern day tasters to evaluate ancient medicinal plants to understand the potential relationships between taste and therapeutic use, lending some support to the idea that the "taste" of a medicine is linked to its effectiveness as a treatment.

Weaknesses:<br /> Because of the limitations of time and the type of botanicals being tested, there is an inherent difficulty in assessing taste intensity. However, because these botanicals are tested by multiple panelists and sometimes tested repeatedly by individual panelists, this helps support the author's analyses.

Reviewer #2 (Public Review):

Erk2 is an essential element of the MAP kinase signaling cascade and directly controls cell proliferation, migration, and survival. Therefore, it is one of the most important drug targets for cancer therapy. The catalytic subunit of Erk2 has a bilobal architecture, with the small lobe harboring the nucleotide-binding pocket and the large lobe harboring the substrate-binding cleft. Several studies by the Ahn group revealed that the catalytic domain hops between (at least) two conformational states: active (R) and inactive (L), which exchange in the millisecond time scale based on the chemical shift mapping. The R state is a signature of the double phosphorylated Erk2 (2P-Erk2), while the L state has been associated with the unphosphorylated kinase (0P-Erk2). Interestingly, the X-ray structures reveal only minimal differences between these two states, a feature that led to the conclusion that active and inactive states are structurally similar but dynamically very different. The Ahn group also found that ATP-competitive inhibitors can steer the populations of Erk2 either toward the R or the L state, depending on their chemical nature. The latter opens up the possibility of modulating the activity of this kinase by changing the chemistry of the ATP-competitive inhibitor. To prove this point, the authors present a set of nineteen compounds with diverse chemical substituents. From their combined NMR and HDX-Mass Spec analyses, fourteen inhibitors drive the kinase toward the R state, while four compounds keep the kinase hopping between the R and L states. Based on these data, the authors rationalize the effects of these inhibitors and the importance of the nature of the substituents on the central scaffold to steer the kinase activity. While all these inhibitors target the ATP binding pocket, they display diverse structural and dynamic effects on the kinase, selecting a specific structural state. Although the inhibited kinase is no longer able to phosphorylate substrates, it can initiate signaling events functioning as scaffolds for other proteins. Therefore, by changing the chemistry of the inhibitors it may be possible to affect the MAP cascade in a predictable manner. This concept, recently introduced as proof of principle, finds here its significance and practical implications. The design of the next-generation inhibitors must be taken into account for these design principles.<br /> The research is well executed, and the data support the author's conclusions.

Reviewer #2 (Public Review):

In this manuscript the authors present and characterize LOVdeg, a modified version of the blue-light sensitive AsLOV2 protein, which functions as a light-inducible degron in Escherichia coli. Light has been shown to be a powerful inducer in biological systems as it is often orthogonal and can be controlled in both space and time. Many optogenetic systems target regulation of transcription, however in this manuscript the authors target protein degradation to control protein levels in bacteria. This is an important advance in bacteria, as inducible protein degradation systems in bacteria have lagged behind eukaryotic systems due to protein targeting in bacteria being primarily dependent on primary amino acid sequence and thus more difficult to engineer. In this manuscript, the authors exploit the fact that the J-alpha helix of AsLOV2, which unwinds into a disordered domain in response to blue light, contains an E-A-A amino acid sequence which is very similar to the C-terminal L-A-A sequence in the SsrA tag which is targeted by the unfoldases ClpA and ClpX. They truncate AsLOV2 to create AsLOV2(543) and combine this truncation with a mutation that stabilizes the dark state to generate AsLOV2*(543) which, when fused to the C-terminus of mCherry, confers light-induced degradation. The authors do not verify the mechanism of degradation due to LOVdeg, but evidence from deletion mutants contained in the supplemental material hints that there is a ClpA dominated mechanism. The LOVdeg is able to target mCherry for protein degradation across different phases of bacterial growth, which is important for regulating processes at stationary phase and a potential additional advantage over transcriptional repression systems. They demonstrate modularity of this LOVdeg by using it to degrade the LacI repressor, CRISPRa activation through degradation of MCP-SoxS, and the AcrB protein which is part of the AcrAB-TolC multidrug efflux pump. In all cases, measurement of the effect of the LOVdeg is indirect as the authors measure reduction in LacI repression, reduction in CRISPRa activation, and drug resistance rather than directly measuring protein levels. Nevertheless the evidence is convincing, although seemingly less effective than in the case of mCherry degradation, although it is hard to compare due to the different endpoints being measured. The authors further modify LOVdeg to contain a known photocycle mutation that slows its reversion time in the dark, so that LOVdeg is more sensitive to short pulses of light which could be useful in low light conditions or for very light sensitive organisms. They also demonstrate that combining LOVdeg with a blue-light transcriptional repression system (EL222) can decrease protein levels an additional 23-fold (relative to 7-fold with LOVdeg alone). Finally, the authors apply LOVdeg to a metabolic engineering task, namely reducing expression of octanoic acid by regulating the enzyme CpFatB1, an acyl-ACP thioesterase. The authors show that tagging CpFatB1 with LOVdeg allows light induced reduction in octanoic acid titer over a 24 hour fermentation. In particular, by comparing control of CpFatB1 with EL222 transcriptional repression alone, LOVdeg, or both the authors show that light-induced protein degradation is more effective than light-induced transcriptional repression. The authors suggest that this is because transcriptional repression is not effective when cells are at stationary phase (and thus there is no protein dilution due to cell division). Overall, the authors have generated a modular, light-activated degron tag for use in Escherichia coli that is likely to be a useful tool in the synthetic biology and metabolic engineering toolkit.

