Reviewer #3 (Public Review):

This paper addresses the impact of non-linear protein degradation on the precision of morphogen gradients. Since the predominant model for the formation of morphogen gradients is a production/diffusion/degradation model understanding the contribution of degradation is an important question. This paper investigates the properties of the simplest and most general mathematical model for gradient formation. As such, this work is of interest. The main conclusion of the paper is that non-linear protein degradation has little impact on the precision of the morphogen gradient near the source of production of the morphogen and it reduces precision far away from the source. These conclusions are supported by the mathematical analysis presented. The paper is a difficult read for people unfamiliar with the current literature.

Reviewer #1 (Public Review):

In this study 1458 Enterobacterales isolates, derived from animals, waste-water and human bloodstream infections, were genetically characterized. This also yielded 3697 plasmids and many AMR genes.

All isolates were derived in a restricted geographical region and within a few years time. They defined "groups of near-identical plasmids" with plasmids derived from different genera, species, and clonal background; 8% of these groups contained plasmids from the different ecological niches and 35% of these cross-niche groups plasmids carried AMR genes. This fits with the concept of recent transfer of AMR plasmids between these ecological niches. Through detailed analyses they provide evidence that for E. coli, AMR dissemination between human and livestock-associated niches is most likely not the result of clonal spread but rather that plasmids transit between ecological niches.

Strengths

This is - to the best of my knowledge - one of the largest and most detailed studies elucidating the epidemiology of plasmids and AMR genes in different ecological niches.

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 #1 (Public Review):

The authors conducted an extensive characterization of canine H3N2 influenza viruses. By analyzing gene sequences of canine H3N2 influenza viruses isolated in their laboratory and those that are available in public databases, they identified various genetic clades (also somehow correlate with antigenic groups identified in serological assays) and human-like amino acid substitutions in these viruses, which indicated the evolution of these viruses towards potentially more adaptive to humans. By experiments with several selected canine H3N2 influenza isolates, they found that more recent canine H3N2 influenza viruses have acquired receptor specificity for both avian- and human-like receptors, enhanced low-pH stability and in vitro growth as well as improved replication and transmission in the dog and ferret models. They further identified amino acid substitutions underlying the improved transmissibility of these canine H3N2 influenza viruses. The study was well-designed and the conclusions in the manuscript are in general well supported by the experimental data. Findings from the study will certainly help understand the evolution of canine influenza viruses and assessing the risk posed by these viruses to public health.

Although the authors have identified some properties/molecular markers of canine H3N2 influenza viruses that highlight the potential for infecting humans, it needs to be cautious to emphasize the threat of these viruses to public health. One fact is that despite the increasing prevalence of these viruses in dogs and the close proximity between dogs and humans, there is so far no report of human infection with canine H3N2 influenza viruses. The authors are wished to discuss this in their manuscript so that the readers can have a more comprehensive understanding of their findings and the public health importance of canine influenza viruses.

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 #3 (Public Review):

The manuscript by Chen et al shows solid evidence that canine origin influenza viruses are evolving towards a more mammalian adapted phenotype. The data also show that humans may lack proper protection against these viruses if they were to evolve more prone to cross to humans. There are some aspects of the ms that need to be addressed: 1) The investigators should run neuraminidase inhibition assays to established the level of cross reactivity of human sera to the canine origin NA (one of reasons proposed as to the lower impact of the H3N2 pandemic was the presence of anti0N2 antibodies in the human population), 2) please tone down the significance of ferret-to-ferret transmission as a predictor of human-to-human transmission. Although flu viruses that transmit among humans do show the same capacity in ferrets, the opposite is NOT always true.

Reviewer #1 (Public Review):

In this manuscript, the authors have elegantly demonstrated the significance of asking fundamental questions in patient-derived models of patient-derived organoids (PDOs). This is especially relevant for studying complex cancers such as High-Grade Serous Ovarian Carcinoma (HSCOG). In addition to developing patient-derived organoids, this study has comprehensively examined transcriptomic, genomic, and single-cell data. In addition, based on this data, the authors have performed a complex drug sensitivity assay that further stratifies the PDOs into drug-sensitive and resistant categories. This approach would be central to identifying therapeutic regimens for difficult-to-treat cancers in the future.

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 #3 (Public Review):

The manuscript adequately demonstrates that genomic instability is maintained in HGSOC tumourspheres. The use of 3-dimensional HGSOC models to more greatly resemble the in vivo environment has been used for more than a decade, but this is the first demonstration using a variety of genomic assessment tools to show genomic instability in the HGSOC tumoursphere model. It is clearly demonstrated that these HGSOC tumourspheres represent copy number variations similar to information in public datasets (TCGA, PAWG, BriTROC-1) and that cellular heterogeneity is present in these tumourspheres. The simple steps outlined to establish and passage tumourspheres will benefit the field to further study mechanisms of genomic instability in HGSOC.

A weakness of the manuscript is the lack of operational definitions for what constitutes an organoid and an appropriate definition to distinguish genomic instability from chromosomal instability (a distinct type of genomic instability). Line 147 states "As PDOs consist of 100% tumour cells...", although this does not appear to have been established by any assessment. This limited characterization of the 3D model is a weakness since no data is provided on whether the tumourspheres constitute only a single cell type (as indicated on line 147) or multiple cell types (e.g., HGSOC cell, mesothelial cells) using markers beyond p53 expression. Based on this information, this model cannot be called a PDO, rather it should be referred to as a tumoursphere.

Chromosome instability (CIN) is a type of genomic instability that is broadly defined as an increased rate of chromosome gains or losses and is best identified through analysis of single cells (e.g., karyotype analysis), something that bulk whole genome sequencing cannot determine since it is a reflection of cell populations and not individual cells. While the data demonstrate genomic instability is retained in the tumourspheres, and chromosome losses or copy-number amplifications were observed using single-cell whole genome sequencing, evaluation of samples from the same patient over time was not evaluated. While there is evidence to support CIN in these samples, in agreement with other published work that has demonstrated CIN in >95% of HGSOC samples analyzed at the single-cell level, this work is not conclusive. The title of the manuscript should be modified to more accurately represent what the evidence supports.

An additional weakness is missing information (e.g., Figure 1d, Supplementary Figure 3b, and Supplementary Table 4 were not included in the manuscript; the 13 anticancer compounds used to test drug sensitivity are not indicated) making an assessment of the data impossible, and assessment of some conclusions difficult.

Reviewer #1 (Public Review):

The basis of this method is to clone guides into a Crispr-based editing plasmid, transfect pools into Leishmania, maintain them as episomes, then look at phenotypes. The guides are designed to cause editing that converts codons to stop codons, and the authors have designed a computational tool that enables the design of guides that work for the first half of each gene. Selection for the episome is necessary and editing efficiencies were variable (99% to 0%) depending on the species, being worst for L. major. The use of premature termination codons also clearly raises issues for false positives and negatives, especially as there is no evidence for nonsense-mediated mRNA decay in Leishmania.

There are already two genome-wide screening options for Leishmania, so the advantages and disadvantages of the method proposed here need to be discussed in a much more detailed and balanced way.<br /> In the "LeishGEM" project (http://www.leishgem.org) all Leishmania mexicana genes will be knocked out and each KO will be bar-coded. At the end, 170 pooled populations of 48 bar-coded mutants will be publicly available. The only real reason the authors of the current paper give for not using this approach is that it is labour-intensive. However, LeishGEM is funded and underway, with several centres involved, so that argument is weak.<br /> There is also a preprint describing RNAi for functional analysis in Leishmania braziliensis.

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 #1 (Public Review):

This umbrella review aims to synthesize the results of systematic reviews of the impact of the COVID-19 pandemic on various dimensions of cancer care from prevention to treatment. This is a challenging endeavour given the diversity of outcomes that can be assessed in cancer care.

Search and review methods are good and are in line with recommendations for umbrella reviews. Perhaps one weakness of the search strategy was that only one database (Pubmed) was searched. The search strategy appears adequate, though perhaps some more search terms related to reviews and cancer could have been included. It is therefore possible that some reviews may have been missed by the search strategy.

It is challenging to perform a good umbrella review that yields novel insights, as it is difficult to combine results from different reviews which themselves combine results from different studies with different methodologies. However, I think perhaps one of the main weaknesses of this study is that it is not clear to me what is the core objective of the umbrella review, and how analyses relate to that core objective. In other words, I do not understand based on the introduction what new information the authors are hoping to learn from their umbrella review that could not be learned from reading the individual systematic reviews, beyond a vague objective of "synthesizing" the literature. Because of this, it is not very clear to me how the data extracted and the analysis fits into the larger objectives, and what the new knowledge generated by this review is. Based on the reported results, it would appear that one of the main goals is to assess the quality of systematic reviews and of the underlying studies in the reviews, but it is hard to tell. I think there are potentially important insights this review could tell us, but the message and implications of current evidence remain for me a little confused in the current manuscript.

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 #1 (Public Review):

The article is a straightforward continuation of their previous 2016 study. The authors demonstrate an organism-level role of intermediate filaments (IFs) in C. elegans with a model highlighting intermediate filament functions in organism development, larval development, oxidative stress-resilience, size, and lifespan.

The study uses endotube morphogenesis in C. elegans as an elegant model to examine the effect of aberrant IF network morphogenesis on endotube morphology and how these effects are reflected in terms of progeny growth and development.

The study identifies the C. elegans IF protein IFB-2 as a core component of IF network morphogenesis where any mutation or dysfunction of IF interacting proteins such as SMA-5, IFO-1, and BBLN1 can be mostly rescued by silencing of IFB-2.

The observed mutations cause a range of systemic and functional defects of which endotube-related defects that include luminal widening and cytoplasmic invaginations are regarded as the key parameters to observe the direct result of IF network perturbation in the study. Based on these parameters authors narrowed down on IFB-2 head domain as a critical interactor in IF network morphogenesis and function.

On the whole, very interesting findings and an elegant study with excellent data that would be of broad interest for cytoskeletal research. The study has clear ramifications also for the understanding of the evolutionary development and roles of IF, both IF aspects that are still very poorly understood.

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 #3 (Public Review):

This manuscript by Geisler and colleagues used suppressor genetics to identify suppressors of the sma-5(n678) allele, which results in a defective gut endotube (an IF layer just under the microvillar structure), small body size, slow development, and short life span. The authors identified an internal deletion allele in ifb-2, which stunningly rescues all of the phenotypes listed above (despite the apparent absence of an endotube). This suppression is also observed with a previously characterized knockout allele. Conversely, this allele also suppresses analogous defects that result from mutations in the ifo-1 gene and bbln-1.

This is an exceptionally rigorous set of experiments, beautifully described in a clear manuscript illustrated by nicely constructed figures. The overall finding, that some IF mutations result in toxic aggregates that can be eliminated by the loss of a single IF protein is interesting both from a fundamental understanding of IF networks and its clinical implications. With one minor exception, the conclusions are well supported by the data presented.

Reviewer #1 (Public Review):

This carefully done research paper presents a fundamental model of techniques that are useful for the elucidation of kinase substrates. The paper utilizes state-of-the-art approaches to define a kinetic phosphoproteome and how to integrate that data with complementary approaches using a chemical probe (in this case KTPyS, a triphosphate) to find these substrates. Using these approaches TgCDPK1 was demonstrated to affect microneme secretion via a direct interaction with a HOOK complex (defined as a HOOK protein TGG1_289100, an FTS TGGT1_264050 and 2 other proteins TGGT1_316650 and 306920).

This work is carefully controlled and the analysis pathways are logical and provide paradigms for how to approach the question of identifying substrates of kinases using proteomic approaches employing genetic and chemical strategies.

The authors succeeded in the identification of candidate substrates for TgCDPK1. Validation of the results was provided by previous studies in the literature that characterized some of these substrates as well as the experiments in this manuscript on the characterization of the HOOK complex that is phosphorylated by CDPK1.

The HOOK complex (defined as a HOOK protein TGG1_289100, an FTS TGGT1_264050, and 2 other proteins TGGT1_316650 and 306920) was clearly demonstrated to be involved in invasion via its role in microneme trafficking.

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 #3 (Public Review):

In this manuscript, Chan and collaborators investigate the role of CDPK1 in regulating microneme trafficking and exocytosis in Toxoplasma gondii. Micronemes are apicomplexan-specific organelles localized at the apical end of the parasite and depending on cortical microtubules. Micronemes contain proteins that are exocytosed in a Ca²+-dependent manner and are required for T. gondii egress, motility, and host-cell invasion. In Apicomplexa, Ca²+ signaling is dependent on Ca²+-dependent protein kinases (CDPKs). CDPK1 has been demonstrated to be essential for Ca²+-stimulated micronemes exocytosis allowing parasite egress, gliding motility, and invasion. It is also known that intracellular calcium storages are mobilized following a cyclic nucleotide-mediated activation of protein kinase G. This step, occurs upstream of CDPK1 functions. However, the exact signaling pathway regulated by CDPK1 remains unknown. In this paper, the authors used phosphoproteomic analysis to identify new proteins phosphorylated by CDPK1. They demonstrated that CDPK1 activity is required for calcium-stimulated trafficking of micronemes to the apical end, depending on a complex of proteins that include HOOK and FTS, which are known to link cargo to the dynein machinery for trafficking along microtubules. Overall, the authors identified evidence for a new protein complex involved in microneme trafficking through the exocytosis process for which circumstantial evidence of its functionality is demonstrated here.

Reviewer #1 (Public Review):

This study builds an odorant organization map as estimated by a neural network trained on several odor perceptual classification databases. The authors come up with an attractive hypothesis about the link of odor perception to metabolic connectedness, as opposed to a range of other ways of classifying odorant compounds. There are several interesting implications of this, which the authors touch upon, but could perhaps frame as specific predictions.

The authors clearly have generated a powerful methodology, a useful classifying network, and a well-organized database. The study would be much stronger if the methodology were more thoroughly explained, with open code and data availability as expected for a computational study, and as a resource for further research on the topic.

It would also be valuable to place the current findings in the context of considerable earlier work that has sought to map odor perception and place it in the context of structural and chemical features.

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 #1 (Public Review):

This work focuses on the characterization of neutralizing antibodies from humans survivors of SNV and ANDV hantavirus infections, including the mapping of epitopes located in the Gn and/or Gc glycoproteins, and their mechanism of viral interference blocking receptor binding or membrane fusion. It also confirms previous data on broadly neutralizing epitopes allowing inhibition of different hantavirus species. The work covers for the first time in vivo evidence of cross-protection against HNTV infection by a broadly neutralizing antibody prepared from SNV infection using a prophylaxis animal model and compares the data with protection from ANDV lethal challenge using ANDV-specific neutralizing antibodies. The work provides valuable information for the development of therapeutic measures that cross-protect against several hantavirus species which seems a promising strategy to rise pharmaceutical interest against a group of viruses causing orphan disease.

The strength of the work is based on the impressive amount of work and versatility of methods to identify residues involved in the binding and/or escape from seven different neutralizing antibody clones that allow for important conclusions on species-specific antigenic regions and confirm data on a region that seems broadly conserved among different hantavirus species. At the same time, the weakness of the work is that data processing does not allow for readers data analysis (Figs. 1b, 2a, 2c, Ext. Data Fig. 4).

The authors clearly achieve their aim of characterizing the antigenic sites of neutralizing antibodies. Yet, the presented data on binding to ANDV mutant constructs and negative-staining EM does not allow for the conclusion that the epitope of the broadly neutralizing antibodies ANDV-44 and SNV-53 involved the Gn capping loop. An alternative explanation of the escape mutations in the Gn capping loop could be produced by an allosteric effect on the Gc fusion loop region, and a role in structuring the Gc fusion loop has been previously demonstrated (References 7 and 9). In addition, it is not clear why SNV-24 has no broad neutralizing activity although escape mutations occurred at the highly conserved residues K833 and D822 in Gc domain I.

Finally, concerning the in vivo protection experiments, it would be important to show viral RNA levels in lungs and kidneys in the lethal ANDV animal model (Fig. 7) to allow for comparison with the prophylaxis from HTNV infection (Fig. 6).

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 #1 (Public Review):

Collins et al use mesoscopic two-photon imaging to simultaneously record activity from basal forebrain cholinergic or noradrenergic axons in several distant regions of the dorsal cortex during spontaneous behavior in head-fixed awake mice. They find that activity in axons from both neuromodulatory systems is closely correlated with measures of behavioral state, such as whisking, locomotion and face movements. While axons were globally correlated with these behavioral state-related metrics across the dorsal cortex, they also find evidence of behavioral state independent heterogenous signals.

The use of simultaneous multiarea optical recordings across a large extent of dorsal cortex with single axon resolution for studying the coherence of neuromodulatory afferents across cortical areas is novel and addresses important questions regarding neuromodulation in the neocortex. The manuscript is clearly written, the data is well presented and, for the most part, carefully analyzed. Parts of the manuscript confirm previous results on the influence of behavioral state on norepinephrine and acetylcholine cortical afferents. However, the observation that these modulations are globally broadcasted to the dorsal cortex while behavioral state independent hetetogenous signals are also present in these axons is novel and important for the field.

While the evidence for a behavioral state driven global modulation of activity in both neuromodulatory systems is quite clear, I have concerns that the apparent heterogeneity in axonal responses might be driven by movement-induced artifacts. Moreover, even in the case that the heterogeneity in calcium activity across axons is confirmed, it might not be driven by differences in spiking activity across neuromodulatory axons as concluded, but by other mechanisms that are not explicitly discussed or considered.

1) Motion artifacts are always a concern when imaging from small structures in behaving animals. This issue is addressed in the manuscript in Fig 2A-C by comparing axonal responses to "autofluorescent blebs that did not have calcium-dependent activity" (line 1011). Still, as calcium-dependent activity and motion artifacts can both be locked to behavioral variables the "bleb" selection criterion seems biased and flawed with a circular logic. "Blebs" presenting motion-induced changes in fluorescence that may pass as neural activity will be wrongly excluded when from the "bleb" control group using this criterion. This will result in an underestimation of the extent of the contamination of the GCaMP signals by movement-induced artifacts. This potential confound might generate apparent heterogeneity across axons and regions as some axons and some cortical areas might be more prone to movements artifacts than others.

2) In the case that the heterogeneity is indeed due to differences in calcium activity, it might be not due to modularity in spiking activity within the LC or the BF as interpreted and discussed in the manuscript. As calcium signaling in axons not only relates to spiking activity but can also reflect presynaptic modulations, the observed heterogeneity might be due to local action of presynaptic modulators in a context of global identical broadcasted activity. The current dataset does not allow distinguishing which of the two different mechanisms underlies to the observed signal heterogeneity.

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 #3 (Public Review):

Acetylcholine and Norepinephrine are two of the most powerful neuromodulators in the CNS. Recently developments of new methods allow monitoring of the dynamic changes in the activity of these agents in the brain in vivo. Here the authors explore the relationship between the dynamic changes in behavioral states and those of ACh and NE in the cortex. Since neuromodulatory systems cover most of the cortical tissue, it is essential to be able to monitor the activity of these systems in many cortical areas simultaneously. This is a daunting task because the axons releasing NE and ACh are very thin. To my knowledge, this study is the first to use mesoscopic imaging over a wide range of the cortex at the single axon resolution in awake animals. They find that almost any observable change in behavioral state is accompanied by a transient change in the activity of cortical ACh and NE axonal segments. Whisking is significantly correlated with ACh and NE. The authors also explore the spatial pattern of activity of ACh and NE axons over the dorsal cortex and find that most of the dynamics is synchronous over a wide spatial scale. They look for deviation from this pattern (which I will discuss later). Lastly, the authors monitor the activity of cortical interneurons capable of releasing ACh.

Comments:<br /> 1. On a broad overview, I find the discussion of behavioral states, brain states, and neuromodulation states quite confusing. To begin with, I am not convinced by the statement that "brain states or behavioral states change on a moment-to-moment basis." I find that the division of brain activity into microstates (e.g., microarousal) is counterproductive. After all, at the extreme, going along this path, we might eventually have an extremely high dimensional space of all neuronal activity, and any change in any neuron would define a new brain state. Similarly, mice can walk without whisking, can whisk without walking, can walk and whisk, are all these different behavioral states? And if so, are they all associated with different brain states? Most importantly, in the context of this manuscript, one would expect that different states (brain, behavior) would be associated with at least four potential states of the ACh x NE system (high ACh and High NE, High ACh and Low NE, etc.). However, the reported findings indicate that the two systems are highly synchronized (or at least correlated), and both transiently go on with any change from a passive state to an active state. Therefore, the manuscript describes a rather confined relationship of the neuromodulation systems with the rather rich potential of brain and behavioral states. Of course, this is only my viewpoint, and the authors are not obliged to accept it, but they should recognize that the viewpoint they take for granted is not shared by all and consider acknowledging it in the manuscript.<br /> 2. Most of the manuscript (bar one case) reports nearly identical dynamics of ACh and NE. Is that a principle? What makes these systems behave so similarly? Why have two systems that act nearly the same? Still, if there is a difference, it is the time scale of the ACh compared to the NE. Can the authors explain this difference or speculate what drives it?<br /> 3. Whisker activity explains most strongly the neuromodulators dynamics, but pupil dilation almost does not (in contrast to many previous reports including reports of the same authors). If I am not mistaken, this was nearly ignored in the presentation of the results and the discussion section. Could the author elaborate more on what is the reason for this discrepancy?<br /> 4. I find the question of homogenous vs. heterogenous signaling of both the ACh and NE systems quite important. It is one thing if the two systems just broadcast "one bit" information to the whole brain or if there are neuromodulation signals that are confined in space and are uncorrelated with the global signal. However, the way the analysis of this question is presented in the manuscript is very difficult to follow, and eventually, the take-home message is unclear. The discussion section indicates that the results support that beyond a global synchronized signal, there is a significant amount of heterogeneous activity. I think this question could benefit from further analysis. I suggest trying to demonstrate more specific examples of axonal ROIs where their activity is decorrelated with the global signal, test how consistent this property is (for those ROIs), and find a behavioral parameter that it predicts. Also, in the discussion part, I am missing a discussion of the potential mechanism that allows this heterogeneity. On the one hand, an area may receive NE/ACh innervation from different BF/LC neurons, which are not completely synchronized. But those neurons also innervate other areas, so what is the expected eventual pattern? Also, do the results support neuromodulation control by local interneuron circuits targeting the axons (as is the case with dopaminergic axons in the Basal Ganglia)?<br /> 5. The axonal signal seems to be very similar across the cortex. I am not sure this is technically possible, but given that NE axons are thin and non-myelinated and taking advantage of the mesoscopic scale, could the author find any clue for the propagation of the signal on the rostral to caudal axis?<br /> 6. While the section about local VCIN is consistent with the story, it is somehow a sidetrack and ends the manuscript on the wrong note. I leave it to the authors to decide but recommend them to reconsider if and where to include it. Unfortunately, the figure attached was on a very poor resolution, and I could not look into the details, so I am afraid that I could not review this section properly.

Reviewer #1 (Public Review):

In this study, the authors aim to identify the cell state dynamics and molecular mechanisms underlying melanocyte regeneration in zebrafish. By analyzing thousands of single-cell transcriptomes over regeneration in both wild-type and Kit mutant animals, they provide thorough and convincing evidence of (1) two paths to melanocyte regeneration and (2) that Kit signaling, via the RAS/MAPK pathway, is a key regulator of this process. Finally, the authors suggest that another proliferative subpopulation cells, expressing markers of a separate pigment cell type, constitute an additional population of progenitors with the ability to contribute to melanocytes. The data supporting this claim are not as convincing, and the authors failed to show that these cells did indeed differentiate into melanocytes. Despite the challenges of describing this third cell state, this study offers compelling new findings on the mechanisms of melanocyte regeneration and provides paths forward to understanding why some animals lack this capacity.

The majority of the main conclusions are well supported by the data, but one claim, in particular, should be revisited by the authors.

(1) Provided evidence that the aox5(hi)mitfa(lo) population of cells contributes to melanocyte regeneration is inconclusive and somewhat circumstantial. First, the transcriptional profiles of these cells are much more consistent with the xanthophore lineage. Indeed, xanthophores have been shown to express mitfa (in embryos in Parichy, et al. 2003 (PMID: 10862741), and in post-embryonic cells in Saunders, et al. 2019). Second, while the authors address this possibility in Supplemental figure 7 by showing that interstripe xanthophores fail to divide following melanocyte ablation, they fail to account for the stripe-resident xanthophores/xanthoblasts. The presence and dynamics of aox5+ stripe-resident xanthophores/xanthoblasts are detailed in McMenamin, et al., 2014 (PMID: 25170046) and Eom, et al., 2015 (PMID: 26701906). Without direct evidence that the symmetrically-dividing, aox5+ cells measured in this study do indeed differentiate into melanocytes, it is more likely that these cells are a dividing population of xanthophores/xanthoblasts. The authors should revise their claims accordingly.

Minor revisions

(1) At line 140, it is noted that Xanthophores are pteridine-producing, but they also get their yellow color from carotenoids (especially in adults). This should be noted as well, especially since the authors display the xanthophore marker, scarb1, which plays a key role in xanthophore carotenoid coloration.<br /> [Mapping expression levels onto UMAP space for scarb1 and perhaps other markers of xan, irid, or proliferation would be helpful as a supplement to the dot plot in Fig 1 and could help to clarify the transcriptomic signature of mitfa+ aox5-hi cells and plausibility of the model that they are an McSC population. -Parichy]

(2) The authors should provide the list of genes that comprise their cluster signatures (line 252) as part of the supplementary tables.

