Reviewer #3 (Public Review):

Cells can oxidize diverse substrates in the mitochondria to sustain cellular energy metabolism. However, all of these substrates require covalent thioester linkage to coenzyme A (CoA). Thus, multiple energy metabolism substrates could potentially compete for a limited pool of mitochondrial CoA. Cells encode a set of mitochondrial acyl-CoA thioesterases (ACOTs) that free CoA up by removing attached substrates. The authors hypothesized that ACOT2, a mitochondrial ACOT with a preference for long-chain acyl-CoA substrates that arise during the oxidation of lipids as a fuel source, could regulate the balance of substrates used in the mitochondria by reducing the oxidation of lipids by removing them from CoA and freeing the mitochondrial pool of CoA for use by other substrates.

To test this hypothesis, the authors generated mice with loss of ACOT2 in the skeletal muscle, where this is most expressed, and assayed the CoA composition of muscle and their glucose/fatty acid catabolism in mice that were challenged with different diets, fasting or exercise to expose the muscle to different substrates conditions. These experiments were complemented with biochemical analysis of mitochondria isolated from the muscle of control and ACOT2 animals exposed to a variety of substrates and challenged with different simulated energy demands.

On the basis of these convincing experiments, the authors argue that loss of ACOT2 both in vivo and in vitro interestingly increases glucose oxidation, while not increasing oxidation of lipids. This is particularly surprising as the CoA competition model would predict that ACOT2 loss would increase lipid oxidation while hindering glucose oxidation. The authors argue that ACOT2 facilitates lipid oxidation due to ACOT2 reversal of lipid ligation to CoA preventing feedback inhibition of the lipid oxidation pathway that occurs when lipid supply outstrips the ability of the lipid oxidation pathway to metabolize the lipids. These findings will be valuable for the field of metabolism providing insight into how ACOTs regulate substrate catabolism in cells and tissues.

Reviewer #1 (Public Review):

The authors focused on genetic variability in relation to insulin resistance. The used genetically different lines of mice and exposed them to the same diet. They found that genetic predisposition impacts the overall outcome of metabolic disturbances.

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):

Summary:<br /> The authors aimed to investigate how genetic and environmental factors influence the muscle insulin signaling network and its impact on metabolism. They utilized mass spectrometry-based phosphoproteomics to quantify phosphosites in skeletal muscle of genetically distinct mouse strains in different dietary environments, with and without insulin stimulation. The results showed that genetic background and diet both affected insulin signaling, with almost half of the insulin-regulated phosphoproteome being modified by genetic background on an ordinary diet, and high-fat high-sugar feeding affecting insulin signaling in a strain-dependent manner.

Strengths:<br /> Study uses state-of-the-art phosphoproteomics workflow allowing quantification of a large number of phosphosites in skeletal muscle, providing a comprehensive view of the muscle insulin signaling network. The study examined five genetically distinct mouse strains in two dietary environments, allowing for the investigation of the impact of genetic and environmental factors on insulin signaling. The identification of coregulated subnetworks within the insulin signaling pathway expanded our understanding of its organization and provided insights into potential regulatory mechanisms. The study associated diverse signaling responses with insulin-stimulated glucose uptake, uncovering regulators of muscle insulin responsiveness.

Weaknesses:<br /> The limitations acknowledged by the authors, such as the need for larger cohorts and the inclusion of female mice. Moreover as acknowledged by authors, they are unable to dissect to what extent the obesity and different life span cycle for different strain affects insulin signaling. This suggest that further research is needed to validate and expand upon the findings.

Reviewer #2 (Public Review):

The authors use a high-throughput sequencing-based enrichment assay to measure how individual amino acids substitutions in the Rep proteins of AAV change the production of AAV. The key experiment involved the creation of all possible single codon mutations of the AAV2 rep gene in a barcoded format, transfection of the library into HEK293T cells for production of AAV, and sequencing to see which rep variants were enriched in the viral particles produced from the library. As the library rep variants were flanked by inverted terminal repeats for packaging into viral particles, the authors could use high-throughput sequencing of the barcodes to determine how much each rep variant supported the production of AAV. The rep gene libraries were cleverly made through a cloning process that ensured each mutant was attached to an exactly known 20nt barcode included in each mutagenic oligo (and subsequently moved to the end of the library genes by another cloning step). This allowed the authors to confidently observe nearly all rep variants in their experiments, resulting in a comprehensive map between Rep protein variants and AAV production. The overall map should act as a useful guide for AAV engineering. Not only did certain variants improve AAV production by ~2-fold and show generality across AAV capsid serotypes, the map might be used to predict greater effects through combinations of mutations, especially if augmented by natural evolutionary datasets and statistical learning.

In interpreting the results of this study, the reader should bear in mind that what has been measured and validated in high throughput is the production of intact genome-containing AAVs. The authors also successfully show transduction for selected high production variants. This is important as the efficiency by which an AAV preparations transduce cells is most relevant property for gene therapy.

Overall, this is a well-executed and well-analyzed study. The results support the conclusions and claims of the work. I see this work as a useful resource for engineering recombinant AAVs to increase their production, which should have broad impact as the use of AAVs in gene therapy grows.

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 #2 (Public Review):

Summary:

In this manuscript Nie et al investigate the effect of PARG KO and PARG inhibition (PARGi) on pADPR, DNA damage, cell viability and synthetic lethal interactions in HEK293A and Hela cells. Surprisingly, the authors report that PARG KO cells are sensitive to PARGi and show higher pADPR levels than PARG KO cells, which is abrogated upon deletion or inhibition of PARP1/PARP2. The authors explain the sensitivity of PARG KO to PARGi through incomplete PARG depletion and demonstrate complete loss of PARG activity when incomplete PARG KO cells are transfected with additional gRNAs in the presence of PARPi. Furthermore, the authors show that the sensitivity of PARG KO cells to PARGi is not caused by NAD depletion but by S-phase accumulation of pADPR on chromatin coming from unligated Okazaki fragments, which are recognized and bound by PARP1. Consistently, PARG KO or PARG inhibition show synthetic lethality with Pol beta, which is required for Okazaki fragment maturation. PARG expression levels in ovarian cancer cell lines correlate negatively with their sensitivity to PARGi.

Strengths:

The authors show that PARG is essential for removing ADP-ribosylation in S-phase.

Weaknesses:

1) This begs the question as to the relevant substrates of PARG in S-phase, which could be addressed, for example, by analysing PARylated proteins associated with replication forks in PARG-depleted cells (EdU pulldown and Af1521 enrichment followed by mass spectrometry).<br /> 2) The results showing the generation of a full PARG KO should be moved to the beginning of the Results section, right after the first Results chapter (PARG depletion leads to drastic sensitivity to PARGi), otherwise the reader is left to wonder how PARG KO cells can be sensitive to PARGi when there should be presumably no PARG present.<br /> 3) Please indicate in the first figure which isoforms were targeted with gRNAs, given that there are 5 PARG isoforms. You should also highlight that the PARG antibody only recognizes the largest isoform, which is clearly absent in your PARG KO, but other isoforms may still be produced, depending on where the cleavage sites were located.<br /> 4) FACS data need to be quantified. Scatter plots can be moved to Supplementary while quantification histograms with statistical analysis should be placed in the main figures.<br /> 5) All colony formation assays should be quantified and sensitivity plots should be shown next to example plates.<br /> 6) Please indicate how many times each experiment was performed independently and include statistical analysis.

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 #1 (Public Review):

Hu et al. performed sc-RNA-seq analyses of kidney cells with or without virus infection, vaccines, and vaccines+virus infections from pooled adult zebrafish. They compared within these experimental groups as well as kidney vs spleen. Their analyses identified expected populations but also revealed new hematopoietic stem/progenitor cell (HSPC), even in the spleen. Their analyses show that HSPCs in the kidney can respond to virus infection differentially and can be trained to recognize the same infection and argue that zebrafish kidney can serve as a secondary immune organ. The findings are important and interesting. The manuscript is well written and a pleasure to read. However, there are several issues with their figure presentation and figure qualities, as well as the lack of clarity in some of figure legends. Some of the data presentation can be improved for better clarity. It is also important to outline what is conserved and what is unique for fish.

Major concerns:

1. The visualization for several figure panels is very poor. Please provide high resolution images and larger font sizes for gene list or Y and X axis labels. This includes Figure 1B, Figure 1-figure supplement 2, Figure 2B-2C, 3A-3D, 4F, 5B, 6G, Figure 6-figure supplement 1B, Figure 6-figure supplement 2. Figure 7B, 8C-8E, Figure 8-figure supplement 1., 10F, 10G-10J, Figure 10-figure supplement 1.<br /> 2. What are the figures at the end of the manuscript without any figure legends?<br /> 3. It would be better to use a Table to organize the gene signatures that define each unique population of immune cells such as T, B, NK, etc.<br /> 4. What are the similarities for HSPC and immune cell populations between fish and man based on this research? It is better to form a table to compare and discuss.<br /> 5. It is highly likely that sex and age could be the biological variation for how HSPC responds to virus infections and vaccination. The author should clearly state the fish sex and age from their samples and discuss their results taking into consideration of these variations.<br /> 6. The authors claim that the spleen and kidney share HSPCs. However, their data did not demonstrate this result clearly in Figure 4A. Perhaps they should use different color to make the overlay becoming more obvious? Or include a table to show which HSPCs are shared between the kidney and spleen? Are they sure if these are just HSPCs seeding the spleen to differentiate into B cells or other immune cells?

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 #1 (Public Review):

Summary:<br /> This useful work provides insight into agonist binding to muscle nicotinic receptors. The authors want to understand the fundamental steps in ligand binding to muscle nicotinic receptors using computational methods. The study builds on a large basis of empirical studies of the various states involved in receptor activation. However, the evidence supporting the conclusions is incomplete, because little support is available for the starting structures that are derived from ligand docking. This work is a useful starting point for more detailed work on ligand binding to this important class of receptors.

Strengths:<br /> The strengths include the number of ligands tried, and the relation to the mature analysis of the receptor function.

Weaknesses:<br /> The weaknesses are the brevity of the simulations, the concomitant lack of scope of the simulations, the lack of depth in the analysis, and the incomplete relation to other relevant work.

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 #3 (Public Review):

Summary:<br /> The authors use docking and molecular dynamics (MD) simulations to investigate transient conformations that are otherwise difficult to resolve experimentally. The docking and simulations suggest an interesting series of events whereby agonists initially bind to the low-affinity site and then flip 180 degrees as the site contracts to its high-affinity conformation. This work will be of interest to the ion channel community and to biophysical studies of pentameric ligand-gated channels.

Strengths:<br /> I find the premise for the simulations to be good, starting with an antagonist-bound structure as an estimate of the low affinity binding site conformation, then docking agonists into the site and using MD to allow the site to relax to a higher affinity conformation that is similar to structures in complex with agonists. I cannot speak to the details of the simulation methods, but the predictions are interesting and provide a view into what a transient conformation that is difficult to observe experimentally might be like.

Weaknesses:<br /> Although the match in simulated vs experimental energies for two ligands was very good, the calculated energies for two other ligands were significantly different than the experiment. It is unclear to what extent the choice of method for the energy calculations influenced the results.

A control simulation, such as for an apo site, is lacking.

Reviewer #4 (Public Review):

Summary:<br /> In their manuscript "Conformational dynamics of a nicotinic receptor neurotransmitter binding site," Singh and colleagues present cogent molecular docking and dynamics simulations to explore the initial conformational changes associated with agonist binding in the muscle nicotinic acetylcholine receptor, aligned with the extensive experimental literature on this system. Their central findings are of a consistently preferred pose for agonists upon initial association with a resting channel, followed by a dramatic rotation of the ligand and contraction of a critical loop over the binding site. Principal component analysis also suggests the formation of an intermediate complex, not yet captured in structural studies. Binding free energy calculations are consistent with the evolution of a higher-affinity complex following agonist binding, with a ligand efficiency notably similar to experimental values. Snapshot comparisons provide a structural rationale for these changes on the basis of pocket volume, hydration, and rearrangement of key residues at the subunit interface.

Strengths:<br /> Docking results are clearly presented and remarkably consistent. Simulations are produced in triplicate with each of four different agonists, providing an informative basis for internal validation. They identify an intriguing transition in ligand pose, not well documented in experimental structures, and potentially applicable to mechanistic or even pharmacological modeling of this and related receptor systems. The paper seems a notable example of integrating quantitative structure-function analysis with systematic computational modeling and simulations, likely applicable to the wider journal audience.

Weaknesses:<br /> Timescales (200 ns) do not capture global rearrangements of the extracellular domain, let alone gating transitions of the channel pore, though this work may provide a launching point for more extended simulations. A more general concern is the reproducibility of the simulations, and how representative states are defined. It is not clear whether replicates were included in principal component analysis or subsequent binding energy calculations, nor how simulation intervals were associated with specific states. Structural analysis largely focuses on snapshots, with limited direct evidence of consistency across replicates or clusters. Figure legends and tables could be clarified.

Reviewer #3 (Public Review):

Summary:

In the manuscript "Ebola Virus Sequesters IRF3 in Viral Inclusion bodies to Evade Host Antiviral Immunity " by Lin Zhu et al, the authors elucidated an evasion mechanism by which EBOV evades host innate immunity.

Strengths:

Using data from immunofluorescence analysis, TEM and Western Blot, the authors conclude that Ebola virus VP35 protein evades host antiviral immunity by interacting with STING to sequester IRF3 into IBs and inhibit type-I interferon production.

Weaknesses:

Similar mechanisms have already been found in other viruses, such as SFTSV, RSV and so on. In addition, the presented results are also relatively rough, and the mechanism explained is not deep enough, so this story is not innovative

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 #1 (Public Review):

Bian et al showed that biomarker-informed PhenoAgeAccel was consistently related to an increased risk of site-specific cancer and overall cancer within and across genetic risk groups. The results showed that PhenoAgeAccel and genetic liability of a bunch of cancers serve as productive tools to facilitate the identification of cancer-susceptible individuals under an additive model. People with a high genetic risk for cancer may benefit from PhenoAgeAccel-informed interventions.

As the authors pointed out, the large sample size, the prospective design UK Biobank study, and the effective application of PhenoAgeAccel in predicting the risk of overall cancer are the major strengths of the study. Meanwhile, the CPRS seems to be a solid and comprehensive score based on incidence-weighted site-specific polygenic risk scores across 20 well-powered GWAS for cancers.

It wouldn't be very surprising to identify the association between PhenoAgeAccel and cancer risk, since the PhenoAgeAccel was constructed as a predictor for mortality which attributed a lot to cancer. Although cancer is an essential mediator for the association, sensitivity analyses using cancer-free mortality may provide an additional angle. It would be interesting to see, to what extent, PhenoAgeAccel could be reversed by environmental or lifestyle factors. G by E for PhenoAgeAccel might be worth a try.

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 #1 (Public Review):

The objective of this study was to investigate the influence of the C. trachomatis effector Cdu1 on the ubiquitination of proteins in infected host cells and its correlation with the previously identified role of Cdu1 in facilitating Golgi distribution around the Chlamydia inclusion.

To achieve this, the authors created a cdu1-null mutant in C. trachomatis and employed proteomics to analyze ubiquitinated proteins in cells infected with Cdu1-producing and Cdu1-deficient chlamydiae, comparing them to mock-infected cells. The results revealed that, among the proteins specifically ubiquitinated after infection with Cdu1-deficient chlamydiae, three were other C. trachomatis effectors (InaC, IpaM, and CTL0480), members of a large family of Chlamydia effectors (Incs) that insert in the inclusion membrane.

Subsequently, the authors focused on understanding how Cdu1 shields InaC, IpaM, and CTL0480 from ubiquitination and the implications of this protection for the protein levels and functions of these Incs during infection. Data is presented showing that Cdu1 can bind to InaC, IpaM, and CTL0480, and protects these Incs and itself from ubiquitination and proteasomal degradation. This protective role of Cdu1 is dependent on its acetylation, but not on its deubiquitinating activity. Host cells infected by the cdu1 null mutant displayed defects resembling those observed in cells infected by inaC, ipaM, or ctl0480 null mutants.

Additionally, it was previously established that CTL0480 inhibits a chlamydial egress pathway involving the extrusion of the inclusion. This study now revealed that InaC and IpaM also play a role in promoting the extrusion of C. trachomatis inclusion, and the cdu1 null mutant exhibited a defect in this process. This leads to the title's conclusion that Cdu1 regulates chlamydial exit from host cells by safeguarding specific C. trachomatis effectors from degradation.

In summary, this work is excellent and impressive, both technically and conceptually, providing mechanistic insights into the action of Cdu1. The data provides convincing support for the proposed model, illustrating how the acetylation activity of Cdu1 protects itself and three Incs (InaC, IpaM, and CTL0480) from degradation. While the study indicates that the observed phenotypes in cells infected by the cdu1 null mutant are linked to reduced levels of InaC, IpaM, and CTL0480, these Incs are still detectable in cells infected by the cdu1 null mutant. Even if very unlikely, this leaves room for the possibility that Cdu1 directly promotes assembly of F-actin and Golgi repositioning around the inclusion, MYPT1 recruitment to the inclusion, and extrusion of the inclusion. Nevertheless, the major significance of this work lies in the integration of proteomics and chlamydial genetics to unveil a unique mechanism in which one effector controls the levels of other effectors, emphasizing the intricate relationships among bacterial effectors injected into host cells.

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 #3 (Public Review):

In this article by Bastidas et al. the authors examine the functions of the Chlamydia deubiquitinating enzyme 1 (Cdu1) during infections of human cells. First, a mutant lacking Cdu1 but not Cdu2 was constructed using targetron and quantitative proteomics was used to identify differences in ubiquitinated proteins (both host and bacterial) during infection. While they found minimal changes in host protein ubiquitination, they identified three Chlamydia effector proteins, IpaM, InaC and CTL0480 were all ubiquitinated in the absence of Cdu1. Microscopy and immunoprecipitations found Cdu1 directly interacts with these Chlamydia effectors and confirmed that Cdu1 mediates the stabilization of these effectors at the inclusion membrane during late infection time points. Surprisingly rather than deubiquitination driving this stabilization, the acetylation function of Cdu1 was required, and acetylation on lysine residues prevented degradative ubiquitination of Cdu1, IpaM, InaC and CTL0480. In line with this observation the authors show that loss of Cdu1 phenocopies the loss of single effector mutants of InaC, IpaM and CTL0480, including golgi stack formation and the recruitment of MYPT1 to the inclusion. The aggregation of changes to the Chlamydia inclusion does not alter growth but controls extrusion of chlamydia from cells with reduced extrusion in Cdu1 mutant Chlamydia infections. The strengths of the manuscript are the range of assays used to convincingly examine the biochemical and cellular biology underlying Cdu1 functions. The finding that acetylation of lysine residues is a mechanisms for bacterial effectors to block degradative ubiqutination is impactful and will open new investigations into this mechanism for many intracellular pathogens. The authors revisions to the manuscript have addressed my primary concerns and the authors present compelling arguments for remaining questions that are outside the scope of this study. Altogether this is an important series of findings that help to understand the mechanisms underpinning Chlamydia pathogenesis using orthologous methods and is an impactful study.

Reviewer #1 (Public Review):

This article by Navratna et al. reports the first structure of human HGSNAT in an acetyl-CoA-bound state. Through careful structural analysis, the authors propose potential reasons why certain human mutations lead to lysosomal storage disorders and outline a catalytic mechanism. The structural data are of good quality, and the manuscript is clearly written. This study represents an important step toward understanding the mechanism of HGSNAT and is valuable to the field. I have the following suggestions:

1. The authors should characterize whether the purified protein is active. Otherwise, how does one know if the detergent used maintains the protein in a biologically relevant state? The authors should at least attempt to do so. If these prove to be challenging, at the very least, the authors should try a cell-based assay to demonstrate that the GFP tag does not interfere with the function.

2. In Figure 5, the authors present a detailed schematic of the catalytic cycle, which I find to be too speculative. There is no evidence to suggest that this enzyme undergoes isomerization, similar to a transporter, between open-to-lumen and open-to-cytosol states. Could it not simply involve some movements of side chains to complete the acetyl transfer?

Reviewer #3 (Public Review):

Summary:<br /> Navratna et al. have solved the first structure of a transmembrane N-acetyltransferase (TNAT), resolving the architecture of human heparan-alpha-glucosaminide N-acetyltransferase (HGSNAT) in the acetyl-CoA bound state using single particle cryo-electron microscopy (cryoEM). They show that the protein is a dimer, and define the architecture of the alpha- and beta- GSNAT fragments, as well as convincingly characterizing the binding site of acetyl-CoA.

Strengths:<br /> This is the first structure of any member of the transmembrane acyl transferase superfamily, and as such it provides important insights into the architecture and acetyl-CoA binding site of this class of enzymes.

The structural data is of a high quality, with an isotropic cryoEM density map at 3.3Å facilitating the building of a high-confidence atomic model. Importantly, the density of the acetyl-CoA ligand is particularly well-defined, as are the contacting residues within the transmembrane domain.

The open-to-lumen structure of HSGNAT presented here will undoubtedly lay the groundwork for future structural and functional characterization of the reaction cycle of this class of enzymes.

Weaknesses:<br /> While the structural data for the open-to-lumen state presented in this work is very convincing, and clearly defines the binding site of acetyl-CoA, to get a complete picture of the enzymatic mechanism of this family, additional structures of other states will be required.

A potentially significant weakness of the study is the lack of functional validation. The enzymatic activity of the enzyme characterized was not measured, and the enzyme lacks native proteolytic processing, so it is a little unclear whether the structure represents an active enzyme.

Reviewer #1 (Public Review):

In this paper, the authors developed an image analysis pipeline to automatically identify individual ‎‎neurons within a population of fluorescently tagged neurons. This application is optimized to deal with ‎‎multi-cell analysis and builds on a previous software version, developed by the same team, to resolve ‎‎individual neurons from whole-brain imaging stacks. Using advanced statistical approaches and ‎‎several heuristics tailored for C. elegans anatomy, the method successfully identifies individual ‎‎neurons with a fairly high accuracy. Thus, while specific to C. elegans, this method can ‎become ‎instrumental for a variety of research directions such as in-vivo single-cell gene expression ‎analysis ‎and calcium-based neural activity studies.‎

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 #1 (Public Review):

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

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 #1 (Public Review):

This study investigates the underlying mechanisms of information-seeking in infancy. Eight-month-old Dutch infants were tested in a screen-based eye-tracking task in which one of two geometrical shape cues (differing in their shape and motion) either announced the location of an upcoming reward cartoon (informative) or not (non-informative). The authors measured the infants' pupil size before the cartoon appeared. Infants showed smaller pupil sizes when presented with the informative cue as compared to the noninformative cue. The decrease in pupil size in the informative condition emerged over the course of trials whereas infants' pupil size remained unchanged in the noninformative condition. The authors interpret their findings as supportive evidence of statistical learning and generalization processes organizing infants' information-seeking.

It was a pleasure to read the paper and I think the study makes a valuable contribution to our understanding of information-seeking in infancy. The manuscript is very well written and the study is cleverly designed. My following comments are based on my reading of the manuscript and the supplemental materials. It should be noted that evaluating the details of the statistical procedure the authors used lies outside my expertise. The same applies to some decisions of the authors related to pre-processing and filtering the pupil data. I very much appreciate that the authors shared all their raw data and analysis scripts openly accessible on the Open Science Framework. The study was unfortunately not preregistered, making it difficult to trace when in the study process certain decisions or assumptions were made.

My two main concerns relate to the conceptualization and definition of information-seeking and the proposed speed of the mechanisms explaining infants' behavior. I outline my general comments below before listing some more concrete issues.

1) While reading the manuscript, I was sometimes confused about what the authors refer to when talking about information-seeking - both in terms of the broader conceptualization of the phenomenon as well as when referring to their own study. What information are infants seeking? The informative value of the cue shape in terms of their motion (because it carries information about the location of a rewarding animation)? Or is the target (the rewarding video) the information being sought? From how the study is set up, I assume the authors refer mainly to the first aspect, but I think the manuscript would benefit from some clearer distinctions and definitions of terms.

More specifically, I think it could help if the authors would specify the different aspects involved in information-seeking in the introduction (e.g., seeking information "directly", seeking cues guiding them towards information, etc.). Secondly, it would help if they would sharpen their (already in some parts existing) definitions for their study and then keep consistent with their definitions throughout the methods, results, and discussion. Is the cue the information being sought or the "behavior" (motion) of the cue? Or is the target animation the information being sought and guided via the cueing?

2) Speed of the generalization process:<br /> From my understanding of the study design, the shape of the geometrical shape gains informative value over time (serving as an informative cue) and the *motion* of the shape is the actual informative or non-informative visual cue in that it either reliably highlights the actual target region (or all regions). In the generalization trials, only the shape was manipulated while the motion aspect remained consistent with the previous trials. Based on infants' behavior across learning and generalization trials, the authors make an argument about two distinct processes taking place: a slower allowing to learn where to find info and a faster generalization process. Apologies if I missed something, but given that the motion remains consistent, it's maybe not surprising that the generalization trials are "faster"? Maybe the generalization process would have been slower if not only the shape had changed but if also a novel informative motion had been introduced. Also, it would be helpful if the authors could clarify what they mean by the statistical learning process being more "data-hungry" (line 274).

3) I would find it very helpful if the authors would discuss statistical learning and information-seeking processes from other possible mechanisms such as reward learning mechanisms. For example, the authors use a "rewarding" (not informative) stimulus as the target-wouldn't it be possible that the results can be also explained by reinforcement learning processes? Relatedly, in line 396 they write that they used TD learning to predict whether "information will be delivered" and contrast this with the approach being used to predict whether a reward will be delivered. But in their study reward was being delivered, too (in the form of the target), in addition to the informative motion of the cue.

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 #3 (Public Review):

Summary:<br /> The study attempts to shed light on the mechanisms underlying information-seeking in infants by investigating whether infants distinguish between informative and uninformative stimuli to resourcefully allocate their attention. The authors show that 8-month-old infants can learn whether a visual stimulus is informative or uninformative about the location of a later appearing rewarding stimulus by employing statistical regularities from the input. Specifically, infants showed decreased pupil dilation for informative over uninformative cues, which developed over the course of trials as more and more information was gathered from the input. The pattern of learning was in line with a reinforcement learning model which employed a steep learning curve in the beginning followed by a more shallow but steady learning growth over trials. After 17 trials, the authors presented novel cues that shared certain visual features with the previous stimuli and showed that pupil dilation was reduced for novel cues that shared features with the previous informative stimuli, suggesting that infants were able to generalize their acquired knowledge about the informativeness of certain features to novel stimuli. The present study adds to the existing literature about the underlying mechanisms of learning by showing that infants cannot only predict an upcoming stimulus based on statistical regularities of a preceding cue but also the informativeness of the cue itself.

