Reviewer #2 (Public Review):

Okuma, Hidehiko et al. investigated the role of dystroglycan N-terminus (alpha-DGN) in matriglycan synthesis and how the resultant shorter matriglycan affects muscle function and anatomy, and neuromuscular junction formation. Using transgenic mice with muscle-specific loss of alpha-DGN, and DAG1 KO mice exogenously expressing alpha-DGN-deficient DG, they found in both types of mice that less and a shorter form of matriglycan was made. The shorter matriglycan is capable of binding laminin. Additional analyses revealed that the alpha-DGN deficient mice have abnormal neuromuscular synapses and reduced lengthening contraction-induced force. Interestingly, exogenous expression of alpha-DGN or LARGE1 overexpression does not restore the full-length matriglycan or rescue the phenotypes. The authors also compared three transgenic mouse models with different matriglycan lengths and found correlations between matriglycan length and eccentric contraction force, centrally located nuclei (inverse correlation), and laminin binding. These data provide additional insights into the mechanisms underlying matriglycan synthesis and dystroglycanopathies.

The main conclusion of this paper, which is that synthesis of full-length matriglycan requires alpha-DGN, is well supported by data. However, the lack of phenotypic rescue by exogenous alpha-DGN expression makes it difficult to draw a more generalized cause-and-effect conclusion between alpha-DGN, matriglycan length, and pathologies.

Reviewer #2 (Public Review):

Horton et al combined computational and functional approaches to identify a role for a mouse transposable element (TE) family in the transcriptional response to interferon gamma (IFNG, also known as type II interferon). This paper builds on previous work, some of which was done by the corresponding author, in which TE families have been shown to contribute transcription factor binding sites to genes in a species-specific manner. In the current work, the authors analyzed datasets from mouse primary macrophages that had been stimulated by IFNG to identify TEs that might contribute to the transcriptional response to IFNG treatment. In addition to previously identified endogenous retrovirus subfamilies, the authors identified sites from another TE family, B2_Mm2, that they found contained STAT1 transcription factor binding sites and whose binding by STAT1 was induced following IFNG stimulation. To test the hypothesis that a B2_Mm2 element was providing IFNG-inducibility to an associated gene, the authors chose one of the 699 mouse genes that had nearby (<50 kb) B2_Mm2 elements and was upregulated upon IFNG treatment in previous datasets. The gene they chose was Dicer1, which also is upregulated by IFNG in mouse macrophages but not in human primary macrophages, furthering the hypothesis that the presence of B2_Mm2 in mouse cells may provide IFNG-inducibility to Dicer1. Following KO of a ~500 bp region in two separate clones of immortalized mouse macrophages, the authors show a decrease in basal as well as IFNG-induced expression of Dicer1, providing support for their conclusion that a B2_Mm2 is important for IFNG-inducibility. The authors further show that two nearby genes that are also upregulated by IFNG, Serpina3f and Serpina3g, are also reduced at basal conditions as well as when stimulated with IFNG. The authors use these data to suggest that additional elements in the B2_Mm2 element in the Dicer1 gene, possibly CTCF elements, are have long distance effects on transcription of nearby genes.

Overall, this is an interesting and well written manuscript. The computational conclusions are supported by their data and add to the growing field of TEs and their role in transcription regulatory network evolution. While the authors do a good job of experimentally validating one example, inclusion of additional data, all of which they already have, as detailed below would substantially increase the applicability of their work and strengthen their conclusions about the broad role of TEs in the IFNG response in mice versus other species.

1) Following their genome-wide comparisons, the authors hone in on Dicer1 as an interesting example in which they hypothesize that a B2_Mm2 element near the Dicer1 gene could be contributing to the fact that this gene is upregulated by IFNG in mouse cells but not human cells. What would be very useful to the readers of this paper is knowing how many other examples there might be like this one. Adding DEseq values from human RNAseq data the authors already use (current references 10 and/or 37) for identifiable human orthologs to Table S7 would thus strengthen their conclusions. If Dicer1 is unique in this aspect of having (a) a nearby B2_Mm2 element and (b) a binary difference between inducibility in mouse versus human cells, that is interesting. If Dicer1 is not unique, that strengthens the authors' assertion that B2_Mm2 insertions have altered the transcriptional network in a host-specific manner. Either way, the answer is interesting, but without including this analysis, the authors leave out an important aspect of their work and it remains unclear how generalizable their conclusions are.

2) The results with Serpina3g and Serpina3F gene expression in the authors' knockout cells are very interesting. However, the authors focus almost exclusively on Serpina3g and Serpina3F, which makes it difficult to understand what is happening genome wide. Are other IFNG-induced genes (including those not on chromosome 12) similarly affected at the level of basal or induced transcription? How many genes are different in WT versus KO cells, both at basal and induced states? Does this correlate with their CUT&TAG data shown in Fig. 5? By focusing only on nearby genes (Serpina3g and Serpina3F), the authors hint that this may be a long range regulatory effect, "potentially mediated by the CTCF binding activity of the element" that they removed. But by only focusing on two nearby IFNG-induced genes, their data do not rule out the (also potentially quite interesting) possibility that there may be a more indirect role for this TE site or Dicer1 in basal transcription of IFNG-induced genes or IFNG-mediated gene expression. Providing more data on other genes throughout the genome in WT and KO cells, which the authors have generated but do not include in the manuscript, would help distinguish between these models. While a broader effect of these KOs on IFNG expression, or gene expression in general, would not fit as neatly with their model for local gene regulation, these analyses are needed to understand the effects of TE insertion on gene regulation.

Reviewer #2 (Public Review):

In this manuscript, the authors are proposing a generalizable solution to masking brains from medical images from multiple species. This is done via a deep learning architecture, where the key innovation is to incorporate domain transfer techniques that should allow the trained networks to work out of the box on new data or, more likely, need only a limited training set of a few segmented brains in order to become successful.

The authors show applications of their algorithm to mice, rats, marmosets, and humans. In all cases, they were able to obtain high Dice scores (>0.95) with only a very small number of labelled datasets. Moreover, being deep-learning-based segmentation once a network has been trained is very fast.

The promise of this work is twofold: to allow for the easy creation of brain masking pipelines in species or modalities where no such algorithms exist, and secondly to provide higher accuracy or robustness of brain masking compared to existing methods.

I believe that the authors overstate the importance of generalizability somewhat, as masking brains is something that we can by and large do well across multiple species. This often uses specialized tools for human brains that the authors acknowledge work well, and in the usually simpler non-human (i.e. lissencephalic rodent) brains also work well using image registration or multi-atlas segmentation style techniques. So generalizability adds definite convenience but is not a game-changer.

The key to the proposed algorithm is thus that it works better than, or at least as well as, existing tools. The authors show multiple convincing examples that this is the case even after retraining with only a few samples. Yet in those examples, the authors proposed retraining the network on even subtle acquisition changes, such as moving in field strength from 7 to 9.4T. I tried it on some T2 weighted ex-vivo and T1 weighted manganese enhanced in-vivo mouse data and found that the trained brain extraction net does not generalize well. None of the pre-trained networks provided by the authors produced reasonable masks on my data. Using their domain adaptation retraining algorithm on ~20 brains each resulted in, as promised, excellent brain segmentations. Yet even subtle changes to out-of-sample inputs degraded performance significantly. For example, one set of data with a slight intensity drop-off due to a misplaced sat band created masks that incorrectly excluded those lower intensity voxels. Similarly, training on normal brains and applying the trained algorithm to brains with stroke-induced lesions caused the lesions to be incorrectly masked. BEN thus seems to be in need of regular retraining to very precisely matched inputs. In both those examples, the usual image registration/multi-atlas segmentation approach we use for brain masking worked without needing any adaptation.

