Reviewer #2 (Public Review):

In their study the authors aimed to investigate the dissemination of Enterobacterales plasmids between geographically and temporally restricted isolates recovered from different niches, such as human blood stream infections, livestock, and wastewater treatment works. By using a very strict similarity threshold (Mash distance < 0.0001) the authors identified so-called groups of near-identical plasmids in which plasmids from different genera, species, and clonal background co-clustered. Also, 8% of these groups contained plasmids from different niches (e.g., human BSI and livestock) while in 35% of these cross-niche groups plasmids carried antimicrobial resistance (AMR) genes suggesting recent transfer of AMR plasmids between these ecological niches.

Next, the authors set-out to examine the wider plasmid population structure by clustering plasmids based on 21-mer distributions capturing both coding and non-coding plasmid regions and using a data-driven threshold to build plasmid networks and the Louvain algorithm to detect the plasmid clusters. This yielded 247 clusters of which almost half of the clusters contained BSI plasmids and plasmids from at least one other niche, while 21% contained plasmids carrying AMR genes. To further assess cross-niche plasmids similarities, the authors performed an additional plasmid pangenome-like analysis. This highlighted patterns of gain and loss of accessory plasmid functions in the background of a conserved plasmid backbone.

By comparing plasmid core gene or plasmid backbone phylogenies with chromosome core gene phylogenies, the authors assessed in more detail the dissemination of plasmids between humans and livestock. This indicated that, at least for E. coli, AMR dissemination between human and livestock-associated niches is most likely not the result of clonal spread but that plasmid movement plays an important role in cross-niche dissemination of AMR.

Based on these data the authors conclude that in Enterobacterales plasmid spread between different ecological niches could be relatively common, even might be occurring at greater rates than estimated, as signatures of near-identity could be transient once plasmids occupy and adept to a different niche. After such a host jump, subsequent acquisition, and loss of parts of the accessory plasmid gene content, as a result of plasmid evolution after inter-host transfer, may obscure this near-identity signature. As stated by the authors, this will raise challenges for future One Health-based genomic studies.

Strengths<br /> The article is well written with a clear structure. The authors have used for their analysis a comprehensive collection of more than 1500 whole genome sequenced and fully assembled isolates, yielding a dataset of more than 3600 fully assembled plasmids across different bacterial genera, species, clonal backgrounds, and ecological niches. A strong asset of the collection, especially when analyzing dissemination of AMR contained on plasmids, is that isolates were geographically and temporally restricted. Bioinformatic analyses used to discern plasmid similarity are beyond state-of-the-art. The conclusions about dissemination of plasmids between genera, species, clonal background and across ecological niches are well supported by the data. Although conclusions about inter-host plasmid dissemination patterns may have been drawn before, this is to my knowledge the first time that patterns of dissemination of plasmids have been studied at such a high-level of detail in such a well selected dataset using so many fully assembled genomes.

Weaknesses<br /> One conclusion that is not entirely supported by the data is the general statement in the discussion that "cross-niche plasmid in not driven by clonal lineages". From the tanglegram, displaying the low congruence between the plasmid and chromosome core gene phylogeny in E. coli, this conclusion is probably valid for E. coli, but this not necessarily means that this is also the case for the other Enterobacterales genera and species included in this study. For these other genera, the data supporting this conclusion are not given, probably because total number of isolates for certain genera were low, or because certain niches were clearly underrepresented in certain genera.

Furthermore, the BSI as well as the livestock niches were analyzed as single niches while the BSI niche included both nosocomial and community-derived BSI isolates and the Livestock niche included samples from different livestock-related hosts. Given the fact that a substantial number of plasmids were available from cattle, sheep, pigs, and poultry, it would be interesting to see whether particular livestock hosts were more frequently found in the cross-niche plasmid clusters than other livestock hosts and whether the BSI plasmids in these cross-niche clusters were predominantly of community or nosocomial origin.

Reviewer #2 (Public Review):

The authors show how an avian influenza A virus that jumped into dogs is now evolving in real time. Though its evolutionary adaptation to dogs, the virus is gaining properties that are increasingly consistent with the potential to infect humans.

The data are alarming, although it should be emphasized that this dog H3N2 influenza virus has not yet infected humans, and perhaps never will. It is also unknown how pathogenic (medically serious) the virus would be in humans if it were to jump. The authors show preliminary data that prior exposure to human seasonal H3N2 will not render us resistant to this dog virus should it jump to humans.

What is most remarkable about this study is the breadth of experimental approaches taken, and the holistic analysis of what is bound to become a classic tale in virus evolution and emergence through an intermediate host.

Reviewer #2 (Public Review):

In this manuscript, Vias and co-authors develop HGSOC PDOs and characterized their genomes, transcriptomes, drug sensitivity, and intra-tumoural heterogeneity. They show that PDOs represent the high variability in copy number genotypes observed in HGSOC patients. Drug sensitivity was reproducible compared to parental tissues and the ability of these models to grow in vivo.

Overall, the manuscript lacks sufficient novelty. Several pieces of information and a number of conclusions that are presented here have been previously published by other groups (Nina Maenhoudt, Stem cell reports, 2020; Shuang Zhang, Cancer Discov, 2021).

Reviewer #2 (Public Review):

This is a well-written and clear manuscript, in which the authors describe the stepwise development of an approach for loss of function screens in a range of different Leishmania species, culminating in a small-scale screen. The method relies on CRSIPR/Cas9 directed mutation of cytosine bases to generate premature STOP codons. The conclusions of the manuscript are well supported by the data presented and this approach appears to have great potential to facilitate functional studies and discovery biology in a range of different species.

The authors have presented the development of their base editing toolbox in a stepwise manner, showing the optimisation steps they took. They initially used a tdTomato expressing cell line to optimise which base editor to use and examine constitutive versus episomal expression approaches. Before analysing specific proteins - PFR2, IFT88, PF16, MFT. This systematic approach gives confidence in their results and the utility of the system. The primer design resource with primer effectiveness score is great to see and will aid the adoption of this approach.

Line 482 - the authors wrote 'As expected, the proportion of cells showing a motility phenotype in the IFT88 targeted L. infantum population decreased further' Why is this result expected? Presumably, this is due to the fact that cells without a functional IFT system lack flagella and grow slower so can be outcompeted by faster-growing mutants. This speaks to the major caveat highlighted by the authors in the discussion and the final small-scale screen. In a population of cells, those with deleterious mutations in an essential gene or one whose disruption results in slower growth will be outcompeted by cells in which a non-deleterious mutation has occurred, which feeds into the issue of timing.

The authors show with CRK3 this process of non-deleterious mutants outcompeting deleterious mutants does result in a detectable drop in the number of parasites with specific CRK3 guides but not in those with IFT88. Is this due to the fact that the outgrowth of the non-deleterious IFT88 mutants occurs rapidly or that the mutation of the targets in IFT88 was ineffective? The data presented in Figure 5 shows that for some species at least a mutation of the IFT88 gene was possible. This might mean that for certain genes the outgrowth occurs within the first 12 days after transfections so will not be seen using this approach, without a wider study, which is beyond the scope of this manuscript it will be difficult to know.

The ability to readily generate cells resistant to miltefosine, highlight the strength of this approach in identifying the mode of actions/resistance mechanisms for anti-leishmanial drugs. Moreover, any screens using this base editing approach, in which cells expressing proteins without a changed functionality/expression are killed will likely be effective in identifying genes of interest. This could mirror the success that the genome-wide RNAi screens have had in Trypanosoma brucei.

This base editing approach now sits alongside using CRISPR/Cas9 to generate full gene deletion mutants and RNAi to help understand gene function in Leishmania. As discussed by the authors in their balanced discussion there are merits. A major advantage of this approach is the ability to simply generate a library of plasmids that will target the entire genome, whereas both full gene deletions and RNAi in L. braziliensis are more time-consuming and the latter lacks inducible control. However, as part of the LeishGEM project pools of barcoded deletion mutants are being generated, which have the potential to be used in other screens. Moreover, this base-editing approach has the potential to identify the function of essential genes, which is not possible when trying to generate stable deletion cell lines. However, this has only been demonstrated for one gene to date and the ability to detect slower-growing mutants varied greatly between different species.

The authors highlight that this base editing approach will leave potentially functional regions of the NT of proteins, which is true and may mean genes are missed. However, this may also provide extra information about the protein's function/domain structure if STOP codons in certain positions showed an effect on function whereas those in others don't.

Overall, the base editing approach in this manuscript looks to have great utility and in reality, is a complementary approach to the genetic tools we already have to study gene function in Leishmania. However, only time will tell how effective this method is through its adoption and effective use by different researchers.

Reviewer #2 (Public Review):

This umbrella review summarizes the results of systematic reviews about the impact of the COVID-19 pandemic on cancer care. PRISMA checklist is used for reporting. The literature search was performed in PubMed and systematic reviews published until November 29th, 2022 were included. The quality of included systematic reviews was appraised using the AMSTAR-2 tool and data were reported descriptively due to the high heterogeneity of 45 included studies. Based on the results of this paper, regardless of the low quality of included evidence, COVID-19 affected cancer care in many ways including delay and postponement of cancer screening, diagnosis, and treatment. Also, patients with cancer had been affected psychologically, socially, and financially during the COVID-19 pandemic.

Strengths:

This umbrella review has summarized many important aspects of cancer care that were affected during the COVID-19 pandemic.

Weakness:

The main limitation of the current study is that the authors have searched only one database, which might have missed some relevant systematic reviews. Also, most of the included reviews in this paper had low and medium methodological quality.

Reviewer #2 (Public Review):

The authors describe in the nematode C. elegans the effects of perturbed organization of Intermediate filaments (IFs), which form the cytoskeleton of animal cells together with actin filaments. They focus on a previously identified mutant of the kinase SMA-5, which when mutated leads to disorganized IF structure in intestinal cells of C. elegans. The authors found that the phenotypes caused by the mutated SMA-5 kinase concerning gut morphology and animal health can be reversed by removing IF network components such as the protein IFB-2. This finding is extended to other components of the IF network, which also display a certain degree of sma-5 phenotype alleviation when depleted.

Strength:<br /> The finding that suppressing the intestinal phenotypes caused in sma-5 mutants can be suppressed by removing functional IF components is an interesting observation. It confirms a previous study showing that bbln-1 mutation-caused IF phenotypes can be suppressed by depleting IFB-2.

Weakness:<br /> 1) The finding of suppressing the intestinal phenotypes caused in sma-5 mutants can be considered a minor conceptual advancement. However, the study comes short of providing insight into the molecular processes of how deranged IF networks and its consequence can be rescued/suppressed by removing e.g. the IFB-2 filaments. Many statements concerning the relationship between SMA-5 and the IFs are based on assumptions. The study requires protein biochemical analysis to show whether SMA-5 phosphorylates the IF proteins - mainly the IFB-2 polypeptide. The relationship between SMA-5 / IFB-2 is a central aspect of this study but the main conclusions are based on the notion that IFB-2 and other IF proteins may be phosphorylated by SMA-5. Mutating putative phosphorylation sites of IFB-2 without having shown any proof that the modification occurs by SMA-5 is futile. This important open question needs to be addressed. And will allow statements whether the ifb-2(kc20) mutant allele-encoded shorter IFB-2 protein lacks phosphorylation or not.

2) No quantification of the morphological defects such as using fluorescent-labeled IF proteins as in previous studies is provided in the manuscript. The EM pictures are not sufficient to provide information on how often the IF network perturbations and morphology defects occur. Also, the rescue of the actual morphological gut defects was not quantified. The assessment of development time and arrest, body length, lifespan, oxidative stress resistance, and others should be related to intestinal tube defects. They are useful and important but are an indirect measure of intestine defects and rescue.

3) It is not clear how exactly the mutant ifb-2 allele kc20 was identified. In the Materials and methods section, the authors provide information on the specific primers for the ifb-2 locus. But how did they know that the mutation lies within this region? Was there mutation mapping or whole-genome sequencing applied?

Reviewer #2 (Public Review):

In this study, the authors take a multipronged approach to identify the substrate repertoire of calcium-dependent protein kinase, CDPK1 in Toxoplasma that includes quantitative phosphoproteomics, myristoylation, thiophosphorylation, immunoprecipitation as well as proximity-based labeling. Their finding also reveals that CDPK1 functions in parasite invasion and egress by phosphorylating different protein candidates. More importantly, the authors successfully determine one branch of the CDPK1 signaling pathway that regulates invasion through the phosphorylation of the HOOK protein involved in the translocation and secretion of micronemal proteins.

Reviewer #2 (Public Review):

In this manuscript, the authors use an embedding of human olfactory perceptual data within a graph neural network (which they term principal odor map, or POM). This embedding is a better predictor of a diverse set of olfactory neural and behavior data than methods that use chemical features as a starting point to create embeddings. The embedding is also seen to be better for comparison of pairwise similarities (distances of various sorts) - the claim is that proximity of pairs of odors in the POM is predictive of their similarity in neural data from olfactory receptor neurons.

A major strength of the paper is the conceptualization of the problem. The authors have previously described a graph neural net (GNN) to predict verbal odor descriptors from molecular features (here, a 2019 preprint is cited, but a newer related one in 2022 describing the POM is not cited). They now use the embedding created by that GNN to predict similarities in large and diverse datasets in olfactory neuroscience (which the authors have curated from published work). They show that predictions from POM are better than just generic chemical features. The authors also present an interesting hypothesis that the underlying latent structure discovered by the GNN relates to metabolic pathway proximity, which they claim accounts for the success in the prediction of a wide range of data (insect sensory neuron responses to human behavior). In addition to the creativity of the project, the technical aspects, are sound and thorough.

There are some questions about the ideas, and the size of the effects observed.

1. The authors frame the manuscript by invoking an analogy to other senses, and how natural statistics affect what's represented (and how similarity is defined). However, in vision or audition, the part of the world that different animals "look at" can be very different (different wavelengths, different textures and spatial frequencies, etc). It is still unresolved why any given animal has the particular range of reception it has. Each animal is presumably adapted for its ecological niche, which can have different salient sensory features. In vision, different animals pick different sound bandwidths or EM spectra. Therefore, it is puzzling to think that all animals will somehow treat chemicals the same way.

2. The performance index could be made clearer, and perhaps raw numbers shown before showing the differences from the benchmark (Mordred molecular descriptor). For example, can we get a sense of how much variance in the data does it explain, what percent of the hold-out tests does it fit well, etc.?

3. The "fitting" and predictions are in line with how ML is used for classification and regression in lots of applications. The end result is a better fit (prediction), but it's not actually clear whether there are any fundamental regularities or orders identified. The metabolic angle is very intriguing, but it looks like Mordred descriptor does a very good job as well (extended figure 5). Is it possible to show the relation between metabolic distance and Mordred distance in Figure 2c? In fact, even there, cFP distance looks very well correlated with metabolic distance (we are talking about r= 0.9 vs r = 0.8). This could simply be due to a slightly nonlinear mapping between chemical similarity and perceptual similarity (which was used to get POM distance).

4. How frequent are such examples shown in Fig 2d? Pentenal and pentenol are actually very similar in many ways, and it may be that Tanimoto distance is not a great descriptor of chemical similarity. cFFP edit distance is quite small, just like metabolic distance. The thiol example on the right is much better. Also, even in Fig 2C POM vs metabolic distance, the lowest metabolic distances have large variations in the POM values - so there too, metabolic reactions that create very different molecules in 1 step can vary widely in POM distance as well.

5. A major worry is that Mordred descriptors are doing fine, and POM offers only a small improvement (but statistically significant of course). Another way to ask this question is this: if you plot pairwise correlation/distance of pairs of odors from POM against that for Mordred, how correlated does this look? My suspicion is that it will be highly correlated.

6. The co-occurrence in mixtures and close POM distance may arise from the way the embedding was done - with perceptual descriptors used as a key variable. Humans may just classify molecules that occur in a mixture as similar just from experiencing them together. Can the authors show that these same molecules in Fig 4d,e have very similar representations in neural data from insects or mice?

Reviewer #2 (Public Review):

Treatment of human illnesses caused by infection by hantaviruses are currently not available and hence research on new therapies are needed. The manuscript by Engdahl et al describes the characterization of four neutralizing antibodies with potency against hantaviruses using several approaches. This knowledge of these antibodies and where they bind in these studies can be used in the design of vaccines or the development of passive immunotherapeutic approaches and are hence very valuable for the advancement of new treatments. Hence this new knowledge is a major strength of the manuscript. the studies, however, the in vitro studies are limited in the use of pseudotyped viruses and not the actual viruses. Inclusion of the potency and binding of these to their native viruses, and standardization of their use in treatments of hamsters with these viruses, would elevate this approach to stand as a valuable contribution to the development of treatments for hantaviruses.

Reviewer #2 (Public Review):

This study uses behavioral monitoring and cutting-edge calcium imaging approaches to track the activity of cholinergic and noradrenergic axons in cortex of head-fixed mice, and correlate activity with behavioral state. The data confirm that much of this activity is dependent on behavioral state, and in particular is strongly correlated with arousal of the animal and is highly coordinated across axons. They also show that a small fraction of axonal activity is heterogenous, and does not seem to be dependent on global behavioral state. They describe additional details of this activity, such as that whisking activity is the best predictor of cholinergic and noradrenergic axon activity, and that noradrenergic activity is more transient during bouts of arousal (whisking) than cholinergic activity. Altogether this manuscript is generally very thorough analytically, most of the data appear technically sound, and the presentation is largely clear. However, the significance of the findings - exactly how much they enhance what is already known - is less clear.

The main advanced novelty of the approach is the use of mesoscale imaging, giving them the ability to analyze the degree to which neuromodulatory cholinergic and noradrenergic signals are uniform across cortex, or might be correlated with distinct behavioral states or events. They attempt to get at this in Figure 4, by determining how much of their detected signal from cholinergic and noradrenergic axon activity comes from a 'common signal' versus how much of the signal is residual once the common signal is subtracted, so presumably reflects a unique influence. This analysis and the reasoning behind it is very hard to follow, and it is not clear to us that these residual signals are truly meaningful (i.e. not coming just from some source of noise). The authors try to get at this meaning in Figure 4K by plotting partial minus ordinary correlations in different arousal states, but it is not clear to us what exactly this difference means, considering the ordinary correlation itself is different in those comparisons as well. The fact that there is a bigger difference between partial and ordinary correlations during whisking than in other states does not give us real information about where the partial correlation is from.

Reviewer #2 (Public Review):

Franz and colleagues set out to understand the mechanisms and cell types that contribute to melanocyte regeneration in the adult skin. Previously, they used genetics and imaging to identify cell populations (progenitors) in the adult skin that they believe contribute to melanocyte regeneration in adult zebrafish (Iyengar et al., 2015). Here, they use scRNA-seq to understand the molecular nature of these cells following melanocyte ablation with the copper chelator, neocuproine. From these studies, they claim to identify three types of progenitors (called melanocyte stem cells, McSCs): cells that give rise directly to differentiated melanocytes and depend on kit signaling; cells that undergo division before becoming fully differentiated; and cells that express high levels of a xanothophore marker (a yellow pigment cell) that also undergo cell division.

Strengths:<br /> The main strength of this work is the generation of scRNA-seq datasets of cells that express a melanocyte marker (mitfa) at multiple time points in adult skin during regeneration. This is an exciting dataset, and unique. The work gives an idea of the complexity of the regeneration process and paves the road for future studies on how McSC lineages contribute to melanoma. It is interesting to see how many of the processes and zebrafish cell types are conserved during evolution. By studying skin-associated melanocyte progenitors in adults, the authors provide insight into mechanisms poorly understood about melanocyte regeneration.

Weaknesses:<br /> (1) Data Interpretation in context: We have concerns regarding the labelling of the cells of interest "stem cells"; we prefer the term the authors themselves use "progenitors" (Iyengar et al., 2015). The authors do not place their work in the context of the wider field, especially with regards to the work on xanthophores and on regenerating melanocytes and adult McSCs in the embryo that contribute to the adult stripe.

(2) Cell type identity: Zebrafish contain another cell type called xanthophores that can also express mitfa and aox5 (Saunders et al., 2019). Indeed, in their supplementary tables, the authors call many of the mitfa+ aox5+ cells "xanthophores" based on their gene expression. There is no evidence here that these cells give rise to melanocytes. In their studies in Figure 7, we think that based on the shape of the cells, they may be looking at dividing xanthophores or unpigmented xanthophore precursors (McMenamin et al., 2014), rather than melanocyte stem cells. We don't know why these cells are dividing, but perhaps the loss of melanocytes in the adult stripe leads to an expansion of xanthophores.

(3) Analysis: The statistical approaches are not always correct, and some choices in the scRNA-seq analysis should be explained and/or revisited.

Reviewer #2 (Public Review):

The paper is intriguing, but to me, a main weakness is that the imaging experiments are done with overexpressed protein. Another is that the different results for the different subunits of TFIID would indicate that there are multiple forms of TFIID in the nucleus, which no one has observed/proposed before. Otherwise, the experimental data would have to be interpreted in a more nuance way. Additionally, there is no real model of how a TBP-depleted TFIID would recruit Pol II. Do the authors suggest that when TBP is present, it is not playing a role in Pol II transcription, despite being at all promoters? Or that in its absence an alternative mechanism takes over? In the latter case, are they proposing that it is just based on the rest of TFIID? How? The authors do not provide a mechanistic explanation of what is actually happening and how Pol II is being recruited to promoters.

Reviewer #2 (Public Review):

In this manuscript, Roberts et al. present XTABLE, a tool to integrate, visualise and extract new insights from published datasets in the field of preinvasive lung cancer lesions. This approach is critical and to be highly commended; whilst the Cancer Genome Atlas provided many insights into cancer biology it was the development of accessible visualisation tools such as cbioportal that democratised this knowledge and allowed researchers around the world to interrogate their genes and pathways of interest. XTABLE is trying to do this in the preinvasive space and should certainly be commended as such. We are also very impressed by the transparency of the approach; it is quite simple to download and run XTABLE from their Gitlab account, in which all data acquisition and analysis code can be easily interrogated.

We would however strongly advocate deploying XTABLE to a web-accessible server so that researchers without experience in R and git can utilise it. We found it a little buggy running locally and cannot be sure whether this is due to my setup or the code itself. Some issues clearly need development; Progeny analysis brings up a warning "Not working for GSE109743 on the server and not sure why". GSEA analysis does not seem to work at all, raising an error "Length information for genome hg38 and gene ID ensGene is not available". In such relatively complex software, some such errors can be overlooked, as long as the authors have a clear process for responding to them, for example using Gitlab issue reporting. Some acknowledgement that this is an ongoing development would be helpful.

The authors discuss some very important differences between the datasets in the text. Most notably they differ in endpoints and in the presence of laser capture. We would advocate including some warning text within the XTABLE application to explain these. For example, the "persistent/progressive" endpoint used in Beane et al (next biopsy is the same or higher grade) is not the same as the "progressive" endpoint in Teixeira et al (next biopsy is cancer); samples defined as "persistent/progressive" may never progress to cancer. This may not be immediately obvious to a user of XTABLE who wishes to compare progressive and regressive lesions. Similarly, the use of laser capture is important; the authors state that not using laser capture has the advantage of capturing microenvironment signals, but differentiating between intra-lesional and stromal signals is important, as shown in the Mascaux and Pennycuick papers. The authors cannot do much about the different study designs, but as the goal is to make these data more accessible We think some brief description of these issues within the app would help to prevent non-expert users from drawing incorrect conclusions.

The authors themselves illustrate this clearly in their analysis of CIN signatures in progression potential. They observe that there is a much clearer progressive/regressive signal in GSE108124 compared to GSE114489 and GSE109743. This does not seem at all surprising, since the first study used a much stricter definition of progression - these samples are all about to become cancer whereas "progressive" samples in GSE109743 may never become cancer - and are much enriched for CIN signals due to laser capture. Their discussion states "CIN scores as a predictor of progression might be limited to microdissected samples and CIS lesions"; you cannot really claim this when "progression" in the two cohorts has such a different meaning. To their credit, the authors do explain these issues but they really should be clearly spelled out within the app.

We are not sure we agree with their analysis of CDK4/Cyclin-D1 and E2F expression in early lesions. The authors claim these are inhibited by CDKN2A and therefore are markers of CDKN2A loss of function. But these genes are markers of proliferation and can be driven by a range of proliferative processes. Histologically, low-grade metaplasias and dysplasias all represent proliferative epithelium when compared to normal control, but most never become cancer. It is too much of a leap to say that these are influenced by CDKN2A because that gene is inactivated in LUSC; do the authors have any evidence that this gene is altered at the genomic level in low-grade lesions?

Overall this tool is an important step forwards in the field. Whilst we are a little unconvinced by some of their biological interpretations, and the tool itself has a few bugs, this effort to make complex data more accessible will be greatly enabling for researchers and so should be commended. In the future, we would like to see additional molecular data integrated into this app, for example, the whole genome and methylation data mentioned in line 153. However, we think this is an excellent start to combining these datasets.