Reviewer #2 (Public Review):

In this study, the authors hypothesized that the balance of mTOR complexes in Sertoli cells may also play a significant role in age-dependent changes in the sperm epigenome. To test this hypothesis, the authors use transgenic mice with manipulated activity of mTOR complexes in Sertoli cells. These results suggest that the mTOR pathway in Sertoli cells may be used as a novel target of therapeutic interventions to rejuvenate the sperm epigenome in advanced-age fathers.

The authors attempt to demonstrate that the balance of mTOR complexes in Sertoli cells regulates the rate of sperm epigenetic aging. The authors have effectively met their research objectives, and their conclusions are supported by the data presented.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors work to extend their previous observation that galectin-9 interacts with arabinogalactans of Mtb in their EMBO reports 2021 manuscript. Here they provide evidence that the CARD2 domain of galectin-9 can inhibit the growth of Mtb in culture. In addition, antibodies that also bind to AG appear to inhibit Mtb growth in culture. These data indicate that independent of the common cell-associated responses to galectin-9 and antibodies, the interaction of these proteins with AG of mycobacteria may have consequences for bacterial growth.

Strengths:

The authors provided several lines of evidence in culture media that the introduction of galectin-9 proteins and antibodies inhibits the growth rate of Mtb.

Weaknesses:

In light of other observations that cleaved galectin-9 levels in the plasma is a biomarker for severe infection (Padilla A et al Biomolecules 2021 and Iwasaki-Hozumi H et al. Biomoleucles 2021) it is difficult to reconcile the author's interpretation that the elevated gal-9 in Active TB patients (Figure 1E) contributes to the maintenance of latent infection in humans. The authors should consider incorporating these observations in the interpretation of their own results.

The anti-AG titers were measured only in individuals with active TB (Figure 3C), generally thought to be a less protective immunological state. The speculation that individuals with anti-AG titers have some protection is not founded. Further only 2 mAbs were tested to demonstrate restriction of Mtb in culture. It is possible that clones of different affinities for AG present within a patient's polyclonal AG-antibody responses may or may not display a direct growth restriction pressure on Mtb in culture. The authors should soften the claims about the presence of AG-titers in TB patients being indicative of protection.

Reviewer #2 (Public Review):

Summary:

The authors generated a DNA methylation score in cord blood for detecting exposure to cigarette smoke during pregnancy. They then asked if it could be used to predict height, weight, BMI, adiposity, and WHR throughout early childhood.

Strengths:

The study included two cohorts of European ancestry and one of South Asian ancestry.

