Reviewer #1 (Public Review):

Wu et al. sought to investigate the biological role of GPR110 in modulating hepatic lipid metabolism. The authors demonstrate a pathological role of GPR110 in promoting hepatic steatosis and generalized metabolic syndrome in a mouse model of diet-induced obesity. Furthermore, the authors identify enhanced SCD1 expression as an underlying mechanism promoting GPR110-induced metabolic dysfunction. Finally, the authors provide clinically relevant human data demonstrating a positive correlation between GPR110 expression and degree of hepatic steatosis. The strengths of this study include the rigorous design and execution of experiments, the utilization of gain and loss of function as well as pharmacological and genetic approaches, and the clinically relevant human data presented. The claims are supported by robust data. These findings have the potential to impact the field of metabolism in general, given their findings indicate targeting GPR110 can reverse diet induced obesity and metabolic syndrome. Only minor weaknesses were noted in regard to further interpretation of the data.

Reviewer #1 (Public Review):

The first defined that FAM76B inhibited the NF-κB-mediated inflammation by modulating translocation of hnRNPA2B1 to cytosol, where hnRNPA2B1 bound to IKK and released active NFkB that translocated into nuclear and initiated inflammation.

Reviewer #1 (Public Review):

The authors have investigated the effect of aerobic exercise on the decline in cerebral blood flow and cognitive function in old mice. Using appropriately two-photon microscopy and optical coherence tomography they found that aerobic exercise restored capillary blood flow and oxygenation in the white matter more than in the grey matter in old female mice. Interestingly, this aerobic exercise also ameliorated cognitive performance in these mice. The data obtained strongly supports the hypothesis and supports the conclusion of the study. Nevertheless, it would be important to compare the effects of aerobic exercise in old mice to its effects in young animals. It will be also interesting to know if the protective effect of exercise is similar in male mice.

This work brings new insights into the comprehension of the age-associated changes in cerebral microcirculation and in the protective effects of aerobic exercise.

Reviewer #1 (Public Review):

The manuscript by Huang, Li, et al. describes the identification of variants in the gene coding for p31 comet, a protein required for silencing the spindle assembly checkpoint or SAC, in women with recurrent pregnancy loss upon IVF. In three families mutations affecting splicing or expression of full-length protein were identified. The authors show that oocytes of the patients arrest in meiosis I, are most likely to fail to inactivate the SAC without a fully functional p31 comet. Indeed, the metaphase I arrest occurring in mouse oocytes upon overexpression of Mad2 can be rescued by overexpression of wild-type p31 comet, but not a truncated version. Injection of wt p31 comet into 6 human oocytes from one patient rescued the meiosis I arrest.

Main points:

The fact that inactivation of the SAC is required for anaphase I onset in human oocytes is not novel. Biallelic mutations of TRIP13 were shown to lead to the same phenotype (Zhang et al. Am J. Hum Gen., 2020).

No new mechanistic insights are obtained.

The authors propose a role for female fertility, however, also a male patient with a p31 comet variant is sterile.

The fact that the C-terminus of p31 comet is required for interaction with Mad2 and hence, turning off the SAC, is already known.

Reviewer #1 (Public Review):

This work reports an important demonstration of how to predict the mutational pathways to antimicrobial resistance (AMR) emergence, particularly in the enzyme DHFR (dihydrofolate reductase). Epistasis, or non-additive effects of mutations due to their background dependence, is a major confounding factor in the predictability of protein evolution, including proteins that confer antimicrobial resistance. In the first approach, they used the Rosetta to predict the mutant DHFR-drug binding affinity and the resulting selection coefficient, which then became inputs to a population genetics model. In the second approach, they use the observed clinical/environmental frequency of the variants to estimate the selection coefficient. Overall, this work is a compelling demonstration that a mechanistic model of the fitness landscape could recapitulate AMR evolution; however, considering that the number of mutations and pathways is small, a more compelling description of the robustness of the results and/or limitations of the model is needed.

Major strengths:<br /> 1. This is a compelling multi-disciplinary work that combines a mechanistic fitness landscape of DHFR (previously articulated in literature and cited by the authors), Rosetta to determine the biophysical effects of mutations, and a population genetics model.<br /> 2. The study takes advantage of extensive data on the clinical/environmental prevalence of DHFR mutations.<br /> 3. Provides a careful review of the surrounding literature.

Major weakness:<br /> 1. Considering that the number of mutations and pathways being recapitulated is rather small, I would suggest a more detailed description of the robustness of the results. For example:<br /> a. Please report the P-value for the correlation of the predicted DDG_{binding, theory} and DDG_{binding, experimental}. If interested in showing the correct assignment of mutational effects, perhaps use a contingency matrix to derive a P-value.<br /> b. Although the DDG_binding calculation in Rosetta seems to converge (Appendix figures 3 and 4), I do not think the DDG values before equilibration should be included in the final DDG estimate. In practice, there is a "burn in" number of runs where the force field optimizes the calculation to account for potential clashes in the structure, etc. This is particularly important since the starting structures are modeled from homology. Consequently, the distributions of DDG that include the equilibration runs are multimodal (Appendix figure 2), which means that calculating an average may be inappropriate.<br /> 2. The geographical areas over which the mutational pathways are independently estimated are not isolated, allowing for the potential that an AMR variant in one region arose due to "migration" from another area. For example, the S58R-S117N is the most frequent double mutant of PvDHFR in geographically proximate Southern/Southeastern Asia (Fig. 4). To a certain extent, similar mutational patterns occur for PfDHFR in Southern/Southeastern Asia (Fig. 3). Although accounting for mutant migration in the model may be beyond the scope of the study, a clear argument for the validity of the "isolated island" assumption is needed.

Reviewer #1 (Public Review):

This work develops new and improved methods for tracking and quantifying yeast cells in time-lapse microscopy. Overall, the manuscript presents exceedingly clever solutions to many practical data analysis problems that arise in microfluidics, some of which may be useful in other image analysis settings.

I find the manuscript is at times very dense and technical and is missing context for a general audience. Hard to know what are the most important contributions, and the authors assume the reader is familiar with many details of their previous work/field. Claims are made with little explanation, context or scientific logic.

Reviewer #1 (Public Review):

Extracellular vesicles (EVs) are emerging as important mediators of cell-to-cell signaling. In this paper the authors aim to demonstrate that Stranded at second (Sas), a Drosophila cell surface protein, binds to dArc1 and Ptp10D to mediate intercellular transport of dArc1 via EVs. dArc1 protein has been shown to form virus-like capsids that carry dArc1 mRNA from neurons to muscle, but little is known about this new intercellular communication pathway. Similarly, not much is known generally about how EVs are targeted to specific cell types, or how specific EV cargo can be delivered. Thus, this work is of interest to cell biologists and neuroscientists. However, the jumbled description of the results and general lack of rigor of experiments diminish the impact and interpretability of the conclusions. Moreover, almost all experiments rely on gain-of-function and over-expression of Sas, thus the relevance to normal physiological signaling is unclear.

Major strengths:<br /> 1. The data showing that Sas is released into EVs and delivered to cells is strong.<br /> 2. The EM data showing Sas localization to EVs is clear.

Major weaknesses:<br /> The description of the results omits some data in the figures and is not in a logical order. This made it hard to read and follow. There is also a lack of rigor and quantification in some experiments. Specifically:

1. Figure 2: Description of dArc1 putative capsids is absent from the results section (2f,g) until describing fig 4 data (line 362). Given that there is no immuno-EM labeling of dArc1 protein, it is not clear if Sas and dArc1 are localized to the same EVs. Nor is it clear if the double membrane EVs are actually EVs that contain capsids. Overall, the EM data lacks quantification. How many EVs on average show Sas labeling? How many EVs have double membranes? The dense protein staining surrounding EVs seems unusual, is this due to artifact of the purification? EV kits are generally non-specific and isolate non-EV membranes, corroboration using ultracentrifugation or size exclusion chromatography methods would be beneficial. SAS-FL overexpression results in more EVs, which confounds subsequent experiments suggesting that Sas targets EVs to specific cell types/regions.

2. Figure 3: There are no data showing the expression of Sas in SG cells using the GAL4 lines. Is this expression restricted to just SG cells? The results jump from a-b to f-g. c-e are out of order. The quantification in g should be broken into two and paired with the actual data (c-e, and f). It is not clear how the quantification in g was performed. How many WBs were analyzed? There seems to be a bubble in the first lane of f, which would preclude quantification. Why is d not quantified and there seems to be an overall increase in background staining in e that is not specific to discs. The source data files are not labelled and these data should be incorporated into annotated supplemental figures. Is transfer in a-b due to Ptp10D? How many WBs were quantified in g?

3. Figure 4: C and d, IP data has no inputs for IPs, no sizing markers, and no IgG controls for antibody specificity. These data would also be more convincing if done with FL Sas and included co-Ips from cell lysates.

4. In general, the WBs in the figures show very white backgrounds with high contrast, which suggests the images may have been manipulated. Total protein controls are also missing.

5. Figure 5: Ashley et al (Cell 2018) showed that dArc1 mRNA transfer required the 3'UTR so it is puzzling that the authors used heterologous UTRs. The results using FISH on endogenous dArc1 mRNA are dramatic. The authors should show definitively that their probe does not pick up over-expressed dArc1.

6. Many of the conclusions would be strengthened by the loss of function experiments, especially showing a requirement for Sas in dArc1 transfer.

Reviewer #1 (Public Review):

The authors of this manuscript address the question of whether vagal and sacral neural crest make distinct contributions to the enteric nervous system (ENS). The ENS regulates intestinal motility and many intestinal homeostatic functions; mutations in genes involved in ENS development lead to defects that can range from mild to catastrophic. The best studied of the ENS neuropathies is Hirschsprung disease, which is thought to result from failure of vagal neural crest cells to migrate properly into the distal intestine to differentiate into ENS neurons and glia. However, sacral neural crest cells are known to contribute to the distal ENS and have to migrate a considerably shorter distance. Thus, understanding whether there are distinct vagal and sacral contributions to the ENS provides insights into basic ENS biology as well as the basis of human disease. Previous transplantation and ablation studies have already revealed that vagal and sacral neural crest have differing ENS developmental potentials, although this has not been directly correlated with discrete cell types. Here the authors combine single cell RNA sequencing and a viral lineage tracing technique that is new to avians to gain insight into the different ENS cell types generated by vagal and sacral neural crest along the length of the intestine. They find that vagal and sacral neural crest exhibit distinct transcriptional profiles and contribute both similar and different progeny to the ENS. For example, both vagal and sacral crest contribute to progenitor cells, connective tissue and neurons, but most secretomotor neurons are vagal crest-derived whereas most adrenergic neurons and melanocytes in the distal intestine are sacral-crest derived. The authors also suggest a role of the local environment in determining the fate of vagal and sacral derivatives. The data presented in this manuscript provide a multitude of hypotheses about similarities and differences between vagal and sacral derived ENS cells. However, a shortcoming of the manuscript is that all of these hypotheses remain untested.

Reviewer #1 (Public Review):

In this project, the authors used a single-cell RNA sequencing technique, created a cell atlas of normal and diseased human anterior cruciate ligaments of 49,356 cells from 8 patients, explored the variations of the cell subtypes' spatial distributions, and found their associations with ligamental degeneration. Using the single-cell RNA sequencing data, the authors identified fibroblast subsets unique to normal and diseased tissues, revealed two processes of acute and chronic disease outcome in ligamental degeneration and found immune cell and stromal cell subclusters changed the extracellular matrix in ligament and contributed to the disease. Combined with spatial transcriptome sequencing, they found the spatial distribution of immune and stromal cells associated with the disease and demonstrated cell-cell communications among endothelial cells, macrophages, and fibroblasts.

Reviewer #1 (Public Review):

It has been previously shown that defective autophagy and disorganized microtubule network contribute to the pathogenesis of Duchenne muscular dystrophy (DMD). The authors previously reported that nitrite oxide synthase 2 (NOX2) regulates these alterations. It was also shown that acetylated tubulin facilitates autophagosome-lysosome fusion and thus autophagy. In the present study, the authors showed that autophagy is differentially regulated by redox and acetylation modifications in dystrophic mdx mice. The ablation of Nox2 in mdx mice activated the autophagosome maturation but not its fusion with the lysosome. On the other hand, the inhibition of histone acetylase 6 (HDAC6) restored microtubule acetylation, promoted autophagosome-lysosome fusion, and improved muscle function in mdx mice. The strength of this paper is the combination of different approaches to decipher the mechanism, including the evaluation of the level and interaction of several proteins involved in the maturation of autophagosomes and in the fusion between autophagosomes and lysosomes.

This study reveals an important molecular mechanism by which increasing microtubule acetylation improves autophagy and muscle function in dystrophic mice. This has a translational impact on several diseases in which autophagy is impaired. The improvement of autophagosome-lysosome fusion with HDAC6 inhibitor is supported by several data, but some parts merit further analysis:

1) To add appropriate controls (e.g. without antibodies) to support protein-protein interaction for all co-immunoprecipitation assays.<br /> 2) The simple evaluation of the protein levels of p62 and LC3-II is not sufficient to claim autophagy improvement after HDAC6 inhibition. It would be good to evaluate the autophagic flux in vivo in all groups of mice (to treat the mice with or without autophagy inhibitor and evaluate whether the difference in the level of LC3-II between the two conditions is higher with HDAC6 inhibitor than without in the mdx mice).

Reviewer #1 (Public Review):

The purpose of this study was to investigate within the diverse Multiethnic Cohort (MEC) study on how COVID-19 impacted access to cancer screenings and treatment through a cross-sectional survey in this study population.

Major strengths were leveraging existing participants in a cohort study that contained a diverse population. The MEC cohort participants have been studied since the 90's. The investigators used a well-designed survey and performed analysis on responses focused on cancer screening attendance. Weaknesses of this study are low response rates that make the results not generalizable to other populations, especially younger populations, and possible bias of specific types of individuals responding.

This study found associations with racial/ethnic, age, comorbidities, and education to be key factors associated with cancer-related screening and healthcare seeking during the COVID-19 pandemic.

Whether the associations observed by the investigators would remain over time is unknown, as health care seeking changed as the pandemic evolved and prevention tools (including mass testing and vaccination) became available. It is important to note that this is a snapshot in time, so while it is informative, it will be important to monitor whether certain groups/populations that may be at high risk for cancer may need to be targeted for early diagnosis and screening.

Reviewer #1 (Public Review):

This study provided evidence to interpret and understand the aging and developmental processes in children. The main strength of the study is it measures a set of biological age measures and a set of developmental measures, thus providing multi-faceted evidence to explain the associations between aging and development in children. The main weakness of this study is that how to measure and test the aging hypothesis of "a buildup of biological capital model" and "wear and tear" is not well-explained. Why the observed associations between biological age measures and developmental measures could support the aforementioned aging theories?

