Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

However, some significant issues need to be addressed.

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

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

Specific comments:

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

The following points may be addressed to improve the study:

Brain age / model performance:

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

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

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

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

RSFC for age prediction:

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

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

The lack of control groups

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

Reviewer #2 (Public Review):

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

Strengths:

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

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

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

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

Weaknesses:

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

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Strengths:

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

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

Weaknesses:

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Specific comments (in order of manuscript):

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

Strengths:

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

Weaknesses:

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

Reviewer #2 (Public Review):

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

Strengths:

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

Weaknesses:

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

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

Strengths

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

Weaknesses

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

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

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

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

Reviewer #2 (Public Review):

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

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

Reviewer #2 (Public Review):

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

Reviewer #2 (Public Review):

This manuscript presents data on multiple experiments regarding the co-evolution of poly-lysogenic and phage-susceptible Klebsiella pneumoniae strains. In particular, the manuscript aimed to determine the mechanisms of resistance that would shape bacterial competition over co-evolutionary timescales. The major finding is that the potential for lysogenization as a phage resistance mechanism is narrow and only likely to occur given certain circumstances. Moreover, the manuscript again reinforces the importance of receptor changes -initially loss, but modification in structure or expression over longer time scales- as a major mechanism of phage resistance that influences bacterial competition.

Strengths<br /> A major strength of this manuscript is the care in designing experiments and conducting follow-up experiments to isolate the essential elements to support each of the conclusions. This includes using orthogonal methods such as sequencing and modeling to support or expand the findings from culturing and experimental evolution. The study features results that were beautifully replicated (e.g. Figure 3) lending confidence to the findings.

Weaknesses<br /> Two weaknesses of the manuscript in its current form are: 1) a need to discuss other studies that also have found context-dependent results and 2) more focus on delivering the key overall "message" of the paper to the reader. Finally, not a weakness, but a (necessary) limitation is the study system, but this manuscript sets a bar for other groups to test in their systems to probe the generality of the findings.

The support for the conclusions is compelling. The findings were counter to the initial expectation (lysogenization as a major feature) and the manuscript does an admirable job of supporting the unexpected conclusion with thorough experimental work, supplemented with modeling.

This manuscript will be of great significance in microbial evolution, both for its implications in limiting the scope of lysogenization as a viable phage resistance mechanism in the long term and for its significant experimental rigor, particularly with regard to the co-evolutionary timescale studied. The study has very important implications for the evolution of antimicrobial resistance and phage therapy.

Reviewer #2 (Public Review):

Insulin exocytosis is a tightly orchestrated process that involves proteins acting in complexes near the plasma membrane. The authors have contributed much of the field's knowledge on how exophilin anchors insulin granules in cortical actin and works with other effectors to prepare granules for exocytosis. Here they find that, while both exophilin and melanophilin localize to the exocyst, functionally they are not equivalent. TIRF imaging of monolayer dispersed beta cells, although a non-physiologic model to study islet cell secretion (which requires homotypic and heterotypic cell coupling), is nonetheless an established method that the authors have used with expert proficiency. The imaging and quantitative methods here should be broadly applicable to those studying secretory events at cellular resolution, and practical details e.g. the need for double transfection in RNAi experiments, are helpful and appreciated.

Reviewer #2 (Public Review):

This manuscript provided evidence that Gaq is a key regulator of the expression of inflammatory cytokines to maintain the proper progress of decidualization of human endometrial stromal cells for successful implantation and pregnancy. The major strength of the manuscript is the experimental design to answer sequential scientific questions regarding the function of Gaq during decidualization in the human endometrium using various molecular and pharmacologic tools. A weak point of this manuscript is that the author did not provide a reason to focus on HDAC5 among various downstream targets for the study of Gaq. In addition, if the authors make a knockout mouse of Gaq and characterize its phenotypes to support what they found in human stromal cells, the findings in this manuscript could become a piece of compelling evidence for the importance of Gaq during decidualization in the human endometrium for a successful pregnancy. This could be the next scientific topic for the authors to pursue this project.

Reviewer #2 (Public Review):

The authors found FOXC2 is mainly expressed in As of mouse undifferentiated spermatogonia (uSPG). About 60% of As uSPG were FOXC2+ MKI67-, indicating that FOXC2 uSPG were quiescent. Similar spermatogonia (ZBTB16+ FOXC2+ MKI67-) were also found in human testis.