Memindex dimensions mentioned in a 1904 advertisement<br /> cards: 2 3/4 x 4 1/2 inches<br /> case: 2 3/4 x 4 3/4 inches

Reviewer #2 (Public Review):

This study by Wang et al. examines changes in YAP expression in embryonic avian cultured explants in response to high and low shear stress, as well as tensile and compressive stress. The authors show that YAP expression is increased in response to low, oscillatory shear stress, as well as high compressive stress conditions. Inhibition of YAP signaling prevents compressive stress-induced increases in circularity, decreased pHH3 expression, and increases VE-cadherin expression. On the other hand, YAP gain of function prevents tensile stress-induced decreases in pHH3 expression and VE-cadherin expansion. It also decreases the strain energy density of embryonic avian valve explants. Finally, using an avian model of left atrial ligation, the authors demonstrate that unloaded regions within the primitive valve structures are associated with increased YAP expression, compared to regions of restricted flow where YAP expression is low. Overall, this study sheds light on the biomechanical regulation of YAP expression in developing valves.

Strengths of the manuscript include:<br /> - Novel insights into the dynamic expression pattern of YAP in valve cell populations during post-EMT stages of embryonic valvulogenesis.<br /> - Identify the positive regulation of YAP expression in response to low, oscillatory shear stress, as well as high compressive stress conditions.<br /> - Identify a link between YAP signaling in regulating stress-induced cell proliferation and valve morphogenesis.<br /> - The inclusion of the atrial left atrial ligation model is innovative, and the data showing distinguishable YAP expression levels between restricted, and non-restricted flow regions is insightful.

This is a descriptive study that focuses on changes in YAP expression following exposure to diverse stress conditions in embryonic avian valve explants. Overall, the study currently lacks mechanistic insights, and conclusions based on data are highly over-interpreted, particularly given that the majority of experimental protocols rely on one method of readout.

Reviewer #2 (Public Review):

The idea of using fluorescently labeled tandem SH2 domains to target tagged RTKs is brilliant and could potentially provide a powerful new way to assess the activation of RTKs in situ and in multiple physiological contexts. Thus, it was disappointing that there was insufficient characterization of the system to be able to interpret the data it generates. Although the paper shows that tagging the EGFR appears to have minimal impact on its biological activity, the readout for receptor kinase activity is % clearance of the fluorescent reporter tag from the cytosol. Such clearance is likely to depend on a variety of different factors, including the ratio of tagged receptors to probe, the number of functional pools in which the probe exists, the exchange rate between these pools, and the affinity of the probes for the tagged receptor. Without determining how each of these factors impacts % clearance, it is difficult to interpret either the dose-response curves or response kinetics.

For example, the difference in activation kinetics between EGFR and ErbB2 is very interesting, but the almost instantaneous rise (Fig S4B) is very surprising. The kinetics of activation of the EGFR have been extensively studied by mass-spectrometry and are generally limited by ligand binding, which has a characteristic time of several minutes, not seconds (pmid: 26929352; pmid: 1975591). Thus, such a response is suggestive of a freely exchanging ZtSH2 reporter pool that is mostly depleted in seconds with the slow secondary kinetics reflecting a slowly exchanging ZtSH2 reporter pool. Alternately, the cells could be accumulating an intracellular pool of activated receptors over time. That the authors are using concentrations of EGF >100-fold physiological levels (pmid: 29268862) further complicates the interpretation of these experiments.

There is also insufficient attention paid to either controlling or measuring important parameters, such as expression levels of tagged receptors or levels of endogenous receptors. 3T3 cells, contrary to the statement of the authors, do not have "negligible" numbers of EGFR: they have ~40K, which is typical for mouse fibroblasts. This is much higher than MCF7 cells, which are frequently used as a model system to study EGFR responses. Yet they do not see transactivation of their ErbB2 construct in 3T3 cells without expressing additional EGFR (Fig. 4C), suggesting low sensitivity of the assay. Conversely, they show a significant response mediated by endogenously tagged EGFR in HEK 293 cells, which are frequently used as an EGFR-negative cell line (PMID: 26368334). This indicates that their assay is extremely sensitive. Which is it? As mentioned above, it likely depends on the expression level and affinity of the different components of their system.

A great advantage of using the EGFR system as a test case for the new system is that thousands of investigations have been performed over the last four decades. This provides a strong foundation for determining whether the new technology is working correctly. For example, the dynamics of EGFR activation and trafficking at the single cell level have been documented in many studies, which show a remarkable consistency (e.g. see pmid: 24259669; pmid: 11408594; pmid: 25650738). Unfortunately, instead of using differences between the new results and previously reported data as a basis for refining their technique, the authors attempt to apply their raw data to address complex questions of EGFR dynamics, with less than satisfactory results.

For example, they attempt to use their technique to understand the basis of different signaling dynamics between EGFR ligands. Rather than being a relatively recent observation, differences in EGFR ligand signaling have been explored for over 30 years (pmcid: PMC361851), and are generally ascribed to differences in trafficking (pmid: 7876195). Based on these observations and resulting mathematical models, novel EGFR ligands have been designed with enhanced potency (pmid: 8195228 , pmid: 9634854 ). All this work was done over 20 years ago. Since then, new natural ligands for the EGFR have been discovered from sequence analysis and differences in their potency have similarly been ascribed to differences in their intracellular trafficking patterns (pmid: 19531065 - cited by the authors). An alternate hypothesis was proposed more recently by Freed et al (2017) as described by the authors, but that is what it is: an alternative hypothesis.

Unfortunately, the model that the authors use to test this hypothesis does not even include endocytosis or receptor trafficking but instead uses variable "scaling" factors to see if the data can fit the dimerization hypothesis. In the supplement, they state that "Since our simulations were run on relatively short time scales (~30 min post-stimulation), we did not consider trafficking and degradation of receptors." However, the half-life of EGFR internalization is generally ~3-4min (pmid: 1975591) and degradation ~1hr, so most of the signal shown in Figure 3 is likely to come from internalized rather than surface-associated ligand-EGFR complexes. A further complication is that internalization rates are strongly influenced by receptor expression levels (pmid: 3262110), which are not controlled for here. Thus, the omission of trafficking in their model is not appropriate. This does not mean that the authors are wrong, it simply means that without validation or calibration, their new technology is not ready to resolve current problems in the field.

Reviewer #2 (Public Review):

Mitterer et al investigated the role of the essential ATPase Spb4 in the maturation of the large ribosomal subunit precursor in the nucleolus using a combination of genetics, biochemistry, and cryo-EM. They suggest that the helicase Spb4 promotes limited RNA strand separation to drive reconfiguration of helices H62/H63/H63a at the base of domain IV of the 25S rRNA. The study also couples an in vitro pre-ribosome maturation assay with cryo-EM visualisation of pre-60S particles to recapitulate a major structural transition that is dependent on the recruitment of the AAA+ ATPase Rea1 to Spb4-bound particles. This structural transition is important as it promotes nucleolar exit of the 60S precursor from the nucleolus following the release of a limited set of ribosome assembly factors including the Ytm1-Erb1 complex together with the helicase Has1. The quality of the new cryo-EM maps provides a wealth of structural detail on the architecture of late pre-60S nucleolar maturation intermediates.

The paper is of high quality and clearly written with appropriately detailed methods. The figures are generally well-presented and informative. A strength of the study is that it provides insight into the function and mechanism of action of a poorly understood class of DEAD-box RNA helicases. The study reports the utility of in vitro pre-ribosome maturation combined with cryo-EM analysis to capture additional ribosome maturation intermediates, an approach that may become more widely adopted in the future among the ribosome synthesis community. The biochemical, genetic, and structural analyses strongly support the proposed mechanism for Spb4 function in reconfiguring helices H62/H63/H63a following induced RNA strand separation prior to the release of the Ytm1-Erb1 complex.

The authors suggest that Spb4 "induces" bending and strand separation of the rRNA at the base of ES27. They also suggest that the C-terminal domain of Spb4 "induces" substrate RNA strand disruption. However, an alternative possibility could be that the rRNA is sampling multiple conformations and that Spb4 stabilises one of these conformers. No direct experimental evidence for "induced" bending and strand separation by Spb4 is provided to support the claims.

The findings in the manuscript are generally consistent with a very recently published study on Spb4 function (Cruz et al., https://doi.org/10.1038/s41594-022-00874-9). However, the authors should cite this work and update the text to take account of this report.

Reviewer #2 (Public Review):

This is an intriguing study investigating the molecular mechanisms of the adhesion G-protein coupled receptor latrophilin-2 control of neural circuit developmental organization. Using the model CA1 to subiculum hippocampal circuit with its spatially segregated axon targeting, the authors experiments find that ectopic Lphn2 expression in CA1 neurons that normally do not express it, leads to axon mistargeting. The authors detail these circuitry alterations with Lphn2 genetic manipulations, finding that axon targeting is dependent on its GPCR signaling, likely through Galpha12/13 coupling.

Strengths: Building off the author's previous studies, the experiments are well designed and analyzed. The advance in this study is finding that Lphn2 expression in CA1 cells that normally do not express impacts its axon targeting. They go on to show compelling data that implicates this mistargeting is dependent on Lphn2 GPCR signaling properties, identified as likely Galpha12/13 dependent.

Weaknesses: The system used is a "misexpression system". By forcing cells with ordinally low levels to overexpress Lphn2, circuitry alterations are observed. While this gain of function assay demonstrates the importance as to why Lphn2 is not expressed in certain cell types, it isn't a physiologically relevant system to investigate Lphn2 dependent circuit development.

To strengthen this study, the following specific points could use addressing:<br /> • While the data is strong, some of the terminology used is unclear, including use of terms "repulsive receptor" and "repulsive ligand". The authors use "repulsive receptor" to describe Lphn2 action for axon targeting, but repulsion and attraction processes are simultaneous. Is Lphn2 really by acting as a repulsive receptor, or alternatively, by acting to shift axon attraction to Lphn2 expressing subiculum neurons?<br /> • For their proposed axon guidance model to work, Lphn2 has to be signaling through G12/13 proteins near the axon growth cone to induce its collapse and retraction. By using Flag-tagged Lphn2 constructs in their assays, this should be visible. Clear Flag-Lphn2 signal is observed in the dendrites of infected cells (Figure1-figure supplement 1; Figure5- figure supplement 1). But does Flag-Lphn2 also localize to the pCA1 axons that are projecting to the subiculum?<br /> • With their previous work, pCA1 to dSub circuit patterning is dependent on Ten3+ to Ten3+ homophilic attraction that exists between the two regions. Its unclear how ectopic Lphn2 is able to override this Ten3+ to Ten3+ connection patterning. Does ectopic Lphn2 outcompete Ten3 function in these neurons? Or alternatively, is Ten3 expression/localization impacted by the presence of ectopic Lphn2?

Reviewer #2 (Public Review):

In this work, the authors did a comprehensive model comparison to find the best predictor of where V genes are trimmed during the V(D)J recombination process, using their DNA sequence alone. This is an important step towards characterizing how the diversity of T-cell receptors and antibodies is generated and to better understanding the function of the enzymes involved in the process, such as Artemis.

The authors find that the best model uses a combination of the sequence-specific position-weight matrix, and the GC content of DNA on both sides of the cutting site, which they relate to the DNA's ability to "breathe." Their conclusions are based on a rigorous comparison of log-likelihoods using independent test data from other loci than the one on which the models were trained. The study also includes myriad tests and controls, increasing confidence in their conclusions.

Reviewer #2 (Public Review):

This is an excellent study performed by a world-leading research group in the field of the neural mechanisms of perceptual processing. The strengths of this work are the application of the MEG-fMRI fusion approach that links spatial locations in fMRI and time points in MEG and rigorous model-based analyses. The weaknesses may be a lack of a more concise visual illustration of the main findings and an in-depth discussion of some of the findings. The weaknesses are minor and the authors' conclusions are well justified by their data.

Reviewer #2 (Public Review):

The authors first characterize Siglec-1 clustering on immature and mature DCs and observe that clustering increases in mature DCs. Concomitantly with clustering, the mobility of Siglec-1 reduced. At the cell periphery of mDCs, Siglec-1 was enriched in actin-rich areas. A role for actin, specifically for the formin-nucleated actin was supported using inhibitors. Concomitantly the clustering of Siglec-1 was reduced. The localization of Siglec-1 to actin-rich filopodia was dependent on formin activation and RhoA, ROCK-mediated ERM phosphorylation. With respect to consequences for the binding of HIV particles, forming, and Rho-dependent Siglec-1 nanoclustering, enhanced binding of virus particles indicating that clustering of Siglec-1 provides for better docking sites. On the ligand side, high amounts of GM1 lipids (4%) were needed for liposomes to be captured by Siglec-1, reinforcing the idea of docking sites. Consistent with the important role of actin in the process, time course studies of virus binding to mDCs revealed dramatic changes in the plasma membrane architecture including the emergence of membrane ruffles, shrinkage of the basal membrane, and constriction of the cell membrane where VLPs accumulate on route to the formation of the virus-containing compartment. Overall, the strength of this report is its comprehensive nature, detailed and quantitative imaging analysis, and confirmation of the importance of Siglec-1 clustering (receptor) with liposomes containing the ligand GM1.

Reviewer #2 (Public Review):

Poison frogs are able to sequester alkaloids to make themselves toxic or unpalatable to predators. Despite much research, the proteins that accomplish this sequestering role are not well known. Here, biochemical and proteomic analysis identifies a liver-derived alkaloid binding globulin (ABG) as the main alkaloid binding molecule in the blood of poison frogs. The results are solid and address a major void in our understanding of plasma alkaloid transport in frogs. While some additional analysis of ABG mutants would further enhance the interpretations, the study represents an important starting point that suggests specific new roles for serpins in animal ecophysiology.

Reviewer #2 (Public Review):

Macrophages have been demonstrated to play a role in retinal diseases. Macrophage infiltration in melanomas is predictive of increased changes in metastases, and sub-types of macrophages play a role in diverse diseases including macular degeneration and diabetic retinopathy. Here the authors using a light-induced retinal degeneration model and using retinal explants, and peripheral blood-derived monocytes from patients with AMD show that M2a polarized macrophages drive this phenotype. The authors demonstrate this both in vivo and ex vivo and also demonstrate a role for cell-based and secreted factors. The work is fairly specialized and of interest to the vision research community but also has implications for macrophage biology. The data also connects systemic immunity to retinal cell death in diseases such as macular degeneration.

Reviewer #2 (Public Review):

The manuscript entitled 'Functional membrane microdomains and the hydroxamate siderophore transporter ATPase FhuC govern Isd-dependent heme acquisition in Staphylococcus aureus' investigates the heme transport over the bacterial cell membrane. The novelty of this paper is proving the requirement of a highly structured cell envelope that depends on functional membrane microdomains FMMs for bacterial nutrient acquisition. The authors showed that the heme-specific permease (IsdF) is associated with FMMs, to directly interact with the FMM scaffolding protein flotillin A (FloA) and to co-localize with the latter on intact bacterial cells since IsdF needs an appropriate location within the membrane for functionality.

The strengths of the manuscript:

It provides new evidence on the different mechanisms used by S. aureus to acquire iron. These new findings are essential in understanding the way this bacterium survives nutritional immunity and thus can be a target for novel therapeutic approaches.<br /> All the results were based on the necessary molecular techniques that strongly support the conclusions.

The weaknesses of the manuscript:

More details concerning different strategies of iron acquisition should be mentioned in the introduction.<br /> Additional bibliographic literature is needed for explaining what unknown ATPase partially substitutes for the function of FhuC.<br /> More experiments are needed in order to verify the speculations presented in the last part of the manuscript.

英勇possessing or acting with bravery or boldness

有組織的歌唱團（如在教堂禮拜中）an organized company of singers (as in a church service)

通過拉動或拉動使（某物）移動:施加牽引力to cause (something) to move by pulling or drawing : to exert traction on

規模大或宏偉large or magnificent in scale

打破慣有的友好關係a break in accustomed friendly relations

無所畏懼 Courageously resolute especially in the face of danger or difficulty

刺穿 To pierce through with or as if with a pointed weapon

突然的衝動或心血來潮 A sudden impulse or whim

傲慢 Having or showing an attitude of superiority and contempt for people or things perceived to be inferior

a feeling of contempt for someone or something regarded as unworthy or inferior 蔑視

1930s Wilson Memindex Co Index Card Organizer Pre Rolodex Ad Price List Brochure

archived page: https://web.archive.org/web/20230310010450/https://www.ebay.com/itm/165910049390

Includes price lists

List of cards includes: - Dated tab cards for a year from any desired. - Blank tab cards for jottings arranged by subject. - These were sold in 1/2 or 1/3 cut formats - Pocket Alphabets for jottings arranged by letter. - Cash Account Cards [without tabs]. - Extra Record Cards for permanent memoranda. - Monthly Guides for quick reference to future dates. - Blank Guides for filing records by subject.. - Alphabet Guides for filing alphabetically.

Memindex sales brochures recommended the 3 x 5" cards (which had apparently been standardized by 1930 compared to the 5 1/2" width from earlier versions around 1906) because they could be used with other 3 x 5" index card systems.

In the 1930s Wilson Memindex Company sold more of their vest pocket sized 2 1/4 x 4 1/2" systems than 3 x 5" systems.

Some of the difference between the vest sized and regular sized systems choice was based on the size of the particular user's handwriting. It was recommended that those with larger handwriting use the larger cards.

By the 1930's at least the Memindex tag line "An Automatic Memory" was being used, which also gave an indication of the ubiquity of automatization of industrialized life.

The Memindex has proved its success in more than one hundred kinds of business. Highly recommended by men in executive positions, merchants, manufacturers, managers, .... etc.

Notice the gendering of users specifically as men here.

Features: - Sunday cards were sold separately and by my reading were full length tabs rather than 1/6 tabs like the other six days of the week - Lids were custom fit to the bases and needed to be ordered together - The Memindex Jr. held 400 cards versus the larger 9 inch standard trays which had space for 800 cards and block (presumably a block to hold them up or at an angle when partially empty).

The Memindex Jr., according to a price sheet in the 1930s, was used "extensively as an advertising gift".

The Memindex system had cards available in bundles of 100 that were labeled with the heading "Things to Keep in Sight".

Notice the 1 1/4" x 3" cards, 2 1/4 x 4" cards in addition to the 3 x 5" and 4 x 6".

Reviewer #2 (Public Review):

In this work, Ball et al. investigated the possibility to generate a novel set of HepG2 liver cell lines to generate "mitochondrial DNA-personalized" models as novel tools to study idiosyncratic drug-induced liver injury related to mitochondrial variation. This work represents the generation of a comprehensive collection of n=10 HepG2 lines, half reflecting haplogroup H and half reflecting haplogroup J. The authors then assessed their impact on basic mitochondrial function in liver cells. Interestingly, they find a greater respiratory complex activity driven by complex I and II of the haplogroup J lines relative to haplogroup H. Finally, the authors make an attempt at using this novel set of lines to probe the consequential effects of mitochondrial genotype on drug-induced liver toxicity. This work provides an interesting proof-of-concept study and is a starting point towards studying and predicting idiosyncratic drug-induced liver injury in a personalized manner. This technique may be broadly extrapolated to other commonly used liver cell models within the toxicology field.

Strengths:

1) This work presents an exciting initiative to study interindividual variability in idiosyncratic drug-induced liver injury focusing on mitochondrial haplotypes. In further follow-ups, this work could be extended to also represent other different haplogroups to establish a thorough "biobank". The established lines allow for future in-depth characterization and testing of many putative hepatotoxic compounds through a variety of toxicity measures that could shed further light on the impact of mitochondrial DNA variation on (idiosyncratic) drug-induced liver injury.

2) This technique may be broadly extrapolated to other commonly used liver cell lines within the toxicology field (e.g. HepaRG cells or iPSC-derived cells) that are potentially also more metabolically competent. A short discussion on this could be added to the current manuscript.

Weaknesses:

1) The major weakness of the current manuscript is the rather large variation across sample measurements regarding the proof-of-concept experiments to study drug effects (fig. 3-6). This makes much of the data rather hard to interpret and to infer conclusions. As an example, proton leak (fig. 3f/4f) seems to 2-fold increase in the J group even under basal conditions (0 uM flutamide/metabolite), while this is not observed in fig. 2a and this effect seems to be also absent under 0 uM tolcapone (fig. 5f). Unfortunately, the current data do not allow to draw confident conclusions about whether the tested drugs have effects on the mitochondrial respiration of the different haplogroups. This may well be linked to the methods used for measuring mitochondrial activity, but since this is the predominant method needed in the current paper, either increasing the number of experiments (across more lines) or identifying a more rigorous methodological manner to obtain consistencies of experiments would help the authors to make more confident claims about their data.

2) The data on the effects of inhibition of complex I/II activity are not sufficiently convincing to support the claim that haplogroup J is more susceptible to flutamide/metabolite (fig. 6). Both seem to respond rather identical to flutamide or its metabolite, i.e. at higher concentrations complex I/II activity decreases, but with the sole difference that the haplogroups represent different basal activity (not influenced by the drug). Estimating fold changes, for example, for both haplogroups, complex I and II activity decreases ca. 2-fold at the highest concentration of the metabolite (fig. 6c-d), therefore concluding that there is no difference between haplogroup susceptibility unlike the authors claim. It is furthermore unclear what the statistical significance currently represents: it should represent whether at different/increasing concentrations the activity of the complexes significantly differs vs. the previous/basal conditions from the same haplogroup. If it represents (which it seems to be) the significance of the haplogroup J vs. the haplogroup H, it is non-informative as it is obvious that haplogroup J presents with a higher baseline.

3) It would help to mention how many lines per haplogroup H/J were used in the analyses across all figures. This should be clarified, as the error bars for most experiments are rather high and therefore statistical significance is lacking, making data interpretation complex. It could be helpful if the authors present at least for some analyses single plots of data obtained across different lines from the same haplogroup to evaluate the consistency of the effects of the genotypes as supplementary figures. If only 1-2 lines were used per group, it would help to perform additional experiments to assess consistencies across groups.

Reviewer #2 (Public Review):

In their manuscript, "Single-Cell RNA-seq of Heart Reveals Intercellular Communication Drivers of Myocardial Fibrosis in Diabetic Mice", Wei Li et al. study the pathogenesis of cardiac fibrosis in mouse hearts in response to high-fat-diet/streptozotocin-induced diabetes. They infer cellular interactions from single nucleus RNA-seq data and highlight some ligand-receptor pairs including PDGFs and PDGFRa. They further aim to identify fibroblast subtypes associated with fibrosis and to identify factors driving diabetic myocardial fibrosis.

This study addresses an important problem (cardiac fibrosis as a consequence of diabetes), using single nucleus RNA-seq and several follow-up experiments in a diabetic mouse model. While many of the described findings, including PDGFRa involvement in fibrosis and a Postn positive fibroblast population (reflecting activated fibroblasts), are expected, the most exciting novel insight would come from the Hrc+ fibroblast population and its characterization. However, based on the currently presented data and analysis it is not clear if this is indeed a fibroblast subtype or due to technical factors.

1) A major point of the manuscript is the description of Hrc+ fibroblasts (Fibroblast 3) as profibrogenic in diabetes. However, fibroblast 3 expresses several cardiomyocyte markers Nppa, Ryr2, Ttn alongside Hrc which is described to play a role in Ca2+ handling at the sarcoplasmic reticulum in cardiomyocytes (Fig. 4C) and shows a low correlation with other fibroblast clusters (Fig. 4B). A possible explanation is technical, e.g. if two nuclei (one fibroblast, one cardiomyocyte) were captured together in one droplet (barcode collisions or doublets). Unfortunately, this uncertainty makes interpretation of all following snRNA-seq analyses based on this fibroblast subpopulation impossible.

2) To follow the study and be able to appreciate the data quality, individual sample metadata and UMAPs colored based on a sample and/or condition (diabetes or control) would be helpful. The paper would benefit from an analysis to show if the differences in the number of detected genes are due to the number of nuclei per cluster or if the bigger clusters are really also the ones with the most dramatic changes. Instead of showing expression levels of differentially regulated genes in distinct clusters (Fig1 S2), the differential expression could be displayed with violin plots or heatmaps that illustrate values for both conditions. Clusters that did not reveal any differential expressed genes, e.g. Adipo can be removed. Fig 1F these KEGG enrichments are hard to interpret since they can be confounded by highly expressed cardiomyocyte genes that are detected in all clusters (1B) and thus drive the GO enrichment of e.g. "cardiac muscle contraction" in T cells.

3) The study looks into the pathogenesis of cardiac fibrosis in diabetic mice. The authors show that downregulation of Itgb1 with siRNA (Fig 6I) leads to less fibrosis in diabetic mice. This effect might be expected since Itgb1 is an extracellular matrix-linked gene and might indicate that downregulation could be beneficial. Given this, it is confusing to see the following analysis which links several genetic variants associated with Type 2 Diabetes to Itgb1 (one leading to premature stop) and its ligand. This analysis seems out of place in relation to the remainder of the study which focuses to identify the downstream effects of diabetes on cardiac fibrosis.