1. Abstract - conclusion: The aging hypothesis of "a buildup of biological capital model" and "wear and tear" were mentioned in the conclusion without an explanation of these theories in the previous section. Readers who are not experts in the field may not understand the logic.<br /> 2. Result - Biological age marker performance: the correlation between transcriptome age and chronological age is very strong (r =0.94). I am afraid that very little age-independent information could be captured by the transcriptome age. Is it possible to down-regulate the age dependency of the transcriptome age in the training process?<br /> 3. The study population comes from several cohorts, which might influence the results. How the cohort effects were controlled for in the analyses?<br /> 4. Figure 3 only showed the number of p values. Can the author also provide the number of point estimates and 95% confidence intervals, perhaps in the supplemental table?

Reviewer #1 (Public Review):

In the manuscript, titled "Comparative single-cell profiling reveals distinct cardiac resident macrophages essential for zebrafish heart regeneration," Wei et al. perform bulk and single-cell RNA-sequencing on uninjured and injured zebrafish hearts with or without prior macrophage depletion by clodronate. For the single-cell RNA sequencing, the authors sort macrophages and neutrophils prior to sequencing by using fluorescent reporters for each of the two lineages. The authors characterize the differential gene expression between injured and uninjured hearts with and without prior macrophage depletion. The single-cell analyses allow the characterization of nine discrete subpopulations of macrophages and two distinct neutrophil types. The manuscript is largely descriptive with lots of discussion of specific differentially expressed genes. The authors conclude that tissue-resident macrophages are important for heart regeneration through the remodeling of the microenvironment and by promoting revascularization. Circulating monocyte-derived macrophages cannot adequately replace the resident macrophages even after recovery from clodronate depletion.

The manuscript presents a very large catalog of useful gene expression data and further characterizes the diversity of macrophages and neutrophils in the heart following injury. Although the conclusions that resident macrophages are important for regeneration and that circulating macrophages cannot adequately substitute for them are not particularly novel, this manuscript provides additional support for those ideas and extends that work by providing a wealth of gene expression data from the different macrophage sub-populations in the zebrafish and how they respond to and promote regeneration. The authors also present a nice analysis supporting the interactions of macrophages with neutrophils via comparing receptors and ligands (from gene expression data) on the two populations - this should be a useful resource.

Reviewer #1 (Public Review):

In the current work, the authors aimed to investigate the genetic and non-genetic factors that impact structural asymmetry.

A major strength is the number of data samples included in the study to assess brain structural asymmetry. A consequence of the inclusion of many samples is then also the sample size. Given that the authors also work with longitudinal data, it would be nice to be able to appreciate the individual effects across time points, this is now a little unclear. A possible less well-developed approach is the genetic basis, as this was stated as the main question, here the investigations are not that deep and may only touch upon the question. Moreover, the association with cognition, handedness, sex, and ICV is somewhat interesting yet seems also a bit minimal to fully grasp its implications.

To some extent, the aim of the study could still be written with more clarity. However, the authors have in part achieved their aims - assuming it is found a consensus on the brain asymmetry patterns in humans as is stated in the abstract. Overall the results support the conclusions, yet the strong interpretation of early life factors in particular is not empirically investigated as far as I gather.

Overall this is a nice and thorough work on asymmetry that may inform further work on brain asymmetry, its genetic basis, development, environmentally induced change, and link to behavioural variation.

Reviewer #1 (Public Review):

This manuscript puts forward the concept that there is a specific time window during which YAP/TAZ driven transcription provides feedback for optimal endothelial cell adhesion, cytoskeletal organization and migration. The study follows up on previous elegant findings from this group and others which established the importance of YAP/TAZ-mediated transcription for persistent endothelial cell migration. The data presented here extends the concept at two levels: first, the data may explain why there are differences between experimental setups where YAP/TAZ activity are inhibited for prolonged times (e.g. cultures of YAP knockdown cells), versus experiments in which the transient inhibition of YAP/TAZ and (global) transcription affects endothelial cell dynamics prior to their equilibrium.

All experiments are convincing, clearly visualized and quantified. I have some questions that the authors may address to strengthen this exciting new concept:

• Point for more elaborate discussion: Apparently the timescale of negative feedback signals is conserved between endothelial cell migration in vitro (with human cells) and endothelial migration during the formation of ISVs in zebrafish. What do you think might be an explanation for such conserved timescales? Are there certain processes within cytoskeletal tension build up that require this quantity of time to establish? Or does it relate to the time that is needed to begin to express the YAP/TAZ target genes that mediate feedback?<br /> • Do you expect different timescales for slower endothelial migratory processes (e.g. for instance during fin vascular regeneration which takes days) ?<br /> • Is the ~4hrs and 8hrs feedback time window a general property or does it differ between specific endothelial cell types? In the veins the endothelial cells generate less stress fibers and adhesions compared to in the arteries. Does this mean that there might be a difference in the feedback time window, or does that mean that certain endothelial cell types may not have such YAP/TAZ-controlled feedback system?<br /> • The experiments are based on perturbations to prove that transcriptional feedback is needed for endothelial migration. What would happen if the feedback systems is always switched on? An experiment to add might be to analyse the responsiveness of endothelial cells expressing constitutively active YAP/TAZ.<br /> • To investigate the role of YAP-mediated transcription in an accurate time-dependent manner the authors may consider using the recently developed optogenetic YAP translocation tool: https://doi.org/10.15252/embr.202154401

Reviewer #1 (Public Review):

The authors set out to develop an organoid model of the junction between early telencephalic and ocular tissues to model RGC development and pathfinding in a human model. The authors have succeeded in developing a robust model of optic stalk(OS) and optic disc(OD) tissue with innervating retinal ganglion cells. The OS and OD have a robust pattern with distinct developmental and functional borders that allow for a distinct pathway for pathfinding RGC neurites.

This study falls short on a thorough analysis of their single cell transcriptomics (scRNAseq). From the scRNAseq it is unclear the quality and quantity of the targeted cell types that exist in the model. A comparative analysis of the scRNAseq profiles of their cell-types with existing organoid protocols, to determine a technical improvement, or with fetal tissue, to determine fidelity to target cells, would greatly improve the description of this model and determine its utility. This is especially necessary for the RGCs developed in this protocol as they recommend this as an improved model to study RGCs.

Future work targeting RGC neurite outgrowth mechanisms will be exciting.

Reviewer #1 (Public Review):

In this article, the authors found a distinct fibroblast subpopulation named AG fibroblasts, which are capable of regulating myeloid cells, T cells and ILCs, and proposed that AG fibroblasts function as a previously unrecognized surveillant to orchestrate chronic gingival inflammation in periodontitis. Generally speaking, this article is innovative and interesting, however, there are some problems that need to be addressed to improve the quality of the manuscript.

Results:

1) It is recommended to add HE staining and immunohistochemistry staining to observe the inflammation, tissue damage, and repair status from 0 to 7 days, so that readers can understand cell phenotype changes corresponding to the periodontitis stage. The observation index can include inflammation and vascular related indicators.

2) Figure 1A-1D can be placed in the supplementary figure.

3) I suggest the authors to put the detection of the existence of AG fibroblasts before exploring its relationship with other types of cells.

4) The layout of the picture should be closely related to the topic of the article. It is recommended to readjust the layout of the picture. Figure 1 should be the detection of AG cells and their proportion changes from 0 to 7 days. In other figures, the authors can separately describe the proportion changes of myeloid cells, T cells and ILCs, and explored the association between AG fibroblasts and these cell types.

Methods:

It is recommended to separately list the statistical methods section. The statistical method used in the article should be one-way ANOVA.

Reviewer #1 (Public Review):

Overall, I find the work performed by the authors very interesting. However, the authors have not always included literature that seems relevant to their study. For instance, I do not understand why two papers Dunican et al 2013 and Dunican et al 2015, which provide important insight into Lsh/HELLS function in mouse, frog and fish were not cited. It is also important that the authors are specific about what is known and in particular about what is not known about CDCA7 function in DNA methylation regulation. Unless I am mistaken, there is currently only one study (Velasco et al 2018) investigating the effect of CDCA7 disruption on DNA methylation levels (in ICF3 patient lymphoblastoid cell lines) on a genome-wide scale (Illumina 450K arrays). Unoki et al 2019 report that CDCA7 and HELLS gene knockout in human HEK293T cells moderately and extremely reduces DNA methylation levels at pericentromeric satellite-2 and centromeric alpha-satellite repeats, respectively. No other loci were investigated, and it is therefore not known whether a CDCA7-associated maintenance methylation phenotype extends beyond (peri)centromeric satellites. Thijssen et al performed siRNA-mediated knockdown experiments in mouse embryonic fibroblasts (differentiated cells) and showed that lower levels of Zbtb24, Cdca7 and Hells protein correlate with reduced minor satellite repeat methylation, thereby implicating these factors in mouse minor satellite repeat DNA methylation maintenance. Furthermore, studies that demonstrate a HELLS-CDCA7 interaction are currently limited to Xenopus egg extract (Jenness et al 2018) and the human HEK293 cell line (Unoki et al 2019). Whether such an interaction exists in any other organism and is of relevance to DNA methylation mechanisms remains to be determined. Therefore, in my opinion, the conclusion that "Our co-evolution analysis suggests that DNA methylation-related functionalities of CDCA7 and HELLS are inherited from LECA" should be softened, as the evidence for this scenario is not very compelling and seems premature in the absence of molecular data from more species.

The authors used BLAST searches to characterize the evolutionary conservation of CDCA7 family proteins in vertebrates. From Figure 2A, it seems that they identify a LEDGF binding motif in CDCA7/JPO1. Is this correct and if yes, could you please elaborate and show this result? This is interesting and important to clarify because previous literature (Tesina et al 2015) reports a LEDGF binding motif only in CDCA7L/JPO2.

To provide evidence for a potential evolutionary co-selection of CDCA7, HELLS and the DNA methyltransferases (DNMTs) the authors performed CoPAP analysis. Throughout the manuscript, it is unclear to me what the authors mean when referring to "DNMT3". In the Material and Methods section, the authors mention that human DNMT3A was used in BLAST searches to identify proteins with DNA methyltransferase domains. Does this mean that "DNMT3" should be DNMT3A? And if yes, should "DNMT3" be corrected to "DNMT3A"? Is there a reason that "DNMT3A" was chosen for the BLAST searches?

CoPAP analysis revealed that CDCA7 and HELLS are dynamically lost in the Hymenoptera clade and either co-occurs with DNMT3 or DNMT1/UHRF1 loss, which seems important. Unfortunately, the authors do not provide sufficient information in their figures or supplementary data about what is already known regarding DNA methylation levels in the different Hymenoptera species to further consider a potential impact of this observation. What is "the DNA methylation status" of all these organisms? This information cannot be easily retrieved from Table S2. A clearer presentation of what is actually known already would improve this paragraph.

Furthermore, A. thaliana DDM1, and mouse and human Lsh/Hells are known to preferably promote DNA methylation at satellite repeats, transposable elements and repetitive regions of the genome. On the other hand, DNA methylation in insects and other invertebrates occurs in genic rather than intergenic regions and transposable elements (e.g. Bewick et al 2017; Werren JH PlosGenetics 2013). It would be helpful to elaborate on these differences.

Reviewer #1 (Public Review):

The manuscript focused on roles of a key fatty-acid synthesis enzyme, acetyl-coA-carboxylase 1 (ACC1), in the metabolism, gene regulation and homeostasis of invariant natural killer T (NKT_ cells and impact on these T cells' roles during asthma pathogenesis. The authors presented data showing that the acetyl-coA-carboxylase 1 enzyme regulates the expression of PPARg then the function of NKT cells including the secretion of Th2-type cytokines to impact on asthma pathogenesis. The results are clearcut and data were logically presented.

Major concerns:

1. This study heavily relied on the CD4-CreACC1fl/fl mice. While using of a-GalCer stimulation and Ja18KO mice mitigated the concern, it is still a major concern that at least some of the phenotype were due to the effect on conventional CD4 T cells. For example, the deletion of ACC1 gene seems also decreased the numbers of conventional CD4 T cells (Fig. 2D, Fig. S1D). Previously there were reports showing ACC1 gene in conventional CD4 T cells also plays a role in lung inflammation (Nakajima et al., J. Exp. Med. 218, 2021). If the authors believe the phenotype observed was mainly due to iNKT cells, rather than conventional CD4 T cells, a compare/contrast of the two studies should be discussed to explain or reconcile the results.

2. The overall significance of the manuscript is related to the potential clinical suppression of ACC1 in human asthma patients. However, the authors only showed the elevated ACC1 genes in these patients, not even in vitro data demonstrating that suppression of ACC1 genes in the iNKT cells from patients could have potential therapeutic effect or suppression of the relevant cytokines.

3. The authors report that a-GalCer administration can induce the AHR, however, in the cited paper (Hachem et al., Eur J. Immunol. 35, 2793, 2005), iNKT cell activation seems to have the opposite effect to inhibit AHR. Did the authors mean to cite different papers?

Reviewer #1 (Public Review):

In the study by Venkat et al. the authors expand the current knowledge of allosteric diversity in the human kinome by c-terminal splicing variants using as a paradigm DCLK1. In this work, the authors provide evolutionary and some mechanistic evidence about how c-terminal isoform specific variants generated by alternative splicing can regulate catalytic activity by means of coupling specific phosphorylation sites to dynamical and conformational changes controlling active site and substrate pocket occupancy, as well as interfering with protein-protein interacting interfaces that altogether provides evidence of c-terminal isoform specific regulation of the catalytic activity in protein kinases.

The paper is overall well written, the rationale and the fundamental questions are clear and well explained, the evolutionary and MD analyses are very detailed and well explained. The methodology applied in terms of the biochemical and biophysical tools falls a bit short in some places and some comments and suggestions are given in this respect. If the authors could monitor somehow protein auto-phosphorylation as a functional readout would be very useful by means of using phospho-specific antibodies to monitor activity. Overall I think this is a study that brings some new aspects and concepts that are important for the protein kinase field, in particular the allosteric regulation of the catalytic core by c-terminal segments, and how evolutionary cues generate more sophisticated mechanisms of allosteric control in protein kinases. However a revision would be recommended.

Reviewer #1 (Public Review):

Original review:

This manuscript by Walker et. al. explores the interplay between the global regulators HapR (the QS master high cell density (HDC) regulator) and CRP. Using ChIP-Seq, the authors find that at several sites, the HapR and CRP binding sites overlap. A detailed exploration of the murPQ promoter finds that CRP binding promotes HapR binding, which leads to repression of murPQ. The authors have a comprehensive set of experiments that paints a nice story providing a mechanistic explanation for converging global regulation. I did feel there are some weak points though, in particular the lack of integration of previously identified transcription start sites, the lack of replication (at least replication presented in the manuscript) for many figures, some oddities in the growth curve, and not reexamining their HapR/CRP cooperative binding model in vivo using ChIP-Seq.