The lineage tracing experiment using Foxc2CRE/+;R26T/Gf/f mice demonstrated that all germ cells were derived from the FOXC2+ uSPG. Furthermore, specific ablation of the FOXC2+ uSPGs using Foxc2Cre/+;R26DTA/+ mice resulted in the depletion of all uSPG population. In the regenerative condition created by busulfan injection, all FOXC2+ uSPG survived and began to proliferate at around 30 days after busulfan injection. The survived FOXC2+ uSPGs generated all germ cells eventually. To examine the role of FOXC2 in the adult testis, spermatogenesis of Foxc2f/-;Ddx4-cre mice was analyzed. From a 2-month-old, the degenerative seminiferous tubules were increased and became Sertoli cell-only seminiferous tubules, indicating FOXC2 is required to maintain normal spermatogenesis in adult testes. To get insight into the role of FOXC2 in the uSPG, CUT&Tag sequencing was performed in sorted FOXC2+ uSPG from Foxc2CRE/+;R26T/Gf/f mice 3 days after TAM diet feeding. The results showed some unique biological processes, including negative regulation of the mitotic cell cycle, were enriched, suggesting the FOXC2 maintains a quiescent state in spermatogonia.

Lineage tracing experiments using transgenic mice of the TAM-inducing system was well-designed and demonstrated interesting results. Based on all data presented, the authors concluded that the FOXC2+ uSPG are primitive SSCs, an indispensable subpopulation to maintain adult spermatogenesis.

The conclusion of the mouse study is mostly supported by the data presented, but to accept some of the authors' claims needs additional information and explanation. Several terminologies define cell populations used in the paper may mislead readers.

1) "primitive spermatogonial stem cell (SSC)" is confusing. SSCs are considered the most immature subpopulation of uSPG. Thus, primitive uSPGs are likely SSCs. The naming, primitive SSCs, and transit-amplifying SSCs (Fig. 7K) are weird. In general, the transit-amplifying cell is progenitor, not stem cell. In human and even mouse, there are several models for the classification of uSPG and SSCs, such as reserved stem cells and active stem cells. The area is highly controversial. The authors' definition of stem cells and progenitor cells should be clarified rigorously and should compare to existing models.

2) scRNA seq data analysis and an image of FOXC2+ ZBTB16+ MKI67- cells by fluorescent immunohistochemistry are not sufficient to conclude that they are human primitive SSCs as described in the Abstract. The identity of human SSCs is controversial. Although Adark spermatogonia are a candidate population of human SSCs, the molecular profile of the Adark spermatogonia seems to be heterogeneous. None of the molecular profiles was defined by a specific cell cycle phase. Thus, more rigorous analysis is required to demonstrate the identity of FOXC2+ ZBTB16+ MKI67- cells and Adark spermatogonia.

3) FACS-sorted GFP+ cells and MACS-THY1 cells were used for functional transplantation assay to evaluate SSC activity. In general, the purity of MACS is significantly lower than that of FACS. Therefore, FACS-sorted THY1 cells must be used for the comparative analysis. As uSPGs in adult testes express THY1, the percentage of GFP+ cells in THY1+ cells determined by flow cytometry is important information to support the transplantation data.

4) The lineage tracing experiments of FOXC2+-SSCs in Foxc2CRE/+;R26T/Gf/f showed ~95% of spermatogenic cells and 100% progeny were derived from the FOXC2+ (GFP+) spermatogonia (Fig. 2I, J) at month 4 post-TAM induction, although FOXC2+ uSPG were quiescent and a very small subpopulation (~ 60% of As, ~0.03% in all cells). This means that 40% of As spermatogonia and most of Apr/Aal spermatogonia, which were FOXC2 negative, did not contribute to spermatogenesis at all eventually. This is a striking result. There is a possibility that FOXC2CRE expresses more widely in the uSPG population although immunohistochemistry could not detect them.

5) The CUT&Tag_FOXC2 analysis on the FACS-sorted FOXC2+ showed functional enrichment in biological processes such as DNA repair and mitotic cell cycle regulation (Fig.7D). The cells sorted were induced Cre recombinase expression by TAM diet and cut the tdTomato cassette out. DNA repair process and negative regulation of the mitotic cell cycle could be induced by the Cre/lox recombination process. The cells analyzed were not FOXC2+ uSPG in a normal physiological state.