Weaknesses:

1. The number of mothers who self-reported any smoking was very low, much lower than in the general population and practically non-existent in the South Asian population. As a result, all analyses appeared to have been underpowered. It is possibly for this reason that the authors chose to generate their DNA methylation model using previously published summary statistics. The resulting score is not of great value in itself due to the low-powered dataset used to estimate covariance between CpG sites. In fact, a score was generated for a much larger, better-powered dataset several years ago (Reese, EHP, 2017, PMID 27323799).

2. The conclusion that "even minimal smoking exposure in South Asian mothers who were not active smokers showed a DNAm signature of small body size and low birthweight in newborns" is not warranted because no analyses were performed to show that the association between DNA methylation and birth size/weight was driven by maternal smoking.

3. Although it was likely that some mothers were exposed to second-hand smoke and/or pollution, data on this was either non-existent or not included in this study. Including this would have allowed a more novel investigation of the effects of smoke exposure on the pregnancies of non-smoking mothers.

4. One of the European cohorts and half of the South Asian cohort had DNA methylation measured on only 2500 CpG sites. This set of sites included only 125 sites previously linked to prenatal smoking. The resulting model of prenatal smoking was small (only 11 CpG sites). It is possible that a large model may have been more powerful.

5. The health outcomes investigated are potentially interesting but there are other possibly more important outcomes of interest such as birth complications, asthma, and intellectual impairment which are known to be associated with prenatal smoking.

Reviewer #2 (Public Review):

Summary:<br /> Peled et al identified HER2 mutations in connection with resistance to the anti-HER2 antibody Pertuzumab-mediated therapy. After constructing a yeast display library of Pertuzumab variants with 3.86×1011 sequences for targeted screening of variant combinations in chosen 6 out of 14 residues, the authors performed experimental screening to obtain the clones that bind to HER2 WT and/or mutants (S310Y and S310F), and then combined new variations to obtain antibodies with a broad spectrum binding to both WT and two HER2 mutants. These are interesting studies of clinical impact and translational potential.

Strengths:<br /> 1. Deep computational analyses of large datasets of clinical data provide useful information about HER2 mutations and their potential relevance to antibody therapy resistance.

2. There is valuable information analyzing the residues within or near the interface between the antigen HER2 and the Pertuzumab antibody (heavy chain). The experimental antibody library screening obtained 90+ clones from 3.86×1011 sequences for further functional validation.

Weaknesses:<br /> 1. There is a lack of assessment for antibody variant functions in cancer cell phenotypes in vitro (proliferation, cell death, motility) or in vivo (tumor growth and animal survival). The only assay was the western blotting of phosphopho-HER3 in Figure 4. However, HER2 levels and phosphor-HER2 were not analyzed.

2. There is a misleading impression from the title of computational engineering of a therapeutic antibody and the statement in the abstract "we designed a multi-specific version of Pertuzumab that retains original function while also bindings these HER2 variants" for a few reasons:<br /> a. The primary method used for variant antibody identification for HER2 mutant binding is rather traditional experimental screening based on yeast display instead of the computational design of a multi-specific version of Pertuzumab.<br /> b. There is insufficient or lack of computational power in the antibody design or prioritization in choosing variant residues for the library construction of 3.86×1011 sequences. It seems random combinations from 6 residues out of 4 groups with 20 amino acid options.<br /> c. The final version of the tri-binding variant is a combination of screened antibody clones instead of computation design from scratch.<br /> d. There is incomplete experimental evidence about the therapeutic values of newly obtained antibody clones.

3. Figures can be improved with better labeling and organization. Some essential pieces of data such as Supplementary Figure 1B on HER2 mutations in S310 that abrogated its binding to Pertuzumab should be placed in the main figures.

4. It is recommended to provide a clear rationale or flowchart overview into the main Figure 1. Figure 2A can be combined with Figure 1 to the list of targeted residues.

5. The quality of Figures such as Figure 2B-C flow data needs to be improved.

Reviewer #2 (Public Review):

Ma X. et al proposed that A. muciniphila was a key strain that promotes the proliferation and differentiation of intestinal stem cells by acting on the Wnt/b-catenin signaling pathway. They used various models, such as the piglet model, mouse model, and intestinal organoids to address how A. muciniphila and B. fragilis offer protection against ETEC infection. They showed that FMT with fecal samples, A. muciniphila or B. fragilis protected piglets and/or mice from ETEC infection, and this protection is manifested as reduced intestinal inflammation/bacterial colonization, increased tight junction/Muc2 proteins, as well as proper Treg/Th17 cells. Additionally, they demonstrated that A. muciniphila protected basal-out and/or apical-out intestinal organoids against ETEC infection via Wnt signaling. While a large body of work has been performed in this study, there are quite a few questions to be addressed.