Review of revised version:

This revised manuscript by Walker et. al. addresses some of the editorial points and conceptual discussion, but in general, most of my suggestions (as the previous reviewer #1) for additional experimentation or addition were not addressed as discussed below. Therefore, my overall review has not changed.

1) For example, in point 1, the suggested analysis was not performed because it is not trivial. My reason for making this suggestion is that the original manuscript was limited to Vibrio cholerae, and the impact of the manuscript would increase if the findings here were demonstrated to be more broadly applicable. I expect papers published in eLife to have such broad applicability. But no changes were made to the manuscript in this regard. The revised version is still limited to only Vibrio cholerae.

2) For point 2, the activity of FLAG-tag luxO could have been simply validated in a complementation assay. Yes, they demonstrated DNA binding, but that is not the only activity of LuxO.

3) For point 7, the transcriptional fusions were not explored at different times or different media, which is also something that was hinted at by other reviewers. In regard to exploring expression at different time points, this seems particularly relevant for QS regulated genes.

4) For point 13, the authors express that doing an additional CHIP-Seq is outside of the scope of this manuscript. Perhaps that is the case, but the point of the comment is to validate the in vitro binding results with an in vivo binding assay. A targeted CHIP-Seq approach specifically analyzing the promoters where cooperative binding was observed in vitro could have addressed this point.

Reviewer #1 (Public Review):

The manuscript describes that cultured mammalian cells adapt to chronic stress by increasing their size and protein translation through Hsp90. The authors extensively use Hsp90 knockout cells and mass spectrometry to provide solid evidence that chronic heat shock response is accompanied by cell size changes and stress resistance in large cells. The major strength of the work is the authors ability to document the heat shock response in detail, while the main weakness is that the cell size changes appear not to be quantitative making it difficult to assess how much the cell density is changed in chronic stress. Nevertheless, the increased stress resistance of large cells is conceptually important and provides one potential explanation why cells need to control their size. This work adds to our understanding of how cellular stress is managed, and while stress responses have been observed previously in relation to cell size, this work provides evidence for increased stress resistance in larger cells.

Reviewer #1 (Public Review):

"MAGIC" was introduced by the Rong Li lab in a Nature letters article in 2017. This manuscript is an extension of this original work and uses a genome wide screen the Baker's yeast to decipher which cellular pathways influence MAGIC. Overall, this manuscript is a logical extension of the 2017 study, however the manuscript is challenging to follow, complicated by the data often being discussed out of sequence. Although the manuscripts makes claims of a mechanism being pinpointed, there are many gaps and the true mechanisms of how the factors identified in the screen influence MAGIC is not clear. A key issue is that there are many assumptions drawn on previous literature, but central aspects of the mechanisms being proposed are not adequately shown.

Key comments:

[1] Reasoning and pipelines presented in the first two sections of the results are disordered and do not follow figure order. In some instances, the background to experimental analyses such as detailing the generation of spGFP constructs in the YKO mutant library, or validation of Snf1 activation are mentioned after respective results are discussed. This needs to be fixed.<br /> [2] In general there is a lack of data to support microscopy data and supporting quantification analysis. The validity of this data could be significantly strengthened with accompanying western blots showing accumulation of a given constructs in mitochondrial sub compartments (as was the case in the labs original paper in 2017).<br /> [3] Much of the mechanisms proposed relies on the Snf1 activation. This is however not shown, but assumed to be taking place. Given that this activation is central to the mechanism proposed this should be explicitly shown here - for example survey the phosophorylation status of the protein.

Joint Public Review:

Smirnova et al. present a cryo-EM structure of human SIRT6 bound to a nucleosome as well as the results from molecular dynamics simulations. The results show that the combined conformational flexibilities of SIRT6 and the N-terminal tail of histone H3 limit the residues with access to the active site, partially explaining the substrate specificity of this sirtuin-class histone deacetylase. The cryo-EM analysis in its current form is incomplete, lacking aspects of validation such as angular distribution information and other standard measurements of the quality of the reconstruction. Biochemical validation of the structural findings is inadequate, relying primarily on previous publications. Importantly, two other groups have recently published cryo-EM structures of SIRT6:nucleosome complexes. This manuscript by Smirnova et al., therefore, confirms and complements these previous findings, with the addition of some novel insights into the role of structural flexibility in substrate selection.

Reviewer #1 (Public Review):

Gehr and colleagues used an elegant method, using neuropixels probes, to study retinal input integration by mouse superior collicular cells in vivo. Compared to a previous report of the same group, they opto-tagged inhibitory neurons and defined the differential integration onto each group. Through these experiments, the author concluded that overall, there is no clear difference between the retina connectivity to excitatory and inhibitory superior colliculus neurons. The exception to that rule is that excitatory neurons might be driven slightly stronger than inhibitory ones. Technically, this work is performed at a high level, and the plots are beautifully conceived, but I have doubts if the interpretation given by the authors is solid. I will elaborate below.

Some thoughts about the interpretation of the results.

My main concern is the "survivor bias" of this work, which can lead to skewed conclusions. From the data set acquired, 305 connections were measured, 1/3 inhibitory and 2/3 excitatory. These connections arise from 83 RGC onto 124 RGC (I'm interpreting the axis of Fig.2 C). Here it is worth mentioning that different RGC types have different axonal diameters (Perge et al., 2009). Here the diameter is also related to the way cells relay information (max frequencies, for example). It is possible that thicker axons are easier to measure, given the larger potential changes would likely occur, and thus, selectively being picked up by the neuropixels probe. If this is the case, we would have a clear case of "survival bias", which should be tested and discussed. One way to determine if the response properties of axonal termini are from an unbiased sample is to make a rough functional characterization as generally performed (see Baden et al. 2006). This is fundamental since all other conclusions are based on unbiased sampling.

One aspect that is not clear to me is to measure of connectivity strength in Figure 2. Here it seems that connectivity strength is directly correlated with the baseline firing rate of the SC neuron (see example plots). If this is a general case, the synaptic strength can be assumed but would only differ in strength due to the excitability of the postsynaptic cell. This should be tested by plotting the correlation coefficient analysis against the baseline firing rate.

My third concern is the assessment of functional similarity in Fig. 3. It is not clear to me why the similarity value was taken by the arithmetic mean. For example, even if the responses are identical for one connected pair that exclusively responds either to the ON or OFF sparse noise, the maximal value can only be 0.67. Perhaps I misunderstood something. Secondly, correlations in natural(istic) movies can differ dramatically depending on the frame rate that the movie was acquired and the way it is displayed to the animal. What looks natural to us will elicit several artifacts at a retinal level, e.g., due to big jumps between frames (no direction-selective response) or overall little modulation (large spatial correlations). I would rather opt for uniform stimuli, as suggested previously. Of course, these are also approximations but can be easily reproduced by different labs and are not subjected to the intricacies of the detailed naturalistic stimulus used.

Fourth. It is important to control the proportion of inhibitory cells activated optogenetically across the recording probe. Currently, it is not possible to assess if there are false negatives. One way of controlling for this would be to show that the number of inhibitory interneurons doesn't vary across the probe.

Fifth. In Fig. 4, the ISI had a minimal bound of 5 ms. Why? This would cap the firing rate at 200Hz, but we know that RGC in explants can fire at higher frequencies for evoked responses. I would set a lower bound since it should come naturally from the after-depolarization block. Another aspect that remains unclear is to what extent the paired-spike ratio depends on the baseline firing rate. This would change the interpretation from the particular synaptic connection to the intrinsic properties of the cell and is plausible since the bassline firing rate varies tremendously. One related analysis would be to plot the change of PSR depending on the ISI. It would be intuitive to make a scatter plot for all paired spikes of all recorded neurons (separated into inhibitory and excitatory) of ISI vs. PSR.

Panel 4E is confusing to me. Here what is plotted is efficacy 1st against PSR (which is efficacy 2nd/efficacy 1st). Given that you have a linear relation between efficacy 1st and efficacy 2nd (panel 4C), you are essentially re-plotting the same information, which should necessarily have a hyperbolic relationship: [ f(x) = y/x ]. Thus, fitting this with a linear function makes no sense and it has to be decaying if efficacy 2nd > efficacy1st as shown in 4C.

Finally, in Figure 5, the perspective is inverted, and the spike correlations are seen from the perspective of SC neurons. Here it would also be good to plot the cumulative histograms and not look at the averages. Regarding the similarity index and use of natural stats, please see my previous comments. Also, would it be possible to plot the contribution v/s the firing rate with the baseline firing rate with no stimulation or full-field stimulation? This is important since naturalistic movies have too many correlations and dependencies that make this plot difficult to interpret.

Overall, the paper only speaks from excitatory and inhibitory differences in the introduction and results. However, it is known that there are three clear morphologically distinct classes of excitatory neurons (wide-field, narrow-field, and stellate). This topic is touched in the discussion but not directly in the context of these results. Smaller cells might likely be driven much stronger. Wide-field cells would likely not be driven by one RGC input only and will probably integrate from many more cells than 6.

Reviewer #1 (Public Review):

Segas et al. present a novel solution to an upper-limb control problem which is often neglected by academia. The problem the authors are trying to solve is how to control the multiple degrees of freedom of the lower arm to enable grasp in people with transhumeral limb loss. The proposed solution is a neural network based approach which uses information from the position of the arm along with contextual information which defines the position and orientation of the target in space. Experimental work is presented, based on virtual simulations and a telerobotic proof of concept.

The strength of this paper is that it proposes a method of control for people with transhumeral limb loss which does not rely upon additional surgical intervention to enable grasping objects in the local environment. A challenge the work faces is that it can be argued that a great many problems in upper limb prosthesis control can be solved given precise knowledge of the object to be grasped, its relative position in 3D space and its orientation. It is difficult to know how directly results obtained in a virtual environment will translate to real world impact. Some of the comparisons made in the paper are to physical systems which attempt to solve the same problem. It is important to note that real world prosthesis control introduces numerous challenges which do not exist in virtual spaces or in teleoperation robotics.

The authors claim that the movement times obtained using their virtual system, and a teleoperation proof of concept demonstration, are comparable to natural movement times. The speed of movements obtained and presented are easier to understand by viewing the supplementary materials prior to reading the paper. The position of the upper arm and a given target are used as input to a classifier, which determines the positions of the lower arm, wrist and the end effector. The state of the virtual shoulder in the pick and place task is quite dynamic and includes humeral rotations which would be challenging to engineer in a real physical prosthesis above the elbow. Another question related to the pick and place task used is whether or not there are cases where both the pick position and the place position can be reached via the same, or very similar, shoulder positions? i.e. with the shoulder flexion-extension and abduction-adduction remaining fixed, can the ANN use the remaining five joint angles to solve the movement problem with little to no participant input, simply based on the new target position? If this was the case, movements times in the virtual space would present a very different distribution to natural movements, while the mean values could be similar. The arguments made in the paper could be supported by including individual participant data showing distributions of movement times and the distances travelled by the end effector where real movements are compared to those made by an ANN.

In the proposed approach users control where the hand is in space via the shoulder. The position of the upper arm and a given target are used as input to a classifier, which determines the positions of the lower arm, wrist and the effector. The supplementary materials suggest the output of the classifier occurs instantaneously, in that from the start of the trial the user can explore the 3D space associated with the shoulder in order to reach the object. When the object is reached a visual indicator appears. In a virtual space this feedback will allow rapid exploration of different end effector positions which may contribute to the movement times presented. In a real world application, movement of a distal end-effector via the shoulder is not to be as graceful and a speed accuracy trade off would be necessary to ensure objects are grasped, rather than knocked or moved.

Another aspect of the movement times presented which is of note, although it is not necessarily incorrect, is that the virtual prosthesis performance is close too perfect. In that, at the start of each trial period, either pick or place, the ANN appears to have already selected the position of the five joints it controls, leaving the user to position the upper arm such that the end effector reaches the target. This type of classification is achievable given a single object type to grasp and a limited number of orientations, however scaling this approach to work robustly in a real world environment will necessitate solving a number of challenges in machine learning and in particular computer vision which are not trivial in nature. On this topic, it is also important to note that, while very elegant, the teleoperation proof of concept of movement based control does not seem to feature a similar range of object distance from the user as the virtual environment. This would have been interesting to see and I look forward to seeing further real world demonstrations in the authors future work.

Reviewer #1 (Public Review):

In this study, Drougard et al. examined the consequences of an acute high fat diet (HFD) on microglia in mice. 3-day HFD influenced the regulation of systemic glucose homeostasis in a microglia-dependent and independent manner, as determined using microglial depletion with PLX5622. 3-day HFD increased microglial membrane potential and the levels of palmitate and stearate in cerebrospinal fluid in vivo. Using confocal imaging, respirometry and stable isotope-assisted tracing in primary microglial cultures, the authors suggest an increase in mitochondrial fission and metabolic remodelling occurs when exposed to palmitate, which increases the release of glutamate, succinate and itaconate that may alter neuronal metabolism. This acute microglial metabolic response following acute HFD is subsequently linked to improved higher cognitive function (learning and memory) in a microglia and DRP1-dependent manner.

Strengths:<br /> Overall, this study is interesting and novel in linking acute high fat diet to changes in microglia and improved learning and memory in mice. The role for microglia and DRP1 in regulating glucose homeostasis and memory in vivo appears to be supported by the data.

Weaknesses:<br /> The authors suggest that utilisation of palmitate by microglia following HFD is the driver of the acute metabolic changes and that the release of microglial-derived lactate, succinate, glutamate and itaconate are causally linked to improvements in learning and memory.<br /> A major weakness is that the authors provide no mechanistic link between beta-oxidation of palmitate (or other fatty acids) in microglia and the observed systemic metabolic and memory phenotypes in vivo. Pharmacological inhibition of CPT1a could be considered or CPT1a-deficient microglia.

Another major weakness is that the authors also suggest that 3-day HFD microglial response (increase membrane potential) is likely driven by palmitate-induced increases in itaconate feedforward inhibition of complex II/SDH. Whilst this is an interesting hypothesis, the in vitro metabolic characterisation is not entirely convincing. The authors suggest that acute palmitate appears to rapidly compromise or saturate complex II activity. Succinate is a membrane impermeable dicarboxylate. It can enter cells via MCT transporters at acidic pH. It is not clear that I) Succinate is taken up into microglia, II) If the succinate used was pH neutral sodium succinate or succinic acid, and III) If the observed changes are due to succinate oxidation, changes in pH or activation of the succinate receptor SUCNR1 on microglia. In the absence of these succinate treatments, there are no alterations in mitochondrial respiration or membrane potential following palmitate treatment, which does not support this hypothesis. Intracellular itaconate measurements and quantification are lacking and IRG1 expression is not assessed. There also appears to be more labelled itaconate in neuronal cultures from control (BSA) microglia conditioned media, which is not discussed. What is the total level of itaconate in neurons from these conditioned media experiments? No evidence is provided that the in vivo response is dependent on IRG1, the mitochondrial enzyme responsible for itaconate synthesis, or itaconate. To causally link IRG1/itaconate, IRG1-deficient mice could be used in future work.