Overall, this paper is filled with excellent ideas for a generalized brain extraction deep learning algorithm that features domain adaptation to allow easy retraining to meet different inputs, be they species or sequence types. The authors are to be highly commended for their work. Yet it appears to at the moment produce overtrained networks that are challenged by even subtle shifts in inputs, something I believe needs to be addressed for BEN to truly meet its promised potential.

Reviewer #2 (Public Review):

This is the first report that establishes gamma-TuNA as an activator of gamma-TuRC-dependent microtubule-nucleation, using purified components. This is an in-depth study that establishes experimental conditions under which gamma-TuNA can function as an activator (dimerization of gamma-TuNA, appropriately sized N-terminal tag) and clarifies why similar attempts to study gamma-TuNA have failed in the past. I think that the information in this manuscript will be of immense value to the scientific community, as it resolves a long-standing mystery concerning the function of gamma-TuNA. A key question that still remains unanswered is whether the gamma-TuNA-dependent activation mechanism involves a conformational change of the gamma-TuRC, from an asymmetric to a ring-shaped template structure, but this may be beyond the scope of the present submission.

Reviewer #2 (Public Review):

Here, the authors aim to address the role of R loops in CSR. Though implicated in CSR since decades, R loops remain enigmatic regarding their true function at the Igh locus during CSR. In particular, its role in AID targeting to S regions remains debated with no direct evidence supporting this claim. In this study, the Barlow lab sheds interesting new light on what R loops may be doing during CSR. They study the response to elevated R loop levels which they achieve using single or double KO of SETX (a helicase that can unwind R loops) and RNaseH2 (which can cleave R loops). In this system, R loop removal is deficient and the effect on CSR and genome instability can be assessed. This is a fresh approach which allows the authors to draw new insights into R loop biology. Overall, the results support the conclusions that the timely removal of R loops is not necessary for optimal CSR but is necessary to maintain genome stability. But there are some experiments that need to be done to solidify this conclusion.

The major findings are that the increase in steady-state R loops in dKO cells does not appear to affect CSR frequency although small increase in mutation is observed. However, in dKO cells, there is a significant increase in gross chromosomal aberrations (translocations and fusions) as well as increased usage of alternative end-joining during CSR. Thus, surprisingly, increased DNA damage and increased reliance on alternative end-joining do not appear to reduce CSR which would have been expected based on many previous studies. Thus, they conclude that R loop removal by SETX and RNaseH2 is necessary to enhance the usage of classical end-joining repair pathways that are more efficient and less prone to genome instability.

The major weakness here is the lack of a proper characterization of B cell development in the mice. They use Cd19-cre which acts earlier in B cell development in the bone marrow and hence it is important to know whether B cell populations were skewed or otherwise influenced by the early KO of Setx and Rnaseh2. Along these lines, gene expression analysis is necessary to know whether the single and double KO (both naïve and activated) splenic B cells have undergone differential expression in DNA repair pathways or other pathways that could impinge upon CSR and contribute to the DNA repair phenotype they observe.

There is no western blot analysis to show how well RNASEH2 is depleted. Cd19-cre is known to have variable effects hence it is unclear whether efficient deletion was obtained in mature B cells.

One puzzling finding is that R loops were increased only in the S-mu but not the S-gamma1 region although both form R loops. Some thoughts on this would be useful for the readers since this implies that R loop resolution at S-gamma1 is independent of both enzymes.

Reviewer #2 (Public Review):

Canetta et al. investigated the time-dependent effects of inhibition of parvalbumin-positive interneurons in the mouse prefrontal cortex on task learning and cognition. The authors have used electrophysiology, optogenetics, behavioral paradigms, and histology. This study provides an interesting angle to understand cell behavior in the mouse prefrontal cortex, which eventually may help in therapies against schizophrenia.

Reviewer #2 (Public Review):

This is an original and carefully argued study on a key question of immunology. The authors detect statistical differences in the TRA and TRB repertoires between negatively selected thymocytes and mature T cells. Discrimination does not work for individual T cell receptor chains, but starts to become reasonably sensitive and specific for quora of 30 alpha chains (I did not find ROCs for beta chains; see below). These results, including the more detailed statistics based on CDR3 sequence, are technically sound and make a unique conceptual contribution to quantitative immunology.

In terms of interpretation, the premise of the paper - that negatively selected and peripheral T cell repertoires should systematically differ in some characteristics because thymocytes could scan only a tiny fraction of self-peptides - is not based on experimental evidence. Experimental data allow for the possibility that a thymocyte scans a much larger fraction of self-peptides than the number given by the authors. Hence this point cannot be maintained as a premise, while the underlying question is key and worth discussing. In this context, I also recommend that the title give a factual account of the main finding, rather than propose a particular hypothetical interpretation (hypotheses will be better placed in the Discussion, possibly the Abstract). These suggested edits do not impact the originality and importance of the experiments and computational results; these will be of wide interest.

Reviewer #2 (Public Review):

The authors hypothesized that T-cells capable of recognizing SARS-CoV-2 specific antigens might be present within the pre-existing CMV specific T cell memory pool in CMV+ individuals. In order to test this hypothesis, the authors used a collection of pre-pandemic samples from CMV+ and CMV- donors. Using the approach described in the manuscript, the authors were able to demonstrate the existence of CMV specific T cells capable of crossreacting with SARS-CoV-2 antigens. In addition they were able to show that this crossreactivity can be mediated by a public TCR. The findings warrant additional studies in larger cohorts of acutely infected individuals. This important finding expands our knowledge of T-cell crossreactivity and heterologous immunity. In addition, this study provides useful information regarding the origin of T-cells that crossreact with SARS-CoV-2.

Reviewer #2 (Public Review):

This manuscript addresses the function of osteocytes that are not well understood. These cells are embedded in the largest organ, bone. In addition to mechanosensing, the concept that bone, and that of the osteocytes can act as endocrine cells, communicating with other organs with soluble factors is beginning to take shape. In addressing the function of osteocytes in mice, the authors specially remove/reduce the number of osteocytes using genetic tools to conditionally activate the expression of diphtheria toxin (DTA) in osteocytes that are expressing the DMP1, thus, killing these cells. The impact on the skeletal system in development and ageing were studied, as well as cells in the bone marrow.

Mice with completed removal of DMP1-expressing osteocytes die before birth. However, mice with partially reduced osteocytes survive with reduced life span associated with severe osteoporosis, kyphosis and sarcopenia, conditions that are age-related, and the authors claimed an association with prematured ageing. The authors showed changes in the balance between the osteoblast, osteoclast and adipocyte lineages as possible mechanisms.

A relationship between bone and muscle is known, especially the contractile muscles. Their finding that there is a continuing body and muscle weight lost substantiates this relationship with focal muscle atrophy and sarcopenia.

Using a similar genetic approach, a previous study by Asada et al (2013) has shown that osteocytes regulate mobilization of haematopoietic stem/progenitor cells in mice. This manuscript extends this relationship in an assessment of the bone marrow cells using single cell RNA sequencing (scRNA-seq), revealing an alteration of the haematopoietic lineage commitment, with a shift from lymphopoiesis to myelopoiesis.

The most novel and interesting finding is the association with senescence. However, this is also perhaps the weakest link in the manuscript, as it presents a big jump in the hypothesis from the single cell data. The hypothesis was substantiated from an assessment of a senescence associated secretory phenotype (ASAP) score from the scRNA-seq data, which was not well explained. Nevertheless, circulatory SASP were elevated in osteocyte compromised mice, and concluded that osteocyte reduction induced senescence in osteoprogenitors and myeloid lineage cells.