Strengths:<br /> The authors use a suitable method to test the highly relevant question of whether and how infants infer the informativeness of stimuli from experience and whether they can generalize this knowledge to new stimuli. Their experiment is carefully designed and well controlled with conditions closely matched (e.g., the shape and color of objects and the structure of each trial). Their measure of interest (i.e., pupil dilation) is also examined at a time point in each trial when the conditions are the most similar, which further points to a thought-through and careful design. This empirical data is backed up with a computational approach (using a Bayesian model and training a reinforcement learning algorithm) to elucidate the learning mechanisms at play. This approach is explained concisely to readers not familiar with the models.

The results are convincing showing a clear difference between informative and uninformative condition and development over trials. Specifically, this difference is not apparent in the first trial (Fig. 2c) but develops over time which supports a learning trajectory. The data support the authors' conclusion that infants learn about the informativeness of the object cue from the input, and the employed learning algorithms give further insights into the learning trajectory of the infants. Overall, the statistical analyses seem solid and the priors for the Bayesian models are well reported.

Data and scripts are openly available fostering transparency.

Overall, the manuscript is very well and concisely written.

Weaknesses:<br /> The authors' conclusion that infants can generalize the acquired knowledge to similar but novel stimuli is weakened by methodological concerns regarding the analysis. It is not fully clear which trials the authors excluded and analyzed as they do not consistently report the trials in the manuscript (e.g., it is stated that after trial 17 the first generalization trial started, but also that trial 17 was excluded as the first trial of the generalization phase). As there are only a few novel trials and novel and familiar trials alternated, the inclusion or exclusion of trial analyses might have a significant impact on the results. Thus, this needs further clarification. The authors also mentioned that the novel stimuli shared relevant as well as irrelevant features, but it was not clear to me whether the authors could establish that only the relevant features contributed to the observed generalization effect.

Some methodological decisions were not explained and need justification, in particular, as the study is not preregistered. This includes, for example, the exclusion criteria and the choice not to analyze all generalization trials. Further, the authors did not perform model comparison (e.g., their model against a null model) and therefore do not report the strength of evidence for a difference in conditions.

Another weakness is that the sample sizes of 30 infants for the initial part and 19 infants for the generalization part of the experiment are rather small (especially with regard to the chosen weakly informative priors).

Reviewer #1 (Public Review):

Summary:<br /> This manuscript uses optical coherence tomography (OCT) to visualize tissue microstructures about 1-2 mm under the finger pad skin surface. Their geometric features are tracked and used to generate tissue strains upon skin surface indentation by a series of transparent stimuli both normal and tangential to the surface. Then movements of the stratum corneum and the upper portion of the viable epidermis are evaluated. Based upon this data, across a number of participants and ridges, around 300 in total, the findings report upon particular movements of these tissue microstructures in various loading states. A better understanding of the mechanics of the skin microstructures is important to understand how surface forces propagate toward the locations of mechanoreceptive end organs, which lie near the edge of the epidermis and dermis, from which tactile responses of at least two peripheral afferents originate. Indeed, the microstructures of the skin are likely to be important in shaping how neural afferents respond and enhance their sensitivity, receptive field characteristics, etc.

Strengths:<br /> The use of OCT in the context of analyzing the movements of skin microstructures is novel. Also novel and powerful is the use of distinct loading cases, e.g., normal, tangential, and stimulus features, e.g., edges, and curves. I am unaware of other empirical visualization studies of this sort. They are state-of-the-art in this field. Moreover, in addition to the empirical imaging observations, strain vectors in the tissues are calculated over time.

Weaknesses:<br /> The interpretation of the results and their framing relative to the overall hypotheses/questions and prior works could be articulated more clearly. In particular, the major findings of the manuscript are in newly describing a central concept regarding "ridge flanks," but such structures are neither anatomically nor mechanistically defined in a clear fashion. For example, "... it appears that the primary components of ridge deformation and, potentially, neural responses are deformations of the ridge flanks and their relative movement, rather than overall bending of the ridges themselves." From an anatomical perspective, I think what the authors mean by "ridge flanks" is a differential in strain from one lateral side of a papillary ridge to the other. But is it unclear what about the continuous layers of tissue would cause such behaviors. Perhaps a sweat duct or some other structure (not visible to OCT) would subdivide the "flanks" of a papillary ridge somehow? If not due to particular anatomy, then is the importance of the "ridge flank" due to a mechanistic phenomenon of some sort? Given that the findings of the manuscript center upon the introduction of this new concept, I think a greater effort should be made to define what exactly are the "ridge flanks." It is clear from the results, especially the sliding case, that there is something important that the manuscript is getting at with this concept.

The OCT used herein cannot visualize deep and fully into what the manuscript refers to as a "ridge" (note others have previously broken apart this concept apart into "papillary", "intermediate" and "limiting" ridges) near locations of the mechanoreceptive end organs lie at the epidermal-dermal border. Therefore, the OCT must make inferences about the movements of these deeper tissues, but cannot see them directly, and it is the movements of these deeper tissues that are likely driving the intricacies of neural firing. Note the word "ridge" is used often in the manuscript's abstract, introduction, and discussion but the definition in Fig. 1 and elsewhere differs in important ways from prior works of Cauna (expert in anatomy). Therefore, the manuscript should clarify if "ridge" refers to the papillary ridge (visible at the exterior of the skin), intermediate ridge (defined by Cauna as what the authors refer to as the primary ridge), and limiting ridge (defined by Cauna as what the authors refer to as the secondary ridge). What the authors really mean (I think) is some combination of the papillary and intermediate ridge structures, but not the full intermediate ridge. The manuscript acknowledges this in the "Limitations and future work" section, stating that these ridges cannot be resolved. This is important because the manuscript is oriented toward tracking this structure. It sets up the narrative and hypotheses to evaluate the prior works of Cauna, Gerling, Swensson, and others who all directly addressed the movement of this anatomical feature which is key to understanding ultimately how stresses at these locations might move the peripheral end organs (i.e., Merkel cells, Meissner corpuscles).

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):

Summary:<br /> The publication presents unique in-vivo images of the upper layer of the epidermis of the glabrous skin when a flat object compresses or slides on the fingertip. The images are captured using OCT, and are the process of recovering the strain that fingerprints experience during the mechanical stimulation.

The most important finding is, in my opinion, that fingerprints undergo pure compression/tension without horizontal shear, hinting at the fact that the shear stress caused by the tangential load is transferred to the deeper tissues and ultimately to the mechanoreceptors (SA-I / RA-I).

Strengths:<br /> - Fascinating new insights into the mechanics of glabrous skin. To the best of my knowledge, this is the first experimental evidence of the mechanical deformation of fingerprints when subjected to dynamic mechanical stimulation. The OCT measurement allows an unprecedented measurement of the depth of the skin whereas previous works were limited to tracking the surface deformation.<br /> - The robust data analysis reveals the continuum mechanics underlying the deformation of the fingerprint ridges.

Weaknesses:<br /> I do not see any major weaknesses. The work is mainly experimental and is rigorously executed. Two points pique my curiosity, however:

1. How do the results presented in this study compare with previous finite element analysis? I am curious to know if the claim that the horizontal shear strain is transferred to the previous layer is also captured by these models. The reason is that the FEA models typically use homogeneous materials and whether or not the behavior in-silico and in-vivo matches would offer an idea of the nature of the stratum corneum.<br /> 2. Was there a specific reason why the authors chose to track only one fingerprint? From the method section, it seems that nothing would have prevented tracking a denser point cloud and reconstructing the stain on a section of the skin rather than just one ridge. With such data, the author could extend their analysis to multiple ridges interaction and get a better sense of the behavior of the entire strip of skin.

Reviewer #1 (Public Review):

This study aims to identify gene expression differences exclusively caused by cis-regulatory genetic changes by utilizing hybrid cell lines derived from human and chimpanzee. While previous attempts have focused on specific tissues, this study expands the comparison to six different tissues to investigate tissue specificity and derive insights into the evolution of gene expression.

One notable strength of this work lies in the use of composite cell lines, enabling a comparison of gene expression between human and chimpanzee within the same nucleus and shared trans factors environment. However, a potential weakness of the methodology is the use of bulk RNA-seq in diverse tissues, which limits the ability to determine cell-type-specific gene expression and chromatin accessibility regions. Their approach, using hybrid lines, naturally accounts for cell type heterogeneity avoiding the risk of false positives introduced by the otherwise confounding differences in cell type abundances between species, albeit the challenge of false negatives remains an issue. The authors now dully acknowledge this limitation in the manuscript.

Another concern is the use of two replicates derived from the same pair of individuals. While the authors produced cell lines from two pairs of individuals in a previous study (Agloglia et al., 2021). The reason for this experimental design is cost limitations. The authors now acknowledge that the use of replicates could enhance the ability to detect "more" species-specific changes in expression and chromatin accessibility. I would emphasize that replicates would increase robustness to the present findings, given that they are derived from a single pair of individuals.

Furthermore, the study offers the opportunity to relate inter-species differences to trends in molecular evolution. The authors discovered that expression variance and haploinsufficiency score do not fully account for the enrichment of divergence in cell-type-specific genes. The reviewer suggested exploring this further by incorporating external datasets that bin genes based on interindividual transcriptomics variation as a measure of extant transcriptomics constraint (e.g., GTEx reanalysis by Garcia-Perez et al., 2023 - PMID: 36777183). The authors considered this question to be out of the scope of the paper, yet in my opinion this would enhance one of the main findings of this study.

Additionally, stratifying sequence conservation on ASCA regions, which exhibit similar enrichment of cell-type-specific features, using the Zoonomia data mentioned also in the text (Andrews et al., 2023 -- PMID: 37104580) could provide valuable insights. While the author did not find Zoonomia Phastcons values available, they used PhastCons derived from a 470-way alignment of mammals. I commend the authors for their diligent efforts, which undoubtedly bolster their findings that an enrichment in ASCA is evident across all levels of sequence conservation. However, this recent analysis indicates the presence of a potential relationship between sequence conservation and ASCA. It may be advantageous to consider evaluating more quantile subdivisions of maxZ values and pPhastCons values, with the inclusion of these results in the supplementary materials. This approach would be preferable, even if the precise reasons behind the observed discrepancy are not fully elucidated.

Another potential strength of this study is the identification of specific cases of paired allele-specific expression (ASE) and allele-specific chromatin accessibility (ASCA) with biological significance. Prioritizing specific variants remains a challenge, and the authors apply a machine learning approach to identify potential causative variants that disrupt binding sites in two examples (FABP7 and GAD1 in motor neurons). However, additional work is needed to convincingly demonstrate the functionality of these selected variants. Strengthening this section with additional validation of ASE, ASCA, and the specific putative causal variants identified would enhance the overall robustness of the paper. The authors have opted to defer these validations to future studies.

Additionally, the authors support the selected ASE-ASCA pairs by examining external datasets of adult brain comparative genomics (Ma et al., 2022) and organoids (Kanton et al., 2019). While these resources are valuable for comparing observed species biases, the analysis is not systematic, even for the two selected genes. For example, it would be beneficial to investigate if FABP7 exhibits species bias in any cell type in Kanton et al.'s organoids or if GAD1 is species-biased in adult primate brains from Ma et al. Comparing these datasets with the present study, along with the Agoglia et al. reference, would provide a more comprehensive perspective. In the revised version of the manuscript the authors have evaluated the expression of GAD1 in Ma et al, and FABP7 in Sousa et al 2017. For instance, GAD1 show cell type specific species biases in the later. The authors opted for not showing this in the manuscript, However, it remains unclear why certain datasets were favored over others, or why FABP7 should not be evaluated in Kanton et al.

The use of the term "human-derived" in ASE and ASCA has now been avoided.

Finally, throughout the paper, the authors refer to "hybrid cell lines." It has been suggested to use the term "composite cell lines" instead to address potential societal concerns associated with the term "hybrid," which some may associate with reproductive relationships (Pavlovic et al., 2022 -- PMID: 35082442). The authors have presented an eloquent and persuasive explanation that I found to be highly informative.

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.

Joint Public Review:

In this manuscript, the authors introduced an explicit ion model using the coarse-grained modelling approach to model the interactions between nucleosomes and evaluate their effects on chromatin organization. The strength of this method lies in the explicit representation of counterions, especially divalent ions, which are notoriously difficult to model. To achieve their aims and validate the accuracy of the model, the authors conducted coarse-grained molecular dynamics simulations and compared predicted values to the experimental values of the binding energies of protein-DNA complexes and the free energy profile of nucleosomal DNA unwinding and inter-nucleosome binding. Additionally, the authors employed umbrella sampling simulations to further validate their model, reproducing experimentally measured sedimentation coefficients of chromatin under varying salt concentrations of monovalent and divalent ions.

The significance of this study lies in the authors' coarse-grained model which can efficiently capture the conformational sampling of molecules while maintaining a low computational cost. The model reproduces the scale and, in some cases, the shape of the experimental free energy profile for specific molecule interactions, particularly inter-nucleosome interactions. Additionally, the authors' method resolves certain experimental discrepancies related to determining the strength of inter-nucleosomal interactions. Furthermore, the results from this study support the crucial role of intrinsic physicochemical interactions in governing chromatin organization within the nucleus.

The authors have successfully addressed the majority of my key concerns. I appreciate the clarification regarding the parameterization from Pablo's lab and the addition of comparisons of energy profiles as a function of inter-nucleosome distances.

However, the statement "The agreement is evident" may not sufficiently capture the essence of Figure S4, as there is a shortage of substantial agreement. The authors rightly acknowledge it but should delineate the nature of the observed discrepancies.

Reviewer #1 (Public Review):

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

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

Major comments:

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

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

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

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

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

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

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

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

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

Reviewer #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 #1 (Public Review):

The study isolated extracellular vesicles (EV) from healthy controls (HCs) and Parkinson patients (PwP), using plasma from the venous blood of non-fasting people. Such EVs were characterized and validated by the presence of markers, their size, and their morphology. The main aim of the manuscript is to correlate the presence of synaptic proteins, namely SNAP-25, GAP-43, and SYNAPTOTAGMIN-1, normalized with HSP70, with the clinical progression of PwP. Changes in synaptic proteins have been documented in the CSF of Alzheimer's and Parkinson's patients. The demographics of participants are adequately presented. One important limiting, as well as puzzling aspect, is the fact that authors did not find differences between groups at the beginning of the study nor after one year, after age and sex adjustment.

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 #1 (Public Review):

Summary:<br /> The authors used several zebrafish reporter lines to demonstrate the presence, regional distribution, and transcriptional profile of the immune cells in adult zebrafish brains. They identified DC-like cells distinct from microglia or other macrophages, resembling murine cDC1s. Analysis of different mutants further revealed that this DC population was dependent on Irf8, Batf3, and Csf1rb, but did not rely on Csf1ra.

Strengths:<br /> It is an elegantly designed study providing compelling evidence for further heterogeneity among brain mononuclear phagocytes in zebrafish, consisting of microglia, macrophages, and DC-like cells. This will provide a better understanding of the immune landscape in the zebrafish brain and will help to better distinguish the different cell types from microglia, and to assign specific functions.

Weaknesses:<br /> While scRNA-seq data clearly revealed different subsets of microglia, macrophages, and DCs in the brain, it remains somewhat challenging to distinguish DC-like cells from P2ry12- macrophages by immunohistochemistry or flow cytometry.

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 #3 (Public Review):

Rovira, et al., aim to characterize immune cells in the brain parenchyma and identify a novel macrophage population referred to as "dendritic-like cells". They use a combination of single-cell transcriptomics, immunohistochemistry, and genetic mutants to conclude the presence of this "dendritic-like cell" population in the brain. The strength of this manuscript is the identification of dendritic cells in the brain, which are typically found in the meningeal layers and choroid plexus. A weakness is the lack of specific reporters or labeling of this dendritic cell population using specific genes found in their single-cell dataset. Additionally, it is difficult to remove the meningeal layers from the brain samples and thus can lead to confounding conclusions. Overall, I believe this study should be accepted contingent on sufficient labeling of this population and addressing comments.

Reviewer #1 (Public Review):

Summary:<br /> This is a detailed description of the role of PKCδ in Drosophila learning and memory. The work is based on a previous study (Placais et al. 2017) that has already shown that for the establishment of long-term memory, the repetitive activity of MP1 dopaminergic neurons via the dopamine receptor DAMB is essential to increase mitochondrial energy flux in the mushroom body.

In this paper, the role of PKCδ is now introduced. PKCδ is a molecular link between the dopaminergic system and the mitochondrial pyruvate metabolism of mushroom body Kenyon cells. For this purpose, the authors establish a genetically encoded FRET-based fluorescent reporter of PKCδ-specific activity, δCKAR.

Strengths:<br /> This is a thorough study of the long-term memory of Drosophila. The work is based on the extensive, high-quality experience of the senior authors. This is particularly evident in the convincing use of behavioral assays and imaging techniques to differentiate and explore various memory phases in Drosophila. The study also establishes a new reporter to measure the activity of PKCδ - the focus of this study - in behaving animals. The authors also elucidate how recurrent spaced training sessions initiate a molecular gating mechanism, linking a dopaminergic punishment signal with the regulation of mitochondrial pyruvate metabolism. This advancement will enable a more precise molecular distinction of various memory phases and a deeper comprehension of their formation in the future.

Weaknesses:<br /> Apart from a few minor technical issues, such as the not entirely convincing visualisation of the localisation of a PKCδ reporter in the mitochondria, there are no major weaknesses. Likewise, the scientific classification of the results seems appropriate, although a somewhat more extensive discussion in relation to Drosophila would have been desirable.

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 #1 (Public Review):

Summary:<br /> This manuscript reports the effects of a heterozygous mutation in the KCNT1 potassium channels on the properties of ion currents and the firing behavior of excitatory and inhibitory neurons in the cortex of mice expressing KCNT1-Y777H. In humans, this mutation as well as multiple other heterozygotic mutations produce very severe early-onset seizures and produce a major disruption of all intellectual function. In contrast, in mice, this heterozygous mutation appears to have no behavioral phenotype or any increased propensity to seizures. A relevant phenotype is, however, evident in mice with the homozygous mutation, and the authors have previously published the results of similar experiments with the homozygotes. As perhaps expected, the neuronal effects of the heterozygous mutation presented in this manuscript are generally similar but markedly smaller than the previously published findings on homozygotes. There are, however, some interesting differences, particularly on PV+ interneurons, which appear to be more excitable than wild type in the heterozygotes but more excitable in the heterozygotes. This raises the interesting question (which could be more explicitly discussed by the authors) as to whether the reported changes represent homeostatic events that suppress the seizure phenotype in the mouse heterozygotes or simply changes in excitability that do not reach the threshold for behavioral outcomes.

Strengths and Weaknesses:<br /> 1) The authors find that the heterozygous mutation in PV+ interneurons increases their excitability, a result that is opposite from their previous observation in neurons with the corresponding homozygous mutation. They propose that this results from the selective upregulation of a persistent sodium current INaP in the PV+ interneurons. While the observations are very interesting, there are three issues concerning this interpretation that should be addressed:<br /> A) The protocol for measuring the INaP current could potentially lead to results that could be (mis)interpreted in different ways in different cells. First, neither K currents nor Ca currents are blocked in these experiments. Instead, TTX is applied to the cells relatively rapidly (within 1 second) and the ramp protocol is applied immediately thereafter. It is stated that, at this time, Na currents and INaP are fully blocked but that any effects on Na-activated K currents are minimal. In theory, this would allow the pre- to post-difference current to represent a relatively uncontaminated INaP. This would, however, only work if activation of KNa currents following Na entry is very slow, taking many seconds. A good deal of literature has suggested that the kinetics of activation of KNa currents by Na influx vary substantially between cell types, such that single action potentials and single excitatory synaptic events rapidly evoke KNa currents in some cell types. This is, of course, much faster than the time of TTX application. Most importantly, the kinetics of KNa activation may be different in different neuronal types, which would lead to errors that could produce different estimates of INaP in PV+ interneurons vs other cell types.<br /> B) As the authors recognize, INaP current provides a major source of cytoplasmic sodium ions for the activation. An expected outcome of increased INaP is, therefore, further activation of KNa currents, rather than a compensatory increase in an inward current that counteracts the increase in KNa currents, as is suggested in the discussion.<br /> C) Numerical simulations, in general, provide a very useful way to evaluate the significance of experimental findings. Nevertheless, while the in-silico modeling suggests that increases in INaP can increase firing rate in models of PV+ neurons, there is as yet insufficient information on the relative locations of the INaP channels and the kinetics of sodium transfer to KNa channels to evaluate the validity of this specific model.

2) The greatest effect of TTX application would be expected to be the elimination of large transient inward sodium currents. Why are no such currents visible in the control (pre-TTX) or the difference currents (Fig. 2)? Is it possible I missed something in the methods?

3) As expected, the changes in many of the measured parameters are smaller in the present study with heterozygotes than those previously reported for the homozygous mutation. Some of the statements on the significance of some of the present findings need to be stated more clearly. For example, in the results section describing Fig. 2, it is stated that "In glutamatergic and NFS GABAergic YH-HET neurons, the overall KNa current was increased ...as measured by a significant effect of genotype ...." Later in the same paragraph it is stated that the increases in KNa current are not significant. Apparently, different tests lead to different conclusions. Both for the purpose of understanding the pathophysiological effects of changes in KNa current and for making further numerical simulations, more explicit clarifying statements should be made.

4) The effects of the KCNT1 channel blocker VU170 on potassium currents are somewhat larger and different from those of TTX, suggesting that additional sources of sodium may contribute to activating KCNT1, as suggested by the authors. Because VU170 is, however, a novel pharmacological agent, it may be appropriate to make more careful statements on this. While the original published description of this compound reported no effect on a variety of other channels, there are many that were not tested, including Na and cation channels that are known to activate KCNT1, raising the possibility of off-target effects.

5) The experiments were carried out at room temperature. Is it possible that different effects on firing patterns in heterozygotes and homozygotes would be observed at more physiological temperatures?

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 #3 (Public Review):

Summary:<br /> The present manuscript by Shore et al. entitled Reduced GABAergic Neuron Excitability, Altered Synaptic Connectivity, and Seizures in a KCNT1 Gain-of-Function Mouse Model of Childhood Epilepsy" describes in vitro and in silico results obtained in cortical neurons from mice carrying the KCNT1-Y777H gain-of-function (GOF) variant in the KCNT1 gene encoding for a subunit of the Na+-activated K+ (KNa) channel. This variant corresponds to the human Y796H variant found in a family with Autosomal Dominant Nocturnal Frontal lobe epilepsy. The occurrence of GOF variants in potassium channel encoding genes is well known, and among potential pathophysiological mechanisms, impaired inhibition has been documented as responsible for KCNT1-related DEEs. Therefore, building on a previous study by the same group performed in homozygous KI animals, and considering that the largest majority of pathogenic KCNT1 variants in humans occur in heterozygosis, the Authors have investigated the effects of heterozygous Kcnt1-Y777H expression on KNa currents and neuronal physiology among cortical glutamatergic and the 3 main classes of GABAergic neurons, namely those expressing vasoactive intestinal polypeptide (VIP), somatostatin (SST), and parvalbumin (PV), crossing KCNT1-Y777H mice with PV-, SST- and PV-cre mouse lines, and recording from GABAergic neurons identified by their expression of mCherry (but negative for GFP used to mark excitatory neurons).

The results obtained revealed heterogeneous effects of the variant on KNa and action potential firing rates in distinct neuronal subpopulations, ranging from no change (glutamatergic and VIP GABAergic) to decreased excitability (SST GABAergic) to increased excitability (PV GABAergic). In particular, modelling and in vitro data revealed that an increase in persistent Na current occurring in PV neurons was sufficient to overcome the effects of KCNT1 GOF and cause an overall increase in AP generation.

Strengths:<br /> The paper is very well written, the results clearly presented and interpreted, and the discussion focuses on the most relevant points.

The recordings performed in distinct neuronal subpopulations are a clear strength of the paper. The finding that the same variant can cause opposite effects and trigger specific homeostatic mechanisms in distinct neuronal populations is very relevant for the field, as it narrows the existing gap between experimental models and clinical evidence.

Weaknesses:<br /> My main concern is in the epileptic phenotype of the heterozygous mice investigated. In fact, in their previous paper the Authors state that "...Kcnt1-Y777H heterozygous mice did not exhibit any detectable epileptiform activity" (first sentence on page 4). However, in the present manuscript, they indicate twice in the discussion section that these mice exhibit "infrequent seizures". This relevant difference needs to be clarified to correctly attribute to the novel pathophysiological mechanism a role in seizure occurrence. Were such infrequent seizures clearly identified on the EEG, or were behavioral seizures? Could the authors quantify this "infrequent" value? This is crucial also to place in the proper perspective the Discussion statement regarding "... the increased INaP contribution to ... network hyperexcitability and seizures".

Also, some statistical analysis seems to be missing. For example, I could not find any for the data shown in Fig. 6. Thus, the following statement: "the model PV neurons responded to KCNT1 GOF with decreased AP firing and an increased rheobase" requires proper statistical evaluation.

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 #3 (Public Review):

Summary:

The fruit fly visual system has provided a powerful context in which to investigate fundamental questions in neural development, phototransduction, and systems neuroscience. Of recent interest is motion processing, particularly how visual motion cues are estimated locally, and then pooled to derive behaviorally meaningful signals. Many of these pooling operations have been shown to take place in the wide-field neurons in the lobula plate, cell types that have been explored using electrophysiological recordings for more than 50 years in a variety of Diptera. However, our understanding of the diversity and connectivity of these cells remains incompletely understood, and is of interest to many.

In this context, Reiser and colleagues describe the anatomy and connectivity of the complete set of Lobula Plate Tangential neurons in Drosophila, using a careful and systematic reconstruction of the FAFB dataset. Leveraging a previous study of retinal geometry, combined with their characterization of the anatomical inputs to the elementary motion detectors, T4 and T5, they then predict the motion sensitivities of each cell, their neurotransmitter identities, and map the connections of many of these cells into the central brain and contralateral optic lobe.

Strengths:

The quality of the connectomic analysis is exceptional, and the quantitative analysis that links connectivity to function is rigorous and impressive. This paper will be an important resource for the community.

Weaknesses:

Some of the findings could be better linked to previously published work in this field, and there may be a minor limitation to the predicted optimal motion axes, given one of the simplifying assumptions made.

Reviewer #1 (Public Review):

Summary: This paper reported interesting aberrant calcium microwaves in the hippocampus when synapsin promoter driven GCaMPs were expressed for a long period of time. These aberrant hippocampal Ca2+ micro-waves depend on the viral titre of the GECI. The microwave of Ca2+ was not observed when GECI was expressed only in a sparse set of neurons.

Strengths: These findings are important to the wide neuroscience community, especially considering a great number of investigators are using similar approaches. Results look convincing and are consistent across several laboratories.

Weaknesses: One important question is needed to further clarify the mechanisms of aberrant Ca2+ microwaves as described below.

Synapsin promoter labels both excitatory pyramidal neurons and inhibitory neurons. To avoid aberrant Ca2+ microwave, a combination of Flex virus and CaMKII-Cre or Thy-1-GCaMP6s and 6f mice were tested. However, all these approaches limit the number of infected pyramidal neurons. While the comprehensive display of these results is appreciated, a crucial question remains unanswered. To distinguish whether the microwave of Ca2+ is caused selectively via the abnormality of interneurons, or just a matter of pyramidal neuron density, testing Flex-GCaMP6 in interneuron specific mouse lines such as PV-Cre and SOM-Cre will be critical.