Reviewer #2 (Public Review):

Using a novel genetic system to conditionally ablate Lepr from Agrp neurons in adults, the authors discovered that leptin-AgRP neuron signaling strongly modulates the DMH and sought to understand the DMH targets and mechanisms of action in the response to AgRP neuron signaling. GABA signaling likely underlies the effects of AgRP neuron-mediated hyperphagia (etc). DMH Mc4R neurons appear to lie downstream of Agrp neurons. GABA in the DMH appears to mediate many of the effects of AgRP neurons on feeding and body weight. Furthermore, Deletion of Lepr from AgRP neurons increases DMH GABA-ARa3, and modulation of this receptor in the DMH alters food intake and the response to leptin.

Unfortunately, there is little quantification or other validation data from many of the systems deployed, and the analysis jumps around a fair amount, without really uniting the results in a way that paints a convincing picture of the final model that they build.

Reviewer #2 (Public Review):

This manuscript presents a thorough set of investigations on the roles of a previously poorly-studied protein, SNX32. SNX32 is a sorting nexin involved in cargo sorting along the endosomal system. SNX32 contains a BAR domain and a PX domain, and the authors have convincingly shown that, by interacting with SNX4 and with phosphoinositides (PI(3)P or PI(4)P), SNX32 localizes to early endosomes and regulates the trafficking of different cargo receptors (transferrin receptor and cation independent mannose-6 phosphate receptor). In a second part, the authors moved to a more physiological context, in which they studied the functions of SNX32 in neuronal differentiation, which they suggest that is linked to the role of SNX32 in mediating the trafficking of Basigin (BSG).

Reviewer #2 (Public Review):

When O. tauri cells are grown under low light, PSI has six classical LHCIs (Lhcas), four on one side of the PSI core and two on another, and three trimers of the "Lhcp" antenna proteins on a third side, thus surrounding the PSI core. Lhcp Trimer 2 consists of 1 Lhcp1 and 2 Lhcp2; Trimers 1 and 3 are solely Lhcp2. Careful examination of carotenoid positions suggested that certain serve as "molecular staples" in holding the three monomers of a trimer together.

The resolution of the structure is high enough to determine the positions of all the chlorophylls and carotenoids and to establish the correct chemical composition. All the proteins determined by LCMS/MS were located and modeled. Of particular interest were the minor polypeptides PsaO, PsaL, PsaH, and PsaK, which are in between the PSI core and the trimers, and are involved in binding the trimers to the core.

There is a very detailed comparison of Lhcp trimers with LHC trimers of plants and Chlamydomonas. One of the conclusions is that Chl b requires a Gln rather than a Glu at a certain position, which may otherwise be occupied by a carotenoid. Another is that the increased distance between Lhca5 and 6 may be responsible for the lack of "red" Chls.

This led to a detailed analysis of potential energy transfer pathways in the holocomplex based on distances between pigments and how the trimers interact with the small PSI subunits PsaO, PsaL, PsaH, and PsaK. This section is unfortunately rather tedious to read because the individual monomers in each different trimer are suddenly designated by capital letters. This is not explained properly in the text or in the legend in Fig. 10.

That being said, my overall judgment of the manuscript up to this point is very favorable - I'm impressed with the high quality of the data and the thoroughness of its analysis. It has long been known that when O. tauri cells are grown under high light, the PSI complex does not have the Lhcp trimers, but just has the Lhca antenna. Returning cells to low light induces the synthesis of the Lhcp trimers and the formation of the holocomplex. This could be looked at as a "low-light acclimation"; in nature, the prasinophytes are found in shallow water and hence high light exposure may be their "normal".

The authors asked if this is related to the situation in higher plants and Chlamydomonas where HL induces phosphorylation of certain LHCII trimers which migrate from the appressed membrane regions and associate with PSI. The common factor of these two phenomena is phosphorylation, but the process referred to as a"State transition" operates in the opposite direction to the situation in O. tauri. The authors did a little experiment to see if the disappearance of the complex was reversible in the same time scale as the "state transitions" of Chlamy and plants, by exposing their normal low light cells to 1 hr of HL, then putting them back in LL. They did show that the amount of phosphorylated Lhcp1 decreased significantly in this time frame and then recovered a significant amount when returned to LL. However, using P700 oxidation to assay Lhcp trimers is not very convincing to my eyes.

In my opinion, this does not provide any evidence for a similar mechanism to "state transitions". A real understanding will have to involve studying PSII and its interaction (if any) with Lhcps. There is no indication of where the Lhcps went in 1 hour of HL--maybe they're just at the top of the gradient, minus any phosphate. I would strongly recommend deleting this section altogether.

My conclusion is that a detailed comparison with plant and Chlamydomonas PSIs shows that there are many different ways in which a photosynthetic eukaryote can evolve an effective antenna system. It gives me great pleasure to see a carefully revealed model of another solution to the light-harvesting problem.

Reviewer #2 (Public Review):

The authors present an interesting study combining deep learning, neuroimaging, and brain stimulation techniques for several neurodegenerative diseases. This has important consequences to understand the connectivity alterations and to design novel therapies to alleviate these changes.

Reviewer #2 (Public Review):

This manuscript by Gao, Penzo and colleagues provides a first pass characterization of PVT neurons using single-cell RNA sequencing. Following identification and characterization of likely unique PVT cell types, the authors use multiplexed in situ hybridization to confirm the existence of differentially expressed genes and their spatial location along the AP, ML, and DV axes of the PVT. Finally, the authors compared their sequencing dataset to an existing single cell sequencing atlas, which includes projection-specific sequencing. Within these experiments, the authors describe the expression and spatial location of unique gene sets that are enriched within the clustered cell types. The authors use hierarchical clustering to suggest the existence of two main cell branches in PVT, with each of those branches having subclassifications for a total of 5 identified cell populations.

Reviewer #2 (Public Review):

This manuscript by Marjaneh et al is an original research article that aimed to understand the genetic complexity of atrial septal defects by using QTL analysis in advanced intercross lines (AIL) QSi5 and 129T2/SvEms mouse strains, which represent mice with extremes of atrial septal phenotypes. This study is built on previous work by the authors (Biben. 2000), in which they developed three quantitative parameters of atrial septal morphology. These quantitative traits were previously proven by the authors to be associated with the prevalence of PFO across a variety of genetic backgrounds. Using an F2 design of the same strains they have previously identified 13 significant or suggestive QTL affecting these quantitative traits, (Kirk. 2006).

The current manuscript extends the previous analysis using the AIL approach at F14. This design, the fine mapping approach, and the rigorous downstream analysis allowed them to refine their previous findings. In addition, several new QTLs were discovered. Remarkably, the resolution was increased and the overlap between QTL for different traits was enhanced. Furthermore, they performed whole genome sequencing of the parental strains and identified high-confidence deleterious variants that are enriched in known human CHD genes as well as the genes within QTL regions that are expressed in the atrial septum, such as SMAD6. They also performed transcriptome analysis of septa at different developmental stages in parental strains and identified networks enriched in the ribosome, nucleosome, mitochondrial, and ECM biosynthesis underlying septal variation.

Overall, the manuscript was built on a clear rationale and employed a suitable genomics approach to address the topic. The results provide a substantial and important extension of the previous work at a larger scale and a higher level of resolution. The findings improve the status of current knowledge and provide valuable resources to unravel the genetic complexity of CHDs, with relevance to human PFO. The significance is deemed to be "Important" given the large-scale approach, the specificity of quantitative measures, and the resolution of the analysis pipeline. Analysis steps are well-designed providing potential candidate targets from their network analysis. Pending functional validation and confirmatory evidence of the causality in future mechanistic studies, the outcomes may lead to novel diagnostic and translational values.

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

This is a well-written paper that reports that the accumulation of LOOH with age and disuse contributes to the loss of skeletal muscle mass and strength. Moreover, the authors report that LOOH neutralization attenuates muscle atrophy and weakness. The mechanism via which LOOH contributes to these phenotypes remains unclear but seems to be mediated by the autophagy-lysosomal axis. In addition, the paper also reports the efficacy of N-acetylcarnosine treatment in ameliorating muscle atrophy in mice.

The authors should consider the following points to improve the manuscript:

- The authors showed that inhibition of the autophagy-lysosome axis by ATG3 deletion or BafA1 was sufficient to reduce LOOH levels induced by GPx4 deletion, erastin, or RSL3. Moreover, they found that 4-HNE co-localizes with LAMP2. However, it remains unclear the precise mechanism via which LOOH contributes to muscle atrophy and how it is amplified by the autophagy-lysosomal axis. The authors could further test the functional interaction of 4-HNE with LAMP2 with additional experiments such as immunoprecipitation.

- A weak point of the paper is not having performed the experiments on 24-month-old-mice. At 20 months of age, the mice do not display any muscle wasting and myofiber atrophy compared to young mice that have completed postnatal muscle growth (=6-month-old-mice). It would be interesting to see the levels of 4-HNE in 24- or 30-month-old mice, and if N-acetylcarnosine treatment in older mice is able to rescue muscle atrophy induced by aging.

Previous studies have shown that inhibition of autophagy accelerates (rather than protect) from sarcopenia, and that autophagy is required to maintain muscle mass (Masiero 2009, PMID: 19945408; Castets 2013, PMID: 23602450; Carnio 2014, PMID: 25176656). On this basis, the authors should test whether their findings are valid only in the context of disuse atrophy or also in the context of sarcopenia (=24-30-month-old mice).

- In Fig.2 the authors report that GPx4 KD, erastin, and RSL3 reduce the diameter of myotubes. For how long and when was the treatment done? Looking at the images, it seems that there are some myoblasts in the cultures treated with GPx4 KD, erastin, and RSL3. Is it possible that these compounds reduce myotube size by inhibiting myoblast fusion rather than by inducing myotube atrophy?

- MDA quantification was done in the gastrocnemius although all the experiments in this paper were performed in the soleus and EDL. It would be good if the authors could explain the reason for this.

Reviewer #2 (Public Review):

This manuscript dives deeply into the localized binding and potential function of the Histone deacetylase Hdac1, the major HDAC expressed in early frog development. The stage-specific binding of Hdac1 changes during early development, correlating with the binding due to maternal factors, then zygotically generally activated or generally repressed genes, and also genes that can be either activated or repressed depending on their context. The protein appears not to bind to constitutive heterochromatin.

The study pursues how the binding changes on Animal Cap versus Vegetal mass expressed genes, and studies how inhibition of Hdac1 with TSA or VPA affects the degree of acetylation and expression. Perhaps the most interesting finding is that inhibition of Hdac1 has large effects on the acetylation and expression of inactive, but facultatively expressed genes, while it has smaller hyperacetylation effects on already active facultatively expressed genes; despite a modest stimulation of the already stimulatory effects of acetylation, the additional acetylation correlates with inhibition of expression of this subset of genes. This result is clearly documented with embryonic region-specific effects on facultatively expressed genes. The effect on inactive genes fits with the general idea that Hdac1 is repressive, but the effect on already acetylated genes is not so easily explained, though some models are proposed.

The overall findings are important background for developmental and chromatin biologists because they add to the documentation of the correlations between acetylation, deacetylation, and expression of genes in development. The correlations allow the inference of potential functions, though these are not tested other than by pharmacological inhibition of Hdac1.

Reviewer #2 (Public Review):

The study by Tam and colleagues addresses the ion-conducting pathway and selectivity of P2X receptor channels. Recent structures of ATP-bound P2X receptors with the activation gate open revealed the presence of a cytoplasmic cap over the central ion permeation pathway. This prompts the authors to examine if lateral fenestrations are potential pathways for ions to permeate the intracellular end of the channel pore, even although they appear to be largely buried within the membrane. Based on sequence alignment, the authors identified a critical residue E17 within the intracellular lateral fenestrations and found that it is accessible to two thiol reactive reagents. Importantly, mutations of E17 also affect the relative permeability of the channels to cations and anions. The work thus solves an ion-conducting mystery of the physiologically important P2X receptor channels. It demonstrates that lateral fenestrations are part of the internal pore of P2X channels and play a critical role in determining ion selectivity.

The structural and sequence analysis is performed carefully, and the electrophysiological experiments are carried out beautifully. Although the data largely seem to support the conclusions, statistical analysis is required to strengthen the claims. Cysteine accessibility experiments may have alternative interpretations; thus, the rigor can be further improved to include the reversibility of the block by treating it with reducing agents.

Reviewer #2 (Public Review):

The present study aimed to demonstrate the utility of brain signal decoding for the differentiation of asynchronous motor signs in Parkinson's disease (PD). To this end, thirty-one PD patients undergoing deep brain stimulation electrode implantation were recruited to participate in an intraoperative motor task. Task performance was compared to extra-operative experiments in healthy subjects. Neural activity and movement traces were segmented into 7-second windows and attributed tremor and slowness measures. To integrate the two symptom domains an additional decoding state termed effective motor control was introduced, which represented the absence of symptoms. Support vector machine regression was used as the model of choice that was trained on individual recording sessions within subjects. All decoding targets from each neurophysiological modality reached significant prediction performances. This represents an important milestone in the current state of research towards machine learning-based intelligent adaptive deep brain stimulation.

Strengths

1. The present analysis is among the first to demonstrate the potential utility of brain signal decoding for the differentiation of asynchronous motor symptoms in Parkinson's disease. In the future, such approaches may be adopted in clinical brain-computer interfaces that can adapt stimulation in real time to concurrent therapeutic demand.

2. The effort from the research team and patients to acquire this important dataset is commendable. The time pressure in the operation room combined with the current trend of asleep surgery for deep brain stimulation makes such data very rare.

3. No relevant difference in decoding performance was found for subthalamic micro vs. macroelectrode recordings. This has practical significance because current sensing-enabled deep brain stimulation implants only allow for macro-recordings, which according to this study has no severe disadvantage over microelectrode recordings for movement decoding. Note that this question could only be answered in the intraoperative setting, which on the other hand can have disadvantages further described below.

4. Beyond the subthalamic nucleus, the authors corroborate the superiority of electrocorticography over subthalamic activity for movement and symptom decoding in Parkinson's disease. This provides further evidence that additional sensing electrodes may complement the subthalamic signals for adaptive deep brain stimulation.

5. Finally, the idea of decoding the presence of an effective motor state is creative and may inspire future developments in adaptive stimulation control algorithms.

Weaknesses

(Note that I take more words for weaknesses, not because they outweigh the strengths, but because I want to justify my criticism in more detail)

1. One inherent limitation of this study is the intraoperative setting, which demands the patients' skull be fixed to the stereotactic frame. This setting is not naturalistic per se and likely comes with additional perturbations in the brain states that are recorded. Thus, the generalization to real-world scenarios is limited. Given the unique opportunity to record invasive brain signals in humans, this limitation has to be accepted and should be taken into account for the interpretation of the results. As mentioned in the strengths, this is currently the only setting that allows for a comparison of micro- and macroelectrode recordings for brain signal decoding.

2. Similarly, the medication state is defined by the intraoperative scenario, as deep brain stimulation implantations are performed after the withdrawal of dopaminergic medication in the so-called dopaminergic OFF state. In this state, PD symptoms are aggravated, which is used clinically to provide a more reliable assessment of deep brain stimulation-induced symptom alleviation. This may also lead to an overestimation of decoding performances as the difference between the absence and presence of PD motor signs in the dopaminergic medication ON state during activities of daily living could be more nuanced.

3. The task design is very interesting as it allows for a continuous definition of symptom severity and motor performance. The comparison to healthy subjects demonstrated clearly higher tremor scores in PD but no significant differences in movement velocity (depicted as trending but p>0.2). This is somewhat unexpected as slowness of movement, also called bradykinesia, is a defining symptom of Parkinson's disease (PD). By definition, this symptom is present in all PD patients, also indicated in the clinical scores shown in the present study. Action tremor, i.e. the presence of tremulous muscle activity during motor performance, is comparatively rare. To support the clinical relevance of the movement tremor observed during the task, the authors show a correlation with the "resting tremor" score from the clinical assessment. It is unclear to me why resting, instead of action tremor scores are shown, as both are part of the clinical assessment (Unified Parkinson's disease rating scale - UPDRS part III). Ultimately, even though resting tremor is significantly more common in Parkinson's disease, not all patients of the current cohort had resting tremor (as indicated in the clinical score correlation). Thus, it remains somewhat puzzling how precise the 3-8 Hz activity actually captures tremor vs. motor noise or inaccuracy. A more fine-grained analysis comparing patients with clinically diagnosed action tremor (as defined by preoperative UPDRS assessment) and without tremor could have helped to support the clinical claims on symptom-specific decoding. On the other hand, the lack of a significant difference in the slowness of movement in the patient cohort relative to healthy controls questions the ability of the task to capture this symptom. Here, I am not sure whether the normalization procedure may have an influence on the comparability. Finally, movement velocity is an easy target that is distributed across a spectrum, so despite the lack of a significant difference in the healthy cohort, I am relatively confident that the decoding of movement slowness in the present cohort is clinically meaningful.

4. Overall, the pathophysiological framework is well placed in the current state of literature, while almost the entire field of brain signal decoding for adaptive deep brain stimulation was neglected. Successful decoding to address Parkinson's and essential tremor (another disorder with more common action tremor) was achieved by multiple groups in impactful studies representing more naturalistic extraoperative or fully embedded settings (Hirschmann et al., 2017, He et al., 2021, Opri et al., 2021). Additionally, other symptoms, like gait disturbances have been the target of machine learning analyses more recently (Louie et al., 2022 and Thenaisie et al., 2022). Here, the manuscript appears to avoid a discussion of the present endeavour in comparison to the current state of the field. One of our own studies has provided the first demonstration of the superiority of electrocorticography over subthalamic LFP for movement decoding, which I am happy to see replicated for the first time in the present manuscript. Importantly, the referenced study showed modality-dependent model performances, with gradient-boosted decision trees performing significantly better than linear models for electrocorticography, while Wiener filters have been repeatedly shown to perform well for subthalamic local field potentials (e.g. see Shah et al., 2018 IEEE Trans Neural Syst Rehabil Eng). The present study does not compare different machine learning architectures. Thus, decoding performances could potentially be further improved with more refined computational approaches. A more thorough overview of the literature from the many laboratories that are invested in this research across the globe would have improved the interpretation with respect to the broader impacts of the present manuscript.

5. The authors also present analyses of the spatial localization of relative decoding performances. They demonstrate higher tremor decoding performance in the dorsolateral subthalamic nucleus and higher decoding performance for the slowness of movement in the more central and ventral subthalamic regions. The authors interpret this as potential evidence to support clinical decision-making for optimized stimulation control of these symptoms at the respective locations. This is overly speculative and currently not backed by the data. First of all, the results only show the contrast of tremor vs. slowness of movement and not each individually. Thus, the spatial peak with each symptom domain could be very similar, e.g. in the dorsolateral STN, but a reversal of the difference only occurs at relatively low performances, e.g. in the ventral STN. Thus, showing both spatial distributions individually could be more informative. However, the claim that this could also be used to adjust stimulation location to alleviate the respective target symptoms is by no means backed by the data and remains an interesting speculation.

6. Finally, as in many brain signal decoding studies, the presented decoding performances are relatively low. The authors decided to present linear correlation metrics as Pearson's r values. These values are by definition higher than the commonly chosen Coefficient of determination or R² that provides a more interpretable performance metric. The amount of variance in the symptom scores that could be explained by the models ranged between 10% and 30% at a temporal resolution of 7 seconds. Moreover, the validity of the linear score is not entirely clear as Pearson's r can be heavily biased by non-normal distributions which were not assessed or at least not reported for the performance evaluation. These considerations do not severely limit the validity of the results themselves, as the authors have convincingly shown that significant decoding performances are possible and other studies in this field range in similar performance ranks. However, this point should remind us that a short-term clinical adoption of such methods is not yet in sight and further research is warranted. Before machine learning-based clinical computer interfaces can reach the clinical routine, the field has to work on more refined methods. In my opinion, the field will have to provide robust decoding performances with R² > 0.8 without patient-specific training to get into the realm of widespread clinical adoption.

Reviewer #2 (Public Review):

Wong et al. studied how dendrites find specific targets during the wiring process. They used the well-established Drosophila olfactory system to address the question. Specifically, they asked how dendrites of monoglomerulous projection neuron (PN) ensemble form a stereotyped topographic map in antennal lobes. They traced the developmental history of each individual projection neuron from anterodorsal (ad) or lateral (l) lineages and found that birth origin and birth order together specify the initial exploration territory and the terminal target. They then took a step further to ask how about the embryonic-born PNs most of which undergo remodeling during metamorphosis: do they maintain their dendritic target through metamorphosis or do they integrate re-extended dendrites into the adult-specific antennal lobes? They showed that ecdysone signaling simultaneously triggers pruning of the dendrites that formed larval antennal lobes and induces the outgrowth of new dendrites to be integrated into the adult antennal lobes. The methodologies, especially ex vivo explant live imaging, established a powerful paradigm to investigate the dynamics of synapse formation during development.

Reviewer #2 (Public Review):

This is a follow-up study by the senior author, who previously showed in a 2021 JBC paper that levels of Paternally Expressed Gene 10 (PEG10) protein, among many other protein changes, are increased in the spinal cord of Ubqln2 knockout (KO) animals (JBC 2021). In this report, they provide more direct evidence that PEG10 levels are regulated by ubqln2 and that PEG10 can be proteolytically cleaved generating fragments, which when overexpressed, induce alterations in gene expression. Through proteomic analysis of spinal cord tissue from control and ALS patients, they found that PEG10 levels and the signature of genes regulated by its products are increased in ALS, proposing that elevation in PEG10 is a novel marker and driver of ALS.

PEG10 resembles a retrotransposon, encoding virus-like gag-pol products. It is only found in eutherian mammals. Although it has lost its ability to transpose, it still retains the retroviral-like translation frameshifting property generating two main products, gag (reading frame 1, RF1) and gag-pol (RF1/2). PEG10 is essential for survival. It plays an important role in RNA-binding and trophoblast stem cell specification, being required for placental development. It is also expressed in several adult tissues, but its function in them is obscure. A recent study showed PEG10 RF1 and RF1/2 bind the deubiquiting enzyme USP9X, and that loss of USP9X destabilizes RF1 but not RF1/2, suggesting USP9X regulates ubiquitination and proteasomal degradation of PEG10 (Abed et al. PLOS One 2021). Additionally, Abed et al. showed PEG10 products support virus-like particle (VLP) assembly and that both RF1 and RF1/2 localize to the cytoplasm, whereas a portion of RF1/2 is found in the nucleus of some cells. They further showed PEG10 binds and regulates RNA expression, most probably through interaction with the 3'-ends of the RNAs but found no common binding motif suggesting interaction could be with the secondary structure.

As mentioned, the senior author previously reported in a JBC article in 2021 that PEG10 levels are elevated in ubqln2 knock out (KO) mice, but that its levels were slightly decreased in the P497S mouse model of ALS. They validated PEG10 as an interactor of ubqln2 by proximity-dependent biotin labeling. A review of the current manuscript follows.

1. Evidence that ubqln2 regulates PEG10 accumulation (Fig 1). The authors use human embryonic stem cells to investigate how knockout (KO) of different ubqln isoforms (1, 2, and 4) affects PEG10 accumulation, showing that only KO of ubqln2 increases the RF1/2 product.

a) There is considerable variation in PEG10 expression in the duplicate sample sets provided, but this is not reflected by the error bars (fig 1 A and B). For example, RF1/2 is quite different in the two ubqln4 KO lysates, yet the error bars do not capture the variation. Better loading and quantification is needed. Also, in the KO cells, gag levels are slightly increased, which is consistent with alterations in proteasomal degradation. Alternatively, the changes in RF1/2 could also result from changes in read-through translation, but this is not investigated. Also, it would be helpful to include blots showing the lower Mol weight PEG10 products, to see how they change relative to Fig 3.

Fig 1G. The authors examined if removal of the poly proline rich region (PPR) from PEG10 affects RF1/2 regulation by ubqln, confirming its requirement.

b) The mechanism why deletion of the PPR abolished RF1/2 regulation by ubqlns was not examined. Is it from accelerated degradation? Also, it is not clear why the authors use the triple ubqln KO cells and did not perform that tests in the different ubqln KO cells. The latter comment applies for several of their investigations, leading to uncertainty regarding the specificity of ubqln2 in PEG10 regulation. It is possible that removal of most ubqlns stalls protein degradation affecting PEG10 turnover?

2. The authors investigated the phylogenetic relationship between PEG10 and ubqln2 demonstrating that PEG10 levels from marsupials that lack a PPR can be increased by appending a PPR from human PEG10. They used triple ubqln KO cells for these investigations.

a) The change they describe is not obvious in Fig2C and E as they appear quite small. They also conclude that ubqln2 regulates PEG10 by these studies, but really the experiments show it is from loss of all ubqlns, not ubqln2 specifically.