Overall, the manuscript was logically presented, and the data in most parts supported the conclusion. The relationship however was mostly through descriptive morphological and biochemical analyses of the mutant mice. While there are weaker areas that need to be further strengthened, there are novel findings providing further insights into the biology of osteocytes and reaffirms the concept of bone as an endocrine organ.

Reviewer #2 (Public Review):

Wang et al. present a detailed description and analysis of the previously reported cranial remains of enantiornithine bird Yuanchuavis. The authors use X-ray CT scan data to reconstruct the cranial elements and retro-deform the facial and palatal skeleton. The authors also use principle component analysis with geometric morphometrics data to investigate where Yuanchuavis falls in palatine phylomorphospace. The authors use these data to make inferences about the kinetics of the Yuanchuavis skull as well as the evolution of cranial kinesis across birds.

Generally, I find the authors' direct interpretation of their anatomical and PCA data to be convincing and compelling. The anatomical description is thorough and accurate. The methods used for the geometrics morphometrics and PC analyses are appropriate. I find compelling the authors' interpretations that Yuanchuavis largely retained the ancestral non-avialan akinetic skull.

One of the greatest strengths of this paper are the extremely attractive figures. In particular, I find figure 4 to be exceptionally useful - this is easily the most effective illustration I have yet seen of avian cranial kinesis and the shifts in cranial morphology that underlie its evolution. I applaud whoever designed this figure.

My one major concern with this paper's methodology is that the palatine used for Ichthyornis is incorrect. Torres et al. (2021) published the correct palatines, which were very different from those incorrectly (but understandably) identified in Field et al. (2018) and used here. I strongly urge the authors to rerun their GMM analysis with corrected data.

The remaining weaknesses I find in this paper are not major but are worth addressing, and generally pertain to the broader discussion of significance of the authors' more direct interpretations of their data. The authors' suggestion that reduction/loss of the jugal process of the palatine was an early step towards the modern kinetic avian skull is logical, but I don't think the GMM analysis presented here demonstrates that (contrary to lines 390-392). The GMM analysis can only help identify such morphological shifts, not connect them to functional shifts. Rather, I think this analysis helps refine when this shift occurred - indicating that, if there is such a functional link, the earliest steps towards the modern kinetic skull occurred early in avialan evolution.

I find the discussion of the evolution of cranial kinesis as exaptation (lines 427-441) confusing, distracting and largely unnecessary. Has anyone previously suggested that avian cranial kinesis is an example of preadaptation?

I am similarly confused by the connection made by the authors of evolutionary modularity, the akinetic skull of enantiornithines and patterns of avialan diversification (lines 442-464). Specifically, I do not understand how the dominance of enantiornithine clade in the Cretaceous is "counterintuitive" (line 452), nor do I understand how this pattern is explained by evolutionary modularity.

Reviewer #2 (Public Review):

In this article entitled "The missing link between genetic association and regulatory function", Connally and colleagues attempted to quantify the extent to which genetic variants affect complex traits by altering the expression levels of putative causal genes. They focused on nine complex traits (including four common diseases) for which large-scale GWAS data were available. They curated 143 candidate genes (127 unique genes) for Mendelian forms of the traits under the assumption that genes causing the Mendelian form of the complex traits should also be the genes influencing complex trait variation in the general population. They found enrichment of the candidate genes in the GWAS regions (+/- 1Mb of a genome-wide significant signal) for all the complex traits but height and breast cancer. They then investigated the proportion of the candidate genes whose eQTL signals are colocalized with the GWAS signals for the nine traits, the proportion of the genes in close physical proximity with the fine-mapped GWAS variants, and the proportion of genes whose functionally active regions annotated using chromatin modification and activity data are overlapped the fine-mapped GWAS variants. All the proportions appeared to be small.

Major comments

The hypothesis that the genes responsible for the Mendelian traits are also the causal genes for the cognate complex traits does not seem to hold, given the prior work and the data shown in the study. For example, if this hypothesis is true, it is unexplained why the candidate genes were not even enriched in the GWAS regions for height and breast cancer.

The only evidence supporting their hypothesis appears to be the enrichment of the candidate genes in the GWAS regions for seven out of the nine traits. However, significant enrichment of the candidate genes in the GWAS regions does not necessarily mean that a large proportion of the candidate genes are the causal genes responsible for the GWAS signals. Analogously, we cannot use the strong enrichment of eQTLs in GWAS regions as evidence to claim that a large proportion of the GWAS signals are driven by eQTLs.

Considering the large numbers of GWAS signals, we would expect a substantial number of genes in the GWAS regions by chance. It would be interesting to quantify the number of genes in the GWAS regions if the 143 genes are randomly selected. Correcting the observed number of genes for that expected by chance (e.g., subtracting the observed number by that expected by chance), the proportion of the candidate genes in the GWAS regions would be small.

The proportion of the candidate genes whose eQTL signals were colocalized with the GWAS signals or in close physical proximity with the fine-mapped GWAS hits was small. However, I would not be surprised if they are significantly enriched, compared with that expected by chance (e.g., quantified by repeated sampling of the 143 genes at random).

It is unclear how the authors selected the breast cancer genes. If the genes were selected based on tumor somatic mutations, it is a problem because there is no evidence supporting that somatic mutation target genes are also cancer germline risk genes.

The authors observed no enrichment of the candidate genes in height and breast cancer GWAS regions. In this case, should these traits and the corresponding genes be removed from the subsequent analyses?

Reviewer #2 (Public Review):

General linear modelling (GLM) forms a cornerstone in analyses of task-based functional magnetic resonance imaging (fMRI) data. Obtaining robust single-trial fMRI beta estimates is a difficult problem given the relatively high levels of noise in the fMRI data. The introduced toolbox significantly improves such estimates using a few key features: 1) estimating a separate hemodynamic response function (HRF) for each voxel, 2) including noise regressors to improve reliability of the betas across repetitions, using a cross-validated approach, 3) using ridge regression on the beta estimates. The authors explain these steps well and compare the results obtained on subsequent metrics when choosing the include, or not, the different features along this procedure. They also compare their new approach to the Least-Squares Separate technique for beta estimation. For their demonstrations, they use two condition-rich datasets (NSD and BOLD5000) to show the improvements that different components of GLMsingle afford.<br /> The metrics used for comparisons are well chosen and relevant. Especially the test-retest reliability of GLM beta profiles is often a prerequisite for most subsequent analyses. Additionally, they consider temporal autocorrelation between beta estimates of neighbouring trials, and a few potential downstream analyses, looking at representational similarity analysis and condition classification. Thus, they really consider a range of possible applications and provide the reader with useful pointers to inspect what is most relevant for a given application.<br /> This manuscript and toolbox present a major advancement for the field of neuroimaging and should be of interest to essentially any researcher working with task-based fMRI data.

The strengths of the manuscript and toolbox are numerous, and presented results are convincing. To further the impact of the toolbox and paper, the authors could provide more guidelines on implementation for various common uses of the toolbox and/or factors to consider when deciding which steps to implement in one's analysis pipeline (FitHRF, DenoiseGLM, RR).