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 #3 (Public Review):

Summary:<br /> The work by Masala and colleagues highlights a striking artifact that can result from a particular viral method for expressing genetically encoded calcium indicators (GECIs) in neurons. In a cross-institutional collaboration, the authors find that viral transduction of GECIs in the hippocampus can result in aberrant slow-traveling calcium (Ca2+) micro-waves. These Ca2+ micro-waves are distinct from previously described ictal activity but nevertheless are likely a pathological consequence of overexpression of virally transduced proteins. Ca2+ micro-waves will most likely obscure the physiology that most researchers are interested in studying with GECIs, and their presence indicates that the neural circuit is in an unintended pathological state. Interestingly this pathology was not observed using the same viral transduction methods in the visual cortex. The authors recommend several approaches that may help other experimenters avoid this confound in their own data such as reducing the titer of viral injections or using recombinase-dependent expression. The intent of this manuscript is to raise awareness of the potential unintended consequences of viral overexpression, particularly for GECIs. A rigorous investigation into the exact causes of Ca2+ micro-waves or the mech

Strengths:

The authors clearly demonstrate that Ca2+ micro-waves occur in the CA1 and CA3 regions of the hippocampus following large volume, high titer injections of adeno-associated viruses (AAV1 and AAV9) encoding GECIs. The supplementary videos provide undeniable proof of their existence.

By forming an inter-institutional collaboration, the authors demonstrate that this phenomenon is robust to changes in surgical techniques or imaging conditions.

Weaknesses:

I believe that the weaknesses of the manuscript are appropriately highlighted by the authors themselves in the discussion. I would, however, like to emphasize several additional points.

As the authors state, the exact conditions that lead to Ca2+ micro-waves are unclear from this manuscript. It is also unclear if Ca2+ micro-waves are specific to GECI expression or if high-titer viral transduction of other proteins such as genetically encoded voltage indicators, static fluorescent proteins, recombinases, etc could also cause Ca2+ micro-waves.

The authors almost exclusively tested high titer (>5x10^12 vg/mL) large volume (500-1000 nL) injections using the synapsin promoter and AAV1 serotypes. It is possible that Ca2+ micro-waves are dramatically less frequent when titers are lowered further but still kept high enough to be useful for in vivo imaging (e.g. 1x10^12 vg/mL) or smaller injection volumes are used. It is also possible that Ca2+ micro-waves occur with high titer injections using other viral promoter sequences such as EF1α or CaMKIIα. There may additionally be effects of viral serotype on micro-wave occurrence.

The number of animals in any particular condition are fairly low (Table 1) with the exception of V1 imaging and thy1-GCaMP6 imaging. This prohibits rigorous comparison of the frequency of pathological calcium activity across conditions.

Reviewer #1 (Public Review):

Summary:<br /> This study delves into the roles of dact1 and dact2 during zebrafish embryonic axis formation and craniofacial morphogenesis. The researchers seek to unravel the mechanisms by which dact1/2 influences Wnt signaling modulation throughout embryonic development and patterning. They propose distinct spatiotemporal roles for Dact1 and Dact2 proteins in zebrafish embryonic development, highlighting their involvement in modulating noncanonical Wnt signaling during convergent extension events. Their findings demonstrate that dact1 and dact2 exhibit distinct spatiotemporal expression domains during development and that dact1/2 mutation leads to convergent extension defects. Furthermore, the study attempts to establish a link between convergent extension defects resulting from dact1/2 mutation and subsequent craniofacial abnormalities during development. To investigate the connection between dact1 and dact2, compound mutants were employed since single mutants did not exhibit craniofacial phenotypes. Additionally, the research encompasses comprehensive transcriptomics and pathway analyses of differentially expressed genes in dact1/2 mutants. This analysis reveals the overexpression of a calcium-dependent cysteine protease, calpain 8. The study suggests a connection between the upregulation of calpain 8 and the observed craniofacial dysmorphology in dact1/2 mutants, implying a potential link between the altered expression of calpain 8 and the craniofacial abnormalities observed in these mutants.

Strengths:<br /> The study beautifully recapitulates previous findings on the role of dact1/2 in modulating convergent extension during zebrafish embryogenesis.

A combination of multiple approaches, including in vivo time-lapse imaging, has been employed to elucidate the etiology of the rod-like neurocranial phenotype in dact1/2 double mutant.<br /> This study utilizes and discusses several 'traditional' mutant lines and newly created ones, analyzing them through single-cell transcriptomics.

Weaknesses:<br /> 1. Enhancing Reproducibility and Robustness:<br /> To enhance the reproducibility and robustness of the findings, it would be valuable for the authors to provide specific numbers of animals used in each experiment.<br /> Explicitly stating the penetrance of the rod-like neurocranial shape in dact1/2-/- animals would provide a clearer understanding of the consistency of this phenotype.

2. Strengthening Single-Cell Data Interpretation:<br /> To further validate the single-cell data and strengthen the interpretation of the gene expression patterns, I recommend the following:<br /> -Provide a more thorough explanation of the rationale for comparing dact1/2 double mutants with gpc4 mutants.<br /> -Employ genotyping techniques after embryo collection to ensure the accuracy of animal selection based on phenotype and address the potential for contamination of wild-type "delayed" animals.<br /> -Supplement the single-cell data with secondary validation using RNA in situ or immunohistochemistry techniques.

3. Directly Investigating Non-Cell-Autonomous Effects:<br /> To directly assess the proposed non-cell-autonomous role of dact1/2, I suggest conducting transplantation experiments to examine the ability of ectodermal/neural crest cells from dact1/2 double mutants to form wild-type-like neurocranium.

4. Further Elucidating Calpain 8's Role:<br /> To strengthen the evidence supporting the critical role of Calpain 8, I recommend conducting overexpression experiments using a sensitized background to enhance the statistical significance of the findings.

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 #3 (Public Review):

Summary:<br /> In this manuscript, the authors explore the roles of dact1 and dact2 during zebrafish gastrulation and craniofacial development. Previous studies used morpholino (MO) knockdowns to show that these scaffolding proteins, which interact with disheveled (Dsh), are expressed during zebrafish gastrulation and suggested that dact1 promotes canonical Wnt/B-catenin signaling, while dact2 promotes non-canonical Wnt/PCP-dependent convergent-extension (Waxman et al 2004). This study goes beyond this work by creating loss-of-function mutant alleles for each gene and unlike the MO studies finds little (dact2) to no (dact1) phenotypic defects in the homozygous mutants. Interestingly, dact1/2 double mutants have a more severe phenotype, which resembles those reported with MOs as well as homozygous wnt11/silberblick (wnt11/slb) mutants that disrupt non-canonical Wnt signaling (Heisenberg et al., 1997; 2000). Further analyses in this paper try to connect gastrulation and craniofacial defects in dact1/2 mutants with wnt11/slb and other wnt-pathway mutants. scRNAseq conducted in mutants identifies calpain 8 as a potential new target of dact1/2 and Wnt signaling.

Strengths:<br /> When considered separately the new mutants are an improvement over the MOs and the paper contains a lot of new data.

Weaknesses:<br /> The hypotheses are very poorly defined and misinterpret key previous findings surrounding the roles of wnt11 and gpc4, which results in a very confusing manuscript. Many of the results are not novel and focus on secondary defects. The most novel result of overexpressing calpain8 in dact1/2 mutants is preliminary and not convincing.

Major Comments:<br /> 1) One major problem throughout the paper is that the authors misrepresent the fact that wnt11f2 and gpc4 act in different cell populations at different times. Gastrulation defects in these mutants are not similar: wnt11 is required for anterior mesoderm CE during gastrulation but not during subsequent craniofacial development while gpc4 is required for posterior mesoderm CE and later craniofacial cartilage morphogenesis (LeClair et al., 2009). Overall, the non-overlapping functions of wnt11 and gpc4, both temporally and spatially, suggest that they are not part of the same pathway.

2) There are also serious problems surrounding attempts to relate single-cell data with the other data in the manuscript and many claims that lack validation. For example, in Fig 1 it is entirely unclear how the Daniocell scRNA-seq data have been used to compare dact1/2 with wnt11f2 or gpc4. With no labeling in panel 1E of this figure these comparisons are impossible to follow. Similarly, the comparisons between dact1/2 and gpc4 in scRNA-seq data in Fig. 6 as well as the choices of DEGs in dact1/2 or gpc4 mutants in Fig. 7 seem arbitrary and do not make a convincing case for any specific developmental hypothesis. Are dact1 and gpc4 or dact2 and wnt11 co-expressed in individual cells? Eyeballing similarity is not acceptable.

3) Many of the results in the paper are not novel and either confirm previous findings, particularly Waxman et al (2004), or even contradict them without good evidence. The authors should make sure that dact2 loss-of-function is not compensated for by an increase in dact1 transcription or vice versa. Testing genetic interactions, including investigating the expression of wnt11f2 in dact1/2 mutants, dact1/2 expression in wnt11f2 mutants, or the ability of dact1/2 to rescue wnt11f2 loss of function would give this work a more novel, mechanistic angle.

4) The identification of calpain 8 overexpression in Dact1/2 mutants is interesting, but getting 1/142 phenotypes from mRNA injections does not meet reproducibility standards.

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 #1 (Public Review):

In this work Wu, J., et al., highlight the importance of a previously overlooked region on kinases: the αC-β4 loop. Using PKA as a model system, the authors extensively describe the conserved regulatory elements within a kinase and how the αC-β4 loop region integrates with these important regulatory elements. Previous biochemical work on a mutation within the αC-β4 loop region, F100A showed that this region is important for the synergistic high affinity binding of ATP and the pseudo substrate inhibitor PKI. In the current manuscript, the authors assess the importance of the αC-β4 loop region using computational methods such as Local Spatial Pattern Alignment (LSP) and MD simulations. LSP analysis of the F100A mutant showed decreased values for degree centrality and betweenness centrality for several key regulatory elements within the kinase which suggests a loss in stability/connectivity in the mutant protein as compared to the WT. Additionally, based on MD simulation data, the side chain of K105, another residue within the αC-β4 loop region had altered dynamics in the F100A mutant as compared to the WT protein. While these changes in the αC-β4 loop region seem to be consistent with the previous biochemical data, the manuscript can be strengthened with additional experiments.

Comments on the revised version:

Additional experiments (both computational and experimental) assessing the role of the αC-β4 loop region (especially residues such as K105) are needed to bolster their hypothesis. My initial assessment therefore remains unchanged. While this manuscript falls short of expectations when it comes to experimental findings, it is an excellent review on the structural elements of kinases and how the newly identified αC-β4 loop region integrates with these important regions. Perhaps the experimental section (LSP analysis and MD simulation data) could be removed and this manuscript could be converted into a Review Article?

Reviewer #1 (Public Review):

Summary:

The authors try to use a gene therapy approach to cure urofacial symptoms in an HSPE2 mutant mouse model.

Strengths:

The authors have convincingly shown the expression of AAV9/HSPE2 in pelvic ganglion and liver tissues. They have also shown the defects in urethra relaxation and bladder muscle contraction in response to EFS in mutant mice, which were reversed in treated mice.

Weaknesses:

Some important and interesting data are missing. For example, whether the gene therapy can extend the life span of these mutants? The overall in vivo voiding function is missing. AAV9/HSPE2 expression in the bladder wall is not shown.

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:

This is a really interesting study, looking at the efficacy of AAV-mediated delivery of wt HSPE2 gene into mouse mutants with the goal of rescuing lower urinary tract defects.

Strengths: Nice analysis of muscle physiology ex vivo, interesting approach.

Weaknesses: lack of rigor (see below). This is an awesome opportunity to learn much more about the disease, its affects on neurons, muscle, etc.

* Single-cell analysis of mutants versus control bladder, urethra including sphincter. This would be great also for the community.

* Detailed tables showing data from each mouse examined.

* Survival curves.

* Use of measurements that are done in vivo (spot assay for example). This sounds relatively simple.

* Assessment of viral integration in tissues besides the liver (could be done by QPCR).

* Discuss subtypes of neurons that are present and targeted in the context of mutants and controls.

Reviewer #3 (Public Review):

Summary of the findings:

The authors explore an important question concerning the underlying mechanism of representational drift, which despite intense recent interest remains obscure. The paper explores the intriguing hypothesis that drift may reflect changes in the intrinsic excitability of neurons. The authors set out to provide theoretical insight into this potential mechanism.

They construct a rate model with all-to-all recurrent connectivity, in which recurrent synapses are governed by a standard Hebbian plasticity rule. This network receives a global input, constant across all neurons, which can be varied with time. Each neuron also is driven by an "intrinsic excitability" bias term, which does vary across cells. The authors study how activity in the network evolves as this intrinsic excitability term is changed.

They find that after initial stimulation of the network, those neurons where the excitability term is set high become more strongly connected and are in turn more responsive to the input. Each day the subset of neurons with high intrinsic excitability is changed, and the network's recurrent synaptic connectivity and responsiveness gradually shift, such that the new high intrinsic excitability subset becomes both more strongly activated by the global input and also more strongly recurrently connected. These changes result in drift, reflected by a gradual decrease across time in the correlation of the neuronal population vector response to the stimulus.

The authors are able to build a classifier that decodes the "day" (i.e. which subset of neurons had high intrinsic excitability) with perfect accuracy. This is despite the fact that the excitability bias during decoding is set to 0 for all neurons, and so the decoder is really detecting those neurons with strong recurrent connectivity, and in turn strong responses to the input. The authors show that it is also possible to decode the order in which different subsets of neurons were given high intrinsic excitability on previous "days". This second result depends on the extent by which intrinsic excitability was increased: if the increase in intrinsic excitability was either too high or too low, it was not possible to read out any information about the past ordering of excitability changes.

Finally, using another Hebbian learning rule, the authors show that an output neuron, whose activity is a weighted sum of the activity of all neurons in the network, is able to read out the activity of the network. What this means specifically, is that although the set of neurons most active in the network changes, the output neuron always maintains a higher firing rate than a neuron with randomly shuffled synaptic weights, because the output neuron continuously updates its weights to sample from the highly active population at any given moment. Thus, the output neuron can read out a stable memory despite drift.

Strengths:

The authors are clear in their description of the network they construct and in their results. They convincingly show that when they change their "intrinsic excitability term", upon stimulation, the Hebbian synapses in their network gradually evolve, and the combined synaptic connectivity and altered excitability result in drifting patterns of activity in response to an unchanging input (Fig. 1, Fig. 2a). Furthermore, their classification analyses (Fig. 2) show that information is preserved in the network, and their readout neuron successfully tracks the active cells (Fig. 3). Finally, the observation that only a specific range of excitability bias values permits decoding of the temporal structure of the history of intrinsic excitability (Fig. 2f and Figure S1) is interesting, and as the authors point out, not trivial.

Weaknesses:

1) The way the network is constructed, there is no formal difference between what the authors call "input", Δ(t), and what they call "intrinsic excitability" Ɛ_i(t) (see Equation 3). These are two separate terms that are summed (Eq. 3) to define the rate dynamics of the network. The authors could have switched the names of these terms: Δ(t) could have been considered a global "intrinsic excitability term" that varied with time and Ɛ_i(t) could have been the external input received by each neuron in the network. In that case, the paper would have considered the consequence of "slow fluctuations of external input" rather than "slow fluctuations of intrinsic excitability", but the results would have been the same. The difference is therefore semantic. The consequence is that this paper is not necessarily about "intrinsic excitability", rather it considers how a Hebbian network responds to changes in excitatory drive, regardless of whether those drives are labeled "input" or "intrinsic excitability".

A revised version of the manuscript models "slope-based" excitability changes in addition to "threshold-based" changes. This serves to address the above concern that as constructed here changes in excitability threshold are not distinguishable from changes in input. However, it remains unclear what the model would do should only a subset of neurons receive a given, fixed input. In that case, are excitability changes sufficient to induce drift? This remains an important question that is not addressed by the paper in its current form.

2) Given how the learning rule that defines the input to the readout neuron is constructed, it is trivial that this unit responds to the most active neurons in the network, more so than a neuron assigned random weights. What would happen if the network included more than one "memory"? Would it be possible to construct a readout neuron that could classify two distinct patterns? Along these lines, what if there were multiple, distinct stimuli used to drive this network, rather than the global input the authors employ here? Does the system, as constructed, have the capacity to provide two distinct patterns of activity in response to two distinct inputs?

A revised version of the manuscript addresses this question, demonstrating that the network is capable of maintaining two distinct memories.

Impact:

Defining the potential role of changes in intrinsic excitability in drift is fundamental. Thus, this paper represents an important contribution. What we see here is that changes in intrinsic excitability are sufficient to induce drift. This raises the question for future work of the specific contributions of changing excitability from changing input to representational drift.

Reviewer #1 (Public Review):

Summary<br /> Here the authors have tethered a Pgp substrate to strategically place cysteine residues in the protein. Notably, the cysteine-linked substate (ANC-DNPT)- stimulate ATP hydrolyse and so are able to undergo IF to OF transitions. The authors then determined cryo-EM structures of these complexes and MD simulations of bound states. By capturing unforeseen OF conformations with substate they propose that TM1 undergoes local conformational changes that are sufficient to translocate substrates, rather than large bundle movements.

Strengths: This paper provides the first substrate (ANC-DNPT)- bound conformations of PgP and a new mechanistic model of how substrates are translocated.

Weaknesses: Although the cross-links stimulate ATP hydrolysis, it is unclear if the TM1 conformations are exactly the same under physiological conditions, since they have been covalently-trapped to the substrate.

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.

Joint Public Review:

The authors previously showed that expressing formate dehydrogenase, rubisco, carbonic anhydrase, and phosphoribulokinase in Escherichia coli, followed by experimental evolution, led to the generation of strains that can metabolise CO2. Using two rounds of experimental evolution, the authors identify mutations in three genes - pgi, rpoB, and crp - that allow cells to metabolise CO2 in their engineered strain background. The authors make a strong case that mutations in pgi are loss-of-function mutations that prevent metabolic efflux from the reductive pentose phosphate autocatalytic cycle. The authors also use proteomic analysis to probe the role of the mutations in crp and rpoB. While they do not reach strong conclusions about how these mutations promote autotrophic growth, they provide some clues, leading to valuable speculation.

Comments on revised version:

The authors have thoroughly addressed the reviewers' comments. The major addition to the paper is the proteomic analysis of single and double mutants of crp and rpoB. These new data provide clues as to the role of the crp and rpoB mutations in promoting autotrophic growth, which the authors discuss. The authors acknowledge that it will require additional experiments to determine whether the speculated mechanisms are correct. Nonetheless, the new data provide valuable new insight into the role of the crp and rpoB mutations. The authors have also expanded their description of the crp and rpoB mutations, making it clearer that the effects of these mutations are likely to be distinct, albeit with potential for overlap in function.

Reviewer #1 (Public Review):

Summary and strengths<br /> This is an interesting paper that concludes that hiring more women will do more to improve the gender balance of (US) academia than improving the attrition rates of women (which are usually higher than men's). Other groups have reported similar findings but this study uses a larger than usual dataset that spans many fields and institutions, so it is a good contribution to the field.

Weaknesses<br /> The paper uses a mixture of mathematical models (basically Leslie matrices, though that term isn't mentioned here) parameterised using statistical models fitted to data. However, the description of the methods needs to be improved significantly. The author should consider citing Matrix Population Models by Caswell (Second Edition; 2006; OUP) as a general introduction to these methods, and consider citing some or all of the following as examples of similar studies performed with these models:<br /> Shaw and Stanton. 2012. Proc Roy Soc B 279:3736-3741<br /> Brower and James. 2020. PLOS One 15:e0226392<br /> James and Brower. 2022. Royal Society Open Science 9:220785<br /> Lawrence and Chen. 2015. [http://128.97.186.17/index.php/pwp/article/view/PWP-CCPR-2015-008]<br /> Danell and Hjerm. 2013. Scientometrics 94:999-1006

The analysis also runs the risk of conflating the fraction of women in a field with gender diversity! In female-dominated fields (e.g. Nursing, Education) increasing the proportion of women in the field will lead to reduced gender diversity. This does not seem to be accounted for in the analysis. It would also be helpful to state the number of men and women in each of the 111 fields in the study.

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 #3 (Public Review):

This manuscript investigates the roles of faculty hiring and attrition in influencing gender representation in US academia. It uses a comprehensive dataset covering tenured and tenure-track faculty across various fields from 2011 to 2020. The study employs a counterfactual model to assess the impact of hypothetical gender-neutral attrition and projects future gender representation under different policy scenarios. The analysis reveals that hiring has a more significant impact on women's representation than attrition in most fields and highlights the need for sustained changes in hiring practices to achieve gender parity.

Strengths:<br /> Overall, the manuscript offers significant contributions to understanding gender diversity in academia through its rigorous data analysis and innovative methodology.

The methodology is robust, employing extensive data covering a wide range of academic fields and institutions.

Weaknesses:<br /> The primary weakness of the study lies in its focus on US academia, which may limit the generalizability of its findings to other cultural and academic contexts. Additionally, the counterfactual model's reliance on specific assumptions about gender-neutral attrition could affect the accuracy of its projections.

Additionally, the study assumes that whoever disappeared from the dataset is attrition in academia. While in reality, those attritions could be researchers who moved to another country or another institution that is not included in the AARC (Academic Analytics Research Centre) dataset.

Reviewer #1 (Public Review):

Summary:<br /> Herneisen et al characterise the Toxoplasma PDK1 orthologue SPARK and an associated protein SPARKEL in controlling important fate decisions in Toxoplasma. Over recent years this group and others have characterised the role of cAMP and cGMP signalling in negatively and positively regulating egress, motility, and invasion, respectively. This manuscript furthers this work by showing that SPARK and SPARKEL likely act upstream, or at least control the levels of the cAMP and cGMP-dependent kinases PKA and PKG, respectively, thus controlling the transition of intracellular replicating parasites into extracellular motile forms (and back again).

The authors use quantitative (phospho)proteomic techniques to elegantly demonstrate the upstream role of SPARK in controlling cAMP and cGMP pathways. They use sophisticated analysis techniques (at least for parasitology) to show the functional association between cGMP and cAMP signalling pathways. They therefore begin to unify our understanding of the complicated signalling pathways used by Toxoplasma to control key regulatory processes that control the activation and suppression of motility. The authors then use molecular and cellular assays on a range of generated transgenic lines to back up their observations made by quantitative proteomics that are clear in their design and approach.

The authors then extend their work by showing that SPARK/SPARKEL also control PKAc3 function. PKAc3 has previously been shown to negatively regulate differentiation into bradyzoite forms and this work backs up and extends this finding to show that SPARK also controls this. The authors conclude that SPARK could act as a central node of regulation of the asexual stage, keeping parasites in their lytic cell growth and preventing differentiation. Whether this is true is beyond the scope of this paper and will have to be determined at a later date.

Strengths:<br /> This is an exceptional body of work. It is elegantly performed, with state-of-the-art proteomic methodologies carefully being applied to Toxoplasma. Observations from the proteomic datasets are masterfully backed up with validation using quantitative molecular and cellular biology assays.

The paper is carefully and concisely written and is not overreaching in its conclusions. This work and its analysis set a new benchmark for the use of proteomics and molecular genetics in apicomplexan parasites.

Weaknesses:<br /> This reviewer did not identify any weaknesses.

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 #3 (Public Review):

Summary:<br /> This paper focuses on the roles of a toxoplasma protein (SPARKEL) with homology to an elongin C and the kinase SPARK that it interacts with. They demonstrate that the two proteins regulate the abundance of PKA and PKG, and that depletion of SPARKEL reduces invasion and egress (previously shown with SPARK), and that their loss also triggers spontaneous bradyzoite differentiation. The data are overall very convincing and will be of high interest to those who study Toxoplasma and related apicomplexan parasites.

Strengths:<br /> The study is very well executed with appropriate controls. The manuscript is also very well and clearly written. Overall, the work clearly demonstrates that SPARK/SPARKEL regulate invasion and egress and that their loss triggers differentiation.

Weaknesses:<br /> 1. The authors fail to discriminate between SPARK/SPARKEL acting as negative regulators of differentiation as a result of an active role in regulating stage-specific transcription/translation or as a consequence of a stress response activated when either is depleted.

2. The function of SPARKEL has not been addressed. In mammalian cells, Elongin C is part of an E3 ubiquitin ligase complex that regulates transcription and other processes. From what I can tell from the proteomic data, homologs of the Elongin B/C complex were not identified. This is an important issue as the authors find that PKG and PKA protein levels are reduced in the knockdown strains

Reviewer #1 (Public Review):

Summary:<br /> The authors previously demonstrated that species-specific variation in primate CD4 impacts its ability to serve as a functional receptor for diverse SIVs. Here, Warren and Barbachano-Guerrero et al. perform population genetics analyses and functional characterization of great ape CD4 with a particular focus on gorillas, which are natural hosts of SIVgor. They first used ancestral reconstruction to derive the ancestral hominin and hominid CD4. Using pseudotyped viruses representing a panel of envelopes from SIVcpz and HIV strains, they find that these ancestral reconstructions of CD4 are more similar to human CD4 in terms of being a broadly susceptible entry receptor (in the context of mediating entry into Cf2Th cells stably expressing human CCR5). In contrast, extant gorilla and chimpanzee CD4 are functional entry receptors for a narrower range of HIV and SIVcpz isolates. Based on these differences, authors next surveyed gorilla sequences and identified several CD4 haplotypes, specifically in the region encoding the CD4 D1 domain, which directly contacts the viral glycoprotein and thus may impact the interaction. Consistent with this possibility, the authors demonstrated that gorilla CD4 haplotypes are, on average, less capable of supporting entry than human CD4, and that some are largely unable to function as SIV entry receptors. Interestingly, individual residues found at key positions in the gorilla CD4 D1 when tested in the context of human CD4 reduce entry of some virions pseudotyped with diverse SIVcpz envelopes, suggesting that individual amino acids can in part explain the observed differences across gorilla CD4 haplotypes. Finally, the authors perform statistical tests to infer that CD4 from great apes with endemic SIV (i.e., chimpanzees and gorillas) but not non-reservoirs (i.e., orangutans, bonobos) or recent spillover hosts (i.e., humans), have been subject to selection as a result of pressure from endemic SIV.

The conclusions of this paper are mostly well supported by data.

Strengths:<br /> The functional assays are appropriate to test the stated hypothesis, and the authors use a broad diversity of envelopes from HIV and SIVcpz strains. The authors also partially characterize one potential mechanism of gorilla CD4 resistance - receptor glycosylation at the derived N15 found in 5/6 gorilla haplotypes.

Ancestral reconstruction provides a particularly interesting aspect of the study, allowing authors to infer the ancestral state of hominid CD4 relative to modern CD4 from gorillas and chimpanzees. This, coupled with evidence supporting SIV-driven selection of gorilla CD4 diversity and the characterization of functional diversity of extant haplotypes provides several interesting findings.