Major comments:

- The similar protective effect of FMT with fecal samples, A. muciniphila or B. fragilis is perhaps not that surprising, considering that FMT likely restores microbiota-mediated colonization resistance against ETEC infection. While FMT with fecal samples increases SCFAs, it is unclear whether/how FMT with A. muciniphila or B. fragilis alter the microbiota composition/abundance as well as metabolites in the current models in a way that offers protection.

- Does ETEC infection in piglets/mice cause histological damage in the intestines? These data should be shown.

- Line 447, "ETEC adheres to intestinal epithelial cells". However, there is no data showing the adherence (or invasion) of ETEC to intestinal epithelial cells, irrespective of piglets/mouse/organoids.

- In both basal-out and apical-out intestinal organoid models, A. muciniphila protects organoids against ETEC infection. Did ETEC enter into intestinal epithelial cells at all after only one hour of infection? Is the protection through certain A. muciniphila metabolites?

Reviewer #2 (Public Review):

In this manuscript, Tiedje and colleagues longitudinally track changes in parasite numbers across four time points as a way of assessing the effect of malaria control interventions in Ghana. Some of the study results have been reported previously, and in this publication, the authors focus on age-stratification of the results. Malaria prevalence was lower in all age groups after IRS. Follow-up with SMC, however, maintained lower parasite prevalence in the targeted age group but not the population as a whole. Additionally, they observe that diversity measures rebounds more slowly than prevalence measures. Overall, I found these results clear, convincing, and well-presented. They add to a growing literature that demonstrates the relevance of asymptomatic reservoirs.

There is growing interest in developing an expanded toolkit for genomic epidemiology in malaria, and detecting changes in transmission intensity is one major application. As the authors summarize, there is no one-size-fits-all approach, and the Bayesian MOIvar estimate developed here has the potential to complement currently used methods. I find its extension to a calculation of absolute parasite numbers appealing as this could serve as both a conceptually straightforward and biologically meaningful metric. However, I am not fully convinced the current implementation will be applied meaningfully across additional studies.

1. I find the term "census population size" problematic as the groups being analyzed (hosts grouped by age at a single time point) do not delineate distinct parasite populations. Separate parasite lineages are not moving through time within these host bins. Rather, there is a single parasite population that is stochastically divided across hosts at each time point. I find this distinction important for interpreting the results and remaining mindful that the 2,000 samples at each time point comprise a subsample of the true population. Instead of "census population size", I suggest simplifying it to "census count" or "parasite lineage count".

It would be fascinating to use the obtained results to model absolute parasite numbers at the whole population level (taking into account, for instance, the age structure of the population), and I do hope this group takes that on at some point even if it remains outside the scope of this paper. Such work could enable calculations of absolute---rather than relative---fitness and help us further understand parasite distributions across hosts.

2. I'm uncertain how to contextualize the diversity results without taking into account the total number of samples analyzed in each group. Because of this, I would like a further explanation as to why the authors consider absolute parasite count more relevant than the combined MOI distribution itself (which would have sample count as a denominator). It seems to me that the "per host" component is needed to compare across age groups and time points---let alone different studies.

3. Thinking about the applicability of this approach to other studies, I would be interested in a larger treatment of how overlapping DBLa repertoires would impact MOIvar estimates. Is there a definable upper bound above which the method is unreliable? Alternatively, can repertoire overlap be incorporated into the MOI estimator?

Smaller comments:<br /> - Figure 1 provides confidence intervals for the prevalence estimates, but these aren't carried through on the other plots (and Figure 5 has lost CIs for both metrics). The relationship between prevalence and diversity is one of the interesting points in this paper, and it would be helpful to have CIs for both metrics when they are directly compared.