Additionally, there are a few considerations that could be addressed directly:<br /> 1) The authors use crossvalidation to determine the number of nuisance regressors to add in the model. Thus, any variability in responses to a single condition is considered to be 'noise'. How might this influence a potential use of single-trial estimates to assess brain-behaviour correlations (e.g. differences in behavioural responses to a single condition), or within-session learning conditions? For such uses, would the authors suggest to instead use LSS or a subset of their features in GLMsingle (i.e. not using GLMdenoise)?<br /> 2) In the results, using a fixed HRF leads to drastically lower performance on a variety of subsequent measures compared to fitting an HRF to each voxel, especially as regards to beta map test-retest reliability (Fig. 2-3). Have the authors ensured that the HRF chosen is the most appropriate one for the region of interest? In other words, is the chosen HRF also the one that most voxels are fitted in the flexible option?

Reviewer #2 (Public Review):

'Hairlessness' has convergently evolved numerous times in mammals. In this paper the authors look for patterns in the rate of DNA sequence evolution across the mammalian phylogeny to identify regions of the genome that are independently evolving at similar rates in hairless mammals. The authors find that signatures of convergent accelerated sequence evolution in hairless mammals is biased towards coding and gene regulatory regions known to be involved in hair biology, likely reflecting genetic drift following hair reduction. This bias toward hair-relevant genomic regions also highlights the utility of this approach to identify new candidate regions of the genome that haven't previously been implicated in hair biology and the authors describe several intriguing coding and non-coding candidates. Authors further find that genes and putative gene-regulatory regions have non-random patterns of drift, with mutations in coding regions biased toward proteins that compose physical aspects of the hair sheath.

The analysis in this paper is centered on the RERconverge tool. Importantly, the authors have taken numerous steps to address potential issues with such an approach. One issue with RERconverge is the need to include/exclude ancestral branches as having a trait, which introduces assumptions about ancestral states. The authors controlled for this by running multiple variations of RERconverge with and without ancestral states as being 'hairless' with no major impact on results. The authors also controlled for whether certain lineages are driving the correlation signal, and found that removal of any given lineage does not impact skin or hair follicle enrichments. Finally, the authors have adequately distinguished whether other common phenotypes in hairless mammals (e.g. marine lifestyle or body size) drive the convergent signals in the dataset and found the reported genetic signatures are best explained by hair loss compared to these other traits.

The paper should be of interest to a broad selection of biologists interested in evolution, development and phylogenomic methods. The candidate genes identified in this paper provide a compelling launching point for future experimental studies into the genetic basis of hair.

Reviewer #2 (Public Review):

This work follows up on an earlier publication that showed PNPase and RNase J2 play important roles in CRISPR RNA processing (doi: 10.7554/eLife.45393). Here, the authors show that RNase R also plays a critical role in CRISPR RNA maturation. In addition, they show that RNase R and PNPase are both recruited to the type III CRISPR complex (Cas10-Csm) via direct interactions with the Cmr5 subunit and that deletion of an intrinsically disordered region (IDR2) on Cmr5 selectively inhibits PNPase recruitment but not RNase R. The authors show unquantified stimulation of PNPase nuclease activity by Cmr5. Phage challenge assays are performed to test the impact of PNPase and RNase R deletion mutations on CRISPR-Cas mediated phage defense. Contrary to expectation, over-expression of the CRISPR system in cells that contain a deletion of PNPase and/or RNase R, maintain robust anti-phage immunity. The interpretation of this experiment is that RNase R and PNPase may be dispensable in an over-expression system that produces high (non-natural) concentrations of the Csm complex. They test this idea using a system that expresses the CRISPR-Cas components off of a chromosomally encoded locus (strain RP62a) and challenge these cells using a plasmid conjugation assay. In this iteration, deletion of PNPase has no impact on CRISPR performance, while deletion of RNase R "exhibited a moderate" attenuation of the immune response. In contrast, to either single gene deletion, the PNPase and RNase R double mutant showed a near complete loss of immunity.

Overall, the paper provides convincing evidence that PNPase and RNase R are involved in crRNA processing, and that they are recruited to the type III complex via Cmr5. The work on RNase R is entirely new and the role of PNPase is expanded. The role of cellular RNases in CRISPR RNA biogenesis is important, though some of the results are subtle and some of the biochemistry would benefit from a more quantitative analysis.

Reviewer #2 (Public Review):

Mounting evidence demonstrates that reversible methylation of mRNA (m6A) is a ubiquitous regulator of mRNA splicing, stability, and translation. The biology of m6A involves writer proteins that add a methyl group to mRNA, reader proteins that mediate the function of the methylated mRNA, and eraser proteins that remove the methyl group upon accomplishing the goal. This manuscript reports a key role of the m6A reader protein YTHDC1 in regulating the function of skeletal muscle stem cells that are crucial for postnatal muscle growth and regeneration.

The strengths of the manuscript include using several tour-de-force techniques to examine m6A and the biological consequence in satellite cells. A large amount of data supports the conclusion. Combining conditional knockout animal models and molecular tools to dissect in vivo functions of YTHDC1 and molecular mechanisms underlying the function.

There are only a few minor weaknesses. The main body is lengthy, and some content can be reduced or condensed. For example, RNA-seq was used to determine gene expression in WT and cKO cells, but the purpose of this is not well justified given that YTHDC1 mainly functions to regulate splicing and nuclear expert of mRNA rather than controlling their expression levels. Does the RNA-seq data suggest that YTHDC1 may also regulate gene expression independent of m6A reader function?

Reviewer #2 (Public Review):

The present study proposes a novel methodology for genetic labeling and manipulation of cerebrospinal fluid-contacting neurons (CSF-cNs). This is based on an impressive quantity of nice images of very high quality, results being obtained both in classical confocal microscopy and electronic microscopy and an advanced images analysis procedure. Anatomical findings are put in a more functional aspect with investigations of neuronal properties and motor function using in vitro and in vivo approaches examining functional consequences of perturbation of CSF-cNs' activity. Conclusions are strongly supported by the data. Nevertheless, it could be important to describe a bit more how the quantity of virus injected can be controlled, to increase the size of the sample for the collection of in vivo data (n=4 presently) and eventually discuss these new anatomical data with the presence of locomotor central pattern generators known to be located in restricted regions of the spinal cord (is there any relation or not). Overall this new method should be of great interest for researchers investigating the anatomy and the role of these still enigmatic cells.

Reviewer #2 (Public Review):

Macaisne and colleagues investigate the assembly and function of a protein module consisting of the kinase BUB-1 and the microtubule binding proteins HCP-1/CENP-F and CLS-2/CLASP, which function at kinetochores during cell division. By replacing endogenous proteins with RNAi-resistant transgenic mutants that are expressed at endogenous levels, the authors screen for protein domains involved in recruitment of the module to meiotic kinetochores in oocytes. This tour de force clarifies the connectivity among the components of the module and confirms a linear assembly hierarchy in which the outer kinetochore protein KNL-1 recruits BUB-1 (surprisingly independently of its binding partner BUB-3), BUB-1 recruits HCP-1, and HCP-1 recruits CLS-2. Having identified deletion mutants that perturb specific interactions among module components, the authors use these separation-of-function mutants to investigate how the module contributes to female meiotic divisions using live cell imaging. The results allow the authors to conclude that the module has both kinetochore-dependent and kinetochore-independent functions and that module integrity is important for spindle assembly and chromosome segregation. In an elegant domain-swapping experiment the authors target CLS-2 directly to BUB-1 so that HCP-1 is no longer necessary for CLS-2 recruitment. Depletion of HCP-1 in this background reveals that HCP-1's role goes beyond that of a CLS-2 recruitment factor. Finally, an in-depth mutational analysis of CLS-2's microtubule binding region shows that only one of the two TOG-like domains is essential for CLS-2 function, consistent with the absence of critical residues in the second TOG-like domain. The extensive in vivo analysis of module mutants is complemented by in vitro assays that directly assess the effect of module components on microtubule dynamics. This confirms CLS-2's role as a microtubule stabilizer but also reveals that addition of the other two components modulates this effect.