While microglial DRP1 is causally implicated the role of palmitate is not convincing. Mitochondrial morphology changes are subtle including TOMM20 and DRP1 staining and co-localization - additional supporting data should be provided. Electron microscopy of mitochondrial structure would provide more detailed insight to morphology changes. Western blot of fission-associated proteins Drp1, phospho-Drp1 (S616), MFF and MiD49/51. Higher magnification and quality confocal imaging of DRP1/TOMM20. Drp1 recruitment to mitochondrial membranes can be assessed using subcellular fractionation. No characterisation of primary microglia from DRP1-knockout mice is performed with palmitate treatment. Authors demonstrate an increase in both stearate and palmitate in CSF following 3-day HFD. Only palmitate was tested in the regulation of microglial responses, but it may be more informative to test stearate and palmitate combined.

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Reviewer #1 (Public Review):

In this manuscript by Douglas et al, the investigative team seeks to identify Staphylococcus aureus genes (and associated polymorphisms) that confer altered susceptibility to human serum, with the hypothesis that such genes might contribute to the propensity of a strain to cause bacteremia, invasive disease, and/or death. Using an innovative GWAS-like approach applied to a bank of over 300 well-characterized clinical S. aureus isolates, the authors discover SNPs in seven different staphylococcal genes that confer increased survival in the setting of serum exposure. The authors then mainly focus on one gene, tcaA, and illustrate a potential mechanism whereby modification of peptidoglycan structure and WTA display leads to altered susceptibility to serum, serum-derived antimicrobial compounds, and antibiotics. One particularly significant finding is that the identified tcaA SNP is significantly associated with patient mortality, in that patients infected with the SNP bearing isolate are less likely to die from infection. It is therefore hypothesized that this SNP represents an adaptive mutation that promotes serum survival while decreasing virulence and host mortality. In a murine model of infection, the strain bearing the WT allele of tcaA is significantly more virulent than the tcaA mutant, suggesting that the role of tcaA in bacteremia is infection-phase dependent.

This manuscript has many strengths. The triangulation of genomic analysis, patient outcomes data, and in vitro and in vivo mechanistic testing adds to the significance of the findings in terms of human disease. Testing the impact of mutating tcaA in multiple staphylococcal lineages and backgrounds also increases the rigor of the study. The identification of bacterial loci that impact susceptibility to both host antimicrobial compounds and commonly used antibiotics is also a strength of this work, given the evolutionary and treatment implications for such genes.

One moderate weakness is that the impact of the identified SNP in tcaA is only tested in some of the assays, whereas the majority of the testing is performed with a whole gene knockout. In some cases this results in more speculative conclusions that will require further testing to validate. All in all, this is an exciting manuscript that will be of interest to the broader research communities focused on staphylococcal pathogenesis, bacterial evolution, and host-pathogen interactions, as well as to clinicians who care for patients with invasive staphylococcal infection.

Reviewer #1 (Public Review):

Using an immobilised metal affinity chromatography (IMAC)-based assay coupled with Western blot immunodetection analysis, SbtB, the regulatory protein for SbtA activity, is shown in itself to be regulated by the local adenylate energy charge (AEC), with inhibitory binding of SbtB to SbtA disfavoured at high ATP:ADP ratios. Such conditions are expected to be encountered during steady-state photosynthesis with the associated cellular demand for Ci and SbtA activity.

By homology with ATP-binding PII proteins, ATP is proposed to interact with a loop region of SbtB, changing its conformation on binding and inhibiting the formation of the (inactive) SbtA:SbtB complex. On the basis of this, the authors propose that SbtB acts an AEC-sensing 'curfew' protein for SbtA activity, tuning bicarbonate import by this protein for situations when carbon fixation would be physiologically (and energetically) advantageous. As SbtA is a HCO3-/Na+ symporter, Na+ homeostasis would also be controlled by regulation of this transporter.

The IMAC assay used to monitor SbtA:SbtB complex stability as a function of AEC seems robust, is relatively straightforward and may be of interest to other researchers investigating adenylate-sensing protein reaction partners (with the usual caveats on extrapolating in vitro results to living systems, as noted by the authors).

In this study, SbtA regulation was also investigated in vivo in a Synechococcus HCO3- transporter knockout mutant via measurement of labelled HCO3- uptake and overall photosynthetic performance (MIMS-monitored O2 evolution as a function of PAR). Here, SbtB was inferred to regulate SbtA activity during the induction of photosynthesis (i.e. at low ATP:ADP) and not when photosynthesis was fully activated and in a steady-state condition. SbtA inactivation on a light-dark transition was also demonstrated in vivo irrespective of the presence SbtB, indicative of additional regulatory pathways affecting the activity of this transporter. These conclusions seem to be well-supported by the presented data.

Reviewer #1 (Public Review):

Here, Ensinck et al. investigated the composition of the yeast mRNA m6A methyltransferase complex required for meiosis. This complex was known to contain three proteins but is much more complex in mammals, insects, and plants. Through IP-MS analysis, they identified three more proteins Kar4, Ygl036w, and Dyn2. Of these, Kar4 and Ygl036w are homologous to Mettl14 and Virma, respectively, and, like the previously described factors, are essential for m6A deposition, mating, and binding of the reader Pho92 to mRNA during meiosis by evidence acquired with appropriate methodology. Dyn2 is a novel factor not described for any m6A complex and is not essential for m6A deposition, mating, and binding of the reader Pho92 to mRNA during meiosis.

In addition, detailed analysis of the Slz1 revealed homology to the mammalian factor m6A complex member ZC3H13 to comprise a conserved complex of five proteins, Mettl3, Mettl14, Mum2/WTAP, Virma, and Slz/ZC3H13. When co-expressed in insect cells, they co-purify stoichiometrically, and the presence of Mum2 as a dimer is also indicated, as shown for WTAP.

Complementary to these data, they show that the stability of the individual complex members is affected in mutants supporting that they are stabilized through complex formation.<br /> Furthermore, the authors then show that kar4 has additional roles in mating that are separable from its role through the m6A complex in meiosis.<br /> The authors employ appropriate methodology throughout to address their aims and present convincing evidence for their claims. The evidence presented here reinforces that the m6A complex is evolutionary and highly conserved, with a broad scope for its functional analysis in humans and model organisms.

Reviewer #1 (Public Review):

The current study was designed to test the hypothesis that neural circuit plasticity during adolescence can be targeted to restore cortical function under conditions of developmental disruptions that are relevant to psychiatric disorders. Specifically, the authors targeted the mesofrontal cortical dopamine system in 2 genetic mouse models of schizophrenia and performed optical recordings in combination with behavior and chemogenetic manipulations. Major findings and strengths include that stimulation of frontal dopaminergic projections in a limited adolescent time window can stably reverse defects in cortical neuronal activity and cognitive control in adulthood in 2 genetic mouse models of psychiatric disorders. While the precise postsynaptic mechanisms underlying the positive impact of adolescent mesofrontal dopamine stimulation were not address, another strength of this study is that the authors performed key manipulations using age and dose/intensity as dependent variables to show that the level of neural circuit activation during adolescence follows an inverted U-shape pattern. Collectively, this is a well-design study with many strengths and novel findings that are likely to positively impact a widespread of disciplines within the biological psychiatry and neuroscience field.

Reviewer #1 (Public Review):

The authors aimed to contrast the effects of pharmacologically enhanced catecholamine and acetylcholine levels versus the effects of voluntary spatial attention on decision making in a standard spatial cueing paradigm. Meticulously reported, the authors show that atomoxetine, a norepinephrine reuptake inhibitor, and cue validity both enhance model-based evidence accumulation rate, but have several distinct effects on EEG signatures of pre-stimulus cortical excitability, evoked sensory EEG potentials and perceptual evidence accumulation. The results are based on a reasonable sample size (N=28) and state-of-the art modeling and EEG methods.

Although the authors draw a few partial conclusions that are not fully supported by the data (see below), I think that the authors' EEG findings provide sufficient support for the overall conclusion that "selective attention and neuromodulatory systems shape perception largely independently and in qualitatively different ways". This is an important conclusion because neuromodulatory systems and selective spatial attention are both known to regulate the neural gain of task-relevant single neurons and neural networks. Apparently, these effects on neural gain affect decision making in dissociable ways.

The effects of donepezil, a cholinesterase inhibitor, were generally less strong than those of atomoxetine, and in various analyses went in the opposite direction. The authors fairly conclude that more work is necessary to determine the effects of cholinergic neuromodulation on perceptual decision making.

1) I believe that the following partial conclusions are not fully supported by the data:

a) In the results section on page 6, the authors conclude that "Attention and ATX both enhanced the rate of evidence accumulation towards a decision threshold, whereas cholinergic effects were negligible." I believe "negligible" is wrong here: the corresponding effects of donepezil had p-values of .09 (effect of donepezil on drift rate), .07 (effect of donepezil on the cue validity effect on drift rate) and .09 (effect of donepezil on non-decision time), and were all in the same direction as the effects of atomoxetine, and would presumably have been significant with a somewhat larger sample size. I would say the effects of donepezil were "in the same direction but less robust" (or at the very least "less robust") instead of "negligible".

b) "In the results section on page 8, the authors conclude that "Summarizing, we show that drug condition and cue validity both affect the CPP, but they do so by affecting different features of this component (i.e. peak amplitude and slope, respectively)."<br /> This conclusion is a bit problematic for two reasons. First, drug condition had a significant effect not only on peak amplitude but also on slope. Second, cue validity had a significant effect not only on slope but also on peak amplitude. It may well be that some effects were more significant than others, but I think this does not warrant the authors' conclusion.

c) In the discussion section on page 11, the authors conclude that "First, although both attention and catecholaminergic enhancement affected centro-parietal decision signals in the EEG related to evidence accumulation (O'Connell et al., 2012; Twomey et al., 2015), attention mainly affected the build-up rate (slope) whereas ATX increased the amplitude of the CPP component (Figure 3D-F)."<br /> As I wrote above, I believe it is not correct that "attention mainly affected the build-up rate or slope", given that the effect of cue-validity on CPP slope was also significant. Also, while the authors' data do support the conclusion that ATX increased the amplitude and not the slope of the CPP component, a previous study in humans found the opposite: ATX increased the slope but did not affect the peak amplitude of the CPP (Loughnane et al 2019, JoCN, https://pubmed.ncbi.nlm.nih.gov/30883291/). Although the authors cite this study (as from 2018 instead of 2019), they do not draw attention to this important discrepancy between the two studies. I encourage the authors to dedicate some discussion to these conflicting findings.

2) On page 12 and page 14 the authors suggest a selective effect of ATX on *tonic* catecholamine activity, but to my knowledge the exact effects of ATX on phasic vs. tonic catecholamine activity are unknown. Although microdialysis studies have shown that a single dose of atomoxetine increases catecholamine concentrations in rodents, it is unknown whether this reflects an increase in tonic and/or phasic activity, due to the limited temporal resolution of microanalysis. Thus, atomoxetine may affect tonic and/or phasic catecholamine activity, and which of these two effects dominates is still unknown, I think.

Reviewer #1 (Public Review):

Ruby et al. have investigated patterns of age-specific mortality in the exceptionally long-lived naked mole-rat (NMR), under captive conditions. The authors first visited this topic five years previously with an unprecedently large data set and concluded that naked mole-rats are 'non-aging': because analyses of their survival did not detect an increasing mortality hazard with age. This result has obvious applied interest in humans because of its implications for maintaining health into later life. One criticism directed at this previous work was that a limited number 'old-aged' individuals in their data set (individuals in what might be expected to be the latter half of the life course) reduced the power with which to detect an age-related increase in mortality - or to convincingly demonstrate its absence. The current study revisits this topic with a larger sample across the life course. The authors also provide additional analyses that explore various predictors of mortality, including breeding status, body weight and colony size, and now also make direct comparisons to mortality patterns in other species of African mole-rat from the Fukomys clade (which share many convergent social and life history features). I found the analyses of Fukomys mortality particularly illuminating. However, while these additional analyses provide some useful context and can generate interesting discussion points about ageing patterns in an extremely unusual species, the principal issue at hand whether the absence of Gompertzian mortality in NMR is a robust pattern.

In this respect, a major limitation of the current study is that only 11% of the animals (n = 755) had died at the point of its conclusion- the remaining 89% being right-censored (n = 6138). This means that, as in the previous analysis, there are still relatively small numbers of individuals that have died in the older age classes (see Fig 1 for the high level of right-censoring between 15-20 years and the low numbers of deaths after this point, also Supp 1 for the raw data): the part of the life course where one would predict mortality rates to increase from an evolutionary perspective. Thus, while the authors claim very generally that the "demographic data has doubled", this in no way reflects whether the new data is informative to the question at hand, which relies on an ability to estimate death rates in older individuals accurately. If one looks more closely at the numbers which do matter, then one can see that the number of deaths in the data set has shifted from 447 in the former treatment (Ruby et al. 2018) to 755 currently, but that the number of later-stage deaths remains somewhat modest (and that this is probably reflected in the large confidence intervals for the mortality hazards at this time). I therefore remain unconvinced that the current study can rule out an exponential increase in hazard in older individuals.

The authors have also not provided any statistical evidence that the mortality hazard changes with age (or not), instead relying on visual comparisons of aggregated data. This is a fundamental problem and demands a more thorough treatment that compares survival models with different shape profiles. If anything, it seems that the hazard rate is declining with age - see Figures 1B & 2C, and while this may strengthen the authors argument if supported statistically, I would still wonder whether the higher mortality in early life - say 6 months to 3 years of age - is a consequence of the costs of early life development and that this is not a useful baseline against which to compare 'adult' mortality. It would also not overcome the data limitations identified above.

An additional concern is that the paper is selective in its presentation of previous work, with the authors focussing on results which support their main interpretations and glossing over those that don't. For example, the study refers to the fact that NMRs are resistant to various age-related diseases and do not show many age-related declines in physiology. Yet, while this argument of negligible senescence might hold generally, the literature contains various reports of later life declines in NMR physiology (Andziak et al. 2006; Edrey et al., 2011). Referring to work from your own group, Braude et al. (2021) write "several typical mammalian age-related lesions of muscles, bone, heart, liver, and eye, including sarcopenia, osteoarthritis, a decline in articular cartilage thickness of the condyles, lipofuscin accumulation in several organs, eye cataracts, and kidney fibrosis have been described in naked mole-rats older than 26 years (Edrey et al., 2011)". A more balanced treatment of physiology in extremely old individuals would prove constructive.