(3) The authors should more clearly describe how they performed lineage tracing (line 339). Additionally, for the corresponding figure 4E, the authors should list the number of cells traced. The source data only contains calculated percentages rather than counts for each type of differentiation. My understanding is that the number listed in the figure legend is the number of fish (i.e. n = 4), but this should be clarified as well.<br /> [A supplementary figure of labeled cells is important here with enough context to show that cells can be re-identified unambiguously. Additionally note that "lineage tracing" will typically be assumed to mean single-cell labeling and tracking, so if that is not the case for these experiments it would be preferable to use an alternative descriptor. -Parichy]

(4) Line 321, the authors list the mean regeneration percentages for the kita and kitlga(lf) mutants, but these differences are not significantly different according to Figure 4B. By listing the means (which should be noted), the authors seem to be highlighting the differences but then do not comment on them. The description and integration of this result into the main text should be clarified.

(5) In Figure 6E, the RNA-velocity result is not particularly consistent with the authors' claims. Visually, the arrows seem fairly randomly directed. The data in 6B, showing gene expression associated with the S phase and G2/M phase much more clearly convey the directionality of the loop (S phase, followed by G2/M). I suggest that the authors weaken their claim about the RNA-velocity result or remove it altogether and focus on the cell cycle-related gene expression signatures.

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 #3 (Public Review):

This manuscript describes McSC states and McSC function during regeneration in zebrafish using both a scRNAseq timecourse and classic zebrafish experimentation, including lineage tracing and mutant lines. Altogether this study provides a more holistic look at pigment regeneration following injury and helps to validate the role of signaling pathways implicated in McSC biology by previous studies. The major question addressed by this manuscript is whether McSC heterogeneity can explain the highly regenerative nature of the zebrafish pigmentary system. The observations reported in this manuscript confirm this view, eloquently using a time course of single-cell transcriptomics for predictive purposes followed up by mechanistic studies to confirm the fate of different McSC subclusters. This study very nicely complements and extends our current understanding of how McSCs function during regeneration and provides novel datasets for further interrogation. Perhaps the most exciting aspect of the data is the identification of a novel marker (aox5) to identify self-renewing McSCs; this tool could be employed to identify these cells and address their potential in the context of expanding these cells for therapeutic purposes or address their contribution as melanoma stem cells. This study will be of general interest to researchers interested in pigment regeneration, stem cell-based therapeutics for pigment disorders, and the basic biology of stem cells and their heterogeneity.

While this paper certainly extends previous observations of McSCs, the idea of McSC heterogeneity is not necessarily novel. In mouse, KIT-dependent and KIT-independent McSC populations have been identified (Ueno 2015) as well as other McSC subpopulations with different potentials (CD34+/-, Joshi 2019). While this manuscript does a much more comprehensive job of describing this heterogeneity, which is fantastic, some of the previous literature on the topic could be better acknowledged and integrated. Despite this criticism, this manuscript provides the most comprehensive look to date at McSC dynamics across the regenerative period and provides ample datasets for secondary analyses to generate/confirm additional hypotheses.

Reviewer #1 (Public Review):

This study addresses the role of the general transcription factor TBP (TATA-binding protein), a subunit of the TFIID complex, in RNA polymerase II transcription. While TBP has been described as a key component of protein complexes involved in transcription by all three RNA polymerases, several previous studies on TBP loss of function and on the function of its TRF2 and TRF3 paralogues have questioned its essential role in RNA polymerase II transcription. This new study uses auxin induced TBP degradation in mouse ES cells to provide strong evidence that its loss does not affect ongoing polymerase II transcription or heat-shock and retinoic acid-induced transcription activation, but severely inhibits polymerase III transcription. The authors coupled TBP degradation with TRF2 knock out to show that it does not account for the residual TBP-independent transcription. Rather the study provides evidence that TFIID can assemble and is recruited to promoters in the absence of TBP.

All together the study provides compelling evidence for TBP-independent polymerase II transcription, but a better characterization of the residual TFIID complex and recruitment of other general transcription factors to promoters would strengthen the conclusions.

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 #3 (Public Review):

In this study, the authors set out to study the requirement of the TATA binding protein (TBP) in transcription initiation in mESCs. To this end they used an auxin inducible degradation (AID) system. They report that by using the AID-TBP system after auxin degradation, 10-20% of TBP protein is remaining in mESCs. The authors claim that as, the observed 80-90% decrease of TBP levels are not accompanied by global changes in RNA polymerase II (Pol II) chromatin occupancy or nascent mRNA levels, TBP is not required for Pol II transcription. In contrast, they find that under similar TBP-depletion conditions tRNA transcription and Pol III chromatin occupancy were impaired. The authors also asked whether the mouse TBP paralogue, TBPL1 (also called TRF2) could functionally replace TBP, but they find that it does not. From these and additional experiments the authors conclude that redundant mechanisms may exist in which TBP-independent TFIID like complexes may function in Pol II transcription.

The major strengths of this manuscript are the numerous genome-wide investigations, such as many different CUT&Tag experiments, and NET-seq experiments under control and +auxin conditions and their analyses. Weaknesses lie in some experimental setups (i.e. overexpression of Halo-tagged TAFs), mainly in the overinterpretation (or misinterpretation) of the data and in the lack of a fair discussion of the obtained data in comparison to observations described in the literature. As a result, very often the interpretation of data does not fully support the conclusions.<br /> Nevertheless, the findings that 80-90% decrease in cellular TBP levels do not have a major effect on Pol II transcription are interesting, but the manuscript needs some tuning down of many of the authors' very strong conclusions, correcting several weaker points and with a more careful and eventually more interesting Discussion.

Reviewer #1 (Public Review):<br /> <br /> Roberts et al have developed a tool called "XTABLE" for the analysis of publicly available transcriptomic datasets of premalignant lesions (PML) of lung squamous cell carcinoma (LUSC). Detection of PMLs has clinical implications and can aid in the prevention of deaths by LUSC. Hence efforts such as this will be of benefit to the scientific community in better understanding the biology of PMLs.

The authors have curated four studies that have profiled the transcriptomes of PMLs at different stages. While three of them are microarray-based studies, one study has profiled the transcriptome with RNA-seq. XTABLE fetches these datasets and performs analysis in an R shiny app (a graphical user interface). The tool has multiple functionalities to cover a wide range of transcriptomic analyses, including differential expression, signature identification, and immune cell type deconvolution.

The authors have also included three chromosomal instability (CIN) signatures from literature based on gene expression profiles. They showed one of the CIN signatures as a good predictor of progression. However, this signature performed well only in one study. The authors have further utilised the tool XTABLE to identify the signalling pathways in LUSC important for its developmental stages. They found the activation of squamous differentiation and PI3K/Akt pathways to play a role in the transition from low to high-grade PMLs

The authors have developed user-friendly software to analyse publicly available gene expression data from premalignant lesions of lung cancer. This would help researchers to quickly analyse the data and improve our understanding of such lesions. This would pave the way to improve early detection of PMLs to prevent lung cancer.

Strengths:

1. XTABLE is a nicely packaged application that can be used by researchers with very little computational knowledge.<br /> 2. The tool is easy to download and execute. The documentation is extensive both in the article and on the GitLab page.<br /> 3. The tool is user-friendly, and the tabs are intuitively designed for successive steps of analysis of the transcriptome data.<br /> 4. The authors have properly elaborated on the biological interest in investigating PMLs and their clinical significance.

Weaknesses:

The article is focused on the development and the utility of the tool XTABLE. While the tool is nicely developed, the need for a tool focussing only on the investigation of PMLs is not justified. Several shiny apps and online tools exist to perform transcriptomic analysis of published datasets. To list a few examples - i) http://ge-lab.org/idep/ ; ii) http://www.uusmb.unam.mx/ideamex/ ; iii) RNfuzzyApp (Haering et al., 2021); iv) DEGenR (https://doi.org/10.5281/zenodo.4815134); v) TCC-GUI (Su et al., 2019). While some of these are specific to RNA-seq, there are plenty of such shiny apps to perform both RNA-seq and microarray data analysis. Any of these tools could also be used easily for the analysis of the four curated datasets presented in this article. The authors could have elaborated on the availability of other tools for such analysis and provided an explanation of the necessity of XTABLE. Since 3 of the 4 datasets they curated are from microarray technology, another good example of a user-friendly tool is NCBI GEO2R. This is integrated with the NCBI GEO database, and the user doesn't need to download the data or run any tools. iDEP-READS (http://bioinformatics.sdstate.edu/reads/) provide an online user-friendly tool to download and analyse data from publicly available datasets. Another such example is GEO2Enrichr (https://maayanlab.cloud/g2e/). These tools have been designed for non-bioinformatic researchers that don't involve downloading datasets or installing/running other tools.

Secondly, XTABLE doesn't provide a solution to integrate the four datasets incorporated in the tool. One can only analyse one dataset at a time with XTABLE. The differences in terms of methodology and study design within these four datasets have been elaborated on in the article. However, attempts to integrate them were lacking.

The tool also lacks the flexibility for users to add more datasets. This would be helpful when there are more datasets of PMLs available publicly.

Understanding the biology of PML progression would require a multi-omics approach. XTABLE analyses transcriptome data and lacks integration of other omics data. The authors mention the availability of data from whole exome, methylation, etc from the four studies they have selected. However, apart from the CIN scores, they haven't integrated any of the other layers of omics data available.

Lastly, the authors could have elaborated on the limitations of the tool and their analysis in the discussion.

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.

German academic publishing in Niklas Luhmann's day was dramatically different from the late 20th/early 21st centuries. There was no peer-review and as a result Luhmann didn't have the level of gatekeeping that academics face today which only served to help increase his academic journal publication record. (28:30)

Reviewer #1 (Public Review):

This manuscript builds on data from the same group showing that Lphn2 functions cell-autonomously as a receptor in CA1 pyramidal axons and cell-non-autonomously as a ligand in the neurons of the subiculum. In either case, binding of teneurin-3 to Lphn2 mediates repulsive events, and since different populations of neurons within each region express differing levels of both proteins, this mechanism allows proximal CA1 pyramidal axons to preferentially project to distal subiculum and distal CA1 pyramidal axons to project to proximal subiculum. The authors now ask mechanistic questions about the role of Lphn2 signaling in these wiring processes.

The authors demonstrate that G-protein signaling downstream of Lphn2, which is mediated by the tethered agonist, is necessary for the ability of ectopically expressed Lphn2 to redirect proximal CA1 axons from distal to proximal subiculum. Moreover, the authors show that while autoproteolytic activity of Lphn2 facilitates G-protein signaling, possibly by making the tethered agonist more available for signaling, it is not necessary for axonal mistargeting. Thus, the authors conclude that tethered agonist-dependent G-protein signaling is required for Lphn2-mediated hippocampal neural circuit assembly. Most of the data shown in support of these conclusions are convincing, though I have some concerns about the expression levels and/or effects of the tethered agonist mutants in CA1, which is important since the analyses assume that any defects are in the repulsive interactions described above.

The authors also use heterologous cells to determine that Lphn2 couples to Ga12/13, but not other heteromeric G-proteina-subunits. Within the context of heterologous cells, these experiments are well controlled and exhaustive, as every mutant used in vivo is carefully analyzed. One potential criticism of this work, however, is that perhaps the authors assume too much in simply translating their results in heterologous cells to neurons, especially when one of the most interesting conclusions of this paper (see below) is that Lphn2 signaling is context-dependent. Without further data to confirm the results of these experiments in the neuronal populations studied, these data primarily illustrate possibilities, but don't exclude other possibilities.

Finally, the authors test the role of Lphn2 functioning as a ligand in the subiculum by driving its expression in the normally Lphn2-low dorsal subiculum. As they reported before, this alteration decreases the ability of proximal CA1 axons to project to this area. Interestingly, and in contrast to the role of Lphn2 as a receptor above, neither Lphn2 autoproteolysis nor tethered agonist function are required for this effect.

In summary, this is an interesting paper that addresses timely and pressing issues in the adhesion-GPCR field.

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 #3 (Public Review):

The function of the nervous system relies on precisely connected neuronal networks. A previous study from the Luo lab reported an important pair of molecular interaction between an adhesion GPCR, latrophilin-2, and teneurin-3 in specifying the connections between CA1 neurons in the hippocampus and the subiculum. This new study continues to investigate the signaling mechanisms, particularly whether the trimeric G proteins are involved. Adhesion GPCRs are in general still under studied, esp in nervous system. This study also used a clever misexpression approach, which provide signaling studies in the in vivo context. The data are of high quality and convincing.

Reviewer #1 (Public Review):

Determination of the biomechanical forces and downstream pathways that direct heart valve morphogenesis is an important area of research. In the current study, potential functions of localized Yap signaling in cardiac valve morphogenesis were examined. Extensive immunostainings were performed for Yap expression, but Yap activation status as indicated by nuclear versus cytoplasmic localization, Yap dephosphorylation, or expression of downstream target genes was not examined. The goal of the work was to determine Yap activation status relative to different mechanical environments, but no biomechanical data on developing heart valves were provided in the study.

There are several major weaknesses that diminish enthusiasm for the study.<br /> 1. The Hippo/Yap pathway activation leads to dephosphorylation of Yap, nuclear localization, and induced expression of downstream target genes. However, there are no data included in the study on Yap nuclear/cytoplasmic ratios, phosphorylation status, or activation of other Hippo pathway mediators. Analysis of Yap expression alone is insufficient to determine activation status since it is widely expressed in multiple cells throughout the valves. The specificity for activated Yap signaling is not apparent from the immunostainings.

2. The specific regionalized biomechanical forces acting on different regions of the valves were not measured directly or clearly compared with Yap activation status. In some cases, it seems that Yap is not present in the nuclei of endothelial cells surrounding the valve leaflets that are subject to different flow forces (Fig 1B) and the main expression is in valve interstitial subpopulations. Thus the data presented do not support differential Yap activation in endothelial cells subject to different fluid forces. There is extensive discussion of different forces acting on the valve leaflets, but the relationship to Yap signaling is not entirely clear.

3. The requirement for Yap signaling in heart valve remodeling as described in the title was not demonstrated through manipulation of Yap activity.

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 #3 (Public Review):

In this manuscript, Wang et al. assess the role of wall shear stress and hydrostatic pressure during valve morphogenesis at stages where the valve elongates and takes shape. The authors elegantly demonstrate that shear and pressure have different effects on cell proliferation by modulating YAP signaling. The authors use a combination of in vitro and in vivo approaches to show that YAP signaling is activated by hydrostatic pressure changes and inhibited by wall shear stress.

There are a few elements that would require clarification:

1) The impact of YAP on valve stiffness was unclear to me. How is YAP signaling affecting stiffness? is it through cell proliferation changes? I was unclear about the model put forward:<br /> - Is it cell proliferation (cell proliferation fluidity tissue while non-proliferating tissue is stiffer?)<br /> - Is it through differential gene expression?<br /> This needs clarification.

2) The model proposes an early asymmetric growth of the cushion leading to different shear forces (oscillatory vs unidirectional shear stress). What triggers the initial asymmetry of the cushion shape? is YAP involved?

3) The differential expression of YAP and its correlation to cell proliferation is a little hard to see in the data presented. Drawings highlighting the main areas would help the reader to visualise the results better.

4) The origin of osmotic/hydrostatic pressure in vivo. While shear is clearly dependent upon blood flow, it is less clear that hydrostatic pressure is solely dependent upon blood flow. For example, it has been proposed that ECM accumulation such as hyaluronic acid could modify osmotic pressure (see for example Vignes et al.PMID: 35245444). Could the authors clarify the following questions:<br /> - How blood flow affects osmotic pressure in vivo?<br /> - Is ECM a factor that could affect osmotic pressure in this system?

Reviewer #1 (Public Review):

Farahani et al. describe the generation of pYtags, recombinant RTKs, and reporters, that exploit phosphotyrosine/tandem SH2 interaction pairs from immune-specific signaling proteins to allow spatiotemporal monitoring of the activation of different ligand-binding (EGFR and FGFR1) or ligandless (ERBB2) RTKs in living cells stimulated with high and low-affinity ligands (e.g. EGF and EREG or EPGN respectively in the case of EGFR). The study is well-explained and the experiments are clear and clean. Although the authors expanded tool generation to different RTKs and different cells, the potential utility of the approach is limited because the broad concept that different receptor dimers activate different downstream signalling pathways is already well established. Additionally, the results only examine the temporal kinetics of the receptors rather than their spatial organization, e.g. in different vesicular/endosomal compartments. The study also describes the use of CRISPR-Cas9 to generate a pYtag knock-in EGFR-expressing HEK 293T cell line to avoid complications arising from over-expression. There were significant differences in terms of receptor activation dynamics comparing knock-in and over-expressed cell lines.

The study is technologically innovative, yet the analysis of RTK spatial signalling over time in ligand-stimulated cells should be improved.

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 #3 (Public Review):

Farahani et al. developed a novel biosensor, pYtag, to monitor receptor tyrosine kinase activity using live cell fluorescence microscopy. The approach to the sensor design relies on adding a tyrosine activation motif to a receptor tyrosine kinase of interest which when phosphorylated recruits a fluorescently-tagged SH2 domain protein. The sensor was used to monitor EGFR and ErbB2 activity and characterize their activity in the presence of different ligands, allowing for the kinetics of receptor activity to be determined in live cells with high temporal resolution.

The design, characterization, and verification of the sensor with controls were rigorously done and the sensor appears to be a good approach to monitoring receptor tyrosine kinases. In addition to this, the biological characterization of RTK signaling kinetics allowed for mathematical modeling to determine the dimerization affinity of ligand-bound receptors is the rate-limiting step of receptor tyrosine kinase signaling dynamics. Proving these sensors can be used to monitor biological activities in live cells.

Initial proof of principles of pYtag was demonstrated in cell lines where the tags were expressed, the authors went beyond this and showed the tagging system could be gene edited to endogenous proteins allowing for the function of receptor tyrosine kinase to be measured under physiological concentrations.

Reviewer #1 (Public Review):

In this study, Mitterer et al continue their comprehensive investigation of the mechanisms underlying the biogenesis of the eukaryotic large, or 60S, ribosomal subunit. Specifically, they elucidate the roles that the DEAD-box helicase Spb4 and its interaction partner, Rrp17, play in the maturation of nucleolar 60S precursor particles. Using cell biology approaches, the authors demonstrate that Spb4 and Rrp17 are associated with late-stage nucleolar 60S precursor particles and that depletion of these factors arrests 60S biogenesis at a step just prior to nucleolar exit. Cryo-EM imaging of particles carrying Spb4 and Rrp17 (purified using affinity-tagged Spb4 or Rrp17) yielded high-quality structures of Spb4- and Rrp17-bound 60S precursor particles. The structures provide novel insights into the roles of Spb4 and Rrp17 in the maturation of nucleolar 60S precursor particles. In addition, the structures provide novel insights into the Spb4 function that may be of interest and importance to the function of other DEAD-box helicases. The authors then establish an in vitro maturation assay that, although unlikely to exactly recapitulate the in vivo maturation process, provides additional insights, particularly when coupled to cryo-EM structures of the in vitro-matured 60S particles.

A major strength of this work is the combination of cell biology, structural biology, and biochemistry. The cell biology-directed preparation of Spb4- and Rrp17-bound 60S precursor particles is particularly powerful and results in high-quality structures of these precursors. Another strength of the work is the remarkable view of a DEAD-box helicase in action and the interesting finding that the RecA domains of the helicase are in the open conformation while the helicase is likely bound to ADP-this will be an interesting and important observation for researchers working in the broader DEAD-box helicase field. An additional strength of the work is the development and use of an in vitro maturation assay that allowed further details of the activities of Spb4 and Rrp17 in nucleolar maturation of 60S precursor particles to be investigated and visualized.

A minor weakness of this work is a question about the confidence with which the authors can conclude, using just the structural data presented here, that Spb4 is bound to ADP rather than to ATP or ATP-Pi.

The considerable strengths of this work far outweigh the minor weakness, and I expect that this work will have a significant impact on 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 #3 (Public Review):

Over the past decade, Cryo-EM analysis of assembling ribosomes has mapped the major intermediates of the pathway. Our understanding of the mechanisms by which ATPases drive the transitions between states has been slower to develop because of the transient nature of these events. Here, the authors use cryo-EM and biochemical and molecular genetic approaches to examine the function of the DEAD-box ATPase Spb4 and the AAA-ATPase Rea1 in RNP remodeling. Spb4 works on the pre-60S in an early nucleolar state. The authors find that Spb4 acts to remodel the three-way junction of H62/H63/H63a at the base of expansion segment ES27. Interestingly, Spb4 appears to interact stably with a folding intermediate in the ADP rather than ATP-bound form. This work represents one of the few cases in which an RNA helicase of ribosome biogenesis has been captured and engaged with its substrate. The authors then show that the addition of the AAA-ATPase Rea1 to Spb4-purified particles results in the release of Ytm1, a known target of Rea1. However, they did not observe an efficient release of Ytm1 when particles were affinity purified via Ytm1, suggesting that the recruitment of Spb4 is important for this step. Cryo-EM analysis of Spb4-particles treated with Rea1 revealed the previously characterized state NE particles but no additional intermediates. Consequently, this analysis of Rea1 is less informative about its function than is their work on Spb4 helicase activity. In general, the data support the authors' conclusions and the data are well presented.

Major points<br /> 1. The Erzberger group has recently published work regarding the function of Spb4. They similarly found that Spb4 is necessary for remodeling the 3-way junction at the base of ES27. Although it was posted to Biorxiv in Feb 2022, it was not formally published until Dec 2022. The authors should cite this work and include a brief discussion comparing conclusions.<br /> 2. L311. The heading "Coupled pre-60S dissociation of the Ytm1-Erb1 complex and RNA helicase Has1" should be changed. Coupling implies a mechanistic interplay. Although the release of Ytm1 and Has1 both depend on Rea1, the data do not support the conclusion of mechanistic coupling. In fact, the authors write in lines 328-329 "Thus, the Rea1-dependent pre-60S release of the Ytm1-Erb1 complex occurs before and independently of Has1..." Independently cannot also imply coupling.<br /> 3. L339-342 Combining data sets for uniform processing was a great idea! This approach should be used more often in cryo-EM analyses of in vitro maturation reactions.<br /> 4. L428 The authors need to amend their comment that this is the first structure of Spb4-bound to the substrate as this has recently been published by the Erzberger group and was first posted as a preprint in early 2022.

Reviewer #1 (Public Review):

A quantitative understanding of the mechanisms underlying VDJ recombination is a prerequisite for a better understanding of adaptive immune repertoire generation. Here, Russel et al. study potential sequence-based factors that may drive VDJ trimming, a mechanism involved in VDJ recombination. This work provides a significant advance in the statistical modeling of immune repertoire generation.

Using a previously-published TCR𝛽 repertoire sequencing data set, the authors designed a probabilistic model of nucleotide trimming that allows the exploration of various mechanistically-interpretable sequence-level features. Using this model, they show that local sequence context and the capacity for sequence-breathing, together, can most accurately predict the trimming probabilities of a given V-gene sequence. Their model suggests that double-stranded DNA needs to be able to "breathe" for trimming to occur and provides evidence of a sequence motif that appears to get preferentially trimmed, independent of breathing. Importantly their findings are not dataset-dependent.

So far, there exists no model for VDJ trimming, a major mechanism in the process of VDJ recombination. With this model, we are now in the position to refine modeling tools for VDJ recombination. Importantly, the model developed by Russel et al. enables exploration of what biological sequence-based factors most contribute to VDJ trimming. To support their conclusions, the authors test their approach on multiple model architectures and AIRR datasets.

While I agree that this is important work, the authors might be overstating the mechanistic insight achieved given that solely statistical inference was used in this work. This is something that requires more discussion and support from the authors.

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 #1 (Public Review):

The study employs state-of-art techniques and model-driven fusion of MEG and 7T to characterize the fine spatiotemporal profiles of object recognition in human brains when stimuli are noisy. By using two models, the recognition and the two-state models, to characterize the representational format, the work demonstrates that the ventral visual pathway is more toward two-state representation while the dorsal visual pathway tends to display the recognition-like profile. Overall it is an interesting work addressing an important question. My major concern is on the two selected models and whether they could be fairly compared to address the question. Moreover, some details need more clarification and statistical support.

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 #1 (Public Review):

Gutiérrez-Martínez et al. present a detailed analysis of Siglec-1 nano-distribution on the surface of dendritic cells (DCs) and the role of Siglec-1 in HIV-1 interactions with DCs.

DCs have been proposed as key cellular intermediates in the transmission of HIV and other viruses. Not only can these cells be crucial for the presentation of virus-derived antigens, but, in tissue culture at least, mature DCs (mDC) have been observed to sequester HIV particles into compartments (virus-containing compartment [VCC]) from which the virus can be subsequently transmitted to CD4+ve T cells through cell-cell contacts often termed virological synapses. This so-called trans-infection mechanism is believed to be important in establishing HIV infection and transmission of the virus to immunological tissues. Although there is considerable evidence for this process, the molecular details of how HIV particles are captured by DCs and transferred to VCC are poorly understood. In recent years Siglec-1 (CD169), a plasma membrane-associated sialic acid-binding lectin expressed on monocytic cells has been implicated in the capture of HIV and other viruses. In this paper, the authors have used super-resolution and other imaging methods to perform a detailed quantitative analysis of the cell surface distribution of Siglec-1 on immature and mature DCs, the relationship between this distribution with actin and regulators of actin polymerization, and then how this impacts on the capture of HIV particles and their association with VCCs.