The experiments presented in this paper are rigorous and succeed in elucidating the functional relevance of the interactions among BUB-1, HCP-1, and CLS-2. The main conclusion of the paper, namely that these components work as a unit, is well supported by the in vivo evidence. What is less clear is whether the effects observed in vitro reflect the activity of the intact module. This part of the paper would profit from analysis of binding-defective mutants. Specifically, including HCP-1 mutants defective in CLS-2 and/or BUB-1 binding would help determine whether the enhancement of microtubule pausing that is observed in the presence of all three components requires assembly of the module.

Reviewer #2 (Public Review):

In this work Kado and colleagues analyzed cell membrane partitioning in Mycobacterium smegmatis. Based on the membrane fluidizing effect of benzyl alcohol they did a transposon sequencing that are sensitive to the treatment. Among a group of genes that code for antiporter, they identify the bifunctional PBP PonA2 to be involved in benzyl alcohol sensitivity. Membrane partitioning in domains with higher and lower fluidity seems to depend on the peptidoglycan cell wall. In particular, de novo partitioning depends on preexisting cell wall, but not on the active synthesis. The authors use a variety of techniques to support their claims.

The authors claim that the membrane in Msmeg is partitioned in IMDs (intracellular membrane domain) and a PM-CW (apparently a more rigid membrane domain). I know that the term IMD has been used before, but I find this misleading. Intracellular means that something is within the cell. Here we are talking about different fluidities within the 2D space of the membrane. I do not think that this term is meaningful and should be used.

The authors suggest that PonA2 regulates the density (or heterogeneity - I assume the authors mean degree of crosslinking?) of the peptidoglycan, thereby influencing membrane partitioning (lines 371-372). This claim would require a PG analysis and a comparison of the cross-linking degree. The influence of PonA2 on membrane partitioning remains somewhat unclear. While the authors claim that PonA2 was also shown to provide a protective effect against other stresses, such as heat, it is not certain that this has to do with membrane partitioning. Although increase in temperature has certainly an effect on membrane dynamics, heat also triggers unfolded protein response. Bacteria furthermore adapt their membranes quickly to changes in temperature and likely adaption also takes place when other stressors influence membrane fluidity. Also, only the TG defective PonA2 led to the phenotype and not the TP mutation, which would argue against a change in crosslinking.

Reviewer #2 (Public Review):

In this study, the authors set out to decode the latency of position representations of static and moving stimuli using EEG multivariate pattern analysis. Linear classifiers were trained on the positions of static stimuli and then generalized to the positions of moving objects in a time-resolved manner. The authors find that the early neural representations of the position of moving stimuli are close to positions in the real world. As neural delays from the retina to the early visual cortex should theoretically induce a latency of ~70 ms their findings suggest that these delays are compensated very early in the visual hierarchy. Furthermore, they find that delays that are accumulated during subsequent processing stages of the visual hierarchy are not compensated, supporting the interpretation of an early compensation mechanism.

I congratulate the authors on this excellent scientific work. I believe its major strength lies in the successful attempt to generalize neural representations of static objects to moving objects. This is made possible due to the large amount of collected EEG data as well as smart task design. Effectively this allows the authors to track which location is currently represented in the brain and how this compares to the actual physical location, all in a time-resolved manner. The approach is remarkably robust against biases due to its relative simplicity, both in task design and analysis.

One of the few limitations of the study is their inability to generalize very early location signals from static to moving objects. This might be indicative of differences in neural codes/mechanisms and in turn, limits the interpretation of which stages of the visual hierarchy are involved in motion extrapolation. That being said, I agree with the authors that this is a fundamentally difficult problem to solve, and importantly it does not negatively impact the main conclusions of this paper.

The current work provides significant methodological and theoretical utility. I am certain that the classification method and principal task design will be used by future studies investigating motion perception due to their effectiveness in tracking internally represented locations. On a theoretical level, the authors' results provide strong evidence that motion compensation processes occur very early in the visual hierarchy. There has been an ongoing debate about how and where this is achieved in the visual system and fMRI studies have only provided limited evidence to solve this issue due to the sluggish nature of the BOLD signal. In addition, the present results challenge previous theories on the role of feed-forward and feedback signals in neural delay compensation and provide concrete directions for future research.

Reviewer #2 (Public Review):

Aims:

This paper asks whether a risk score integrating the impact of common genetic variants across the genome (polygenic risk score) on Type II Diabetes is also to any degree predictive of diabetes in pregnancy (Gestational Diabetes Mellitus or GDM). A number of quantitative endpoints relevant to the risk of GDM are also evaluated. The authors also test for any evidence of statistical interaction between the GDM polygenic risk score and some predictive risk factors - asking if a high polygenic risk score has a more (or less) powerful effect on GDM risk in certain strata of BMI and diet quality. They find no evidence of such interaction.

Strengths/Weaknesses:

The cohorts are strong for the investigation of this question. The paper integrates data from well phenotyped pregnant South Asian women participants on two continents - 837 participants from the Canadian START study and 4372 participants from the UK Born in Bradford cohort. Among these, 734 women had GDM.

There are some differences between the cohorts - for example the occurrence of GDM was about 25% in the START study participants and only around half that in the BIB study, there were differences in the specific origins of the two cohorts within South Asia, and there were life course and lifestyle differences. Appropriate caveats are made by the authors.

The T2D PRS used was derived from previously published data in which only 18% of the population was of South Asian ethnic origins. This could lead to some inaccuracy when applied to an entirely South Asian population, which the authors acknowledge. It seems the "best available" approach to the problem.

Regarding the analyses for interaction, even these cohorts seem likely underpowered to detect this.

Aims achieved?

The authors achieved their aims and showed that the PRS for T2D had small magnitude, but highly significant, association with fasting plasma glucose, two hour post OGTT glucose, and the risk of GDM (47% increase in risk overall). They calculated the population attributable fraction of being in the top tertile of PRS compared with the bottom two tertiles. They did not find any evidence of interactions.

Likely impact:

This paper adds to the literature supporting the hypothesis that genetic factors predisposing to T2D and GDM substantially overlap.

Reviewer #2 (Public Review):

The authors use Jurkat CD4 T-cells stimulated with either antigen (via B-cells or immobilised) or using ionomycin and PMA to broadly stimulate as a model for T-cell activation. They have previously used this system to show that nuclear actin controls expression of some cytokines during T-cell activation. They describe a burst of actin assembly in the nucleus, followed by cytoplasmic actin assembly and organisation into an actin ring synapse in the case of the B-cell stimulation. The main novel observation is that knockdown of either ARPC5 or ARPC5L subunits of the Arp2/3 complex give different impairment of nuclear vs cytoplasmic actin assembly depending on the stimulus. The data are mostly clear and convincing and seem to be appropriately analysed. This study raises the interesting point that signal-induced actin assembly might use different isoforms of Arp2/3 complex depending on the context. These observations are of interest and reveal potential signal-dependent functions of the Arp2/3 subunits, but the study doesn't reveal a biological importance of these differences (e.g. consequences for gene expression or signaling) or explain how/why the different ARPC5 subunits can have different functions.