Another way in which the study fails to fully represent the literature is with respect to the divergence in ageing rates between breeders and non-breeders. This pattern has proved seductive for various mole-rat researchers because of its similarities to social insects and the suggestion that it is reproduction itself which delays ageing. While this is a clear possibility with some empirical support, it is important to also consider the question from the other way: which is to ask why non-breeders die at higher rates than breeders. For other cooperative breeders such as meerkats, the answer is clear: dominant, breeding individuals evict subordinates and once evicted from the group, the chances that these individuals will survive plummets (e.g. Cram et al. 2028). Is it possible that a similar form of dominance control might contribute the shorter life span of non-breeders in captivity? You reference Toor et al. (2020) elsewhere and this is relevant here again.<br /> Captivity also prevents non-breeders from dispersing when they would otherwise ordinarily do so (Braude 2000): is it possible that this also affects their mortality in captivity? Perhaps not being able to disperse induces chronic stress (see for example the discussion in Novikov et al. 2015). The idea that breeders show a lower intrinsic rate of aging is attractive, but many factors could contribute to this and alternatives should be considered unless they can be strongly refuted.

Lastly, it would be very beneficial to have more information on how individuals become breeders in the captive population/s. For the purposes of the analyses, individuals have been categorised as a breeder or a non-breeder based on whether they bred or not at some point in their life (i.e., they are a "breeder" for their whole life for the purposes of the Kaplan Meier curves and the estimation of mortality hazards). I think it is therefore important to rule out the possibility that only high-quality individuals become breeders and that this is what drives the contrast in breeder and non-breeder mortality. In short, is it the case that most breeders are created through the random pairing of a male and a female? Or do new breeders inherit the position once the old queen dies? The latter could lead to breeders being of generally higher quality, which might affect their mortality hazard independently of status.

Overall, I think that the authors can confidently conclude that any onset of actuarial senescence is heavily delayed in naked mole-rats, but the main conclusion that naked mole-rats "defy Gompertzian mortality" is based on inadequate evidence. It seems very possible that the inability to detect an increasing mortality hazard in such a long-lived species arises from data limitations. The central finding of the study should therefore be viewed very critically.

Refs:<br /> Anziak et al. (2006) Aging Cell 5:463-471.<br /> Braude et al. (2021) Biological Reviews 96: 376-293.<br /> Cram et al. (2018) Current Biology 28: 1-6.<br /> Edrey et al. (2011) ILAR Journal 52:41-53.<br /> Novikov et al. (2015) Biogerontology 16: 723-732.<br /> Toor et al. (2020) Animal Behaviour 168: 45-58.

Reviewer #1 (Public Review):

This is a well-written manuscript, aiming to seek experimental evidence to establish anatomical and functional connectivity between the cerebellum and the nucleus accumbens (NAc). The authors combined anatomical, neural tracing, and electrophysiological approaches with electrical stimulation and optogenetics and provided a novel and solid set of data supporting the existence of disynaptic connections between the cerebellum and the NAc. The results are convincing and the main conclusion is supported by the data. Overall, this was a well-conceived project, and the experiments were conducted carefully, though some gaps remain to be filled. The knowledge generated from this manuscript will build a foundation for further research focusing on the interaction between cerebellum and limbic system as well as the role of such interaction in controlling motivated behavior.

Overall, this is a well-conceived project. The experiments were conducted carefully. The results support the conclusion of the existence of disynaptic circuits from the cerebellum to the NAc.

Reviewer #1 (Public Review):

In this work, Vezina et al. present Bactabolize, a rapid reconstruction tool for the generation of strain-specific metabolic models. Similar to other reconstruction pipelines such as CarveMe, Bactabolize builds a strain-specific draft reconstruction and subsequently gap-fills it. The model can afterwards be used to predict growth in any defined medium the user specifies. The authors constructed a pan-model of the Klebsiella pneumoniae species complex (KpSC) and used it as input for Bactabolize to construct a genome-sale reconstruction of K. pneumoniae KPPR1. They compared the generated reconstruction with a reconstruction built through CarveMe as well as a manually curated reconstruction for the same strain. They then compared predictions of carbon, nitrogen, phosphor, and sulfur sources and found that the Bactabolize reconstruction had the overall highest accuracy. Finally, they built draft reconstructions for 10 clinical isolates of K. pneumoniae and evaluated their predictive performance. Overall, this is a useful tool, the data is well-presented, and the paper is well-written. However, the predictions are only compared with two existing reconstruction tools though more have been recently published.

Reviewer #1 (Public Review):

In this manuscript, the authors aimed to provide information about the likely function of uncharacterised genes in fission yeast. The authors highlight the bias in the literature to well-studied genes/proteins and the fact that the functions of many proteins that are conserved from yeast to humans remain unknown. Initial functional characterisation could provide the impetus for researchers to dedicate time and resources to detailed investigations of protein function. The authors subject the fission yeast deletion set to a battery of perturbations (drug treatments etc) and measured the resultant colony size. In total, 131 conditions were analysed for nearly 3,500 mutants, representing a rich dataset. Clustering analysis was then used to identify common phenotype patterns and thereby infer protein functions using a "guilt by association approach. To assign potential GO terms to uncharacterised proteins, the authors developed a new computational approach (NET-FF) which combined two previous approaches, which they validated against curated annotations on the S. pombe database Pombase. Finally, the authors chose a group of genes which their analysis predicted to be involved in cellular ageing for experimental validation, cross-validating a priority unstudied novel gene (SPAC23C4.09c) to be involved in this process. Overall, the functional analysis performed in this manuscript is rigorous, thorough and incorporates some novel approaches leading to new insights and predicted protein functions. It will be an important resource for the fission yeast community.

Joint Public Review:

The manuscript by Lolicato and colleagues characterizes the role of FGF2 dimerization in the unconventional secretion of this signaling molecule using a combination of cell-based and in vitro assays. FGF2 is secreted from the cell via an unconventional mechanism because it lacks a signal sequence. Previous studies by the same group have established a compelling model in which FGF2 forms an oligomer in a PIP2-dependent manner at the plasma membrane, which drives its translocation to the cell exterior. The same group also identified two cysteine residues (C77 and C95) critical for FGF2 oligomerization and secretion.

In this study, the authors analyzed the impact of single cysteine to alanine substitution on the oligomerization and secretion of FGF2. They found that C95 but not C77 is required for PIP2-dependent membrane binding, FGF2 oligomerization, and secretion. On the other hand, C77 regulates the interaction of FGF2 with the plasma membrane Na, K-ATPase, which is thought to enhance the FGF2-PIP2 interaction. Using a set of bi-functional crosslinkers, the authors were able to capture an FGF2 homo-dimer whose formation is dependent on C95.

They propose that FGF2 forms a disulfide-bridged dimer via C95, the building block for FGF2 oligomerization in the plasma membrane.

While most experiments were carefully designed and the data are of high quality, a few issues need further clarification.

A significant concern is a need for more direct evidence for the proposed disulfide-bridged FGF2 dimer in the cytoplasm despite multiple assays highlighting the critical role of C95 in FGF2 oligomerization and secretion. The crosslinking experiments only suggest that C95 is close to another C95 in crosslinked FGF2 dimers. Given that the reducing cytosolic environment does not usually support disulfide bond formation and that no electron acceptor has been identified to support this unusual model, the reviewers feel that the authors should consider an alternative and more plausible explanation for their observations, which is that the C95A mutation disrupts the dimerization interface. This is actually the author's explanation for why the C77A FGF2 mutant fails to bind Na, K-ATPase. For these reasons, the reviewers feel it is an overstatement to claim that FGF2 forms a disulfide dimer in the cytoplasm.

Furthermore, the authors propose that FGF2 dimers can assemble into a transient higher-order FGF2 oligomer to form a toroidal pore for protein secretion. This is supported by a computational simulation study, which suggests that FGF2 dimers exhibit a higher affinity for PI(4,5)P2 than monomers. However, the model would be much stronger if the authors could provide additional experimental validation.

Additionally, the authors propose that C95-dependent FGF2 dimerization may generate a signaling module. They cited a few structure papers on page 9 (Plotnikov et al., 1999; Plotnikov et al., 2000; Schlessinger et al., 2000), suggesting that the FGF2 dimer reported here may be the primary signaling unit. However, this statement may mislead the reader, as it has been clearly stated in these papers that FGF2 does not form a dimer directly. Instead, heparin facilitates the dimerization of the FGF receptor, which results in the recruitment of two FGF2 molecules.

Reviewer #1 (Public Review):

This fascinating paper by A.L. Schneider et al. describes voyAGEr, a shiny-based interface for easy exploration of the GTEx dataset by non- or novice programmers. Importantly, voyAGEr is open source and available from github, which could greatly accelerate additional development and further uses of this interesting tool.

The authors developed a pipeline for modeling age-related changes in gene expression in the GTEx data called ShARP-LM, fitting a linear model for age, sex, and age&sex interaction terms. This pipeline underlies the later analyses that can be applied within voyAGEr. These analyses are labeled by tissue so that users can easily begin a query based on a tissue or a gene of possible interest.

voyAGEr implements many kinds of interesting R-based tools such as pathway overrepresentation analysis and gene co-expression module analysis, in a way that makes these approaches accessible to non-bioinformaticist aging researchers.

As the tidal wave of publicly available large, high-dimensional datasets such as transcriptomes continues to grow exponentially, the usefulness of tools such as voyAGEr will only increase. While test users may be able to imagine features or refinements they wish were already present, due to the open source approach they or anyone else including but not limited to the present authors can implement additional features in the future. I look forward to using this tool and to staying abreast of its future development.

Overall, this study describes a new tool of interest to the field. The manuscript is clearly written overall. The figures and supplementary information are all clear and all add to the manuscript.

Reviewer #1 (Public Review):

This study by Sokač et al. entitled "GENIUS: GEnome traNsformatIon and spatial representation of mUltiomicS data" presents an integrative multi-omics approach which maps several genomic data sources onto an image structure on which established deep-learning methods are trained with the purpose of classifying samples by their metastatic disease progression signatures. Using published samples from the Cancer Genome Atlas the authors characterize the classification performance of their method which only seems to yield results when mapped onto one out of four tested image-layouts.

Major recommendations:

- In its current form, GENIUS analysis is neither computationally reproducible nor are the presented scripts on GitHub generic enough for varied applications with other data. The GENIUS GitHub repository provides a collection of analysis scripts and not a finished software solution (e.g. command line tool or other user interface) (the presented scripts do not even suffice for a software prototype). In detail, the README on their GitHub repository is largely incomplete and reads analogous to an incomplete and poorly documented analysis script and is far from serving as a manual for a generic software solution (this claim was made in the manuscript). The authors should invest substantially into adding more details on how data can be retrieved (with example code) from the cited databases and how such data should then be curated alongside the input genome to generically create the "genomic image". In addition, when looking at the source code, parameter configurations for training and running various modules of GENIUS were hard-coded into the source code and users would have to manually change them in the source code rather than as command line flags in the software call. Furthermore, file paths to the local machine of the author are hard-coded in the source code, suggesting that images are sourced from a local folder and won't work when other users wish to replicate the analysis with other data. I would strongly recommend building a comprehensive command line tool where parameter and threshold configurations can be generically altered by the user via command line flags. A comprehensive manual would need to be provided to ensure that users can easily run GENIUS with other types of input data (since this is the claim of the manuscript). Overall, due to the lack of documentation and hard-coded local-machine folder paths it was impossible to computationally reproduce this study or run GENIUS in general.

- In the Introduction the authors write: "To correct for such multiple hypothesis testing, drastic adjustments of p-values are often applied which ultimately leads to the rejection of all but the most significant results, likely eliminating a large number of weaker but true associations.". While this is surely true for any method attempting to separate noise from signal, their argument fails to substantiate how their data transformation will solve this issue. Data transformation and projection onto an image for deep-learning processing will only shift the noise-to-signal evaluation process to the postprocessing steps and won't "magically" solve it during training. In addition, multiple-testing correction is usually done based on one particular data source (e.g. expression data), while their approach claims to integrate five very different genomic data sources with different levels and structures of technical noise. How are these applications comparable and how is the training procedure able to account for these different structures of technical noise? Please provide sufficient evidence for making this claim (especially in the postprocessing steps after classification).

- I didn't find any computational benchmark of GENIUS. What are the computational run times, hardware requirements (e.g. memory usage) etc that a user will have to deal with when running an analogous experiment, but with different input data sources? What kind of hardware is required GPUs/CPUs/Cluster?

- A general comment about the Methods section: Models, training, and validation are very vaguely described and the source code on GitHub is very poorly documented so that parameter choices, model validation, test and validation frameworks and parameter choices are neither clear nor reproducible. Please provide a sufficient mathematical definition of the models, thresholds, training and testing frameworks.

- In chapter "Latent representation of genome" the authors write: "After successful model training, we extracted the latent representations of each genome and performed the Uniform Manifold Approximation and Projection (UMAP) of the data. The UMAP projected latent representations into two dimensions which could then be visualized. In order to avoid modeling noise, this step was used to address model accuracy and inspect if the model is distinguishing between variables of interest.". In the recent light of criticism when using the first two dimensions of UMAP projections with omics data, what is the evidence in support of the author's claim that model accuracy can be quantified with such a 2D UMAP projection? How is 'model accuracy' objectively quantified in this visual projection?

- In the same paragraph "Latent representation of genome" the authors write: "We observed that all training scenarios successfully utilized genome images to make predictions with the exception of Age and randomized cancer type (negative control), where the model performed poorly (Figure 2B).". Did I understand correctly that all negative controls performed poorly? How can the authors make any claims if the controls fail? In general, I was missing sufficient controls for any of their claims, but openly stating that even the most rudimentary controls fail to deliver sufficient signals raises substantial issues with their approach. A clarification would substantially improve this chapter combined with further controls.

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), 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 very 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. First, it is unclear how the authors selected PFG as a candidate protein as the protein lacks any known DNA binding or regulatory domain. Detailing the reasoning that led to the identification of PFG would make the entire study more appealing. 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. Finally, 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 #1 (Public Review):

Thermogenic adipocyte activity associate with cardiometabolic health in humans, but decline with age. Identifying the underlying mechanisms of this decline is therefore highly important.

To address this task, Holman and co-authors investigated the effects of two major determinants of thermogenic activity: cold, which induce thermogenic de novo differentiation as well as conversion of dormant thermogenic inguinal adipocytes: and aging, which strongly reduce thermogenic activity. The authors study young and middle-aged mice at thermoneutrality and following cold exposure.

Using linage tracing, the authors conclude that the older group produce less thermogenic adipocytes from progenitor differentiation. However, they found no differences between thermogenic differentiation capacity between the age groups when progenitors are isolated and differentiated in vitro. This finding is consistent with previous findings in humans, demonstrating that progenitor cells derived from dormant perirenal brown fat of humans differentiate into thermogenic adipocytes in vitro. Taken together, this underscores that age-related changes in the microenvironment rather than autonomous alterations in the ASPCs explain the age related decline in thermogenic capacity, This is an important finding in terms of identifying new approaches to switch dormant adipocytes into an active thermogenic phenotype.