3. The authors show PEG10 is capable of self-cleavage of the RF1 product, generating 2 detectable N-terminal products, and several other fragments, including a C-terminal nuclear capsid (NC) fragment (Fig3). They show expression of HA-tagged NC fragment localizes to mainly the nucleus, whereas several other PEG10 products and fragments localize to the cytoplasm. They provide strong support that PEG10 is capable of self-cleavage by mutation of an aspartate residue (D) in a DSG motif in the protein to alanine (A to → ASG), which abolished cleavage. They also conducted a nice experiment showing the ASG mutant can be cleaved in trans by introduction of WT PEG10.<br /> a) The authors never show evidence for liberation and accumulation of the NC fragment, only for an artificially tagged protein by immunofluorescence. Use of a tag to study its localization and affects is problematic as the could influence its properties. They need to show that the fragment is detectable because of their central claim that it is responsible for inducing changes in genes. Biochemical fractionation studies could also reveal the extent of the partitioning of the fragment in the nucleus and cytoplasm. The mechanism by which the NC fragment induces changes in gene expression is not clear.

4. The authors show differences in gene expression upon transfection of HEK293 cells with PEG10 RF1, RF1/2, and NC expression constructs. They show that two PEG10-regulated genes, DCLK1 and TXNIP, are both increased in the spinal cord in sporadic ALS cases compared to controls.<br /> a) It is not clear from the studies whether the changes found in ALS are related to changes in PEG10 specifically, or for other reasons. Additionally, more rigorous comparison in many more ALS and controls is needed. PEG10 levels increase upon cell differentiation (Abed et al.) so the changes in ALS may reflect a compensatory and protective response.

5. To investigate if PEG10 RF1/1 levels are altered by ALS mutations in ubqln2 they transfected ubqln TKO cells with either wt ubqln2, or with mutants carrying either the P497H or P506T ALS mutations. They show PEG10 RF1/2 levels are reduced by overexpression of both the wt and P497H mutant, but not by the P506T mutant. They claim that P497H expression did not affect RF1/2 levels. The authors conducted a proteomic comparison of extracts from the spinal cord of two controls, one P497H ubqln2 case, and six sporadic ALS cases. They found increased levels of RF1/2 in the ALS cases. They also found neurofilament medium and neurogranin were both reduced in the ALS cases. Based on these changes they speculate that PEG10 is a novel marker for ALS.<br /> a) The conclusion that the P497S mutant did not affect RF1/2 is incorrect. It reduced RF1/2 slightly more than wt ubqln2. In fact, it appears that expression of all three (wt and the 2 ALS mutants) ubqln2 proteins reduce RF1/2 significantly, compared to the TKO cells.<br /> b) The changes in PEG10 found in the ALS cases are difficult to evaluate because too few controls and ALS cases were used for the comparison. Huge variations in the levels of PEG10 and of the other proteins graphed In Fug 6B-F were seen in the two controls. The comparison needs to be done with many more samples for sound statistical comparison. Were the samples compared from the same region of the spinal cord?

General comments

1. In the Discussion the authors write that because ubqln2 is the only ubqln capable of regulating PEG10 RF1/2 levels, the PXX domain that is only present in ubqln2 is likely responsible for the regulation. There is no proof in support of this hypothesis. Only one ALS-causing mutation (P506T) in the PXX domain, but not the P497H mutation in the same PXX domain, affected RF1/2 accumulation, inconsistent with general involvement of the PXX domain in PEG10 regulation.

2. The authors claim that ubqln2 may have specifically evolved to restrain PEG10 expression, but don't mention USP9X as being another regulator. The common theme that emerges from these studies is that PEG10 levels are regulated by any mechanism that interferes with ubiquitination/proteasomal degradation. Indeed, immunoblots of the gag-pol (RF1/2) in the different ubqln KO cells show a smear at high molecular weight consistent with the accumulation of ubiquitinated PEG10. The authors imply that the transcriptional changes caused by the alteration in PEG10 levels by ubqln2 are responsible for ALS (title of the paper), but this is merely speculation as the effects of the changes are not known. The changes found could be protective. They also claim PEG10 may serve as a novel biomarker for ALS, but such a claim is not justified from the limited analysis conducted so far, which will require more extensive proof.

Reviewer #2 (Public Review):

This is an interesting manuscript that explores the hypothesis that inhomogeneities in visual sensitivity across the visual field are not solely driven by cortical magnification factors. Specifically, they examine the possibility that polar angle asymmetries are subserved by differences not necessarily related to the neural density of representation. Indeed, when stimuli were cortically magnified, pure eccentricity-related differences were minimized, whereas applying that same cortical magnification factor had less of an effect on mitigating polar angle visual field anisotropies. The authors interpret this as evidence for qualitatively distinct neural underpinnings. The question is interesting, the manuscript is well written, and the methods are well executed.

1) The crux of the manuscript appears to lean heavily on M-scaling constants, to determine how much to magnify the stimuli. While this does appear to do a modest job compensating for eccentricity effects across some spatial frequencies within their subject pool, it of course isn't perfect. But what I am concerned about is the degree to which the M-scaling that is then done to adjust for presumed cortical magnification across meridians is precise enough to rely on entirely to test their hypothesis. That is, do the authors know whether the measures of cortical magnification across a polar angle that are used to magnify these stimuli are as reliable across subjects as they tend to be for eccentricity alone? If not, then to what degree can we trust the M-scaled manipulation here? In an ideal world, the authors could have empirically measured cortical surface area for their participants, using a combination of retinotopy and surface-based measures, and precisely compensated for cortical magnification, per subject. It would be helpful if the authors better unpacked the stability across subjects for their cortical magnification regime across polar angles.

2) Related to this previous point, the description of the cortical magnification component of the methods, which is quite important, could be expanded on a bit more, or even placed in the body of the main text, given its importance. Incidentally, it was difficult to figure out what the references were in the Methods because they were indexed using a numbering system (formatted for perhaps a different journal), so I could only make best guesses as to what was being referred to in the Methods. This was particularly relevant for model assumptions and motivation.

3) Another methodological aspect of the study that was unclear was how the fitting worked. The authors do a commendably thorough job incorporating numerous candidate CSF models. However, my read on the methods description of the fitting procedure was that each participant was fitted with all the models, and the best model was then used to test the various anisotropy models afterwards. What was the motivation for letting each individual have their own qualitatively distinct CSF model? That seems rather unusual. Related to this, while the peak of the CSF is nicely sampled, there was a lack of much data in the cutoff at higher spatial frequencies, which at least in the single subject data that was shown made the cutoff frequency measure seem like it would be unreliable. Did the authors find that to be an issue in fitting the data?

4) The manuscript concludes that cortical magnification is insufficient to explain the polar angle inhomogeneities in perceptual sensitivity. However, there is little discussion of what the authors believe may actually underlie these effects then. It would be productive if they could offer some possible explanation.

Reviewer #2 (Public Review):

The manuscript "Neural dynamics underlying self-control in the primate subthalamic (STN) nucleus" builds on a substantial literature indicating a role for the STN in impulsive actions, i.e. responding too early in tasks that require patience. The authors trained two monkeys to move a cursor to a target and then hold still, waiting for a reward. A visual cue indicated the reward magnitude and time interval that the monkeys were required to wait on each trial in order to get the reward. Understanding the mechanism by which the STN supports behavioral inhibition is important since the STN is a common target for deep brain stimulation for both neurological and psychiatric disorders. The authors claim that their results indicate that the STN integrates reward and delay information and that this representation is anatomically varied along the axis of the STN.

Plots of "rejection rate" (trials where the monkeys failed to wait until the rewards) as a function of delay and reward size seem to indicate that the monkeys understood the visual cue. The rejection rates were very low (less than 4% for almost all conditions) which indicates that the monkeys did not have a hard time inhibiting their behavior. It also meant that the authors could not compare trials where the monkeys successfully waited with trials where they failed to wait. This missing comparison weakens the link between the neurophysiological observations and the conclusions the authors made about the signals they observed.

The authors examined the STN activity aligned to the start of the delay and also aligned to the reward. Most of the "delay encoding" in the STN activity was observed near the end of the waiting period. The trouble with the analysis is that a neuron that responded with exactly the same response on short and long trials could appear to be modulated by delay. This is easiest to see with a diagram, but it should be easy to imagine a neural response that quickly rose at the time of instruction and then decayed slowly over the course of 2 seconds. For long trials, the neuron's activity would have returned to baseline, but for short trials, the activity would still be above baseline. As such, it is not clear how much the STN neurons were truly modulated by delay.

Another concern is the presence of eye movement variables in the regressions that determine whether a neuron is reward or delay encoding. If the task variables modulated eye movements (which would not be surprising) and if the STN activity also modulated eye movements, then, even if task variables did not directly modulate STN activity, the regression would indicate that it did. This is commonly known as "collider bias". This is, unfortunately, a common flaw in neuroscience papers.

Overall, while the work is potentially interesting, these methodological issues weaken the link between the data and the conclusions of the paper.

Reviewer #2 (Public Review):

The work integrated genomic and transcriptomic data to reconstruct the origin of the svPDE gene from the ancestral ENPP3 gene. The authors also analyzed the expression of svPDE along different snake lineages and different tissues in three species of venomous snakes. Finally, they purified an svPDE from the venom of Naja atra and analyzed its crystallographic structure and enzymatic function. The experiments are adequately designed and carefully planned and the conclusions made by the authors are well supported by evidence.

I have the following suggestions:

I could not find a section where the authors provided information regarding the origin of the analyzed venom and tissues. i.e. muscle tissue from Naja atra and venom for purification of svPDE. It is important to include this information.

The authors mention (Line 156) that "the genomic sequences of svPDE-E1a were present in all species of Serpentes but not in the species of Dactyloidae, Varanidae, and Typhlopidae.". As I understand it, the family Typhlopidae is included in the Suborder Serpentes. The conclusions stand of course, but I believe it is worth revising, for accuracy.

During the discussion (Line 315), it is stated that the expression of svPDE in Lamprophiidae is probably associated with the adaptation of prey selection as a dietary generalist compared to Viperidae and Elapidae. Provided that both of these clades have several species considered dietary generalists, I believe this statement is not strongly supported.

Also in the discussion (Line 320), the authors mention that Colubridae is traditionally regarded as a non-venomous clade. This statement is far from accurate given that Colubridae is a very diverse clade and several species within it have been shown to be at least moderately venomous. Various species have been shown to produce secretions comparable to those of front-fanged snakes.<br /> Furthermore, despite their difference in morphology, I believe there is little to no evidence that suggests Duvernoy's glands in colubrids have any functions differing from the venom glands of front-fanged snakes.

Reviewer #2 (Public Review):

Mahbub et al further elucidate the structural and functional consequences of the ARL15-CNNM2 interaction for divalent cation transport. They show that ARL15 has low GTP binding affinity and could not detect GTPase activity, questioning whether ARL15 functions as a GTPase. Although the interaction of ARL15 and CNNMs has been demonstrated by multiple groups before, this study addresses some of the key questions that are central within the TRPM-CNNM-PRL-ARL15 field. Particularly, the authors have identified residues in both ARL15 and CNNM proteins which are required for their binding to one another. In addition, they have also illustrated how PRL proteins compete with ARL15 for their binding to CNNMs. Lastly, the functional consequences of ARL15 binding to CNNMs are shown by TRPM7-mediated Zn2+ transport assays.

However, the current dataset also comes with limitations. Previous studies demonstrated that PRLs interact with the CBS domains of CNNMs and lock them in their so-called "flat" confirmation. It remains unclear how ARL15 affects the structure of the CBS domains, especially in the presence of ATP. The subcellular localisation of these interactions has not been examined. Moreover, the consequences of ARL15 on TRPM7 activity are not completely elucidated. It remains unclear whether this functional effect is CNNM-dependent. Moreover, how the zinc uptakes translate to other divalent ion transport, such as magnesium, has not been examined. These questions should be answered to confirm the model as presented in Figure 7.

Reviewer #2 (Public Review):

Experiments were designed to determine if the adult offspring of mothers exposed to intermittent hypoxia (IH) during late gestation show reduced compensatory respiratory motor neuron plasticity, which is defined as an increase in respiratory motor system output that persists for a long-time following cessation of the perturbing stimulus. Here, the team uses a clever approach to evoke plasticity, which they term inactivity-induced respiratory motor facilitation. This approach has been shown to be repeatable and robust, and therefore useful for evaluating the impact of experimental interventions on compensatory respiratory motor system responses. The model is a paralyzed, mechanically ventilated, anesthetized rat in which the activity of a phrenic nerve is used as an index of excitability of the phrenic motor neuron population, which drives the diaphragm muscle in mammals. Importantly, the activity of the respiratory control system in the brainstem can be terminated by reducing the pH of the blood and cerebrospinal fluid (CSF) to a value that is unique to each animal. This value is called the central apneic threshold, and it occurs because pH-sensitive receptors in the brainstem provide critical excitatory synaptic input to the respiratory controller. Since the pH of the blood and CSF depends importantly on the corresponding levels of CO2 the pH can be adjusted up or down by manipulating the blood CO2. To evoke inactivity-induced respiratory motor facilitation, the group first sets the mechanical ventilator at a rate sufficient to reduce CO2 below the apneic threshold to stop phrenic motor output and then keeps the ventilator output at this level. Then, CO2 is added to the ventilator to raise the blood CO2 to levels just above the apneic threshold, which establishes the baseline level of phrenic motor neuron output. They then periodically stop adding CO2 to the inspired gas mixture, which allows CO2 to fall below the apneic threshold, which abolishes phrenic nerve activity. After 1 minute of apnea, the CO2 is reintroduced, blood CO2 levels rise and phrenic nerve activity resumes. This sequence of 1 minute of central apnea followed by 5 minutes of phrenic motor activity is repeated 5 times, and the recording continues for 60 minutes after the fifth apneic episode. As shown in figure 1, a progressive and long-lasting increase in phrenic nerve activity is observed in both male and female control animals, consistent with compensatory respiratory neuroplasticity. Interestingly, the neuroplastic response in the male offspring of animals exposed to intermittent hypoxia throughout gestation was abolished but was unchanged in the female offspring.

This striking, sex-dependent loss of respiratory motor neuroplasticity in the offspring of IH-exposed mothers was associated with increased inflammatory response in the cervical spinal cord, but not in the brainstem. In addition, the transcriptomes of both the spinal cord and brainstems from male offspring of IH-exposed mothers differed from control, with upregulation of genes targeting transcription factors involved in the inflammatory response, specifically the NF-kB/STAT pathways. Accordingly, additional experiments were done to demonstrate that blocking STAT transcription factor activation with intrathecally-delivered drugs restored the plastic response in the male offspring of IH-exposed mothers.

These are novel and interesting observations showing that GIH is associated with a strong, microglia-mediated inflammatory response in the spinal cord of adult males, but not female offspring. The inflammatory response was associated with a loss of compensatory neuroplasticity in phrenic motoneurons. The techniques employed include difficult and labor-intensive whole animal physiology experiments to RNA sequencing and microglial functional analyses. These data are thus important and of wide interest, as they link a gestational insult with spinal cord inflammation, microglial dysfunction, and a sex-dependent alteration in the ability to generate neuromotor plasticity that persists into adulthood. The main caveat is that IH does not model either obstructive or central apnea as both are associated with combined episodic hypoxia and hypercapnia. Moreover, whereas excitatory synaptic input to the phrenic motoneurons was periodically silenced to evoke "inactivity", patients with upper airway obstruction during sleep take great breathing efforts. The model used here seems more like central apnea; do pregnant humans typically have central or obstructive sleep apnea? Nonetheless, the experiments provide important insight into the impact of gestational hypoxia on the development of breathing control in male offspring.

Reviewer #2 (Public Review):

The current manuscript presents a new toolbox to apply temporal response functions (TRFs) usable in python. TRFs are becoming more widely used and providing an accessible toolbox for a wider audience is very important and should be promoted. Overall, it also seems that the code accompanying the manuscript provides all the steps to do the analysis and could potentially be very useful. However, in the current version, the toolbox relies on one single way to solve the TRF estimation problem, which is the boosting algorithm. Providing a single algorithm makes it difficult to compare results from this toolbox with outcomes of other toolboxes which rely on different methods to solve the regression. The user is forced to work with this choice and is not provided other options (or easy ways to implement new options). Additionally, it seems unclear whether the toolbox is fully able to provide the means to generate predictors that are typically used in a TRF analysis. The github code provided for generating the predictors does not seem to be fully integrated with eelbrain and relies on code in the trftools toolbox, which contains code that the authors deem not yet stable enough to be released. Finally, the overall logic and idea behind the toolbox could have been explained better to make it more accessible to use.

Reviewer #2 (Public Review):

In this manuscript, Hüsler et al. aimed to evaluate the contribution of LDs, Sey1, and FadL to intracellular replication and palmitate catabolism of L. pneumophila in D. discoideum. The authors found that Sey1 regulates LD proteome composition and promotes Icm/Dot-dependent LCV-LD interactions as well as FadL-dependent fatty acid metabolism of intracellular L. pneumophila. The study is in general well-designed and performed. The data are clearly presented and valuable in enhancing awareness of the mechanisms of L. pneumophila infection. The evidence supporting the claims of the authors is solid, although the inclusion of additional controls and clarifications would have strengthened the study.

Reviewer #2 (Public Review):

The authors describe the derivation of new and stable fly cell lines through a strategy of tissue-specific RasV12 expression and in some cases single cell cloning. Lines with molecular and, in some cases, phenotypic characteristics of the targeted tissue are identified: muscle, neural, glial, epithelial, and macrophage-like. These are (for the most part) karyotypically normal and amenable to genetic manipulation including transient and attP-mediated insertion. This paper reports a publicly available resource that will be of great use to many. The cell lines are ready for the well-established tools available for high-throughput screening using CRISPR, RNAi, and small molecules, and allow scalable biochemistry which has been a limitation of using Drosophila for some research questions. Moreover, the Ras-targeting approach is potentially a general way to make additional tissue-specific cells, and the authors describe several failures as well as successes in deriving tissue-specific lines. Overall it is a highly valuable piece of work. Ways that the paper reporting this work could be enhanced for the reader include 1) a more critical analysis of the limitations of these lines to represent their prospective in vivo tissues; 2) a more explicit comparison of these lines next to existing fly cell lines including but not limited to the workhorse S2, and 3) any information on the ease of use and behavior of these cells in the types of high-throughput/high-volume formats where they are likely to be most valuable.

Reviewer #2 (Public Review):

In this manuscript, the authors investigated the association of household close-range contact patterns with SARS-CoV-2 transmission in the household using proximity sensors deployed after the identification of SARS-CoV-2 in the household. They recruited participants in two urban communities in South Africa, Klerksdorp (North West Province) and Soweto (Gauteng Province) from October 2020 through September 2021. Their analysis suggests the lack of an association between close-range proximity events and SARS-CoV-2 household transmission.

Their study design looks reasonable, with useful household contacts data collected in the study. However, their regression analysis only considered a limited set of contact parameters (i.e., median measurements of duration, frequency, and average duration). It's not clear if this limitation will bias the conclusion regarding the lack of an association between close-range proximity events and SARS-CoV-2 household transmission.

Reviewer #2 (Public Review):

In this manuscript, the authors proposed a mathematical model to describe analog and digital modes of gene regulation using FCA-mediated FLC regulation as a model. Previously, a similar approach revealed that the repression of FLC by vernalisation is digital. The authors utilized allelic variations of fca mutants (fca-1; a strong allele and fca-3; a weak allele), which resulted in the different levels of FLC de-repression. Unlike FLC in fca-1, where FLC is robustly ON or OFF states in the root cells, authors observed "intermediate" FLC-expressed cells (weak ON) in fca-3. The authors argued that these "intermediate" levels of FLC expression in root cells might indicate the presence of the analog mode of gene expression. In addition, the authors used the "age"-dependent FLC repression to validate whether digital mode can occur in fca-3 and concluded that it does happen. However, digital OFF does not occur in fca-1, and the authors speculated that this might be due to a "high" level of FLC transcription. Based on these observations, the authors developed a simple mathematical model to predict the transition from analog to digital gene regulation at the cell population level. It is an intriguing model/conclusion to show the "analog" mode of gene regulation, and FLC regulation has been an excellent model system for understanding various modes of gene regulation.

However, some significant issues need to be addressed.

1. Mechanistic details of how FCA regulates FLC have been extensively studied, and both transcriptional and co-transcriptional regulations occur. I understand that FCA affects the 3'end processing of antisense COOLAIR RNAs, which regulate FLC. FCA also physically interacts with COOLAIR RNAs and other proteins, including chromatin-modifying complexes, which establish epigenetic repression of FLC regardless of vernalisation. In addition, FCA appears to function to resolve R-loop at the 3' end FLC, and FLC preferentially interacts with m6A-modified COOLAIR by forming liquid condensates. FCA is also alternatively spliced in an autoregulatory manner, and fca-1 mutant was reported to be a null allele as fca-1 cannot produce the functional form of FCA transcripts (r-form).

However, I could not find any information on the fca-3 allele, which was reported to exhibit a weaker phenotype in terms of flowering time (Koornneef et al., 1991). In this manuscript, the authors showed that the level of FLC expression is lower than fca-1 and higher than Ler WT, but I could not find any other relevant information on the nature of the fca-3 allele. Given the known details on the function of FCA, the authors should explain how fca-3 shows an "intermediate" phenotype, which is highly relevant to the argument for an "analog" mode of regulation in fca-3. Therefore, the nature of the fca-3 mutant should be described in detail.

2. The authors used a transgene (FLC-venus) in which an FLC fragment from ColFRI was used. Both fca-1 and fca-3 is Ler background where FLC sequence variations are known. I understand that the authors introgressed the transgenic in Ler background to avoid the transgene effect, but it is not known whether fca-1 or fca-3 mutations have the same impact on Col- FLC.

3. Fig. 3A: I understand that Fig 3A is the qRT-PCR data using whole seedlings, and the gradual reduction of FLC from 7 DAG to 21 DAG was used to test the "analog" vs. "digital" mode of gene regulation in fca-1 and fca-3. I am not sure whether this is biologically relevant.

3-a. The authors wrote that "This experiment revealed a decreasing trend in fca-3 and Ler (Fig. 3A)". But, I do also see a "decreasing trend" in fca-1 as well (although I understand that they may not be statistically significant). I also noticed that the level of FLC in fca-1 at 7 day has a greater variation. Is there any explanation?

3-b. The decreasing trend observed in Ler (although the expression of FLC is already relatively low in Ler) may be the basis for the biological relevance. But Fig. 3D shows that the FLC-venus intensity in Ler root is not "decreasing".<br /> The authors interpreted that "root tip cells in Ler could switch off early, while ON cells still remain at the whole plant level that continue to switch off, thereby explaining the decrease in the qPCR experiment."<br /> Does this mean that the root tip system with FLC-venus cannot recapitulate other parts of plants (especially at the shoot tip where FLC function is more relevant)?

The authors utilize the root system with transgenes in mutant backgrounds to observe and model the gene repression (transgene repression, to be exact). If the root tip cells behave differently from other parts of plants, how could the authors use data obtained from the root tip system?

4. I do see both fca-1 and fca-3 can express FCA at a comparable level (Fig. 3B); thus, I guess that the authors are measuring total FCA transcripts and that fca-3 may result in different levels of "functional form" of FCA. But this is not clearly discussed.

5. Quantification based on image intensity needs to be carefully controlled. Ideally, a threshold to call "ON" or "OFF" state should be based on the comparison to internal control and it is not clear to me how the authors determined which cells are ON or OFF based on image intensity (especially in fca-3).

6. In many parts, I had to guess how the experiments were performed with what kind of tissues/samples. The methods section can benefit from a more thorough description.

Reviewer #2 (Public Review):

The authors set out to characterize the genetic architecture for aposematic color polymorphism in a species of tiger moths. It was previously known that the color polymorphism showed a non-sex-linked Mendelian inheritance pattern, and was thus likely controlled by an allelic change at a single autosomal locus. Based on observations in other species that traits with a similar simple inheritance pattern of polymorphic aposematic colors often involve supergenes, which refers to a tightly linked cluster of co-adapted loci, the authors tested the hypotheses that a supergene may be involved here in tiger moth polymorphism. To test this hypothesis, they used a combination of QTL mapping, GWAS, and RNA-seq approaches to identify regions of the genome that showed an association with the color pattern polymorphism. The genetic mapping approaches identified a candidate genomic region that contained >20 genes, including the genes yellow-e, and its paralog, valkea. The RNA-seq data showed these genes to be expressed differently in the developing wings of the different color morphs. The valkea paralog is associated with a duplicated chromosomal region that appears to only be present in the genomes of yellow-colored morphs. A phylogenetic-weighting approach was also used to attempt to distinguish the strength of associations of the yellow-e and valkea genes with the color polymorphism and found evidence suggesting valkea was the likely genetic switch for the color polymorphism. Lastly, the authors provide evidence that the differences in coloration involve a change in melanins, through chemical characterization of pigments extracts. Collectively, the authors provide a comprehensive examination of the color pattern genetics and compelling evidence that the polymorphism in pigmentation is controlled by an allelic change at a single autosomal locus that includes the yellow-e/valkea genes that show different expression patterns in the differently colored morphs.