Reviewer #2 (Public Review):

Summary:<br /> In the manuscript by Luo et al, the authors investigated the nature and function of TRAIL-HS binding for the regulation of TRAIL-mediated apoptosis in cancer cells. The authors discovered that TRAIL binds to 12mer HS and identified the amino acid residues critical for the binding. The authors further nicely showed that 12mer HS binds to TRAIL homotrimer and larger HS can further promote the formation of larger TRAIL oligomers. Structural analyses were conducted to characterize the binding of TRAIL/HS complexes. At functional level, the authors demonstrated that HS promotes the cell surface binding of TRAIL to enhance TRAIL-mediated apoptosis in a variety of cancer cells. Moreover, the ability of TRAIL to induce apoptosis is correlated with cell surface HS level. Lastly, the authors showed that HS forms complex with TRAIL and its receptor DR5 and promotes DR5 internalization.

Strengths:<br /> Overall, this is a nicely executed study providing both mechanistic and functional insight for TRAIL-mediated apoptosis. It conducted detailed characterization on the direct binding between HS and TRAIL and provided solid evidence supporting the role of such interaction for the regulation of TRAIL-induced apoptosis. The experiments were well-designed with proper controls included. The data interpretation is accurate. The manuscript was clearly written and easy to follow by general readers.

Weaknesses:<br /> There is no major weakness identified from this study. As the authors pointed out, the current relationship between cell surface HS level and sensitivity to TRAIL-mediated apoptosis is still correlative and will need further investigation in the future.

Reviewer #2 (Public Review):

In this study, the authors seek to answer two main questions: 1) Whether interfering with lactate availability in hepatocytes through depletion of hepatocyte specific MCT-1 depletion would reduce steatosis, and 2) Whether MCT-1 in stellate cells promote fibrogenesis. While the first question is based on the observation that haploinsufficiency of MCT-1 makes mice resistant to steatosis, the rationale behind how MCT-1 could impact fibrogenesis in stellate cells is not clear. A more detailed discussion regarding how lactate availability would regulate two different processes in two different cell types would be helpful. The authors employ several mouse models and in vitro systems to show that MCT1 inhibition in hepatic stellate cells reduces the expression of COL-1.

The authors have sufficiently addressed prior comments and added new experiments to provide details on possible mechanisms.

Reviewer #2 (Public Review):

Summary:

In their manuscript, the authors address the question of whether the inversion polymorphism in D. melanogaster can be explained by sexually antagonistic selection. They designed a new simulation tool to perform computer simulations, which confirmed their hypothesis. They also show a tradeoff between male reproduction and survival. Furthermore, some inversions display sex-specific survival.

Strengths:<br /> It is an interesting idea on how chromosomal inversions may be maintained

Weaknesses:<br /> General points:<br /> The manuscript lacks clarity of writing. It is impossible to fully grasp what the authors did in this study and how they reached their conclusions. Therefore, I will highlight some cases that I found problematic.<br /> Although this is an interesting idea, it clearly cannot explain the apparent influence of seasonal and clinal variation on inversion frequencies.

Specific points:<br /> The simulations are highly specific and make very strong assumptions, which are not well-justified.

Reviewer #2 (Public Review):

Ehring et al. analyze contributions of Dispatched, Scube2, serum lipoproteins and Sonic Hedgehog lipid modifications to the generation of different Shh release forms. Hedgehog proteins are anchored in cellular membranes by N-terminal palmitate and C-terminal cholesterol modifications, yet spread through tissues and are released into the circulation. How Hedgehog proteins can be released, and in which form, remains controversial. The authors systematically dissect contributions of several previously identified factors, and present evidence that Disp, Scube2 and lipoproteins concertedly act to release a novel Shh variant that is cholesterol-modified but not palmitoylated. The results provide new insights into the function of Disp and Scube2 in Hedgehog release. The findings concerning the function of lipoproteins and cholesterol in Hedgehog release are largely confirmatory (PMID 23554573, 20685986). However, in light of the multitude of competing models for Hedgehog release, the present study is a valuable contribution that provides further insights into the relevance of lipoproteins in this process.