6) Wei et al (Stem Cells Dev 27, 624-636) have published that FOXC2 is expressed predominately in As and Apr spermatogonia and requires self-renewal of mouse SSCs; however, the authors did not mention this study in Introduction, but referred shortly this at the end of Discussion. Their finding should be referred to and evaluated in advance in the Introduction.

Reviewer #2 (Public Review):

This study uses DNA metabarcoding to identify vertebrates and kākāpō DNA in soils from sites where they are known to occur and from control sites housing related birds. The authors then attempt to identify individual kākāpō birds that have contributed DNA into just three samples with high kākāpō DNA content. For this, they use Oxford Nanopore adaptive sequencing, haplotype identification, and two statistical approaches to determine the number of individuals that contributed to a sample and which specific individuals contributed. This study builds on recent developments in the field that move eDNA into population genomics and individual surveillance.

The manuscript introduction does a satisfactory job of contextualizing the need for this study and the state of the field. It does not detail the challenges of applying adaptive ONT to eDNA samples and the kinds of choices such as selective assays available. I think the authors are using confusing language in the abstract and throughout that is not clear enough to be useful to a reader community that is interested in adopting ONT but not already using it.

As for the methods chosen for this study, I found it peculiar that the authors did not use qPCR specific to kākāpō to estimate the relative proportion of kākāpō eDNA to other vertebrate DNA in the total sample. A fair comparison of methods would make this study more useful to guide the field forward. qPCR should be more sensitive than metabarcoding and is the standard approach for the relative abundance that the terrestrial eDNA community uses for targeted studies.

There is a lot of work done in this study that would be useful to the eDNA community if it were presented clearly. Paragraphs are written often without topic sentences, headings are vague, specific objectives are not clearly outlined, and too many questions remain about why certain approaches were used. For example, there is a selective and non-selective approach used for ONT sequencing. In some places, is not clear what exactly the authors did, and it's not clear why the non-selective approach was preferred by the authors (as they describe in the discussion). The ONT portion of the methods seems written out of order and with frivolous choices about what details to include and omit. No mention of the pore destruction of selective/adaptive sequencing is described, so this study creates hyperbole about the promise of ONT unblocking pores for future research. There are drawbacks! Further, there surely is going to be a lot of interest in the statistical approaches to infer individuals and the number of individuals that shed DNA into a sample but this is not clearly explained. An effort to improve the writing quality throughout is needed prior to publication.

The study fails to describe the scale of the sites and how they are managed. As such, we cannot assess the distance from the site and why kākāpō DNA was found at an abandoned nest site. Maybe it was clear but the names of the sites are inconsistent throughout the ms, and there are assumptions that readers know about this field setting already, which is not a good assumption to make.

The discussion cites nobody and does not put the results back into the broader context of where the science is today. It is a weak discussion that just reiterates the results, but then boasts about the significance of the results when those results referred to were insufficiently described in the manuscript.

Altogether, I think this study has potential if the paper can be improved in clarity and quality. The science is solid and the topic is of great interest to a broad community.

Reviewer #2 (Public Review):

Medwig-Kinney et al. explore the role of the transcription factor NHR-67 in distinguishing between AC and VU cell identity in the C. elegans gonad. NHR-67 is expressed at high levels in AC cells where it induces G1 arrest, a requirement for the AC fate invasion program (Matus et al., 2015). NHR-67 is also present at low levels in the non-invasive VU cells and, in this new study, the authors suggest a role for this residual NHR-67 in maintaining VU cell fate. What this new role entails, however, is not clear. The model in Figure 7E shows NHR-67 switching from a transcriptional activator in ACs to a transcriptional repressor in VUs by virtue of recruiting translational repressors. In this model, NHR-67 actively suppresses AC differentiation in VU cells by binding to its normal targets and acting as a repressor rather than an activator. Elsewhere in the text, however, the authors suggest that NHR-67 is "post-translationally sequestered" (line 450) in nuclear condensates in VU cells. In that model, the low levels of NHR-67 in VU cells are not functional because inactivated by sequestration in condensates away from DNA. Neither model is fully supported by the data, which may explain why the authors seem to imply both possibilities. This uncertainty is confusing and prevents the paper from arriving at a compelling conclusion. What is the function, if any, of NHR-67 and so-called "repressive condensates" in VU cells?