The principal findings, which for the most part are well supported by the data, suggest that small clusters of Siglec-1, which are restricted in their mobility by formin-associated actin, provide platforms with increased avidity for binding virus particles or large unilamellar vesicles through sialic-acid containing gangliosides. In mDCs at least this binding appears to induce the sequestration of bound particles into VCC-like structures. This is a topical and detailed study that addresses important questions of how viral engagement with cell surface receptors leads to events crucial for viral infection and, potentially, pathogenesis. These types of analyses have only recently become feasible with the implementation of super-resolution imaging and few virus-host cell systems have been examined in detail. Thus, this study has relevance not only to HIV but potentially to many other viruses.

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 #3 (Public Review):

In this manuscript, the authors address the mechanism of concentration of HIV-1 particles following interaction with Siglec-1 and define important differences in this process between immature DCs and mature DCs. The methods are largely derived from imaging that is followed by quantitation of nanoclustering of Siglec-1, distance from the center of the cell, and the effects of inhibitors of actin and RhoA pathways. The quantitative imaging approach is a strength and appears quite carefully done. Another strength is the new findings regarding the role of the formin-dependent actin cytoskeletal rearrangements and RhoA activation on clustering and polarization leading to the formation of the virus-containing compartment (VCC). The results are convincing that mature DCs demonstrate more nanoclustering and that formins and RhoA are important in the clustering that occurs of viruses or virus-like particles following capture by Siglec-1. This information should be valuable to the field.

The weaknesses are not in the methods and major conclusions themselves, but there are a number of aspects of the study that could be strengthened. The definition of a VCC here is simply a spot of Siglec-1 that has coalesced with VLPs. A more complete study would include typical VCC markers such as CD81, CD9, and others and would extend the findings to prove that the mechanism invoked actually elicits VCC formation, as opposed to clustering of Siglec-1 and VLPs along the surface of the cell. This study does not establish the mechanism of membrane invagination or tubule formation that occurs with VCC formation, so perhaps it is really describing the initial, surface-related steps of VCC formation but not subsequent internalization events required to form the deeper, vacuole-like VCC.

Nevertheless, this study provides new insights into the initial steps of VCC formation and is provocative regarding how this can be achieved by Siglec-1 in the absence of the need for a cytoplasmic tail. The formin-dependence of VCC formation will be of interest in future studies of HIV uptake and trans-infection events mediated by dendritic cells and macrophages. Some of the findings can be directly translated to the biological context of how VCCs form in HIV-infected macrophages. These will all likely be of substantial interest to those working on HIV and other viruses that are captured by Siglec-1.

Reviewer #1 (Public Review):

In this exciting and well-written manuscript, Alvarez-Buylla and colleagues report a fascinating discovery of an alkaloid-binding protein in the plasma of poison frogs, which may help explain how these animals are able to sequester a diversity of alkaloids with different target sites. This work is a major advance in our knowledge of how poison frogs are able to sequester and even resist such a panoply of alkaloids. Their study also adds to our understanding of how toxic animals resist the effects of their own defenses. Although target site insensitivity and other mechanisms acting to prevent the binding of alkaloids to their targets (often ion channels) are well characterized now in poison frogs, less is known regarding how they regulate the movement of toxins throughout the animal and in blood in particular. In the fugu (pufferfish) a protein binds saxitoxin and tetrodotoxin and in some amphibians possibly the protein saxiphilin has been proposed to be a toxin sponge for saxitoxin. However, little is known about poison frogs in particular and if toxin-binding proteins are involved in their sequestration and auto-resistance mechanisms.

The authors use a clever approach wherein a fluorescently labeled probe of a pumiliotoxin analog (an alkaloid toxin sequestered by some poison frogs) is able to be crosslinked to proteins to which it binds. The authors then use sophisticated mass spectroscopy to identify the proteins and find an outlier 'hit' that is a serpin protein. A competition assay, as well as mutagenesis studies, revealed that this ~50-60 kDa plasma protein is responsible for binding much of the pumiliotoxin and a few other alkaloids known to be sequestered in the in vivo assay, but not nicotine, an alkaloid not sequestered by these frogs.

In general, their results are convincing, their methods and analyses robust and the writing excellent. Their findings represent a major breakthrough in the study of toxin sequestration in poison frogs. Below, a more detailed summary and both major and minor constructive comments are given on the nature of the discoveries and some ways that the manuscript could be improved.

Detailed Summary

The authors functionally characterize a serine-protease inhibitor protein in Oophaga sylvatica frog plasma, which they name O. sylvatica alkaloid-binding globulin (OsABG), that can bind toxic alkaloids. They show that OsABG is the most highly expressed serpin in O. sylvatica liver and that its expression is higher than that of albumin, a major small molecule carrier in vertebrates. Using a toxin photoprobe combined with competitive protein binding assays, their data suggest that OsABG is able to bind specific poison frog toxins including the two most abundant alkaloids in O. sylvatica skin. Their in vitro isolation of toxin-bound OsABG shows that the protein binds most free pumiliotoxin in solution and suggests that OsABG may play an important role in its sequestration. The authors further show that mutations in the binding pocket of OsABG remove its ability to bind toxins and that the binding pocket is structurally similar to that of other vertebrate serpins.

These results are an exciting advance in understanding how poison frogs, which make and use alkaloids as chemical defenses, prevent self-intoxication. The authors provide convincing evidence that OsABG can function as a toxin sponge in O. sylvatica which sets a compelling precedent for future work needed to test the role of OsABG in vivo.

The study could be improved by shifting the focus to O. sylvatica specifically rather than the convergent evolution of sequestration among different dendrobatid species. The reason for this is that most of the results (aside from some of the photoprobe binding results presented in Fig. 1 and Fig. 4) and the proteomics identification of OsABG itself are based on O. sylvatica. It's unclear whether ABG proteins are major toxin sponges in D. tinctorius or E. tricolor since these frogs may contain different toxin cocktails. The competitive binding results suggest that putative ABG proteins in D. tinctorius and E. tricolor have reduced binding affinity at higher toxin concentrations than ABG proteins in O. sylvatica. Although molecular convergence in toxin sponges may be at play in the dendrobatid poison frogs, more work is needed in non-O. sylvatica species to determine the extent of convergence.

Major constructive comments:

Although the protein gels in Fig.1-2 show clearly the role of ABG, a ~50 kDa protein, it's unclear whether transferrin-like proteins, which are ~80 kDa, may also play a role because the gels show proteins between 39-64 kDa (Fig.1). The gel in Fig.2A is specific to one O. sylvatica and extends this range, but the gel does not appear to be labeled accordingly, making it unclear whether other larger proteins could have been detected in addition to ABG. Clarifying this issue would facilitate the interpretation of the results.

There is what seems to be a significant size difference between the O. sylvatica bands and bands from the other toxic frog species, namely D. tinctorius and E. tricolor. Could the photoprobe be binding to other non-ABG proteins of different sizes in different frog species? Given that O. sylvatica bands are bright and this species was the only one subject to proteomics quantification, a possible conclusion may be that the ABG toxin sponge is a lineage-specific adaptation of O. sylvatica rather than a common mechanism of toxin sequestration among multiple independent lineages of poison frogs. It would be helpful if the authors could address this observation of their binding data and the hypothesis flowing from that in the manuscript.

Figure 1B: The species names should be labeled alongside the images in the phylogeny. In addition, please include symbols indicating the number of times toxicity has evolved (for example, once in the ancestors of O. sylvatica and D. tinctorius frogs and once in the ancestors of E. tricolor frogs).

Figure 4B-C: Photoprobe binding results in the presence of epi and nicotine appear to be missing for D. tinctorius and those in the presence of PTX and nicotine are missing for D. tricolor. Adding these results would make for a more complete picture of alkaloid binding by ABG in non-O. sylvatica species.

Using recombinant proteins with mutations at residues forming the binding pocket of O. sylvatica ABG (as inferred from docking simulations), the authors found that all binding pocket mutations disrupted photoprobe binding completely in vitro (L221-222, Fig. 4E). However, there is no information presented on non-binding pocket mutations. Mutations outside of the binding pocket would presumably maintain photoprobe binding - barring any indirect structural changes that might disrupt binding pocket interactions with the photoprobe. This result is important for the conclusion that the binding pocket itself is the sole mediator of toxin interactions. The authors do show that one binding pocket mutation (D383A) results in some degree of photoprobe binding (Fig. 4E) but more detail on the mutations in the binding pocket per se being causal would be helpful.

Please include concentrations in the descriptions of gel lanes in the main figures. The relative concentrations of the photoprobe and other toxins (eg., PTX, DHQ, epi, and nic) are essential for interpreting the competitive binding images. For example, this was done in Fig. S1 (e.g., PB + 10x PTX).

For clarity, the section "OsABG sequesters free PTX in solution with high affinity" could be presented directly after the section titled "Proteomic analysis identifies an alkaloid-binding globulin". The former highlights in vitro experiments confirming the binding affinity of the ABG protein identified in the latter.

Fig. 6E-F should be included as part of Fig. 1 or 2. Although complementary to the RNA sequencing data, these protein results are more closely related to the results in the first two figures which show the degree of competitive binding affinity of PB in the presence of different toxins. The expanded competitive binding results for total skin alkaloids and the two most abundant skin alkaloids from wild samples are most appropriate here.

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 #1 (Public Review):

Elbaz-Hayoun et al. investigate the role of macrophages in the gliotic response of retinal Müller glia and photoreceptor cell death. Monocytes (a precursor of macrophages) were isolated from age-related macular degeneration (AMD) patients. When injected into light-damaged retinas, a reduction in the number of photoreceptors and ERG b-wave strength (evidence of abnormal photoreceptor function) was observed. The authors reasoned that macrophages generated from the injected monocytes might be responsible for the retinal damage. To test this hypothesis, macrophage subtypes were generated from AMD-derived human monocytes and injected into light-damaged mouse eyes. Interstingly, only the human hM2a macrophage subclass mimicked the retinal degeneration of monocyte injection in mouse retinas. Similarly, human M2a (hM2a) cells cultured on mouse retinal explants and even serum-free hM2a culture supernatant were sufficient to induce photoreceptor apoptosis. These effects were not observed with hM1 cells. To identify possible diffusible factors responsible, proteins present in hM2a and hM1 culture supernatants were identified. Nine cytokines were found at higher levels in the hM2a supernatant, and three of these were ligands for the C-C chemokine receptor CCR1. The authors confirmed CCR1 expression in the retina, which was predominantly detected in Müller glia. Importantly, Müller cell expression of CCR1 in the mouse retina was significantly increased following light damage. In contrast, CCR2 and CCR5 levels were unchanged in Müller cells. The increase in CCR1 expression, gliosis, and photoreceptor death was also observed in the rd10 mouse model of retinitis pigmentosa. Inhibiting CCR1 activity in light-damaged eyes using the drug BX471 had impressive effects. Müller activation and photoreceptor cell death were reduced and ERG b-wave levels were partially recovered - clearly indicating a role for CCR1 in retinal degeneration. Additional evidence was provided suggesting that CCR1 activation in M2a macrophages might also play a role in stimulating the movement of other macrophages into the retina and activating retinal microglia, which migrate to the ONL. These data identify a new link between cells of the immune system and those within the retina which contribute to the progression of retinal degeneration.

The data mostly support the conclusions of this paper. However, additional controls need to be added to some experiments.

Concerns:

1) To determine the effect of diseased monocytes on retinal health, light-injured mouse retinas were injected with monocytes isolated from AMD patients (Figure 1 - figure supplement 1). This resulted in a reduction in photoreceptor number and ERG b-wave amplitude. However, the light-injured control eye was injected with PBS only, so no cells were present. The reasoning for using this control was not provided. The appropriate injection control would include monocytes isolated from non-AMD patients. This control should be performed side-by-side with cells from AMD patients.

2) The authors hypothesize, from the experiments presented in Figure 1 - figure supplement 1, that the injected monocytes generated macrophages in the retina, which were responsible for the observed neurotoxicity (Lines 143-145). However, no direct evidence was presented. This idea should be tested in vivo. This could be done by injecting tracer-labeled human AMD-derived monocytes into light-injured mouse retinas. If the authors' hypothesis is true, collected retinas should contain tracer-labeled cells that express macrophage markers. Tracer-labeled M2a macrophage cells should be present since subsequent experiments identify this subclass as being associated with retinal cell death.

3) Photoreceptor number and b-wave amplitudes were measured in light-injured retinas injected with one of four macrophage cell types generated from human AMD-derived monocytes. The authors conclude that only injection of M2a cells reduced photoreceptor number and b-wave amplitudes (Figure 1C, E). This may be true, but it is difficult for the reader to make a conclusion (especially in Fig. 1E) due to the large error bars and five different traces overlapping each other. To make these results easier to interpret, graph control cells with only one experimental sample (cell type) at a time.

4) Most injected macrophages were located in the vitreous. In the case of M2a cells, the authors note that "several of the cells migrated across the retinal layers reaching the subretinal space" (Lines 167,168). One possible explanation for why M0, M1, and M2c macrophages did not induce retinal degeneration is that they did not migrate to the subretinal space and around the optic nerve head. Supplementary figures should be added to demonstrate that this is not the case.

5) Figure 1 - figure supplement 2: Panel A, B cells were stained with CD206 to demonstrate the presence of M2a macrophages (panel B). The authors conclude that panel A contains M1 and panel B contains M2a cells. The lack of CD206 expression illustrates that panel A cells are not M2a macrophages but do not demonstrate they are M1 macrophages. A control using an M1 cell marker is necessary to show that panel A cells are M1 and M1 cells are not detected in M2a cultures.

6) Ex vivo, apoptotic photoreceptor and RPE cells are observed when cultured with M2a macrophages (Figure 2). Do injected M2a cells also induce apoptosis of RPE cells in vivo? This is important to establish that retinal explants are a good model for in vivo experiments.

7) Reactive oxygen species (ROS) production was measured to determine if M2a cell-mediated neurotoxicity was due to oxidative stress. It is concluded that a ROS increase is partly responsible (Line 218). The data do not support this conclusion. ROS was detected in cultured M2a macrophages. More importantly, however, there was no increase in oxidative damage in vivo. The in vivo and cell culture results contradict each other so no conclusion can be made. The lack of in vivo confirmation weakens the argument that ROS drives M2a neurotoxicity. Text suggesting a role for ROS in neurotoxicity should be appropriately edited (Lines including 218, 244, 401,406,481).

8) The authors ask if the photoreceptor cell death is cytokine-mediated. Multiple cytokines were enriched in M2a-conditioned media. Of particular interest were CCR1 ligands MPIF1 and MCP4. The implication is that these two ligands mediate the M2a macrophages to photoreceptor cell death through CCR1. However, there is no attempt to show that either MPIF1 or MCP4 are present in vivo, or are sufficient to induce the retinal response observed. This could be demonstrated by injection of MPIF1 or MCP4. Evidence that either ligand phenocopies M2a macrophage injection would be direct evidence that CCR1 ligands activate the retinal response. Furthermore, co-injection with BX174 should block the effect of these ligands if they work through CCR1.

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 #3 (Public Review):

The authors perform an elegant study where they show that intravitreal injection of human monocytes from patients with AMD cause reduced ERG B-wave amplitudes and photoceptor cell loss compared to controls in the photic retinal injury model. Differentiation of human monocytes from patients with AMD into M2a macrophages caused increased photoreceptor cell loss compared to M1 macrophages. Next, the authors show that after co-culturing retinal explants with M1 and M2a human macrophages followed by TUNEL staining, M2 human macrophages had significantly more apoptotic photoreceptor cells than M1 human macrophages. The authors show that human M2a macrophages have significantly more ROS compared to M0 and M1 human macrophages; however, injection of human M2a macrophages did not cause increased oxidative damage compared to control conditions. Using a multiplex cytokine assay of 120 cytokines between human M1 and M2a macrophages-conditioned medium, the authors found increased levels of 9 cytokines, including three HCC-1, MCP-4, and MPIF-1, which are ligands of the C-C chemokine receptor CCR1. Co-staining showed CCR1 expression in Muller cells following photic injury. In the rd10 mouse model of retinal degeneration as well as aged BALB/c mice CCR1 is upregulated in Muller cells. Injection of mice with the CCR1-specific inhibitor BX471 caused increased photoreceptor numbers and B-wave amplitudes in the photic-injury model. Overall the experiments are well performed and of interest to the field.

Joint Public Review:

The authors employ a range of microscopy, biochemical, and virologic techniques to evaluate the efficacy of CRISPR-nuPin to relocalize DNA and the subsequent impact of HSV-1 replication. There are many compelling experiments that utilize solid approaches to HSV-1 transcription, replication, and histone association. The microscopy images are particularly stunning, strongly supported by biochemical evaluation, and consistent with most of the authors' interpretations. Overall, the manuscript presents data that suggests the dCas9-emerin fusion protein can be used to manipulate the nuclear localization of smaller DNA elements like the HSV-1 viral genome. Chromosomal DNA, as tested by telomere targeting, reveal reduced capacity and elongated kinetics for retargeting. Using this system, authors find differing effects on HSV-1 replication based on the timing of sgRNA electroporation post-infection. Further experiments support that the transcriptional effects of either inhibitory or enhancing treatments may be related to chromatin modifications and expression of the viral protein ICP0.

There are many strengths to both the methodology and analysis in this work. That said, there are several areas where a more expansive explanation of methods and data analysis combined with tempered interpretations and language will greatly improve the manuscript.

Reviewer #1 (Public Review):

In this manuscript, the authors set out to identify the energy-generating protein responsible for powering heme transport through the Isd system of Staphylococcus aureus.

The manuscript convincingly demonstrates that FhuC is required for heme iron utilization and presents strong data to implicate FhuC in binding to IsdF. The authors report that IsdF localizes to functional membrane microdomains in S. aureus. These experiments would benefit from controls showing that the DRM fraction contains the functional membrane microdomains and that the fractionation was successful.

The authors also present strong data demonstrating that loss of floA prevents IsdF incorporation into the membrane although these data would also benefit from genetic complementation.

In a surprising result, the authors report that the IsdA protein is not localized in the functional membrane microdomains which are confounding since IsdA is modeled to work in concert with IsdF. These data suggest there is much more to learn regarding the spatial distribution of this transport system.

Finally, the authors report that FMMs are required for heme transport in the related organism Staphylococcus lugdunensis demonstrating the conservation of this localization across the genus.

Taken together, these exciting and significant data reveal how the canonical heme transporter of S. aureus is regionally localized and acquires energy for heme transport across the membrane.

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.

Reviewer #1 (Public Review):

Idiosyncratic drug-induced liver injury is a disease that appears to be linked to mitochondrial DNA (mtDNA), but there is a lack of model cell lines for the study of this link. To help address this problem, the authors developed ten cybrid HepG2 cell lines that have had their mitochondrial DNA replaced with the mitochondrial DNA of ten human donors. Analysis of single nucleotide polymorphisms in all of the patients' mtDNA allowed the authors to assign the donors to two haplogroups (H and J) with five patients each. The authors also present the results of several assays (e.g. oxygen consumption, ATP production) performed on all ten cell lines in the absence and presence of five clinically-relevant drugs (or drug metabolites). Significant attention was paid to differences observed between the cell lines in the H and J haplogroups. The work is methodologically and scientifically rigorous, ethically conducted, and objectively presented according to the appropriate community standards.

While I feel that the manuscript will be useful to the research field and is an important step towards improving patient outcomes, I feel that the work lacks a broad interest. Much of the paper is spent discussing small and/or statistically insignificant differences between haplogroups H and J. While some interesting interpretations and suggestions are presented in the discussion, the authors didn't perform follow-up experiments to try to nail down any particular mechanistic insights that would be useful to the broader community. I also didn't feel a strong sense that the paper produced any specific suggestions for how clinical outcomes could be improved. Accordingly, any clear insights that would be interesting to a broad scientific community would probably require follow-up studies. The structure of the paper is also not friendly to a broad audience; the results are presented without interspersed commentary that could help the reader understand the meaning or utility of the results as they are being presented. Accordingly, I often felt unsure about how the results being presented were relevant to solving the broader problem established nicely in the introduction. Finally, it wasn't clear that the generated cell lines were made available for anyone to purchase through a cell bank (perhaps the authors did do this, but I don't recall seeing a mention of it). As these cell lines appear to be the primary output of this work, it seems important to better highlight the extent to which they are being made accessible to the scientific community.

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 #1 (Public Review):

Utilizing mouse models as well as in-vitro studies, the authors demonstrate that cardiac cell mapping provides novel insights into intercellular communication drivers underlying pathological extracellular matrix remodeling during diabetic myocardial fibrosis.The work provides new perspectives to help understanding the cellular and molecular mechanisms of diabetes-induced cardiac pathology.

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 #3 (Public Review):

The authors attempted to dissect the intercellular mechanisms implicated in the development of diabetic cardiomyopathy. They used one time point to determine the expressional changes in the STZ-high caloric diet model vs non-diabetic. They also attempted to interfere with fibrosis using a PFGFRa antagonist and silencing of Itgb1. Finally, they looked at some variants of the Itgb1 in patients with diabetes to determine a possible association.

Strengths: This is one of the first transcriptomics study a single cell level of the mouse diabetic heart. The study is technically sound.

Weakness: The study is mainly associative. A cause relationship effect is difficult to be extracted. A major problem is that they studied only a single time point at an advanced stage of the disease, therefore it is difficult to determine if the observed changes are epiphenomena. They also use only one diabetic model where STZ was superimposed on the high caloric diet. STZ can cause unspecific effects and more models are generally requested. They also used male mice only while diabetic cardiomyopathy is more prevalent in females. No functional data are provided to study the capacity of treatment to rescue cardiac contractility and diastolic function, which is certainly affected by fibrosis.<br /> The methodological part can help further studies provided the limits indicated above are considered.

Reviewer #1 (Public Review):

Han et al use sophisticated genetic approaches to investigate leptin-responsive neural circuits. Overall, this is an impressive series of studies that provide fairly convincing evidence for a key inhibitory pathway downstream of AGRP neurons. A few data sets require additional validation or explanation.

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 #3 (Public Review):

The manuscript by Han et al characterizes a pathway from AgRP(LepR) neurons to DMH(MC4R) neurons that is involved in energy balance control. They use a conditional knockout strategy to show that AgRP(LepR) knockout increases body weight and this effect was reversible by blocking GABA signaling. They also showed that activation of AgRP-DMH projection increases food intake, and highlighted a role for alpha3-GABAA receptor signaling in the DMH for regulating feeding behavior. While these data highlight a potential circuit that modulates feeding, there are concerns about the paper in its current form that diminish enthusiasm. The lack of proper controls in many of the experiments raises doubts about the findings.

Strengths: The authors use new tools to characterize a new circuit for leptin-mediated energy balance control. The conditional knockout has several advantages over previous techniques that are described within the manuscript. Further, the authors use combinations of different techniques (gene knockout, optogenetic manipulation, in vivo activity monitoring) to make observations at multiple levels of analysis.

Weaknesses: Several experiments within the paper have worrisome caveats or lack proper controls, raising concerns about the overall conclusions made.

Reviewer #1 (Public Review):

Demographic inference is a notoriously difficult problem in population genetics, especially for non-model systems in which key population genetic parameters are often unknown and where the reality is always a lot more complex than the model. In this study, Rose et al. provided an elegant solution to these challenges in their analysis of the evolutionary history of human specialization in Ae. aegypti mosquitoes. They first applied state-of-the-art statistical phasing methods to obtain haplotype information in previously published mosquito sequences. Using this phased data, they conducted cross-coalescent and isolation-with-migration analyses, and they innovatively took advantage of a known historical event, i.e., the spread of Ae. aegypti to South America, to infer the key model parameters of generation time and mutation rate. With these parameters, they were able to confirm a previous hypothesis, which suggests that human specialists evolved at the end of the African Humid Period around 5,000 years ago when Ae. aegypti mosquitoes in the Sahel region had to adapt to human-derived water storage as their breeding sites during intense dry seasons. The authors further carried out an ancestry tract length analysis, showing that human specialists have recently introgressed into Ae. aegypti population in West African cities in the past 20-40 years, likely driven by rapid urbanization in these cities.

Given all the complexities and uncertainties in the system, the authors have done outstanding jobs coming up with well-informed research questions and hypotheses, carrying out analyses that are most appropriate to their questions, and presenting their findings in a clear and compelling fashion. Their results reveal the deep connections between mosquito evolution and past climate change as well as human history and demonstrate that future mosquito control strategies should take these important interactions into account, especially in the face of ongoing climate change and urbanization. Methodologically, the analytical approach presented in this paper will be of broad interest to population geneticists working on demographic inference in a diversity of non-model organisms.

In my opinion, the only major aspect that this paper can still benefit from is more explicit and in-depth communication and discussion about the assumptions made in the analyses and the uncertainties of the results. There is currently one short paragraph on this in the discussion section, but I think several other assumptions and sources of uncertainties could be included, and a few of them may benefit from some quantitative sensitivity analyses. To be clear, I don't think that most of these will have a huge impact on the main results, but some explicit clarification from the authors would be useful. Below are some examples:

1. Phasing accuracy: statistical phasing is a relatively new tool for non-model species, and it is unclear from the manuscript how accurate it is given the sample size, sequencing depth, population structure, genetic diversity, and levels of linkage disequilibrium in the study system. If authors would like to inspire broader adoption of this workflow, it would be very helpful if they could also briefly discuss the key characteristics of a study system that could make phasing successful/difficult, and how sensitive cross-coalescent analyses are to phasing accuracy.