A novel and surprising finding of the present study is the differential removal of Shh N- or C-terminal lipid anchors depending on the presence of HDL and/or Disp. In particular, the identification of a non-palmitoylated but cholesterol-modified Shh variant that associates with lipoproteins is potentially important. The authors use RP-HPLC and defined controls to assess the properties of processed Shh forms, but their precise molecular identify remains to be defined. A caveat is the strong reliance on over-expression of Shh in a single cell line. The authors detect Shh variants that are released independently of Disp and Scube2 in secretion assays, which however are excluded from interpretation as experimental artifacts. Thus, it would be important to demonstrate key findings in cells that secrete Shh endogenously.

Reviewer #2 (Public Review):

By mapping the sites of the Mcm2-7 replicative helicase loading across the budding yeast genome using high-resolution chromatin endogenous cleavage or ChEC, Bedalov and colleagues find that these markers for origins of DNA replication are much more broadly distributed than previously appreciated. Interestingly, this is consistent with early reconstituted biochemical studies that showed that the ACS was not essential for helicase loading in vitro (e.g. Remus et al., 2009, PMID: 19896182). To accomplish this, they combined the results of 12 independent assays to gain exceptionally deep coverage of Mcm2-7 binding sites. By comparing these sites to previous studies mapping ssDNA generated during replication initiation, they provide evidence that at least a fraction of the 1600 most robustly Mcm2-7-bound sequences act as origins. A weakness of the paper is that the group-based (as opposed to analyzing individual Mcm2-7 binding sites) nature of the analysis prevents the authors from concluding that all of the 1,600 sites mentioned in the title act as origins. The authors also show that the location of Mcm2-7 location after loading are highly similar in the top 500 binding sites, although the mobile nature of loaded Mcm2-7 double hexamers prevents any conclusions about the location of initial loading. Interestingly, by comparing subsets of the Mcm2-7 binding sites, they find that there is a propensity of at least a subset of these sites to be nucleosome depleted, to overlap with at least a partial match to the ACS sequence (found at all of the most well-characterized budding yeast origins), and a GC-skew centered around the site of Mcm loading. Each of these characteristics is related to previously characterized S. cerevisiae origins of replication.

Overall, this manuscript greatly broadens the number of sites that are capable of loading Mcm2-7 in budding yeast cells and shows that a subset of these additional sites act as replication origins. Although these studies show that the sequence specificity of S. cerevisiae replication origins still sets it apart from metazoan origins, the ability to license and initiate replication from sites with increasing sequence divergence suggests a previously unappreciated versatility.

Reviewer #2 (Public Review):

This is a thorough and convincing body of work that represents an incremental but significant improvement on iterations of this method of CRISPR-based Sterile Insect Technique ('pgSIT'). In this version, compared to previous, the authors target more genes than previously, in order to induce both female inviability (targeting the genes intersex and doublesex, compared to fem-myo previously) and male sterility (targeting a beta-tubulin, as previously in the release generation.

The characterization of the lines is extensive and this data will be useful to the field. However, what is lacking is some context as to how this formulation compares to the previous iteration. Mention is made of the possible advantage of removing most females, compared to just making them flightless (as previously) but there is no direct comparison, either experimental, or theoretical i.e. imputing the life history traits into a model. For me this is a weakness, yet easily addressed. In a similar vein, much is made in alluding to the 'safety concerns of gene drive' and how this is a more palatable half-way house, just because it has CRISPR component within it; it is not. It would be much more sensible, and more informative, to compare this pgSIT technology to RIDL (release of insects carrying a dominant lethal), which is essentially a transgene-based version of the Sterile Insect Technique, as is the work presented here.

The authors achieve impressive results and show that these strains, under a scenario of high levels of release ratios compared to WT, could achieve significant local suppression of mosquito populations. The sensitivity analysis that examines the effect of changing different biological or release parameters is well performed and very informative.

The authors are honest in acknowledging that there are still challenges in bringing this to field release, namely in developing sexing strains and optimizing release strategies.

Reviewer #2 (Public Review):

In this work, Vollenweider et al. examine the effectiveness of using natural products, specifically molecules that chelate iron, to treat infectious agents. Through the purification of 320 environmental isolates, 25 potential candidates were identified from natural products based on inhibition assays and were further screened. The structural information and chemical composition were determined.