To gain insight into the age-related changes, the authors use single cell and single nuclei RNA sequencing mapping of their two age groups, comparing thermoneutral and cold conditions between the two groups. Interestingly, where the literature previously demonstrated that de novo lipogenesis (DNL) occurs in relation to thermogenic activation, the authors show that DNL in fact is activated in a white adipocyte cell type, whereas the beige thermogenic adipocytes form a separate cluster.

Considering recent findings, that adipose tissue contains several subtypes of ASPCs and adipocytes, mapping the changes at single cell resolution following cold intervention provides an important contribution to the field, in particular as an older group with limited thermogenic adaptation is analyzed in parallel with a younger, more responsive group. This model also allowed for detection of microenvironment as a determining factor of thermogenic response.

The use of only two time points (young and middle-aged) along the aging continuum limits the conclusions that can be made on aging as the only driver of the observed differences between the groups. It should for example be noted that the older mice had higher weights and larger fat depots, thus the phenotype is complex and this should be taken into consideration when interpreting the data.

In conclusion, this study provides an important resource for further studies on how to reactivate dormant thermogenic fat and potentially improve metabolic health.

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 including 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. However, the authors should better explain what is the added value of this contribution with respect to the previous one, also in terms of computational methods.

In general, the method proposed relies on several hyperparameters and cost functions that have been optimized for the specific datasets. A sensitivity analysis should be performed, varying these parameters and reporting the performance of the framework.

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 #1 (Public Review):

Despite numerous studies on quinidine therapies for epilepsies associated with GOF mutant variants of Slack, there is no consensus on its utility due to contradictory results. In this study Yuan et al. investigated the role of different sodium selective ion channels on the sensitization of Slack to quinidine block. The study employed electrophysiological approaches, FRET studies, genetically modified proteins and biochemistry to demonstrate that Nav1.6 N- and C-tail interacts with Slack's C-terminus and significantly increases Slack sensitivity to quinidine blockade in vitro and in vivo. This finding inspired the authors to investigate whether they could rescue Slack GOF mutant variants by simply disrupting the interaction between Slack and Nav1.6. They find that the isolated C-terminus of Slack can reduce the current amplitude of Slack GOF mutant variants co-expressed with Nav1.6 in HEK cells and prevent Slack induced seizures in mouse models of epilepsy. This study adds to the growing list of channels that are modulated by protein-protein interactions, and is of great value for future therapeutic strategies.

I have a few comments with regard to how Nav1.6 sensitize Slack to block by quinidine.

It is not clear to me if the Slack induced current amplitude varies depending on the specific Nav subtype. To this end, it would be valuable to test if Slack open probability is affected by the presence of specific Nav subtypes. Nav induced differences in Slack current amplitude and open probability could explain why individual Nav subtypes show varied ability to sensitize Slack to quinidine blockade.

It has previously been shown that INaP (persistent sodium current) is important for inducing Slack currents. Here the authors show that INaT (transient sodium current) of Nav1.6 is necessary for the sensitization of Slack to quinidine block whereas INaP surprisingly has no effect. The authors then show that the N-tail together with C-tail of Nav1.6 can induce same effect on Slack as full-length Nav1.6 in presence of high intracellular concentrations of sodium. However, it is not clear to me how the isolated N- and C-tail of Nav1.6 can induce sensitization of Slack to quinidine by interacting with C-terminus of Slack, while sensitization also is dependent on INaT. The authors speculate on different slack open conformation, but one could speculate if there is a missing link, such as an un-identified additional interacting protein that causes the coupling.

Reviewer #1 (Public Review):

With genephys, the author provides a generative model of brain responses to stimulation. This generative model allows mimicking of specific parameters of a brain response at the sensor level, to test the impact of those parameters on critical analytic methods utilized on real M/EEG data. Specifically, they compare the decoding output for differently set parameters to the decoding pattern observed in a classical passive viewing study in terms of the resulting temporal generalization matrix (TGM). They identify that the correspondence between the mimicked and the experimental TGM depends on an oscillatory component that spans multiple channels, frequencies, and latencies of response; and an additive, slower response with a specific (cross-frequency) relation to the phase of the oscillatory, faster component.

A strength of the article is that it considers the complexity of neural data that contributes to the findings obtained in stimulation experiments. An additional strength is the provision of a Python package that allows scientists to explore the potential contribution of different aspects of neural signals to obtained experimental data and thereby to potentially test their theoretical assumptions critical parameters that contribute to their experimental data.

A weakness of the paper is that the power of the model is illustrated for only one specific set of parameters, added in a stepwise manner and the comparison to on specific empirical TGM, assumed to be prototypical; And that this comparison remains descriptive. (That is could a different selection of parameters lead to similar results and is there TGM data which matches these settings less well.) It further remained unclear to me, which implications may be drawn from the generative model, following from the capacities to mimic this specific TGM (i) for more complex cases, such as the comparison between experimental conditions, and (ii) about the complex nature of neural processes involved.

Towards this end, I would appreciate (i) a more profound explanation of the conclusions that can be drawn from this specific showcase, including potential limitations, as well as wider considerations of how scientists may empower the generative model to (ii) understand their experimental data better and (iii) which added value the model may have in understanding the nature of underlying brain mechanism (rather than a mere technical characterization of sensor data).

Reviewer #1 (Public Review):

The paper by Dongsheng Xiao, Yuhao Yan and Timothy H Murphy presents a timely approach to record neuronal activity at multiple temporal and spatial scales. Such approaches are at the forefront of system neuroscience and a few examples include, among others, fMRI alongside electrophysiology (Logothetis et al, 2021. Nature) or widefield calcium imaging (Lake et al, 2020. Nat Meth) , or functional ultrasound imaging and multi unit recording (Claron et al, 2023 Cell Reports), The method presented here combines "low resolution" (i.e. cortical regions) widefield calcium imaging across most of the dorsal portions of the murine cortex combined with electrical recording of single neurons in specific cortical and subcortical locations (as a matter of fact, this later components can be used everywhere in the murine brain).

The method presented here is straightforward to implement and very well documented. Examples of novel insights that this approach can generate are well presented and demonstrate the strength of the presented approach, some aspects of the analysis require clarification.

For example, the author reveal Spike-Triggered average cortical activation Maps (STMs) linked to the activity of single neurons (Figs 4 and 5) This allows to directly asses the functional connectivity between cortical and sub-cortical areas. It nevertheless unclear what is the stability of the established relationships. The nature of the "recordings" in Fig 4. is unclear. It looks like these are imaging sessions on the same day, the length of these recordings as well as the interval between them is not stated. It will be fundamental to build a metric to compare STMs variability across sessions/recordings/days; a root-mean-square from an average map across all recordings could provide a starting point.

Also with respect to the STMs analysis, the data-driven choice of 10 clusters might need a bit more explorations. While the silhouette clustering accuracy peaks at 10 (Fig 5A), this metrics comes without a confidence intervals making it difficult to know if a difference of less than 10% (i.e. 11 or 13 clusters) should be deemed different. Maybe a bootstrapping approach could be used here to build such confidence intervals. Another approach to reach the number of cluster to use could be based on "consensus" between different partitioning algorithms (e.g. Strehl, A. & Ghosh, J. itions. J. Mach. Learn. Res. 3, 583-617 (2001). A much stronger argument should be provided to use the 0.3 correlation cutoff value which seems to be arbitrarily low. The main point here is that the authors should show that their conclusions hold within a range of parameter values (number of clusters and correlation threshold).

Reviewer #1 (Public Review):

In this study, Hoops et al. showed that Netrin-1 and UNC5c can guide dopaminergic innervation from nucleus accumbens to cortex during adolescence in rodent models. They found that these dopamine axons project to the prefrontal cortex in a Netrin-1 dependent manner and knocking down Netrin-1 disrupted motor and learning behaviors in mice. Furthermroe, the authors used hamsters, a seasonal model that is affected by the length of daylight, to demonstrate that the guidance of dopamine axons is mediated by the environmental factor such as daytime length and in sex dependent manner. While this study highlighted the important roles of two neurodevelopmental markers, netrin-1 and UNC5C, in the projection of dopaminergic azons in the adolescence/adult brain, the major weakness is that the data are quite superficial and do not establish any definitive evidence to support the causal relationship between the expression of netrin-1 and UNC5C in the projection of dopaminergic axons remain unclear.<br /> Below are several major concerns regarding the data presented in this manuscript:

1. Despite the well-established role of Netrin-1 and UNC5C axon guidance during embryonic commissural axons, it remains unclear which cell type(s) express Netrin-1 or UNC5C in the dopaminergic axons and their targets. For instance, the data in Figure 1F-G and Figure 2 are quite confusing. Does Netrin-1 or UNC5C express in all cell types or only dopamine-positive neurons in these two mouse models? It will also be important to provide quantitative assessments of UNC5C expression in dopaminergic axons at different ages.

2. Figure 1 used shRNA to knockdown Netrin-1 in the Septum and these mice were subjected to behavioral testing. These results, again, are not supported by any valid data that the knockdown approach actually worked in dopaminergic axons. It is also unclear whether knocking down Netrin-1 in the septum will re-route dopaminergic axons or lead to cell death in the dopaminergic neurons in the substantia nigra pars compacta?

3. Another issue with Figure1J. It is unclear whether the viruses were injected into a WT mouse model or into a Cre-mouse model driven by a promoter specifically expresses in dorsal peduncular cortex? The authors should provide evidence that Netrin-1 mRNA and proteins are indeed significantly reduced. The authors should address the anatomic results of the area of virus diffusion to confirm the virus specifically infected the cells in dorsal peduncular cortex.

4. The authors need to provide information regarding the efficiency and duration of knocking down. For instance, in Figure 1K, the mice were tested after 53 days post injection, can the virus activity in the brain last for such a long time?

5. In Figure 1N-Q, silencing Netrin-1 results in less DA axons targeting to infralimbic cortex, but why the Netrin-1 knocking down mice revealed the improved behavior?

6. What is the effect of knocking down UNC5C on dopamine axons guidance to the cortex?

7. In Figures 2-4, the authors only showed the amount of DA axons and UNC5C in NAcc. However, it remains unclear whether these experiments also impact the projections of dopaminergic axons to other brain regions, critical for the behavioral phenotypes. What about other brain regions such as prefrontal cortex? Do the projection of DA axons and UNC5c level in cortex have similar pattern to those in NAcc?

8. Can overexpression of UNC5c or Netrin-1 in male winter hamsters mimic the observations in summer hamsters? Or overexpression of UNC5c in female summer hamsters to mimic the winter hamster? This would be helpful to confirm the causal role of UNC5C in guiding DA axons during adolescence.

9. The entire study relied on using tyrosine hydroxylase (TH) as a marker for dopaminergic axons. However, the expression of TH (either by IHC or IF) can be influenced by other environmental factors, that could alter the expression of TH at the cellular level.

10. Are Netrin-1/UNC5C the only signal guiding dopamine axon during adolescence? Are there other neuronal circuits involved in this process?

11. Finally, despite the authors' claim that the dopaminergic axon project is sensitive to the duration of daylight in the hamster, they never provided definitive evidence to support this hypothesis.

Reviewer #1 (Public Review):

This paper describes the development and initial validation of an approach-avoidance task and its relationship to anxiety. The task is a two-armed bandit where one choice is 'safer' - has no probability of punishment, delivered as an aversive sound, but also lower probability of reward - and the other choice involves a reward-punishment conflict. The authors fit a computational model of reinforcement learning to this task and found that self-reported state anxiety during the task was related to a greater likelihood of choosing the safe stimulus when the other (conflict) stimulus had a higher likelihood of punishment. Computationally, this was represented by a smaller value for the ratio of reward to punishment sensitivity in people with higher task-induced anxiety. They replicated this finding, but not another finding that this behavior was related to a measure of psychopathology (experiential avoidance), in a second sample. They also tested test-retest reliability in a sub-sample tested twice, one week apart and found that some aspects of task behavior had acceptable levels of reliability. The introduction makes a strong appeal to back-translation and computational validity, but many aspects of the rationale for this task need to be strengthened or better explained. The task design is clever and most methods are solid - it is encouraging to see attempts to validate tasks as they are developed. There are a few methodological questions and interpretation issues, but they do not affect the overall findings. The lack of replicated effects with psychopathology may mean that this task is better suited to assess state anxiety, or to serve as a foundation for additional task development.

Reviewer #1 (Public Review):

Sensory neurons of the mechanosensory bristles on the head of the fly project to the sub esophageal ganglion (SEZ). In this manuscript, the authors have built on a large body of previous work to comprehensively classify and quantify the head bristles. They broadly identify the nerves that various bristles use to project to the SEZ and describe their region-specific innervation in the SEZ. They use dye-fills, clonal labelling, and electron microscopic reconstructions to describe in detail the phenomenon of somatotopy - conserved peripheral representations within the central brain - within the innervation of these neurons. In the process they develop novel tools to access subsets of these neurons. They use these to demostrate that groups of bristles in different parts of the head control different aspects of the grooming sequence.

Reviewer #1 (Public Review):

This important study reveals the structure of human STEAP2 for the first time and suggests the electron transport pathway, but some questions remain regarding the interpretation of the in vitro electron transport experiments, the lack of available redox couples, and potential alternative hypotheses that would if addressed, strengthen the claims in the manuscript.

Strengths

One of the clear strengths of the manuscript that stands out is the determination of the structure of human STEAP2. The structures of some other homologs are known, but STEAP2's structure was not, and STEAP2 seems to have an unusually low activity towards certain metal chelates. The approach of producing the human STEAP2 in insect cells with the supplementation of cofactor biogenesis components nicely results in cofactor-replete protein. The structure of STEAP2 reveals a domain-swapped trimer, with the NADPH-binding domain of the neighboring protomer within electron-transport distance of the FAD-heme axis. The FAD has an interesting and somewhat unusual extended conformation and abuts a Leu residue that may regulate electron transport. Another strength of the manuscript is the demonstration that STEAP1, which does not have the internal NADPH binding domain, can interact modestly and shuttle electrons to the heme in STEAP1 through FAD. These experiments nicely expand information on the function of STEAP1 and provide a structural basis for electron transport in STEAP2.

Weaknesses

A major weakness in the manuscript lies with the kinetics data and how the data, as presented, are unclear to the reader regarding their impact and their connection to the purported electron transport scheme. While multiple sets of data are reported, the analysis in all cases is simply the reduction of a hexacoordinate heme and its related spectra and kinetic parameters. In most cases, it's unclear to the reader which part of the electron pathway is being tested in which experiment. Simple diagrams would be helpful in each case. However, it's also unclear if all of the potential order of addition experiments were actually performed; i.e., flavin but no NADPH; NADPH but no flavin; flavin before NADPH; flavin after NADPH, etc. As there are multiple permutations that should be tested to demonstrate the electron transport pathway, presenting the data in a way that makes it clear to the reader is challenging. Particularly missing are the determined redox potentials of the hemes in both STEAP1 and STEAP2. Could differences in these heme redox potentials be drivers of the difference in metal reduction rates? Also, the text indicates that STEAP2 does not show a reduction rate dependence on the [Fe3+-NTA], but Figure 1A shows a difference in rates dependent on [Fe3+-NTA], and the shape of the reduction curve also changes based on [Fe3+-NTA]. This discrepancy should be rectified.