Strengths:<br /> This study provides a comprehensive mapping effort to identify a locus responsible for modulating adaptive variation in natural populations of the tiger moth. This is an ideal trait and system to study the genetic basis of adaptive variation, as the trait variation has clear impacts on fitness and is under strong selection in natural populations. The genomes of Lepidoptera and their amenability for laboratory research and molecular methods make them well-suited for such mapping efforts. The authors used an impressive number of offspring from genetic crosses to conduct QTL mapping, which was nicely complemented with a population genomic GWAS approach to further narrow the candidate locus. The addition of the RNA-seq provides compelling evidence that genes at this locus are clearly involved in differences in wing pattern development.

The greatest strength of this study is perhaps its finding of "something new, using something old". I am referring to the finding of a novel duplication of the yellow gene being involved in pigment variation. Yellow is well-known to be involved in color pattern development in Drosophila and butterflies, but its role in the tiger moths is completely novel. A recent duplication of yellow being involved in adaptive variation is completely new and quite exciting. With other recent examples of gene duplications being involved in differences in butterfly color pattern development, there are now numerous cases of the rapid evolution of gene duplicates involved in generating wing pattern variation. Thus, the findings here should be of broad interest to those interested in the genetic changes involved in generating adaptive variation in natural populations.

Another strength of the study is the characterization of the melanic pigment changes involved in the polymorphism. Such detailed phenotypic analyses can offer critical insights into how the genetic differences found to be associated with color pattern variation, may function and influence wing pattern development.

Weaknesses:<br /> Despite narrowing the locus to a small number of genes through mapping efforts, the study falls short in identifying the genetic switch and sufficient evidence to confirm valkea's role in the color polymorphism.

The mapping efforts identified a narrow locus covering multiple genes from the yellow gene family and RNA-seq data clearly identified valkea and yellow-e as being differentially expressed between color morphs thereby implicating their involvement in differences in wing color pattern development. However, the type and number of genetic changes at this locus involved in generating the color polymorphism remain unresolved. Tree topology provides only suggestive evidence that genotypes at valkea show a stronger association with color pattern differences than at the other nearby yellow genes, and offers limited further resolution as the where the genetic switch may be (e.g. within coding or non-coding regions across the locus).

I am unconvinced that framing this study as a test for the role of a supergene, or "to test whether the polymorphism is associated with large structural rearrangements controlling multiple phenotypic elements, or the result of a single gene mutation" is most appropriate or strengthens the study. The alternative hypotheses of "large structural rearrangements" versus "single gene mutation" do not necessarily reflect the possible, or most likely hypotheses, and neither are not necessarily clearly supported by the results of the study. In other studies of wing color pattern polymorphisms in butterflies, the genetic changes controlling the variation have been non-coding mutations in putative cis-regulatory elements (CREs) that control the expression of a nearby gene involved in wing pattern development (see examples from Heliconius butterflies). These would be considered changes in CREs, not "single gene mutations". There are instances in which such changes impacting color pattern variation have been captured within structural rearrangements, such as polymorphic inversions of Heliconius numata, the single gene or CRE mutation and structural rearrangements both being involved are not mutually exclusive, thus it is difficult to frame this study as testing them as alternative hypotheses. The data presented in the study celery implicate a genomic region with multiple genes differentially expressed (DE) between color morphs, with one of the DE genes residing within a structural variation (insertion/deletion polymorphism). However, the study is unable to resolve if the large structural rearrangement is involved, or if a single versus multiple genes or CRE changes may be involved. Thus, I find it challenging and perhaps a weakness of the paper to frame the study as a test of these alternative hypotheses that are not necessarily mutually exclusive or able to be distinguished using the data in the study. I have similar concerns with the focus on supergenes (i.e. co-adapted gene complex) being a weakness for the paper, as the results of the study don't directly test for the presence or role of a co-adapted gene complex at the locus identified.

Reviewer #2 (Public Review):

In this manuscript, Hoffmann et al. introduce a novel and innovative method to validate and study the mechanism of action of essential genes and novel putative drug targets. In the wake of many functional genomics approaches geared towards identifying novel drug targets or synthetic lethal interactions, there is a dire need for methods that allow scientists to ablate a gene of interest and study its immediate effect in culture or in xenograft models. In general, these genes are lethal, rendering conventional genetic tools such as CRISPR or RNAi inept.

The ARTi system is based on expression of a transgene with an artificial RNAi target site in the 3'-UTR as well as a TET-inducible miR-E-based shRNAi. Using this system, the authors convincingly show that they can target strong oncogenes such as EGFRdel19 or KRasG12 as well as synthetic lethal interactions (STAG1/2) in various human cancer cell lines in vivo and in vitro.

The system is very innovative, likely easy to be established and used by the scientific community and thus very meaningful.

Reviewer #2 (Public Review):

The authors aimed to test the effects of smoking on the methylome while controlling for genetics to test for evidence of whether previous studies on genetically-unrelated individuals were confounded by genetics.

The strengths of this study of genetics-independent associations between smoking exposure and DNA methylation using an epigenome-scale approach are (1) its moderate sample size for a twin study (50-100 ) to detect some of the larger effects sizes (10-15%) found in this study; (2) the thorough EWAS methodology including adjusting for cellular heterogeneity and the use of Bonferroni correction; (3) the use of a within identical twin pair design; (4) the strong overlap between the results and those of previous similar studies in genetically unrelated individuals. Weaknesses include the use of methylation arrays that although targeted to putative regulatory regions, cover only around 2.5% of genomic CpGs, and the use of only a single tissue (blood). Both are acknowledged by the authors.

The authors achieved their aims and were able to test all their hypotheses. In general, the authors' claims were supported by their data, but they could empirically test for an association between methylation and expression at all top CpGs rather than just stating that a subset significantly associated.

This is an important set of findings for the field because genetic confounding has been levelled as a criticism of epigenomewide association studies. It therefore strengthens the evidence that environment (smoking) can change the methylome, assuming that the methylomes of each pair were similar prior to exposure.

Reviewer #2 (Public Review):

The authors explored non-redundant, and potentially contrasting, roles of the Hippo effector transcription factors, YAP and TAZ, in the epithelial regenerative response to non-infectious lung injury. The strength of the work is the use of genetic mouse models that explored inducible loss of function of YAP and/or TAZ in an alveolar epithelial type 2 (AT2) specific manner. The main weakness of the work is that gene(s) inactivation was performed prior to lung injury and, therefore, does not take into account the contextual and dynamic nature of YAP/TAZ signaling; for example, work by other groups have shown that YAP/TAZ is activated early following injury followed by a decrease in activity, thus balancing proliferation and differentiation of AT2 cells (for review, see PMID: 34671628).

Reviewer #2 (Public Review):

The report was based on three nation-wide cancer screening programs (breast, bowel, and cervix cancer). This paper attempts to simulate the potential impact of screening disruption on the cancer detection. The authors raised an important concern; that the screening disruption by COVID-19 pandemic would led to an increase in cervical cancer but a reduction in detection of breast and bowel cancer.

There are some issues that must be addressed to ensure the analysis and conclusions can be clearly studied. Importantly, it is not entirely clear if the simulation methodology applied to arrive at a scientific conclusion. The authors could provide more insights on how they will address not only the change of cancer detection but also the driving some improved methods for screening helping return to pre-pandemic levels.

1. A quasi-experimental before and after design as the methodological intention should be stated in the article. Although there are equally powerful alternatives with arguably less-stringent requirements that are appropriate and well-tested for natural experiments such as that intervened by the COVID-19 pandemic given the simulation methods, as of now obtaining the actual stage distribution of cancer and the cancer-specific mortality rates before and after the pandemic is possible for making scientifically valid conclusions based on observed data to support the simulation study.

2. The screening disruption is the only concerned parameter in modelling the change of cancer progression in this study. But delayed diagnosis after screening as another concern could be possibly affected by the pandemic. This should be taken into consideration in the simulation. The authors also claimed the cancer treatment could be also be affected by the pandemic, the evaluation on mortality is therefore not feasible. However, the impacts of COVID-19 pandemic on the delayed treatment and cancer treatment are important issues which should be covered by simulation study.

3. By simulations, the confident intervals for the outcomes should be provided as the requirement to determine the required reliability for the estimates.

Reviewer #2 (Public Review):

The authors aimed to explore the relationship between life course SES and BMI trajectories. They achieve the aim partially, and they could present the results more clearly. The work is interesting and will inform China's obesity public health programs and policies, but it is also interesting for other countries and communities. The exploration of life course exposures is relevant in many ways, and the authors did a good job conceptualizing the BMI and SES trajectories. However, some issues need to be improved, such as the discussions about bias and improvements in the writing and presentation of results.

Reviewer #2 (Public Review):

The authors try to introduce the encoding time factor into theories of optimal encoding of information in the nervous system

The major strength is in the rigorous analysis and in the simple yet important take home message.

The authors achieved their aim by proving their point with rigorous analyses and the results support their conclusions

The paper makes a simple yet important addition and will likely call for neuroscientists to include more carefully the importance of stimulus encoding time in their formulations of models of neural coding and in the interpretations of results.

ez a rész egyetlen mondat, képzeletbeli párbeszéd zajlik Radnóti és egy bombázó repülőgép pilótája között. a költő érveket sorakoztat fel, miért ne pusztítsa el hazáját. a szerkesztési elv roppant egyszerű: a pilóta fentről szemláli a tájat; csak egy térképet lát, míg a költő lentről figyeli a tájat, látja az apró életképeket, melyek a térképet élettel töltik meg. a költő ellentétpárokkal dolgozik: háborús célpontok állnak szemben a békés mindennapok képével (vad laktanya - szelíd tanya; gyárat - dolgozót; gyárüzem - gyerek). a pusztítandó célpontok Radnóti számára a hazát jelentik, amelyet ismer és szeret. visszatérnek az első rész képei is: erdő, füttyös gyümölcsös, sírok közt anyóka, bakterház udvarán játszó gyermekek. végül személyes emlékeket idéz fel: szerelmeit, egy apró követ mely szerencsét hozott neki diák korában. egyszóval felidézi a még háború előtti szelíd és békés gyermek- és fiatalkorát. mindez fentről nem látható, de talán átérezhető

Reviewer #2 (Public Review):

Endothelial cells mediate the growth of the vascular system but they also need to prevent vascular leakage, which involves interactions with neighboring endothelial cells (ECs) through junctional protein complexes. Buglak et al. report that the EC nucleus controls the function of cell-cell junctions through the nuclear envelope-associated proteins SUN1 and Nesprin-1. They argue that SUN1 controls microtubule dynamics and junctional stability through the RhoA activator GEF-H1.

In my view, this study is interesting and addresses an important but very little-studied question, namely the link between the EC nucleus and cell junctions in the periphery. The study has also made use of different model systems, i.e. genetically modified mice, zebrafish, and cultured endothelial cells, which confirms certain findings and utilizes the specific advantages of each model system. A weakness is that some important controls are missing. In addition, the evidence for the proposed molecular mechanism should be strengthened.

Specific comments:

1) Data showing the efficiency of Sun1 inactivation in the murine endothelial cells is lacking. It would be best to see what is happening on the protein level, but it would already help a great deal if the authors could show a reduction of the transcript in sorted ECs. The excision of a DNA fragment shown in the lung (Fig. 1-suppl. 1C) is not quantitative at all. In addition, the gel has been run way too short so it is impossible to even estimate the size of the DNA fragment.

2) The authors show an increase in vessel density in the periphery of the growing Sun1 mutant retinal vasculature. It would be important to add staining with a marker labelling EC nuclei (e.g. Erg) because higher vessel density might reflect changes in cell size/shape or number, which has also implications for the appearance of cell-cell junctions. More ECs crowded within a small area are likely to have more complicated junctions.<br /> Furthermore, it would be useful and straightforward to assess EC proliferation, which is mentioned later in the experiments with cultured ECs but has not been addressed in the in vivo part.

3) It appears that the loss of Sun1/sun1b in mice and zebrafish is compatible with major aspects of vascular growth and leads to changes in filopodia dynamics and vascular permeability (during development) without severe and lasting disruption of the EC network. It would be helpful to know whether the loss-of-function mutants can ultimately form a normal vascular network in the retina and trunk, respectively. It might be sufficient to mention this in the text.

4) The only readout after the rescue of the SUN1 knockdown by GEF-H1 depletion is the appearance of VE-cadherin+ junctions (Fig. 6G and H). This is insufficient evidence for a relatively strong conclusion. The authors should at least look at microtubules. They might also want to consider the activation status of RhoA as a good biochemical readout. It is argued that RhoA activity goes up (see Fig. 7C) but there is no data supporting this conclusion. It is also not clear whether "diffuse" GEF-H1 localization translates into increased Rho A activity, as is suggested by the Rho kinase inhibition experiment. GEF-H1 levels in the Western blot in (Fig. 6- supplement 2C) have not been quantitated.

5) The criticism raised for the GEF-H1 rescue also applies to the co-depletion of SUN1 and Nesprin-1. This mechanistic aspect is currently somewhat weak and should be strengthened. Again, Rho A activity might be a useful and quantitative biochemical readout.

Reviewer #2 (Public Review):

This work aims to understand genomic imprinting in the mouse and provide further insight to challenges and patterns identified in previous studies.

Firstly, genomic imprinting studies have been surrounded by controversy especially ~10 years ago when the explosion of sequencing data but immature methods to analyze it lead to highly exaggerated claims of widespread imprinting. While the methods have improved, clear standards are not set and results still have some inconsistencies between studies. The authors first do a meta-analysis of previous studies, comparing their results and doing a useful reanalysis of the data. This provides some valuable insights into the reasons for inconsistencies and guides towards better study designs. While this work does not exactly set a common standard for the field, or provide a full authoritative catalog of imprinted loci in mouse tissues, it provides a step in that direction. I find these analyses relatively simple and straightforward, but they seem solid.

Previous studies have described a relatively common pattern of subtle expression bias towards one parental allele, rather than the classical imprinting pattern of fully monoallelic expression. This work digs deeper into this phenomenon, using first the meta-analysis data and then also targeted pyrosequencing analysis of selected loci. The analysis is generally well done, although I did not understand why gDNA amplification bias was not systematically corrected in all cases but only if it was above a given (low) threshold. I doubt this would affect the results much though. To some extent the results confirm previously observed patterns (bimodal distribution of either subtle or full bias, and effect of distance from the core of the imprinted locus). The novel insights mostly concern individual loci, with discovery and validation of some novel genes, typically with a subtle or context-specific parental bias.

The study also provides some insights into mechanisms, especially by analysis of existing mouse models with a deletion of the ICR of specific loci. The change in the parental bias pattern was then used to infer potential methylation and chromatin-related mechanisms in these imprinted loci, including how the subtle bias further away is achieved. There are interesting novel findings here, as well as hypotheses for further research. However, this is an area where the conclusions rely quite heavily on published research especially as this study doesn't include single-cell resolution, and it's not entirely clear how much of e.g. the Figure 7 mechanisms part is based on discoveries of this study.

Imprinting is a fascinating phenomenon that can be informative of mechanisms of genome regulation and parental effects in general. It is a bit of a niche area though, and the target audience of this study is likely going to be limited to specialists doing research on this specific topic. As the authors point out, the functional importance of the findings is unknown.

Reviewer #2 (Public Review):

Previous work from the authors' lab has shown that the classical 'Minute' phenotypes in Drosophila depend on the ribosomal protein Rps12, suggesting that Rps12 is a sensor of deficits in other ribosomal proteins (Rp). Increasing the dose of Rps12 enhances 'Minute' phenotypes, while loss of Rps12 suppresses them. However, Rps12+/- heterozygous flies do not display 'Minute' phenotypes.

In the current manuscript, the authors examine the consequences of deleting Rps12 in mice to explore its potential role in translational regulation and hematopoiesis. Homozygosity for an Rps12 null mutation is embryonic lethal, while heterozygous Rps12+/- mutant mice exhibit defects in growth, skeletal abnormalities, hydrocephalus and stroke. Consistent with other mouse Rp mutants, Rps12+/- mutant mice have a block in erythroid maturation and reduced spleen size. Hematopoietic stem and progenitor cell (HSPC) numbers are reduced in the bone marrow and are defective in repopulation transplant assays. Unexpectedly, Rps12+/- mutants show loss of HSC quiescence associated with AKT/MTOR and ERK pathway activation and increased global translation, a phenomenon that has not previously been reported in other Rp mutants. The authors conclude that Rps12 is critical for the maintenance of HSC quiescence and function.

Strengths<br /> The data reported in this manuscript nicely complement the existing literature on the functional effects of Rp mutations in mammalian hematopoiesis and development with loss of HSC quiescence and increased global translation in the Rps12 deficient mice. These unexpected findings will be of broad interest to scientists working in the field of ribosome assembly, ribosomopathies and hematopoiesis.

Weaknesses<br /> It remains unclear mechanistically how Rps12 haploinsufficiency activates the AKT/MTOR and ERK signaling pathways. It is also unclear to what extent the reported phenotypes might be indirect consequences of perturbing the expression of two small nucleolar RNA genes that are present in Rps12 introns 4 and 5 or a consequence of TP53 activation, which is known to influence the phenotype in other examples of Rp deletion mouse models. To fully justify the conclusions that the authors wish to draw, it would be important to assess the effect of the heterozygous Rps12+/- mutation on Rps12 protein expression, ribosomal subunit assembly and rRNA processing.

Reviewer #2 (Public Review):

Weaver et al. used video analysis of flies that were feeding in their previously developed FLIC assay to begin to dissect the mechanisms of feeding. FLIC or Fly Liquid Interaction Counter records electrical signals that are generated when a fly touches a liquid food substrate with its legs or proboscis or both. Using video data of the liquid food interactions in the FLIC assay allowed the authors to precisely identify what a fly is doing in the feeding chamber and what the relationship is between the flies' behavior and the electrical signal recorded in the assay. This analysis produced the first detailed behavioral profile of feeding flies and allowed the authors to categorize different types of feeding in the FLIC assay, from tasting food (using their legs) to fast and long feeding bouts (using their proboscis).

After establishing what FLIC signals correspond to the different types of feeding, they used these signals to examine the food choices of starved and sated flies when presented with a sugar-rich (2% sucrose) or protein-rich (2% yeast + 1% sucrose) liquid food source. To represent hedonic feeding, they also presented flies with a choice between super sweet (20% sucrose) food or protein-rich (2% yeast + 1% sucrose) liquid food. Although fully fed flies show no difference in the number of times they visit either food choice, the flies spend more time feeding during their visits on 20% sucrose food than they do on regular sugar and on the yeast food source, suggesting that 20% sucrose is a more pleasurable food source. To make sure this was not due to the higher caloric content of 20% sucrose, they also offered flies food with the same sweetness as 20% sucrose (2% sucrose + 18% arabinose) but without caloric content and food with the same caloric content but the sweetness of 2% sucrose (2% sucrose + 18% sorbitol). This experiment showed that sweetness was the driver for the longer feeding bouts, confirming that sweeter food is apparently perceived as more pleasurable. They also looked at the effect of starving flies on the hedonic drive and found that starvation increases the time spent feeding on pleasurable food, consistent with findings in mammals that homeostatic feeding affects the hedonic drive.

To begin dissecting circuits underlying hedonic drive, the authors used CaMPARI expression in all neurons. CaMPARI is a green fluorescent reporter that turns red in the presence of Ca2+ (a measure of neuronal activity) and UV exposure. Fully fed flies in the super sweet food choice condition showed more red fluorescence in the mushroom bodies. Inhibiting a subset of these neurons acutely shows that horizontal lobes are required for the increased duration of feeding bouts on super sweet food. These lobes are innervated by a cluster of DA neurons and inhibiting them also blocks the increased super sweet feeding times.

The data in the paper largely support the conclusions. The application of this tool to distinguish between homeostatic and hedonic feeding is innovative and very compelling. As proof of principle of the strength of their paradigm, the authors identify a distinct brain circuit involved in hedonic feeding. The methods established in the paper make a deeper understanding of feeding mechanisms possible at both a genetic and brain circuit level.

Some of the data presentation is dense and could be improved to make this paper easier for readers to understand.

1) The dissection of feeding into distinct behavioral elements and its correlation with electrical FLIC signals that allow interpreting feeding types is a fundamental new method to dissect feeding in flies. However, the categories of micro-behaviors in Table 1 are not intuitive.

2) The details for the behavioral data analysis are not clear and should be made more obvious. For example, how many males and females were used in each experiment? Were any of the females mated or were they all virgins? If all virgins, why not use mated females? Mating status may have an effect on the feeding drive. If mated and virgin females were used, are there any differences between them? Similarly, for diurnal feeding experiments, it is not immediately clear from the graphs how many animals were used and how the frequencies were obtained (Fig. 1F, presumably averages for each category per fly but that is inconsistent with the legend in the supplement for this figure). Why does the transition heat map not include all micro-behaviors (Fig. 1E, no LQ data which are significant in diurnal feeding)?

3) The CaMPARI images do not look great, particularly in the pan-neuronal condition (Fig. 5A). It would be useful to include the movie of the stack. Did any other brain regions show activity differences, such as SEZ or PI? These regions are known to be involved in feeding so it seems surprising they show no effect.

Reviewer #2 (Public Review):

This paper explores the possibility of integrating diverse and multiple DNA fragments in the genome taking advantage of plasmids in arrays, and CRISPR-Cas.

Since the efficiency of integration in the genome is low, they, as others in the field, use selection markers to identify successful events of integration. The use of these selection markers is common and diverse, but they use a couple of distinct strategies of selection to:

– Introduce bar codes in the genome of individuals at one specific genomic site (gene for Hygromycin resistance with bar code in an intron with homology arms to complete a functional gene);

– Introduce promoters at two specific genomic landing pads downstream of fluorescent reporters.

The strengths of the study rely on the clever design of the selection markers, which enrich the collection of this type of markers. The weaknesses are the lack of novelty in the field in theoretical or practical terms. In fact, they do not show any innovative application of these approaches. Moreover, they show a limited number of experiments in the manuscript, or at least insufficient in my opinion for an article that is based on a methodology.

This work adds to other recent studies, e.g. from Nonet, Mouridi et al., and Malaiwong et al, that use the integration of single and multiple/diverse DNA sequences in the C. elegans genome, and thus is not as groundbreaking as claimed. The real test of this method will be its use to address biological questions.

Reviewer #2 (Public Review):

In this work, the authors used machine learning techniques to predict chronological age in the large UK Biobank dataset using structural neuroimaging measures of regional brain volumes and cortical thickness in sex-stratified models. From these predictions, the authors calculated the brain-age delta, which is thought to reflect biological brain aging. The authors applied these models to four independent cohorts and calculated brain-age delta, which they then associated with several markers of Alzheimer's disease pathology, neurodegeneration, and cerebrovascular disease. The aim of these analyses was to validate brain-age delta as a clinically relevant marker of AD.

Strengths<br /> This is a well-written manuscript that explains a well-powered study of multiple deeply-phenotyped cohorts. An impressive amount of work went into this manuscript and that is evident from reading it. The manuscript was enjoyable to read and easy to follow, and the authors provided an informative summary figure visualizing the analysis plan of this work. More specifically there are five key strengths in this present work.<br /> First, instead of aiming for a brain-predicted age model with optimal predictive accuracy, as is typically the case in studies using brain-age delta measures, the authors used a model with a restricted feature set and a limited age range to allow for better neurobiological interpretability and to increase the relevance of this model to ageing cohorts.<br /> Second, the authors corrected for the proportional bias that is seen in brain age models and controlled subsequent analyses (i.e. associations between brain-age delta and markers of AD pathology, etc.) for chronological age. This is an important and necessary step when working with brain-age delta but is not always implemented across studies.<br /> Third, the authors computed Shapley Additive explanation values (SHAP) which quantified the contribution of different brain regions to the brain age prediction. This ensured that the model had neurobiological interpretability which is not always the case with brain-age prediction models. This was further improved by using a relatively restricted feature set that is often used in brain-age prediction studies as the most important regions could be easily visualized and therefore more readily interpreted. This is in contrast to other models that use a large number of smaller brain features, which are less easily vizualised and less interpretable.<br /> Fourth, importantly, the authors used sex-stratified models as they generated the brain-age delta measures separately in men and women. This allowed for sex-specific analyses of the associations between brain-age delta and markers of AD pathology, cerebrovascular disease, and neurodegeneration, which is important given evidence of sex differences in AD. These sex-stratified models also enabled the authors to compare the most relevant brain regions in the brain age prediction models. While previous work has reported sex differences in brain-age delta, the sex-specific contribution of specific brain features is important information that is not usually reported.<br /> Finally, in addition to investigating the association of brain-age delta with specific markers of AD pathology, cerebrovascular disease, and neurodegeneration, the authors also analyzed the association between brain-age delta and amyloid and tau status stages which provides important clinically relevant information. This information is important if future work aims to further investigate the use of brain-age delta in the field of AD.