Reviewer #2 (Public Review):

In this manuscript, Marti-Solans et al., investigate how ASICs have been employed during early bilaterian evolution. Using thorough phylogenetic investigation of transcriptomes of metazoan DEG/ENaC genes, they identify ASICs through the Bilateria. ASIC genes are present in 3 major bilaterian groups, and absent from all other lineages. With the help of in situ hybridization and electrophysiology they demonstrate anatomical expression and functional properties of diverse ASICs from each major bilaterian lineage. They find that ASIC expression is broader than expected and is present centrally and peripherally, suggesting integrative and sensory roles. By heterologous expression of the ASIC channels of interest in oocytes, they characterize electrophysiological currents to expose that proton activation properties, Na/K permeability, and inactivation kinetics are diverse across the different lineages. The manuscript is well written, and the results support their conclusions. The results from this study aid the authors in hypothesizing that ASICS were a bilaterian innovation, and, perhaps they were first expressed in the periphery before being incorporated into the brain.

Reviewer #2 (Public Review):

As the first comprehensive integrative analysis on TCR convergence, this study provided several interesting insights:

1) Convergence might be induced by an ongoing immune response against viral infection or tumor; 2) in the tumor, there is a positive association between TCR convergence and tumor mutation load, and neoantigen-specific T cells are enriched for convergent TCRs, both observations further supporting the tumor-reactive hypothesis; 3) a potentially new diagnostic predictor for ICB treatment. Given these strengths, this work is of general interest to a broad audience.

Reviewer #2 (Public Review):

This theoretical study looks at individuals' strategies to acquire information before and after the introduction of pathogens into the system. The manuscript is well-written and gives a good summary of the previous literature. I enjoyed reading it and the authors present several interesting findings about the development of social movement strategies. The authors successfully present a model to look at the costs and benefits of sociality.

I have a couple of major comments about the work in its current form that I think are very important for the authors to address. That said, I think this is a promising start and that with some revisions, this could be a valuable contribution to the literature on behavioral ecology.

Before starting, I would like to be precise that, given the scope of the models and the number of parameter choices that were necessary, I am going to avoid criticisms of the decisions made when designing the models. However, there are a few assumptions I rather find problematic and would like to give proper attention to.

The first regards social vs. personal information. Most of the model argumentation is based on the reliance on social information (considering four, but to me overlapping, social strategies that are somehow static and heritable) but in fact, individuals may oscillate between relying on their personal information and/or on social information -- which may depend on the availability of resources, population density, stochastic factors, among others (Dall et al. 2005 Trends Ecol. Evol., Duboscq et al. 2016 Frontiers in Psychology). In my opinion, ignoring the influence of personal and social information decreases the significance of this work. I am aware that the authors consider the detection of food present in the model, but this is considered to a much smaller extent (as seen in their weight on individual decisions) than the social information cues.

Critically, it is also unclear how, if at all, the information and pathogen traits are related to each other. If a handler gets sick, how does this affect its foraging activity (does it stop foraging, slow its activities, or does it show signs of sickness)? Perhaps this model is attempting to explore the emergence of social movement strategies only, but how they disentangle an individual's sickness status and behavioral response is unclear.

Very little is presented about the virulence of the pathogens and how they could affect the emergence of social strategies. The authors keep their main argumentation based on the introduction of novel pathogens (without distinctions on their pathogenicity), but a behavioral response is rather influenced by how fast individuals are infected and which are their chances of recovering. Besides, they consider that only one or two social interactions would be enough for pathogen transmission to occur.

Another important component is that individuals do not die, and it seems that they always have a chance (even if it is small) to reproduce. So, how the authors consider unsuccessful strategies in the model outputs or how these social strategies would be potentially "dismissed" by natural selection are not considered.

Reviewer #2 (Public Review):

In this paper, Yang et al. seek to show the importance of the lncRNA VPS9D1-AS1 in the biology and pathology of colorectal cancer (CRC). Starting with the analysis of patient data, and proceeding to cellular and animal cancer models.

Specifically, the authors report higher VPS9D1-AS1 levels in tumor tissues in two independent cohorts of CRC patients. There was a positive association between VPS9D1-AS1 levels and molecules involved in TGFb signaling, yet a negative association between VPS9D1-AS1 levels and levels of tumor-infiltrating CD8+ T cells (and a negative correlation of these levels of tumor-infiltrating CD8+ T cells and protein expression of molecules involved in TGFb signaling). Cell line studies revealed a positive feedback loop between VPS9D1-AS1 and TGFb signaling molecules, with a cell-intrinsic, pro-proliferative, and pro-survival effect of VPS9D1-AS1 on CRC cancer cells. VPS9D1-AS1 also controls the expression of several genes in the IFN pathway, in particular the ISGs IFI27 and OAS1. In addition, IFI27 and OAS1 expression are controlled by TGFb, TGFBR1, and SMAD1, and the promoter of OAS1 is targeted by SMAD4 (but also TGFb), which binds to it. VPS9D1-AS1 expression in tumor cells promotes PD1 expression and negatively affects IFNAR1 on T cells to reduce their effector functions. In vivo, MC38 CRC cells overexpressing VPS9D1-AS1 show increased tumor growth in mice, and animals with transgenic VPS9D1-AS1 expression in the intestine develop larger CRC lesions upon AOM/DSS treatment. Finally, in vivo targeting of VPS9D1-AS1 using anti-sense oligo reduced tumor size. The data indicate a series of intricate molecular and cellular interactions and suggest that VPS9D1-AS1 can help with patient stratification, improving prognostic prediction and allowing for personalized treatment.<br /> Taken together, there is a multitude of datasets and several complementary experiments using patient-derived samples, genetically engineered cell lines, and mouse models. Definitely, the paper includes many avenues of inquiry that cover the broad field of cancer molecular biology, biochemistry, and pathogenesis. However, this broad approach renders the paper difficult to follow at times and also leads to numerous typographical and interpretive (but, largely, not methodological), mistakes. In addition, the quality of some of the figures needs to be improved before they can be properly evaluated.

In methodology, the authors are largely successful, and I would not recommend major changes to the work, other than to recommend a "focusing" of the manuscript objectives, or a paring of the data to better convey the desired story.

The experiments presented herein, particularly those that test the efficacy of the lncRNA as cancer therapeutics are important for the field, and should be of high import to other cancer biologists.

Reviewer #2 (Public Review):

This study used electrophysiological data acquired from neurons in the dorsal raphe to model 5-HT output in response to extrinsic excitatory inputs based on the intrinsic properties of 5-HT neurons and local network connectivity with GABAergic neurons. Specifically, general and modified integrate-and-fire single cell models, together with local network models among 5-HT neurons and local GABAergic neurons providing feedforward inhibition (FFI), are used to simulate the firing output of 5-HT neurons in response to transient and prolonged depolarizations. The conclusions are as follows. 1) 5-HT neurons display prominent spike frequency adaptation, resulting from afterhyperpolarization potentials and change in firing threshold, and inactivating K current characteristic of A-type K current (I-A). These two features cause the rapid decline in firing responses at the onset of depolarizing input. 2) Heterogeneous FFI due to heterogeneous electrophysiological properties of local GABA neurons lead to divisive inhibition of 5HT neuron firing (i.e., change in the slope of input-output function) in the network model. 3) Using a ramp depolarization, the authors found that 5-HT neurons encode the temporal derivative of depolarization, i.e., the slope of ramp depolarization. This property can be ascribed to the prominent spike-frequency adaptation observed in 5-HT neurons. Overall, this study provides new insights into the control of 5-HT output by single cell and network mechanisms.