A second major weakness is the lack of any verification of the relevance of the STEAP2 oligomerization to its in vivo function. Is the same domain-swapped trimer known to exist in vivo? If the protein were prepared in other detergents, is the oligomerization preserved? It is alluded to in the text that another STEAP protein is also a trimer. Was this oligomerization verified in vivo? Could this oligomerization be disrupted to impede or abrogate electron transport to underscore the oligomerization relevance? This point is germane, as it would further suggest that the domain-swapped trimer observed in the STEAP2 cryo-EM structure is physiologically relevant, especially given how far the distance between the NADPH and the FAD would otherwise be to support electron transport.

Beyond these two areas in which the manuscript could be improved there are a couple of minor considerations. First, the modest resolution of the STEAP2 structure prevents assigning the states of NADP+/NADPH and FAD/FADH2 with confidence. An orthogonal measure would be useful for modeling the accurate states in the structure. Finally, the BLI b5R/STEAP1 binding/unbinding fits seem somewhat poor, especially at high concentrations of b5R in the dissociation regime, which likely influences the derived value of Kd. A different fitting equilibrium might yield better agreement between the experimental and theoretical results. Moreover, whether this binding strength is influenced by the reduction state of the NADPH would be helpful in understanding and contextualizing the weak binding affinity.

this could help me

Reviewer #1 (Public Review):

This study aims to identify the existence of hedonic feeding and to distinguish it from homeostatic feeding, in Drosophila. The authors use direct observation of feeding events, a novel automated feeding event detector, inventive behavioral assays, and genetics to separate out the ways that the Drosophila interacts with food. Using two choice assays, the authors find an increased duration of interactions with high-concentration sugars under conditions of expected satiety, which is considered to be hedonic feeding.

The technical advances in the measurement of animal interactions with food will help advance the understanding of feeding behavior and motivational states. The correlation of specific types of food interactions across satiation state, sex, and circadian time will help drive forward the understanding of the scope of an animal's goals with feeding, and likely their relation between species and eating disorders. The assessment of mushroom body circuitry in a type of food interaction is helpful for understanding the coding of feeding control in the brain.

The bulk of the feeding data presented in the manuscript are from the interactions of individual flies with a source of liquid food, where interaction is defined as 'physical contact of a specific duration.' Although the assay they use allows for measurements to be made at high temporal resolution, the authors include some data showing that solid food consumption follows the same trend.

Reviewer #1 (Public Review):

The authors use a combination of crop modeling and field experiments to argue that drought during seedling establishment likely severely impacts the yield of pearl millet, an important but understudied cereal crop, and that rapid seedling root elongation could play a major role in mitigating this. They further argue that this trait has a strong genetic basis and that major polymorphisms in candidate genes can be identified using standard methods from modern genetics and genomics. Finally, they use homology with the model plant Arabidopsis thaliana to argue that the function of one putatively causal gene is to regulate root cell elongation.

The major strength of this paper is that it convincingly demonstrates how modern methods from plant breeding and model organisms can be combined to address questions of great practical importance in important but poorly understood crops. The notion that it is possible to connect single-locus polymorphism and cellular biology to drought tolerance and crop yield in pearl millet is not a trivial one.

The weakness is obvious: while the argument made is convincing, it must be recognized that the strength of the evidence is by no means of the level expected in a model organism. Conclusions could easily be wrong, and there is no direct evidence that regulatory variation in PgGRXC9 leads to higher crop yield via cell elongation and seedling drought tolerance. However, generating such evidence in a poorly studied crop would be a monumental undertaking, and should probably not be the priority of people working on pearl millet!

The utility of this work is that it suggests that it is practicable to gain valuable insight into crop adaptation by clever use of modern methods from a variety of sources.

Reviewer #1 (Public Review):

Specifically controlling the level of proteins in bacteria is an important tool for many aspects of microbiology, from basic research to protein production. While there are several established methods for regulating transcription or translation of proteins with light, optogenetic protein degradation has so far not been established in bacteria. In this paper, the authors present a degradation sequence, which they name "LOVtag", based on iLID, a modified version of the blue-light-responsive LOV2 domain of Avena sativa phototropin I (AsLOV2). The authors reasoned that by removing the three C-terminal amino acids of iLID, the modified protein ends in "-E-A-A", similar to the "-L-A-A" C-terminus of the widely used SsrA degradation tag. The authors further speculated that, given the light-induced unfolding of the C-terminal domain of iLID and similar proteins, the "-E-A-A" C-terminus would become more accessible and, in turn, the protein would be more efficiently degraded in blue light than in the dark.

Indeed, several tested proteins tagged with the "LOVtag" show clearly lower cellular levels in blue light than in the dark. While the system works efficiently with mCherry (10-20x lower levels upon illumination), the effect is rather modest (2-3x lower levels) in most other cases. Accordingly, the authors propose to use their system in combination with other light-controlled expression systems and provide data validating this approach. Unfortunately, despite the claim that the "LOVtag" should work faster than optogenetic systems controlling transcription or translation of protein, the degradation kinetics are not consistently shown; in the one case where this is done, the response time and overall efficiency are similar or slightly worse than for EL222, an optogenetic expression system.

The manuscript and the figures are generally very well-composed and follow a clear structure. The schematics nicely explain the underlying principles. However, limitations of the method in its main proposed area of use, protein production, should be highlighted more clearly, e.g., (i) the need to attach a C-terminal tag of considerable size to the protein of interest, (ii) the limited efficiency (slightly less efficient and slower than EL222, a light-dependent transcriptional control mechanism), and (iii) the incompletely understood prerequisites for its application. In addition, several important controls and measurements of the characteristics of the systems, such as the degradation kinetics, would need to be shown to allow a comparison of the system with established approaches. The current version also contains several minor mistakes in the figures.

Joint Public Review:

The manuscript presented by Pabba et al. studied chromatin dynamics throughout the cell cycle. The authors used fluorescence signals and patterns of GFP-PCNA (GFP tagged proliferating cell nuclear antigen) and CY3-dUTP (which labels newly synthesized DNA but not the DNA template) to determine cell cycle stages in asynchronized HeLa (Kyoto) cells and track movements of chromatin domains. PCNA binds to replication forks and form replication foci during the S phase. The major conclusions are: (1) Labeled chromatin domains were more mobile in G1/G2 relative to the S-phase. (2) Restricted chromatin motion occurred at sites in proximity to DNA replication sites. (3) Chromatin motion was restricted by the loading of replisomes, independent of DNA synthesis. This work is based on previous work published in 2015, entitled "4D Visualization of replication foci in mammalian cells corresponding to individual replicons," in which the labeling method was demonstrated to be sound. Although interesting, reduced chromatin mobility in S relative to G1 phase is not new to the field. The genome in HeLa cells is greatly abnormal with heterogeneous aneuploidy, which makes quantification complicated and weakens the conclusions.

Reviewer #1 (Public Review):

She et al studied the evolution of gene expression reaction norms when individuals colonise a new environment that exposes them to physiologically challenging conditions. Their objective was to test the "plasticity first" hypothesis, which suggest that traits that are already plastic (their value changes when facing a new environment compared to the original environment) facilitates the colonisation of novel environments, which, if true, would be predicted to result in the evolution of gene expression values that are similar in the population that colonised the new environment and evolved under these particular selection pressures. To test this prediction, they studied gene expression in cardiac and muscle tissues in individuals originating from three conditions: lowland individuals in their natural environment (ancestral state), lowland individuals exposed to hypoxia (the plastic response state), and a highland population facing hypoxia for several generations (the coloniser state). They classified gene expression patterns as maladaptive or adaptive in lowland individuals responding to short term hypoxia by classifying gene expression patterns using genes that differed between the ancestral state (lowland) and colonised state (highland). Genes expressed in the same direction in lowland individuals facing hypoxia (the plastic state) as what is found in the colonised state are defined as adaptative, while genes with the opposite expression pattern were labelled as maladaptive, using the assumption that the colonised state must represent the result of natural selection. Furthermore, genes could be classified as representing reversion plasticity when the expression pattern differed between the plasticity and colonised states and as reinforcement when they were in the same direction (for example more expressed in the plastic state and the colonised state than in the ancestral state). They found that more genes had a plastic expression pattern that was labelled as maladaptive than adaptive. Therefore, some of the genes have an expression pattern in accordance with what would be predicted based on the plasticity-first hypothesis, while others do not.

As pointed out by the authors themselves, the fact that temperature was not included as a variable, which would make the experimental design much more complex, misses the opportunity to more accurately reflect the environmental conditions that the colonizer individuals face at high altitude. Also pointed out by the authors, the acclimation experiment in hypoxia lasted 4 weeks. It is possible that longer term effects would be identifiable in gene expression in the lowland individuals facing hypoxia on a longer time scale. Furthermore, a sample size of 3 or 4 individuals per group depending on the tissue for wild individuals may miss some of the natural variation present in these populations. Stating that they have a n=7 for the plastic stage and n= 14 for the ancestral and colonized stages refers to the total number of tissue samples and not the number of individuals, according to supplementary table 1. Finally, I could not find a statement indicating that the lowland individuals placed in hypoxia (plastic stage) were from the same population as the lowland individuals for which transcriptomic data was already available, used as the "ancestral state" group (which themselves seem to come from 3 populations Qinghuangdao, Beijing, and Tianjin, according to supplementary table 2) nor if they were sampled in the same time of year (pre reproduction, during breeding, after, or if they were juveniles, proportion of males or females, etc). These two aspects could affect both gene expression (through neutral or adaptive genetic variation among lowland populations that can affect gene expression, or environmental effects other than hypoxia that differ in these populations' environments or because of their sexes or age). This could potentially also affect the FST analysis done by the authors, which they use to claim that strong selective pressure acted on the expression level of some of the genes in the colonised group.

Impact of the work<br /> There has been work showing that populations adapted to high altitude environments show changes in their hypoxia response that differs from the short-term acclimation response of lowland population of the same species. For example, in humans, see Erzurum et al. 2007 and Peng et al. 2017, where they show that the hypoxia response cascade, which starts with the gene HIF (Hypoxia-Inducible Factor) and includes the EPO gene, which codes for erythropoietin, which in turns activates the production of red blood cell, is LESS activated in high altitude individuals compared to the activation level in lowland individuals (which gives it its name). The present work adds to this body of knowledge showing that the short-term response to hypoxia and the long term one can affect different pathways and that acclimation/plasticity does not always predict what physiological traits will evolve in populations that colonize these environments over many generations and additional selection pressure (UV exposure, temperature, nutrient availability).

Altogether, this work provides new information on the evolution of reaction norms of genes associated with the physiological response to one of the main environmental variables that affects almost all animals, oxygen availability. It also provides an interesting model system to study this type of question further in a natural population of homeotherms.

Erzurum, S. C., S. Ghosh, A. J. Janocha, W. Xu, S. Bauer, N. S. Bryan, J. Tejero et al. "Higher blood flow and circulating NO products offset high-altitude hypoxia among Tibetans." Proceedings of the National Academy of Sciences 104, no. 45 (2007): 17593-17598.

Peng, Y., C. Cui, Y. He, Ouzhuluobu, H. Zhang, D. Yang, Q. Zhang, Bianbazhuoma, L. Yang, Y. He, et al. 2017. Down-regulation of EPAS1 transcription and genetic adaptation of Tibetans to high-altitude hypoxia. Molecular biology and evolution 34:818-830.

Reviewer #1 (Public Review):

In this paper, authors used IL-33KO and ILC2KO mice to generate evidence for pregnancy specific enrichment of ILC2s and their unique molecular signatures. They documented that uterine ILC2s are distinct from the ILC2s residing in lung and lymph nodes. They have provided solid evidence that although litter size did not change, but fetuses from ILC2KO mice showed growth restriction. In the absence of ILC2s, LPS injections lead to increased abortion rates and fetal loss suggesting the critical role of ILC2s in protection against infection induced pathology. Most of the results presented in the paper support the conclusion, but in some instances the evidence is indirect. Some clarifications on experimental interpretations are required as below.

• The immunohistology experiments revealed that IL-33 predominately co-localizes with ILC2 in the myometrium, but the staining appears to be diffused throughout the myometrium. It is difficult to pinpoint ILC2 specific IL-33 colocalization. Is the decidual expression of IL-33 largely restricted to ILC2s? Also, IL-33 is produced by a variety of other cell types including endothelial cells, which is difficult to ascertain from the images in Fig 1.<br /> • Authors report that ILC2s are responsible for fetal growth restriction (FGR) noted in the ILC2 KO mice. They attribute FGR to utero-placental abnormalities but the experimental evidence, including spiral arterial remodelling and glucose transporter gene expression are indirect. Is there any compensation from other cell types such as macrophages in the absence of ILC2s as they reported increased dendritic cells, neutrophils, and macrophages in ILC2KO mice. Clarify whether IL-33 levels were consistent between WT and ILC2KO mice. How do these other increased numbers of macrophages, DCs and neutrophils fit in the FGR context besides gene expression changes they captured in DCs and macrophages.<br /> • They captured spiral arterial wall to lumen ratio alterations in ILC2KO mice suggesting sub-optimal vascular changes in ILC2KO mice. They did not find any changes in the uNK cells or IFN-production between WT and ILC2KO. What would be the mechanistic link between ILC2 and spiral arterial vascular changes. They indirectly link it to the IL1B gene expression.<br /> • In the LPS induced abortion in ILC2 KO mice experiments, how do they reconcile the predominant role of macrophages and LPS induced TNF-a in the pathology? They did not find any differences in the gene expression in the LPS induced signature cytokine, TNF-a despite increased numbers of macrophages in ILC2 KO mice. Clarification is required on whether these inflammatory alterations that they captured directly linked to utero-placental insufficiency between WT and ILC2KO. The type 2 cytokines were barely detectable in ILC2KO mice, which likely predispose them for utero-placental alterations.

Reviewer #1 (Public Review):

In this study Guss and colleagues identify a requirement of the ECM component Perlecan for the maintenance of neuronal structures. The authors convincingly demonstrate that the absence of Perlecan (in the entire organism) causes a severe perturbation of the ECM-based neural lamella, a support structure surrounding axon bundles and, to a lesser extent, the neuromuscular junction (NMJ). Likely because of these ECM perturbations, axons and even entire nerve bundles break at sites prior to the innervation of the peripheral muscles. Within hemisegments all affected motoneurons show signs of degeneration and synapses are retracted (degenerate). Through targeted genetic approaches in combination with immunohistochemical and electrophysiological approaches the authors aim to elucidate cell specific requirements of Perlecan. Interestingly, knock down of Perlecan in single tissues but also in combinations of tissues (neurons, glia and muscles) was not sufficient to recapitulate the phenotypes observed after ubiquitous knock down. Similarly, a rescue of these phenotypes via motoneuron expression in null mutants was not successful.