Weaknesses:<br /> The major inference of the work is that SIV infection of gorillas drove the observed diversity in gorilla CD4. This is supported by the majority of SNPs being localized to the CD4 D1, which directly interacts with the envelope, and the demonstrated functional consequences of that diversity for viral entry. However, SIVgor (to the best of my knowledge) only infects Western lowland gorillas (Gorilla gorilla gorilla), and one Gorilla gorilla diehli and three Gorilla beringei graueri individuals were included in the haplotype and allele frequency analyses. The presence of these haplotypes or the presence of similar allele frequencies in Eastern lowland and mountain gorillas would impact this conclusion. It would be helpful for the authors to clarify this point.

The authors appear to use a somewhat atypical approach to assess intra-population selection to compensate for relatively small numbers of NHP sequences (Fig. 6). However, they do not cite precedence for the robustness of the approach or the practice of grouping sequences from multiple species for the endemic vs other comparison. They also state in the methods that some genes encoded in the locus were removed from the analysis "because they have previously been shown to directly interact with a viral protein." This seems to undercut the analysis and prevents alternative explanations for the observed diversity in CD4 (e.g., passenger mutations from selection at a neighboring locus).

Data in Figure 5 is graphed as % infected cells instead of virus titer (TDU/mL). It's unclear why this is the case, and prevents a comparison to data in Figure 2 and Figure 4.

The lack of pseudotyping with SIVgor envelope is a surprising omission from this study, that would help to contextualize the findings. Similarly, building gorilla CD4 haplotype SNPs onto the hominin ancestor (as opposed to extant human CD4) may provide additional insights that are meaningful toward understanding the evolutionary trajectory of gorilla CD4.

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 #1 (Public Review):

Wang et al investigated the evolution, expression, and function of the X-linked miR-506 miRNA family. They showed that the miR-506 family underwent rapid evolution. They provided evidence that miR-506 appeared to have originated from the MER91C DNA transposons. Human MER91C transposon produced mature miRNAs when expressed in cultured cells. A series of mouse mutants lacking individual clusters, a combination of clusters, and the entire X-linked cluster (all 22 miRNAs) were generated and characterized. The mutant mice lacking four or more miRNA clusters showed reduced reproductive fitness (litter size reduction). They further showed that the sperm from these mutants were less competitive in polyandrous mating tests. RNA-seq revealed the impact of deletion of miR-506 on the testicular transcriptome. Bioinformatic analysis analyzed the relationship among miR-506 binding, transcriptomic changes, and target sequence conservation. The miR-506-deficient mice did not have apparent effect on sperm production, motility, and morphology. Lack of severe phenotypes is typical for miRNA mutants in other species as well. However, the miR-506-deficient males did exhibit reduced litter size, such an effect would have been quite significant in an evolutionary time scale. The number of mouse mutants and sequencing analysis represent a tour de force. This study is a comprehensive investigation of the X-linked miR-506 miRNA family. It provides important insights into the evolution and function of the miR-506 family.

The conclusions of this preprint are mostly supported by the data except being noted below. Some descriptions need to be revised for accuracy.

L219-L285: The conclusion that X-linked miR-506 family miRNAs are expanded via LINE1 retrotransposition is not supported by the data. LINE1s and SINEs are very abundant, accounting for nearly 30% of the genome. In addition, the LINE1 content of the mammalian X chromosome is twice that of the autosomes. One can easily find flanking LINE1/SINE repeat. Therefore, the analyses in Fig. 2G, Fig. 2H and Fig. S3 are not informative. In order to claim LINE1-mediated retrotransposition, it is necessary to show the hallmarks of LINE1 retrotransposition, which are only possible for new insertions. The X chromosome is known to be enriched for testis-specific multi-copy genes that are expressed in round spermatids (PMID: 18454149). The conclusion on the LINE1-mediated expansion of miR-506 family on the X chromosome is not supported by the data and does not add additional insights. I think that the LINE1 related figure panels and description (L219-L285) need to be deleted. In discussion (L557-558), "...and subsequently underwent sequence divergence via LINE1-mediated retrotransposition during evolution" should also be deleted. This section (L219-L285) needs to deal only with the origin of miR-506 from MER91C DNA transposons, which is both convincing and informative.

Fig. 3A: can you speculate/discuss why the miR-506 expression in sperm is higher than in round spermatids?

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 #3 (Public Review):

Summary:

In this manuscript, the authors conducted a comprehensive study of the X-linked miR-506 family miRNAs in mice on its origin, evolution, expression, and function. They demonstrate that the X-linked miR-506 family, predominantly expressed in the testis, may be derived from MER91C DNA transposons and further expanded by retrotransposition. By genetic deletion of different combinations of 5 major clusters of this miRNA family in mice, they found these miRNAs are not required for spermatogenesis. However, by further examination, the mutant mice show mild fertility problem and inferior sperm competitiveness. The authors conclude that the X-linked miR-506 miRNAs finetune spermatogenesis to enhance sperm competition.

Strengths:

This is a comprehensive study with extensive computational and genetic dissection of the X-linked miR-506 family providing a holistic view of its evolution and function in mice. The finding that this family miRNAs could enhance sperm competition is interesting and could explain their roles in finetuning germ cell gene expression to regulate reproductive fitness.

Weaknesses:

The authors specifically addressed the function of 5 clusters of X-link miR-506 family containing 19 miRNAs. There is another small cluster containing 3 miRNAs close to the Fmr1 locus. Would this small cluster act in concert with the 5 clusters to regulate spermatogenesis? In addition, any autosomal miR-506 like miRNAs may compensate for the loss of X-linked miR-506 family. These possibilities should be discussed.

Direct molecular link to sperm competitiveness defect remains unclear but is difficult to address.

Reviewer #1 (Public Review):

Summary:<br /> The cohesin complex maintains sister chromatid cohesion from S phase to anaphase. Beyond that, DSBs trigger cohesin recruitment and post-replication cohesion at both damage sites and globally, which was originally reported in 2004. In their recent study, Ayra-Plasencia et al reported in telophase, DSBs are repaired via HR with re-coalesced sister chromatids (Ayra-Plasencia & Machín, 2019). In this study, they show that HR occurs in a Smc3-dependent way in late mitosis.

Strengths:<br /> The authors take great advantage of the yeast system, they check the DSB processing and repair of a single DSB generated by HO endonuclease, which cuts the MAT locus in chromosome III. In combination with cell synchronization, they detect the HR repair during G2/M or late mitosis. and the cohesin subunit SMC3 is critical for this repair. Beyond that, full-length Scc1 protein can be recovered upon DSBs.

Weaknesses:<br /> These new results basically support their proposal although with a very limited molecular mechanistic progression, especially compared with their recent work.

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 #1 (Public Review):

Summary:

This manuscript reports that a combination of two small molecules, 2C (CHIR99027 and A-485) enabled to induce the dedifferentiation of hESC-derived cardiomyocytes (CMs) into regenerative cardiac cells (RCC). These RCCs had disassembled sarcomeric structures and elevated expression of embryonic cardiogenic genes such as ISL1, which exhibited proliferative potential and were able to differentiate into cardiomyocytes, endothelial cells, and smooth muscle cells. Lineage tracing further suggested that RCCs originated from TNNT2+ cells, not pre-existing ISL1+ cells. Furthermore, 2C treatment increased the numbers of RCC cells in neonatal rat and adult mouse hearts and improved cardiac function post-MI in adult mice. Mechanistically, bulk RNA-seq analysis revealed that 2C led to elevated expression of embryonic cardiogenic genes while down-regulation of CM-specific genes. Single-cell RNA-seq data showed that 2C promoted cardiomyocyte transition into an intermediate state that is marked with ACTA2 and COL1A1, which subsequently transformed into RCCs. Finally, ChIP-seq analysis demonstrated that CHIR99027 enhanced H3K9Ac and H3K27Ac modifications in embryonic cardiac genes, while A-485 inhibited these modifications in cardiac-specific genes. These combined alterations effectively induced the dedifferentiation of cardiomyocytes into RCCs.

Strengths:

Overall, this work is quite comprehensive and is logically and rigorously designed. The phenotypic and functional data on 2C are strong.

Weaknesses:

The mechanistic insights of 2C are primarily derived from transcriptomic and genomic datasets without experimental verification.

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 #3 (Public Review):

Summary:

The manuscript by Zhou and colleagues describes the potential of a two-compound combination (2C), CHIR99021 and A-485, which can generate regenerative cardiac cells (RCCs) from human embryonic stem cell-derived TNNT2+ cardiomyocytes. The authors have also demonstrated this phenomenon in neonatal rats CMs in vitro. Further, the administration of 2C can generate RCCs in adult mouse hearts and significantly improve survival and cardiac function in mice subjected to myocardial infarction. Interestingly, 2C treatment induces global changes in transcription and epigenetic modifications.

Strengths of the study:

1. This study describes the potential of 2C in improving the regeneration of the heart post-MI. The findings may have a translation potential. The idea of promoting the regenerative capacity of the heart by reprogramming CMs into RCCs is interesting.

2. The authors have validated the effect of 2C independently in hESCs, rat CMs, and a model of MI.

3. The authors explored the mechanism by Single-cell RNA-seq and Chip-Seq, which points to the transcriptional and epigenetic activation of genes essential for RCC.

Weaknesses of the study:

1. The mechanism involved in the 2C-mediated generation of RCCs is still unclear. The leads found in the RNA-seq and ChIP-seq were not validated experimentally.

2. Considering the very low number of RCCs (0.6%-1.5% of cells) generated, I cannot comprehend how the heart is protected from MI. Did the author believe 2C would affect the survival or metabolism of existing CM under hypoxia? What percentage of cells were regenerated by 2C treatment post-MI?

3. I would like to know about administering 2C in mice, which could have generated RCCs- dedifferentiated CMs in the heart. Does 2C affect the cardiac functions in mice under basal conditions? Also, does 2C administration affect any physiology in mice? The cardiac structural and functional parameters are required post-2C administration.

4. It is also not tested whether 2C would affect other cell types of the heart, including fibroblasts and endothelial cells, in vitro and in vivo. Assuming the level of protection by 2C in mice, it would affect other cell types.

5. It is still being determined how the authors chose the dose of 2C for in vivo and in vitro studies, although the concentration used for screening is different. Assessing the effect of 2C in a dose-dependent manner is essential.

6. A-485 affects H3K27Ac but not H3K9Ac. However, data show that both H3K27Ac and H3K9Ac are affected. An explanation is required.

7. The authors use "regeneration" even at the screening stage. I am wondering if regeneration could be assessed by the experimental approach they adopted.

Reviewer #4 (Public Review):

Overview:

The present manuscript by Zhou and colleagues investigates the impact of a new combination of compounds termed CHIR99021 and A-485 on stimulating cardiac cell regeneration. This manuscript fits the journal and addresses an important contribution to scientific knowledge. However, the following major revisions need to be addressed as stated below.

Major comments:

-The authors should include more information that clarifies and justifies their hypothesis.<br /> -The story line is not well developed and thus not convincing since the results from different sections are not well connected.<br /> -The main text needs to be improved, and authors should explain their purpose in choosing to study ISL1-CMs. Also, to well argument why they conducted this study and its significance.<br /> -Page 3, row 57-58: Please add the references.<br /> -Page 3-4, row 67-68, authors stated "When CMs resumed contraction, they were treated with individual small molecules from a collection of over 4,000 compounds for 3 days (SI Appendix, Fig. S1C and Table S1), and then fixed and immunostained with ISL1". Please explain better, and show the results of the selected screening compounds.<br /> -Authors must make an effort to discuss their findings in a bold way in order to provide a comprehensive and articulate explanation of their results to the readers. There is much information missing from this section. This should also propose new research avenues and foresee the challenges in future investigations.<br /> -Authors must include a conclusion and future perspectives of this study.<br /> - Page 4, row 73, the authors stated that the unique compound combination 'CHIR99021 and A-485' was found to be the most efficient in promoting ISL1 expression with a healthy cell state. However, the authors should prove that by showing at least the cell viability of these compound combinations at different concentrations and timings as a supplementary figure.<br /> -There is some missing information in the methods part, for example, "Images were captured using a confocal Zeiss LSM710 and Olympus IX83 inverted microscope"; authors should include the objective used and the image size, and should include which method they used to analyze the acquired images.<br /> -Figure S3A shows that the TNNT2 mRNA expression was completely absent after 60 hours of 2C administration. Authors should explain this further.<br /> -Figure 3J, there is high variability in the graph of mCherry cells (%). Please choose a better graph, or increase the independent experiment.<br /> -Authors did not explain/discuss their results of the DNA-binding motif analysis of ISL1 in the cells treated with A-485 or 2C (Figure 7K).<br /> -Figure S1B and D: the image's labeling is not clear. In the exact same figure S1B, how can the authors explain the reduction of ISL cells? Do the authors make the treatment with the compound CHIR99021 as shown in figure S1A? If so, the authors should clarify the ISL reduction in Figure S1B.<br /> -Figure 1H: please improve the immunoblot, the level of B-actin does not match among the different conditions, or provide a relative quantification of the proteins.<br /> -Please indicate further information in the methodology part about the compounds used in this study.<br /> -Figures are not well justified and figure legends are not sufficient enough to explain the figures.<br /> -Please improve the figure legends by including more further information; for example, in Figure 2SH it is highlighted only the "DAPI (4′,6-diamidino-2- phenylindole) staining labeled nuclei as blue" but how about the other markers?<br /> -Figure 2F: the graph shows some high variations in "ns" between NC at 2C and in 60h+3d. I would recommend increasing the independent experiments. Similar observation goes also for figure 2E.<br /> -Authors should provide limitations of this study.

Reviewer #1 (Public Review):

Summary:<br /> The manuscript authored by Stockner and colleagues delves into the molecular simulations of Na+ binding pathway and the ionic interactions at the two known sodium binding sites site 1 and site 2. They further identify a patch of two acidic residues in TM6 that seemingly populate the Na+ ions prior to entry into the vestibule. These results highlight the importance of studying the ion-entry pathways through computational approaches and the authors also validate some of their findings through experimental work. They observe that sodium site 1 binding is stabilized by the presence of the substrate in the s1 site and this is particularly vital as the GABA carboxylate is involved in coordinating the Na+ ion unlike other monoamine transporters and binding of sodium to the Na2 site stabilizes the conformation of the GAT1 by reducing flexibility among the helical bundles involved in alternating access.

Strengths:<br /> The study displays results that are generally consistent with available information from experiments on SLC6 transporters particularly GAT1 and puts forth the importance of this added patch of residues in the extracellular vestibule that could be of importance to the ion permeation in SLC6 transporters. This is a nicely performed study and could be improved if the authors could comment on and fix the following queries.

Weaknesses:<br /> 1. How conserved are the residue pair of D281-E283 in other SLC6 transporters. The authors commented on the presence of these residues in SERT but it would be nice to know how widespread these residues are in other SLC6 transporters like NET, GlyT, and DAT.

2. Further, one would like to see the effect of individual mutations D281A and E283A on transport, surface expression, and EC50 of Na+ to gauge the effect on transport.

3. A clear figure of the S1 site where Na+ tends to stay prior to Na1 site interactions needs to be provided with a clear figure. Further, it is not entirely clear how access to S1 is altered if the transporter is in an outward-occluded conformation if F294 is blocking solvent access. Please comment.

4. The p-value of the EC50 differences between GAT1WT and GAT1double mutant need to be mentioned. The difference in sodium dependence EC50 seems less than twofold and it would be useful to mention how critical the role of the recruitment site is. Since the transport is not affected the site could play a transient role in attracting ions.

5. It would be very nice to know how K+ ions are attracted by this recruitment site. This could further act as a control simulation to test the preference for Na+ ions among SLC6 members.

6. Some of the important figures are not very clear. For instance, there should be a zoomed-in view of the recruitment site. The current one in Fig. 1b and 1c could be made clearer. Similarly as mentioned earlier the Na residence at the S1 site away from the Na1 and Na2 sites needs to be shown with greater clarity by putting side chain information in Fig. 6d.

7. The structural features that comprise the two principle components PC1 and PC2 should be described in greater detail.

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 #1 (Public Review):

Summary:<br /> Bendzunas, Byrne et al. explore two highly topical areas of protein kinase regulation in this manuscript. Firstly, the idea that Cys modification could regulate kinase activity. The senior authors have published some standout papers exploring this idea of late, and the current work adds to the picture of how active site Cys might have been favoured in evolution to serve critical regulatory functions. Second, BRSK1/2 are understudied kinases listed as part of the "dark kinome" so any knowledge of their underlying regulation is of critical importance to advancing the field.

Strengths:<br /> In this study, the author pinpoints highly-conserved, but BRSK-specific, Cys residues as key players in kinase regulation. There is a delicate balance between equating what happens in vitro with recombinant proteins relative to what the functional consequence of Cys mutation might be in cells or organisms, but the authors are very clear with the caveats relating to these connections in their descriptions and discussion. Accordingly, by extension, they present a very sound biochemical case for how Cys modification might influence kinase activity in cellular environs.

Weaknesses:<br /> I have very few critiques for this study, and my major points are barely major.

Major points<br /> 1. My sense is that the influence of Cys mutation on dimerization is going to be one of the first queries readers consider as they read the work. It would be, in my opinion, useful to bring forward the dimer section in the manuscript.

2. Relatedly, the effect of Cys mutation on the dimerization properties of preparations of recombinant protein is not very clear as it stands. Some SEC traces would be helpful; these could be included in the supplement.

3. Is there any knowledge of Cys mutants in disease for BRSK1/2?

4. In bar charts, I'd recommend plotting data points. Plus it is crucial to report in the legend what error measure is shown, the number of replicates, and the statistical method used in any tests.

5. In Figure 5b, the GAPDH loading control doesn't look quite right.

6. In Figure 7 there is no indication of what mode of detection was used for these gels.

9. Recombinant proteins - more detail should be included on how they were prepared. Was there a reducing agent present during purification? Where did they elute off SEC... consistent with a monomer of higher order species?

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.

Joint Public Review:

This article is a direct follow-up to the paper published last year in eLife by the same group. In the previous article, the authors discovered a zinc finger protein, Kipferl, capable of guiding the HP1 protein Rhino towards certain genomic regions enriched in GRGGN motifs and packaged in heterochromatin marked by H3K9me3. Unlike other HP1 proteins, Rhino recruitment activates the transcription of heterochromatic regions, which are then converted into piRNA source loci. The molecular mechanism by which Kipferl interacts specifically with Rhino (via its chromodomain) and not with other HP1 proteins remained enigmatic.

In this latest article, the authors go a step further by elucidating the molecular mechanisms important for the specific interaction of Rhino and not other HP1 proteins with Kipferl. A phylogenetic study carried out between the HP1 proteins of 5 Drosophila species led them to study the importance of an AA Glycine at position 31 located in the Rhino chromodomain, an AA different from the AA (aspartic acid) found at the same position in the other HP1 proteins. The authors then demonstrate, through a series of structure predictions, biochemical, and genetic experiments, that this specific AA in the Rhino-specific chromodomain explains the difference in the chromatin binding pattern between Rhino and the other Drosophila HP1 proteins. Importantly, the G31D conversion of the Rhino protein prevents interaction between Rhino and Kipferl, phenocopying a Kipfer mutant.

Strengths:

The authors' effective use of phylogenetic analyses and protein structure predictions to identify a substitution in the chromodomain that allows Rhino's specific interaction with Kipferl is very elegant. Both genetic and biochemical approaches are applied to rigorously probe the proposed explanation. They used a point mutation in the endogenous locus that replaces the Rhino-specific residue with the aspartic acid residue present in all other HP1 family members. This novel allele largely phenocopies the defects in hatch rate, chromatin organization, and piRNA production associated with kipferl mutants, and does not support Kipferl localization to clusters. The data are of high quality, the presentation is clear and concise, and the conclusions are generally well-supported.

Weaknesses:

The reviewers identified potential ways to further strengthen the manuscript.

1) The one significant omission is RNAseq on the rhino point mutant, which would allow direct comparison to cluster, transposon, and repeat expression in kipferl mutants.

2) The manuscript would benefit from adding more evolutionary comparisons. The following or similar analyses would help put the finding into a broader evolutionary perspective: i) Is Kipferl's surface interacting with Rhino also conserved in Kipferl orthologs? In other words, are the Rhino-interacting amino acids of Kipferl under any pressure to be conserved? ii) The remarkable conservation of Rhino's G31 is at odds with the arms race that is proposed to be happening between the fly's piRNA pathway proteins and transposons. Does this mean that Rhino's chromodomain is "untouchable" for such positive selection?

Reviewer #1 (Public Review):

My main concern is the use of the 700K SNP dataset. This set of SNPs suffers from a heavy ascertainment bias, which can be seen in the PCA in the supplementary material where all the aurochs cluster in the center within the variation of cattle. Given the coverage of some of the samples, multiple individuals would have less than 10K SNP covered. The majority of these are unlikely to be informative here given that they would just represent fixed positions between taurine and indicine or SNPs mostly variable in milk cattle breeds. The authors would get a much better resolution (i.e. many more SNPs to work with their very low genome coverage data) using the 1000 bull genome project VCF data set: https://www.ebi.ac.uk/ena/browser/view/PRJEB42783 which based on whole genome resequencing data from many cattle. This will certainly help with improving the resolution of qpAdm and f4 analysis, which have huge confidence intervals in most cases. Right now some individuals have huge confidence intervals ranging from 0 to 80% auroch ancestry...

I agree with the authors that qpAdm is likely to give quite a noisy estimate of ancestry here (likely explain part of the issue I mentioned above). Although qpAdm is good for model testing here for ancestry proportion the authors instead could use an explicit f4 ratio - this would allow them to specify a model which would make the result easier to interpret.

The interpretation of the different levels of allele sharing on X vs autosome being the result of sex-bias admixture is not very convincing. Could these differences simply be due to a low recombination rate on the X chromosome and/or lower effective population size, which would lead to less efficient purifying selection?

The authors suggest that 2 pop model rejection in some domestic population might be due to indicine ancestry, this seems relatively straightforward to test.

The first sentence of the paper is a bit long-winded, also dogs were domesticated before the emergence of farming societies.

It would be good to be specific about the number of genomes and coverage info in the last paragraph of the intro.

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 #3 (Public Review):

Summary:<br /> Günther and colleagues leverage ancient DNA data to track the genomic history of one of the most important farm animals (cattle) in Iberia, a region showing peculiarities both in terms of cultural practices as well as a climatic refugium during the LGM, the latter of which could have allowed the survival of endemic lineages. They document interesting trends of hybridisation with wild aurochs over the last 8-9 millennia, including a stabilisation of auroch ancestry ~4000 years ago, at ~20%, a time coincidental with the arrival of domestic horses from the Pontic steppe. Modern breeds such as the iconic Lidia used in bullfighting or bull running retain a comparable level of auroch ancestry.

Strengths:<br /> The generation of ancient DNA data has been proven crucial to unravel the domestication history of traditional livestock, and this is challenging due to the environmental conditions of the Iberian peninsula, less favourable to DNA preservation. The authors leverage samples unearthed from key archaeological sites in Spain, including the karstic system of Atapuerca. Their results provide fresher insights into past management practices, and permit characterisation of significant shifts in hybridization with wild aurochs.

Weaknesses:<br /> - Treatment of post-mortem damage: the base quality of nucleotide transitions was recalibrated down to a quality score of 2, but for 5bp from the read termini only. In some specimens (e.g. moo022), the damage seems to extend further. Why not use dedicated tools (e.g. mapDamage), or check the robustness by conditioning on nucleotide transversions?

- Their more solid analyses are based on qpAdm, but rely on two single-sample donor populations. As the authors openly discuss, it is unclear whether CPC98 is a good proxy for Iberian aurochs despite possibly forming a monophyletic clade (the number of analysed sites is simply too low to assess this monophyly; Supplementary Table S2). Additionally, it is also unclear whether Sub1 was a fully unadmixed domestic specimen, depleted of auroch ancestry. The authors seem to suggest themselves that sex-biased introgression may have already taken place in Anatolia ("suggesting that sex-biased processes already took place prior to the arrival of cattle to Iberia").

Alternatively, I recommend using Struct-f4 as it can model the ancestry of all individuals together based on their f4 permutations, including outgroups and modern data, and without the need to define pure "right" and "left" populations such as CPC98 and Sub1. It should work with low-coverage data, and allows us to do f4-based MDS plots as well as to estimate ancestry proportions (including from ghost populations).

- In the admixture graph analyses (supplementary results), the authors use population groups based on a single sample. If these samples are pseudohaploidised (or if coverage is insufficient to estimate heterozygosity - and it is at least for moo004 and moo014), f3 values are biased, implying that the fitted graph may be wrong. The graph shown in Fig S7 is in fact hard to interpret. For example, the auroch Gyu2 from Anatolia but not the auroch CPC98 also from Anatolia received 62% of ancestry from North Africa? The Neolithic samples moo004 and moo014 also show the same shocking disparity. I would consider re-doing this analysis with more than a sample per population group.

Reviewer #1 (Public Review):

Summary:<br /> Doxorubincin has long been known to cause bone loss by increasing osteoclast and suppressing osteoblast activities. The study by Wang et al. reports a comprehensive investigation into the off-target effects of doxorubicin on bone tissues and potential mechanisms. They used a tumor-free model with wild-type mice and found that even a single dose of doxorubicin has a major influence by increasing leukopenia, DAMPs, and inflammasomes in macrophages and neutrophils, and inflammation-related cell death (pyroptosis and NETosis). The gene knockout study shows that AIM2 and NLRP3 are the major contributors to bone loss. Overall, the study confirmed previous findings regarding the impact of doxorubicin on tissue inflammation and expanded the research further into bone tissue. The presented data are consistent; however, a major question remains regarding whether doxorubicin drives inflammation and its related events. Most in vitro studies showed that the effect of doxorubincin cannot be demonstrated without LPS priming. This observation raises the question of whether doxorubincin itself could activate the inflammasome and the related events. In vivo study, on the other hand, suggested that it doesn't require LPS. The inconsistency here was not explained further. Moreover, a tumor-free mouse model was used for the study; however, immune responses in tumor-bearing models would likely be distinct from tumor-free ones. The justification for using tumor-free models is not well-established.

Strengths:<br /> The paper includes a comprehensive study that shows the effects of doxorubincin on cytokine levels in serum, the release of DAMPs and NETosis, and leukopenia using both in vivo and in vitro models. Bone marrow cells, macrophages, and neutrophils were isolated from the bone marrow, and the levels of cytokines in serum were also determined.

They employed multiple knockout models with a deficiency in Aim 2, Nlirp3, and double deficiencies to dissect the functional involvement of these two inflammasomes.

The experiments in general are well designed. The paper is also logically written, and the figures were clearly labeled.

Weaknesses:<br /> Most of the data presented are correlative, and there is not much effort to dissect the underlying molecular mechanism.

It is not entirely clear why a tumor-free model is chosen to study immune responses, as immune responses can differ significantly with or without tumor-bearing.