Below we list problems with data interpretation and key missing experiments:

1) The authors report that NHR-67 forms "repressive condensates" (aka. puncta) in the nuclei of VU cells and imply that these condensates prevent VU cells from becoming ACs. Fig. 3A, however, shows an example of an AC that also assemble NHR-67 puncta (these are less obvious simply due to the higher levels of NHR-67 in ACs). The presence of NHR-67 puncta in the AC seems to directly contradict the author's assumption that the puncta repress the AC fate program. Similarly, Figure 5-figure supplement 1A shows that UNC-37 and LSY-22 also form puncta in ACs. The authors need to analyze both AC and VU cells to demonstrate that NHR-67 puncta only form in VUs, as implied by their model.

2) While a pool of NHR-67 localizes to "repressive condensates", it appears that a substantial portion of NHR-67 also exists diffusively in the nucleoplasm. This would appear to contradict a "sequestration model" since, for such a model to work, a majority of NHR-67 should be in puncta. What proportion of NHR-67 is in puncta? Is the concentration of NHR-67 in the nucleoplasm lower in VUs compared to ACs and does this depend on the puncta?

3) The authors do not report whether NHR-67, UNC-37, LSY-22, or POP-1 localization to puncta is interdependent, as implied in the model shown in Fig. 7.

4) The evidence that the "repressor condensates" suppress AC fate in VUs is presented in Fig. 4D where the authors deplete the presumed repressor LSY-22. First, the authors do not examine whether NHR-67 forms puncta under these conditions. Second, the authors rely on a single marker (cdh-3p::mCherry::moeABD) to score AC fate: this marker shows weak expression in cells flanking one bright cell (presumably the AC) which the authors interpret as a VU AC transformation. The authors, however, do not identify the cells that express the marker by lineage analyses and dismiss the possibility that the marker-positive cells could arise from the division of an AC-committed cell. Finally, the authors did not test whether marker expression was dependent on NHR-67, as predicted by the model shown in Fig. 7.

5) Interaction between NHR-67 and UNC-37 is shown using Y2H, but not verified in vivo. Furthermore, the functional significance of the NHR-67/UNC-37 interaction is not tested.

6) Throughout the manuscript, the authors do not use lineage analysis to confirm fate transformation as is the standard in the field. There are 4 multipotential gonadal cells with the potential to differentiate into VUs or ACs. Which ones contribute to the extra ACs in the different genetic backgrounds examined was not determined, which complicates interpretation. The authors should consider and test the following possibilities: disruption of NHR-67 regulation causes 1) extra pluripotent cells to directly become ACs early in development, 2) causes VU cells to gradually trans-fate to an AC-like fate after VU fate specification (as implied by the authors), or 3) causes an AC to undergo extra cell division(s)?? In Fig. 1F, 5 cells are designated as ACs, which is one more that the 4 precursors depicted in Fig. 1A, implying that some of the "ACs" were derived from progenitors that divided.

In conclusion, while the authors report on interesting observations, in particular the co-localization of NHR-67 with UNC-37/Groucho and POP-1 in nuclear puncta, the functional significance of these observations remains unclear. The authors have not demonstrated that the "repressive condensates" are functional and play a role in the suppression of AC fate in VU cells as claimed. The colocalization data suggest that NHR-67 interacts with repressors, but additional experiments are needed to demonstrate that these interactions are specific to VUs, impact VU fate, and sequester NHR-67 from its targets or transform NHR-67 into a transcriptional repressor.

Reviewer #2 (Public Review):

The authors describe a bioinformatic platform that allows for unbiased pathway analysis from multiomics data. The concept is based on correlation, stoichiometry scores and their combination to evidence interaction between two proteins, transcripts or phosphosites in an omic dataset. This platform was developed and validated on both previously published and in house omics data. I really appreciate that the paper is well written and clear, and I would like to acknowledge the amount of work generated to produce the in-house dataset.