2. Estimation of mutation rate and generation time: the estimation of these important parameters is made based on the assumption that they should maximize the overlap between the distribution of estimated migration rate and the number of enslaved people crossing the Atlantic, but how reasonable is this assumption, and how much would the violation of this assumption affect the main result? Particularly, in the MSMC-IM paper (Wang et al. 2020, Fig 2A), even with a simulated clean split scenario, the estimated migration rate would have a wide distribution with a lot of uncertainty on both sides, so I believe that the exact meaning and limitations of such estimated migration rate over time should be clarified. This discussion would also be very helpful to readers who are thinking about using similar methods in their studies. Furthermore, the authors have taken 15 generations per year as their chosen generation time and based their mutation rate estimates on this assumption, but how much will the violation of this assumption affect the result?

3. The effect of selection: all analyses in this paper assume that no selection is at play, and the authors have excluded loci previously found to be under selection from these analyses, but how effective is this? In the ancestry tract length analysis, in particular, the authors have found that the human-specialist ancestry tends to concentrate in key genomic regions and suggested that selection could explain this, but doesn't this mean that excluding known loci under selection was insufficient? If the selection has indeed played an important role at a genome-wide level, how would it affect the main results (qualitatively)?

Reviewer #1 (Public Review):

In this manuscript Sugatha et al. present a comprehensive study on sorting nexin 32 (SNX32) with a wide-spectrum of methodologies and model systems. Authors investigate binding to other sorting nexins involved in the same pathways (SNX1 and SNX4) as well as to its cargo in biochemical and cell-based experiments. They show the importance and explore mechanisms of SNX32 in Transferrin Receptor and Cation Independent Mannose-6-Phosphate Receptor trafficking. Moreover, this work also demonstrates the role of SNX32 in concert with Basigin in neuron differentiation.

Authors with the help of structure modelling and subsequent biochemical experiments find specific residues within the BAR domain of SNX32 that are crucial for heterodimer formation with its interaction partners on endosomal membranes: SNX1 and SNX4. Moreover, this study, by using various microscopy techniques, also demonstrates localization of SNX32 to early endosomes as well as its co-trafficking with Rab11 and Golgi marker. Furthermore, authors with knock-down and rescue experiments investigate the role of SNX32 in Transferrin Receptor and Cation Independent Mannose-6-Phosphate Receptor trafficking. With co-immunoprecipitation they show that the cargo interaction occurs via the conserved stretch in the PX domain and that single amino acid substitution can disrupt this binding. This feature is utilized in a subsequent neuroblastoma cell-based SILAC screen for SNX32 interactome that identifies Basigin (a transmembrane receptor belonging to the superfamily of immunoglobulins) as one of the most prominent interactors in these cells. Finally, authors identify SNX32 and Basigin as crucial factors involved in neurite outgrowth and network formation. Experiments demonstrate that SNX32, but not its homolog SNX6, assists in the surface localization of Basigin where this protein could potentially interact with monocarboxylate transporters crucial for neuro-glial coordination.

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 #3 (Public Review):

The paper by Sugatha et al. examines the role of SNX32 in membrane trafficking. They found SNX32 interacts with SNX4, SNX32 binds the TfR and CIMPR and is required for their intracellular trafficking, and the intracellular location of SNX32 to endosomes was through lipid binding to PI(3)P or PI(4)P. This study further demonstrated that SNX32 plays a role in BSG trafficking to the cell surface. And lastly, they demonstrated SNX32 plays a role in neuronal differentiation likely through its regulation of BSG trafficking.

Reviewer #1 (Public Review):

This is a carefully written manuscript describing the structure of a low-light inducible PSI complex from Ostreococcus tauri. The work expands our knowledge of how photosynthetic systems react to changes in light conditions and shows how this ecologically important green alga utilizes its unique antenna, Lhcp.

In general, I find that the work described in the manuscript is of high quality. The cryoEM maps obtained by the authors clearly show the addition of lhcp trimers to PSI under low light conditions and the distinction between lhcp1 and lhcp2 appears sound together with the identification of the phosphorylation site and its binding in the PSI complex.

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 #3 (Public Review):

The manuscript by Ishii et al describes the structural characteristics of the Ostreococcus tauri photosystem I (PSI) light-harvesting complexes, mostly under low light conditions. The bulk of the work comes from cryo-EM studies that show changes in the supercomplex structure at low light, and suggest a model where additional light harvesting complexes are recruited to the supercomplex to increase light capturing. Interestingly, the evidence presented suggests that this mechanism is distinct from the classical antenna state transitions seen in other organisms studied thus far.

The structural studies are quite interesting and overall suggest an interesting mechanism for adjusting light harvesting by PSI in this heretofore understudied species. These are exciting findings and a great example of how new structural approaches can lead to new functional discoveries.

The manuscript is weaker when it comes to connecting these new structures to functions, and definitive cause-effect relationships are not yet provided, nor are any extensive studies on the effects of redox regulation, physiological state, etc. of the putative state transition reported, preventing a more definitive assessment of the mechanisms and physiological importance of the observed changes.

Nevertheless, the results indicate that a different (previously unknown) mode of regulation, or at least alteration, of light capture, is likely to occur in this species, adding substantially to our knowledge of the diversity of photosynthetic responses, and setting up the field to investigate the underlying mechanisms.

Reviewer #1 (Public Review):

In this article, Sanz Perl and colleagues set out to use a computational whole-brain model to simulate the patterns of functional connectivity (as observed from functional MRI) that characterise different forms of dementia, namely Alzheimer's Disease (AD) and behavioural variant frontotemporal dementia (bvFTD). To overall goal is to develop a paradigm to model a specific disorder, and then develop an in silico assessment of the effects of different interventions. They show that superior fitting of the simulated data to the empirical data of both pathologies can be achieved when a Hopf model of brain activity is informed by patterns of combined AD and bvFTD atrophy, or by the intrinsic organisation of brain regions into canonical resting-state networks. They also show that regional differences in the fitted parameters pertain to AD and bvFTD, both in terms of location, and in terms of dynamical regime. They then use a machine learning algorithm, the variational auto-encoder (VAE), to compress functional connectivity patterns into a 2-dimensional space (given by the relative activation of the VAE's two hidden neurons). This space reveals that AD and bvFTD perturb brain connectivity along two distinct dimensions, further stratifying sub-categories of AD. Finally, through visualisation in this latent space, the authors can assess the effects of different simulated interventions on the models previously fitted to AD and bvFTD: namely, stimulation of different regions and with different dynamical regimes, to evaluate whether the resulting model is moved closer to the region occupied by healthy controls.

A strength of this work is its creative combination of different modelling approaches, combining the more biologically-informed Hopf model, which incorporates atrophy maps and connectivity, with the VAE for the purpose of dimensionality reduction and visualisation. Another strength is the use of different controls, such as an atrophy map from a different disorder (Parkinson's) or the use of randomised heterogeneities, showing that the improved fit is not just due to increased degrees of freedom: an important concern for high-dimensional models, which the authors lay to rest.

Admittedly, the stimulation paradigm shows limited success at bringing the disorder-fitted models back to the region occupied by controls - except for the AD- sub-category, which is the least affected and shows the most promise in the authors' in-silico trial. The limited success of this approach in this specific context does not invalidate the framework's promise. This may also be attributed to the fact that the authors do not use disease-specific atrophy maps to model AD and bvFTD: rather, they use a single atrophy map obtained by combining the two and use this joint atrophy map both to model AD, and to model bvFTD. Likewise, the connectivity of the model is the same for all conditions.

A weakness of this work is that, as the authors themselves acknowledge, the brain regions whose stimulation pushes the model to be least far from controls in the latent space did not match with those presenting different bifurcation parameters. In fact, it is not clear whether this is because stimulation fails to reverse the regional alterations of the dynamical regime, or whether it does succeed, but introduces new alterations - although it should be possible to extract this information from the model, to provide additional insight. This raises the intriguing question of the biological meaning of the latent space. Although the authors do show what kinds of FC correspond to the different values of the VAE hidden neurons' activation, the latent space effectively acts as a 2-dimensional goodness-of-fit - raising the question of how much of the stimulation results could be captured by simply evaluating the stimulated model's GOF against controls (while acknowledging that this would conflate the two distinct dimensions along which AD and bvFTD differ from controls).

Since stimulation is intended to mimic the effects of different real-life interventions such as tACS and tDCS, it would be helpful to see whether the regions that are suggested as most promising for stimulation, do in fact match the regions that have shown the most success in actual clinical trials that have already been carried out. This would be a powerful validation from model to real applicability.

In its essence, the work makes progress towards the authors' goal of modelling different pathologies by incorporating biologically-derived information, highlighting their differences, and enabling the evaluation of different stimulation strategies. This computational framework is widely applicable to a variety of pathological (and even non-pathological) conditions, combining evaluation and intervention in a single workflow.

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 #1 (Public Review):

In this manuscript the authors use single nucleus sequencing together with in situ to profile neurons from the paraventricular nucleus of the thalamus. The PVT has been implicated in diverse functions and here the authors use snRNAseq to try to assign those functions to distinct cell types within the structure. They first use punches of PVT and iterative clustering and filtering to find neuronal clusters with known PVT markers. Other cell types and neurons from surrounding brain regions were also present in the dataset. These data both support the previous division of PVT neurons into Drd2+/- cells and suggest these two groups can be further subdivided into 5 distinct clusters. In a nice in situ experiment the authors assessed top marker gene expression for each cluster across the anterior-posterior axis of the PVT. This showed that the five types were largely in distinct spatial locations. Follow-up in situ with an additional set of marker genes supported the same conclusion but also showed that expression of single genes even within a cell "type" can vary. The authors discuss how the transcriptomes of the cell types could map onto known function of anterior and posterior PVT neurons. Finally, the authors integrate their sequencing data with a dataset of thalamic neurons with specific known projection patterns. Of the cells that co-cluster between the datasets, they identify specific transcriptomic populations of cells that best overlap different cortical projection patterns. The authors identify Col12a1 as a marker of one particular population of PFC-projecting cells.

The idea of spatial gradients of transcription in brain regions rather than discrete cell "types" has been shown in a number of recent studies that combine transcriptomics and in situ hybridization. Application of this idea to other important functional areas of the brain like the PVT generally enhances understanding of the parcellation of neuronal function. Combining these data with mapping of projection patterns by a lab interested in the function of this region, will be of interest to other researchers who study PVT and its role in brain circuits. The data appear to be of high quality and the discussion is scholarly.

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 #3 (Public Review):

This paper from Gao et al., uses single nuclei RNA sequencing to identify cell types and their putative gene markers for the paraventricular thalamus, a small midline brain region important for arousal and motivation. The dataset, collected from male mice, contains ~13,000 single nuclei transcriptomes from the PVT and surrounding regions. Overall, the collected data itself is generally of high quality, and the authors describe some gene markers and putative cell types in the PVT. The authors then go on to characterize PVT cell types from ~4,000 nuclei they identified from the first round of clustering as cell originating from the PVT. They go onto to use fluorescence in situ hybridization to show the spatial patterning of 5 putative marker genes they identified and provide summary disk plot data for the expression of genes for neuromodulator receptors, ion channel subunits, calcium binding proteins, and neuromodulators. The authors then integrate the data with a published 'thalamoseq' dataset of an additional ~2K neurons to show there may be some overlap with cell types identified in previous thalamic sequencing attempts and the current data. Overall, this is a nice start for understanding cell types in the PVT. While the data collected so far is of high quality, and will likely be of interest to the field, the total number of putative PVT cells are quite low (4K or so), which may be impacting the ability to accurately identify cell types. Consistent with this, it is unclear whether the data is best explained by 5 unique PVT neuronal cell types as they describe, or whether the clustering resolution is set too high, which is forcing cells into somewhat arbitrary clusters. By eye, the clusters in Figure 2 do not seem well separated in Umap space. This would likely be improved by additional cells added to the dataset or by demonstrating by other means that the current clustering resolution is appropriate. Alternatively, repeated data integration steps used to try and correct for batch effects may also be causing this.

Reviewer #1 (Public Review):

Marjaneh et al. studied the atrial septal variation through QTL mapping of inbred mouse strains which show extremes of septal phenotypes. The analysis discovered many interesting septal QTLs. Furthermore, the authors identified high-confidence candidate deleterious variants through whole genome sequencing of parental strains and analyzed variant architecture across gene features.

Overall, this is a comprehensive study that will provide a useful reference for the field. It will be a useful tool for hypothesis generation, which could lead to research on therapies that target atrial septal or common congenital heart disease.

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 #3 (Public Review):

In previous studies, Harvey and colleagues described several genetically-influenced biometric parameters correlated with the patent foramen ovale (PFO) cardiac defect (Biben et al., 2000) and identified 13 quantitative trait loci (QTL) that affect these traits using a murine F2 intercross design with mouse strains demonstrating extreme septal phenotypes (Kirk et al., 2006). In the submitted manuscript, Marjaneh et al. follow up and refine these studies with a more in-depth QTL analysis utilizing an advanced intercross design (F14), combined with genome and transcriptome sequencing data supporting a role for the identified QTL in atrial septation. The paper is mostly genetic analysis with follow-up informatics and one example of a validated variant. The results are important, and implicate dozens of loci and hundreds of genes (including those in the BMP pathway, and others known to be essential for cardiac morphogenesis) in atrial septum formation, highlighting the complexity of the processes involved. This paper will be an important resource for the field and sets the stage for a follow-up to validate the many candidates identified that may impact cardiac morphogenesis and atrial septation, specifically. The manuscript is well-written and straightforward and does not suffer from major errors in logic or interpretation. The identification of implicated genetic variants will benefit the field of cardiac development and may inform the advancement of future therapeutics for human patients with PFO (for identified coding variants, in particular).

Reviewer #1 (Public Review):

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

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

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

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

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 #3 (Public Review):

The authors set out to explore how neurons in the rodent parahippocampal area code for environmental and behavioral variables in a complex goal-directed task. The task required animals to learn the association between a cue and a spatial response and to use this information to guide behavior flexibly on a trial-by-trial basis. The authors then used a series of sophisticated analytical techniques to examine how neurons in this area encode spatial location, task-relevant cues, and correct vs. incorrect responding. While these questions have been addressed in studies of hippocampal place cells, these questions have not been addressed in these upstream parahippocampal areas.

Strengths:

1) The study presents data from ensembles of simultaneously recorded neurons in the parahippocampal region. The authors use a sophisticated method for ensuring they are not recording from the same neurons in multiple sessions and yet still report impressive sample sizes.

2) The use of the complex behavioral task guards against stereotyped behavior as rats need to continually pay attention to the relevant cue to guide behavior. The task is also quite difficult ensuring rats do not reach a ceiling level of performance which allows the authors to examine correct and incorrect trials and how spatial representations differ between them.

3) The authors take the unusual approach of not pre-processing the data to group neurons into categories based on the type of spatial information that they represent. This guards against preconceived assumptions as to how certain populations of neurons encode information.

4) The sophisticated analytical tools used throughout the manuscript allow the authors to examine spatial representations relative to a series of models of information processing.

5) The most interesting finding is that neurons in this region respond to situations where rewards are not received by increasing their firing rates. This error or mismatch signal is most commonly associated with regions of the basal ganglia and so this finding will be of particular interest to the field.

Weaknesses:

1) The histological verification of electrode position is poor and while this is acknowledged by the authors it does limit the ability to interpret these data. Recent advances have enabled researchers to look at very specific classes of neurons within traditionally defined anatomical regions and examine their interactions with well-defined targets in other parts of the brain. The lack of specificity here means that the authors have had to group MEC, PaS, and PrS into a functional group; the parahippocampus. Their primary aim is then to examine these neurons as a functional group. Given that we know that neurons in these areas differ in significant ways, there is not a strong argument for doing this.

2) The analytical/statistical tools used are very impressive but beyond the understanding of many readers. This limits the reader's ability to understand these data in reference to the rest of the literature. There are lots of places where this applies but I will describe one specific example. As noted above the authors use a complex method to examine whether neurons are recorded on multiple consecutive occasions. This is commendable as many studies in the field do not address this issue at all and it can have a major impact as analyses of multiple samples of the same neurons are often treated as if they were independent. However, there is no illustration of the outputs of this method. It would be good to see some examples of recordings that this method classifies as clearly different across days and those which are not. Some reference to previously used methods would also help the reader understand how this new method relates to those used previously.

3) The effects reported are often subtle, especially at the level of the single neuron. Examples in the figures do not support the interpretations from the population-level analysis very convincingly.

The authors largely achieve their aims with an interesting behavioral task that rats perform well but not too well. This allows them to examine memory on a trial-by-trial basis and have sufficient numbers of error trials to examine how spatial representations support memory-guided behavior. They report ensemble recordings from the parahippocampus which allows them to make conclusions about information processing within this region. This aim is relatively weak though given that this collection of areas would not usually be grouped together and treated as a single unitary area. They largely achieve their aim of examining the mechanisms underlying how these neurons code task-relevant factors such as spatial location, cue, and presence of reward. The mismatch or error-induced rate remapping will be a particularly interesting target for future research. It is also likely that the analytical tools used in this study could be used in future studies.

Reviewer #1 (Public Review):

In this paper, the authors present evidence from studies of biopsies from human subject and muscles from young and older mice that the enzyme glutathione peroxidase 4 (GPx4) is expressed at reduced levels in older organisms associated with elevated levels of lipid peroxides. A series of studies in mice established that genetic reduction of GPx4 and hindlimb unloading each elevated lipid peroxide levels and reduced muscle contractility in young animals. Overexpression of GPx4 or N-acetylcarnosine blocked atrophy and loss of force generating capacity resulting from hindlimb unloading in young mice. Cell culture experiments in C2C12 myotubes were used to develop evidence linking elevated lipid peroxide levels to atrophy using genetic and pharmacologic approaches. Links between autophagy and atrophy were suggested.

Experiments on GPx4 expression levels, lipid peroxide levels, muscle mass and muscle force generating capacity were internally consistent and convincing. I thought the experiments supporting the view that autophagy contributed to atrophy were convincing. The hypothesis that altered lipidation of autophagy factors contributed was tested or supported in my view. Evidence for muscle atrophy in response to genetic or pharmacologic manipulations is a bit inconsistent throughout the paper, possibly because the small N of some experiments does not provide sufficient power to detect observed numeric differences in the means. The pattern of muscle fiber atrophy by fiber type is consistent throughout the paper but there is variability in which comparisons reached the threshold for significance, again, possibly because of the small N of the experiments. I agree with the authors that altered activity of enzymes in the contractile apparatus provides one explanation for the observed weakness but respectfully wish to point out there are others such as impaired excitation-contraction coupling which is well known to occur in aging.

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 #1 (Public Review):

In this study, the authors characterize the impact of histone deacetylation on spatial regulation of gene expression in the early gastrula embryo. They utilize Xenopus tropicalis as a vertebrate model embryo and focus on maternal HDAC1 and HDAC2 deacetylases to characterize the regulatory role of histone acetylation on zygotic transcription. In particular, they are interested in whether this epigenetic mark positively or negatively regulates gene expression for the presumptive germ layer and contributes spatially to cell lineage integrity in gastrulation.

Using gene expression analysis, they find that HDAC1 and HDAC2 are present maternally in the egg and throughout blastula and gastrula stages. By performing HDAC1 ChIP-Seq, they find that the deacetylase is already bound as early as the Stage 8 blastula - time of genome activation - and that HDAC1 peaks located within promoter regions generally increase over time from blastula to early gastrula, Stage 10.5. Interestingly, the binding of HDAC1 is not dependent on the zygotic transcript, as HDAC1 ChIP-seq peaks show little difference upon alpha-amanitin treatment. Many of the HDAC1 peaks correlate with peaks of both FoxH1 and Sox3, suggesting their role in its deacetylase recruitment to the genome. Examination of epigenetic signatures of HDAC1 bound regions using previously published datasets identifies distinct chromatin binding categories: authors find a strong correlation with H3K27-Ac and pan-H3Kac, and that HDAC1 generally binds to regions free of repressive marks such as H3K9-me3. The authors find that a majority of HDAC1 peaks contain H3K27Ac but not H3K37me3 peaks and approximately ten percent of HDAC1 loci have both activating and repressive marks.

The authors investigate a functional role for histone deacetylation by inhibiting it, using the broad inhibitor TSA, and HDAC1 specific inhibitor VPA. Importantly, they spatially characterize pan-H3K acetylation and gene expression changes in animal cag (AC) and vegetal mass (VG) regions on the embryo. These are very useful datasets that provide new insights into how histone acetylation is tied to the maintenance of lineage integrity. At a global level, they find that TSA inhibition leads to gastrulation arrest and leads to widespread upregulation of H3K acetylation (pan-H3Kac); suggesting that proper regulation of histone acetylation is required for development. Further, they find that previously repressed regions, marked by H3K27me3 show the most upregulation of pan-H3Kac upon TSA treatment. Regionally, they find a number of interesting results upon inhibition of histone acetylation. First, TSA treatment causes dysregulation - upregulation - of the animal cap (AC) pan-H3Kac peaks in vegetal mass (VG), and upregulation of VG peaks in the animal cap. This suggests that lineage specifically is likely maintained in part by HDAC-mediated de-acetylation of germ layer genes. Gene expression characterization in AC and VG explants +/- TSA treatment supports this conclusion as inappropriate upregulation of VG gene expression is found in AC and inappropriate upregulation of AC genes is found in VG. Somewhat surprisingly, HDACs also appears to play a positive regulatory role in germ layer expression. Focusing on genes near HDAC1 peaks containing H3K27Ac, the authors show that genes downregulated upon TSA treatment tend to be spatially restricted; downregulated genes in AC tended to be AC genes and downregulated genes in VG tended to be VG genes. This suggests that HDACs play both positive and negative roles in regulating germ layer expression in the gastrula.

Strengths of the work include the demonstration that histone deacetylase HDAC1 binds to the genome by the onset of genome activation, accumulates in promoters as the embryo develops through early gastrula, and that inhibition of histone deacetylation disrupts germ layer lineage integrity. New datasets include ChIP-seq of HDAC1 from blastula to gastrula, panH3Kac ChIP-seq within animal and vegetal regions of the embryo, and regional RNA-seq of embryos with and without TSA inhibition of histone acetylation. This study helps demonstrate and clarify that HDAC enzymes play both a positive and negative role in gene expression regulation, and that histone acetylation is required to maintain spatial specificity of germ layer expression in gastrula. Some of the weaknesses of the work include the correlative nature of the experiments and missing analysis. Overall, the research is interesting and impactful, contributing to a growing body of work about the role of histone acetylation in the spatial regulation of earliest cell fate decisions in the embryo.

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 #3 (Public Review):

This paper investigates how the epigenetic landscape is set up during early frog embryogenesis focusing on the role of the histone deacetylase, HDAC1, in the regulation of histone acetylation around the period of Zygotic gene activation. The authors document the progressive binding of HDAC1 to the embryonic genome around the time of ZGA and on genomic sites harbouring binding motifs for maternally provided transcription factors. The authors classify HDAC1 binding sites based on their association with different epigenetic markings on H3K27 (acetylation and/or methylation) in embryonic chromatin. They infer from the observed co-occurrence of "incompatible" acetylation and methylation marks on H3K27 residue on a subset of HDAC1 binding sites, that these H3K27 modifications occur in different parts of the embryos. Subsequently, they inhibit histone deacetylase activity by TSA and document its impact on the genomic distribution of acetylated histones as well as transcriptional deregulation in explant from different parts of the embryos. By cross comparing these data to the different classes of HDAC1-associated genomic regions, they conclude that HDAC1 is involved in the spatial regulation of embryonic gene expression. Altogether this work reveals how maternally provided transcription factors could direct chromatin modifiers to shape the epigenome of the developing embryos. The work however relies mostly on indirect evidence and it would be important in particular to confirm (i) that maternal factors are indeed required for HDAC1 targeting to chromatin and (ii) that the documented effect of TSA treatment is mediated through its inhibition of HDAC1.

Reviewer #1 (Public Review):

In this work, the authors investigated the mechanism by which ions are selected in ATP-gated P2X receptor channels using patch-clamp electrophysiology. P2X receptors are known to be cation-selective channels, but one of them (the P2X5) also displays anion permeability through a molecular mechanism that is unclear. Here, the authors identify in P2X2 a glutamate residue (E17) which plays a critical role in determining ion selectivity. This residue is localized in the intracellular side of the permeating pathway and is part of three large intracellular lateral fenestrations that are thought to be potential exit/entry pathways for ions. The authors elegantly show that when the side chain of E17 was substituted for cysteine, it became accessible to water-soluble, thiol-reactive methanethiosulfonate (MTS) derivatives that were applied from both sides of the membrane. By mutating E17 into lysine, which reverts the charge, they show that mutated channels displayed increased anion permeability, although channels still remained largely cation selective. However, reverting the charge in the mouse P2X5 (K17E and K17D), they provide evidence for a complete ion selectivity switch (that is mutated P2X5 became cation selective). Therefore, although the mechanism by which P2X2 selects cation versus anion still remains incompletely understood, it seems that K17 is a key determinant for P2X5 anion permeability.