Immune responses in isolated macrophages, neutrophils, and bone marrow cells require priming with LPS, while such responses are not observed in vivo. There is no explanation for these differences.

The band intensities on Western blots in Fig. 4 and Fig. 5 are not quantified, and the numbers of repeats are also not provided.

Many abbreviations are used throughout the text, and some of the full names are not provided.

Fig. 5B needs a label on the X axis.

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 #1 (Public Review):

Summary and strengths:

This is an interesting, timely and informative article. The authors used publicly available data (made available by a funding agency) to examine some of the academic characteristics of the individuals recipients of the National Institutes of Health (NIH) k99/R00 award program during the entire history of this funding mechanism (17 years, total ~ 4 billion US dollars (annual investment of ~230 million USD)). The analysis focuses on the pedigree and the NIH funding portfolio of the institutions hosting the k99 awardees as postdoctoral researchers and the institutions hiring these individuals. The authors also analyze the data by gender, by whether the R00 portion of the awards eventually gets activated and based on whether the awardees stayed/were hired as faculty at their k99 (postdoctoral) host institution or moved elsewhere. The authors further sought to examine the rates of funding for those in systematically marginalized groups by analyzing the patterns of receiving k99 awards and hiring k99 awardees at historically black colleges and universities.

The goals and analysis are reasonable and the limitations of the data are described adequately. It is worth noting that some of the observed funding and hiring traits are in line with the Matthew effect in science (Merton, 1968: https://www.science.org/doi/10.1126/science.159.3810.56) and in science funding (Bol et al., 2018: https://www.pnas.org/doi/10.1073/pnas.1719557115). Overall, the article is a valuable addition to the research culture literature examining the academic funding and hiring traits in the United States. The findings can provide further insights for the leadership at funding and hiring institutions and science policy makers for individual and large-scale improvements that can benefit the scientific community.

Weaknesses:

The authors have addressed my recommendations in the previous review round in a satisfactory way.

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 #3 (Public Review):

Summary:

The researchers aim add to the literature on faculty career pathways with particular attention to how gender disparities persist in the career and funding opportunities of researchers. The researchers also examine aspects of institutional prestige that can further amplify funding and career disparities. While some factors about individuals' pathways to faculty lines are known, including the prospects of certain K award recipients, the current study provides the only known examination of the K99/R00 awardees and their pathways.

Strengths:

The authors establish a clear overview of the institutional locations of K99 and R00 awardees and the pathways for K99-to-R00 researchers and the gendered and institutional patterns of such pathways. For example, there's a clear institutional hierarchy of hiring for K99/R00 researchers that echo previous research on the rigid faculty hiring networks across fields, and a pivotal difference in the time between awards that can impact faculty careers. Moreover, there's regional clusters of hiring in certain parts of the US where multiple research universities are located. Moreover, documenting the pathways of HBCU faculty is an important extension of the study by Wapman et al. (2022: https://www.nature.com/articles/s41586-022-05222-x), and provides a more nuanced look at the pathways of faculty beyond the oft-discussed high status institutions. (However, there is a need for more refinement in this segment of the analyses). Also, the authors provide important caveats throughout the manuscript about the study's findings that show careful attention to the complexity of these patterns and attempting to limit misinterpretations of readers.

Weaknesses:

The authors have addressed my recommendations in the previous review round in a satisfactory way.

Reviewer #1 (Public Review):

This manuscript deftly combines cryo-EM and electrophysiology to investigate gating mechanisms of human CLC-2. Although another structure of CLC-2 was recently reported, this is the first structure to report density for the absolutely critical gating glutamate, and - an even more exciting result - the first structure to identify the N-terminal gating peptide that is the heart of this manuscript. There has been previous controversy over such a gating peptide in CLC-2, but the combined structural/functional approach appears to establish a role for this peptide in gating, and sets up future experiments to understand why its effects might change under different physiological scenarios. The experiments reported here are thoughtful and well-controlled and the data presentation is excellent. For the electrophysiology experiments, the use of inhibitor AK-42 (developed by the current senior author's lab) to establish a zero current level is a welcome advance and should become standard for electrophysiological studies of CLC-2.

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 #3 (Public Review):

Summary<br /> CLC-2 channels play an important role in cellular homeostasis and electrical excitability, and dysfunctions are associated with aldosteronism and leukodystrophy. Structural insights into the functioning of CLC-2 are just emerging. CLC-2 channels are distinct among the members of the CLC family in that they are activated by hyperpolarization. Earlier studies have implicated channel regulation by a "ball-and-chain" type of channel block mechanism which underlies its strong rectification and use-dependent "run-up" properties. Structural insights into these mechanisms are currently lacking. In this manuscript, Xu et al present CryoEM structures of CLC-2 in the apo and inhibitor-bound conformations in the 2.5-2.7 A resolution range. Several novel structural features are presented that lend support to the "ball-and chain" model, identify an interesting role for the c-terminal domain in gating, and establish the interaction pocket for AK-42. Electrophysiology and simulations nicely support the structural work. Overall, an elegant study, with high-quality data, and a well-presented manuscript.

Strengths<br /> 1. The cryoEM data presented reveals that the channel is in a closed conformation at depolarizing potential (0 mv). Structures for the closed state of CLCs were not previously available. A strong density for Glu205, which constitutes the Egate, allows for an unambiguous assignment of its position at the Scen Cl-binding site, thereby establishing the basis for the block in the closed channel.<br /> 2. The apo state particles were sorted into two classes that differ in the conformation of the CTD. A previously unobserved rearrangement of the CBS region in the CTD is reported wherein the CTD is positioned closer to the TM region in one of the subunits, breaking the C2 symmetry. The data implicates a role for the conformational flexibility of CTD in gating.<br /> 3. The most interesting finding of this work, is perhaps, the presence of an additional density, corresponding to a hairpin-like structure, that is seen only at the subunit where the CTD is positioned away from the TMD. The authors propose that the additional density corresponds to a 13 aa stretch in the N-terminal region. The position of the hairpin at the intracellular mouth of the CL-permeation pathway is likely to impede ion conduction, by a mechanism analogous to the "ball-and-chain" proposed in other voltage-gated channels.<br /> 4. The structure of CLC-2 in complex with a selective inhibitor AK-42 is in a conformation very similar to that of the apo state, with a clear additional density for the AK-42 molecule. Binding site interaction provides insights into AK-42 selectivity for CLC-2 vs CLC-1.

Reviewer #1 (Public Review):

In the best genetically and biochemically understood model of eukaryotic DNA replication, the budding yeast, Saccharomyces cerevisiae, the genomic locations at which DNA replication initiates are determined by a specific sequence motif. These motifs, or ARS elements, are bound by the origin recognition complex (ORC). ORC is required for loading of the initially inactive MCM helicase during origin licensing in G1. In human cells, ORC does not have a specific sequence binding domain and origin specification is not specified by a defined motif. There have thus been great efforts over many years to try to understand the determinants of DNA replication initiation in human cells using a variety of approaches, which have gradually become more refined over time.

In this manuscript Tian et al. combine data from multiple previous studies using a range of techniques for identifying sites of replication initiation to identify conserved features of replication origins and to examine the relationship between origins and sites of ORC binding in the human genome. The authors identify a) conserved features of replication origins e.g. association with GC-rich sequences, open chromatin, promoters and CTCF binding sites. These associations have already been described in multiple earlier studies. They also examine the relationship of their determined origins and ORC binding sites and conclude that there is no relationship between sites of ORC binding and DNA replication initiation. While the conclusions concerning genomic features of origins are not novel, if true, a clear lack of colocalization of ORC and origins would be a striking finding. However, the majority of the datasets used do not report replication origins, but rather broad zones in which replication origins fire. Rather than refining the localisation of origins, the approach of combining diverse methods that monitor different objects related to DNA replication leads to a base dataset that is highly flawed and cannot support the conclusions that are drawn, as explained in more detail below.

Methods to determine sites at which DNA replication is initiated can be divided into two groups based on the genomic resolution at which they operate. Techniques such as bubble-seq, ok-seq can localise zones of replication initiation in the range ~50kb. Such zones may contain many replication origins. Conversely, techniques such as SNS-seq and ini-seq can localise replication origins down to less than 1kb. Indeed, the application of these different approaches has led to a degree of controversy in the field about whether human replication does indeed initiate at discrete sites (origins), or whether it initiates randomly in large zones with no recurrent sites being used. However, more recent work has shown that elements of both models are correct i.e. there are recurrent and efficient sites of replication initiation in the human genome, but these tend to be clustered and correspond to the demonstrated initiation zones (Guilbaud et al., 2022).

These different scales and methodologies are important when considering the approach of Tian et al. The premise that combining all available data from five techniques will increase accuracy and confidence in identifying the most important origins is flawed for two principal reasons. First, as noted above, of the different techniques combined in this manuscript, only SNS-seq can actually identify origins rather than initiation zones. It is the former that matters when comparing sites of ORC binding with replication origin sites, if a conclusion is to be drawn that the two do not co-localise.

Second, the authors give equal weight to all datasets. Certainly, in the case of SNS-seq, this is not appropriate. The technique has evolved over the years and some earlier versions have significantly different technical designs that may impact the reliability and/or resolution of the results e.g. in Foulk et al. (Foulk et al., 2015), lambda exonuclease was added to single stranded DNA from a total genomic preparation rather than purified nascent strands), which may lead to significantly different digestion patterns (ie underdigestion). Curiously, the authors do not make the best use of the largest SNS-seq dataset (Akerman et al., 2020) by ignoring these authors separation of core and stochastic origins. By blending all data together any separation of signal and noise is lost. Further, I am surprised that the authors have chosen not to use data and analysis from a recent study that provides subsets of the most highly used and efficient origins in the human genome, at high resolution (Guilbaud et al., 2022).

References

Akerman I, Kasaai B, Bazarova A, Sang PB, Peiffer I, Artufel M, Derelle R, Smith G, Rodriguez-Martinez M, Romano M, Kinet S, Tino P, Theillet C, Taylor N, Ballester B, Méchali M (2020) A predictable conserved DNA base composition signature defines human core DNA replication origins. Nat Commun, 11: 4826

Foulk MS, Urban JM, Casella C, Gerbi SA (2015) Characterizing and controlling intrinsic biases of lambda exonuclease in nascent strand sequencing reveals phasing between nucleosomes and G-quadruplex motifs around a subset of human replication origins. Genome Res, 25: 725-735

Guilbaud G, Murat P, Wilkes HS, Lerner LK, Sale JE, Krude T (2022) Determination of human DNA replication origin position and efficiency reveals principles of initiation zone organisation. Nucleic Acids Res, 50: 7436-7450

Update in response to authors' comments on the original review:

While the authors have clarified their approach to some aspects of their analysis, I believe they and I are just going to have to disagree about the methodology and conclusions of this work. I do not find the authors responses sufficiently compelling to change my mind about the significance of the study or veracity of the conclusions. In my opinion, the method for identification of strong origins is not robust and of insufficient resolution. In addition, the resolution and the overlap of the MCM Chip-seq datasets is poor. While the conclusion of the paper would indeed be striking and surprising if true, I am not at all persuaded that it is based on the presented data.

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 #3 (Public Review):

Summary: The authors present a thought-provoking and comprehensive re-analysis of previously published human cell genomics data that seeks to understand the relationship between the sites where the Origin Recognition Complex (ORC) binds chromatin, where the replicative helicase (Mcm2-7) is loaded, and where DNA replication actually begins (origins). The view that these should coincide is influenced by studies in yeast where ORC binds site-specifically to dedicated nucleosome-free origins where Mcm2-7 can be loaded and remains stably positioned for subsequent replication initiation. However, this is most certainly not the case in metazoans where it has already been reported that chromatin bindings sites of ORC and Mcm2-7 do not necessarily overlap, nor do they always overlap with origins. This is likely due to Mcm2-7 possessing linear mobility on DNA (i.e., it can slide) such that other chromatin-contextualized processes can displace it from the site in which it was originally loaded. Additionally, Mcm2-7 is loaded in excess and thus only a fraction of Mcm2-7 would be predicted to coincide with replication start sites. This study reaches a very similar conclusion of these previous studies: they find a high degree of discordance between ORC, Mcm2-7, and origin positions in human cells.

Strengths: The strength of this work is its comprehensive and unbiased analysis of all relevant genomics datasets. To my knowledge, this is the first attempt to integrate these observations. It also is an important cautionary tale to not confuse replication factor binding sites with the genomic loci where replication actually begins, although this point is already widely appreciated in the field.

Weaknesses: The major weakness of this paper is the lack of novel biological insight and that the comprehensive approach taken failed to provide any additional mechanistic insight regarding how and why ORC, Mcm2-7, and origin sites are selected or why they may not coincide.

Reviewer #1 (Public Review):

The mutation rate and spectrum have been found to differ between populations as well as across individuals within the same population. Hypothesizing that some of the observed variation has a genetic basis, the authors of this paper have made important contributions in the past few years in identifying genetic variants that modify mutation rate or spectrum in natural populations. This paper makes one significant step further by developing a new method for mapping genetic variants associated with the mutation spectrum, which reveals new biological insights.

Using traditional quantitative trait locus (QTL) mapping in the BXD mouse recombinant inbred lines (RILs), the authors of this paper previously identified a genetic locus associated with C>A mutation rate. However, this approach has limited power, as it suffers from multiple testing burden as well as noise in the "observed mutation spectrum phenotype" due to rarity and randomness of mutation events. To overcome these limitations, the authors developed a new method that they named "aggregate mutation spectrum distance" (AMSD), which in short measures the difference in the aggregate mutation spectrum between two groups of individuals with distinct genotypes at a specific genomic locus. With this new approach, they recover the previously reported candidate mutator locus (near Mutyh gene) and identify a new candidate locus that modifies the C>A mutation rate on only the mutator allele genetic background at the Mutyh locus. Using more rigorous statistical testing, the authors show convincingly synergistic epistatic effects between the mutator alleles at the two loci.

Overall, the analyses presented are well done and provide convincing evidence for the major findings, including the new candidate mutator locus and its epistatic interaction with the Mutyh locus. The new AMSD method introduced is innovative and outperforms traditional QTL mapping under most conditions, as demonstrated by extensive simulations. I identify no major issues with this paper and think it is very well written.

One of the major advantages of the AMSD method over QTL mapping is alleviation of the multiple testing burden, as one comparison tests for any changes in the mutation spectrum, including simultaneous, small changes in the relative abundance of multiple mutation types. The flip side of this advantage of AMSD is that, when a significant association is detected, it is not immediately clear which mutation type is driving the signal. To narrow the signal to specific candidate mutation type(s), additional analyses are needed, such as testing for differential proportions of each mutation type between individuals with or without the candidate mutator allele. However, such analysis may be less powerful when the mutator allele leads to small changes in the relative abundance of multiple mutation types. This will be an area of improvement for future studies.

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 #3 (Public Review):

Sasani et al. develop and implement a new method for mutator allele discovery in the BXD mouse population. This new method, termed "aggregate mutation spectrum distance" or AMSD, carries several notable strengths, including the ability to aggregate de novo mutations across individuals to reduce data sparsity and to combine mutation rate frequencies across multiple nucleotide contexts into a single estimate. As demonstrated by simulations, this method is better suited to mutator discovery under certain scenarios, as compared to conventional QTL or association mapping. Overall, the theoretical premise of the AMSD method is judged to be both strong and innovative, and the methodology could be extended to other species and populations to enable discovery of additional mutator alleles.

The authors then apply their method to the BXD mouse recombinant inbred mapping population. As proof-of-principle, they first successfully re-identify a known mutator locus in this population on chr4. Next, to assess possible genetic interactions involving this known mutator, Sasani et al. condition on the chr4 mutator genotype and reimplement the AMSD scan. This strategy led them to identify a second locus on chr6 that interacts epistatically with the chr4 locus; mice with "D" alleles at both loci exhibit a significantly increased burden of C>A de novo mutations, even though mice with the D allele at the chr6 locus alone show no appreciable increase in the C>A mutation fraction. This exciting discovery not only adds to the catalog of known mutator alleles, but also reveals key aspects of mutator biology and reinforces the hypothesis that segregating variants in genes associated with DNA repair influence germline mutation spectra.

Despite a high level of overall enthusiasm for this work, there are some limitations to the AMSD method. However, it is my judgement that the authors present a balanced summary of the strengths and weaknesses of their method in the revised manuscript. I also think that the authors' conclusions may actually somewhat undersell the scientific impact of their findings. As the authors note, few mutation rate modifiers have been identified in mammals. This is potentially because large- and moderate-effect modifiers are rapidly selected against due to their deleterious effects, but could also be due to pervasive epistasis wherein modifiers are only expressed on certain "permissive" genetic backgrounds, such as the chr6 locus the authors discover in this paper. The potential background dependence of mutator expression could partially shelter it from the action of selection, allowing the allele persist in populations. This discovery has significant implications for our understanding of mutation rate evolution, but only earns a cursory mention in the paper.

Reviewer #1 (Public Review):

This is an interesting study by Pinos and colleagues that examines the effect of beta carotene on atherosclerosis regression. The authors have previously shown that beta carotene reduces atherosclerosis progress and hepatic lipid metabolism, and now they seek to extend these findings by feeding mice a diet with excess beta carotene in a model of atherosclerosis regression (LDLR antisense oligo plus Western diet followed by LDLR sense oligo and chow diet). They show some metrics of lesion regression are increased upon beta carotene feeding (collagen content) while others remain equal to normal chow diet (macrophage content and lesion size). These effects are lost when beta carotene oxidase (BCO) is deleted. The study adds to the existing literature that beta carotene protects from atherosclerosis in general, and adds new information regarding regulatory T-cells. However, the study does not present significant evidence about how beta-carotene is affecting T-cells in atherosclerosis. For the most part, the conclusions are supported by the data presented, and the work is completed in multiple models, supporting its robustness. However there are a few areas that require additional information or evidence to support their conclusions and/or to align with the previously published work.

Specific additional areas of focus for the authors:<br /> The premise of the story is that b-carotene is converted into retinoic acid, which acts as a ligand of the ROR transcription factor in T-regs. The authors measure hepatic markers of retinoic acid signaling (retinyl esters, Cyp26a1 expression) but none of these are measured in the lesion, which calls into question the conclusion that Tregs in the lesion are responsible for the regression observed with b-carotene supplementation.

There does not appear to be a strong effect of Tregs on the b-carotene induced pro-regression phenotype presented in Figure 5. The only major CD25+ cell dependent b-carotene effect is on collagen content, which matches with the findings in Figure 1 +2. This mechanistically might be very interesting and novel, yet the authors do not investigate this further or add any additional detail regarding this observation. This would greatly strengthen the study and the novelty of the findings overall as it relates to b-carotene and atherosclerosis.

The title indicates that beta-carotene induces Treg 'expansion' in the lesion, but this is not measured in the study.

Revised manuscript:<br /> In the revised manuscript, the authors provide quantification of an RA-responsive gene in the plaque as evidence that RA signalling is indeed elevated upon b-carotene supplementation. It is not reduced upon blocking CD25 (Tregs) which implies that other cells in addition to Tregs are impacted by b-carotene supplementation that favourably remodels the plaque. The authors properly account for this by tempering their conclusions and recognize that Tregs are only partially responsible for the plaque phenotype upon b-carotene supplementation.

The authors chose not to further investigate why b-carotene impacted collagen production, instead including a discussion point. In this reviewer's opinion, it is a missed opportunity but hopefully something that can be investigated further by others.

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 #1 (Public Review):

Summary:

The authors demonstrate that the immunosuppressive environment in pancreatic ductal adenocarcinoma (PDAC) can be mitigated by a combination of ionizing radiation (IR), CCR5 inhibition, and PD1 blockade. This combination therapy increases tissue-resident natural killer (trNK) cells that facilitate CD8 T cell activity, resulting in a reduction of E-cadherin positive tumor cells. They identify a specific "hypofunctional" NK cell population in both mouse and human PDAC that supports CD8 T cell involvement. A trNK signature is found to be associated with better survival outcomes in PDAC and other solid tumors.

Strengths:

Overall, I think this is an interesting study that combines testing of therapeutic concepts in mice with bioinformatics analysis of single-cell transcriptome data in primary tumors and exploration of clinical outcomes using signature genes in TCGA data. The key finding is that immunoregulatory properties of tumor-infiltrating/resident CD56-bright NK cells (assumed to be non-cytotoxic) are beneficial for outcome through cross-talk with DC and recruitment of CD8 T cells. The latter is specifically induced by irradiation combined with CCR5i and PD1 blockade.

"These results collectively support the notion that IR/CCR5i/αPD1 combination treatment alters immune infiltration by reducing Tregs and increasing NK and CD8 T cells, thereby resulting in greater local tumor control." I agree with this conclusion.

Weaknesses:

There are a few points to discuss and that the authors may want to address.

1) "Notably, CCR5i significantly reduced Treg infiltration but had no effect on the infiltration of other immune cells, indicating the active recruitment of CCR5+ Tregs in PDAC (Figure 2B)."<br /> CCR5i treatment seems to inhibit infiltration of CD8 T cells and NK cells to a greater extent, in relative terms, compared to Treg, albeit it is not statistically significant. If this visual inspection of the graph does not reflect reality, additional experiments may be needed to verify the selective targeting of Tregs or confirm the fact that also CD8 T cells and NK cells are affected by single agent CCR5i. The reduced recruitment of Treg, NK cells, and CD8T cells was completely reversed when combined with irradiation. In the data shown in Figure 3E it seems as if CCR5i induced infiltration of Tregs along with other immune cells. However, this said, I agree with the conclusion of the authors that this combined treatment leads to an altered immune composition and ratio between Tregs and effector cells (CD8T cells and NK cells). Could this altered composition be displayed more clearly?

2) The definition of active and hypofunctional NK cells based on solely NKG2D expression alone seems like an oversimplification. I realize it is not trivial to test tumor-infiltrating NK cells from these tumors functionally but perhaps scRNAseq of the tumors would allow for characterization of cytotoxicity scores using KEGG or GO analysis or reversed gene set enrichment in responders/non-responders. It seems as if the abstract refers to this phenotype incorrectly since the "hyporesponsive" subset is described as NKG2C-negative.

3) "The NK_C1 cluster correlates best with the hypofunction NK phenotype observed in mice as similarly displayed reduced activation (reduced NKG7, NKp80, GZMA, and PRF1) with additional expression of tissue residency markers CD103, CD49a and, surprisingly, the adaptive activating receptor NKG2C (KLRC2) (Figure 5B, C)."

There is no doubt that NK_C1 represents tumor-infiltrating NK cells with a CD56bright gene signature with a strong tissue resident score. However, the transcriptional expression of KLRC2 on these is not surprising! It is well established that KLRC2 transcripts (but not protein) are highly expressed on conventional CD56bright NK cells. There are several published sources where the authors can find such data for confirmation. Thus, this is not to be confused with adaptive NK cells having an entirely different transcriptional signature and expressing high levels of NKG2C at the cell surface. I strongly recommend re-interpreting the results based on the fact that KLRC2 is expressed at high levels in conventional CD56bright NK cells. If not, it would be important to verify that these tissue-resident NK cells express NKG2C and not NKG2A at the cell surface.

4) NCAM1 transcript alone is not sufficient to deconvolute CD56bright NK cells in TCGA data (Figure 7A). As a single marker, it likely reflects NK cell infiltration without providing further evidence on the contribution of the bright/dim components. Therefore, the use of the bright Tr NK signature described in Table 1 is very important (Figure 7B). Table 1 is not provided. Nor Supplementary Table 1. There is only one supplementary figure in the ppt attached.

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):

Summary:

In the submitted manuscript by Go et al, the authors evaluated the tumor microenvironment in pancreatic ductal adenocarcinoma (PDAC) and made a number of interesting observations, including the following: 1) CCL5 expression within the tumor microenvironment negatively correlated with clinical outcomes in human patients with PDAC; 2) there were both positive and negative correlations between CCL5 expression and the expression of specific genes (e.g. those encoding CD56 and CD16, respectively) included among gene signature lists for Treg, MDSC, TAM, and NK cells; 3) CCR5 inhibition with the inhibitor, maraviroc, reduced Treg infiltration but not that of other immune cell types in an orthotopic murine model of PDAC; 4) CCR5 inhibition augmented anti-PD1 immunotherapy when combined with ionizing radiation (IR) therapy in the murine model; 5) the above therapy resulted in increased infiltration of CD8+ cytotoxic T cells as well as of a subset of NKG2D-negative, tissue-residency (tr) marker expressing NK cells (deemed Cluster 1 NK in their data sets) that inversely correlated with the number of E-cadherin+ cells (i.e. tumor cells) and showed predicted interactions with cDC1 dendritic cells (including XCL1/XCL2 expressed by the NK and XCR1 expressed by the cDC1); 6) the authors identified a number of putative signals stemming from the trNK (e.g. IL-16, TNFSF14, FASLG, CSF, MIF) as well as incoming from cDC1s to NK (e.g. BAG6-NKp30); 7) these trNK cells positively correlated with good outcomes and with CD8+ T cell infiltrations in human PDAC as well as in many other solid tumor types; and 8) importantly, the benefit of IR therapy was specific to the subset of PDAC patients (represented in the TCGA dataset) that were predicted to have low amounts of trNK cells. The authors used murine experimental models, multiplexed imaging analyses, and a number of publicly available sequencing data sets from human tumor samples to perform their investigations. Based on their findings, the authors proposed that combining IR with CCR5 inhibition and anti-PD1 immunotherapy is a promising strategy to treat solid cancers.

Strengths:

Overall, the collective analyses and conclusions appear to be novel and could be of high and rapid impact on the field, particularly in terms of directing clinical trials to incorporate IR with CCR5 inhibition and immunotherapy. The manuscript is well written; the figures are for the most part clear; and the Discussion is very thoughtful.

Weaknesses:

There were a number of minor typographical errors, missing references, or minor issues with the figures. In general, while many of the observations provided strong suggestive evidence of relationships, phenotypes, and functions, the authors often used language to indicate that such things were confirmed, validated, or proven. In fact, there was a paucity of such functional/confirmatory experiments. This does not necessarily detract from the overall significance, excitement for, and potential impact of the study; but the language could likely be adjusted to be more in keeping with the true nature of the findings. The main title and running title are a bit different; consider making them more similar.

Reviewer #3 (Public Review):

This work by Fleck et al. and colleagues documented the auxin feeding-induced effects in adult flies, since auxin could be used in temporally control gene expression using a modified Gal4/Gal80 system. Overall, the experiments were well designed and carefully executed. The results were quantified with appropriate statistical analyses. The paper was also well written and the results were presented logically. Their findings demonstrate that auxin-fed flies have significantly lower triglyceride levels than the control flies using Ultra High-pressure Liquid Chromatography-Mass Spectrometry (UHPLC-MS)-based metabolomics assays. Further transcriptome analyses using the whole flies show changes of genes involved in fatty acid metabolism. However, female oogenesis and fecundity do not seem to be affected, at least using the current assays. These results indicate that auxin may not be used in experiments involving lipid-related metabolism, but could be appropriate to be applied for other biological processes. Researchers need to be careful when applying this strategy in their own experimental design and should perform proper controls.