Reviewer #2 (Public Review):

In this study, the authors assessed the role of the ER protein VAPA in cell migration and regulation of focal adhesions dynamics. The authors used CRISPR/Cas9 knock-out of VAPA in Caco-2 cells. They demonstrate that VAPA KO cells have slower migration capacity which is linked to a slower FA disassembly rate. Interestingly, the VAPA KO cells don't show any defects of PI4P level at endosomes nor at the Golgi complex but have a decreased PI4,5P2 level, probably linked to the redundant function of VAPB at endosomes and Golgi while VAPA might be solely responsible for effects on migration.

The results provided by the authors support their conclusions. The experiments performed are well carried out. The VAPA KO cells used in this study are originating from a clonal population but the authors used rescue experiments expressing the VAPA wild-type of the KDMD mutant to demonstrate the role of VAPA in the phenotype. In addition, appropriate and careful quantifications are provided with the different experiments, strengthening the conclusions. The data provided in this manuscript suggest a role for the ER-resident membrane contact protein VAPA in cell migration potentially independent of lipid homeostasis.

Reviewer #2 (Public Review):

This study demonstrates that AdipoQ+ cells, which constitute approximately 0.8% of bone marrow mesenchymal cells, are major producers of M-CSF (Csf1) in murine bone marrow. The initial finding was discovered in scRNA seq datasets and studied in depth here with animal models and cellular assays. Deletion of Csf1 with AdipoQ-Cre increased trabecular bone mass in long bones and reduced the number of osteoclasts on trabecular bone surfaces. Cd11b+ F4/80+ macrophage numbers were also reduced in bone marrow. Bone loss from ovariectomy was prevented in Csf1∆AdipoQ female mice. Strengths of this study include use of a tissue-directed knock out (Adipo-Cre) model system to understand the relative contribution of AdipoQ+ cells to Csf1 levels and trabecular bone mass, careful examination of other adipose tissues for Csf1 expression, challenging bone responses in Csf1∆AdipoQ female mice with ovariectomy, and studying the effect of Csf1 deletion in macrophage levels. Mechanical studies of bone strength were not included but would be necessary to determine if deletion of Csf1 in AdipoQ+ cells is sufficient to cause osteopetrosis as concluded by the authors. Additional information on other molecular changes Csf1∆AdipoQ mice would provide insights into how deletion of Csf1 in AdipoQ+ cells affects bone remodeling. Overall, this is a very important study that has a lot of merit. It's impact on the field will be high because it is challenging the paradigm that osteoblasts and osteocytes are the major sources of M-CSF in the bone marrow.

Reviewer #2 (Public Review):

This cell atlassing study used single nuclei RNA-sequencing to profile cell type-specific transcriptional response to COVID-19 across multiple organs. The authors surveyed a cohort of 20 patients including 15 COVID-19 donors and 6 organs including the lung, liver and heart. They then annotated major cell types across these tissues and performed systematic differential gene expression analysis to propose cell type-specific shared transcriptional responses in macrophages and endothelial cells across multiple tissues. Finally, they inferred COVID-19 enriched cell interactions between macrophages and endothelia across multiple organs.

The strengths of the study include cross organ profiling from COVID-19 patients beyond the lungs, the immediate availability of this snRNAseq dataset as a resource and the systematic gene expression analysis that compares cell type specific disease programs across the body. There are several novel observations including dysregulation of insulin signalling in the liver and the heart. Most notable are the putative receptor-ligand interactions identified between macrophages and endothelial cells, an understudied aspect of COVID-19 tissue pathology.

However, the study presents weaknesses that diminish the impact of the resource. First, tissue profiling depth/coverage is lower than existing resources with relatively few number of cells per tissue and, more importantly, a very coarse grained cellular annotation. Second, the extent of coordinated gene expression changes across different organs is not very clear from the analysis presented in the paper, especially for macrophages. Finally, the comparisons to existing resources are not very strong and it would be more impactful to see the orthogonal (IHC or smFISH) validation of the novel snRNASeq observations in this study (e.g. endothelial-macrophage interactions).

Major comments:

1. While multiple organs have been profiled, the overall cell numbers are low (~85k nuclei across six organs) compared to existing studies (Delorey study from broad with ~100k nuclei from lung alone). There is also cell # and type bias towards certain donors - 6 donors (donors 15-20) have significantly more cells than others and majority of certain cell types come from a handful of donors (e.g. fibroblasts in covid lung). There is no analysis or discussion to compare the statistical power of this study to other resources - I expect it is limited in recovering DE genes compared to other resources, especially given patient heterogeneity in COVID-19.