The conclusions of this paper are well supported by data. The work should advance our understanding of ion selectivity in P2X receptors and will likely provide the foundation for further studies.

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 #3 (Public Review):

P2X channels are homomeric or heteromeric, non-selective cation channels that are gated by extracellular ATP. They are found in many tissues and are implicated in bodily functions including digestion and urination, and other processes such as pain and immune response. Recent atomic resolution structures of P2X3 and P2X7 have captured the principal gating states likely conserved within this channel family. Among novel structural features that were identified was a cytoplasmic cap that appears to stabilize the intracellular region of the pore in the open state. This cap is not present in the closed and inactivated states. From these data, it has been proposed that the intracellular side of a conductive P2X pore is formed by a cytoplasmic-exposed portion of a larger, membrane-embedded fenestration, a somewhat unusual characteristic for ion channels. In this manuscript, the authors delineated the region of the fenestration that is likely exposed to the cytoplasm and identify a residue that is negatively charged in P2X1-4 and P2X7 but positively charged in P2X5-6. They suggest that not only could this residue line the ion pore, but also it may contribute to differences in cation-to-anion permeability previously observed between these P2X subfamilies. They demonstrate by electrophysiology that E17 lines the ion pore through a series of classical MTS blocking experiments. They further demonstrate that the charge of this residue confers partial or strong cation to anion permeability in rP2X2 and mP2X5, respectively. This is an elegant investigation of the internal pore of P2X channels and the experiments presented in this work are of high quality.

Reviewer #1 (Public Review):

Authors aimed to decode signatures linked to tremor, slowness and effective motor control using different types of signals acquired from a group of Parkinson's disease patients during deep brain stimulation surgery. They were able to identify distinct frequency bands which corresponded to different symptoms and conclude that multi frequency band and cortical decoding surpass single frequency band and subthalamic nucleus-based decoding.

The main strength of the study is the recording types used to decode symptoms emerging during the same experimental task: authors leveraged micro and macro level recordings from the subthalamic nucleus and ECoG recordings from the motor cortex, enabling them to provide decoding performance across distinct recording scales and from two critical structures linked to Parkinson's pathophysiology. This allowed the authors to contrast rhythm-based signatures and timescales of Parkinson's disease motor symptoms.

The primary weakness is the level of description used to describe key methods which makes it difficult to unpack the results: authors should pay particular attention to validating and justifying metrics used for assessing behaviour (e.g., tremor, slowness, and effective motor control). Also, the relationship between behavioural measures and UPDRS scores should be further justified. For instance, (1) what is the definition of tremor amplitude probability density in the absence of tremor and what is its relationship to relevant subcategories of clinical tremor severity?; (2) why did the authors link tremor while performing a task to UPDRS rest tremor scores? ; (3) why did the authors opt for normalised cursor speed as a metric for slowness?; (4) Are there any implications of this normalisation when exploring slowness across participants? Authors consider cortical and subthalamic recordings separately: if these recordings were acquired simultaneously, analysing the relationship between the two signals (i.e., envelope, phase, phase-amplitude) would significantly improve the paper.

Authors aimed to decode signatures linked to different symptoms of Parkinson's disease. Results support their primary conclusions that cortical decoding performs better than subthalamic decoding and that using a multi-dimensional feature space improves the performance of the decoder. The paper and data generated will contribute to movement control, movement disorders, and brain stimulation fields.

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 #3 (Public Review):

In this manuscript, the authors examine microelectrode and macroelectrode recordings from the human STN, as well as electrocorticography from the sensorimotor cortex in order to examine the neurophysiological biomarkers underlying tremor and bradykinesia. This is an important and timely topic, as the detection of such biomarkers can have implications for developing effective closed-loop DBS devices. Currently, there is some uncertainty as to which biomarkers may be relevant for which particular symptoms. Here the authors examine signals recorded from multiple depths within the STN and regress those signals onto behavioral measures of tremor and slowness as captured using a novel behavioral paradigm in which patients track movements on a screen in the intraoperative setting. This group has published on this paradigm previously, and here they now use support vector regressions to examine how the physiological data relates to these behavioral measures. In brief, they find that tremors and bradykinesia (slowness) correlate with different neural signatures from different locations. Overall, the results seem well supported, and the methods and statistical tests are sound.

Reviewer #1 (Public Review):

Temporal patterning allows a neural stem cell to generate different neural identities through the course of development. While this relationship has been demonstrated in many instances of stem cells and/or neurons, it is unclear how birth order translates to target specification. In this manuscript, the authors use live imaging and new tools generated from single-cell RNA sequencing data to address this issue.

They find that neurons born from a given time window (at the resolution of early>middle>late) innervate together - and distinctly from - those born at different temporal windows, though the specifics of the innervations differ between neural stem cell lineages. They also find that neurons achieve this by extending their dendrites in exploratory directions and selectively stabilising the ones in the appropriate direction. This process likely occurs at the sub-second timescales. Finally, they also demonstrate that embryonic-born (larval-specific) neurons that remodel to integrate into adulty-specific circuitry simultaneously perform pruning and dendrite extension to integrate into the circuitry at the appropriate time.

This is a valuable description of how developmental programmes imparted to neurons at the time of their birth might translate to their targetting and connectivity. It lays down a framework for understanding the defects in these processes.

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 #3 (Public Review):<br /> <br /> In this study, Wong et al, generate tools to genetically follow many of the Drosophila olfactory projection neurons. The antennal lobe, where 50 projection neurons need to form a stereotypic map where information from 50 types of olfactory receptor neurons is relayed to higher brain regions, is an exquisite system to study principles of neural circuit wiring. As such, the Luo lab has led the field in uncovering the mechanisms also generating tools that are needed to describe the system in unparalleled temporal and cell-type resolution. Here, they use cutting-edge genetic tools and imaging techniques to provide us with a better-than-ever understanding of the early phases of dendrite targeting and patterning of projection neurons.

Using these refined genetic tools, often allowing them to visualize two types of projection neurons at a time, they uncovered several important principles of dendrite targeting. They found that dendrite targeting is divided into two major steps - first, projection neurons target their dendrites to a few distinct locations, thereby forming a proto-map. This initial targeting is dictated by the combination of their birth time and lineage. As a second step, neurons pattern their dendrites into the adult-specific location by a dynamic process in which net growth is dictated by a balance between stabilization and retraction of dendritic processes. Finally, they found that the embryonic-born projection-neurons, which undergo developmental remodeling that include pruning of their connections to the larval antennal lobe (as it undergoes degeneration) and regrowth into the adult antennal lobe. Surprisingly, and in contrast to other remodeling neurons in Drosophila, pruning and regrowth occur simultaneously.

While the strong part of the paper is the cutting-edge tools, coupled with exceptional imaging strategies, its main weakness stems from the decision to remain in the descriptive realm.

Reviewer #1 (Public Review):

Previous studies from this group reported that PEG10 is increased in the spinal cord from Ubiquilin 2-/- mice as well as PEG10 being elevated in models of Ubiquilin 2-mediated ALS. In this study, the authors provide evidence supporting the concept that the proteasome factor Ubiquilin 2 regulates the activity of the Gag-pol retrotransposon gene (PEG10). Mutations in Ubiquilin 2 underlie a portion of the familial forms of ALS. It is found that in spinal cord tissue from sporadic ALS patients PEG10-pol levels are elevated, leading to the conclusion that altered regulation of PEG10 levels by Ubiquilin 2 and a subsequent alteration in genes regulating axon remodeling may contribute to all forms of ALS pathogenesis. Strengths of this work include the extensive analyses and direct data on the mechanism by which Ubiquilin 2 regulates PEG10 levels in human cells resulting in less PEG10. They further show that peptides generated by the self-cleavage of PEG10 alter the expression of genes involved in axon function. The major weakness of this study is the complete absence of data that directly show that an alteration of PEG10 by Ubiquilin 2 is critical for ALS pathogenesis. As noted by the authors, multiple pathways have been broadly implicated in genetics and sporadic forms of ALS. Thus while the study provides interesting data on the regulation of PEG10 by Ubiquilin 2, the extent to which this pathway underlies ALS pathogenesis and/or progression is speculative.

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 #3 (Public Review):

Ubiquilin 2 (UBQLN2) encoded by a familial predisposition gene for Amyotrophic Lateral Sclerosis (ALS) is a proteosome shuttle protein shown to associate with Cxx2 and mammalian Ty3/gypsy-like retrotransposon PEG10 in previous work by this author. Other work has shown that PEG10 is expressed from an imprinted gene required for placental development and in adrenal and brain tissues. Increases in PEG10 expression are also implicated in some cancers. Building on previous work by the senior authors (Whitely et al., 2021) which showed that PEG10 interacts with components of the UBQLN machiner and is elevated in UBQUILN TKO human and mouse cells, this work focuses on the interaction of UBQLN proteins and PEG10 target. Using a HEK human kidney cell line, authors show first that targeting of PEG10 depends upon a proline-rich repeat at the carboxy terminus of Gag-Pol unique to eutherian animals; second, that the aspartyl protease previously implicated in gag-pol processing can release the gag carboxyl-terminal CHCC zinc knuckle nucleocapsid to concentrate in the nucleus and correlates with changes in expression of genes related to neuronal development. Finally, they show that PEG10 is elevated in human spinal cord neuron cell lines.

Strengths:

The primary strengths of this manuscript lie in the multiple experiments linking UBQLN2 activity to the target PEG10 PPR motif and in potentially linking Gag-Pol and NC production to changes in cellular gene expression. The authors knock out multiple UBQLN genes in various species and demonstrate the phylogenetic correspondence between UBQLN2 PEG10 levels.

Weaknesses:

Although this manuscript links elevated PEG10 protein levels to fALS mutated UBQLN2 and changes in neuronal gene expression, it does not as the title suggests demonstrate that UBQLN2 control of PEG10 is required for "neuronal health in ALS". This is an awkward title and the link between neuronal health and the ability to turnover PEG10 is not clearly established since most of the experiments were conducted in non-neuronal human cell lines.

Authors could more completely set the context for their work including their own work (Whiteley et al., 2021) and findings in Angelman (UBE3A, Pandya et al., 2021) and Parkinsons (Sakharkar et al., 2019) which, rather than detracting from their work, would confer greater interest. In addition, they mention in passing that in the absence of familial predisposition mutations, in ALS UBQLN2 can be inactivated by trapping in aggregates. This undermines their comparison of fALS and sALS cells.

The multiple western blots while consistent with authors conclusions, do not show greater than two-fold differences in PEG10 protein levels in the absence of UBQLN2 proteins so that there are likely other factors besides UBQLN2 influencing PEG10 levels. For example, the authors do not comment on PEG10 extracellular VLP production which occurs in some cells or that other proteins previously implicated as targets of PEG10 could be influencing the neuronal phenotypes of fALS. In addition, clarification of the different phenotypes of fALS mutations in the UBQLN2 hotspot would have addressed concerns that more than UBQLN2 is involved in the phenotype.

Reviewer #1 (Public Review):

In this study, Jigo et al. measured the entire contrast sensitivity function and manipulated eccentricity and stimulus size to assess changes in contrast sensitivity and acuity for different eccentricities and polar angles. They found that CSFs decreased with eccentricity, but to a lesser extent after M scaling while compensating for striate-cortical magnification around the polar angle of the visual field did not equate to contrast sensitivity.

In this article, the authors used classic psychophysical tests and a simple experimental design to answer the question of whether cortical magnification underlies polar angle asymmetries of contrast sensitivity. Contrast sensitivity is considered to be the most fundamental spatial vision and is important for both normal individuals and clinical patients in ophthalmology. The parametric contrast sensitivity model and the extraction of key CSF attributes help to compare the comparison of the effect of M scaling at different angles. This work can provide a new reference for the study of normal and abnormal space vision.

The conclusions of this paper are mostly well supported by data, but some aspects of data collection and analysis need to be clarified and extended. 1) In addition to the key CSF attributes used in this paper, the area under the CSF curve is a common, global parameter to figure out how contrast sensitivity changes under different conditions. An analysis of the area under the CSF curve is recommended. 2) In Figure 2, CRFs are given for several SFs, but were the CRFs at the cutof-sf well-fitted? The authors should have provided the CRF results and corresponding fits to make their results more solid. 3) The authors suggested that the apparent decrease in HVA extent at high SF may be due to the lower cutoff-SF of the perifoveal VM. Analysis of the correlation between the change in HVA and cutoff SF after M scaling may help to draw more comprehensive conclusions. 4) In Figure 6, it would be desirable to add panels of exact values of HVA and VMA effects for key CSF attributes at different eccentricities, as shown in Figures 4B, D, and F, to make the results more intuitive.

More discussions are needed to interpret the results. 1) Due to the different testing distances in VM and HM, their retinae will be in a different adaptation state, making any comparison between VM and HM tricky. The author should have added a discussion on this issue. 2) In Figure 4, the HVA extent appears to change after M-scaling, although the analysis shows that M-scaling only affects the HVA extent at high SF. In contrast, the range of VMA was almost unchanged. The authors could have discussed more how the HVA and VMA effects behave differently after M-scaling. 3) The results in Figure 4 also show that at 11.3 cpd, the measurement may be inaccurate. This might lead to an inaccurate estimate of the M scaling effect at 11.3 cpd. The authors should discuss this issue more. 4) The different neural image-processing capabilities among locations, which is referred to as the "Qualitative hypothesis", is the main hypothesis explaining the differences around the polar angle of the visual field. To help the reader better understand this concept, the author should provide further discussions.

The authors should also provide more details about their measures. For example, high grayscale is crucial in contrast sensitivity measurements, and the authors should clarify whether the monitor was calibrated with high grayscale or only with 8-bit. Since the main experiment was measuring CS at different locations, it should also be clarified whether the global uniformity of the display was calibrated. In addition, their method of data analysis relies on parametric contrast sensitivity model fitting. One of the concerns is whether there are enough trials for each SF to measure the threshold. The authors should have included in their method the number of trials corresponding to each SF in each CSF curve.

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 #3 (Public Review):

Jigo, Tavdy & Carrasco used visual psychophysics to measure contrast sensitivity functions across the visual field, varying not only the distance from fixation (eccentricity) but also the angular position (meridian). Both parameters have been shown to affect visual sensitivity: spatial visual acuities generally fall off with eccentricity, it is now widely accepted that it is superior along the horizontal than the vertical meridian, and there may also be differences between the upper and lower visual field, although this anisotropy is typically less pronounced. The eccentricity-dependent decrease in performance is thought to be due to reduced cortical magnification in peripheral compared to central vision; that is, the amount of brain tissue devoted to mapping a fixed amount of visual space. The authors, therefore, include a crucial experimental condition in which they scale the size of their stimuli to account for reduced cortical magnification. They find that while this corrects for reduced performance related to stimulus eccentricity, it does not fully explain the variation in performance at different visual field meridians. They argue that this suggests other neural mechanisms than cortical magnification alone underlie this intra-individual variability in visual perception.

The experiments are done to an extremely high technical standard, the analysis is sound, and the writing is very clear. The main weakness is that as it stands the argument against cortical magnification as the factor driving this meridional variability in visual performance is not entirely convincing. The scaling of stimulus size is based on estimates in previous studies. There are two issues with this: First, these studies are all quite old and therefore used methods that cannot be considered state-of-the-art anymore. In turn, the estimates of cortical magnification may be a poor approximation of actual differences in cortical magnification between meridians. Second, we now know that this intra-individual variability is rather idiosyncratic (and there could be a wider discussion of previous literature on this topic). Since these meridional differences, especially between upper and lower hemifields, are relatively weak compared to the variance, a scaling factor based on previous data may simply not adequately correct these differences. In fact, the difference in scaling used for the upper and lower vertical meridian is minute, 7.7 vs 7.68 degrees of visual angle, respectively. This raises the question of whether such a small difference could really have affected performance.

That said, there have been reports of meridional differences in the spatial selectivity of the human visual cortex (Moutsiana et al., 2016; Silva et al., 2017) that may not correspond one-to-one with cortical magnification. This could be a neural substrate for the differences reported here. This possibility could also be tested with their already existing neurophysiological data. Or perhaps, there could be as-yet undiscovered differences in the visual system, e.g., in terms of the distribution of cells between the ventral and dorsal retina. As such, the data shown here are undoubtedly significant and these possibilities are worth considering. If the authors can address this critique either by additional experiments, analyses, or by an explanation of why this cannot account for their results, this would strengthen their current claims; alternatively, the findings would underline the importance of these idiosyncrasies in the visual cortex.

Reviewer #1 (Public Review):

Pasquereau and Turner investigated the encoding of reward and delay information in subthalamic (STN) neurons in behaving macaques. They record during a forced-choice task with three levels of reward and two levels of delay, using rejection rates to model subjective value. Task-dependent neurons, those which encoded reward and/or delay, were identified with a sliding-window regression model. They then investigated the time course of reward and delay information using a principal component analysis approach. They find that the strength of the first and four principal components varies systematically along the anteroposterior axis of the STN, suggesting a spatial distribution of value coding. These data, recorded in a controlled task, add to the understanding of STN function.

The data, collected from a well-defined brain area and with appropriate motor and oculomotor controls included during a straight-forward task, are a good foundation for investigating STN function. However, the statistical procedures used are not completely described and may not be appropriate, particularly in the sliding window analysis. Given this analysis underlies some of the further analyses, it must be clarified or corrected for the conclusions to stand. Further, the analysis only explores the encoding of delay at the time of a cue and does not consider how the value of delay may change over time.

The sliding window analysis, a common approach in investigating time-course data, necessitates multiple comparisons (188 time-bins here) and so requires a controlling procedure to keep the family-wise error-rate low. The authors describe, not completely, how the pre-instruction period was used to establish the boundaries for significance for each coefficient. The pre-instruction period, by the authors' own account, is a period of lower variance and so it would be expected that the boundaries for significance would be lower and the number of task-dependent neurons is therefore an overestimate. The shuffling process the authors use when they determine significance in their principal components analysis is a more appropriate method.

The task design and analysis provide a limited test of delay encoding. As only two levels of delay were tested, it is not possible to directly test whether the subjective discounting function is hyperbolic or exponential and hence whether the delay is encoded subjectively or objectively. Further, the task has several variable interval lengths (hold in: 1.2-2.8 s, short delay: 1.8-2.3 s, long delay: 3.5-4s) that frustrate interpretation. The distribution of these delays is not described, for example as it reads it seems possible that some long delay rewards are delivered with shorter latency between cue and reward than some short delay rewards (1.2 + 3.5 = 4.7s vs. 2.8+2.3 = 5.1 s). The authors have not considered that if the delay value is encoding, then the value, both objectively and subjectively, may be changing as the delay elapses. The variation of these task intervals may have an effect on the value of delay.

The principal components analysis is an interesting way to explore patterns of encoding and the spatial distribution of these patterns. In particular, the finding that Discounting- neurons, those whose firing rate increases with increasing reward cues and decreases with increasing delay cues, are preferentially found in the posterior STN, which the authors demonstrate with both the principal component analysis and the sliding-window classification analysis, challenges previous ideas of STN organization.

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 #3 (Public Review):

The authors have been challenged to figure out the neural processing of delay discounting during waiting for upcoming reward outcomes after behavioral controls in the subthalamic nucleus, where unique brain regions as a part of the basal ganglia for cognitive and motor functions. They described the activity property of STN neurons for the delay gratification at the single neuron level and population level, using both conventional and recently developing approaches. The finding is novel, but the details of the analysis are sometimes inaccurate and needed to be improved. Their claims are now partially supported. If their analyses are improved, their findings have a significant impact on understanding the neural basis of delay discounting, which is one of the predominant behavioral characteristics among organisms.

Reviewer #1 (Public Review):

In this work, Pan et al. investigate the properties of the underexplored snake venom phosphodiesterase (svPDE) from a genomic, transcriptomic, and structural perspective. These analyses are complemented by comparisons with similar ENPP proteins to better understand the elements that may underline the specific role of svPDE in envenomation. The data support a role for svPDE that may be related to its interactions with partner proteins or due to its phosphodiesterase activity to enhance the cytotoxic effects of other venoms present in the environment.

Overall, the authors have done a good job of investigating the origins and function of svPDE. The evolutionary analyses are adequate and informative, which are expanded by further experiments to determine the structure and interactions of svPDE. The protein-protein interaction experiments and the svPDE activity experiments with different substrate types shed light on the possible role of the protein in the context of its cellular environment and point to the potential role of glycosylation as part of the mode of action of svPDE. These results will pose a good prelude for further research into the mechanism and interactions of svPDE from other species. Further, the mechanistic insights from this work may also help the development of antivenom compounds that target svPDE.

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 #3 (Public Review):

The biochemical identity and the crystal structure of the snake venom phosphodiesterase (svPDE) were determined using protein purified from the crude venom of a snake (Naja atra) captured in Taiwan. The crystal structure was determined with and without AMP bound. The quality of the structure is excellent and the coordination of the bound AMP makes sense based on the coordination by side-chain residues and the known coordination of bound AMP to structural homologues (ENPP3). Naturally, it's interesting that snake venom produces a soluble variant of the membrane-anchored PDE found in humans.

Although the structure and the catalytic site seem overall similar, it is unclear what the role of the snake enzyme is in the host infection. Furthermore, there are a number of human ENPP enzymes and they have different substrate preferences and physiological roles. More detailed biochemistry would help to put the role of the svPDE into a physiological context.

Reviewer #1 (Public Review):

ARL15 forms a complex with the TRPM7 channel and CNNM transporters and is involved in the regulation of the TRPM7 function. To understand the regulatory mechanism, the authors performed biochemical and structural characterizations. In this work, they determined the crystal structure of ARL15 in complex with CNNM2 CBS domain, performed the mutational analysis based on the structure, and successfully revealed the binding mechanism between ARL15 and CNNM.

However, the detailed mechanism of TRPM7 inhibition by ARL15 remains unclear because the structure of TRPM7 in complex with ARL15 is still unknown. Furthermore, despite the structure determination of ARL15 in complex with CNNM, the effect of ARL15 on CNNM function is still unclear.

Nevertheless, the structural information on the ARL15-CNNM complex provided by the authors is valuable for the related research field, and the structure-based CNNM mutants specifically targeting disruption of binding to either ARL15 or PRL would also be useful.

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 #3 (Public Review):

The authors studied the interaction between Arl15 and CNNMs using various biochemical and biophysical approaches. Significantly, they solved the crystal structure of Arl15 and the CBS-pair domain of CNNM2 and demonstrated that PRLs and Arl15 could compete for binding to CNNMs. The study should advance our understanding of how cellular divalent ions are regulated via Arl15, CNNMs, and TRPM7, although some issues regarding the guanine nucleotide-binding of Arl15 need to be addressed.

Reviewer #1 (Public Review):

These authors use a mouse model of gestational intermittent hypoxia (GIH), a component of sleep apnea during pregnancy, to test the hypothesis that GIH induces inflammation in the central nervous system that impairs respiratory functions, in a sex-dependent manner. The major finding of this work is that spinal cord inflammation, mainly driven by activated microglia cells, impairs inactivity-induced inspiratory motor facilitation (iMF). The authors successfully test this hypothesis and their results support their conclusion.

Major strengths of this work include a robust study design, a well-defined translational model (GIH that sets on later in pregnancy), complementary biochemical and experimental methods such that correlated findings are followed up by interventional studies, and sufficient power to evaluate sex differences. In particular, the authors note the upregulation of several NF-kB regulates genes and increased concentration expression of inflammatory markers in the spinal cords of male mice. These mice also have deficits in the iMF response. By depleting microglia and blocking Ik-kinases, the authors convincingly demonstrate that the increased spinal inflammation is causative in the disruption of respiratory plasticity.

The major limitation as the manuscript is currently written is a clear rationale for evaluating the iMF response as a primary endpoint. One of the corresponding authors is an expert in iMF, but there is no rationale for why it is expected that this aspect of plasticity might be disrupted. The authors discuss breathing and respiratory function in the introduction, but these have not been measured here. It is not known whether GIH impacts respiratory response or baseline breathing in a spontaneous breathing model, including baseline frequency and tidal volume and the ventilatory responses to hypoxia and hypercapnia. Shortening the introduction to offer a clear rationale would be beneficial, given the wide audience of this journal. The limitations of this model, including vagotomy, mechanical ventilation, hyperoxic ventilation, and recording from the phrenic nerve in lieu of respiratory measures, should also be discussed in the discussion for readers not familiar with this model outside of the respiratory control field.

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 #3 (Public Review):

The role of maternal sleep apnea on neurological and physiological function in the offspring is of substantial interest and the investigators have contributed significantly to this emerging field via prior publications. Recent work has evidenced that recurrent bouts of gestational intermittent hypoxia (GIH) result in life-long changes in cardiovascular, cognitive, and metabolic function in the offspring. Recently, investigators have shown that GIH reprograms the neuroinflammatory response of neonates, such that the newborn offspring's normal immune response is attenuated following a Lipopolysaccharides (LPS) exposure and respiratory rhythm generation is considerably altered (Johnson et al. Respir Physiol Neurobiol. 2018). The present study by Mickelson et al. substantially extends these previous findings by showing that GIH results in region and sex-specific changes in the microglial activation of adult rats. In male rats, these changes are indicative of an increased pro-inflammatory profile and contribute to the blunted ability to elicit respiratory neuroplasticity following apneic challenge-induced breathing instability. While a robust attenuation of key inflammation-related genes was observed in spinal and brainstem regions of GIH-exposed female rats, these results were not pursued further and present another exciting area of investigation. Nonetheless, the primary goal of these studies was to elucidate the potential role of spinal microglial activation in decreasing respiratory neuroplasticity in adult rats, which has been investigated in-depth using clever and appropriate experimental approaches.