Reviewer #1 (Public Review):

In recent years, Auxin treatment is frequently used for inducing targeted protein degradation in Drosophila and various other organisms. This approach provides the way to acutely alter the levels of specific proteins. In this manuscript, the authors carefully examine the impact of Auxin treatment and provide strong evidence that Auxin treatment elicits alterations in feeding activity, survival rates, lipid metabolism, and gene expression patterns. Researchers need to be aware of these effects to design experiment/controls and interpret their data.

Strengths:<br /> Regarding widespread usage of Auxin mediated gene manipulation method, it is important to address whether the application of Auxin itself causes any physiological changes. Authors provide evidence of several Auxin effects on lipid metabolism, feeding behavior and gene expression changes. Experiments are suitably designed with appropriate sample size, data analysis methods.

Weaknesses:<br /> Data shown here are limited for certain method of treatment. No time course, dose dependency information is provided, and cell-type-specific responses are unknown. Therefore, this work basically provides the cautionary note for the field for researchers who use this method suggesting the importance that they should thoroughly check the gene expression pattern for their specific tissue of interest under their normal standard or altered food conditions.

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 #3 (Public Review):

Summary of Work<br /> This paper conducts the largest GWAS study of A. thaliana in response to a viral infection. The paper identifies a 1.5 MB region in the chromosome associated with disease, including SNPs, structural variation, and transposon insertions. Studies further validate the association experimentally with a separate experimental infection procedure with several lines and specific T-DNA mutants. Finally, the paper presents a geographic analysis of the minor disease allele and the major association. The major take-home message of the paper is that structural variants and not only SNPs are important changes associated with disease susceptibility. The manuscript also makes a strong case for negative frequency-dependent selection maintaining a disease susceptibility locus at low frequency.

Strengths and Weaknesses<br /> A major strength of this manuscript is the large sample sizes, careful experimental design, and rigor in the follow-up experiments. For instance, mentioning non-infected controls and using methods to determine if geographic locus associations were due to chance. The strong result of a GWAS-detected locus is impressive given the complex interaction between plant genotypes and strains noted in the results. In addition to the follow-up experiments, the geographic analysis added important context and broadened the scope of the study beyond typical lab-based GWAS studies. I find very few weaknesses in this manuscript.

Support of Conclusions<br /> The support for the conclusions is exceptional. This is due to the massive amount of evidence for each statement and also due to the careful consideration of alternative explanations for the data.

Significance of Work<br /> This manuscript will be of great significance in plant disease research, both for its findings and its experimental approach. The study has very important implications for genetic associations with disease beyond plants.

Reviewer #1 (Public Review):

In this manuscript, Butkovic et al. perform a genome-wide association (GWA) study on Arabidopsis thaliana inoculated with the natural pathogen turnip mosaic virus (TuMV) in laboratory conditions, with the aim to identify genetic associations with virus infection-related parameters. For this purpose, they use a large panel of A. thaliana inbred lines and two strains of TuMV, one naïve and one pre-adapted through experimental evolution. A strong association is found between a region in chromosome 2 (1.5 Mb) and the risk of systemic necrosis upon viral infection, although the causative gene remains to be pinpointed.

This project is a remarkable tour de force, but the conclusions that can be reached from the results obtained are unfortunately underwhelming. Some aspects of the work could be clarified, and presentation modified, to help the reader.

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):

Summary:

The study demonstrates that deletion of a small cytoplasmic membrane protein, Tmem263, caused severe impairment of longitudinal bone growth and that the impaired bone growth was caused by suppression of expression and/or protein levels of growth hormone receptor in the liver.

Strengths:

The experimental design of the study is sound and the results are in general of supportive of the conclusions.

Weaknesses:

The study lacks mechanistic investigation into how the deletion of a gene corresponding to a small cytoplasmic membrane protein would lead to substantial reduction in the gene expression of growth hormone receptor, which takes place in the nuclei. Accordingly, the manuscript is of largely descriptive nature.

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 #1 (Public Review):

Summary<br /> Developing vaccination capable of inducing persistent antibody responses capable of broadly neutralizing HIV strains is of high importance. However, our ability to design vaccines to achieve this is limited by our relative lack of understanding of the role of T-follicular helper (Tfh) subtypes in the responses. In this report Verma et al investigate the effects of different prime and boost vaccination strategies to induce skewed Tfh responses and its relationship to antibody levels. They initially find that live-attenuated measles vaccine, known to be effective at inducing prolonged antibody responses has a significant minority of germinal center Tfh (GC-Tfh) with a Th1 phenotype (GC-Tfh1) and then explore whether a prime and boost vaccination strategy designed to induce GC-Tfh1 is effective in the context of anti-HIV vaccination. They demonstrate that a vaccine formulation referred to as MPLA induces higher GC-Tfh1 and link this to increased antibody production.

Strengths:<br /> While there is a lot of literature on Tfh subtypes in blood, how this related to the germinal centers is not always clear. The strength of this paper is that they use a relevant model to allow some longitudinal insight into the detailed events of the germinal center Tfh (GC-Tfh) compartment across time and how this related to antibody production.

Weaknesses:<br /> The authors focus strongly on the proportion of GC-Tfh1 of GC-Tfh. There seems to be an assumption that since the MPLA vaccine has a higher number of GC-Tfh1 that this explains the higher levels of antibodies. This is not an entirely unreasonable assumption but the mechanistic link between the two is never tested.

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 #1 (Public Review):

The authors isolated a novel marine Planctomycetes bacterium with unique characteristics using a budding mode of division from the deep-sea cold seep sediment and named it Poriferisphaera heterotrophicis ZRK32. This work demonstrated that strain ZRK32 preferred nutrient-rich medium, moreover, the addition of nitrate or ammonia promoted the growth of strain ZRK32 and further caused the release of bacteriophage without killing the host. These results are interesting, well presented and documented in the revised manuscript.

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 #1 (Public Review):

In this article, Vardakalis et al. propose a novel model of hippocampal oscillations whereby an external input (emulating the medial septum) can drive theta rhythms. This model displays phase-amplitude coupling of gamma oscillations, as well as theta resetting, which are known features of physiological theta that have been missing in previous models. The end goal proposed by the authors is to have a framework to explore the mechanisms of neurostimulation, which have shown promising applications in pathological conditions, but for which the underlying dynamics remain largely unknown. To reach this objective, the authors implement an existing biophysical model of the hippocampus that is able to generate gamma oscillations, and receives inputs from a set of Kuramoto oscillators to emulate theta drive originating from the medial septum.

Overall, the hypotheses and results are clearly presented and supported by high quality figures. The study is presented in a didactic way, making it easy for a broad audience to understand the significance of the results. The study does present some weaknesses that could easily be addressed by the authors. First, there are some anatomical inaccuracies: line 129 and fig1C, the authors omit medial septum projections to area CA1 (in addition to the entorhinal cortex). Moreover, in addition to CA1, CA3 also provides monosynaptic feedback projections to the medial septum CA3. Finally, an indirect projection from CA1/3 excitatory neurons to the lateral septum, which in turn sends inhibitory projections to the medial septum could be included or mentioned by the authors. This could be of particular relevance to support claims related to effects of neurostimulations, whereby minutious implementation of anatomical data could be key. If not updating their model, the authors could add this point to their limitation section, where they already do a good job of mentioning some limitations of using the EC as a sole oscillatory input to CA1. The authors test conditions of low theta inputs, which they liken to pathological states (line 112). It is not clear what pathology the authors are referring to, especially since a large amount of 'oscillopathies' in the septohippocampal system are associated with decreased gamma/PAC, but not theta oscillations (e.g. Alzheimer's disease conditions). While relevant for the clinical field, there is overall a missed opportunity to explain many experimental accounts with this novel model. Although to this day, clinical use of DBS is mostly restricted to electrical (and thus cell-type agnostic) stimulation, recent studies focusing on mechanisms of neurostimulations have manipulated specific subtypes in the medial septum and observed effects on hippocampal oscillations (e.g. see Muller & Remy, 2017 for review). Focusing stimulations in CA1 is of course relevant for clinical studies but testing mechanistic hypotheses by focusing stimulation on specific cell types could be highly informative. For instance, could the author reproduce recent optogenetic studies (e.g. Bender et al. 2015 for stimulation of fornix fibers; Etter et al., 2019 & Zutshi et al. 2018 for stimulation of septal inhibitory neurons)? Cell specific manipulations should at least be discussed by the authors.

Beyond these weaknesses, this study has a strong utility for researchers wanting to explore hypotheses in the field of neurostimulations. In particular, I see value in such models for exploring more intricate, phase specific effects of continuous, as well as close loop stimulations which are on the rise in systems neuroscience.

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.

Joint Public Review:

In this work, Xie et al. developed SCA-seq, which is a multiOME mapping method that can obtain chromatin accessibility, methylation, and 3D genome information at the same time. SCA-seq first uses M.CviPI DNA methyltransferase to treat chromatin, then perform proximity ligation followed by long-read sequencing. This method is highly relevant to a few previously reported long read sequencing technologies. Specifically, NanoNome, SMAC-seq, and Fiber-seq have been reported to use m6A or GpC methyltransferase accessibility to map open chromatin, or open chromatin together with CpG methylation; Pore-C and MC-3C have been reported to use long read sequencing to map multiplex chromatin interactions, or together with CpG methylation. Therefore, as a combination of NanoNome/SMAC-seq/Fiber-seq and Pore-C/MC-3C, SCA-seq is one step forward. The authors tested SCA-seq in 293T cells and performed benchmark analyses testing the performance of SCA-seq in generating each data module (open chromatin and 3D genome). The QC metrics appear to be good and I am convinced that this is a valuable addition to the toolsets of multi-OMIC long-read sequencing mapping.

Reviewer #1 (Public Review):

This research article by Watabe T and colleagues characterizes PKA waves triggered by prostaglandin E2 (PGE2). What the author discovered is that waves of PKA occur both in vitro, in MDCK epithelial monolayers, and in vivo, in the ear epidermis in mice. The PKA waves are the consequence PGE2 discharge, that in turn is triggered by Calcium bursts. Calcium level and ERK activity intensity control that mechanism by acting at different levels.<br /> This article is a technological tour de force using different biosensors and optogenetic actuators. However, what makes this article interesting is the ability of combining these tools together to dissect a complex signaling pathway at the single-cell level and with highly dynamic processes. For this reason, this paper represents the essence of modern cell biology and paves the way for the cell biology of the future.

However, we think that the paper in this stage is still partly descriptive in its nature, and more measurements are needed to increase the strength of the mechanistic insights. Here below the points that we believe that need some improvement.

1)Even though the phenomenon of PGE2 signal propagation is elegantly demonstrated and well described, the whole paper is mostly of descriptive nature - the PGE2 signal is propagated via intercellular communication and requires Ca transients as well as MAPK activity, however function of these RSPAs in dense epithelium is not taken into consideration.<br /> What is the function of these RSPAs in cellular crowding? - Does it promote cell survival or initiate apoptosis? Does it feed into epithelial reorganization during cellular crowding? Still something else? The authors discuss possible roles of this phenomenon in cell competition context, but show no experimental or statistical efforts to answer this question. I believe some additional analysis or simple experiment would help to shed some light on the functional aspect of RSPAs and increase the importance of all the elegant demonstrations and precise experimental setups that the manuscript is rich of. Monolayer experiments using some perturbations that challenge the steady state of epithelial homeostasis - drug treatments/ serum deprivation/ osmotic stress/ combined with live cell imaging and statistical methods that take into account local cell density might provide important answers to these questions. The authors could consider following some of these ideas to improve the overall value of the manuscript.

2) In the line 82-84 the authors claim: "We found that the pattern of cAMP concentration change is very similar to the activity change of PKA, indicating that a Gs protein-coupled receptor (GsPCR) mediates RSPA". In our opinion, this conclusion is not well-supported by the results. The authors should at least show that some measurement of the two patterns show correlation. Are the patterns of cAMP of the same size as the pattern of PKA? Do they have the same size depending on cell density? Do they occur at the same frequency as the PKA patterns, depending on the cell density? Do they have an all or nothing activation as PKA or their activation is shading with the distance from the source?

3) In general, the absolute radius of the waves is not a good measurement for single-cell biology studies, especially when comparing different densities or in vivo vs in vitro experiments. We suggest the authors to add the measurement of the number of the cells involved in the waves (or the radius expressed in number of cells).

4) In 6D, the authors should also show the single-cell trajectories to understand better the correlation between PKA and ERK peaks. Is the huger variability in ERK activity ratio dues to different peak time or different ERK activity levels in different cells? The authors should show both the variability in the time and intensity.

5) In lines 130-132, the authors write, "This observation indicates that the amount of PGE2 secretion is predetermined and that there is a threshold of the cytoplasmic calcium concentration for the triggered PGE2 secretion". How could the author exclude that the amount of PGE2 is not regulated in its intensity as well? For sure, there is a threshold effect regarding calcium, but this doesn't mean that PGE2 secretion can be further regulated, e.g. by further increasing calcium concentration or by other mechanisms.

6) The manuscript shows that not all calcium transients are followed by RSPAs. Does the local cell density/crowding increase the probability of overlap between calcium transients and RSPAs?

The revision of the Watabe T paper provides additional data and analyses in response to the reviewers' comments. On our side, we are satisfied by these improvements.<br /> In the answer to our first question, the authors claim that they did multiple experiments to understand the function of RSPA in MDCK cell, all providing negative results. The authors could consider publishing the negative results as well, as they can be useful for the community.

In sum, we are convinced of the value of this article, and we thank the authors for the work that has been done.

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 #1 (Public Review):

Summary:<br /> Here, Boor et al focus on the regulation of daf-7 transcription in the ASJ chemosensory neurons, which has previously shown to be sensitive to a variety of external and internal signals. Interestingly, they find that soluble (but not volatile) signals released by food activate daf-7 expression in ASJ, but that this is counteracted by signals from the ASIC channels del-3 and del-7, previously shown to detect the ingestion of food in the pharynx. Importantly, the authors find that ASJ-derived daf-7 can promote exploration, suggesting a feedback loop that influences locomotor states to promote feeding behavior. They also implicate signals known to regulate exploratory behavior (the neuropeptide receptor PDFR-1 and the neuromodulator serotonin) in the regulation of daf-7 expression in ASJ. Additionally, they identify a novel role for a pathway previously implicated in C. elegans sensory behavior, HEN-1/SCD-2, in the regulation of daf-7 in ASJ, suggesting that the SCD-2 homolog ALK may have a conserved role in feeding and metabolism.

Strengths:<br /> The studies reported here, particularly the quantitation of gene expression and the careful behavioral analysis, are rigorously done and interpreted appropriately. The results suggest that, with respect to food, DAF-7 expression encodes a state of "unmet need" - the availability of nearby food to animals that are not currently eating. This is an interesting finding that reinforces and extends our understanding of the neurobiological significance of this important signaling pathway. The identification of a role for ASJ-derived daf-7 in motor behavior is a valuable advance, as is the finding that SCD-2 acts in the AIA interneurons to influence daf-7 expression in ASJ.

Weaknesses:<br /> A limitation of the work is that some mechanistic relationships between the identified signaling pathways remains unclear, but this provides interesting opportunities for future work. There are some minor concerns about the statistical analysis in the paper, but these are unlikely to affect the authors' interpretation of their results.

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 #3 (Public Review):

Summary:<br /> In this interesting study, the authors characterize the mechanisms whereby a C. elegans TGF-beta DAF-7 responds to various forms of food cues to regulate foraging.<br /> Building on their previous findings that characterized the functional role of daf-7 in the ASJ sensory neurons in response to a bacterial pathogen and in regulating searching behaviors, the authors of this manuscript show that ingestion of E. coli OP50, a common laboratory food for the worms, suppresses ASJ expression of daf-7 and secreted water-soluble cues of OP50 increase it. They further show that the level of daf-7 expression in ASJ is positively associated with a higher level of roaming/exploration. The authors identify that the function of a C. elegans ortholog of Anaplastic Lymphoma Kinase in the interneurons AIA regulates ASJ expression of daf-7 in response to food information and the related searching behavior.

Strengths:<br /> The study addresses an important question that appeals to a wide readership. The findings are demonstrated by strong results produced from well designed experiments.

Reviewer #1 (Public Review):

The authors' aim was to test to what extent atypical organization of language is associated with a mirrored brain organization of other cognitive functions. In particular, they focused on the inferior frontal gyri (IFG) by studying the inhibitory control network. This allowed them to directly test support for the Causal hypothesis of hemispheric specialization, arguing for fast sequences of cognitive processes being better performed by a single hemisphere, versus the Statistical hypothesis of lateralization, postulating an independent lateralization of each cognitive function.

Previous studies on this topic did not focus on functions involving homotopic language regions. This limitation is bypassed in this study by assessing inhibition with a Stop-Signal Task which also engages the IFG in the contralateral site to the verb generation task. By studying a combination of structural and functional information, in addition to the activation contrasts, the authors are able to test whether atypical organization is accompanied by stronger interhemispheric connectivity. Although relying mainly on correlations and lacking important methodological information that may be critical to understand the reported effects, the results are quite straightforward. However the bilingual/monolingual status and gender of the participants is not reported which might affect the relationship between language and inhibitory control.

The conclusions of the paper are supported by the data. With their design, the authors observed that, as a group, individuals with atypical organization show a mirror organization of the whole inhibitory network to the contralateral site, supporting the Causal hypothesis at the group level. However, individual data support the Statistical hypothesis, since the segregation between language and inhibition was not observed in all individuals and a variety of configurations in bilateral and bilateral organisation of language and inhibition were also observed.

The results of this study have important implications for our understanding of the independence of different cognitive functions, which is crucial when addressing brain damage and rehabilitation. This aspect also indirectly speaks to researchers interested in evolution and in bilingualism and its relation to cognitive control. These aspects are not discussed but incorporating them would broaden the interest of the paper beyond the current implications mentioned.

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.

Joint Public Review

In this study, Mitra and coworkers extend their previous analyses of the functional role of Orai in the excitability of central dopaminergic neurons in Drosophila. The authors show that a dominant-negative mutant of Orai (OraiE180A) significantly alters the gene expression profile of flight-promoting dopaminergic neurons (fpDANs), including that of Set2, E(z), and Trl, thereby shifting the level of epigenetic signatures that modulate gene expression. The Orai-Trl-Set2 pathway modulates the expression of voltage gated calcium channels, which, in turn, are involved in dopamine release. The study is generally well-done, is in-depth, and comprehensive. The finding that SOCE regulates a wide range of neuronal genes necessary for neuronal excitability and effector signaling by controlling chromatin remodeling genes is a noteworthy discovery.

The authors have adequately answered the previous concerns.

Joint Public Review:

Summary:

The existence of hox gene complexes conserved in animals with bilateral symmetry and in which the genes are arranged along the chromosome in the same order as the structures they specify along the anteroposterior axis of organisms is one of the most spectacular discoveries of recent developmental biology. In brief, homeotic mutations leads to the transformation of a given body segment of the fly into the copy of the next adjacent segment. For the sake of understanding the main observation of this work, it is important to know that in loss-of-function (LOF) alleles, a given segment develops like a copy of the segment immediately anterior to it, and in gain-of-function mutations (GOF), the affected segment develop like a copy of the immediately posterior segment. Over the last 30 years the molecular lesions associated with GOF alleles led to a model where the sequential activation of the hox genes along the chromosome result from the sequential opening of chromosomal domains. Most of these GOF alleles turned out to be deletions of boundary elements (BE) that define the extend of the segment-specific regulatory domains. The fruit fly Drosophila is a highly specialized insect with a very rapid mode of segmentation. Furthermore, the hox clusters in this lineage have split. Given these specificities it is legitimate to question whether the regulatory landscape of the BX-C we know of in D.melanogaster is the result of very high specialization in this lineage, or whether it reflects a more ancestral organization. In this article, the authors address this question by analyzing the continuous hox cluster in butterflies. They focus on the integenic region between the Antennapedia and the Ubx gene, where the split occurred in D.melanogaster. Hi-C and ATAC-seq data suggest the existence of a boundary element between 2 Topologically-Associated-Domain (TAD) which is also characterized by the presence of CTCF binding sites. Butterflies have 2 pairs of wings originating form T2 (forewing) specified by Antp and T3 specified by Ubx (hindwing). Remarkably, CRISPR mutational perturbation of this boundary leads to the hatching of butterflies with homeotic clones of cells with hindwings identities in the forewing (a posteriorly oriented homeotic transformation). In agreement with this phenotype, the authors observe ectopic expression of Ubx in these clones of cells. In other words, CRISPR mutagenesis of this BE region identified by molecular tool give rise to homeotic transformations directed towards more posterior segment as the boundary mutations that had been 1st identified on the basis of their posterior oriented homeotic transformation in Drosophila. None of the mutant clones they observed affect the hindwing, indicating that their scheme did not affect the nearby Ubx transcription unit. This is a reassuring and important 1st evidence that some of the regulatory paradigm that have been proposed in fruit flies are also at work in the common ancestor to Drosophilae and Lepideptora.

Given the large size of the Ubx transcription unit and its associated regulatory regions it is not surprising that the authors have identified ncRNA that are conserved in 4 species of Nymphalinae butterflies, some of which also present in D.melanogaster. Attempts to target the promoters by CRISPR give rise to clones of cells in both forewings and hindwings, suggesting the generation of regulatory mutations associated with both LOF and GOF transformations. The presence of clones with dual homeosis suggest the targeting of Ubx activator and repression CRMs. Unfortunately, these experiments do not allow us to make further conclusions on the role of these ncRNA or in the identification of specific regulatory elements. To the opinion of this referee, some recent papers addressing the role that these ncRNA may play into boundary function should be taken with caution, and evidences that ncRNA(s) regulate boundaries in the BX-C in a WT context are still lacking.

Strengths: the convincing GOF phenotype resulting from the targeting of the Antp-Ubx_BE

Weaknesses: the lack of comparisons with the equivalent phenotypes obtained in D.melanogaster with for example the Fub mutation

Reviewer #1 (Public Review):

Gametocytes are erythrocytic sexual stages of the malaria-causing parasite Plasmodium, and are essential for parasite transmission and reproduction in the mosquito vector. In this study, Murata et al investigated the mechanisms of gene regulation in female gametocytes in the rodent malaria model parasite Plasmodium berghei. According to current views, gene regulation in Plasmodium parasites is dominated by the family of AP2 transcription factors (TFs), such as the AP2-G TF, which drives sexual commitment. The same authors previously identified one AP2 TF, called AP2-FG, as an essential TF mediating differentiation of female gametocytes. Here, they identified a novel protein, called PFG (for partner of AP2-FG, also described as Fd2 in a recently published study), which cooperates with AP2-FG to regulate a subset of female gametocyte genes.

PFG was identified among AP2-G targets, but possesses no known DNA binding or other characterized domain. The authors show that PFG-knockout P. berghei parasites can form male and female gametocytes yet cannot transmit to mosquitoes, due to a defect in female gametocyte development. Using RNA-seq, they show that many female-specific genes are down-regulated in PFG(-)parasites. Chromatin immunoprecipitation combined with DNA sequencing (ChIP-seq) revealed that PFG colocalizes with AP2-FG on a ten-base motif that is enriched upstream of female-specific genes. Importantly, the ChIP-seq profile of PFG is unchanged in the absence of AP2-FG, suggesting that PFG binds to DNA independently of AP2-FG. Mutation of the ten-base motif in one target gene using CRISPR-Cas9 demonstrates that this motif is required for PFG localization at the gene locus. The data also show that binding of AP2-FG is affected in the absence of PFG, with disruption of AP2-FG interaction with the ten-base motif, but conservation of AP2-FG binding to distinct five-base motifs. Using a recombinant AP2 domain from AP2-FG, the authors demonstrate that the AP2 domain of AP2-FG binds to the five-base motifs. Using CRISPR they show that disruption of the five-base motifs in the genome abrogates AP2-FG binding, and using a reporter expression system they confirm that these motifs act as a cis-activating promoter element.

Through the analysis of target genes based on the presence of the ten- versus five-base motifs, the authors propose a model where AP2-FG can function in two forms, associated or not with PFG, and acting on the ten- or five-base motifs, respectively, to regulate distinct gene subsets during development of female gametocyte development.

The paper is well written, with a clear narrative, and the work is very well performed, relying on robust molecular approaches. Generally the conclusions and the model proposed by the authors are well supported by the data. Nevertheless, the study as it stands raises a number of questions. While the data convincingly show that PFG and AP2-FG cooperate to regulate the expression of a subset of female-specific genes, the paper does not show whether the two proteins actually interact with each other to form a complex. Also, how PFG binds to DNA and whether the protein has transactivating activity remains elusive, as the protein apparently possesses no known DNA-binding or activating domain. These points could be discussed in more detail in the manuscript and/or be the subject of follow up studies.

In summary, this work reveals the essential role of a Plasmodium protein with no known DNA binding or regulatory domain, illustrating that unknown facets remain to be uncovered in this fascinating pathogen.

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 #3 (Public Review):

This study is well designed and executed and provides new and important insights into the role of two TFs during the maturation of female gametocytes and fertilization in the mosquito midgut. However, it but would benefit from a more thorough characterization of the phenotype to understand at which step of development these factors are required.

Overall the authors have shown only limited willingness to comprehensively address reviewer concerns and incorporate their suggestions.

Joint Public Review:

Xie et al. propose that the asymmetric segregation of the NuRD complex is regulated in a V-ATPase-dependent manner, and plays a crucial role in determining the differential expression of the apoptosis activator egl-1 and thus critical for the life/death fate decision.

While the model is very intriguing, the reviewers raised concerns regarding the rigor of the method. One issue is with statistics (either insufficient information or inadequate use of statistics), and second is the concern that the asymmetry observed may be caused by one cell dying (resulting in protein degradation, RNA degradation etc). We recommend that the authors address these issues.

Major #1:

There are still many misleading statements/conclusions that are not rigorously tested or that are logically flawed. These issues must be thoroughly addressed for this manuscript to be solid.

1. Asymmetry detected by scRNA seq vs. imaging may not represent the same phenomenon, thus should not be discussed as two supporting pieces of evidence for the authors' model, and importantly each method has its own flaw. First, for scRNA seq, when cells become already egl-1 positive, those cells may be already dying, and thus NuRD complex's transcripts' asymmetry may not have any significance. The data presented in FigS1D, E show that there are lots of genes (6487 out of 8624) that are decreased in dying cells. Thus, it is not convincing to claim that NuRD asymmetry is regulated by differential RNA amount.