The conclusions are well supported by combination of rigorous brain slice electrophysiological recordings of the two types of neurons in the dorsal raphe, i.e., 5-HT neurons and somatostatin-positive GABA neurons, which are identified by the usage of transgenic mice where these neurons are fluorescently labeled, and the application of single cell and network models.

As the authors state, the most striking finding of this study is that 5-HT neurons encode temporal derivative of excitatory inputs, as it may relate to reinforcement learning models. Here, this feature is captured using a ramp depolarization and is solely modeled with intrinsic property of 5-HT neurons, i.e., spike-frequency adaptation. Instead of using a ramp depolarization, using repetitive brief depolarizations with changing intervals/frequency will be more informative. Further, incorporating the network model with FFI, in particular the delay in inhibition following excitation associated with FFI when same inputs (single and repetitive) feed into 5-HT neurons and GABA neurons, may be more relevant to the reinforcement learning algorithms (e.g., see Fig. 6a in J. Neurosci. 2008, 28: 9619-9631).

Reviewer #2 (Public Review):

The authors examine the effects of depletion of an accessory subunit of the V-ATPase, ATP6AP2, using recombination of a floxed gene with osteocalcin promoter cre recombinase. Major findings are that defects and death in osteocytes occur, with mass spectrometry sequencing showing that matrix metalloproteinase, MMP14, which is involved in collagen remodeling in a number of other contexts, regulates bone matrix remodeling and osteocyte differentiation downstream of ATP6AP2. Further, ATP6AP2 depletion was counteracted in part by direct expression of MMP14 in ATP6AP2 depleted osteoblast-lineage cells.

Major strengths of the work include a clear description of methods and most results, as well as a concise and clear discussion.<br /> - There is an extensive description of the bone with a detailed discussion of micro computed tomography and staining results.<br /> - Interesting findings include retention of woven bone, and labeling for secondary indicators including cleaved caspase 3, RunX2, and sclerostin.<br /> - Osteocyte tomato labeling of the ATP6AP2Ocn-cre animals is a very good confirmation of the histomorphometric analysis.<br /> - The KI67 labeling of proliferative cells is very interesting but should be introduced more clearly. Similarly, cleaved caspase 3 is very useful but a sentence stating why this was done would be useful for clarity.<br /> - Interaction of ATP6AP2 directly with MMP14 is very interesting and useful in wrapping up the paper.

Weaknesses include:<br /> - When introducing assays, a brief description of why this is done would make the paper more accessible.<br /> - The reviewer would like to see a clearer description of the depletion of ATP6AP2 by cre-lox recombination.<br /> - Results showing calcein deposition, not on the surface of the cortical bone requires more data to strengthen this finding.<br /> - Retention of woven bone suggests a defect in resorption, but a clear description of the resorbed area is not seen.

The reviewer is enthusiastic about the manuscript.

Reviewer #2 (Public Review):

The authors use microfluidic devices to follow single swimmers for long periods, measuring their movement in detail and allowing detailed statistics at a level that has never been possible before and machine learning.

Its strength is the extraordinary detail and the doors opened by the quality of the resultant data. As such it makes a substantial contribution to a narrow field and adds slightly more subtly to an important field of full mathematically accessible descriptions of migration phenotypes.

Its weakness is that these tools are not yet used for any particularly enlightening tests. The directed probability fluxes are interesting, but not surprising. The strength of this paper is in the method, the analysis, and the ability to generate rigorous datasets.

Reviewer #2 (Public Review):

Krehenwinkel et al. investigated the long-term temporal dynamics of arthropod communities using environmental DNA (eDNA) remained in archived leave samples. The authors first developed a method to recover arthropod eDNA from archived leave samples and carefully tested whether the developed method could reasonably reveal the dynamics of arthropod communities where the leave samples originated. Then, using the eDNA method, the authors analyzed 30-year-long well-archived tree leaf samples in Germany and reconstructed the long-term temporal dynamics of arthropod communities associated with the tree species. The reconstructed time series includes several thousand arthropod species belonging to 23 orders, and the authors found interesting patterns in the time series. Contrary to some previous studies, the authors did not find widespread temporal α-diversity (OTU richness and haplotype diversity) declines. Instead, β-diversity among study sites gradually decreased, suggesting that the arthropod communities are more spatially homogenized in recent years. Overall, the authors suggested that the temporal dynamics of arthropod communities may be complex and involve changes in α- and β-diversity and demonstrated the usefulness of their unique eDNA-based approach.

Strengths:<br /> The authors' idea that using eDNA remained in archived leave samples is unique and potentially applicable to other systems. For example, different types of specimens archived in museums may be utilized for reconstructing long-term community dynamics of other organisms, which would be beneficial for understanding and predicting ecosystem dynamics.

A great strength of this work is that the authors very carefully tested their method. For example, the authors tested the effects of powdered leaves input weights, sampling methods, storing methods, PCR primers, and days from last precipitation to sampling on the eDNA metabarcoding results. The results showed that the tested variables did not significantly impact the eDNA metabarcoding results, which convinced me that the proposed method reasonably recovers arthropod eDNA from the archived leaf samples. Furthermore, the authors developed a method that can separately quantify 18S DNA copy numbers of arthropods and plants, which enables the estimations of relative arthropod eDNA copy numbers. While most eDNA studies provide relative abundance only, the DNA copy numbers measured in this study provide valuable information on arthropod community dynamics.

Overall, the authors' idea is excellent, and I believe that the developed eDNA methodology reasonably reconstructed the long-term temporal dynamics of the target organisms, which are major strengths of this study.

Weaknesses:<br /> Although this work has major strengths in the eDNA experimental part, there are concerns in DNA sequence processing and statistical analyses.

Statistical methods to analyze the temporal trend are too simplistic. The methods used in the study did not consider possible autocorrelation and other structures that the eDNA time series might have. It is well known that the applications of simple linear models to time series with autocorrelation structure incorrectly detect a "significant" temporal trend. For example, a linear model can often detect a significant trend even in a random walk time series.

Also, there are some issues regarding the DNA sequence analysis and the subsequent use of the results. For example, read abundance was used in the statistical model, but the read abundance cannot be a proxy for species abundance/biomass. Because the total 18S DNA copy numbers of arthropods were quantified in the study, multiplying the sequence-based relative abundance by the total 18S DNA copy numbers may produce a better proxy of the abundance of arthropods, and the use of such a better proxy would be more appropriate here. In addition, a coverage-based rarefaction enables a more rigorous comparison of diversity (OTU diversity or haplotype diversity) than the read-based rarefaction does.

These points may significantly impact the conclusions of this work.

Reviewer #2 (Public Review):

In this article, the authors leveraged patterns on the empirical genomic data and the power of simulations and statistical inferences and aimed to address a few biologically and culturally relevant questions about Cabo Verde population's admixture history during the TAST era. Specifically, the authors provided evidence on which specific African and European populations contributed to the population per island if the genetic admixture history parallels language evolution, and the best-fitting admixture scenario that answers questions on when and which continental populations admixed on which island, and how that influenced the island population dynamics since then.

Strengths:

1) This study sets a great example of studying population history through the lens of genetics and linguistics, jointly. Historically most of the genetic studies of population history either ignored the sociocultural aspects of the evidence or poorly (or wrongly) correlated that with genetic inference. This study identified components in language that are informative about cultural mixture (strictly African-origin words versus shared European-African words), and carefully examined the statistical correlation between genetic and linguistic variation that occurred through admixture, providing a complete picture of genetic and sociocultural transformation in the Cabo Verde islands during TAST.

2) The statistical analyses are carefully designed and rigorously done. I especially appreciate the careful goodness-of-fit checking and parameter error rates estimation in the ABC part, making the inference results more convincing.