The respiratory motor system employs homeostatic neuroplastic mechanisms at the spinal level to increase phrenic motor output in response to reduced neural activation of respiratory pathways (also called inactivity-induced inspiratory motor facilitation (iMF)). Under carefully controlled conditions, lowering inspired CO2 levels causes cessation of phrenic inspiratory output (central apnea). The authors have previously utilized a protocol of recurrent central apneas to elicit iMF in phrenic motor output. In the present study, authors utilize this neurophysiological outcome to test the impact of GIH on altering the neuroplastic capacity of adult rats. A key finding of this study is that GIH attenuates iMF in male rats. This attenuation is not observed in female rats. To test the role of inflammation (in particular microglia-driven inflammation), the authors employ two approaches to inactivate spinal inflammatory pathways or deplete microglia in adult male rats. Building upon the 29 out of 12982 differentially expressed genes in cervical spinal cord microglia in GIH vs GNX (control exposure rats), the authors targeted the NF-κB pathway using intrathecally delivered TPCA-1 (NF-κB inhibitory subunit (IκB) inhibitor). Indeed, spinal TPCA-1 application restored iMF in GIH-exposed male rats. The second approach employed global microglial depletion using an orally delivered CSF1R inhibitor Pexidartinib (PLX3397) to show that iMF could be provoked in GIH-exposed male rats. It is important to note that although the authors do not report changes in microglial expression in GIH vs. GNX rats, they conclude that there are alterations in microglial activation that contribute to the GIH-induced attenuation of the neuroplastic capacity of respiratory motor networks.

A few questions emerge from this study. In the previous study by the group investigating changes in the inflammatory profile of newborns exposed to GIH, Cox-2 mRNA expression was shown to be elevated in the spinal cords of male rats. This is an interesting finding that has not been tested in GIH-exposed adult male rats in this study and it would be interesting to follow up on whether these changes in microglial profiles are conserved from newborn to adult stages. Indeed, the authors identify additional changes in hypoxia-responsive signaling pathways of GIH rats whose role in impaired respiratory plasticity would be an exciting follow-up to the current study.

The authors emphasize that the reduction in iMF capacity is due to changes in local spinal microglia activation. They do also report that 4 genes were upregulated in the brainstem region of GIH rats as compared to GNX rats. Without an appropriate anatomical control (such as hypoglossal motor output), it would not be appropriate to conclude that microglial activation resulting from GIH has no impact on respiratory networks. Further, the inclusion of bursting frequency data could provide some insight into neural drive originating in brainstem regions.

In summary, this study by Mickelson et al. provides a valuable framework for mechanisms imposing long-lasting changes in respiratory motor control following gestational exposure to episodes of sleep apnea. Furthermore, the work completed here may very well be relevant to other motor systems in which spinal microglia modulate the capacity to elicit homeostatic plastic changes. These changes are particularly important in the context of disease and injury and may impair the capacity of GIH-exposed individuals to elicit neuroplastic changes at the motor neuron level.

Reviewer #1 (Public Review):

The paper starts with a general explanation of the method behind temporal response functions (TRFs), an analysis technique for M/EEG data that has led to many new findings in the last few years. The authors touch upon convolution and show how a linear model can be used to model non-linear responses. The methods section provides a practical introduction to the TRF, in which advice on general analysis steps - such as EEG preprocessing and centering the predictor variables - is intertwined with explanations of the use of the toolbox Eelbrain. The results section outlines how to use the outcome of a TRF model to answer (cognitive) neuroscientific questions and provides a comparison between ERPs and TRFs. The discussion section touches upon a couple of considerations, the most important one being a discussion of the Sparsity prior/Boosting algorithm.

A first great merit of this paper is that it manages to clearly explain both the analysis and the important decisions a researcher needs to make in just a few pages. When following the steps outlined in (in particular) the methods section, the researcher will know how to implement a TRF model using Eelbrain, as well as have a general idea about the decisions that one needs to make in the process. Furthermore, the explicit comparison between ERPs and TRFs will help many understand what TRFs are, and in which ways they allow for more fine-grained analysis of the data than ERPs. For these reasons, this work is a suitable starting point for anyone who wants to get started with TRFs, and a good addition to the existing set of papers on this topic, such as Crosse, Di Liberto, Bednar, and Lalor (2016) and Sassenhagen (2019).

An important contribution of this work is the implementation of the Boosting algorithm. Although it is yet to be determined whether this algorithm creates better models of the neural data than previous implementations of the TRF, the authors provide good arguments for the suitability of this algorithm for the analysis of neural time-series data.

On the practical side, the tutorial analyses are well-designed for the target audience, with interpretable questions and contrast relevant to the field of cognitive neuroscience. The corresponding scripts are clear and well-commented. Finally, the implementation of this method in Python will be greatly appreciated - especially by those who do not have access to a MATLAB license.

All in all, this is a highly didactic paper that will help many researchers get started with temporal response functions both theoretically (to understand the method) and practically (to work with the toolbox). As such, this work has the potential to be of great importance in the field of cognitive neuroscience.

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 #1 (Public Review):

The authors propose that the ER-resident large GTPase Sey1, a homolog of mammalian atlastin, localizes to LDs and promotes their association with the Legionella-containing vacuole (LCV); They also propose that the effector LegG1 contributes to this process by activating the host GTPase RanA on the LCV surface. Once LDs associate with the LCV, the authors favor a model where LDs are taken up into the LCV lumen where they are consumed by L. pneumophila as a carbon source. They propose that the fatty acid transporter FadL, Lpg1810, is involved in the transport of palmitate across the bacterial membrane.

Strong points of this study are the use of Dictyostelium as a genetically tractable model system, the finding that FadL and the addition of exogenous palmitate positively affect intracellular bacterial growth, and the fact that LDs can be detected within LCVs which, if confirmed, would be of significant biological importance.

The main concern is that the molecular mechanism underlying LCV-LD dynamics and LD uptake have only been superficially described. It needs to be determined how exactly proteins like Sey1 or LegG1 promote LD recruitment to LCVs. Does this process really depend on Ran GTPases and if so, do constitutively inactive Ran mutants phenocopy the defects? And by what mechanism are LDs delivered across the LCV membrane into their lumen? The authors themselves raise that question in the discussion, but provide no explanation or supporting data. How commonly can LD uptake into LCVs be observed across a population of cells? And are the phenotypes observed upon deletion of Sey1 direct effects, or are global changes in the ER/host cell protein or lipid landscape indirectly causing those phenotypes? These are some of the questions that, once addressed, would improve the impact of this study.

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 #1 (Public Review):

In this manuscript, the authors were trying to achieve the generation of continuous cell lines after lineage-restricted mis-expression of RasV12 in vivo followed by primary cell culture. They used glia-, epithelia-, and muscle-specific Gal4s, to get glial, epithelial, and muscle cell lines, as well as the RU-inducible Gene Switch Gal4, to get neuronal and blood cell lines. They performed RNA-seq analysis of the cell lines and showed that they are most similar to each by principal component analysis. They compared their RNA-seq to the Fly Cell Atlas and showed that the cell lines were quite similar to their in vivo counterparts. They treated cell lines with the steroid hormone ecdysone and found that many of the cell lines differentiate. These cell lines also contain an attP site, allowing for CRISPR-based screens. These cell lines could be passaged for many generations, but robust effects were found in the early passages. These cell lines have been deposited at a public resource center (The Drosophila Genomics Resource Center, DGRC).

The major strengths of the paper include rigorous analysis of characteristics, gene expression, and differentiation potential of the cell lines. There were only a few minor weaknesses related to editorial changes in the manuscript.

The authors provide convincing results that support their conclusions and as such the authors achieved their aims.

This work is likely to have a positive impact on the Drosophila community. These cell lines will serve as a solid foundation for both low- and high-throughput screens.

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 #3 (Public Review):

This is a clearly written, straightforward, resource paper describing the creation of several new cell lines that may prove useful to the Drosophila community. They are to be distributed through the Drosophila Genomics Resource Center and might be put to use at the Drosophila RNAi screening center.

Reviewer #1 (Public Review):

This study aimed to estimate contact parameters associated with the transmission of SARS-CoV-2 in unvaccinated South African households over one year. The authors found no correlation between the frequency or duration of contacts and infection risk. Similar parameters (e.g., sharing a room with the index patient) also failed to yield an association. Reassuringly, a robust association was found with the Ct of the index case; female sex and individuals aged 13-17 years were also associated with increased risk. In a more general analysis, obesity, age >5 and <60 y, and non-smoking status were associated with increased risk.

Strengths of the study are its relatively large size (131 households involving 497 people) with detailed proximity data; frequent testing to enable high ascertainment of infections; and ability to exclude individuals seropositive at baseline. Additionally, several outcomes were evaluated in the models, partly to accommodate uncertainty in the index case. Different model structures were evaluated to gauge robustness.

Limitations of the study include the fact that many index cases were likely enrolled after their infectious period, and it is possible that apparent secondary cases in the household arose from a shared exposure with the index case but had a longer latent period. Each of these factors could weaken the perceived effect of close contacts. Statistically, there is the vexing question of what age (gender, smoking, etc.) really represents mechanistically, and whether the models may be conditioning on a collider. Another statistical consideration is that many household contacts were excluded from the study because they were seropositive at baseline. In effect, their households may already have been "challenged" with the virus, and there may be heterogeneities in household susceptibility that are not fully considered by the simple exclusion of individuals with evidence of prior infection. Separating these household types in the analysis might have yielded different results.

All that said, it is telling that in these households, infection is not clearly linked to typically defined close contacts. This is an important result that complements other strong evidence that aerosols are the dominant route of transmission for SARS-CoV-2. This information is critical for the design of effective intervention strategies. Additionally, the authors outline how future studies can be designed to improve on this work.

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 #3 (Public Review):

The manuscript by Kleynhans et al analyzes data from household contacts of SARS-CoV-2 cases at two sites in South Africa. Proximity sensors were distributed to household members following diagnosis of the "index case" and measured the frequency and duration of close contacts (defined as being face-to-face within 1.5 meters for at least 20 seconds). The authors then examined the association between the duration, frequency, and average duration of contacts and the risk of a diagnosis of SARS-CoV-2 among household members in the subsequent two weeks, for both contact with the index case and all cases within the household. The risk of infection among household members was high (~60%), but was not significantly associated with the contact metrics examined. The findings may indicate that aerosols may be the predominant mode of SARS-CoV-2 transmission within households; however, there are also a number of limitations associated with the design and analysis of the study, which the authors acknowledge and which may limit the interpretability of the conclusions of this study.

One important study limitation has to do with the design of the study: Sensors were not distributed to household members until a day or two after the diagnosis of the index case. Since individuals are most infectious with SARS-CoV-2 just prior to symptom onset, contact patterns were measured only after most transmission from the index case likely occurred. Furthermore, household members may have limited their contact with the index case, particularly if the index case attempted to isolate following their diagnosis, so the contact patterns measured are unlikely to be representative of typical mixing within the household.

Another important limitation has to do with the analytical approach: The logistic regression model assumes that the first person in the household to test positive for SARS-CoV-2 (i.e. the index case) infected all subsequent cases within the household. However, this approach does not account for chains of transmission within the household or transmission from outside the household (possibly from the same source that infected the index case). While this concern is partially addressed by also assessing the association between the risk of infection and contact with all infected household members, more sophisticated methods could be used to infer the most likely infector of each case. The possibility of multiple introductions of the virus from outside the household is also only partially addressed by excluding households in which more than one variant was detected. While these limitations (and others) are appropriately acknowledged by the authors in the Discussion, nevertheless they limit the conclusions that can be drawn from the study results.

It is also worth noting that the contact metrics as defined and analyzed in the model may not be the measures that are most relevant to transmission. The authors examined three different contact measures: the median daily duration of contact, the median daily frequency of contact, and the median daily average duration of contact (i.e. the ratio of the two previous measures). They chose to examine the median daily values because contact duration was heavily skewed and the number of days of follow-up varied after data cleaning, but it may be that longer-duration contacts important to transmission are not appropriately captured by these metrics. Indeed, the median daily duration of the contact is quite short (only ~18 minutes on average). It would be useful to also evaluate a measure such as the total cumulative duration of contact and frequency of contacts divided by the number of days of follow-up, which differs from the measures they calculate and would take into account more prolonged and frequent contacts.

Lastly, the measures of association reported in the manuscript are the odds ratios (ORs) associated with one additional second of contact per day. This is not a very biologically meaningful unit of measure, and when rounded to two significant digits, the ORs are not surprisingly 1.0 with 95% confidence intervals that also round to 1.0. It would be more interpretable to report the ORs associated with a 1-minute (rather than 1-second) increase in the duration of contact, and the biological interpretation of the ORs should be described in the text.

Reviewer #1 (Public Review):

FLC is a gene involved in cold-dependent induction of flowering, as prolonged cold exposure leads to a progressive decrease in the level of this floral repressor as a result of a digital switch from an ON to an OFF state occurring asynchronously in cell populations. In this work, the authors analyze the contribution of analog and digital regulation to FLC expression in the absence of cold exposure. To do so, they use a genetic trick to be able to perform this analysis in the wild-type Ler ecotype where the molecular tools are available to do such an analysis. In Ler, an activator of FLC is missing due to a natural mutation and FLC expression is repressed during vegetative development by a pathway called the autonomous repressive pathway, allowing for a rapid transition to flowering. The authors used two mutant allele in one component of the autonomous pathway, the FCA gene. In the strongest allele, FLC is highly expressed and the plant are late flowering while in the weaker allele FLC shows a weaker expression and the plant requires an intermediate time between Ler and the strong fca allele to flower.

The authors demonstrate that the expression levels of the FLC gene vary quantitatively in the three genetic background they use (Ler and two fca alleles), and that mutating FCA leads to an analog increase in FLC expression. The quantifications performed by the authors indicate that increased level of FLC correlate with a decrease in the proportion of cells that can switch OFF FLC, with the strong fca allele showing a negligible amount of cells that can switch OFF FLC. The authors further measure the half-life of FLC mRNA and FLC protein, and show that FLC expression switch from ON to OFF is a one-way-switching. They used these data to build a computational model of the regulation of FLC expression and show that the model can reproduce the dynamics of FLC protein level at the cell population level in a time-course with measurement at 7, 15 and 21 days after sowing. Taken together their work suggest that, at least in the weak fca mutant, a combination of analog and digital regulation of transcription explains the population-wide dynamics of FLC expression. The authors propose that this regulation could be explained by high level of transcription of FLC preventing the digital switch, as a result of the short half-lives of FLC mRNA and FLC protein.

The finding of this work are potentially of wide interest to understanding transcriptional regulation by providing a functional link between the digital and analog mode of regulation of gene expression. However, the evidence of a link between expression levels resulting from analog regulation and the digital regulation are only partly supported by correlations from cell population-wide analysis of FLC expression. The authors did not provide experiments to more directly test that higher level of transcription could indeed prevent the OFF switch of FLC. It is likely but not shown that the ON to OFF switch of FLC is regulated similarly in the absence of cold exposure (this study) and upon cold exposure. Also, in their model, the authors used the assumption that FLC switches off at division but they do not test this important assumption. Finally it is unclear whether this combination of analog and digital regulation is relevant to FLC regulation in wild-type plants or is only relevant to the laboratory-induced mutants studied in this work.

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 #3 (Public Review):

Gene regulation at the single cell level can appear in two fundamentally different modes: a digital mode, in which a certain gene is either ON or OFF, and an analog mode, where a gene can gradually modulate its expression in a range of values. Yet, it is unclear how such two modes might operate together. In the work by Antoniou-Kourounioti et al, the authors argue that the Arabidopsis floral repressor FLOWERING LOCUS C (FLC) exhibits such two regulatory modes in the Arabidopsis root before cold exposure, with analog preceding digital.

This work has the strength of performing an elegant combination of experimental and modelling approach to solve a non-trivial and fundamental question on gene regulation. At the experimental level, the authors are able to quantify the number of FLC transcripts as well as their protein levels at the single cell level in the studied Arabidopsis lines, and they elegantly recapitulate some of their experimental results with an in silico root model.

Although this work has a very high potential, I find there are several important aspects that require some attention.

I think further explanations and clarity are needed to help the readership understand the differences between digital and analog regulation, beyond the explanations illustrated by Fig 1. In my understanding, digital regulation will involve observing some kind of bimodality when quantifying expression levels at the single cell level (see Bintu et al 2016), but from the definitions of ON and OFF cells the authors did in this work (see below), and the modelling they propose, it seems not to be the case. Given the authors derive very strong conclusions from their quantifications on what is digital and what is analog, I think it is important to be clearer in this regard. Also, to clarify the possible scenarios of interplay between analog and digital, I believe it would help to emphasize and better connect the modelling part to the experimental part.

Another major concern to me is whether the extracted conclusions rely too much on certain choices the authors made when doing the quantifications from the experimental data. In particular,

1) The way the authors define ON and OFF cells sounds a bit arbitrary to me and, in my understanding, can affect a lot the outcomes and derived conclusions. The authors define ON cells to those cells having more than one transcript, or when they are above the value of 0.5 of the Venus intensity measure - what would it happen if the thresholds are slightly above these levels? And why such thresholds should be the same for the studied lines Ler, fca-3 and fca-1? By looking at the distributions of mRNAs and Venus intensities in Ler and fca-3 plants, one could argue that all cells are in an OFF, 'silent' state, and that what is measured is some 'leakage', noise or simply cell heterogeneity in the expression levels. If there is a digital regulation, I would expect to see this bimodality more clearly at some point, as it was captured in Berry et al (2015) - perhaps cells in fca-1 show at a certain level of bimodality? When seeing bimodality, one could separate ON and OFF states by unmixing gaussians, or something in these lines that makes the definition less arbitrary and more robust.

2) The authors use means in all their plots for histograms and data, and perform tests that rely on these means. However, many of these plots are skewed right distributions, meaning that mean is not a good measure of center. I think using median would be more appropriate, and statistical tests should be rather done on medians instead of means. If tests using medians were performed, I believe that some of the pointed results will be less significant, and this will affect the conclusions of this work.

3) Some data might require more repeats, together with its quantification. For instance, the expression levels for fca-1 in Fig 2E and Fig 3D at 7 days after sowing look qualitatively different to me - not just the mean looks different, but also the distribution; fca-1 in Fig 3D looks more monomodal, while in Fig 2E it looks it shows more a bimodal distribution. Having these two different behaviours in these two repeats indicates that, more ideally, three repeats might be needed, together with their quantification. Fig. 2C would also need some repeats. In Fig 1S1 C and D, it would be good to clarify in which cases there are 2 or more repeats -3 repeats might be needed for those cases in Fig 1S1 C-D that have large error bars.

Also, when doing the time courses, I find it would be very beneficial to capture an earlier time point for all the lines, to see whether it is easier to capture the digital nature of the regulation. Note that the authors have already pointed that 7 days after sowing might be too late for Ler line to capture the switch.

If the above comments are addressed and the authors manage to clarify how the digital and analog regulation are integrated in the chosen system, I believe this work would have a strong impact on a very wide scientific community, given it tackles a very fundamental question in gene regulation.

Reviewer #1 (Public Review):

The authors present a nice collection of analyses identifying the likely causal locus and pigmentation basis underlying color polymorphism in a model aposematic moth system. In general, the writing and presentation are very clear. There are several areas of the text, however, that could benefit from more clarity and attention to detail. Those changes should be very simple for the authors to make.

My primary concern however is the interpretation of their findings, in light of the lack of analysis of recombination, as well as the flanking region of their identified gene duplication. Specifically, while the authors do an OK job characterizing the genomic region 3' of their identified novel insertion/duplication associated with white coloration, I could not find an analysis of the 5' region, in which there could be other functional elements that could give rise to their "complex polymorphism". Additionally, the authors discuss their findings and the potential of their duplicated region to "provide a region of reduced recombination" (lines 249-251). However, they need to be much more clear with the reader that this is a hypothesis that they have not measured (even though they have WGS data from a sufficient number of individuals estimating LD, which I find strange).

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 #3 (Public Review):

This article aims at investigating the genetic and developmental basis underlying colour pattern polymorphism in the wood tiger moth. It combines GWAS and QTL data pointing at a candidate gene from the yellow gene family. The pattern of gene expression during wing disk development is then consistent with a potential role of this gene in the control of colour pattern variation but functional validation is lacking. The pigment analyses reveal the presence of pheomelanin on the wings, whose synthesis is known to be controlled by a pathway regulated by genes from the yellow family. The identification of these pigments suggests that variations in the colours of the wings in this species could indeed be caused by the regulation of the yellow pathway. Although functional validation establishing the exact role of the valkea gene is lacking, the data provided are in line with a pleiotropic effect of controlled by a small region of the genome enabling the series of phenotypic variations associated with the white coloration. The duplication event restricted to a single haplotype also provides a convincing mechanism for the restriction of recombination in this genomic region. However, the fact that the valkea gene is truncated questions its functionality. It remains possible that the developmental switch could be rather caused by the variations detected in the non-coding part of the duplicated region, causing differential patterns of expression in different genes, including yellow-e. Some deeper discussion is needed on the putative role of the valkea gene vs. of the regulatory regions in controlling the developmental switch between yellow and white morphs.

Altogether, this interesting study provides original and important results on the genetic architecture underlying balanced polymorphism in the wild.

Reviewer #1 (Public Review):

In this manuscript the authors proposed a novel system by which they can suppress the expression of any gene of interest precisely and efficiently with a pre-validated, highly specific and efficient synthetic short-hairpin RNA. The idea of identifying potent artificial RNAi (ARTi) triggers is intriguing, and the authors successfully identify six ARTi with robust knockdown efficiency and limited to no off-target effects. As a proof-of-concept, the authors examined three oncology targets for validation, including EGFRdel19 (which already has a clinically approved drug for validation), KRASG12R (for which there are no in vivo compatible inhibitors yet) and STAG1 (which has a synthetic lethal interaction with recurrent loss-of-function mutations of STAG2). The authors demonstrated significant suppression of colony formation and in vivo tumor growth for all three oncology targets.

This novel system could serve as a powerful tool for loss-of-function experiments that are often used to validate a drug target. Not only this tool can be applied in exogenous systems (like EGFRdel19 and KRASG12R in this paper), the authors successfully demonstrated that ARTi can also be used in endogenous systems by CRISPR knocking in the ARTi target sites to the 3'UTR of the gene of interest (like STAG2 in this paper).

ARTi enables specific, efficient, and inducible suppression of these genes of interest, and can potentially improve therapeutic target validations. However, the system cannot be easily generalized as there are some limitations in this system:

• The authors claimed in the introduction sections that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the advantages of ARTi over CRISPR are likely less than claimed.

• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins.

• The approach of knocking-out an endogenous gene followed by replacement of a regulatable gene can also be achieved using regulated degrons, and by tet-regulated promoters included in the gene replacement cassette. The authors should include a discussion of the merits of these approaches compared with ARTi.

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 #1 (Public Review):

Van Dongen et al. investigated the methylation signature of smoking found in the blood among monozygotic twins ascertained from the Netherlands Twin Register. With their unique study design (which by design controls for the influence of age and sex), the authors shed light on DNA methylation levels that vary with smoking status, as well as with smoking cessation. The authors novel study design examined of twin pairs concordant or discordant for smoking status (current, former, never). The authors performed an epigenome-wide association study (EWAS) and identified 13 genome-wide significant CpGs that were differentially methylated between the discordant twin current-never smoking pairs. Another EWAS conducted by the authors found 5 additional genome-wide significant CpGs among current-former smoking discordant pairs. Each of the 13 identified CpG sites between current-former twins have been previously identified as associated with smoking. The authors found that 3 of these 13 CpGs are located within 1Mb of a single nucleotide polymorphism (SNP) previously associated with smoking initiation, suggesting a role for the SNP in both genetic susceptibility of smoking as well as methylation. The authors tested for enrichment of the 13 CpGs within traits and pathways and found enrichment among smoking related traits, as well as the dopaminergic synapse pathway. Interestingly, the authors found that twin pairs discordant for former smoking (former smoking-never pair) had methylation levels that nearly returned to baseline (never smoking) after smoking cessation. These data broaden our understanding of methylation signatures in the blood using a concordant/discordant smoking and twin study design. The authors evaluated within-twin pair methylation differences for the 13 significant CpGs and found twins concordant for smoking status had very little difference between their methylation levels, yet those discordant for smoking status had larger differences with the current-never smoking twins having the largest differences. Importantly, using a dataset with both methylation and RNA sequencing data, the authors found higher methylation at three CpGs was associated with lower gene expression providing functional context for their findings. The authors correctly acknowledge the limitations of only having blood to evaluate methylation signatures and using a methylation array rather than bisulfite sequencing.

There are a couple of aspects that would be useful to help with interpretation of their findings, such as whether presentation of a formal test for trend shows a linear relationship between overall DNA methylation and smoking pack-years and smoking quit time. It would help the reader if the authors could put their findings into context with what has been previously identified in studies such as the Framingham Heart Study or the prior twin study with concordant/discordant twins. While the findings are interesting, the moderate sample size and use of a methylation array rather than sequencing may ultimately lead this work to have only moderate impact on the field.