The paper is well-structured and thorough; targeting virulence factors in this manner is a great idea. My enthusiasm is dampened by the mediocre effects of the compounds. The lack of a dose-response curve in the survivability assays suggests a limited scope for these molecules. While it is encouraging that the best survivability occurred at the lowest toxicity level, it opens questions as to how effective such molecules can be. Either the reduction in mortality was offset by using higher concentrations, which was not observed in the compound-alone test, or there is no dose-response curve. The latter would suggest to me that the variation in survivability is not due to the addition of siderophores.

I would also like to see how these molecules compare to other iron-chelating molecules. Desferoxamine is a bacteria-derived siderophore that is FDA-approved. However, it is not used to treat infections. Would the author consider comparing their candidate molecules to well-studied molecules? This also raises questions about the novelty of this work; I think the authors could rephrase the discussion to better reflect that bioprospecting for iron-chelating molecules has previously occurred and been successful.

Finally, I am concerned about the few mutations reported in the resistance study. Looking at the SI, it appears that very few mutations were seen. It is unclear what filtering the authors used to arrive at such a low number of mutations. Even filtering against mutations that were selected by adaptation to the media, it seems low that only a handful of clones had distinct mutations.

This paper has a lot of strengths. The workflow is logical and well-executed; the only significant weakness is the effect of the molecules and the lack of an explanation for a dose-response curve in the survivability assay, especially when compared to the data reported in Figure 3. As the authors describe in lines 214-217.

Reviewer #2 (Public Review):

Summary:<br /> Dr. Khulman and colleagues present a very interesting experimental and mathematical modeling work on IS608 transposition. The system has a number of unique advantages that create experimental possibilities utilized here to investigate transposition dynamics. This kind of approach is badly missing from the field and I believe the experiments and modeling work shown here and the type of results that can be derived are groundbreaking and certainly deserve high visibility.

Strengths:<br /> The attempt to measure and model transposition dynamics in cells.

Weaknesses:<br /> - Lack of controls using an active site mutant of TnpB.<br /> - Lack of control in RecA- cells as transposon restoration is proposed to be dependent on homologous recombination.<br /> - Lack of consideration of the levels of ωRNA present.

Reviewer #2 (Public Review):

Hagihara et al. conducted a study investigating the correlation between decreased brain pH, increased brain lactate, and poor working memory. They found altered brain pH and lactate levels in animal models of neuropsychiatric and neurodegenerative disorders. Their study suggests that poor working memory performance may predict higher brain lactate levels.

However, the study has some significant limitations. One major concern is that the authors examined whole-brain pH and lactate levels, which might not fully represent the complexity of disease states. Different brain regions and cell types may have distinct protein and metabolite profiles, leading to diverse disease outcomes. For instance, certain brain regions like the hippocampus and nucleus accumbens exhibit opposite protein/signaling pathways in neuropsychiatric disease models.

Moreover, the memory tests used in the study are specific to certain brain regions, but the authors did not measure lactate levels in those regions. Without making lactate measurements in brain-regions and cell types involved in these diseases, any conclusions regarding the role of lactate in CNS diseases is premature.

Additionally, evidence suggests that exogenous treatment with lactate has positive effects, such as antidepressant effects in multiple disease models (Carrard et al., 2018, Carrard et al., 2021, Karnib et al., 2019, Shaif et al., 2018). It also promotes learning, memory formation, neurogenesis, and synaptic plasticity (Suzuki et al., 2011, Yang et al., 2014, Weitian et al., 2015, Dong et al., 2017, El Hayek et al. 2019, Wang et al., 2019, Lu et al., 2019, Lev-Vachnish et a.l, 2019, Descalzi G et al., 2019, Herrera-López et al., 2020, Ikeda et al., 2021, Zhou et al., 2021,Roumes et al., 2021, Frame et al., 2023, Akter et al., 2023).

In conclusion, the relevance of total brain pH and lactate levels as indicators of the observed correlations is controversial, and evidence points towards lactate having more positive rather than negative effects. It is important that the authors perform studies looking at brain-region-specific concentrations of lactate and that they modulate lactate levels (decrease) in animal models of disease to validate their conclusions. It is also important to consider the above-mentioned studies before concluding that "altered brain pH and lactate levels are rather involved in the underlying pathophysiology of some patients with neuropsychiatric disorders" and that "lactate can serve as a potential therapeutic target for neuropsychiatric disorders".