Weaknesses

1) Most of the methods in the main analyses here were previously developed (eg. MDS, MetHis, RF/NN-ABC). However, when being introduced and applied here, the authors didn't reinstate the necessary background (strength and weakness, limitations and usage) of these methods to make them justifiable over other methods. For example, why ADS-MDS is used here to examine the genetic relationship between Cabo Verde populations and other worldwide populations, rather than classic PCA and F-statistics?

2) The senior author of this paper has an earlier published article (Verdu et al. 2017 Current Biology) on the same population, using a similar set of methods and drew similar conclusions on the source of genetic and linguistic variation in Cabo Verde. Although additional samples on island levels are added here and additional analyses on admixture history were performed, half of the main messages from this paper don't seem to provide new knowledge than what we already learned from the 2017 paper.

3) Furthermore, there are a few essential factors that could confound different aspects of the major analyses in this article that I believe should be taken into account and discussed. Such factors include the demographic history of source populations prior to admixture, different scenarios of the recipient population size changes, differences in recombination rates across the genome and between African and European populations, etc.

Overall, the paper is of interest to the field of human evolutionary genetics - that not only does it tell the story of a historically important population, but also the methodology behind this paper sets a great example for future research to study genetic and sociocultural transformations under the same framework.

Reviewer #2 (Public Review):

This work conducted a Mendelian randomization analysis between TG and a large number of disease traits in biobanks. They leverage the publicly available summary statistics from the European samples from the UK Biobank and FinnGen. A solid but routine standard summary-statistics based MR study is conducted. Several significant causal associations from TG to phenotypes are called by setting p-value cutoff with some Bonferroni correction. Sensitivity statistical analyses are conducted which generate largely consistent results. The research problem is important and relevant for public health as well we drug development. Overall this is a solid execution of current methods over appropriate data source and yields a convincing result. The interpretation of the results in discussion is also well-balanced.

While the paper does have strengths in principle, a few technical weaknesses are observed.

They used UK Biobank as the discovery and FinnGen as the replication. But the two cohorts are rather used symmetrically. Especially for the Tier 3 (NB), it seems to be an attempt of reusing the replication cohort as the discovery. I wonder if that would create additional multiple testing burden as a greater number of hypotheses are considered.

The replication p-value cutoff is a bit statistically lenient. In a typical discovery-replication setting the two stages are conducted sequentially and replication should go through the Bonferroni adjustment on the number of significant signals from discovery that is tested in the replication. For example, in this case, in tier 2, the cutoff should be 0.05/39. This may make the association of leiomyoma of the uterus slightly non-significant though. Similar cutoff should be applied to tier 3 as well.

The causal effect of TG to leiomyoma of the uterus is weak, as indicated by both the sub-significant in the replication and the non-significant of MR-PRESSO. Similarly, I would recommend more caution on the weak statistical rigor when interpreting Tier 2 and Tier 3 results.

Another methodological choice that might need justification is the use of UKB TG GWAS loci (1,248 SNPs) are the instrument for FinnGen. This may create some subtle interference with the use of UKB as outcomes in the discovery analysis. It may be minor but some justification or at least some discussions of potential limitations should be mentioned. What about the alternative of using GLGC as instruments in replication?

For disease outcomes (line 188), UKB European sample size is ~400,000 rather than ~500,000. Can the author clarify the sample size they used?

It would be reassuring to the reader if the TG measurements were measured in a treatment-naïve manner.

"Phenome-wide MR is a high-throughput extension of MR that, under specific assumptions, estimates the causal effects of an exposure on multiple outcomes simultaneously." - I guess it is more informative to mention the specific assumptions, at least briefly, in the introduction so it is easier for the reader to interpret the results.

Reviewer #2 (Public Review):

In this new exciting manuscript, Möller and colleagues studied different behavioral patterns of human and non-human primate subjects in a transparent social coordination game. In the task, two subjects chose between two visible options, in which each subject preferred a different option. Critically, the reward level also varied based on a payoff matrix. Choosing the non-preferred options by both subjects resulted in the lowest rewards, whereas choosing the preferred options by both resulted in medium-sized rewards for both. However, when both subjects chose the same option (i.e., coordinated), which was preferred by one subject but not preferred by the other subject, both received the highest rewards, with the subject who indicated the preferred option receiving a higher reward than the other. Therefore, the optimal strategy would be a dynamic turn-taking strategy in which both subjects choose the same option while taking turns over time. The authors found that about half of the human pairs adopted the turn-taking strategy. On the other hand, monkeys performed the task mostly in a selfish manner - both monkeys tended to choose their preferred options. Interestingly, in the human-monkey pairing, the monkeys could learn the turn-taking patterns. Furthermore, a detailed examination showed that turn-taking patterns in humans indicated a prosocial strategy, while turn-taking patterns in monkeys reflected a competitive strategy, where a slow-responding monkey followed the option of the fast-responding monkey. Together, the results convincingly demonstrate very interesting similarities and differences between humans and monkeys in carrying out social coordination.

Strength: This study provides convincing results with good sample size and rigorous data analyses. The transparent task design uniquely allowed the authors to examine the visual social aspects underlying social coordination. The direct comparison between human and monkey subjects, as well as examining human-monkey pairs were important and informative. Overall, the results provide novel insights into other studies in non-human primates that aim to understand the common social decision-making mechanism of both human and non-human primates.

Weakness: In the situation when the human subjects were paired with monkey subjects, it was unclear what detailed aspects of this experience directly led to the increase in the turn-taking behavior in the monkey subjects. About half of the human subjects behaved more like the monkey subjects by not exhibiting the dynamic turn-taking behavior, yet the reasons behind this within-group difference were unclear.

Reviewer #2 (Public review):

The present studies by Foster and colleagues use mouse genetics to show that pyruvate kinase 1 and 2 (PKM1 and PKM2) regulate ATP-sensitive K+ channel activity (KATP channel) through mitochondrial PEP-dependent cytoplasmic ATP/ADP increases, leading to first phase insulin secretion. During the second phase of insulin secretion, when ATP hydrolysis is maximal, oxidative phosphorylation is engaged to sustain ATP/ADP ratios and KATP channel closure. As such, the work challenges the consensus view of KATP channel activity, which states that ATP derived from oxidative phosphorylation in the mitochondrial matrix increases cytoplasmic ATP/ADP ratio, thus closing KATP channels and increasing Ca2+ fluxes.

Strengths of the study include: 1) careful experimental design and execution; 2) use of comprehensive mouse genetics to pinpoint roles of PKM1, PKM2 and phosphoenolpyruvate carboxykinase 2 (which produces PEP from oxoaloacetic acid); and 3) multiple lines of corroboratory evidence that the PEP-PKM1/2 system influences KATP channel activity and downstream signaling, via changes in non-mitochondrial ATP/ADP.

Weaknesses include: 1) lack of in vivo data to support a role of PKM1/PKM2 in determining glucose levels; and 2) over-reliance on mouse models, meaning that translational relevance to human biology is unclear.

Nonetheless, on balance, the authors have achieved their aims of showing that PEP and PKM1/PKM2 are critical regulators of KATP channel activity, Ca2+ fluxes and insulin secretion.

Overall, this is a potentially important study, which updates the textbook view of KATP-channel regulation, the major signaling mechanism through which pancreatic beta cells couple blood glucose levels to insulin release.

COVID research updates: Coronavirus antibodies last for months — if you have them. (2021). Nature. https://doi.org/10.1038/d41586-020-00502-w

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