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 #3 (Public Review):

In order to address their study question of a potential shared genetic predisposition to both smoking and DNA methylation level, they indicate that a MZ discordant pair analysis would be very powerful.

The authors draw on the well-characterized and very large prospective study of twins and family members from the Netherlands, the Netherlands Twin Register (NTR). Over 3000 cohort members have DNA methylation assessed by arrays (450k and Epic). Monozygotic twin pairs discordant and concordant for smoking are included in epigenome-wide analyses, and followed-up using enrichment and gene expression studies.

The results demonstrate that the strongest associations that have been seen in unrelated individuals (such as for AHRR) are seen in the discordant pairs but do not have the statistical power to confirm or reject weaker (yet consistently seen) associations

Some mention of the effect of second-hand smoke (SHS) could be made as it is an exposure to smoke not due to one's own active smoking. As twin pairs often reside together or are in frequent contact/visiting - MZs more than DZ and females more than males, SHS may be attenuating differences between current and non-current smokers in discordant pairs rather than shared genetics. Likewise twin pairs often have the same or related occupation, and if smoking is common at their typical workplace (even if they work at different companies/employers), the non-smoking twin may be exposed to more tobacco than an unrelated never-smoker.

The study sample should be better described, especially with regard to how smoking behavior was assessed, and whether the twins in pairs discordant for smoking differ in characteristics that can affect DNA methylation. These details would be essential for understanding to what degree the observed findings are attributable to smoking.

The study provides important information on the smoking methylation relationship and supports the generally held view that smoking has a direct effect on methylation. Hence, methylation changes are a useful biomarker of current and past smoking. The current results indicate that confounding due to shared genetics is unlikely to be a major factor but some role cannot be excluded.

Reviewer #1 (Public Review):

The manuscript by Warren et al., presents evidence suggesting that aberrant Yap signaling plays a role in epithelial progenitor cell dysregulation in lung fibrosis. This work builds on a body of work in the literature that Hippo signaling is aberrantly regulated in idiopathic pulmonary fibrosis. They use a combination of single nuclear and spatial transcriptomics, together with in vivo conditional genetic perturbations of Hippo signaling in mice, to investigate roles for Yap/Taz signaling in alveolar epithelial homeostasis and remodeling associated with exposure to a fibrosing agent, bleomycin. They show that Taz and Tead1/4 are most abundantly expressed by alveolar type 1 (AT1) cells, but Nf2 immunoreactivity (upstream activator of Hippo) is observed predominantly within airway and AT2 cells. Bleomycin exposure was associated with reduced p-Mst in regenerating alveolar epithelium, that inactivation of Yap/Taz arrested AT2>AT1 differentiation, and inactivation of either Nf2 or Mst1/2 promoted AT1 differentiation after bleomycin exposure and reduced matrix deposition/fibrosis. They go on to show that compromised alveolar regeneration resulting from inactivation of Yap/Taz results in enhanced bronchiolization of injured alveoli. Experiments are well designed and include quantitative endpoints where appropriate, data of high quality, and results are generally supportive of conclusions. These studies provide valuable new data relating to roles for the Hippo pathway in regulation of alveolar homeostasis and epithelial regeneration/remodeling in injury/repair and fibrosis.

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 #3 (Public Review):

The manuscript entitled "Hippo signaling impairs alveolar epithelial regeneration in pulmonary fibrosis" is a rigorous and timely report detailing the significance of Hippo signaling, Taz and Yap in AT2/AT1 differentiation and the subsequent impact on the progression of lung fibrosis versus repair/ regeneration. The authors experimental design and results support their conclusions. The identification of the distinct effects of Taz and Yap in these processes highlight the pathway and specific molecules as potential therapeutic targets.

The major strengths of these studies lie in the rigor of the elegant transgenic developmental/adult injury-repair mouse models combined with spatial transcriptomics and analyses. The weaknesses include a lack of detail presented in the methods, some legends and discussion.

Reviewer #1 (Public Review):

The authors of the current study investigated the effect of the suspension of the Australian breast, bowel and cervical cancer screening program for 3, 6, 9, or 12 months on cancer outcomes and cancer services.

The major strengths of the current study are the usage of the validated Policy1 modelling platform to estimate the effects of delays in the screening program on cancer outcomes. Furthermore, they described a wide range of different scenarios and looked at all three national screening programs together. A clear and detailed description of the screening programs was given. The results are well-described and detailed.

The authors reached their aim. They showed how a disruption of the breast cancer screening program of 12 months led to less screen-detected and interval invasive cancers, and to an increase in the percentage of tumours with a tumour size of more than 15mm or with nodal involvement. In addition, suspension of the bowel screening program for 12 months led to upstaging for 891 tumors. Suspension of the cervix screening program for 12 months let to 27 upstaged tumors, and to 69 extra tumors. On the contrary, suspension of the breast screening for 3 months did not lead to a higher percentage of tumours with a tumor size of more than 15 mm or to a higher percentage of tumors with nodal involvement. Suspension of the bowel screening program for 3 months led to upstaging of 261 tumors, and suspension of the cervical screening program for 6 months led to 21 extra tumors and to 9 upstaged tumors. The conclusion of the authors that 'maintaining screening participation is critical to reducing the burden of cancer at a population level' is therefore not completely correct, as suspension for 3 months might be needed in situations with limited resources and will not have a very large impact on the cancer burden.

This paper predicts upstaging due to the disruptions in the screening program. This information can be used by hospitals so they know what they can expect, and can be used in the future if decisions need to made about suspending the screening program.

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 #3 (Public Review):

This is an interesting manuscript with an important subject pertaining to the impact of COVID-19 pandemic on various delayed schedules of population-based cancer screening, leading to the reduction of screen-detected cancers and the possible upstaging cancers. The results were assessed by simulation model (Policy I modelling) with the demonstration of Australia scenarios including three major cancers, including breast cancer, colorectal cancer, and cervical cancer.

Assess the impacts of COVID-19 disruption to population cancer screening for three major cancers on short-term and long-term outcomes for policy analysis.

The merit of this study is to provide a series of simulated results under disruption scenarios but the weakness are several-fold including lacking of mortality estimates, inadequate assessments and inaccurate reports on missed cancers (interval cancers) and upstaging.

Policy analysis based on disruption scenario through the simulation model would be very informative to guide policy-makers for designing a salvage program to minimize the impacts of COVID-19 disruptions.

Direct reporting data on the empirical disruption scenario instead of relying on the sensitivity analysis of disruption scenario is more transparent and convincing for the public.

Reviewer #1 (Public Review):

Overall, the paper by Dang and colleagues is an interesting addition to the field. This study investigates the relationship between socioeconomic status and lifetime obesity using group-based trajectory modeling. The authors identified three trajectories overall, the most prevalent being stable normal BMI. Overall, higher SES was associated with a greater risk of obesity, which is contradictory to studies that examine the relationship among developed countries. Their findings and conclusions are supported by their analysis/data, however, some consideration and additional details are needed to help understand and be more confident in the final results.

Strengths of this study include:

- The use of novel techniques to investigate the relationship between SES and lifetime obesity, which is important for understanding the life course of disease and for designing future public health interventions and strategies.<br /> - A large sample size.<br /> - The use of a population-based sampling strategy to recruit participants, which helps the generalizability of findings and limits volunteer bias.<br /> - The availability of data on SES and height/weight over a 20-year follow-up, including objectively measured weight and height.<br /> - The availability of important confounders (e.g., physical activity, energy intake).

While overall it is an interesting study, there are some considerations and unclarities that should be addressed.

Weaknesses of this study include:

- Lack of clarity on how the authors conceptualize and define socioeconomic status in some sections of the paper. A limitation is the definition of SES only encompasses educational attainment and occupation, and not other aspects (e.g. income, social class). However, most studies published to date also focus mostly on education and occupation.<br /> - A large majority (~90%) of participants were excluded from the analysis due to missing data on exposures and outcomes. This is a substantial proportion, and it is quite possible that this may have resulted in selection bias for those included vs. those not included, and may limit the generalizability of the findings.<br /> - As with all studies that use self-reported data, there is some potential for information bias. However, the authors do acknowledge this as a limitation in their study.<br /> - There is a lack of clarity with some of the methods (e.g. how multinomial logistic regression was used, latent classes, and how confounders were chosen). The paper would benefit from the inclusion of these details.

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 #1 (Public Review)

There has been a lot of work showing that multi-peaked tuning curves contain more information than single peaked ones. If that's the case, why are single-peaked tuning curves ubiquitous in early sensory areas? The answer, as shown clearly in this paper, is that multi-peaked tuning curves are more likely to produce catastrophic errors.

This is an extremely important point, and one that should definitely be communicated to the broader community. And this paper does an OK job doing that. However, it suffers from two (relatively easily fixable) problems:

I. Unless one is an expert, it's very hard to extract why multi-peaked tuning curves lead to catastrophic errors.

II. It's difficult to figure out under what circumstances multi-peaked tuning curves are bad. This is important, because there are a lot of neurons in sensory cortex, and one would like to know whether multi-peaked tuning curves are really a bad idea there.

And here are the fixes:

I. Fig. 1c is a missed opportunity to explain what's really going on, which is that on any particular trial the positions of the peaks of the log likelihood can shift in both phase and amplitude (with phase being more important). However Fig. 1c shows the average log likelihood, which makes it hard to understands what goes wrong. It would really help if Fig. 1c were expanded into its own large figure, with sample log likelihoods showing catastrophic errors for multi-peaked tuning curves but not for single peaked ones. You could also indicate why, when multi-peaked tuning curves do give the right answer, the error tends to be small.

II. What the reader really wants to know is: would sensory processing in real brains be more efficient if multi-peaked tuning curves were used? That's certainly hard to answer in all generality, but you could make a comparison between a code with single peaked tuning curves and a _good_ code with multi-peaked tuning curves. My guess is that a good code would have lambda_1=1 and c around 0.5 (you could use the module ratio the grid cell people came up with -- I think 1/sqrt(2) -- although I doubt if it matters much). My guess is that it's the total number of spikes, rather than the number of neurons, that matters. Some metric of performance (see point 1 below) versus the contrast of the stimulus and the number of spikes would be invaluable.

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.

Reviewer #1 (Public Review):

Buglak et al. describe a role for the nuclear envelope protein Sun1 in endothelial mechanotransduction and vascular development. The study provides a full mechanistic investigation of how Sun1 is achieving its function, which supports the concept that nuclear anchoring is important for proper mechanosensing and junctional organization. The experiments have been well designed and were quantified based on independent experiments. The experiments are convincing and of high quality and include Sun1 depletion in endothelial cell cultures, zebrafish, and in endothelial-specific inducible knockouts in mice.

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 #3 (Public Review):

Here, Buglak and coauthors describe the effect of Sun1 deficiency on endothelial junctions. Sun1 is a component of the LINC complex, connecting the inner nuclear membrane with the cytoskeleton. The authors show that in the absence of Sun1, the morphology of the endothelial adherens junction protein VE-cadherin is altered, indicative of increased internalization of VE-cadherin. The change in VE-cadherin dynamics correlates with decreased angiogenic sprouting as shown using in vivo and in vitro models. The study would benefit from a stricter presentation of the data and needs additional controls in certain analyses.

1. The authors implicate the changes in VE-cadherin morphology to be of consequence for "barrier function" and mention barrier function frequently throughout the text, for example in the heading on page 12: "SUN1 stabilizes endothelial cell-cell junctions and regulates barrier function". The concept of "barrier" implies the ability of endothelial cells to restrict the passage of molecules and cells across the vessel wall. This is tested only marginally (Suppl Fig 1F) and these data are not quantified. Increased leakage of 10kDa dextran in a P6-7 Sun1-deficient retina as shown here probably reflects the increased immaturity of the Sun1-deficient retinal vasculature. From these data, the authors cannot state that Sun1 regulates the barrier or barrier function (unclear what exactly the authors refer to when they make a distinction between the barrier as such on the one hand and barrier function on the other). The authors can, if they do more experiments, state that loss of Sun1 leads to increased leakage in the early postnatal stages in the retina. However, if they wish to characterize the vascular barrier, there is a wide range of other tissue that should be tested, in the presence and absence of disease. Moreover, a regulatory role for Sun1 would imply that Sun1 normally, possibly through changes in its expression levels, would modulate the barrier properties to allow more or less leakage in different circumstances. However, no such data are shown. The authors would need to go through their paper and remove statements regarding the regulation of the barrier and barrier function since these are conclusions that lack foundation.<br /> 2. In Fig 6g, the authors show that "depletion of GEF-H1 in endothelial cells that were also depleted for SUN1 rescued the destabilized cell-cell junctions observed with SUN1 KD alone". However, it is quite clear that Sun1 depletion also affects cell shape and cell alignment and this is not rescued by GEF-H1 depletion (Fig 6g). This should be described and commented on. Moreover please show the effects of GEF-H1 alone.<br /> 3. In Fig. 6a, the authors show rescue of junction morphology in Sun1-depleted cells by deletion of Nesprin1. The effect of Nesprin1 KD alone is missing.

Reviewer #1 (Public Review):

In mammals, a small subset of genes undergoes canonical genomic imprinting, with highly biased expression in function of parent of origin allele. Recent studies, using polymorphic mouse embryos and tissues, have reevaluating the number of allele-specific expressed genes (ASE) to 3 times more than previously thought, however with most of these novel genes showing a very low ASE (50%-60% bias toward one parental allele). Here, the authors undergo a comparison of 4 datasets and complete bioinformatic reanalysis of 3 recent allele specific RNAseq to study potential novel imprinted genes, using recently released iSoLDE pipeline. Very few genes have been confirmed with true ASE in the different studies and/or validated by pyrosequencing analysis, However, the authors show that most of the newly discovered ASE genes are lying in close proximity of already known imprinted loci and could be co-regulated by these imprinted clusters. This is important to understand how and to which extent imprinted control regions control gene expression.

This manuscript highlights the number of potential false discovered imprinted genes in previous datasets that could result to either lack of replicates, weak allelic ratio or low gene expression and lack of read depth. But the lack of overlap in the ASE called genes (at the exception to the known imprinted genes) between the different datasets is worrying and important to discuss, as the authors did. I would have appreciated more details into the differences between the different datasets that could explain the lack of reproducibility : library preparation protocol, sequencer technology, SNP calling, number of reads per SNP, bioinformatics pipeline.

Studying allele specific expression of lowly expressed genes is difficult by technology based on PCR amplification (library preparation, pyrosequencing) and could result on a bias expression only due to the random amplification of a small pool of molecules. Could the author compare the level of expression of their different classes of genes? The more robust ASE genes in their study could be the more highly expressed? Several genes were identified only in one or two of the previous studies, were they expressed in the other studies when not define as ASE? This would also allow defining a threshold of expression to study allelic bias in the future. To conclude, this study is an important resource for the epigenetic field and better understand genomic imprinting.

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 #3 (Public Review):

Genomic imprinting is a striking example of epigenetic inheritance in mammals with profound influence on growth and development. A powerful experimental approach to the study of imprinting involves reciprocal mouse F1 crosses; it allows direct assessment of the parent-of-origin effects in a genetically uniform setting that is also an order of magnitude richer in polymorphism than human samples. Use of RNA sequencing is a natural fit to systematic quantitative analysis of allele-specific expression; however, multiple RNA-seq studies of imprinting in F1 mouse tissues wildly disagree in the estimated numbers of novel imprinted genes and in the extent of allelic bias in these genes. In their study, Edwards et al. start with an observation that existing studies varied in their experimental design and data analysis procedures. To assess to what extent disagreements between findings are due to different data processing, they re-analyzed several published datasets using a single pipeline. Furthermore, they performed experimental validation of a number of the novel candidate imprinted genes using primer extension on RT-PCR products (pyrosequencing), to estimate the number of false positives.

Between re-analysis of RNA-seq datasets and the validation experiments, this study presents convincing evidence that most candidate novel imprinted genes are artefactual. The discordant predictions between studies remain even after processing all the data following ISoLDE protocol. Importantly, validated candidate genes tended to be on the periphery of known imprinted domains, suggesting that their boundaries are yet to be finalized.

This work brings into focus an important issue of reproducible analysis and interpretation of RNA sequencing data, especially the analysis of allele-specific expression, including in the specific case of imprinted genes. With novel molecular mechanisms described recently (such as H3K27me3-related parent-of origin gene regulation) and greater accuracy of measuring subtle allelic bias afforded by deep sequencing, the authors' suggested classification (canonical, weak canonical, non-canonical, and weakly biased) is a useful pragmatic step in dealing with the confusing terminology in different studies.

The authors make a strong case that the data analysis methods used in the analyzed studies are prone to false positives. However, the approaches they use are more of an invitation to further dialogue than a definitive recipe to follow. For example, the authors mention that combining the results of several analytical approaches should increase accuracy. However, if those approaches are erroneous, this could lead to two types of error: (1) tools might be erroneous in a similar way, then consistency of results might be taken as confirmation of correctness, (2) averaging results from tools with opposite biases would lead to loss of signal. In the long run, there is no substitute to developing statistically accurate tools and validating that they correctly deal with noise in the data. On the experimental side, Pyrosequencing also involves PCR. This does not change the main conclusions of this study but going forward, it is worth focusing on the methods less affected by amplification (such as allele-specific FISH, ddPCR, or direct RNA sequencing).

Reviewer #1 (Public Review):

In humans, mutations in specific ribosomal protein genes and ribosome assembly factors cause a group of diseases collectively known as ribosomopathies. Patients with these diseases typically display a number of remarkably similar tissue specific phenotypes including anemia and craniofacial abnormalities. The causes of the tissue specificity of these disorders have long remained an outstanding question in the field, and more recent evidence points to the induction of nucleolar stress which triggers a p53-dependent response and cell death. In previous work, the authors have shown that loss and gain of Drosophila Rps12 causes a number of unexpected phenotypes. This current paper seeks to investigate the function of Rps12 in mice.

The authors generate a conditional knockout allele within the mouse Rps12 locus and show that homozygous loss of Rps12 results in early embryonic lethality, while heterozygous mutants display a number of cell specific defects in the hematopoietic system. The authors provide evidence that haploinsufficiency of Rps12 results in erythropoiesis defects that worsen with age, a decrease in the number of hematopoietic progenitor cells, and disruption of hematopoietic stem cell (HSC) quiescence correlated with a failure of mutant HSCs to reconstitute peripheral blood. Strikingly, loss of Rps12 results in increased translation in HSCs and early progenitors, marked by activation of MEK/ERK and ARK/TOR signaling pathways.

Strengths<br /> The paper provides new evidence that loss of Rps12 results in a number of specific defects in the hematopoietic system. The phenotypic characterization is rigorous and clearly described in the text. The observations Rps12 heterozygotes exhibit increases in protein synthesis and loss of HSC quiescence are interesting and warrant further investigation. This paper will have broad appeal to those interested in development, stem cell maintenance, ribosome biology, and ribosomopathies.

Weaknesses<br /> The Rps12 gene has two embedded snoRNAs, the disruption of which could contribute to all of the described phenotypes. Additional work is needed to confirm that the mutant phenotypes are caused specifically by loss of Rps12

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 #3 (Public Review):

In this manuscript, the authors studied the erythropoiesis and hematopoietic stem/progenitor cell (HSPC) phenotypes in a ribosome gene Rps12 mutant mouse model. They found that RpS12 is required for both steady and stress hematopoiesis. Mechanistically, RpS12+/- HSCs/MPPs exhibited increased cycling, loss of quiescence, protein translation rate, and apoptosis rates, which may be attributed to ERK and Akt/mTOR hyperactivation. Overall, this is a new mouse model that sheds light into our understanding of Rps gene function in murine hematopoiesis. The phenotypic and functional analysis of the mice are largely properly controlled, robust, and analyzed.

A major weakness of this work is its descriptive nature, without a clear mechanism that explains the phenotypes observed in RpS12+/- mice. It is possible that the counterintuitive activation of ERK/mTOR pathway and increased protein synthesis rate is a compensatory negative feedback. Direct mechanism of Rps12 loss could be studied by ths acute loss of Rps12, which is doable using their floxed mice. At the minimum, this can be done in mammalian hematopoietic cell lines.

Below are some specific concerns need to be addressed.

1. Line 226. The authors conclude that "Together, these results suggest that RpS12 plays an essential role in HSC function, including self-renewal and differentiation." The reviewer has three concerns regarding this conclusion and corresponding Figure3. 1) The data shows that RpS12+/- mice have decreased number of both total BM cells and multiple subpopulations of HSPCs. The frequency of HSPC subpopulations should also be shown to clarify if the decreased HSPC numbers arises from decreased total BM cellularity or proportionally decrease in frequency. 2) This figure characterizes phenotypic HSPC in BM by flow and lineage cells in PB by CBC. HSC function and differentiation are not really examined in this figure, except for the colony assay in Figure 3K. BMT data in Figure4 is actually for HSC function and differentiation. So the conclusion here should be rephrased. 3) Since all LT-, ST-HSCs, as well as all MPPs are decreased in number, how can the authors conclude that Rps12 is important for HSC differentiation? No experiments presented here were specifically designed to address HSC differentiation.

2. Figure 3A and 5E. The flow cytometry gating of HSC/MPP is not well performed or presented, especially HSC plot. Populations are not well separated by phenotypic markers. This concerns the validity of the quantification data.

3. It is very difficult to read bone marrow cytospin images in Fig 6F without annotation of cell types shown in the figure. It appears that WT and +/- looked remarkably different in terms of cell size and cell types. This mouse may have other profound phenotypes that need detailed examination, such as lineage cells in the BM and spleen, and colony assays for different types of progenitors, etc.

4. For all the intracellular phospho-flow shown in Fig7, both a negative control of a fluorescent 2nd antibody only and a positive stimulus should be included. It is very concerning that no significant changes of pAKT and pERK signaling (MFI) after SCF stimulation from the histogram in WT LSKs. There are no distinct peaks that indicate non-phospho-proteins and phospho-proteins. This casts doubt on the validity of results. It is possible though that Rsp12+/- have very high basal level of activation of pAKT/mTOR and pERK pathway. This again may point to a negative feedback mechanism of Rps12 haploinsufficiency.

5. The authors performed in vitro OP-Puro assay to assess the global protein translation in different HSPC subpopulations. 1) Can the authors provide more information about the incubation media, any cytokine or serum included? The incubation media with supplements may boost the overall translation status, although cells from WT and RpS12+/- are cultured side by side. Based on this, in vivo OP-Puro assay should be performed in both genotypes. 2) Polysome profiling assay should be performed in primary HSPCs, or at least in hematopoietic cell lines. It is plausible that RpS12 haploinsufficiency may affect the content of translational polysome fractions.

Reviewer #1 (Public Review):

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

Strengths:

The technical advances in the measurement of animal interactions with food will help advance the understanding of feeding behavior and motivational states.

The correlation of specific types of food interactions across satiation state, sex, and circadian time will help drive forward the understanding of the scope of an animal's goals with feeding, and likely their relation between species and eating disorders.

The assessment of mushroom body circuitry in a type of food interaction is helpful for understanding the coding of feeding control in the brain.

Limitations:

All feeding data presented in the manuscript are from the interactions of individual flies with a source of liquid food, where interaction is defined as 'physical contact of a specific duration.' It would be helpful to approach the measurement of feeding from multiple angles to form the notion of hedonic feeding since the debate around hedonic feeding in Drosophila has been ongoing for some time and remains controversial. One possibility would be to measure food intake volumetrically in addition to food interaction patterns and durations (e.g. via the modified CAFE assay used by Ja).

Some of the statistical analyses were presented in a way that may make understanding the data unnecessarily difficult for readers. Examples include:

1) In Table I the authors present food interaction classifications based on direct observation. These are helpful. However, the classification system is updated or incompletely used as the manuscript progresses, most importantly changing from four categories with seven total subcategories to three categories and no subcategories. In subsequent data analyses, only one or two of these categories are assessed. It would be helpful, especially when moving from direct observation to automated categorization, to quantify the exact correspondences between all of the prior and new classifications, as well as elaborate on the types of data that are being excluded.

2) The authors switch between a variety of biological and physiological conditions with varying assays, which makes following the train of reasoning nearly impossible to follow. For example, the authors introduce us to circadian aspects of feeding behavior to introduce the concept of 'meal' and 'non-meal' periods of the day. It is then not clear in which of the subsequent experiments this paradigm is used to measure food interactions. Is it the majority of the subsequent figure panels? However, the authors also use starved flies for some assays, which would be incompatible with circadian-locked meals. The somewhat random and incompletely reported use of males and females, which the authors show behave differently, also makes the results more difficult to parse. Finally, the authors are comparing within-fly for the 'control environment' and between flies for their 'hedonic environment' (Figure 3A and subsequent panels), which I believe is not a good thing to do.

3) Statistical analyses are not always used consistently. For example, in Figures 3B and C, post hoc test results are shown for sucrose vs. yeast interactions, but no such statistics are given for 3E and 3F, preventing readers from assessing if the assay design is measuring what the authors tell us it is measuring.

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 #1 (Public Review):

This work describes a novel high-throughput approach to diverse transgenesis which the authors have named TARDIS for Transgenic Arrays Resulting in Diversity of Integrated Sequences. The authors describe the general approach: the generation of a synthetic 'landing pad' for transgene insertion (as previously reported by this group) that has a split selection hygromycin resistance gene, meaning that only perfect integration with the insert confers resistance to the otherwise lethal hygromycin drug. The authors then demonstrate two possible applications of the technology: individually barcoded lineages for lineage tracing and transcriptional reporter lines generated by inserting multiple promoters. In both cases, the authors did a limited 'proof of concept' study including many important controls, showcasing the potential of the method. The conclusions for applications of this method in C. elegans are supported by the data and the authors discuss important observations and considerations. In the discussion, the authors expand the application of the method beyond C. elegans, which is speculative at this point given that a nontrivial aspect of the success of the method in worms is the self-assembly of DNA into heritable extrachromosomal arrays (a feature that is absent in most other systems).