The authors very convincingly demonstrate that in the absence of Perlecan synaptic terminals degenerate and that axon and neural lamella morphology and structure is perturbed. All processes were analyzed using multiple and complementary approaches including live-imaging and electrophysiology. The precise correlation of these phenotypes and especially the careful classification into degenerated and non-affected NMJs revealed that the cause for all phenotypes is likely the disruption of the neural lamella that - through thus far unknown mechanisms - cause axonal breakage and subsequently synaptic retractions.

This study highlights the importance of the ECM to maintain neuronal structures, however, the precise source of Perlecan and the precise cause of axonal breakage remains still unresolved.

Further rescue experiments would be necessary to resolve the source of Perlecan. This requires a first demonstration that a rescue is possible with the available tools using a ubiquitous-expression analogous to the RNAi-experiments.<br /> In addition, a careful longitudinal analysis of the integrity of individual axons (e.g. MN1 or MN4) combined with an ECM analysis may provide insights into the place and cause of the axonal breakages that are likely causal for all other observed phenotypes. As pointed out in the discussion a disruption of the blood-brain barrier at specific (?) vulnerable sites seems currently the most reasonable explanation for the observed effects. Surprisingly, the authors did not observe any rescue effect after the inhibition of Wallarian degeneration mechanisms highlighting that the cellular mechanisms underlying these two forms of degeneration in which axons are disrupted may be different.

Reviewer #1 (Public Review):

We conclude that this descriptive study has some strengths but additionally, we propose several ways in which to increase its potential impact and to strengthen some of the claims. This study describes the remodeling of Merkel cells and their innervating sensory axons in the skin. By using transgenic mouse lines in which these cells were genetically fluorescently labeled, the authors performed a series of analyses mostly focusing on the number and location of Merkel cells and the sensory axons that innervate them.

One of the major strengths of the study is the establishment of intravital imaging techniques to investigate the dynamic simultaneous behavior of Merkel cells and their innervation during homeostasis and hair regeneration. However, how the findings integrate into the existing knowledge of skin development is unfortunately only partially addressed.

To the best of our understanding, a few technical limitations of the study define its major weaknesses: First, Merkel cell loss is dramatic and it's unclear whether this reduction is part of a developmentally controlled reduction in cell number, or whether additional cells are expected to be integrated into the system. Longer windows of imaging might help here. Second, the depilation agent might be too aggressive and lead to cell death and thus better controls might be suggested. Similarly, ablating Merkal cells throughout development might cause developmental issues that might mask the proposed homeostasis analyses. A controlled adult specific ablation might be suggested. Finally, the TrkC based transgenic mouse is expected to be heterozygous - could that be an issue? Either better controls, or a textual addressing of this topic are advised.

All in all, we think this study has the potential to establish a high resolution description of Merkel cells - sensory axon dynamic interactions. We hope that the authors will be encouraged to improve the paper based on our comments, something that will likely improve its potential significance and impact.

Reviewer #1 (Public Review):

In the manuscript, the authors tried to explore the molecular alterations of adipose tissue and skeletal muscle in PCOS by global proteomic and phosphorylation site analysis. In the study, the samples are valuable, while there are no repeats for MS and there are no functional studies for the indicted proteins, phosphorylation sites. The authors achieved their aims to some extent, but not enough.

Reviewer #1 (Public Review):

Holzinger et al. investigated potential antimicrobial compounds for cystic fibrosis (CF) infection with similarity to a host-derived antimicrobial, bactericidal permeability-increasing protein (BPI). Human BPI (huBPI) is neutralised by anti-BPI antibodies, rendering it ineffective at eradicating Pseudomonas aeruginosa infection in a large proportion of people with cystic fibrosis. BPI produced by mice (muBPI), scorpionfish (scoBPI), and oysters (oyBPI) was evaluated on their anti-inflammatory, bactericidal, and immunogenic potency. The authors showed that each BPI orthologue evaded recognition by anti-BPI. The cationic BPI orthologues also reduced bacterial burden in vitro, reducing the expression of proinflammatory cytokines (IL-6 and TNF) and significantly decreasing cell culture density in the laboratory and multidrug-resistant P. aeruginosa strains. ScoBPI was the most potent, with greater anti-inflammatory and bactericidal activity than huBPI and all other orthologues.

This study investigates the action of BPI orthologues as potential CF antimicrobials. While scoBPI appears significantly more effective as an anti-inflammatory and bactericidal agent compared to huBPI, the orthologue has not been tested in environments that model the CF lung environment. The authors describe the cationic BPI as binding to the LPS via electrostatic interaction. This interaction could be limited in vivo, as anionic extracellular DNA, cationic metal ions and polyamines, and other charged substances may impede interaction. Further, delivery of scoBPI to the infection site may be detrimentally impacted, due to the viscous mucous in the lungs and the biofilm mode of growth of P. aeruginosa. The discussion of the study could be improved by describing important considerations for future development. These could include in vitro testing against P. aeruginosa biofilms in relevant sputum-mimicking media, and in vivo validation in Galleria mellonella, and CF and non-CF mouse models.

Overall, the authors present an interesting study that provides a compelling basis for a potential novel antimicrobial for CF chronic airway infection. The authors' claims are well supported by their data, which they present in a clear, logical manner. To build on these findings, the authors could test scoBPI in models that recapitulate core factors of the CF environment.

Reviewer #1 (Public Review):

In the present study, Yasuko Isoe, Ryohei Nakamura & colleagues follow a lineage analysis study aiming at identifying the clonal organization of the dorsal telencephalon. The authors use the teleost fish medaka to conduct their experiments since it displays a clearly delineated dorsal pallium. After identifying the clonal units that constitute the dorsal telencephalon, they analyze the epigenetic landscape in each unit. The authors identify then differential open chromatin patterns that they relate to functional aspects of each unit, and additionally, use the epigenetic landscape to infer the identity of transcription factors operating as putative regulators. Overall, the study consists of an impressive amount of data that shed light on the organization of a central brain region in vertebrates.

The findings in the manuscript are organized into two main sections: lineage analysis and epigenetic organization. The authors combine genetic tools with laser dissections of specific clones and ATAC-seq and RNA-seq analysis in multiple samples, an approach that is very elegant and follows high technical standards. For lineage analysis, the authors used a basic, but appropriate, tool to induce and follow clones generated in early embryos, with the side note that lineages are followed using a non-ubiquitous promoter so that the authors restrict their analysis to neural progenitors. My overall impression is that the authors have collected a massive amount of high-quality data, which unfortunately is not properly integrated or discussed in the manuscript. There is only a superficial effort in incorporating the two main findings, which contrasts with the depth of acquired data.

The observation of clonal sectors in the pallium is a great finding that deserves a more detailed analysis in terms of their developmental and evolutionary origin: How may progenitors are used to set up the entire pallium? What is the smallest clone that contributes to it? Is there any laterality bias in the clonal architecture? Is the clonal architecture exclusive for progenitors or does it extend to neurons as well? How has the clonal architecture impacted the morphological diversity of the pallium among teleosts? What are possible evolutionary paths to explain this phenomenon? The authors' discussion on this point circles around one concept, illustrated in the following sentence: " (The clonal architecture) ... possibly explains how the difference in diversity between the pallium and subpallium has emerged: the subpallium is conserved because cells belong to various clonal units intertwined with each other, which has constrained their modification during evolution; whereas the pallium is diverse because of the modular nature of the clonal units which allows for the emergence of diversity". This is the concept that I have the most problems with. The authors' reason that a more defined clonal structure (pallium) makes a system more prone to evolutionary novelties, while a region where clones intermingle (subpallium) is more rigid and therefore more conserved between species.  Is there experimental data that backs up this statement in any other systems? If there is, I urge the authors to share these here. If this is just a speculation, then the argument would benefit from an explanation of how this clonal organization allows for evolutionary novelty. Would it happen by the appearance of more clones at the early stages of development? The authors leave this central point untouched even when discussing the evolutionary origin of the pallium in teleosts.

Having shown the clonal architecture of the pallium and conducted a detailed epigenetic analysis in clones, the authors could also speculate on what is special about this type of organisation. Particularly, how they envision that cells belonging to the same clone inherit a common epigenetic landscape that will define their function later on. There is little analysis of the cellular organization of each clone, mainly because the authors labeled only a subset of the real, genetic clone. The authors present images of entire brains and optical horizontal and transverse sections, which largely sustain their claims for a clonal organization. The difference in the clonal arrangements between the Dld and the Vd is clear, but the authors could provide a higher-resolution image of some clones in the telencephalon to get an idea of the cellular composition of the regions they use for their analysis. What is the extent of non-GFP cells in the regions they use for RNAseq and ATACseq? From the images shown it is very difficult to realize whether all cells in the clonal sector do indeed belong to the clone.

Reviewer #1 (Public Review):

In this paper, the authors use patch-clamp recordings of immature (4w and 5w) and mature granule cells (GC) in hippocampal slices to study stimulus-response properties at different cell ages. First analyzing spike trains generated by a fluctuating stimulus, they show that the reliability of spiking responses increases with cell age. They then fit a Spike Response Model (SRM), a type of GLM that translates inputs to membrane potential and then membrane potential to spikes. Using this model they compare the model parameters from different cells. Time constants for the input-voltage filter are faster for the mDGCs than the 4w, with 5w intermediate, and time constants across all cells appear to be faster when reliability is higher. They analyze stimulus reconstruction and stimulus-response information using the recordings and then extend this to pseudo-populations to test how heterogeneous properties contribute to coding efficacy. They find that mixed pools of neurons, including cells of multiple ages, decode stimuli more accurately.

Overall, this is a cleverly designed study with sound methodology. A major contribution of the paper is demonstrating with precise, quantitative methods how a degree of heterogeneity that naturally arises in neural populations may be beneficial to decoding the stimulus, despite the fact that some of the heterogeneity arises from variability in single cells. This is an intriguing result showing how neural coding and decoding may actually benefit from heterogeneous response properties rather than only be hindered by variation.

The paper has a couple of weaknesses. First, it is difficult to assess how meaningful the effects that the authors measure are. For example, is a 3% improvement in decoding (Fig. 4H) with mixed populations of GCs substantial? A second issue not currently addressed in the paper is the relative roles of age-dependent variability and within-group variability: how much of the improvement in stimulus decoding/information encoding is achieved by heterogeneity across model parameters that appears in each age group? Further analyses and clarifications in this vein are suggested.

Reviewer #1 (Public Review):

This manuscript by Toshima et al. describes a study of the organization of traffic in the endomembrane system of the budding yeast Saccharomyces cerevisiae. The authors address the relation between endocytosis and the Golgi (TGN: a collection of maturing membrane elements derived from the trans-Golgi). The study builds on a previous article by the group of Benjamin Glick. In that study (Day et al., 2018), it was postulated that the TGN is the first destination for yeast endocytic traffic after internalization from the plasma membrane. Additionally, Day et al. had shown that endocytic recycling traffic towards the plasma membrane departs from the TGN as well. Therefore, early endosome and recycling endosome compartments would be identical to the TGN or contained within it. Here, Toshima et al. use super-resolution confocal live imaging microscopy (SCLIM) to refine a model of endocytic pathway organization. This powerful imaging technology allows them to show that out of two partially overlapping TGN markers, namely Tlg2 and Sec7, the syntaxin Tlg2 correlates better with the arrival of fluorescently labeled endocytic cargo than alternative TGN marker Sec7. Building on this main finding, the authors conclude that a specific part of the TGN (an "independent sub-compartment") functions as the early endosome. Further experiments in mutants for GGA clathrin adaptors, required for departure of endocytic cargo from the TGN to the Rab5-positive prevacuolar endosome, show again that endocytosed cargo accumulation correlates better with Tlg2 than with Sec7. Furthermore, in GGA mutants the overlap between Tlg2 and Sec7 is decreased, suggesting that GGA is required for maturation of this Tlg2 sub-compartment.

The study is well conducted and its main conclusion that a Tlg2 subregion within the TGN functions as the early endosome seems well supported by the superb live imaging and the analysis of GGA mutants.

Although a technical feat in live superresolution imaging, this single kind of data (moving, shape-shifting blobs of fluorescently-labeled proteins) does not totally fill with meaning the terms "compartment", "sub-compartment", or "independent sub-compartment". This, I think, is the main limitation of the study. Are these compartments or sub-compartments individuated membrane elements, collections of vesicles, regions of the same cisterna or saccule? For this, electron microscopy would be needed.

Reviewer #1 (Public Review):

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

General/major concerns:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

Major comments:

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

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

MAJOR CONCERNS

1. Classifier as an evaluation method.

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

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

2. Local stimulation effects in the striatum

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

3. Autocorrelation findings.

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

4. Connectivity results

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

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

Reviewer #1 (Public Review):

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

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

Reviewer #1 (Public Review):

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

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

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

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

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

Reviewer #1 (Public Review):

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

Reviewer #1 (Public Review):

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

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

Reviewer #1 (Public Review):

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

Strengths:

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

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

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

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

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

Weaknesses:

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

Reviewer #1 (Public Review):

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

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

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

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

Reviewer #1 (Public Review):

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

Strengths:

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

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

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

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

Public Review:

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

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

Joint Public Review:

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

Reviewer #1 (Public Review):

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

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

Reviewer #1 (Public Review):

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

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

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

Reviewer #1 (Public Review):

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

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

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

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

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

Reviewer #1 (Public Review):

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

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

Reviewer #1 (Public Review):

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

Reviewer #1 (Public Review):

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

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

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

Reviewer #1 (Public Review):

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

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

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

Reviewer #1 (Public Review):

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

Reviewer #1 (Public Review):

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

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

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

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

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

Reviewer #1 (Public Review):

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

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

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

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

Reviewer #1 (Public Review):

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

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

Reviewer #1 (Public Review):

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

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

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

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

Reviewer #1 (Public Review):

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

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

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

Reviewer #1 (Public Review):

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

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

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

Joint Public Review:

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

Major points:

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

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

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

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

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

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

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

Joint Public Review:

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

Reviewer #1 (Public Review):

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

Reviewer #1 (Public Review):

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

Major comments

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

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

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

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

Reviewer #1 (Public Review):

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

The manuscript has numerous strengths:

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

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

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

The paper has only a few weaknesses:

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

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

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

Reviewer #1 (Public Review):

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

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

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

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

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

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

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

Reviewer #1 (Public Review):

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

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

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

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

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

Reviewer #1 (Public Review):

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

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

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

Reviewer #1 (Public Review):

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

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

Reviewer #1 (Public Review):

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

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

Reviewer #1 (Public Review):

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

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

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

Reviewer #1 (Public Review):

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

# Main comments

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

## a. Structured smoothing

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

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

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

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

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

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

Reviewer #1 (Public Review):

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

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

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

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

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

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