Limitations<br /> There are three important weaknesses in this present work. First, the conclusion that "These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration" (from the Abstract) may be overstated. While we assume that brain-age delta reflects an accelerated ageing process, this is still a cross-sectional measure and the results show cross-sectional associations with markers of AD and neurodegeneration. For true validation of this measure as a non-invasive marker of biological brain aging with respect to markers of AD and neurodegeneration, we would need longitudinal data to show that changes in brain age are longitudinally associated with changes in markers of AD and neurodegeneration.<br /> Second, the authors reported that brain-age delta was not related to longitudinal brain change ('aging signature change'), which supports a recent finding that cross-sectional brain-age delta was not associated with longitudinal brain change but was associated with birthweight and polygenic risk scores for brain-age delta (Vidal-Pineiro et al., 2021 eLife). This previous finding led to the conclusion that brain-age delta may reflect early-life factors more so than longitudinal brain change or 'accelerated brain ageing'. This is a critical issue to contend with if we really wish to pursue further validation of the brain-age delta as a potential marker of aging<br /> Third, the analyses for the associations between brain-age delta and other variables are not corrected for multiple comparisons, even though a large number of comparisons are conducted. This means that some of the apparently significant results could be false positives. Appropriately correcting these analyses for multiple comparisons would strengthen the results, allowing for greater confidence in the significant results, and would avoid mistaken interpretations of false positive findings.

Appraisal<br /> The authors developed accurate and generalizable sex-specific measures of the brain-age delta. The authors demonstrated that brain-age delta was associated with measures of AD pathology and neurodegeneration. These have the potential to be useful findings that may promote the use of the brain-age delta in AD research. However, as these results are not corrected for multiple comparisons it is possible that some of these results may be false positives. Moreover, the finding that brain-age delta was not associated with longitudinal brain change may undermine the conclusions, as it could suggest that brain-age delta is not reflective of accelerated brain ageing.

Impact<br /> I believe that this work has two important impacts. First, the methods demonstrated in the present study highlight that sex-stratified models may be necessary for future brain-age delta studies, and given that the models were externally validated in four separate cohorts, a key impact is that future researchers will be able to apply the well-described brain-age models here in their own work. Second, the finding that brain-age delta was not related to longitudinal brain change or atrophy, supports previous similar findings and could suggest that brain-age delta does not, as previously assumed, reflect accelerated brain ageing. This may indicate that the brain-age delta is not a satisfactory marker of brain ageing and therefore could discourage future work with this metric that attempts to validate it is a clinical marker of brain ageing. If this issue could be alternatively explained or if brain-age delta is, in fact, shown to reflect brain ageing, then an additional potential impact is that it may support the future investigation into the use of brain-age delta in longitudinal studies of brain ageing and neurodegeneration.

Reviewer #2 (Public Review):

Wilson et al. investigated the development of thalamocortical tracts in the fetal brain using in vivo diffusion magnetic resonance imaging (dMRI). In their results, fiber tracts terminating in the prefrontal, superior parietal, and visual cortex connect to discrete areas of the thalamus in an anterior-to-posterior manner. The reported fetal thalamus parcellation is remarkably consistent with parcellation observed in adults, which has significant implications for the development of experience-expectant vs. experience-dependent neurocircuitry. Using along-tract analysis, the authors also identify distinct trajectories of tissue maturation along tracts connecting the thalamus to the medial prefrontal cortex, visual cortex, and superior parietal cortex. Next, these maturation maps were segmented using a histologically defined fetal atlas, which revealed unique maturation within fetal neural compartments across gestation. The study introduces an exciting analytical model for bridging the gap between histology and dMRI, enhancing both the interpretability of dMRI metrics in the fetal brain and validating dMRI as a sensitive tool that can reveal organizing principles of fetal brain development. The sample size is impressive for fetal imaging and analyses were completed in individual subject spaces, which helps to minimize the warping of dMRI data.

The conclusions of the paper are largely well-supported by the data, but some aspects of sample composition and data analysis require clarification and extension to ensure the generalizability of the results.

1. Sociodemographic makeup of the sample is insufficiently considered. The authors provide information about fetal gestational age and fetal sex, but no other information about the sample is provided. Readers familiar with the developing human connectome project will know the data was collected in the United Kingdom, but this is not stated explicitly in the manuscript. There is no other information provided about the sample, so it is unclear whether the included 140 maternal-fetal dyads are representative of the broader population. Complex social experiences that vary as a function of income, racial and ethnic identity, and education are potent influences on the developing brain, and there is notable meta-analytic work demonstrating the sociodemographic makeup of a sample alters trajectories of brain development. Brain development in utero has also been shown to vary among fetuses who are later born preterm, yet there is no information about pregnancy complications or delivery (e.g., gestational age at birth) reported in the manuscript. This lack of sociodemographic and health information significantly impedes inference regarding result generalizability.

2. Over half of the collected data were discarded because of failing data quality checks. This is common in fetal data, but it is unclear what thresholds were used to determine exclusion and whether the excluded cases fall evenly along the age spectrum. Typically, MRI data from younger fetuses show greater motion artifacts compared to data collected in older fetuses, which presents a significant confound for the present study that requires careful consideration. It is also unclear whether the motion correction strategies employed in the present study work equally well for all fetal ages. In short, additional analysis and information are required to ensure age-related motion is not unduly impacting the present results.

3. Given that the youngest age group was much smaller than the other groups (n=13), more data is also needed to assess the robustness of the tissue maturation trajectories reported for this young age group.

4. Sensitivity analyses that illustrate the findings are robust to different preprocessing choices would enhance analytic rigor.

Reviewer #2 (Public Review):

Harada et al. investigated the mechanism by which high mannose levels inhibit cellular proliferation and enhance chemotherapy. The authors used CRISPR-Cas9 to delete mannose phosphate isomerase (MPI), a key enzyme for metabolizing mannose, in human cancer cells. They found that MPI knockout leads to decreased proliferation of cancer cells when challenged with supraphysiologic concentrations of mannose. Mannose challenge increased sensitivity to both cisplatin and doxorubicin chemotherapy. It also induced slow cell-cycling with impaired entry into the S phase and progression to mitotic phase. Proteomic analysis revealed down-regulation of cell-cycle related proteins following mannose challenge. Specifically, MCM2-7 proteins are decreased, indicating a failure of replication fork progression. The authors show that high mannose conditions disengage dormant origin sites from DNA synthesis during replication stress induced by cisplatin, confirming relevance to induced chemotherapy sensitivity. Metabolic analysis revealed decreased glycolytic activity, increased oxidative phosphorylation, and depleted nucleotides. Finally, pharmacologic inhibition of de novo dNTP biosynthesis using hydroxyurea treatment produced similar effects on cell-cycle progression, chemotherapy sensitivity, and inhibition of DNA synthesis from dormant origins, indicating that high mannose induced depletion of dNTP pools may be the major mechanism behind the anti-cancer effects of mannose.

Strengths: Overall, the authors used a robust approach with several techniques showing consistent results. The use of multiple clones and cell lines increases confidence in the reported findings. Additionally, the re-expression of MPI in MPI-KO cells eliminated the sensitivity to high mannose conditions, increasing confidence that the findings are not due to off-target effects. The authors are thorough in characterizing the defects in cell-cycle progression and have robust molecular evidence to support the failure of DNA synthesis from dormant origins during chemotherapy-induced replication stress. The use of both proteomics and metabolomic techniques generates a robust picture of molecular effects of mannose challenge. Lastly, the demonstration of similar mechanistic effects by pharmacologic inhibition of de novo dNTP synthesis provides support that depletion of dNTPs is a major cause for the anti-cancer effects of high mannose.

Weaknesses: While the conclusions of this paper are supported by strong and consistent evidence, there are limitations in the relevance of the models used. The study was conducted using cancer cells genetically engineered to not express MPI. However, cancer cells ubiquitously express MPI. Drawing conclusions about metabolic remodeling based on metabolite pool sizes alone is not recommended, as pool sizes can increase or decrease due to changes in production or consumption. Isotope labeling studies would reconcile the reasons for accumulation or depletion of metabolite pool sizes. Lastly, in Figure 3, the authors show down regulation of cell cycle progression genes in response to mannose challenge. However, there is also upregulation of proteins related to various cell death mechanisms including ferroptosis and necrosis, suggesting there may be additional mechanisms to explain the effects of mannose challenge. It is unclear why the cell-cycle explanation was pursued without addressing other possibilities.

Reviewer #2 (Public Review):

In this study, Levakov et al. investigated brain age based on resting-state functional connectivity (RSFC) in a group of obese participants following an 18-month lifestyle intervention. The study benefits from various sophisticated measurements of overall health, including body MRI and blood biomarkers. Although the data is leveraged from a solid randomized control set-up, the lack of control groups in the current study means that the results cannot be attributed to the lifestyle intervention with certainty. However, the study does show a relationship between general weight loss and RSFC-based brain age estimations over the course of the intervention. While this may represent an important contribution to the literature, the RSFC-based brain age prediction shows low model performance, making it difficult to interpret the validity of the derived estimates and the scale of change. The study would benefit from more rigorous analyses and a more critical discussion of findings. If incorporated, the study contributes to the growing field of literature indicating that weight-reduction in obese subjects may attenuate the detrimental effect of obesity on the brain.

The following points may be addressed to improve the study:

Brain age / model performance:

1. Figure 2: In the test set, the correlation between true and predicted age is 0.244. The fitted slope looks like it would be approximately 0.11 (55-50)/(80-35); change in y divided by change in x. This means that for a chronological age change of 12 months, the brain age changes by 0.11*12 = 1.3 months. I.e., due to the relatively poor model performance, an 80-year-old participant in the plot (fig 2) has a predicted age of ~55. Hence, although the age prediction step can generate a summary score for all the RSFC data, it can be difficult to interpret the meaning of these brain age estimates and the 'expected change' since the scale is in years.

2. In Figure 2 it could also help to add the x = y line to get a better overview of the prediction variance. The estimates are likely clustered around the mean/median age of the training dataset, and age is overestimated in younger subs and overestimated in older subs (usually referred to as "age bias"). It is important to inspect the data points here to understand what the estimates represent, i.e., is variation in RSFC potentially lost by wrapping the data in this summary measure, since the age prediction is not particularly accurate, and should age bias in the predictions be accounted for by adjusting the test data for the bias observed in the training data?

3. In Figure 3, some of the changes observed between time points are very large. For example, one subject with a chronological age of 62 shows a ten-year increase in brain age over 18 months. This change is twice as large as the full range of age variation in the brain age estimates (average brain age increases from 50 to 55 across the full chronological age span). This makes it difficult to interpret RSFC change in units of brain age. E.g., is it reasonable that a person's brain ages by ten years, either up or down, in 18 months? The colour scale goes from -12 years to 14 years, so some of the observed changes are 14 / 1.5 = 9 times larger than the actual time from baseline to follow-up.

- The questions above should be investigated and addressed in the context of potential challenges with using brain age as a marker (see e.g., https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.25837, https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.26144).

RSFC for age prediction:

1. Several studies show better age prediction accuracy with structural MRI features compared to RSFC. If the focus of the study is to use an accurate estimate of brain ageing rather than specifically looking at changes in RSFC, adding structural MRI data could be helpful.

2. If changes in RSFC is the main focus, using brain age adds a complicated layer that is not necessarily helpful. It could be easier to simply assess RSFC change from baseline to follow up, and correlate potential changes with changes in e.g., BMI.

The lack of control groups

1. If no control group data is available, it is important to clarify this in the manuscript, and evaluate which conclusions can and cannot be drawn based on the data and study design.

Reviewer #2 (Public Review):

This work by Bray et al. presented a customized way to induce small electrolytic lesions in the brain using chronically implanted intracortical multielectrode arrays. This type of lesioning technique has the benefit of high spatial precision and low surgical complexity while allowing simultaneous electrophysiology recording before, during, and after the lesion induction. The authors have validated this lesioning method with a Utah array, both ex vivo and in vivo using pig models and awake-behaving rhesus macaques. Given its precision in controlling the lesion size, location, and compatibility with multiple animal models and cortical areas, the authors believe this method can be used to study cortical circuits in the presence of targeted neuronal inactivation or injury and to establish causal relationships before behavior and cortical activity.

Strengths:

Great presentation of design considerations that addressed the gaps of current lesioning and neuronal inactivation methods, especially the cross-compatibility that allows this method to be used across different cortical regions and in different animal models, making it easy to be adopted into a variety of electrophysiology studies.

This method can induce lesions that are highly precise and repeatable in size and location, allowing for robust investigation of neuronal circuit function. When combined with the ability to record without disruption both in the acute and chronic phase after lesioning, this would create a great tool to study neural adaptation and reorganization.

The customized current source is simple, low-cost, yet effective in delivering precise, controllable current for electrolytic lesioning, and thus easy to adopt for a range of neuroscience applications.

Extensive ex vivo testing and validation were performed before moving into in vivo and eventually nonhuman primate (NHP) experiments, successfully reducing animal use.

Weaknesses:

In many of the figures, it is not clear what is shown and the analysis techniques are not well described.

The flexibility of lesioning/termination location is limited to the implantation site of the multielectrode array, and thus less flexible compared to some of the other termination methods outlined in Appendix 2.

Although the extent of the damage created through the Utah array will vary based on anatomical structures, it is unclear what is the range of lesion volumes that can be created with this method, given a parameter set. It was also mentioned that they performed a non-exhaustive parameter search for the applied current amplitude and duration (Table S1/S2) to generate the most suitable lesion size but did not present the resulting lesion sizes from these parameter sets listed. Moreover, there's a lack of histological data suggesting that the lesion size is precise and repeatable given the same current duration/amplitude, at the same location.

It is unclear what type of behavioral deficits can result from an electrolytic lesion this size and type (~3 mm in diameter) in rhesus macaques, as the extent of the neuronal loss within the damaged parenchyma can be different from past lesioning studies.

The lesioning procedure was performed in Monkey F while sedated, but no data was presented for Monkey F in terms of lesioning parameters, lesion size, recorded electrophysiology, histological, or behavioral outcomes. It is also unclear if Monkey F was in a terminal study.

As an inactivation method, the electrophysiology recording in Figure 5 only showed a change in pairwise comparisons of clustered action potential waveforms at each electrode (%match) but not a direct measure of neuronal pre and post-lesioning. More evidence is needed to suggest robust neuronal inactivation or termination in rhesus macaques after electrolytic lesioning. Some examples of this can be showing the number of spike clusters identified each day, as well as analyzing local field potential and multi-unit activity.

The advantages over recently developed lesioning techniques are not clear and are not discussed.

There is a lack of quantitative histological analysis of the change in neuronal morphology and loss.

There is a lack of histology data across animals and on the reliability of their lesioning techniques across animals and experiments.

There is a lack of data on changes in cortical layers and structures across the lesioning and non-lesioning electrodes.

Reviewer #2 (Public Review):

The manuscript presents a computational model of how an organism might learn a map of the structure of its environment and the location of valuable resources through synaptic plasticity, and how this map could subsequently be used for goal-directed navigation.

The model is composed of 'map cells', which learn the structure of the environment in their recurrent connections, and 'goal-cell' which stores the location of valued resources with respect to the map cell population. Each map cell corresponds to a particular location in the environment due to receiving external excitatory input at this location. The synaptic plasticity rule between map cells potentiates synapses when activity above a specified threshold at the pre-synaptic neuron is followed by above-threshold activity at the post-synaptic neuron. The threshold is set such that map neurons are only driven above this plasticity threshold by the external excitatory input, causing synapses to only be potentiated between a pair of map neurons when the organism moves directly between the locations they represent. This causes the weight matrix between the map neurons to learn the adjacency for the graph of locations in the environment, i.e. after learning the synaptic weight matrix matches the environment's adjacency matrix. Recurrent activity in the map neuron population then causes a bump of activity centred on the current location, which drops off exponentially with the diffusion distance on the graph. Each goal cell receives input from the map cells, and also from a 'resource cell' whose activity indicates the presence or absence of a given values resource at the current location. Synaptic plasticity potentiates map-cell to goal-cell synapses in proportion to the activity of the map cells at time points when the resource cell is active. This causes goal cell activity to increase when the activity of the map cell population is similar to the activity where the resource was obtained. The upshot of all this is that after learning the activity of goal cells decreases exponentially with the diffusion distance from the corresponding goal location. The organism can therefore navigate to a given goal by doing gradient ascent on the activity of the corresponding goal cell. The process of evaluating these gradients and using them to select actions is not modelled explicitly, but the authors point to the similarity of this mechanism to chemotaxis (ascending a gradient of odour concentration to reach the odour source), and the widespread capacity for chemotaxis in the animal kingdom, to argue for its biological plausibility.

The ideas are interesting and the presentation in the manuscript is generally clear. The two principle limitations of the manuscript are: i) Many of the ideas that the model implements have been explored in previous work. ii) The mapping of the circuit model onto real biological systems is pretty speculative, particularly with respect to the cerebellum.

Regarding the novelty of the work, the idea of flexibly navigating to goals by descending distance gradients dates back to at least Kaelbling (Learning to achieve goals, IJCAI, 1993), and is closely related to both the successor representation (cited in manuscript) and Linear Markov Decision Processes (LMDPs) (Piray and Daw, 2021, https://doi.org/10.1038/s41467-021-25123-3, Todorov, 2009 https://doi.org/10.1073/pnas.0710743106). The specific proposal of navigating to goals by doing gradient descent on diffusion distances, computed as powers of the adjacency matrix, is explored in Baram et al. 2018 (https://doi.org/10.1101/421461), and the idea that recurrent neural networks whose weights are the adjacency matrix can compute diffusion distances are explored in Fang et al. 2022 (https://doi.org/10.1101/2022.05.18.492543). Similar ideas about route planning using the spread of recurrent activity are also explored in Corneil and Gerstner (2015, cited in manuscript). Further exploration of this space of ideas is no bad thing, but it is important to be clear where prior literature has proposed closely related ideas.

Regarding whether the proposed circuit model might plausibly map onto a real biological system, I will focus on the mammalian brain as I don't know the relevant insect literature. It was not completely clear to me how the authors think their model corresponds to mammalian brain circuits. When they initially discuss brain circuits they point to the cerebellum as a plausible candidate structure (lines 520-546). Though the correspondence between cerebellar and model cell types is not very clearly outlined, my understanding is they propose that cerebellar granule cells are the 'map-cells' and Purkinje cells are the 'goal-cells'. I'm no cerebellum expert, but my understanding is that the granule cells do not have recurrent excitatory connections needed by the map cells. I am also not aware of reports of place-field-like firing in these cell populations that would be predicted by this correspondence. If the authors think the cerebellum is the substrate for the proposed mechanism they should clearly outline the proposed correspondence between cerebellar and model cell types and support the argument with reference to the circuit architecture, firing properties, lesion studies, etc.

The authors also discuss the possibility that the hippocampal formation might implement the proposed model, though confusingly they state 'we do not presume that endotaxis is localized to that structure' (line 564). A correspondence with the hippocampus appears more plausible than the cerebellum, given the spatial tuning properties of hippocampal cells, and the profound effect of lesions on navigation behaviours. When discussing the possible relationship of the model to hippocampal circuits it would be useful to address internally generated sequential activity in the hippocampus. During active navigation, and when animals exhibit vicarious trial and error at decision points, internally generated sequential activity of hippocampal place cells appears to explore different possible routes ahead of the animal (Kay et al. 2020, https://doi.org/10.1016/j.cell.2020.01.014, Reddish 2016, https://doi.org/10.1038/nrn.2015.30). Given the emphasis the model places on sampling possible future locations to evaluate goal-distance gradients, this seems highly relevant. Also, given the strong emphasis the authors place on the relationship of their model to chemotaxis/odour-guided navigation, it would be useful to discuss brain circuits involved in chemotaxis, and whether/how these circuits relate to those involved in goal-directed navigation, and the proposed model.

Finally, it would be useful to clarify two aspects of the behaviour of the proposed algorithm:

1) When discussing the relationship of the model to the successor representation (lines 620-627), the authors emphasise that learning in the model is independent of the policy followed by the agent during learning, while the successor representation is policy dependent. The policy independence of the model is achieved by making the synapses between map cells binary (0 or 1 weight) and setting them to 1 following a single transition between two locations. This makes the model unsuitable for learning the structure of graphs with probabilistic transitions, e.g. it would not behave adaptively in the widely used two-step task (Daw et al. 2011, https://doi.org/10.1016/j.neuron.2011.02.027) as it would fail to differentiate between common and rare transitions. This limitation should be made clear and is particularly relevant to claims that the model can handle cognitive tasks in general. It is also worth noting that there are algorithms that are closely related to the successor representation, but which learn about the structure of the environment independent of the subjects policy, e.g. the work of Kaelbling which learns shortest path distances, and the default representation in the work of Piray and Daw (both referenced above). Both these approaches handle probabilistic transition structures.

2) As the model evaluates distances using powers of adjacency matrix, the resulting distances are diffusion distances not shortest path distances. Though diffusion and shortest path distances are usually closely correlated, they can differ systematically for some graphs (see Baram et al. cited above).

Reviewer #2 (Public Review):

Lauterbur et al. present a description of recent additions to the stdpopsim simulation software for generating whole-genome sequences under population genetic models, as well as detailed general guidelines and best practices for implementing realistic simulations within stdpopsim and other simulation software. Such realistic simulations are critical for understanding patterns in genetic variation expected under diverse processes for study organisms, training simulation-intensive models (e.g., machine learning and approximate Bayesian computation) to make predictions about factors shaping observed genetic variation, and for generating null distributions for testing hypotheses about evolutionary phenomena. However, realistic population genomic simulations can be challenging for those who have never implemented such models, particularly when different evolutionary parameters are taken from a variety of literature sources. Importantly, the goal of the authors is to expand the inclusivity of the field of population genomic simulation, by empowering investigators, regardless of model or non-model study system, to ultimately be able to effectively test hypotheses, make predictions, and learn about processes from simulated genomic variation. Continued expansion of the stdpopsim software is likely to have a significant impact on the evolutionary genomics community.

Strengths:

This work details an expansion from 6 to 21 species to gain a greater breadth of simulation capacity across the tree of life. Due to the nature of some of the species added, the authors implemented finite-site substitution models allowing for more than two allelic states at loci, permitting proper simulations of organisms with fast mutation rates, small genomes, or large effect sizes. Moreover, related to some of the newly added species, the authors incorporated a mechanism for simulating non-crossover recombination, such as gene conversion and horizontal gene transfer between individuals. The authors also added the ability to annotate and model coding genomic regions.

In addition to these added software features, the authors detail guidelines and best practices for implementing realistic population genetic simulations at the genome-scale, including encouraging and discussing the importance of code review, as well as highlighting the sufficient parameters for simulation: chromosome level assembly, mean mutation rate, mean recombination rate or recombination map if available, effective size or more realistic demographic model if available, and mean generation time. Much of these best practices are commonly followed by population genetic modelers, but new researchers in the field seeking to simulate data under population genetic models may be unfamiliar with these practices, making their clear enumeration (as done in this work) highly valuable for a broad audience. Moreover, the mechanisms for dealing with issues of missing parameters discussed in this work are particularly useful, as more often than not, estimates of certain model parameters may not be readily available from the literature for a given study system.

Weaknesses:

An important update to the stdpopsim software is the capacity for researchers to annotate coding regions of the genome, permitting distributions of fitness effects and linked selection to be modeled. However, though this novel feature expands the breadth of processes that can be evaluated as well as is applicable to all species within the stdpopsim framework, the authors do not provide significant detail regarding this feature, stating that they will provide more details about it in a forthcoming publication. Compared to this feature, the additions of extra species, finite-site substitution models, and non-crossover recombination are more specialized updates to the software.

When it comes to simulating realistic genomic data, the authors clearly lay out that parameters obtained from the literature must be compatible, such as the same recombination and mutation rates used to infer a demographic history should also be used within stdpopsim if employing that demographic history for simulation. This is a highly important point, which is often overlooked. However, it is also important that readers understand that depending on the method used to estimate the demographic history, different demographic models within stdpopsim may not reproduce certain patterns of genetic variation well. The authors do touch on this a bit, providing the example that a constant size demographic history will be unable to capture variation expected from recent size changes (e.g., excess of low-frequency alleles). However, depending on the data used to estimate a demographic history, certain types of variation may be unreliably modeled (Biechman et al. 2017; G3, 7:3605-3620). For example, if a site frequency spectrum method was used to estimate a demographic history, then the simulations under this model from stdpopsim may not recapitulate the haplotype structure well in the observed species. Similarly, if a method such as PSMC applied to a single diploid genome was used to estimate a demographic history, then the simulations under this model from stdpopsim may not recapitulate the site frequency spectrum well in the observed species. Though the authors indicate that citations are given to each demographic model and model parameter for each species, this may not be sufficient for a novice researcher in this field to understand what forms of genomic variation the models may be capable of reliably producing. A potential worry is that the inclusion of a species within stdpopsim may serve as an endorsement to users regarding the available simulation models (though I understand this is not the case by the authors), and it would be helpful if users and readers were guided on the type of variation the models should be able to reliably reproduce for each species and demographic history available for each species.