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 #1 (Public Review):

This work provides a comprehensive assessment of volumetric-MRI-based brain age estimates in relation to AD-related biomarkers and AD risk factors. Brain age modeling has been studied extensively in recent years. Brain age estimates are suggested surrogate markers for aging-associated changes in the brain. This paper provides findings on how brain age estimates are associated with AD-related amyloid and tau accumulation, cerebrovascular white matter disease, and unspecific neurodegeneration detected by plasma NfL and to some extent CSF NfL as well. The authors also provide important results on sex-specific differences in these associations.

Strengths:

Modeling and analyses were performed on different observational cohorts. Analysis was repeated for the cognitively unimpaired, and individuals with MCI separately.

Weaknesses:

Although the authors concluded that brain age prediction is a biomarker of AlD pathology, only associations were assessed in this study. Further analyses are required to truly assess the biomarker value of brain age prediction for AD pathology.

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 #3 (Public Review):

Cumplido-Mayoral and colleagues' study focused on the brain-age paradigm in the context of Alzheimer's disease risk. The goal was to valid brain-age 'deltas' by assessing how they relate to Alzheimer's biomarkers and related neurodegenerative measures. They did this by training a new brain-age model on FreeSurfer phenotypes (cortical and subcortical) using the UK Biobank dataset. They then tested multiple datasets including ALFA, ADNI, OASIS, and EPAD, focusing on cognitively unimpaired people and people with mild cognitive impairment. Using brain-age deltas calculated in the test sets, the authors then tested associations with a range of dementia-related measures, including the presence of MCI, APOE e4, amyloid and tau positivity, white matter hyperintensity volume and NfL levels from plasma or CSF.

Strengths include using multiple independent datasets from different sources. This provides large sample sizes and access to different data types. Another strength is the efforts to understand drivers of brain age prediction, by using the SHAP technique. The authors include a newly trained brain-age prediction model, which appears to work as well as existing alternative methods.

A weakness is the number of tests conducted and the absence of multiple comparison corrections. A problem with the SHAP analysis is that it does not account for the correlated nature of the input features.

Overall, the study met the stated aims, and I anticipate the results to make a positive contribution to the research field. The results tended to support the conclusions, particularly regarding the relationship between brain-age delta and the markers of neurodegeneration, AD risk, and cerebrovascular health. The only concern around this is whether the number of tests conducted has inflated the type I error rate and resulted in some false positives. This could have been explored further. The conclusions are sex differences are less well supported by the evidence. While some delta-by-sex interactions were significant, others were not (e.g., Figure 3), however, the interpretation focuses only on the significant ones to support blanket statements about the differences between males and females with regard to neurodegeneration. Given the issues about multiple comparisons, this seems premature and somewhat uneven.

Reviewer #1 (Public Review):

This study uses a rigorous methodological approach to chart thalamocortical tracts originating from distinct thalamic nuclei, coupled with a model to characterize relative tissue and fluid components along these tracts. This allows a precise description of changes specific to tracts between thalamic nuclei and distinct cortical projection areas. In conjunction with analyses of the microstructure at various distances along the tracts between the thalamus and cortex, these results demonstrate a remarkably consistent organization of thalamic projections as early as 23w, while also highlighting specific gestational-age (GA) dependent processes specific to each tract. This provides a strong step forward in characterizing the development of fetal white matter tracts from non-invasive diffusion MRI data.

Performing detailed neuroimaging analyses of fetal brain development incurs myriad technical challenges, and significant effort has been applied to overcome these. Nevertheless, several aspects of the approaches employed would benefit from better justification. For example, while acquisition parameters necessarily differ from those used in studies in post-natal developmental, or even adult, diffusion MRI studies, this raises several questions regarding the applicability of the modeling analyses employed (in particular, MSMT-CSD with low b-value dMRI data). Additionally, the normalization approach for assessing location-specific differences along each tract is complicated by the gross changes in brain size occurring during this period. Distinguishing the contribution of location-specific changes in microstructure from topographical change (e.g., terminal zones may constitute a smaller relative portion of the tract at later GAs), would enhance the inferences drawn from these results.

It's unclear from the methods how mothers were recruited to get the range of GAs represented, and whether this incurred any demographic correlations to GA. Some more description of recruitment, and a demographic comparison to GA, to clarify that there was not likely to be bias in who was scanned at different times (e.g., 2nd vs 3rd trimester) would strengthen the generalizability of these results.

The statistical basis for comparison among GA groups in the analysis of location-dependent changes in microstructure is not clear. E.g., the characterization of the depths at which GA-dependent differences in tissue fraction occur should be more clearly laid out, such that these observations can be demonstrated quantitatively, rather than reported descriptively.

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 #3 (Public Review):

The period that is examined is in the range (21 to 37GW) and uses tractography to delineate five distinct thalamocortical pathways. The paper generates anatomically constrained whole-brain connectomes for each gestational week. The authors parcellate the thalamus according to to streamline connectivity that has been published about two decades ago. The authors delineate the developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. The study included the primary motor cortex, primary sensory cortex, posterior parietal cortex, dorsolateral prefrontal cortex, and primary visual cortex. With the limitations of the method, the authors delineated five major thalamocortical pathways in each gestational week.

The study finds consistent and distinct origins of different tracts, resembling the adult topology of thalamic nuclei as early as 23W gestation. The study monitors the transient compartment of the subplate and intermediate zone, internal capsule, and establishes references to complement histological knowledge.

The paper's hypothesis is straightforward: "the biological processes occurring in different fetal compartments leads to predictable changes in diffusion metrics along tracts, reflecting the appearance and resolution of these transient zones." Study transient structures, such as subcortical plate or subplate. The authors predict that as subplate neurons disappear the tissue fraction is becoming relatively higher in the deep grey matter and the cortical plate and lower in the subplate. The authors investigate this by characterising the entire trajectory of tissue composition changes between the thalamus and the cortex, to explore the role of transient fetal brain developmental structures on white matter maturational trajectories. The authors demonstrate that along-tract sampling of diffusion metrics can capture temporal and compartmental differences in the second to the third trimester, reflecting the maturing neurobiology of the fetal brain described in histology studies.

Reviewer #1 (Public Review):

In this manuscript, Harada et al. build upon prior studies in honeybees and mammalian cells that high levels of mannose impair proliferation, glucose entry into glycolysis. Here, they find that an inability to adequately metabolize mannose results in dNTP depletion and impaired DNA synthesis at replication forks, which sensitizes to chemotherapy. They provide solid evidence that dNTP depletion is sufficient to impair proliferation and increase chemosensitivity, although causality in the context of an inability to metabolize mannose is not established.

Strengths:<br /> This is a very rigorous, well-designed study and the findings are valuable and broadly interesting for the metabolism and cancer communities. The methods are comprehensive and the experimental details in the legends are complete.

Weaknesses:<br /> When giving context to their work, the authors focus heavily on what is known about mannose metabolism in honeybees and do not discuss thoroughly what is known in cancer cells, including prior work that performed very in-depth metabolic phenotyping of mannose phosphate isomerase low and high cells. The claim that the activity of the pentose phosphate pathway is not affected by mannose is not completely justified by the data presented, as pathway flux is not examined. Moreover, the mechanistic connection between mannose and dNTP depletion is not established. Finally, causality for dNTP depletion in cell cycle perturbation and chemosensitivity is not established.

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 #3 (Public Review):

The manuscript approaches an important problem associated with mannose challenge and subsequent changes in metabolism and DNA replication. The researchers employed MPI-KO human cancer cells to explore the key mechanism behind the anti-cancer activity of mannose, and demonstrated that the large influx of mannose exceeding the capacity to metabolize it, that is, the onset of 'honeybee syndrome', induced dramatic metabolic remodeling that led to dNTP loss.

• They established MPI-KO human cancer cells using the CRISPR-Cas9 system, and exploited the mannose auxotrophy and sensitivity observed in MPI-KO mouse embryonic fibroblasts (MPI- KO MEFs) (DeRossi et al., 2006). The addition of a physiological concentration of mannose (50 μM, unchallenged) to culture medium supported the proliferation of MPI-KO MEFs. However, mannose challenge increased the sensitivity of MPI-KO HT1080 cells to DNA replication inhibitors (i.e., cisplatin and doxorubicin) when the cells had been preconditioned with excess 5 mannose prior to the drug treatment.<br /> • Thus, induction of honeybee syndrome suppresses cell proliferation and increases chemosensitivity in MPI-KO human cancer cell models.<br /> • These results suggest that mannose challenge severely impairs the entry of the cells into S phase and its progression to mitotic phase. Strikingly, however, switching of the mannose-challenge medium to the mannose-unchallenged medium after long-term mannose challenge (6 days) resulted in robust cell proliferation.<br /> • The researchers observed downregulation of proteins related to the cell cycle and DNA replication in mannose-challenged cells (Fig. 3A), which were enriched with the mini-chromosome maintenance 2-7 (MCM2-7) complex.<br /> • Together, these results indicate that mannose challenge disengages dormant origins from DNA synthesis during replication stress, thus exacerbating DNA damage.<br /> • Our finding that DNA synthesis from dormant origins during replication stress is highly sensitive to the dNTP pool size is in good agreement with the therapeutic advantages of RNR inhibition in enhancing the efficacy of radiochemotherapy (Kunos and Ivy, 2018).<br /> The work is of potentially great importance in understanding the action of mannose on cancer cells and the resulting sensitization to anti-cancer agents.

Reviewer #1 (Public Review):

It is now widely accepted that the age of the brain can differ from the person's chronological age and neuroimaging methods are ideally suited to analyze the brain age and associated biomarkers. Preclinical studies of rodent models with appropriate neuroimaging do attest that lifestyle-related prevention approaches may help to slow down brain aging and the potential of BrainAGE as a predictor of age-related health outcomes. However, there is a paucity of data on this in humans. It is in this context the present manuscript receives its due attention.

Comments:

1) Lifestyle intervention benefits need to be analyzed using robust biomarkers which should be profiled non-invasively in a clinical setting. There is increasing evidence of the role of telomere length in brain aging. Gampawar et al (2020) have proposed a hypothesis on the effect of telomeres on brain structure and function over the life span and named it as the "Telomere Brain Axis". In this context, if the authors could measure telomere length before and after lifestyle intervention, this will give a strong biomarker utility and value addition for the lifestyle modification benefits.

2) Authors should also consider measuring BDNF levels before and after lifestyle intervention.

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 #3 (Public Review):

The authors report on an interesting study that addresses the effects of a physical and dietary intervention on accelerated/decelerated brain ageing in obese individuals. More specifically, the authors examined potential associations between reductions in Body-Mass-Index (BMI) and a decrease in relative brain-predicted age after an 18-months period in N = 102 individuals. Brain age models were based on resting-state functional connectivity data. In addition to change in BMI, the authors also tested for associations between change in relative brain age and change in waist circumference, six liver markers, three glycemic markers, four lipid markers, and four MRI fat deposition measures. Moreover, change in self-reported consumption of food, stratified by categories such as 'processed food' and 'sweets and beverages', was tested for an association with change in relative brain age. Their analysis revealed no evidence for a general reduction in relative brain age in the tested sample. However, changes in BMI, as well as changes in several liver, glycemic, lipid, and fat-deposition markers showed significant covariation with changes in relative brain age. Three markers remained significant after additionally controlling for BMI, indicating an incremental contribution of these markers to change in relative brain age. Further associations were found for variables of subjective food consumption. The authors conclude that lifestyle interventions may have beneficial effects on brain aging.

Overall, the writing is concise and straightforward, and the langue and style are appropriate. A strength of the study is the longitudinal design that allows for addressing individual accelerations or decelerations in brain aging. Research on biological aging parameters has often been limited to cross-sectional analyses so inferences about intra-individual variation have frequently been drawn from inter-individual variation. The presented study allows, in fact, investigating within-person differences. Moreover, I very much appreciate that the authors seek to publish their code and materials online, although the respective GitHub project page did not appear to be set to 'public' at the time (error 404). Another strength of the study is that brain age models have been trained and validated in external samples. One further strength of this study is that it is based on a registered trial, which allows for the evaluation of the aims and motivation of the investigators and provides further insights into the primary and secondary outcomes measures (see the clinical trial identification code).

One weakness of the study is that no comparison between the active control group and the two experimental groups has been carried out, which would have enabled causal inferences on the potential effects of different types of interventions on changes in relative brain age. In this regard, it should also be noted that all groups underwent a lifestyle intervention. Hence, from an experimenter's perspective, it is problematic to conclude that lifestyle interventions may modulate brain age, given the lack of a control group without lifestyle intervention. This issue is fueled by the study title, which suggests a strong focus on the effects of lifestyle intervention. Technically, however, this study rather constitutes an investigation of the effects of successful weight loss/body fat reduction on brain age among participants who have taken part in a lifestyle intervention. In keeping with this, the provided information on the main effect of time on brain age is scarce, essentially limited to a sign test comparing the proportions of participants with an increase vs. decrease in relative brain age. Interestingly, this analysis did not suggest that the proportion of participants who benefit from the intervention (regarding brain age) significantly exceeds the number of participants who do not benefit. So strictly speaking, the data rather indicates that it's not the lifestyle intervention per sé that contributes to changes in brain age, but successful weight loss/body fat reduction. In sum, I feel that the authors' claims on the effects of the intervention cannot be underscored very well given the lack of a control group without lifestyle intervention.

Another major weakness is that no rationale is provided for why the authors use functional connectivity data instead of structural scans for their age estimation models. This gets even more evident in view of the relatively low prediction accuracies achieved in both the validation and test sets. My notion of the literature is that the vast majority of studies in this field implicate brain age models that were trained on structural MRI data, and these models have achieved way higher prediction accuracies. Along with the missing rationale, I feel that the low model performances require some more elaboration in the discussion section. To be clear, low prediction accuracies may be seen as a study result and, as such, they should not be considered as a quality criterion of the study. Nevertheless, the choice of functional MRI data and the relevance of the achieved model performances for subsequent association analysis needs to be addressed more thoroughly.

Reviewer #1 (Public Review):

In the paper, the authors illustrated a novel method for Electrolytic Lesioning through a microelectronics array. This novel lesioning technique is able to perform long-term micro-scale local lesions with a fine spatial resolution (mm). In addition, it allows a direct comparison of population neural activity patterns before and after the lesions using electrophysiology. This new technique addresses a recent challenge in the field and provides a precious opportunity to study the natural reorganization/recovery at the neuronal population level after long-term lesions. It will help discover new causal insights investigating the neural circuits controlling behavior.

Several minor concerns are summarized below:

It was not always clear what the lesion size was. This information is important for future applications, for example, in the visual cortex, where neurons are organized in retinotopy patterns.

It would be helpful if the author could add some discussion about whether and how this method could be used in other types of array/multi-contact electrodes, such as passive neuropixels, S-probes, and so on. In addition, though an op-amp was used in the design, it would still be helpful if the author could provide a recommended range for the impedance of the electrodes.

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 #1 (Public Review):

This paper presents a highly compelling and novel hypothesis for how the brain could generate signals to guide navigation towards remembered goals. Under this hypothesis, which the authors call "Endotaxis", the brain co-opts its ancient ability to navigate up odor gradients (chemotaxis) by generating a "virtual odor" that grows stronger the closer the animal is to a goal location. This idea is compelling from an evolutionary perspective and a mechanistic perspective. The paper is well-written and delightful to read.

The authors develop a detailed model of how the brain may perform "Endotaxis", using a variety of interconnected cell types (point, map, and goal cells) to inform the chemotaxis system. They tested the ability of this model to navigate in several state spaces, representing both physical mazes and abstract cognitive tasks. The Endotaxis model performed reasonably well across different environments and different types of goals.

The authors further tested the model using parameter sweeps and discovered a critical level of network gain, beyond which task performance drops. This critical level approximately matched analytical derivations.

My main concern with this paper is that the analysis of the critical gain value (gamma_c) is incomplete, making the implications of these analyses unclear. There are several different reasonable ways in which the Endotaxis map cell representations might be normalized, which I suspect may lead to different results. Specifically, the recurrent connections between map cells may either be an adjacency matrix, or a normalized transition matrix. In the current submission, the recurrent connections are an un-normalized adjacency matrix. In a previous preprint version of the Endotaxis manuscript, the recurrent connections between the map cells were learned using Oja's rule, which results in a normalized state-transition matrix (see "Appendix 5: Endotaxis model and the successor representation" in "Neural learning rules for generating flexible predictions and computing the successor representation", your reference 17). The authors state "In summary, this sensitivity analysis shows that the optimal parameter set for endotaxis does depend on the environment". Is this statement, and the other conclusions of the sensitivity analysis, still true if the learned recurrent connections are a properly normalized state-transition matrix?

Overall, this paper provides a very compelling model for how neural circuits may have evolved the ability to navigate towards remembered goals, using ancient chemotaxis circuits.

This framework will likely be very important for understanding how the hippocampus (and other memory/navigation-related circuits) interfaces with other processes in the brain, giving rise to memory-guided behavior.

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 #3 (Public Review):

This paper argues that it has developed an algorithm conceptually related to chemotaxis that provides a general mechanism for goal-directed behaviour in a biologically plausible neural form.

The method depends on substantial simplifying assumptions. The simulated animal effectively moves through an environment consisting of discrete locations and can reliably detect when it is in each location. Whenever it moves from one location to an adjacent location, it perfectly learns the connectivity between these two locations (changes the value in an adjacency matrix to 1). This creates a graph of connections that reflects the explored environment. In this graph, the current location gets input activation and this spreads to all connected nodes multiplied by a constant decay (adjusted to the branching number of the graph) so that as the number of connection steps increases the activation decreases. Some locations will be marked as goals through experiencing a resource of a specific identity there, and subsequently will be activated by an amount proportional to their distance in the graph from the current location, i.e., their activation will increase if the agent moves a step closer and decrease if it moves a step further away. Hence by making such exploratory movements, the animal can decide which way to move to obtain a specified goal.

I note here that it was not clear what purpose, other than increasing the effective range of activation, is served by having the goal input weights set based on the activation levels when the goal is obtained. As demonstrated in the homing behaviour, it is sufficient to just have a goal connected to a single location for the mechanism to work (i.e., the activation at that location increases if the animal takes a step closer to it); and as demonstrated by adding a new graph connection, goal activation is immediately altered in an appropriate way to exploit a new shortcut, without the goal weights corresponding to this graph change needing to be relearnt.

Given the abstractions introduced, it is clear that the biological task here has been reduced to the general problem of calculating the shortest path in a graph. That is, no real-world complications such as how to reliably recognise the same location when deciding that a new node should be introduced for a new location, or how to reliably execute movements between locations are addressed. Noise is only introduced as a 1% variability in the goal signal. It is therefore surprising that the main text provides almost no discussion of the conceptual relationship of this work to decades of previous work in calculating the shortest path in graphs, including a wide range of neural- and hardware-based algorithms, many of which have been presented in the context of brain circuits.

The connection to this work is briefly made in appendix A.1, where it is argued that the shortest path distance between two nodes in a directed graph can be calculated from equation 15, which depends only on the adjacency matrix and the decay parameter (provided the latter falls below a given value). It is not clear from the presentation whether this is a novel result. No direct reference is given for the derivation so I assume it is novel. But if this is a previously unknown solution to the general problem it deserves to be much more strongly featured and either way it needs to be appropriately set in the context of previous work.

Once this principle is grasped, the added value of the simulated results is somewhat limited. These show: 1) in practical terms, the spreading signal travels further for a smaller decay but becomes erratic as the decay parameter (map neuron gain) approaches its theoretical upper bound and decreases below noise levels beyond a certain distance. Both follow the theory. 2) that different graph structures can be acquired and used to approach goal locations (not surprising) .3) that simultaneous learning and exploitation of the graph only minimally affects the performance over starting with perfect knowledge of the graph. 4) that the parameters interact in expected ways. It might have been more impactful to explore whether the parameters could be dynamically tuned, based on the overall graph activity.

Perhaps the most biologically interesting aspect of the work is to demonstrate the effectiveness, for flexible behaviour, of keeping separate the latent learning of environmental structure and the association of specific environmental states to goals or values. This contrasts (as the authors discuss) with the standard reinforcement learning approach, for example, that tries to learn the value of states that lead to reward. Examples of flexibility include the homing behaviour (a goal state is learned before any of the map is learned) and the patrolling behaviour (a goal cell that monitors all states for how recently they were visited). It is also interesting to link the mechanism of exploration of neighbouring states to observed scanning behaviours in navigating animals.

The mapping to brain circuits is less convincing. Specifically, for the analogy to the mushroom body, it is not clear what connectivity (in the MB) is supposed to underlie the graph structure which is crucial to the whole concept. Is it assumed that Kenyon cell connections perform the activation spreading function and that these connections are sufficiently adaptable to rapidly learn the adjacency matrix? Is there any evidence for this? As discussed above, the possibility that an algorithm like 'endotaxis' could explain how the rodent place cell system could support trajectory planning has already been explored in previous work so it is not clear what additional insight is gained from the current model.

Reviewer #1 (Public Review):

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

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

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

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

Reviewer #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 #3 (Public Review):

Lauterbur et al. present an expansion of the whole-genome evolution simulation software "stdpopsim", which includes new features of the simulator itself, and 15 new species in their catalog of demographic models and genetic parameters (which previously had 6 species). The list of new species includes mostly animals (12), but also one species of plant, one of algae, and one of bacteria. While only five of the new animal species (and none of the other organisms) have a demographic model described in the catalog, those species showcase a variety of demographic models (e.g. extreme inbreeding of cattle). The authors describe in detail how to go about gathering genetic and demographic parameters from the literature, which is helpful for others aiming to add new species and demographic models to the stdpopsim catalog. This part of the paper is the most widely relevant not only for stdpopsim users but for any researcher performing population genomics simulations. This work is a concrete contribution towards increasing the number of users of population genomic simulations and improving reproducibility in research that uses this type of simulations.

Reviewer #1 (Public Review):

Sun and colleagues outline structural and mechanistic studies of the bacterial adhesin PrgB, an atypical microbial cell surface-anchored polypeptide that binds DNA. The manuscript includes a crystal structure of the Ig-like domains of PrgB, cryo-EM structures of the majority of the intact polypeptide in DNA-bound and free forms, and an assessment of the phenotypes of E. faecalis strains expressing various PrgB mutants.

Generally, the study has been conducted with a good level of rigor, and there is consistency in the findings. However, I do have some specific technical concerns relating to the study. Although the PX work has been expertly undertaken, the Cryo-EM structures reported are both at ~10-angstrom resolution. Visual inspection indicates that the positioning of the PrgB domains into the EM envelopes is somewhat questionable and this needs to be addressed. The narrative of the manuscript very much hinges on this being correct. In addition, wrt the PrgB mutant studies, it could be that the loss of function observed in specific mutants is simply a consequence of mutation-induced misfolding of those polypeptides. Experimental evidence supporting the direct interaction between the PAD and the stalk domains in PrgB is also lacking.

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 #1 (Public Review):

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

Strengths:

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

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

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

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

Weaknesses:

- Very few weaknesses.

- The in vivo efficacy of MS023 does not seem to be very great. The mice treated with MS023 display a very small reduction in ADMA levels and a small increase in SDMA levels (Fig S6A).

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 #3 (Public Review):

Dominici et al studied the effects of the type I PRMT inhibitor MS023 on skeletal muscle stem cells (MuSCs) and on muscle strength in dystrophin-deficient mdx mice. The authors observed an enhanced proliferative capacity of cultured MuSCs with an increase of Pax7+/MyoD- cells. The observations are more or less in line with previous studies of the same group, describing reduced differentiation but enhanced proliferation of MuSCs after genetic inactivation of Prmt1. scRNA-seq identified different subpopulations of MuSCs, showing a shift to increased Pax7 expression and elevated oxidative phosphorylation and glycolysis after treatment with MS023. Treatment of MuSC with MS023 during expansion in vitro enhanced engraftment of MuSCs and treatment of dystrophic mdx mice increased muscle strength.

Overall, the manuscript provides new insights into the beneficial effects of the type I PRMT inhibitor MS023 for skeletal muscle regeneration. The description of the MS023-induced transcriptional and metabolic changes in MuSC is interesting and the effects on MuSC transplantation and muscle strength are stunning. However, the proposed underlying mechanism, which is claimed to rely on the expansion of MuSC and 'reprograming' of MuSCs towards a "unique and previously uncharacterized identity" is not sufficiently supported. The extent of the description of scRNA-seq data is inappropriate. Some conclusions from the scRNA-seq data appear to be overinterpreted or are rather trivial. It remains completely unclear whether the MS023-stimulated increase of metabolic pathway activity (OXPHOS, glycolysis) plays any role for preserving stem cell properties of MuSC during expansion and improves engraftment. Additional functional and mechanistic studies are required to explore the underlying molecular processes. Furthermore, it remains completely unclear whether the acclaimed increase in grip and tetanic strength of mdx mice after MS023 treatment relies on enhanced expansion of MuSC mediated by PRMT1 inhibition.