Reviewer #2 (Public Review):

The study is novel and valuable to the field and provides new and important insights into the role of lipid mediators in VML injuries. By expanding our understanding of the mechanisms that regulate muscle regeneration following VML injuries, the study has the potential to guide the development of novel therapeutic interventions that promote tissue repair and recovery. The data presented in the manuscript is of good quality. The findings and conclusions are supported by a variety of different analyses (e.g., gene expression, histology, flow cytometry).

Despite the strengths of the study, some limitations are identified. Specifically, the impact of maresin 1 on macrophage phenotypes (M1/M2) could have been explored in more detail using histological or protein expression analysis. Moreover, additional data are needed to substantiate the claims about increased muscle regeneration. Lastly, the study does not address myofiber innervation, myofiber-type transitions, or motor unit remodeling.

Reviewer #2 (Public Review):

In this study, the goal is to leverage the power of Bayesian inference to estimate online the probability that any given arbitrarily chosen strategy is being used by the decision-maker. By computing the trial-by-trial MAP and variance of the posterior distribution for each candidate strategy, the authors can not only see which strategy is primarily being used at every given time during the task and when strategy changes occur but also detect when the target rule of a learning task becomes the front-running strategy, i.e., when successful learning occurs.

Strengths:<br /> 1. The proposed approach adds to recent methods for capturing the dynamics of decision-making at finer temporal resolution (trials) (Roy et al., 2021; Ashwood et al., 2022) but it is novel and differs from these in that it is suited especially well for analyzing when learning occurs, or when a rule switches and learning must recommence, and it does not necessitate large numbers of trials.

2. The manuscript starts with a validation of the approach using synthetic data and then is applied to datasets of trial-based two-alternative forced choice tasks ranging from rodent to non-human primate to human, providing solid evidence of its utility.

3. Compared to classic procedures for identifying when an animal has learned a contingency which typically needs to be conservative in favor of better accuracy, this method retrieves signs of learning happening earlier (~30 trials earlier on average). This is achieved by identifying the moment (trial) when the posterior probability of the correct "target" rule surpasses the probability of all other strategies. Having greater temporal precision in detecting when learning happens may have a very significant impact on studies of the neural mechanisms of learning.

4. This approach seems amenable to testing many different strategies depending on the purpose of the analysis. In the manuscript, the authors test target versus non-target strategies (correct versus incorrect) and also in another version of the analysis, they test what they call "exploratory" strategies.

5. One of the main appeals of this method is its apparent computational simplicity. It necessitates only updating on every trial the parameters of a beta distribution (prior distribution for a given strategy) with the evidence that the behavior on trial was either consistent or inconsistent with the strategy. Two scalars, the mode of the posterior (MAP) and the inverse of the variance, are all that are required for identifying the decision criterion (highest MAP and if tied lowest variance) and the learning criterion (first trial where MAP for target strategy is higher than chance).

Weaknesses:<br /> 1. It seems like a limitation of this approach is that the candidate strategies to arbitrate between must be known ex-ante. It is not clear how this approach could be applied to uncover latent strategies that are not mixtures of the strategies selected.

2. Different strategies may be indistinguishable from each other and thus it may not be possible to distinguish between them. Similarly, the fact that two strategies seem to be competing for the highest MAP doesn't necessarily mean that those are correct strategies and perhaps interchangeable as the manuscript seems to suggest.

3. The decay parameter is a necessary component to make the strategy selection non-stationary and accommodate data sets where the rules are changing throughout the task. However, the choice of the decay parameter value bounds does not seem very principled. Having this parameter as a free-parameter adds a flexibility that seems to have significant effects on when the strategy switch is detected and how stable the detected switch is.

4. This method is a useful approach for arbitrating between strategies and describing the behavior with a temporal precision that may prove important for studies attempting to tie these precise events to changes in neural activity. However, it seems limited in its explanatory power. In its current form, this method does not provide a prediction of the probability to transition from one strategy to another. And, because the MAP of different strategies may be close at any given moment, it is hard to imagine using this approach to tease out the different "mental states" that represent each strategy being at play.