Reviewer #2 (Public Review):

Having previously solved the X-ray crystallographic structure of the polymer adhesin domain (PAD) of PrgB from E. faecalis, the authors looked to build on that work by crystallizing a nearly full-length construct of PrgB. Though they were successful in their crystallization endeavors, the crystal contained only what was previously thought to be two domains with RGD motifs. The authors' high-resolution structure shows that in fact the C-terminal portion of PrgB is made up of four immunoglobulin-like domains. The authors then set out to collect single-particle cryoEM data in a bid to obtain a full-length structure of PrgB, both in the presence and absence of ssDNA. The authors were only able to obtain quite low-resolution data, which they fit their crystal structures into. The authors then used these structures to inform the design of novel deletion mutants and point mutations, as well as to rationalize years of phenotypic data from other published mutants.

The X-ray crystallographic structure is beautiful and in combination with their in vivo data allowed them to propose a model where PrgB positions cells at an appropriate distance for conjugation. The cryoEM data are not convincing in their current state, and I, therefore, don't believe that their model of the immunoglobulin domains acting to protect the PAD domain of PrgB from PrgA is well supported. Perhaps there are 2D classes or other data that make a case for the fit of the crystal structures into the cryoEM volumes, but without a PAD deletion or perhaps a dataset including a PAD-specific antibody, I don't feel the fit is supported.

The in vivo experiments appear to be done well and the authors' discovery that the Ser-Asn-Glu is not important for generalized aggregation but has an additional yet unknown role in conjugation and biofilm formation is exciting and well supported by their data.

Reviewer #2 (Public Review):

In this manuscript, Dominici et al. aim to determine whether the reversible inhibition of the type I protein arginine methyltransferases (PRMT) would maintain the stemness of muscle stem cells in culture and enable subsequent regenerative capacities. They demonstrate that the type I PRMT inhibitor MS023 enhances self-renewal and in vitro expansion of muscle stemm cells isolated from mice. Using a very rigorous single cell RNA-sequencing approach, they further demonstrate that a distinct sub-populations of cells emerge under type I PRMT inhibition and that these cells entered the differentiation program more efficiently. Moreover, they revealed a shift in metabolism in these cells, which they confirmed in vitro. Finally, they demonstrate that MS023 enhances muscle stem cells engraftment in vivo and that the direct injection of MS023 increases muscle strength in a mice model of Duchenne muscular dystrophy.<br /> This study will have a great impact in the field of stem cells and offer potential therapeutic avenues for diseases such as Duchenne muscular dystrophy.

Two weaknesses are noted which lie in overstatements of the findings. There are six type I PRMTs (PRMT1, 2, 3, 6, 8, and CARM1), all of which are inhibited by MS023. While the authors demonstrate that their observations are not due to the inhibition of CARM1, they do not demonstrate that it is due to the inhibition of PRMT1, as they suggest.

Furthermore, this study suggests that the switch and elevated cellular metabolism in muscle stem cells due to MS023 enhanced self-renewal and engraftment capabilities but does not demonstrate this fact directly as stated.

Reviewer #2 (Public Review):

The authors performed a retrospective cohort study using claims data to assess the causal relationship between bisphosphonate (BP) use and COVID-19 outcomes. They used propensity score matching to adjust for measured confounders. This is an interesting study and the authors performed several sensitivity analyses to assess the robustness of their findings. The authors are properly cautious in the interpretation of their results and justly call for randomized controlled trials to confirm a causal relationship. However, there are some methodological limitations that are not properly addressed yet.

Strengths of the paper include:<br /> - Availability of a large dataset.<br /> - Using propensity score matching to adjust for confounding.<br /> - Sensitivity analyses to challenge key assumptions (although not all of them add value in my opinion, see specific comments)<br /> - Cautious interpretation of results, the authors are aware of the limitations of the study design.

Limitation of the paper are:<br /> - This is an observational study using register data. Therefore, the study is prone to residual confounding and information bias. The authors are well aware of that.<br /> - The authors adjusted for Carlson comorbidity index whereas they had individual comorbidity data available and a dataset large enough to adjust for each comorbidity separately.<br /> - The primary analysis violates the positivity assumption (a substantial part of the population had no indication for bisphosphonates; see specific comments). I feel that one of the sensitivity analyses 1 or 2 would be more suited for a primary analysis.<br /> - Some of the other sensitivity analyses have underlying assumptions that are not discussed and do not necessarily hold (see specific comments).

In its current form the limitations hinder a good interpretation of the results and, therefore, in my opinion do not support the conclusion of the paper.

The finding of a substantial risk reduction of (severe) COVID-19 in bisphosphonate users compared to non-users in this observational study may be of interest to other researchers considering to set up randomized controlled trials for evaluation of repurpose drugs for prevention of (severe) COVID-19.

Specific comments (in order of manuscript):

Methods:<br /> - Line 158: it is unclear how the authors dealt with patients who died during the follow-up period. The wording suggests they were excluded which would be inappropriate.<br /> - Why did the authors use CCI for propensity matching rather than the individual comorbid conditions? I presume using separate variables will improve the comparability of the cohorts. The authors discuss imbalances in comorbidities as a limitation but should rather have avoided this.<br /> - Line 301-10: it seems unnecesary to me to adjust for the given covariates while these were already used for propensity score matching (except comorbidities, but see previous comment). The manuscript doesn't give a rationale why did the authors choose for this 'double correction'.<br /> - In causal research a very important assumption is the 'positivity assumption', which means that none of the individuals has a probability of zero or one to be exposed. Including everyone would therefore not be appropriate. My suggestion is to include either all patients with an indication (based on diagnosis) or all that use an anti-osteoporosis (AOP) drug (or one as the primary and the other as the sensitivity analysis) instead of using these cohorts as sensitivity analyses. The choice should in my opinion be based on two aspects: whether it is likely that other AOP drugs have an effect on the COVID-19 outcomes and whether BP users are deemed to be more similar (in their risk of COVID-19 outcomes) to non-users or to other AOP drug users. Or alternatively, the authors might have discussed the positivity assumption and argue why this is not applicable to their primary analysis.<br /> - Sensitivity Analysis 3: Association of BP-use with Exploratory Negative Control Outcomes: what is the implicit assumption in this analysis? I think the assumption here is that any residual confounding would be of the same magnitude for these outcomes. But that depends on the strength of the association between the confounder and the outcome which needs not be the same. Here, risk avoiding behavior (social distancing) is the most obvious unmeasured confounder, which may not have a strong effect on other health outcomes. Also it is unclear to me why acute cholecystitis and acute pancreatitis-related inpatient/emergency-room were selected as negative controls. Do the authors have convincing evidence that BPs have no effect on these outcomes? Yet, if the authors believe that this is indeed a valid approach to measure residual confounding, I think the authors might have taken a step further and present ORs for BP → COVID-19 outcomes that are corrected for the unmeasured confounding. (e.g. if OR BP → COVID-19 is ~ 0.2 and OR BP → acute cholecystitis is ~ 0.5, then 'corrected' OR of BP → COVID-19 would be ~ 0.4.<br /> - Sensitivity Analysis 4: Association of BP-use with Exploratory Positive Control Outcomes: this doesn't help me be convinced of the lack of bias. If previous researchers suffered from residual confounding, the same type of mechanisms apply here. (It might still be valuable to replicate the previous findings, but not as a sensitivity analysis of the current study.)<br /> - Sensitivity Analysis 5: Association of Other Preventive Drugs with COVID-19-Related Outcomes: Same here as for sensitivity analysis 3: the assumption that the association of unmeasured confounders with other drugs is equally strong as for BPs. Authors should explicitly state the assumptions of the sensitivity analyses and argue why they are reasonable.

Results:<br /> - The data are clearly presented.<br /> - The C-statistic / ROC-AUC of the propensity model is missing.

Discussion:<br /> - When discussing other studies the authors reduce these results to 'did' or 'did not find an association'. Although commonly practiced, it doesn't justify the statistical uncertainty of both positive and negative findings. Instead I encourage the authors to include effect estimates and confidence intervals. This is particularly relevant for studies that are inconclusive (i.e. lower bound of confidence interval not excluding a clinically relevant reduction while upper bound not excluding a NULL-effect).<br /> - Line 1145 "These retrospective findings strongly suggest that BPs should be considered for prophylactic and/or therapeutic use in individuals at risk of SARS-CoV-2 infection." I agree for prophylactic use but do not see how the study results suggest anything for therapeutic use.<br /> - The authors should discuss the acceptability of using BPs as preventive treatment (long-term use in persons without osteoporosis or other indication for BPs). This is not my expertise but I reckon there will be little experience with long-term inhibiting osteoblasts in people with healthy bones. The authors should also discuss what prospective study design would be suitable and what sample size would be needed to demonstrate a reasonable reduction. (Say 50% accounting for some residual confounding being present in the current study.)<br /> - The authors should discuss the fact that confounders were based on registry data which is prone to misclassification. This can result in residual confounding.

DOI: 10.1093/humrep/dead031

Resource: RRID:ZFIN_ZDB-GENO-150810-2

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-150810-2

What is this?

DOI: 10.1093/humrep/dead031

Resource: ZFIN_ZDB-GENO-181011-2

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-GENO-181011-2

What is this?

Reviewer #2 (Public Review):

This study used tandem mass isobaric tags (TMT) and LC-MS/MS analyses to complete proteomic analyses of whole extensor digitorum longus (EDL), soleus, and extraocular muscles (EOM) excised from 3 month old male WT (n=5) and dHT (n=5) mice. The major strengths of the work include the comprehensive nature of the unbiased muscle proteome studies, validation of the experimental approach by confirming several well-known differences between fast and slow twitch muscles in the WT EDL and soleus proteome data, and the identification of distinct proteome changes and alterations in core ECC and SOCE complex stoichiometry in the three different muscles from dHT mice. The main limitation of this study is that the results are primarily descriptive in nature, and thus, do not provide mechanistic insight into how Ryr1 disease mutations lead to the muscle-specific changes observed in the EDL, soleus and EOM proteomes.

Results comparing fast twitch (EDL) and slow twitch (soleus) muscles from WT mice confirmed several known differences between the two muscle types (e.g. elevated type I myosin, slow troponin I/T/C isoforms, SERCA2, calsequestrin-2, and carbonic anhydrase 3 in soleus; elevated type IIb myosin, SERCA1, calsequestrin-1, collagen I, and parvalbumin in EDL), as well as an overall decrease in oxidoreductase activity associated proteins and increase in extracellular matrix proteins in EDL muscle. Relative levels of select proteins involved in muscle contraction, ECC, extracellular matrix, heat shock response, ribosomes, FK 506 binding, and calcium dependent kinase activity were are compared. Similar analyses between EOM/EDL and EOM/soleus muscles from WT mice were not conducted.

The authors next assessed changes in the EDL, soleus and EOM proteomes in muscles excised from dHT mice, which were previously shown to exhibit an early myopathy characterized by reductions in Ryr1 expression, muscle mass and specific force production. This analysis revealed that in addition to the expected decrease in Ryr1 levels in all three muscles, a large number of additional proteins were significantly increase/decreased altered in EDL (848 proteins), soleus (509 proteins), and EOM (677 proteins). Data in Fig. 3 indicate that more proteins were significantly upregulated than downregulated in all three dHT muscle groups. While a reactome pathway analysis for proteins changes observed in EDL is shown in Supplemental Figure 1, the authors do not fully discuss the nature of the proteins and corresponding pathways impacted in the other two muscle groups analyzed.

The authors conducted a targeted analysis of proteins involved in several select pathways known to be important for skeletal muscle (e.g. ECC proteins, contractile proteins, heat shock proteins, ribosomal proteins, FK506 binding proteins, calcium dependent protein kinases). Increases in some FK506 binding proteins were seen in EDL and EOM muscles of dHT mice, while increases in calcium dependent proteins kinases were observed in all three muscle groups. Overall, fewer protein changes were observed in soleus muscles of dHT mice, with most alterations impacting ECC and ribosomal proteins. Beyond the EDL reactome pathway analysis and author-selected protein analyses shown in Tables 2-4, the nature of the totality of proteins altered in each muscle group, the corresponding pathways involved, and the relative degree to which changes are conserved or unique across all three muscle groups analyzed are not fully evaluated or discussed.

The final part of this study used spiked-in labeled peptides in combination with parallel reaction-monitoring and high resolution TMT mass spectrometry to quantify several key proteins involved in coordinating the ECC (Ryr1 and Cacna1s) and SOCE (Stim1 and Orai1) processes. These analyses provide the first mass spectrometry-based quantification of the concentration (mol/kg) and stoichiometry (e.g. Ryr1/cacna1s, Stim1/Orai1, etc) of these proteins across the three different muscles in both WT and dHT mice. The results indicate that while the stoichiometry of the core ECC complex (Ryr1/Cacna1s ~0.6-0.7) is similar across all muscles in WT, this ratio is reduced in EDL and EOM (but not soleus) of dHT mice. Moreover, the stoichiometry of the core SOCE complex indicates that Orai1 levels are limiting in EDL and EOM muscle (Stim1/Orai1 ~25-50), while Orai1 protein was below detectable levels in soleus. Unlike the core ECC complex, core SOCE complex stoichiometry was unaltered in muscles of dHT mice. These findings have important implications regarding ECC and SOCE function in the three different muscle groups under both normal conditions and a mouse model of RYR1-related myopathy.

Reviewer #2 (Public Review):

This work provides a direct extension of the authors' previously published paper "Charting brain growth and aging at high spatial precision" (Rutherford et al. 2022), expanding their highly valuable existing repository of pre-trained normative models to now also include cortical thickness, surface area, and functional connectivity data.

Strengths<br /> Building on previously published and validated methodology, this work significantly expands an existing modelling toolbox with new data modalities, particularly functional connectivity measures.

Model comparisons show that deviation scores derived from normative models perform as well, or better than, raw data models across three different benchmarking tests (group differences, classification, regression). The authors clearly demonstrate the utility of deviation scores in the assessment of both group and individual differences.

All code, including pre-trained normative models, tutorials, and analysis scripts are available online and very well documented. In addition, the authors are promising to make an easy-to-use online portal available soon.

Weaknesses<br /> Although still an impressively large multi-site data set, the sample size of the functional data (N=22k) is considerably smaller than that of the structural data (N=58k) which implies higher uncertainty in the functional normative model estimates.

The scope of functional normative models computed and shared by the authors is limited to coarse parcellations (based on the Yeo-17 and Smith-10 atlases). High-dimensional functional normative models, for now, still belong to the realm of future work.

Interpretation of deviation scores in classification and prediction tasks is not straightforward. Unlike raw data models, these derived summary measures do not have biological or clinical meaning on their own and can only be interpreted with respect to a pre-defined set of reference data.

Reviewer #2 (Public Review):

The work is rather interesting and novel because for the first time, the authors employed knowledge graph, a cutting-edge technique in the domain of artificial intelligence, to identify a novel herbal drug combination for the treatment of PCM. The results of the clinical trial study clearly demonstrated that the drug combination is effective to ameliorate the symptoms of PCM patients and improve the general health status of the patients. Overall, the strategy of this manuscript may provide a paradigm for the design of drug combination towards many other human disorders.

title

Reviewer #2 (Public Review):

In this paper, the authors illustrate how a One Health approach can strengthen our understanding of the dynamics of the spread and the control of rabies. This is done by analyzing multiple epidemiological and sequence data from both dogs and humans, on the island of Pemba. The joint analyses of these data make it possible to reconstruct the history of rabies introduction and circulation on the island and to quantify the impact of different control measures in particular the cost per death averted.

Data documenting rabies epidemics tend to be rare and of limited quality so the effort to collect these data and analyze them with state-of-the-art statistical techniques should be saluted.

Reviewer #2 (Public Review):

The paper of Tran et al. introduces the concept of 'skeletal age' as a means of conveying the combined risk of fracture and fracture-associated mortality for an individual. Skeletal age is defined as the sum of chronological age and the number of years of life lost associated with a fracture. Using the very comprehensive Danish national registry and employing Cox's proportional hazards model they estimated the hazard of mortality associated with a fracture. Skeletal age was estimated for each age and fracture site stratified by gender. The authors propose to replace the fracture probability with skeletal age for individualized fracture risk assessment.

Strengths of the study lie in the novelty of the concept of 'skeletal age' as an informative metric to internalize the combined risks of fracture and mortality, the very large and well-described Danish National Hospital Discharge Registry, the sophisticated statistical analysis and the clear messages presented in the manuscript. The limitations of the study are acknowledged by the authors.

Reviewer #2 (Public Review):

In Bridi et al the authors convincingly show alteration of the E/I ratio oscillation in two mouse models (Fmr1 and BTBR) of ASD. They go on to examine two possible mechanisms that may underlie these changes, 1) sleep/wake cycle and timing and 2) eCB signaling, both of which have been shown to change E/I ratio oscillations. They find that eCB signaling is altered in both models while sleep/wake timing and cycle are normal, concluding that dysfunctional eCB signaling is likely contributing to the changes in E/I oscillation. The experiments are extremely well done, and conclusions are mostly supported by the data, however, there are some concerns with the interpretation of their findings which I will detail below.

1) The authors describe the changes in E/I ratio that they observe in the BTBR mouse line as a "phase-shift". However, to actually show a true phase shift they should record at all of the same time points as they did in the Fmr1 model. Based on just two time points the authors have not shown a "Phase-shift" a phase shift would have to show that the other two time points (Z6 and Z18) follow the predicted (-6hr?) shift. These data would also help define the length of the shift.<br /> 2) Are the changes in E/I ratio presynaptic or postsynaptic? The authors seem to suggest that the synaptic changes they observe are a loss or gain in synapses. Mini-analysis alone is not sufficient for this conclusion. Even if the authors have shown in a previous paper that PPR is unchanged in control mice, presynaptic effects could be contributing to the observed changes in the mouse models studied here. As eCB signaling is thought to be primarily presynaptic this lends additional motivation to explore presynaptic contributions to the observed phenotypes.<br /> 3) The authors do not make any comparisons between control and ASD model mice at any of their time points. It would be helpful to have additional comparisons between ASD model and control at each time point tested in Fig 1 to relate back to previously published studies that mostly record in the animals' light phase. In other words, please clarify at which phases the ASD E/I ratio is different from the control.

Reviewer #2 (Public Review):

Cecon et al presented a series of tau biosensors using the NanoBiT complementation system to monitor tau intramolecular and intermolecular interactions. Three major findings shown in the paper are discussed below.

(1) The authors added two modifications to the existing NanoBiT complementation-based biosensors including K18(P301L) and TauP301L which have the capabilities of monitoring tau-tau interactions in response to phosphorylation and seeding. It is important to first have a thorough characterization of the biosensors such as the basal comparative signals among the different isoforms/mutations (the data in the paper are mostly normalized) and how these signals correspond to their functional units such as whether they are monomers, oligomers or fibrils as confirmed by other biochemistry assays e.g. ThS staining. The interpretation on the functional effect of these biosensors in response to stimulation such as addition of seeds have to be discussed. For example, K18(P301L) biosensor is responding to both mK18 and aggK18 as well as aggTau but not mTau or oAB. It appears that the biosensor is unable to differentiate monomeric and aggregated species of K18 tau. Also, beta-amyloid oligomers have been shown to seed tau aggregation, but this is not the case shown by the study which warrants some discussion. A more thorough characterization of the luciferase biosensors would be essential before moving into other assays and high-throughput screening as it is important to know exactly what kind of tau species are being targeted.

(2) The authors added colchicine, a MT destabilizing drug, to the luciferase biosensor systems and showed that phosphorylation of WT tau takes place when it is still bound to MTs, as colchicine prevented its phosphorylation and suggested that tau species comprising of K18 and full-length WT tau might represent an interesting new therapeutic target, as K18 tau and tau with P301L mutation renders full-length WT tau responsive to seeding. It is an interesting concept to study how tau aggregation changes with respect to MT destabilization. However, it is worth noting that treatment with chemical compounds may cause many other effects that need to be well controlled/eliminated before reaching a conclusion. The authors showed that treatment with colchicine reduces luciferase signals of the tau biosensors and suggested that the luciferase signals arise from MT bound tau which is interesting. While colchicine is a well-known MT destabilization drug, it is still important to test if colchicine itself is perturbing tau-tau interaction as other studies have shown that colchicine might promote tau aggregation and cause cognitive dysfunction. From a different perspective, one might consider that MT destabilization may result in more tau in the cytosols due to their detachment from MTs and hence resulting in enhanced tau-tau interactions which would be reflected by an increased in biosensor signals. Furthermore, if tau proteins are already interacting when they are on the MTs, a disruption in MTs may not disrupt tau-tau interactions and might lead to enhanced tau-tau interactions. However, this is not the case shown in this study and perhaps a discussion on this interpretation would help to clarify some questions. The luciferase signal for tau on MTs might be due to tau being near one another when they are residing on MTs which acts as a scaffold to hold them together and not exactly due to tau-tau interactions. Hence, upon MT destabilization, the tau proteins lost the scaffolds that hold them together and hence results in a reduction in the luciferase signals. In terms of the therapeutic targeting of K18-WT tau complex, it is important to note that K18 has increased the responsiveness of WT tau to seeding by 2-fold as compared to the 107-fold change upon seeding of K18-K18 tau biosensor. Although significant, it is a very small change as compared to the signal obtained from K18 biosensors.

(3) Finally, the authors conducted a proof-of-concept study to illustrate the potential of the luciferase biosensor to be used in high-throughput screening drug discovery. The authors used tau seeds (Tg brain lysates), and not small molecules, to show the increase in luciferase signals with Z-factors of >0.5, which indicates excellent assay condition. The authors then further showed that known compounds reduced tau aggregation in Tg brain lysates and reduced luciferase signals of the biosensors. High throughput screening capability typically refers to the perturbation of biosensors or tau-tau interactions directly by drug compounds. From the experimental setup, it seems like the authors will be using luciferase biosensor in the presence of Tg brain lysates (together as a system) to screen for drug candidates, instead of using the biosensor directly to screen for compounds that have a direct effect in perturbing the biosensor. In this case, the Z-factor should be calculated for positive-control compounds that are applied to the biosensor+Tg lysates system. The IC50 of the compounds tested in this system should be determined and compared with the known IC50 values of these compounds in the available literature. It appears that the compounds are only exhibiting good inhibition at very high concentrations, suggesting the need to test and eliminate any non-specific effect such as compound aggregation at a very high concentration.

Reviewer #2 (Public Review):

In this paper, the authors provide evidence to support the longstanding proposition that a dual-learning system/systems-level consolidation (hippocampus attains memories at a fast pace which are eventually transmitted to the slow-learning neocortex) allows rapid acquisition of new memories while protecting pre-existing memories. The authors leverage many techniques (behavior, pharmacology, electrophysiology, modelling) and report a host of behavioral and electrophysiological changes on induction of increased medial prefrontal cortex (mPFC) plasticity which are interesting and will be of significant interest to the broad readership.

The experimental design and analyses are convincing (barring some instances which are discussed below). The following recommendations will bolster the strength/quality of the manuscript:

1. Certain concerns regarding the interpretation and analysis of the behavioral data remain. The authors need to clarify if increased mPFC plasticity leads to only an increase in one-shot memory or 'also' interference of previous information. It seems that the behavioral results could also be explained by the more parsimonious explanation that one-shot memory is improved. Do the current controls tease apart these two scenarios? Additionally, the authors need to clarify why the 'no trial' and 'anisomycin' controls for the stable task perform at chance levels on exposure to a new object-place association on test day (Fig 1D). Finally, further description of how the discrimination index (exploration time of novel-exploration time of familiar/sum of both) is recommended i.e., in the stable condition, which 'object' is chosen as 'novel' (as both are in the same locations) for computing the index (Fig 1). Do negative DI values imply a neophobia to novel objects (and thus are a form of memory; this is also crucial because the modelling results (Fig 1E) use both neophilia and neophobia while negative discrimination indexes are considered similar to 0 for interpreting the behavioral results, as stated on page 3, lines 84-86?

2. The authors report lower firing rates in RGS14414 animals during the task in Fig 2F. It is indeed remarkable how large the reported differences are. The authors need to rule out any differences in the behavioral state of the animals in the two groups during the task, i.e., rest vs. active exploration/movement dynamics. Are only epochs during the task while the animals interact with the objects used for computing the firing rates (same epochs as Fig 1)? If not, doing so will provide a useful comparison with Fig 1. Additionally, although the authors make the case for slow firing rate neurons being important for plasticity (based on Grosmark and Buzsaki, 2016), it is crucial to note that the firing rate dynamic (slow vs. fast) in that study for the hippocampus is defined based on the whole recorded session (predominated by sleep), indeed the firing rates of the two groups (slow vs. fast/plastic vs. rigid) during the task/maze-running do not differ in that study. Therefore, the results here seem incongruent with the Grosmark and Buzsaki paper. Since this finding is central to the main claim of the authors, it either warrants further investigation or a re-interpretation of their results.