2. Regarding NuRD protein's asymmetry, there are still multiple issues. Most likely explanation of their asymmetry is purely daughter size asymmetry. Because one cell is much bigger than the other (3 times larger), NuRD components, which are not chromatin associated, would be inherited to the bigger cell 3 times more than the smaller daughter. Then, upon nuclear envelope reformation, NuRD components will enter the nucleus, and there will be 3 times more NuRD components in the bigger daughter cell. It is possible that this is actually the underling mechanism to regulate gene expression differentially, but this possibility is not properly acknowledged. Currently, the authors use chromatin associated protein (Mys-1) as 'symmetric control', but this is not necessarily a fair comparison. For NuRD asymmetry to be meaningful, an example of protein is needed that is non-chromatin associated in mitosis, distributed to daughter cells proportional to daughter cell size, and re-enter nucleus after nuclear envelope formation to show symmetric distribution. And if daughter size asymmetry is the cause of NuRD asymmetry, other lineages that do not undergo apoptosis but exhibit daughter size asymmetry would also show NuRD asymmetry. The authors should comment on this (if such examples exist, it is fine in that in those cell types, NuRD asymmetry may be used for differential gene expression, not necessarily to induce cell death, but such comparison provides the explanation for NuRD asymmetry, and puts the authors finding in a better context).

3. For the analysis of protein asymmetry between two daughters in Fig S4C, the method of calibration is unclear, making it difficult to interpret the results.

4. As for pHluorin experiments, the authors were asked to test the changes in fluorescence observed are due to changes in pH or changes in the amount of pHluorin protein. They need to add a ratio-metric method in this manuscript. A brief mention to Page 12 line 12 is insufficient to clarify this issue.

Major #2:

Some issues surrounding statistics must be resolved.

1. Fig. 1FG, 2D, 3BDEG, 5BD and 6B used either one-sample t-test or unpaired two-tailed parametric t-test for statistical comparison. These t-tests require a verification of each sample fitting to a normal distribution. The authors need to describe a statistical test used to verify a normal distribution of each sample.

2. Fig. 2D, 3D, and 3G have very small sample size (N=3-4, N=6, N=3, respectively), it is possible that a normal distribution cannot be verified. How can the authors justify the use of one-sample t-test and unpaired parametric t-test ?

3. Statistical comparison in Fig. 2D and Fig. 6B should be re-assessed. For Fig. 2D, the authors need to compare the intensity ratio of HDA-1/LIN53 between sister cells dying within 35 min and those over 400 min. For Fig. 6B, they need to compare the intensity ratio of VHA-17 between DMSO- and BafA1- treated cells at the same time point after anaphase.

Reviewer #1 (Public Review):

Huang C-K. and colleagues in this work address the understudied role of environmental conditions and external forces in cell extrusion as a fundamental part of epithelial homeostasis. They suggest that hydrostatic stress plays a significant role in counteracting cell extrusion forces through the indirect regulation of the focal adhesion kinase (FAK) - protein kinase B (AKT) survival pathway. The team nicely exploits their expertise in fabricating cell culture substrates to control hydrostatic stress on a common epithelial cell model from the kidney (i.e., MDCK). This was done by creating waving surfaces with different lengths from 50µm to 200 µm, thus creating a heterogenous distribution of monolayer forces towards the substrate. Finally, using a specific inhibitor for FAK, they suggest that the survivor pathway FAK-AKT is involved in the observed phenomenon.

In conclusion, the presented data underline the importance of considering external forces and tissue geometry in regulating epithelial homeostasis and the selective transport of water and solutes. These results may have a significant impact on understanding the basic mechanisms of epithelial physiology and pathology, such as in the kidney, intestine, or retina.

Comments on the revised version:

Overall, most of my comments were reasonably addressed. Nevertheless, one comment was not convincingly addressed ("Recommendation 5" - reviewer #1).

The authors did not show that the FAK inhibitor directly induced the reduction of AKT phosphorylation but used this experiment to conclude that FAK - AKT survivor pathway is involved in the observed phenomenon (Fig. 4). The authors mentioned that additional immunoblotting experiments are currently underway. This is a minor control for the manuscript message, but I feel it is necessary. The connection between the levels of FAK and p-AKT shown in Fig. 4E is purely correlative and can be caused by ECM adhesion-independent reasons.

Alternatively, the authors could reduce the stress on the FAK - AKT survivor pathway's involvement and conclude only on the involvement of FAK.

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 #1 (Public Review):

The authors use a combination of structural and MD simulation approaches to characterize phospholipid interactions with the pentameric ligand-gated ion channel, GLIC. By analyzing the MD simulation data using clusters of closed and open states derived previously, the authors also seek to compare lipid interactions between putative functional states. The ultimate goal of this work is to understand how lipids shape the structure and function of this channel.

The strengths of this article include the following:

1) The MD simulation data provide extensive sampling of lipid interactions in GLIC, and these interactions were characterized in putative closed and open states of the channel. The extensive sampling permits confident delineation of 5-6 phospholipid interaction sites per subunit. The agreement in phospholipid binding poses between structures and the all-atom MD simulations supports the utility of MD simulations to examine lipid interactions.

2) The study presents phospholipid binding sites/poses that agree with functionally important lipid binding sites in other pLGICs, supporting the notion that these sites are conserved. For example, the authors identify interactions of POPC at an outer leaflet intersubunit site that is specific for the open state. This result is quite interesting as phospholipids or drugs that positively modulate other pLGICs are known to occupy this site. Also, the effect of mutating W217 in the inner leaflet intersubunit site suggests that this residue, which is highly conserved in pLGICs, is an important determinant of the strength of phospholipid interactions at this site. This residue has been shown to interact with phospholipids in other pLGICs and forms the binding site of potentiating neurosteroids in the GABA(A) receptor.

Comments on the revised version:

We appreciate the authors' thorough response and revisions.

Specifically, the authors address the issue of interaction times by providing measures of the diffusion coefficients and mean displacements of the lipids. These show that there is sufficient movement of lipids within the first shell to indicate that certain residues are forming binding interactions with lipids while others are not. Longer simulation times would be necessary to determine the strength of these interactions and how they may differ between different conformations.

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 #1 (Public Review):

In this study, single neurons were recorded, using tetrodes, from the parahippocampal cortex of 5 rats navigating a double-Y maze (in which each arm of a Y-maze forks again). The goal was located at any one of the 4 branch terminations, and rats were given partial information in the form of a light cue that indicated whether the reward was on the right or left side of the maze. The second decision point was un-cued and the rat had no way of knowing which of the two branches was correct, so this phase of the task was more akin to foraging. Following the outbound journey, with or without reward, the rat had to return (inbound journey) to the maze start, to begin again.

Neuronal activity was assessed for correlations with multiple navigation-relevant variables including location, head direction, speed, reward side, and goal location. The main finding is that a high proportion of neurons showed an increase in firing rate when the animal made a wrong turn at the first branch point (the one in which the correct decision was signalled). This increase, which the authors call rate remapping, persisted throughout the inbound journey as well. It was also found that head direction neurons (assessed by recording in an open field arena) in the same location in the room were more likely to show the rate change. The overall conclusion is that "during goal-directed navigation, parahippocampal neurons encode error information reflective of an animal's behavioral performance" or are "nodes in the transmission of behaviorally relevant variables during goal-directed navigation."

Overall I think this is a well-conducted study investigating an important class of neural representation: namely, the substrate for spatial orientation and navigation. The analyses are very sophisticated - possibly a little too much so, as the basic findings are relatively straightforward and the analyses take quite a bit of work to understand. A difficulty with the study is that it was exploratory (observational) rather than hypothesis-driven. Thus, the findings reveal correlations in the data but do not allow us to infer causal relationships. That said, the observation of increased firing in a subset of neurons following an erroneous choice is potentially interesting. However, the effect seems small. What were the actual firing rate values in Hz, and what was the effect size?

I also feel we are lacking information about the underlying behavior that accompanies these firing rate effects. The authors say "one possibility is that the head-direction signal in the parahippocampal region reflects a behavioral state related to navigational choice or the lack of commitment to a particular navigational route" which is a good thought and raises the possibility that on error trials, rats are more uncertain and turn their heads more (vicarious trial and error) and thus sample the preferred firing direction more thoroughly. Another possibility is that they run more slowly, which is associated with a higher firing rate in these cells. I think we therefore need a better understanding of how behaviour differed between error trials in terms of running speed, directional sampling, etc. A few good, convincing raw-data plots showing a remapping neuron on an error trial and a correct trial on the same arm would also be helpful (the spike plots were too tiny to get a good sense of this: fewer, larger ones would be more helpful). It would be useful to know at what point the elevated response returned to baseline, how - was it when the next trial began, and was the drop gradual (suggesting perhaps a more neurohumoral response) or sudden?

Comments on the revised submission:

The authors have clarified a number of points arising from my original review but some remain.

On the issue of hypotheses: I was really referring, and apologies that I was unclear on this, to the hypothesis about the neural responses predicted in this experiment. The authors aimed to "examine whether spatial representations flexibly adapt to behaviorally relevant factors" but this is not really a hypothesis as such, in the true mechanistic sense so much as "let's see what we can find" which is not an invalid reason to do this type of study. However, no manipulations were made that test causal relationships arising from the study. It thus remains observational. It does however raise testable hypotheses which is valuable. The strongest in my mind is that the rise in firing rates is a catecholamine response to frustration, a conclusion supported by the slow temporal dynamics of the changes.

On the issue of running speed: it needs to be ruled out that this might have been the cause of the altered firing rates since running speeds were different. More generally, the lack of other concurrent behavioral data means we cannot rule out other possible behavioral bases to this effect that are unrelated to error but are related to the motor correlates of the error.

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 #3 (Public Review):

Summary & Strengths:

This study is useful in revealing the neural correlates of goal-directed navigation in the rodent parahippocampal regions, including the medial entorhinal cortex, presubiculum, and parasubiculum. It shows that task-relevant information represented by the parahippocampus is strongly related to task performance. It also reports the relationships of navigational factors (e.g., head direction signal) recorded during foraging in an open field with task variables.

Gonzalez and Giocomo investigated the neural activities in the parahippocampal cortex modulated by visual cues and error signals while the animal performed a goal-directed navigation task on the tree maze. They confirmed that the firing rates and spatial firing patterns in the parahippocampus were significantly correlated with the animal's task performance and the general navigational coding in the open field arena. The authors have concluded that the parahippocampal neurons encode mismatch-like signals, suggesting the functional role of the parahippocampus as a feedback system in a goal-directed task. However, a few major concerns should be addressed more closely to support the conclusion.

1) Due to the limitations of histological verification, the neural responses in the medial entorhinal cortex, presubiculum, and parasubiculum are analyzed together, and this limits the study from understanding the differential information processing across these regions. Because the medial entorhinal cortex and the pre/parasubiculum are believed to be located in very different positions in the information flow within the rodent medial temporal lobe with different anatomical connections, it would have been more convincing if the distinctive functions between the regions could be identified.

2) The authors should carefully differentiate rate remapping and global remapping in their analysis. Rate remapping generally indicates firing rate modulation with little or no shift of spatial firing fields (Leutgeb et al., 2005; Colgin et al., 2008). Therefore, the neurons exhibiting global remapping should not be included in the analysis suited for rate remapping (e.g., the encoding model that considers the cue-dependent rate-remapping effect).

3) One of the major findings in this study is that the parahippocampal neural responses to a visual cue or reward were correlated with task performance. One can expect that cue representation before the decision point is likely to have a greater impact on task performance. Although the Uz score between the left cue and right cue seemed not significantly different from zero on the stem, it would be beneficial if the authors verify whether the remapping score based on the firing rate maps will still be correlated with the task performance when examined only before the decision point, not for the entire maze.

4) There is a need to set the analytic epoch in more detail. The boundary between outbound and inbound journeys was set as 'last goal well visit.' However, even in a correct trial, if the reward was not received in the first goal well, an error signal could occur before the animal triggered the second goal well which was rewarding. This might have caused the rate remapping between two cue conditions, specifically on the arms. To eliminate this possibility, it is recommended to set the outbound journey from the home well trigger to the first goal well approach or to select only trials where the animal received rewards from the first goal well triggering.

Weaknesses:

Incomplete results could limit support for the arguments of the study and may require more rigorous analytical methods.

Reviewer #1 (Public Review):

The authors set out to determine the causal influence of the rIFG on stop-signal inhibition by using the innovative method of focused ultrasound to modulate this area during a stop-signal task. They report that tFUS during the stop signal only (and not the go) affected the probability of making a stop (only for long SSD) and reduced reaction time. tFUS also looked to affect some ERP components thus lending 'causal' evidence for the role of rIFG in stopping behavior and N200/P300 dynamics. The background and premise seem solid, the experimental design looks appropriate with good controls however, I do not think the authors' conclusions are supported. The methods are difficult to understand, and lack citations (background for performing these analyses/pre-processing) - some are listed but not in the reference list - but also leave out important methodology and detail. Despite the fact that there are many statistical tests in the results there are none for their main conclusions that the P300 latency indexes stop-signal inhibition - this is only descriptive. Individuals with expertise in the field of stop signal inhibition are encouraged to read this pre-print to gauge the veracity of the authors' conclusions and the appropriateness of their methodology.

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 #3 (Public Review):

The paper by Li et al. describes the role of the TOR pathway in Aspergillus flavus. The authors tested the effect of rapamycin in WT and different deletion strains. This paper is based on a lot of experiments and work but remains rather descriptive and confirms the results obtained in other fungi. It shows that the TOR pathway is involved in conidiation, aflatoxin production, pathogenicity, and hyphal growth. This is inferred from rapamycin treatment and TOR1/2 deletions. Rapamycin treatment also causes lipid accumulation in hyphae. The phenotypes are not surprising as they have been shown already for several fungi. In addition, one caveat is in my opinion that the strains grow very slowly and this could cause many downstream effects. Several kinases and phosphatases are involved in the TOR pathway. They were known from S. cerevisiae or filamentous fungi. The authors characterized them as well with knock-out approaches.

Reviewer #1 (Public Review):

Their absolute quantification PCR results with the sumo reference gene led the authors to conclude that A. flavus has two copies of tor and tapA in its genome. However, the the genomic location of the additional copies of tor and tapA are unknown.

I have concerns about the conclusion for the following reasons:

First, the authors should provide more convincing data showing that tor and tapA genes are indeed duplicated genes in A. flavus. The authors appeared to use the A. flavus PTS strain as a parental strain for constructing the tor and tapA mutants. If so, the A. flavus CA14 strain (Hua et al., 2007) should be the parental wild-type strain for the A. flavus PTS strain. I did a BLAST search in NCBI for the torA (AFLA_044350) and tapA (AFLA_092770) genes using the most recent CA14 genome assembly sequence (GCA_014784225.2) and only found one allele for each gene: torA on chromosome 7 and tapA on chromosome 3. I could not find any other parts with similar sequences. Even in another popular A. flavus wild-type strain, NRRL3357, both torA and tapA exist as a single allele. Based on the published genome assembly data for A. flavus, there is no evidence to support the idea that tor and tapA exist as copies of each other. Therefore, the authors could perform a Southern blot analysis to further verify their claim. If torA and tapA indeed exist as duplicate copies in different chromosomal locations, Southern blot data could provide supporting results.

If the tor and tapA genes indeed exist as dual copies, do the duplicate genes have identical DNA and protein sequences? If they have different DNA or protein sequences, they should be named differently as paralogs, such as torA and torB or tapA and tapB.

Second, the authors should consider the possibility of aneuploidy for their constructed mutants. When an essential gene is targeted for deletion, aneuploidy often occurs even in a fungal strain without the "ku" mutation, which results in seemingly dual copies of the gene. As the authors appear to use the A. flavus PTS strain having the "ku" mutation, the parental strain has increased genome instability, which may result in enhanced chromosomal rearrangements. So, it will be necessary to Illumina-sequence their tor and tapA mutants to make sure that they are not aneuploidy.

Furthermore, the genetic nomenclature +/- and -/- should be reserved for heterozygous and homozygous mutants in a diploid strain. As A. flavus is not a diploid strain, this type of description could cause confusion for the readers.

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 #1 (Public Review):

Summary:<br /> In their revised manuscript, the authors analyze the evolution of the gasdermin family and observe that the GSDMA proteins from birds, reptiles and amphibians does not form a clade with the mammalian GSDMAs. Moreover, the non-mammalian GSDMA proteins share a conserved caspase-1 cleavage motif at the predicted activation site. The authors provide several series of experiments showing that the non-mammalian GSDMA proteins can indeed be activated by caspase-1 and that this activation leads to cell death (in human cells). They also investigate the role of the caspase-1 recognition tetrapeptide for cleavage by caspase-1 and for the pathogen-derived protease SpeB.

Strengths:<br /> The evolutionary analysis performed in this manuscript appears to use a broader data basis than what has been used in other published work. An interesting result of this analysis is the suggestion that GSDMA is evolutionary older than the main mammalian pyroptotic GSDMD, and that birds, reptiles and amphibians lack GSDMD but use GSDMA for the same purpose. The consequence that bird GSDMA should be activated by an inflammatory caspase (=caspase1) is convincingly supported by the experiments provided in the manuscript.

Weaknesses:<br /> While the cleavability of bird/reptile GSDMA by caspase-1 is well-supported by several experiments, the role of this cleavage for pyroptotic cell killing is addressed more superficially. The experiments performed to this end all use human cells; it is likely - but not guaranteed - that the human model recapitulaes the physiological role of non-mammalian GSDMA proteins. While the data provided in this paper help to understand GSDMA evolution and the activation mechanism of bird/reptile GSDMA, it does not address the still elusive activation mechanism for mammalian GSDMA

As a consequence, the significance of this finding is mostly limited to birds and reptiles.

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 #1 (Public Review):

Summary:<br /> The preprint by Laganowsky and co-workers describes the use of mutant cycles to dissect the thermodynamic profile of specific lipid recognition by the ABC transporter MsbA. The authors use native mass spectrometry with a variable temperature source to monitor lipid binding to the native protein dimer solubilized in detergent. Analysis of the peak intensities (that is, relative abundance) of 1-3 bound lipids as a function of solution temperature and lipid concentration yields temperature-dependent Kds. The authors use these to then generate van't Hoff plots, from which they calculate the enthalpy and entropy contributions to binding of one, two, and in some cases, three lipids to MsbA. The authors have previously demonstrated that MS can indeed extract thermodynamic contributions to lipid binding. The authors then employ mutant cycles, in which basic residues involved in headgroup binding are mutated to alanine. By comparing the thermodynamic signatures of single and double (and in one instance triple) mutants, they aim to identify cooperativity between the different positions. They furthermore use inward and outward locking conditions which should control access to the different binding sites determined previously. The main conclusion is that lipid binding to MsbA is driven mainly by energetically favorable entropy increase upon binding, which stems from the release of ordered water molecules that normally coordinate the basic residues, which helps to overcome the enthalpic barrier of lipid binding. The authors also report an increase in lipid binding at higher temperatures which they attribute to a non-uniform heat capacity of the protein. Although they find that most residue pairs display some degree of cooperativity, particularly between the inner and outer lipid binding sites, they do not provide a structural interpretation of these results.

Strengths:<br /> The use of double mutant cycles and mass spectrometry to dissect lipid binding is novel and interesting. For example, the observation that mutating a basic residue in the inner and one in the outer binding site abolishes lipid binding to a greater extent than the individual mutations is highly informative even without having to break it down into thermodynamic terms. The method and data reported here opens new avenues for the structure/activity relationship of MsbA. The "mutant cycle" approach is in principle widely applicable to other membrane proteins with complex lipid interactions.

Weaknesses:<br /> The use of double mutant cycles to dissect binding energies is well-established, and has, as the authors point out, been employed in combination with mass spectrometry to study protein-protein interactions. Its application to extract thermodynamic parameters is robust in cases where a single binding event is monitored, e.g. the formation of a complex with well-defined stoichiometry, where dissociation constants can be determined with high confidence. It is, however, complicated significantly by the fact that for MsbA-lipid interactions, we are not looking at a single binding event, but a stochastic distribution of lipids across different sites. Even if the protein is locked in a specific conformation, the observation of a single lipid adduct does not guarantee that the one lipid is always bound to a specific site. The authors discuss this issue in the manuscript. As they point out, one can assume that the most high-affinity sites will be populated first. Hence, the Kd values determined by MS likely describe (mostly) lipid binding to these sites, although this does not seem to hold universally true, as seen for example for the two (in principle equivalent) binding sites in the vanadate-locked protein. In addition, mutation of a binding site (which the authors show reduces lipid binding) may instead allow the lipid to bind to a lower-affinity site elsewhere. In summary, the Kds are an approximation.<br /> (Minor comment: The protein concentrations used for MS titration experiments should be stated in the methods.)

The authors conclude that solvation entropy is a major factor driving lipid binding (Figure 6). If the increase in entropy upon lipid binding comes from the release of ordered water molecules around the basic residues, we should see a smaller increase in entropy for proteins where several basic residues have been changed to alanine, which is not the case. The authors explain this by stating that other entropic factors likely are at play. Judging from their data, that is certainly correct, but why then focus on solvation entropy in the discussion if its contribution to the total entropy change cannot be determined?

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.

Joint Public Review:

Bacteria exhibit species-specific numbers and localization patterns of flagella. How specificity in number and pattern is achieved in Gamma-proteobacteria needs to be better understood but often depends on a soluble GTPase called FlhF. Here, the authors take an unbiased protein-pulldown approach with FlhF, resulting in identifying the protein FipA in V. parahaemolyticus. They convincingly demonstrate that FipA interacts genetically and biochemically with previously known spatial regulators HubP and FlhF. FipA is a membrane protein with a cytoplasmic DUF2802; it co-localizes to the flagellated pole with HubP and FlhF. The DUF2802 mediates the interaction between FipA and FlhF, and this interaction is required for FipA function. Altogether, the authors show that FipA likely facilitates the recruitment of FlhF to the membrane at the cell pole together with the known recruitment factor HupB. This finding is crucial in understanding the mechanism of polar localization. The authors show that FipA co-occurs with FlhF in the genomes of bacteria with polarly-localized flagella and study the role of FipA in three of these organisms: V. parahaemolyticus, S. purtefaciens, and P. putida. In each case, they show that FipA contributes to FlhF polar localization, flagellar assembly, flagellar patterning, and motility, though the details differ among the species. By comparing the role of FipA in polar flagellum assembly in three different species, they discover that, while FipA is required in all three systems, evolution has brought different nuances that open avenues for further discoveries.<br /> <br /> Strengths:

The discovery of a novel factor for polar flagellum development. The solid nature and flow of the experimental work.

The authors perform a comprehensive analysis of FipA, including phenotyping of mutants, protein localization, localization dependence, and domains of FipA necessary for each. Moreover, they perform a time-series analysis indicating that FipA localizes to the cell pole likely before, or at least coincident with, flagellar assembly. They also show that the role of FipA appears to differ between organisms in detail, but the overarching idea that it is a flagellar assembly/localization factor remains convincing.

The work is well-executed, relying on bacterial genetics, cell biology, and protein interaction studies. The analysis is deep, beginning with discovering a new and conserved factor, then the molecular dissection of the protein, and finally, probing localization and interaction determinants. Finally, the authors show that these determinants are important for function; they perform these studies in parallel in three model systems.

Weaknesses:

The comparative analysis in the different organisms was on balance, a weakness. Mixing the data for the organisms together made the text difficult to read and took away key points from the results. The individual details crowded out the model in its current form. Indeed, because some of the phenotypes and localization dependencies differ between model systems, the comparison is challenging to the reader. The authors could more clearly state what these differences mean, why they arise, and (in the discussion) how they might relate to the organism's lifestyle. 

More experiments would be needed to fully analyze the effects of interacting proteins on individual protein stability; this absence slightly detracted from the conclusions.

Reviewer #1 (Public Review):

This study delineates an important set of uninjured and injured periosteal snRNAseq data that provides an overview of periosteal cell responses to fracture healing. The authors also took additional steps to validate some of the findings using immunohistochemistry and transplantation assays. This study will provide a valuable publicly accessible dataset to reexamine the expression of the reported periosteal stem and progenitor cell markers.

Strengths:<br /> 1. This is the first single-nuclei atlas of periosteal cells that are obtained without enzymatic cell dissociation or targeted cell purification by FACS. This integrated snRNAseq dataset will provide additional opportunities for the community to revisit the expression of many periosteal cell markers that have been reported to date.

2. The authors delved further into the dataset using cutting-edge algorithms, including CytoTrace, SCENIC, Monocle, STRING, and CellChat, to define the potential roles of identified cell populations in the context of fracture healing. These additional computation analyses generate many new hypotheses regarding periosteal cell reactions.

3. The authors also sought to validate some of the computational findings using immunohistochemistry and transplantation assays to support the conclusion.

Weaknesses:<br /> 1. The current snRNAseq datasets contain only a small number of nuclei (1,189 nuclei at day 0, 6,213 nuclei on day 0-7 combined). It is unclear if the number is sufficient to discern subtle biological processes such as stem cell differentiation.

2. The authors' designation of Sca1+CD34+ cells as SSPCs is not sufficiently supported by experimental evidence. It will be essential to demonstrate stem/progenitor properties of Sca1+CD34+ cells using independent biological approaches such as CFU-F assays. In addition, the putative lineage trajectory of SSPCs toward IIFCs, osteoblasts, and chondrocytes remains highly speculative without concrete supporting data.

3. The designation of POSTN+ clusters as injury-induced fibrogenic cells (IIFCs) is not fully supported by the presented data. The authors' snRNAseq datasets (Figure 1d) demonstrate that there are many POSTN+ cells prior to injury, indicating that POSTN+ cells are not specifically induced in response to injury. It has been widely recognized that POSTN is expressed in the periosteum without fracture. This raises a possibility that the main responder of fracture healing is POSTN+ cells, not SSPCs as they postulate. The authors cannot exclude the possibility that Sca1+CD34+ cells are mere bystanders and do not participate in fracture healing.

4. Detailed spatial organization of Sca1+CD34+ cells and POSTN+ cells in the uninjured periosteum with respect to the cambium layer and the fibrous layer is not demonstrated.

5. Interpretation of transplantation experiments in Figure 5 is not straightforward, as the authors did not demonstrate the purity of Prx1Cre-GFP+SCA1+ cells and Prx1Cre-GFP+CD146- cells to pSSPCs and IIFCs, respectively. It is possible that these populations contain much broader cell types beyond SSPCs or IIFCs.

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 #3 (Public Review):

In this manuscript, the authors explored the transcriptional heterogeneity of the periosteum with single nuclei RNA sequencing. Without prior enrichment of specific populations, this dataset serves as an unbiased representation of the cellular components potentially relevant to bone regeneration. By describing single-cell cluster profiles, the authors characterized over 10 different populations in combined steady state and post-fracture periosteum, including stem cells (SSPC), fibroblast, osteoblast, chondrocyte, immune cells, and so on. Specifically, a developmental trajectory was computationally inferred using the continuum of gene expression to connect SSPC, injury-induced fibrogenic cells (IIFC), chondrocyte, and osteoblast, showcasing the bipotentials of periosteal SSPCs during injury repair. Additional computational pipelines were performed to describe the possible gene regulatory network and the expected pathways involved in bone regeneration. Overall, the authors provided valuable insights into the cell state transitions during bone repair and proposed sets of genes with possible involvements in injury response.

While the highlights of the manuscript are the unbiased characterization of periosteal composition, and the trajectory of SSPC response in bone fracture response, many of the conclusions can be more strongly supported with additional clarifications or extensions of the analysis.