3. A concern remains as to how many of the electrophysiological changes they observe (firing rate differences, LFP differences including coupling, sleep state differences, Figs. 2-4) support their main hypothesis or are a by-product of injection of RGS14414 (for instance, one might argue that an increased 'capability' to learn new information/more plasticity might lead to more NREM sleep for consolidation, etc.). The authors need to carefully interpret all their data in light of their main hypothesis, which will substantially improve the quality/strength of the manuscript.

Reviewer #2 (Public Review):

The manuscript by Yildiz et al investigates the early response of BECs to high fatty acid treatment. To achieve this, they employ organoids derived from primary isolated BECs and treat them with a FA mix followed by viability studies and analysis of selected lipid metabolism genes, which are upregulated indicating an adjustment to lipid overload. Both organoids with lipid overload and BECs in mice exposed to a HFD show increased BEC proliferation, indicating BEC activation as seen in DR. Applying bulk RNA-sequencing analysis to sorted BECs from HFD mice identified four E2F transcription factors and target genes as upregulated. Functional analysis of knock-out mice showed a clear requirement for E2F1 in mediating HFD induced BEC proliferation. Given the known function of E2Fs the authors performed cell respiration and transcriptome analysis of organoids challenged with FA treatment and found a shift of BECs towards a glycolytic metabolism.

The study is overall well-constructed, including appropriate analysis. Likewise, the manuscript is written clearly and supported by high-quality figures. My major point is the lack of classification of the progression of DR, since the authors investigate the early stages of DR associated with lipid overload reminiscent of stages preceding late NAFLD fibrosis. How are early stages distinguished from later stages in this study? Molecularly and/or morphologically? While the presented data are very suggestive, a more substantial description would support the findings and resulting claims.

Reviewer #2 (Public Review):

It is believed that the reason why women generally have lower rates of atherosclerotic events than men until menopause is due to the beneficial effects of estrogen on the cardiovascular system. The paper attempts to explain why hormone replacement therapy with estrogen is not effective in preventing atherosclerosis in post-menopausal women. The authors posit that accumulation of iron after menopause inhibits estrogen receptor expression and makes estrogen ineffective. Using mouse model of atherosclerosis and iron overload, they demonstrate that 1)atherosclerosis is increased in overectomized mice 2) estrogen supplement seems to further exacerbate atherosclerosis and this is accompanied by increased total body iron; 3) iron itself causes a decrease in ERa via increased proteasome degradation of Era via E3 ligase MDM2 and 4) iron chelation rescues the protective effects of estrogen in overectomized mice on atherosclerosis progression.

While interesting in terms of hypothesis, I found the manuscript (not the overall themes) but the individual experimental logic difficult to follow with unclear rationale for many of the experiments and timepoints chosen. Moreover the human data supporting these claims are weak in terms of what is shown. The authors only partially achieve their aims as many of the experiments in mice appear incomplete in terms of data shown and transparency. Some important controls are also missing.

This work has important potential to understand the causes of accelerated atherosclerosis in women after menopause and how to better prevent atherosclerosis in women of this age group

Reviewer #2 (Public Review):

In this manuscript, Nguyen et al. make use of recently determined cryo-EM structures of Nav1.7 channels in complex with ProTX-II, a peptide spider toxin that binds to VSD2 and stabilizes the deactivated state of the channel in addition to reducing peak currents. Previous work on making modified spider toxin peptides as potent and selective Nav1.7 inhibitors by Merck, Amgen, and others was conducted in a structure-blind manner. This manuscript demonstrates that it is possible to use structure data and computational tools to identify modified spider toxin peptides that show even better potency and selectivity properties.

The authors did a very nice job presenting their detailed results. This detailed material should be very helpful to researchers wanting to expand on this work toward the development of peptide-based pain drugs that selectively target Nav1.7. Their in-vitro electrophysiological analysis is excellent, showing full selectivity profiles (including difficult to work with channels such as hNav1.8 and hNav1.9) from HEK293 cells and also showing inhibition of the TTX-S current with both mouse and human cultured DRG neurons. The in-vivo work shows very strong analgesia in the hotplate model as well as in a model of oxaliplatin-induced peripheral neuroparthy, showing that PTx2-3127 is a powerful analgesic in rats.

Overall, this is an excellent investigation into the feasibility of using structural information and computational tools to design potent and selective Nav1.7 inhibitors. Such peptide-based inhibitors might be developed in the future as novel pain drugs.

Reviewer #2 (Public Review):

Jelen et al. developed a new taste conditioning paradigm where they pair a tastant (CS) with optogenetic activation of either sensory neurons or dopamine neurons. Activation of different cell types in training led to decreased sugar attraction or decreased salt avoidance. Depending on the activated cell type, the authors could even induce LTM with optogenetic activation. They found that the neural requirement for aversive or appetitive taste learning widely overlaps with the requirement for learning with other modalities (olfaction). They focus also on appetitive taste LTM formation, which requires caloric food intake after training similar to olfactory LTM.

Strengths:

The newly developed operant paradigm has several advantages compared to previous taste learning paradigms. The flies are freely walking and can be monitored throughout training and test. This allowed the authors to describe the temporal dynamics of learning and learned behavior. They could show that a specific type of dopamine neuron enhances salt sipping during training but was not sufficient to induce learning. Furthermore, they could now investigate both, appetitive and aversive learning, which was not possible before in immobilized flies. Optogenetic activation as the US in training allowed the authors to disentangle the need for caloric value in short-term and long-term memory.

Weaknesses:

Artificial activation of neurons seems to be sufficient to induce different memories in the fly. However, as the flies do not receive actual food in the training, those results may not represent the naturally used neural circuits, or only partial circuits underlying the normal taste learning. Also, the new paradigm has operant training, which might change the requirement or recruitment of learning circuits. Thus, the authors find similar neurons involved as in classical conditioning, which is very interesting, but also some differences.

Reviewer #2 (Public Review):

Kankaanpää and colleagues studied how lifestyle factors in adolescence (e.g., smoking, BMI, alcohol and exercise) associate with advanced epigenetic age in early adulthood.

Strengths:

The manuscript is very well written. Although the analyses and results are complex, the authors manage very well to convey the key messages.<br /> The twin dataset is large and longitudinal, making this an excellent resource to assess the research questions.<br /> The analyses are advanced including LCA capitalizing on the strength of these data.<br /> The authors also include a wider range of epigenetic age measures (n=6) as well as a broader range of lifestyle habits. This provides a more comprehensive view that also acknowledges that associations were not uniform across all epigenetic age measures.

Weaknesses:

The accuracy of the epigenetic age predictions was moderate with quite large mean absolute errors (e.g., +7 years for Horvath and -9 years for PhenoAge). Also, no correlations with chronological age are presented. With these large errors it is difficult to tease apart meaningful deviations (between chronological and biological age) from prediction error.

The authors claim that 'the unhealthiest lifestyle class, in which smoking and alcohol use co-occurred, exhibited accelerated biological aging...'. However, this is only partially true. For example, PhenoAge was not accelerated in lifestyle class C5. Similarly, all classes showed some degree of deceleration (not acceleration) with respect to DunedinPACE (Figure 3D). The large degree of heterogeneity across different epigenetic age measures needs to be acknowledged.

The authors claim that 'Practically all variance of AAPheno and DunedinPACE common with adolescent lifestyle was explained by shared genetic factors'. However, Figure 4 suggest that most of the variation (up to 96%) remained unexplained and genetics only explained around 10-15% of total variation. The large amount of unexplained variation should be acknowledged.

Reviewer #2 (Public Review):

Little is known about how the circadian clock regulates the timing of anthesis. This manuscript shows that the circadian clock regulates the diurnal rhythms in floral development of the sunflower. The authors have developed a new method to characterize the timing of floral development under normal conditions as well as constant dark and light conditions. The results from the treatment of darkness during the subjective night and day suggest that the circadian clock regulates the growth of ovary, stamen, and style differently.

All clock papers claim that the circadian clock regulates the fitness of organisms, however, it is hard to evaluate how the circadian clock affects the fitness of organisms. The timing of pollen release and stigma maturity is directly related to plant fitness. That's why the authors suggest that the circadian clock in sunflowers increases plant fitness by regulating the timing of anthesis.

Although the authors manipulated the light and temperature to examine the role of the circadian clock in floral development, the weakness of this manuscript is that there is no molecular evidence to show how the clock regulates floral development.

Reviewer #2 (Public Review):

The present manuscript takes a new perspective and investigates the functional relevance of traveling alpha waves' direction for visual spatial attention. While the modulation of alpha oscillatory power - and especially the lateralization of alpha power - has been associated with spatial attention in the literature, the present investigation offers a new perspective that helps understand and differentiate the functional roles of alpha oscillations in the ipsi- versus contralateral hemisphere for spatial attention.

The present study uses a straightforward approach and provides an analysis of two EEG datasets, which are convergingly in line with the authors' claim that two patterns of travelling alpha waves need to be differentiated in visual spatial attention. First, backward waves in the ipsilateral hemisphere, and second, forward waves in the contralateral hemisphere, which are only observed during visual stimulation. Importantly, the authors test the relation of these patterns of traveling waves to the overall power of alpha oscillations and to the hemispheric lateralization of alpha power. Furthermore, to test the functional significance, the authors demonstrate that the pattern of forward and backward waves around stimulus onset differentiates between hits and misses in task performance.

Although the results are in line with the conclusions drawn, some questions remain. The authors investigate the relationship between traveling alpha waves and the hemispheric lateralization of alpha power, which is a well-established neural signature of spatial attention. Surprisingly, the lateralization of alpha power shown in Figure 3B appears relatively weak in the present dataset (by visual inspection), which raises the question of whether the investigation of a relation between lateralized alpha power and alpha traveling waves is warranted in the first place.

Furthermore, the authors employ between-subject correlations (with N = 16) to test the relationship between alpha traveling waves and (lateralized) alpha power. However, as inter-individual differences in patterns of travelling waves are not the main focus here, within-subject analyses of the same relations would be able to test the authors' hypotheses much more directly.

It needs to be appreciated that the authors analyze two datasets in the present study. However, the question remains whether the absence of the forward waves effect in paradigms without visual stimulation is a general one and would replicate in other datasets. Moreover, the manuscript would benefit from a discussion of the potential implications of traveling waves for functional connectivity between posterior and anterior regions.

Reviewer #2 (Public Review):

The authors report a conserved spike S2 hinge epitopes and two conformationally selective antibodies that help elucidate spike behavior. This work defines a third class of S2 antibody and provides insights into the potency and limitations of targeting this S2 epitope for future pan-coronavirus strategies.

Reviewer #2 (Public Review):

The manuscript by Li et al demonstrates the role of Nphp2/Invs in renal epithelia in preventing NPHP-like phenotypes, such as epithelial/stromal proliferation and stromal fibrosis, in mice. Previously, mutants of the Nphp2 allele in mice, generated by insertional mutagenesis, showed severe cystic kidney disease and fibrosis in neonates.

The authors nicely show that the NPHP-like phenotypes in mutant kidneys arise from abnormal signaling specifically within and from renal epithelial cells. Furthermore, the fibrotic response and abnormal increase of cell proliferation precede cyst formation and could be initiated independently of cyst formation. The authors also show that the removal of cilia reduces the severity of Nphp2 phenotypes. The authors suggest that similar to polycystins, NPHP2 inhibits a cilia-dependent cyst and fibrosis-activating pathway. Finally, the histone deacetylase (HDAC) inhibitor valproic acid (VPA) reduces these phenotypes and preserves kidney function in Nphp2 mutant mice, supporting HDAC inhibitors as potential candidate drugs for treating NPHP.

Overall, understanding the mechanisms driving NPHP phenotypes is important and drugging relevant pathways in treating this disease is an important unmet need in patients. The authors have provided insights into both these aspects in this study. The manuscript is nicely written, and the assays shown are rigorous and insightful.

Reviewer #2 (Public Review):

The work presented in the manuscript addresses regulatory mechanisms in a complex genome locus, the Bithorax-Complex (BX-C) in Drosophila. Here three homeotic genes are controlled by multiple regulatory domains, each of which comprises distinct sets of cis-regulatory elements including insulators, enhancers, Polycomb responsive elements, and promoters for coding and non-coding transcripts. Despite such complexity, the authors have made good efforts to explain the context for the study and the question that they are interested in, what is the function of an evolutionarily conserved but newly defined cis-element, Fub-1?

Fub-1 localizes at the chromatin boundary between the homeotic gene Ubx and the bxd/pbx regulatory domain, which thus predicts it is a chromatin insulator. To dissect the function of Fub-1, the authors utilized powerful and versatile gene exchange cassettes (phiC31/attp; FRT/FLP; Cre/Loxp) to engineer both the endogenous locus of Fub-1 and another insulator site Fab-7 to introduce exogenous Fub-1. Using these transgenic tools, they tested the insulator activity of Fub-1. They first confirmed that deleting Fub-1 causes changes in chromosomal configuration in the flanking region using Micro-C. However, unexpectedly, they found that Fub-1 depletion does not cause homeotic transformation, a phenotype that is expected to occur when the expression of the homeotic gene is changed due to the loss of chromatin insulators. Instead, they observed that only a sub-element within Fub-1 has an insulator function while the other sub-element that contains an active promoter suppresses insulator activity. They further demonstrated that although there is no detectable phenotype when both sub-elements are deleted, changing the direction of the promoter or stopping transcription by adding an SV40 terminator in between the two sub-elements could relieve the suppression of insulator activity. From this evidence, the authors conclude that transcriptional read-through from the active promoter of a non-coding transcript regulates the insulator activity of Fub-1.

The finding provides a new angle to examine regulation by insulators and reveals a new function of active promoters of non-coding transcripts. The work also leaves further questions, for example, how general is such a mechanism in the genome organization of Drosophila and other organisms, and what is the significance of the mechanism given that deleting the Fub-1 insulator does not cause phenotypic outcomes in Drosophila? In the discussion, the authors elaborated on possibilities to discuss these questions.

Reviewer #2 (Public Review):

Maksim et al. present Phantasus, a web application for interactive gene expression analysis. The tool allows the user to load microarrays and RNA-Seq data from NCBI GEO.<br /> The user is able to explore, normalize, filter and perform differential expression analysis using limma or DESeq2 pipelines for microarray and RNA-Seq data, respectively. The web tool is capable of generating figures such as PCA and volcano plots and performing gene set enrichment analysis. Phantasus has some advantages when compared to the set of tools already available, showing a good trade-off between ease of use, access to data and different functions. Furthermore, the application is open source and the pre-processed cache files are provided by the authors. Thus, the more experienced user can install the tool on a local computer.

Finally, Phantasus is limited to standardized analyzes available in its internal methods and databases, which may not meet the needs of researchers who wish to apply different types of quantification and normalization. However, this is the ideal tool for the non-bioinformatics user who wants to reanalyze public data or perform simple differential expression analyzes on their own data.

Reviewer #2 (Public Review):

The work by Bravi et al. introduces a learning technique based on Restricted Boltzmann machines, that uses analog to differential learning to model two distinct datasets being part of a common biophysical framework but that behave differently depending on a set of parameters with "background" and "select" features. The biological problem tackled by the authors is the prediction of immunogenetic peptides versus non-immunogenetic ones, as well as determining the sequence features related to binding recognition.

My assessment of the strengths and weaknesses of this work is the following:

Strengths

The authors propose a novel and technically robust solution to a significant and currently unsolved problem in molecular immunology. They are detailed and exhaustive in the description of the formulation of their model as well as in the assessment analysis. Being this a hard problem, the results presented seem a very important step forward not only to solve some of these problems but also to provide convincing arguments that this methodology is more general than other previous approaches; that it can be applied to both immunogenicity prediction as well as binding specificity and is of generative nature. This can have a significant use in therapeutic applications. Another strength of this work is that their methodology could be easily applicable to other biological problems that deal with general versus selected features. For instance, specificity in recognition of other protein-protein interactions, protein-RNA recognition as well as the analysis of ever-growing SELEX and in vitro evolution datasets. Finally, I thought that the efforts of this work to provide "interpretable" learning models are important and definitely a strength of this work.

Weaknesses

As stated before, this work is detailed in nature and contains technical details to make it reproducible. However, in the attempt of the authors to compare against the large number of alternative approaches to this model, I felt that the readability of the article is affected. If this article is meant to be read by broader audiences that might utilize this framework in immunology research, at points the manuscript feels lost in comparison and descriptions of other methods. This is due to the fact that every time a new technical method is introduced, readers want to know about a comparison with other methods, but I feel that the manuscript can be rewritten in such a way that those technical comparisons don't become the major point of the paper and focuses more on how the predictive results of the model can be then applied in immunology. A similar point can also be raised about the methods section, although it has the advantage of being exhaustive and detailed, it also makes it hard for the reader to focus on the most important parts of the work. Perhaps, a better distribution of the methods and SI methods could help streamline the readability of this interesting work.

Reviewer #2 (Public Review):

This manuscript describes the involvement of CD73 in tumor cell metabolism by inhibiting CD73 expression in a CD73-positive tumor cell line. The authors demonstrated that CD73 deletion decreases aspartate synthesis via the alteration of mitochondrial respiration. The study is well-designed and the data are convincing.

Reviewer #2 (Public Review):

The study of Bonnet et al. focuses on how proteins 4.1N and SAP97 affect intracellular trafficking (IT) and externalisation of AMPA receptors (AMPARs) in cultured rat hippocampal neurons. To specifically look at IT, the authors combine the so-called Ariad approach with confocal spinning disc microscopy and photobleaching of dendritic regions, developed in their previous paper (Hangen et al., 2018). This allowed them to synchronously release newly synthesized AMPARs from the ER (upon addition of a synthetic ligand) and measure the number of vesicles carrying AMPARs, their velocity as well as time spent moving and pausing. To detect the insertion of AMPARs at the plasma membrane, live immunolabelling was used. Using RNA-based knock-outs of 4.1N and SAP97 proteins as well as mutants of the AMPAR C-terminus which mediates interactions with these two proteins, in basal conditions and during chemically induced long-term potentiation (cLTP), they could show that the two proteins play different roles in AMPAR trafficking, with SAP97 more profoundly affecting IT compared to 4.1N in basal conditions.

The unique approach allowing observation of IT of AMPARs and a series of tested mutants in basal and cLTP conditions are the main strengths of the paper and also result in the main new finding which is differential regulation of AMPAR IT by 4.1N and SAP97. The measurements of IT parameters and externalisation of unmodified AMPARs across different conditions (and the previous publication) are very reproducible and that makes the whole approach very reassuring.

However, a few points regarding the methodology and analysis remained after reading the manuscript:<br /> Due to the tested mutants, I find the data for the 4.1N-AMPAR interaction particularly strong, but less so for SAP97. For SAP97, sh-RNA experiments are performed and the delta7 mutant is tested. In the case of 4.1N, sh-RNA knockouts were found to be affected by interactions other than AMPAR-4.1N, so the same might be the case for SAP97. Delta4.1N mutant was found to be less reliable than the S816A S818A mutant, in which the AMPAR C-terminus length was retained and 4.1N binding abolished via two mutations. Although only 4 amino acids were removed in the delta7 mutant, this still changes the length of the AMPAR C-terminus. It would be good to acknowledge these aspects of SAP97 experiments.

As there is a number of conditions tested in the paper and to make the conclusions clearer, it might be useful to provide a summary table. It seems to me there are conditions where IT parameters remain unchanged, but no condition where externalisation is not reduced compared to the relevant control condition. Hence, I would agree that 4.1N might be less relevant than SAP97 for IT, but I am not sure it is clear that 4.1N plays a bigger role in externalisation than SAP97, which is what the conclusion figure (Fig. 7) seems to be implying.

DOI: 10.1016/j.cub.2023.01.039

Resource: RRID:ZFIN_ZDB-ALT-030716-2

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-030716-2

What is this?

Reviewer #2 (Public Review):

Spielvogel and colleagues report in vitro studies investigating the development of de novo resistance of HIV to Darunavir. Darunavir is one of the most widely used protease inhibitors worldwide, but pathways for the development of de novo resistance are uncertain, as many individuals have had prior protease inhibitor experience prior to treatment with darunavir. As such studies of the kind reported here are essential. The authors have performed foundational studies using compelling and complementary approaches to characterize the emergence of protease drug resistance. They have investigated darunavir, as well as a series of 10 structurally related compounds to provide a clear picture of the role of side chains in the development of resistance. They have complemented these studies with precise structural studies of the interactions of drug with WT and mutant viruses. These data are relevant to the understanding of clinical responses to darunavir and are important in developing new protease inhibitors.

Reviewer #2 (Public Review):

In this manuscript, Thakkar and colleagues evaluate the immunogenicity of 3rd and 4th doses of SARS-CoV2 vaccinations in patients with cancer. The authors find that additional vaccine doses are able to seroconvert a subset of patients and that antibody levels correlate with T-cell responses and viral neutralization.

The main strengths of this manuscript are:<br /> 1) The authors systemically performed a broad array of immunological assessments, including assessments of antibody levels, T cell activity, and neutralization assays, in a large cohort of patients with cancer receiving 3rd and 4th doses of COVID vaccines.<br /> 2) The authors recruited an ethnically diverse cohort of patients with diverse cancer types, though enrolled participants were enriched for hematological malignancies.<br /> 3) Prior to FDA/CDC guidance supporting a 4th vaccine dose, the authors recruited participants with no or inadequate responses into a prospective clinical trial of a 4th dose, the results of which are outlined here.<br /> 4) The authors' findings that patients with hematologic malignancies and those receiving anti-CD20/BTK inhibitors have lower immunological responses to SARS-CoV-2 vaccines are consistent with multiple prior studies, including prior studies from these authors.<br /> 5) The authors also find that 3rd and 4th COVID vaccine doses are able to seroconvert a subset of patients with no or "inadequate" responses, though it's unclear whether seroconversion is enough for true protection from SARS-CoV-2 infection.

The main weaknesses of the manuscript include:<br /> 1) The study cohorts disproportionately enrolled patients with hematological malignancies who have been previously shown to mount lower immunological responses to COVID-19 vaccines; thus, the findings may not be representative of a typical oncology patient population.<br /> 2) The subgroup analyses were relatively small.<br /> 3) The nomenclature used in the manuscript was confusing when it came to "baseline" assessments and boosters versus additional doses of vaccines.<br /> 4) Ultimately, the major limitation of this manuscript is that antibody levels/T-cell responses/neutralization are surrogates for immune protection against SARS-CoV-2, but it's unclear what defines the ideal cutoffs for protection. Simply seroconverting may still be insufficient. The authors don't provide data showing antibody levels as relates to breakthrough infection, likely because they are underpowered for this analysis.

Reviewer #2 (Public Review):

In their paper, Diekmann and Cheng describe a model for the generation of so-called hippocampal replay sequences - a process thought to play a central role in planning, decision making and the consolidation of new memories. Given the diversity of functions replay has been purported to support coming up with a single mechanism for it has remained a challenge. Diekmann and Cheng are able to achieve this with a relatively simple and intuitive model. Specifically, in their model replay is determined based on a finite number of factors; namely, the likelihood and reward-association of an experience, how similar an experience is with an agent's/animal's current state and whether an experience matches *too* much the current state (so to avoid replaying persistently the same state). With these few ingredients the authors are able to replicate important replay findings. Further, the authors emphasise that their model has the significant advantage of being more biologically feasible than other contemporary models in the field.

The model achieves its objectives broadly however the authors have not sufficiently explained the advantage of their model over other models - i.e. how they address the limitations of previous models - nor have they attempted to replicate multiple important features of replay - such as that it can often be non-local. Finally, the details of the biological implementation of their model, particularly with regard to the two modes it can operate in, have not been fleshed out. These points limit the potential impact of the model.

Reviewer #2 (Public Review):

This is a technical study by Ji and colleagues that uses adaptive optics to correct for the intrinsic aberrations of the mouse eye to improve the quality of in vivo two-photon retinal imaging. Currently, the most common approach to retinal imaging is to use isolated ex vivo retina preparations for direct access to the tissue. However, in vivo retinal imaging offers the unique advantage of tracking long-term changes in vascular/cellular structure and function in disease or development. The authors describe an optimized adaptive optical two-photon microscope setup for imaging fluorescent markers through the mouse eye and evaluate the effect of the wavefront sensing area on the imaging quality. They further demonstrate the power of this setup by monitoring the focal vascular leakage in a mouse model of proliferative vascular retinopathy and by monitoring drug-induced population activity changes using GCaMP6s in a mouse model of photoreceptor degeneration. Together, these results provide a valuable, enabling technical resource for applying AO-two-photo imaging to study outstanding questions in retinal biology that require long-term in vivo imaging. Overall, this is an important development with a broad impact on the investigation of neuronal and vascular functions in the retina.