1. As described in the method section, both the steady-state data and full dataset underwent integration before dimensional reduction and clustering. It would be appreciated if the authors could compare the post-integration landscapes of uninjured cells between steady state and full dataset analysis. Specifically, fibroblasts were shown in Figure 1C and 1E, and such annotations did not exist in Figure 2B. Will it be possible that the original 'fibroblasts' were part of the IIFC population?

2. According to Figure 2, immune cells were taking a significant abundance within the dataset, specifically during days 3 & 5 post-fracture. It will be interesting to see the potential roles that immune cells play during bone repair. For example, what are the biological annotations of the immune clusters (B, T, NK, myeloid cells)? Are there any inflammatory genes or related signals unregulated in these immune cells? Do they interact with SSPC or IIFC during the transition?

3. The conclusion of Notch and Wnt signaling in IIFC transition was not sufficiently supported by the analysis presented in the manuscript, which was based on computational inferences. It will be great to add in references supporting these claims or provide experimental validations examining selected members of these pathways.

Reviewer #1 (Public Review):

In this manuscript Rubin and Aso provide important new tools for the study of learning and memory in Drosophila. In flies, olfactory learning and memory occurs at the Mushroom Body (MB) and is communicated to the rest of the brain through Mushroom Body Output Neurons (MBONs). Previously, typical MBONs were thoroughly studied. Here, atypical MBONs that have dendritic input both within the MB lobes and in adjacent brain regions are studied. The authors describe new cell-type-specific GAL4 drivers for the majority of atypical MBONs (and other MBONs) and using an optogenetic activation screen they examined their ability to drive behaviors and learning.

The experiments in this manuscript were carefully performed and the results are clear. The tools provided in this manuscript are of great importance to the field.

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 #1 (Public Review):

Summary:<br /> The manuscript "Drosophila Visuomotor Integration: An Integrative Model and Behavioral Evidence of Visual Efference Copy" provides an integrative model of the visuomotor control in Drosophila melanogaster. This model presents an experimentally derived model based on visually evoked wingbeat pattern recordings of three strategically selected visual stimulus types with well-established behavioral response characteristics. By testing variations of these models, the authors demonstrate that the virtual model behavior can recapitulate the recorded wing beat behavioral results and those recorded by others for these specific stimuli when presented individually. Yet, the novelty of this study and their model is that it allows predictions for natural visual scenes in which multiple visual stimuli occur simultaneously and may have opposite or enhancing effects on behavior. Testing three models that would allow interactions of these visual modalities, the authors show that using a visual efference copy signal allows visual streams to interact, replicating behavior recorded when multiple stimuli are presented simultaneously. Importantly, they validated the prediction of this model in real flies using magnetically tethered flies, e.g., presenting moving bars with varying backgrounds. In conclusion, the presented manuscript presents a commendable effort in developing and demonstrating the validity of a mixture model that allows predictions of the behavior of Drosophila in natural visual environments.

Strengths:<br /> Overall, the manuscript is well-structured and clear in its presentation, and the modeling and experimental research are methodically conducted and illustrated in visually appealing and easy-to-understand figures and their captions.

The manuscript employs a thorough, logical approach, combining computational modeling with experimental behavioral validation using magnetically tethered flies. This iterative integration of simulation and empirical behavioral evidence enhances the credibility of the findings.

The associated code base is well documented and readily produces all figures in the document.

Suggestions:<br /> However, while the experiments provide evidence for the use of a visual efference copy, the manuscript would be even more impressive if it presented specific predictions for the neural implementation or even neurophysiological data to support this model. Or, at the very least, a thorough discussion. Nonetheless, these models and validating behavioral experiments make this a valuable contribution to the field; it is well executed and addresses a significant gap in the modeling of fly behavior and holistic understanding of visuomotor behaviors.

Here are a few points that should be addressed:<br /> 1. The biomechanics block (Figure 2) should be elaborated on, to explain its relevance to behavior and relation to the underlying neural mechanisms.<br /> 2. It is unclear how the three integrative models with different strategies were chosen or what relevance they have to neural implementation. This should be explained and/or addressed.<br /> 3. There should be a discussion of how the visual efference could be represented in the biological model and an evaluation of the plausibility and alternatives.

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 #3 (Public Review):

Summary:<br /> Canelo et al. used a combination of mathematical modeling and behavioral experiments to ask whether flies use an all-or-none EC model or a graded EC model (in which the turn amplitude is modulated by wide-field optic flow). Particularly, the authors focus on the bar-ground discrimination problem, which has received significant attention in flies over the last 50-60 years. First, they use a model by Poggio and Reichardt to model flight response to moving small-field bars and spots and wide-field gratings. They then simulate this model and compare simulation results to flight responses in a yaw-free tether and find generally good agreement. They then ask how flies may do bar-background discrimination (i.e. complex visual environment) and invoke different EC models and an additive model (balancing torque production due to background and bar movement). Using behavioral experiments and simulation supports the notion that flies use an all-or-none EC since flight turns are not influenced by the background optic flow. While the study is interesting, there are major issues with the conceptual framework.

Strengths:<br /> - They ask a significant question related to efference copies during volitional movement.<br /> - The methods are well detailed and the data (and statistics) are presented clearly.<br /> - The integration of behavioral experiments and mathematical modeling of flight behavior.<br /> - The figures are overall very clear and salient.

Weaknesses:<br /> - Omission of saccades: While the authors ask a significant question related to the mechanism of bar-ground discrimination, they fail to integrate an essential component of the Drosophila visuomotor responses: saccades. Indeed, the Poggio and Reichardt model, which was developed almost 50 years ago, while appropriate to study body-fixed flight, has a severe limitation: it does not consider saccades. The authors identify this major issue in the Discussion by citing a recent switched, integrate-and-fire model (Mongeau & Frye, 2017). The authors admit that they "approximated" this model as a smooth pursuit movement. However, I disagree that it is an approximation; rather it is an omission of a motor program that is critical for volitional visuomotor behavior. Indeed, saccades are the main strategy by which Drosophila turn in free flight and prior to landing on an object (i.e. akin to a bar), as reported by the Dickinson group (Censi et al., van Breugel & Dickinson [not cited]). Flies appear to solve the bar-ground discrimination problem by switching between smooth movement and saccades (Mongeau & Frye, 2017; Mongeau et al., 2019 [not cited]). Thus, ignoring saccades is a major issue with the current study as it makes their model disconnected from flight behavior, which has been studied in a more natural context since the work of Poggio.

Critically, recent work showed that a group of columnar neurons (T3) appear specialized for saccadic bar tracking through integrate-and-fire computations, supporting the notion of parallel visual circuits for saccades and smooth movement (Frighetto & Frye, 2023 [not cited]).

A major theme of this work is bar fixation, yet recent work showed that in the presence of proprioceptive feedback, flies do not actually center a bar (Rimniceanu & Frye, 2023). Furthermore, the same study found that yaw-free flies do not smoothly track bars but instead generate saccades. Thus prior work is in direct conflict with the work here. This is a major issue that requires more engagement by the authors.

- Relevance of the EC model: EC-related studies by the authors linked cancellation signals to saccades (Kim et al, 2014 & 2017). Puzzlingly, the authors applied an EC model to smooth movement, when the authors' own work showed that smooth course stabilizing flight turns do not receive cancellation signals (Fenk et al., 2021). Thus, in Fig. 4C, based on the state of the field, the efference copy signal should originate from the torque commands to initiate saccades, and not from torque to generate smooth movement. As this group previously showed, cancellation signals are quantitatively tuned to that of the expected visual input during saccades. Importantly, this tuning would be to the anticipated saccadic turn optic flow. Thus the authors' results supporting an all-or-none model appear in direct conflict with the author's previous work. Further, the addition-only model is not particularly helpful as it has been already refuted by behavioral experiments (Rimneceanu & Frye, Mongeau & Frye).

- Behavioral evidence for all-or-none EC model: The authors state "unless the stability reflex is suppressed during the flies' object evoked turns, the turns should slow down more strongly with the dense background than the sparse one". This hypothesis is based on the fact that the optomotor response magnitude is larger with a denser background, as would be predicted by an EMD model (because there are more pixels projected onto the eye). However, based on the authors' previous work, the EC should be tuned to optic flow and thus the turning velocity (or amplitude). Thus the EC need not be directly tied to the background statistics, as they claim. For instance, I think it would be important to distinguish whether a mismatch in reafferent velocity (optic flow) links to distinct turn velocities (and thus position). This would require moving the background at different velocities (co- and anti-directionally) at the onset of bar motion. Overall, there are alternative hypotheses here that need to be discussed and more fully explored (as presented by Bender & Dickinson and in work by the Maimon group).

Reviewer #1 (Public Review):

The proposed study provides an innovative framework for the identification of muscle synergies taking into account their task relevance. State-of-the-art techniques for extracting muscle interactions use unsupervised machine-learning algorithms applied to the envelopes of the electromyographic signals without taking into account the information related to the task being performed. In this work, the authors suggest to include the task parameters in extracting muscle synergies using a network information framework previously proposed. This allows the identification of muscle interactions that are relevant, irrelevant, or redundant to the parameters of the task executed.

The proposed framework is a powerful tool to understand and identify muscle interactions for specific task parameters and it may be used to improve man-machine interfaces for the control of prostheses and robotic exoskeletons.

With respect to the network information framework recently published, this work added an important part to estimate the relevance of specific muscle interactions to the parameters of the task executed.

It is not clear how the well-known phenomenon of cross-talk during the recording of electromyographic muscle activity may affect the performance of the proposed technique and how it may bias the overall outcomes of the framework.

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 #3 (Public Review):

In this study, the authors developed and tested a novel framework for extracting muscle synergies. The approach aims at removing some limitations and constrains typical of previous approaches used in the field. In particular, the authors propose a mathematical formulation that removes constrains of linearity and couple the synergies to their motor outcome, supporting the concept of functional synergies and distinguishing the task-related performance related to each synergy. While some concepts behind this work were already introduced in recent work in the field, the methodology provided here encapsulates all these features in an original formulation providing a step forward with respect to the currently available algorithms. The authors also successfully demonstrated the applicability of their method to previously available datasets of multi-joint movements.

Preliminary results positively support the scientific soundness of the presented approach and its potential. The added values of the method should be documented more in future work to understand how the presented formulation relates to previous approaches and what novel insights can be achieved in practical scenarios and confirm/exploit the potential of the theoretical findings.

Reviewer #1 (Public Review):

Gap junction channels establish gated intercellular conduits that allow the diffusion of solutes between two cells. Hexameric connexin26 (Cx26) hemichannels are closed under basal conditions and open in response to CO2. In contrast, when forming a dodecameric gap-junction, channels are open under basal conditions and close with increased CO2 levels. Previous experiments have implicated Cx26 residue K125 in the gating mechanism by CO2, which is thought to become carbamylated by CO2. Carbamylation is a labile post-translational modification that confers negative charge to the K125 side chain. How the introduction of a negative charge at K125 causes a change in gating is unclear, but it has been proposed that carbamylated K125 forms a salt bridge with the side chain at R104, causing a conformational change in the channel. It is also unclear how overall gating is controlled by changes in CO2, since there is significant variability between structures of gap-junction channels and the cytoplasmic domain is generally poorly resolved. Structures of WT Cx26 gap-junction channels determined in the presence of various concentrations of CO2 have suggested that the cytoplasmatic N-terminus changes conformation depending on the concentration of the gas, occluding the pore when CO2 levels are high.

In the present manuscript, Deborah H. Brotherton and collaborators use an intercellular dye-transfer assay to show that Cx26 gap-junction channels containing the K125E mutation, which mimics carbamylation caused by CO2, is constitutively closed even at CO2 concentrations where WT channels are open. Several cryo-EM structures of WT and mutant Cx26 gap junction channels were determined at various conditions and using classification procedures that extracted more than one structural class from some of the datasets. Together, the features on each of the different structures are generally consistent with previously obtained structures at different CO2 concentrations and support the mechanism that is proposed in the manuscript. The most populated class for K125E channels determined at high CO2 shows a pore that is constricted by the N-terminus, and a cytoplasmic region that was better resolved than in WT channels, suggesting increased stability. The K125E structure closely resembles one of the two major classes obtained for WT channels at high CO2. These findings support the hypothesis that the K125E mutation biases channels towards the closed state, while WT channels are in an equilibrium between open and closed states even in the presence of high CO2. Consistently, a structure of K125E obtained in the absence of CO2 appeared to also represent a closed state but at lower resolution, suggesting that CO2 has other effects on the channel beyond carbamylation of K125 that also contribute to stabilizing the closed state. Structures determined for K125R channels, which are constitutively open because arginine cannot be carbamylated, and would be predicted to represent open states, yielded apparently inconclusive results.

A non-protein density was found to be trapped inside the pore in all structures obtained using both DDM and LMNG detergents, suggesting that the density represents a lipid rather than a detergent molecule. It is thought that the lipid could contribute to the process of gating, but this remains speculative. The cytoplasmic region in the tentatively closed structural class of the WT channel obtained using LMNG was better resolved. An additional portion of the cytoplasmic face could be resolved by focusing classification on a single subunit, which had a conformation that resembled the AlphaFold prediction. However, this single-subunit conformation was incompatible with a C6-symmetric arrangement. Together, the results suggest that the identified states of the channel represent open states and closed states resulting from interaction with CO2. Therefore, the observed conformational changes illuminate a possible structural mechanism for channel gating in response to CO2.

Some of the discussion involving comparisons with structures of other gap junction channels are relatively hard to follow as currently written, especially for a general readership. Also, no additional functional experiments are carried out to test any of the hypotheses arising from the data. However, structures were determined in multiple conditions, with results that were consistent with the main hypothesis of the manuscript. No discussion is provided, even if speculative, to explain the difference in behavior between hemichannels and gap junction channels. Also, no attempt was made to measure the dimensions of the pore, which is relevant because of the importance of identifying if the structures indeed represent open or closed states of the channel.

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 #3 (Public Review):

Summary:<br /> The mechanism underlying the well-documented CO2-regulated activity of connexin 26 (Cx26) remains poorly understood. This is largely due to the labile nature of CO2-mediated carbamylation, making it challenging to visualize the effects of this reversible posttranslational modification. This paper by Brotherton et al. aims to address this gap by providing structural insights through cryo-EM structures of a carbamylation-mimetic mutant of the gap junction protein.

Strengths:<br /> The combination of the mutation, elevated PCO2, and the use of LMNG detergent resulted in high-resolution maps that revealed, for the first time, the structure of the cytoplasmic loop between transmembrane helix (TM) 2 and 3.

Weaknesses:<br /> The presented maps merely reinforce their previous findings, wherein wildtype Cx26 favored a closed conformation in the presence of high PCO2. While the structure of the TM2-TM3 loop may suggest a mechanism for stabilizing the closed conformation, no experimental data was provided to support this mechanism. Additionally, the cryo-EM maps were not effectively presented, making it difficult for readers to grasp the message.

Reviewer #1 (Public Review):

Summary:<br /> This study identifies a family of solute transports in the enteric protist, Blastocystis, that may mediate the transport of glycolytic intermediates across the mitochondrial membrane. The study builds on previous observations suggesting that Blastocystis (and other Stramenopiles) are unusual in having a compartmentalized glycolytic pathway with enzymes involved in upper and lower glycolysis being located in the cytosol and mitochondria, respectively. In this study, the authors identified two putative Stamenopile metabolite transporters that are related to plant di/tricarboxylic acid transporters that might mediate the transport of glycolytic intermediates across the mitochondrial membrane. These GIC-transporters were localized to the Blastocystis mitochondrion using specific rabbit antibodies and shown to bind several glycolytic intermediates (including GAP, DHAP, and PEP) based on thermostability shift assays. Direct evidence for transport activity was obtained by reconstituting native proteins in proteoliposomes and measuring the uptake of 14C-malate or 35S-sulphate against unlabelled substrates. This assay showed that GIC-2 transported DHAP, GAP, and PEP. However, significant transport activity was not observed for bGIC-2. Overall, the study provides strong, but not conclusive evidence that bGIC-1 is involved in transporting glycolytic intermediates across the inner membrane of the mitochondria, while the function of GIC-2 remains unclear, despite exhibiting the same metabolite binding properties as bGIC-2 in thermostability assays.

Strengths:<br /> Overall, the findings are of interest in the context of understanding the diversity of core metabolic pathways in evolutionarily diverse eukaryotes, as well as the process by which cytosolic glycolysis evolved in most eukaryotes. The experiments are carefully performed and clearly described.

Weaknesses:<br /> The main weakness of the study is the lack of direct evidence that either bGIC-1 and/or bGIC2 are active in vivo. While it is appreciated that the genetic tools for disrupting GIC genes in Blastocystis are limited/lacking, are there opportunities to ectopically express or delete these genes in other Stamenopiles, such as Phaeodactylum triconuteum, to demonstrate function in vivo?

The authors demonstrate that both bGIC-1 and bGIC-2 are targeted to the mitochondrion, based on immunofluorescence studies. However, the precise localization and topology of these carriers in the inner or outer membrane are not defined. The conclusions of the study would be strengthened if the authors could show that one/both transporters are present in the inner membrane using protease protection experiments following differential solubilization of the outer and inner mitochondrial membranes.

It is not clear why hetero-exchange reactions were not performed for bGIC-1 (only for bGIC-2).

The summary slide depicted in Fig 7 is somewhat simplified and inaccurate. First, the authors show that TPI is located in the mitochondria in this study, while in the summary figure, TPI is shown to be present in both the cytosol and mitochondrial matrix. A cytosolic localization for TPI provides a functional rationale for having a triose-P carrier in the inner membrane - however, this is not supported by the data shown here. Second, if bGIC1/2 uses PEP as a counter ion to import GA3P and DHAP into the mitochondrion, as proposed in Fig 7, the lower glycolytic pathway would be effectively truncated at PEP, removing substrate for pyruvate kinase and formation of pyruvate/ATP. Third, the authors suggest that DHAP may have other functions in the mitochondria although these are not shown in the figure.

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 #3 (Public Review):

Summary:<br /> Unlike most eukaryotes, Blastocystis has a branched glycolysis pathway, which is split between the cytoplasm and the mitochondrial matrix. An outstanding question was how the glycolytic intermediates generated in the 'preparatory' phase' are transported into the mitochondrial matrix for the 'pay off' phase. Here, the authors use bioinformatic analysis to identify two candidate solute carrier genes, bGIC-1, and bGIC-2, and use biochemical and biophysical methods to characterise their substrate specificity and transport properties. The authors demonstrate that bGIC-2 can transport dihydroxyacetone phosphate, glyceraldehyde-3-phosphate, 3-phosphoglycerate, and phosphoenolpyruvate, establishing this protein as the 'missing link' connecting the two split branches of glycolysis in this branch of single-celled eukaryotes. The authors also present their data on bGIC-1, which suggests a role in anion transport and bOGC, which is a close functional homologue of the human oxoglutarate carrier (hOGC, SLC25A11) and human dicarboxylate carrier (hDIC, SLC25A10).

Strengths:<br /> The results are presented in a clear and logical arrangement, which nicely leads the reader through the process of gene identification and subsequent ligand screening and functional reconstitution. The results are compelling and well supported - the thermal stabilisation data is supported by the exchange studies. Caveats, where apparent, are discussed and rational explanations are given.

Weaknesses:<br /> The study does not contain any significant weaknesses in my view. I would like to see the authors include the initial rate plots used in the main figures (possibly as insets), so we can observe the data points used for these calculations. It would also have been interesting to include the AlphaFold models for bGIC-1 and bGIC-2 and a discussion/rationalisation for the substrate specificity discussed in the study.

Reviewer #1 (Public Review):

Summary:<br /> In this study, Yan et al. investigate the molecular bases underlying mating type recognition in Tetrahymena thermophila. This model protist possesses a total of 7 mating types/sexes and mating occurs only between individuals expressing different mating types. The authors aimed to characterize the function of mating type proteins (MTA and MTB) in the process of self- and non-self recognition, using a combination of elegant phenotypic assays, protein studies, and imaging. They showed that the presence of MTA and MTB in the same cell is required for the expression of concavalin-A receptors and for tip transformation - two processes that are characteristic of the costimulation phase that precedes cell fusion. Using protein studies, the authors identify a set of additional proteins of varied functions that interact with MTA and MTB and are likely responsible for the downstream signaling processes required for mating. This is a description of a fascinating self- and non-self-recognition system and, as the authors point out, it is a rare example of a system with numerous mating types/sexes. This work opens the door for the further understanding of the molecular bases and evolution of these complex recognition systems within and outside protists.

The results shown in this study point to the unequivocal requirement of MTA and MTB proteins for mating. Nevertheless, some of the conclusions regarding the mode of functioning of these proteins are not fully supported and require additional investigation.

Strengths:<br /> 1. The authors have established a set of very useful knock-out and reporter lines for MT proteins and extensively used them in sophisticated and well-designed phenotypic assays that allowed them to test the role of these proteins in vivo.

2. Despite their apparent low abundance, the authors took advantage of a varied set of protein isolation and characterization techniques to pinpoint the localization of MT proteins to the cell membrane, and their interaction with multiple other proteins that could be downstream effectors. This opens the door for the future characterization of these proteins and further elucidation of the mating type recognition cascade.

Weaknesses:<br /> The manuscript is structured and written in a very clear and easy-to-follow manner. However, several conclusions and discussion points fall short of highlighting possible models and mechanisms through which MT proteins control mating type recognition:

1. The authors dismiss the possibility of a "simple receptor-ligand system", even though the data does not exclude this possibility. The model presented in Figure 2 S1, and on which the authors based their hypothesis, assumes the independence of MTA and MTB proteins in the generation of the intracellular cascade. However, the results presented in Figure 2 show that both proteins are required to be active in the same cell. Coupled with the fact that MTA and MTB proteins interact, this is compatible with a model where MTA would be a ligand and MTB a receptor (or vice-versa), and could thus form a receptor-ligand complex that could potentially be activated by a non-cognate MTA-MTB receptor-ligand complex, leading to an intracellular cascade mediated by the identified MRC proteins. As it stands, it is not clear what is the proposed working model, and it would be very beneficial for the reader for this to be clarified by having the point of view of the authors on this or other types of models.

2. The presence of MTA/MTB proteins is required for costimulation (Figure 2), and supplementation with non-cognate extracellular fragments of these proteins (MTAxc, or MTBxc) is a positive stimulator of pairing. However, alone, these fragments do not have the ability to induce costimulation (Figure 5). Based on the results in Figures 5 and 6 the authors suggest that MT proteins mediate both self and non-self recognition. Why do MTAxc and MTBxc not induce costimulation alone? Are any other components required? How to reconcile this with the results of Figure 2? A more in-depth interpretation of these results would be very helpful, since these questions remain unanswered, making it difficult for the reader to extract a clear hypothesis on how MT proteins mediate self- and non-self-recognition.

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 #3 (Public Review):

The authors describe the role, location, and function of the MTA and MTB mating type genes in the multi-mating-type species T. thermophila. The ciliate is an important group of organisms to study the evolution of mating types, as it is one of the few groups in which more than two mating types evolved independently. In the study, the authors use deletion strains of the species to show that both mating types genes located in each allele are required in both mating individuals for successful matings to occur. They show that the proteins are localized in the cell membrane, not the cilia, and that they interact in a complex (MTRC) with a set of 6 associated (non-mating type-allelic) genes. This complex is furthermore likely to interact with a cyclin-dependent kinase complex. It is intriguing that T. thermophila has two genes that are allelic and that are both required for successful mating. This coevolved double recognition has to my knowledge not been described for any other mating-type recognition system. I am not familiar with experimental research on ciliates, but as far as I can judge, the experiments appear well performed and mostly support the interpretation of the authors with appropriate controls and statistical analyses.

The results show clearly that the mating type genes regulate non-self-recognition, however, I am not convinced that self-recognition occurs leading to the suppression of mating. An alternative explanation could be that the MTA and MTB proteins form a complex and that the two extracellular regions together interact with the MTA+MTB proteins from different mating types. This alternative hypothesis fits with the coevolution of MTA and MTB genes observed in the phylogenetic subgroups as described by Yan et al. (2021 iScience). Adding MTAxc and/or MTBxc to the cells can lead to the occupation of the external parts of the full proteins thereby inhibiting the formation of the complex, which in turn reduces non-self interactions. Self-recognition as explained in Figure 2S1 suggests an active response, which should be measurable in expression data for example. This is in my opinion not essential, but a claim of self-recognition through the MTA and MTB should not be made.

The authors discuss that T. thermophila has special mating-type proteins that are large, while those of other groups are generally small (lines 157-160 and discussion). The complex formed is very large and in the discussion, they argue that this might be due to the "highly complex process, given that there are seven mating types in all". There is no argument given why large is more complex, if this is complex, and whether more mating types require more complexity. In basidiomycete fungi, many more mating types than 7 exist, and the homeodomain genes involved in mating types are relatively small but highly diverse (Luo et al. 1994 PMID: 7914671). The mating types associated with GPCR receptors in fungi are arguably larger, but again their function is not that complex, and mating-type specific variations appear to evolve easily (Fowler et al 2004 PMID: 14643262; Seike et al. 2015 PMID: 25831518). The large protein complex formed is reminiscent of the fusion patches that develop in budding or fission yeasts. In these species, the mating type receptors are activated by ligand pheromones from the opposite mating type that induce polarity patch formation (see Sieber et al. 2023 PMID: 35148940 for a recent review). At these patches, growth (shmooing) and fusion occur, which is reminiscent (in a different order) of the tip transformation in T. thermophilia. The fusion of two cells is in all taxa a dangerous and complex event that requires the evolution of very strict regulation and the existence of a system like the MTRC and cyclin-dependent complex to regulate this process is therefore not unexpected. The existence of multiple mating types should not greatly complicate the process, as most of the machinery (except for the MTA and MTB) is identical among all mating types.

The Tetrahymena/ciliate genetics and lifecycle could be better explained. For a general audience, the system is not easy to follow. For example, the ploidy of the somatic nucleus with regards to the mating type is not clear to me. The MAC is generally considered "polyploid", but how does this work for the mating type? I assume only a single copy of the mating type locus is available in the MAC to avoid self-recognition in the cells. Is it known how the diploid origin reduces to a single mating type? This does not become apparent from Cervantes et al. 2013. Also, the explanation of co-stimulation is not completely clear (lines 49-60). Initially, direct cell-cell contact is mentioned, but later it is mentioned that "all cells become fully stimulated", even when unequal ratios are used. Is physical contact necessary? Or is this due to the "secrete mating-essential factors" (line 601)? These details are essential, for interpretation of the results and need to be explained better.

Abstract and introduction: Sexes are not mating types. In general, mating types refer to systems in which there is no obvious asymmetry between the gametes, beyond the compatibility system. When there is a physiological difference such as size or motility, sexes are used. This distinction is of importance because in many species mating types and sexes can occur together, with each sex being able to have either (when two) or multiple mating types. An example are SI in angiosperms as used as an example by the authors or mating types in filamentous fungi. See Billiard et al. 2011 [PMID: 21489122] for a good explanation and argumentation for the importance of making this distinction.