2. The results on ABI/Transitional AT2 and PATS cells in the lung are not clear. While the increased basal cells are presented as likely ABIs, the label transfer seems to map most of this signature to AT1 cells (Fig 2E). Fig 2F presents gene expression similarities - but it is difficult to see them on the heatmap (there are few cells and this reviewer is color blind). A more quantitative approach or clear visualisation of shared definitive marker gene expression is needed. Regarding PATS, with the limited number of nuclei & patients profiled here, I am not confident in the label transfer based comparison to the Broad study.

3. More granular annotation of endothelial and macrophage subtypes would improve the utility of the resource. For example, lymphatic vs vascular endothelial cells in the lung show different responses to COVID-19 with the former population increasing in abundance in disease while the latter population diminishes (e.g. Broad delorey study). Such phenotypes cannot be extracted from the current annotation.

4. The extent of the cross organ coordinated response is not very clear. Fig 5A and Fig 5 sup fig suggest common DEG genes in macrophages and endothelial cells respectively across organs, but Fig 5F and G seem to suggest that DE coordination is close to random or not significant (except endothelial cells). Fig 5B-E correlations also seem limited. Fig 6C-E finds few cell-cell interactions conserved between macrophages and endothelial cells. In addition, endothelial cells change in abundance in opposite directions in the lung vs heart, suggesting divergent responses.

5. How many STR genes are there and are they conserved across different cell types?

6. Orthogonal validation of some of the novel findings with IHC or smFISH would confirm the robustness of novel findings and utility as a resource. The validation of hepatocyte insulin dysregulation or the vascular-macrophage cell interactions would add great value.

Reviewer #2 (Public Review):

The authors sought to be able to examine what cellular mechanisms underlie increases in mature blood cell production upon immune challenge. To this end they devised a new in vitro organ culturing system for the lymph gland, the main hematopoetic organ of the fruit fly Drosophila melanogaster; the fly serves as an excellent model for studying fundamental questions in immunology, as it allows live imaging combined with genetic manipulation, and the molecular pathways and cellular functions of its innate immune system are highly conserved with vertebrates.

The authors provide compelling evidence that the cultured lymph gland shows a similar time scale, dynamics, and capacity for cell division as was observed in vivo, and does not undergo undue oxidative stress in their optimized culture conditions. This technique will prove extremely useful to the large community studying the fly lymph gland, and potentially vertebrate immunologists seeking to expand the models they utilize.

In these cultured glands, the authors identify progenitors undergoing symmetric cell divisions and provide some evidence that is consistent with, but does not prove, that these two cells maintain their proliferative capacity. They detect equivalent levels in the two equally sized daughter cells of dome-Meso-GFP, a marker for JAK-STAT activity; however, this could be due to an equal inheritance of the protein from the mother, not an equivalent maintenance of a proliferative capacity.

The authors develop a technique to conduct tracking of progenitor cell size over time in the cultured lymph glands and identify a switch increase in growth after division, as well as two orientations of the divisions, with the main one occurring 90% of the time.

They show that bacterial infection results in a significant decrease in the division of Blood progenitors and the elimination of the minor orientation of division, but no obvious change in the rate of division.

By imaging two markers, Dome-GFP for the progenitor state and Eater dsRed for the differentiated one, they examine the trajectories by which differentiation occurs in the wild-type lymph gland. They describe two main categories of fate transitions. In one that they call linear, the blood cells express high levels of the differentiation marker along with the progenitor marker before turning off the progenitor marker. The dynamics of how these progenitor cells get to the state of expressing both the differentiation and progenitor marker at high levels is not described. In the other, which they call sigmoidal, cells express only high levels of the progenitor marker, and the differentiation marker increases after or as the progenitor marker decreases. The authors show that upon infection there is a large increase in the amount of the linear type of differentiation.<br /> But how this change in the type of differentiation upon infection explains the increased amount of differentiation is not clear.

A potential explanation comes from an aspect of their data that the authors don't comment upon. In their live analysis of lymph glands at a distinct time point in the uninfected state (Fig 7M-N), 95% of the cells they analyze traversing the sigmoidal path are in the intermediate step. This would predict that the cells on this path spend a much longer time stuck in this intermediate state before traversing to the final differentiated one, or that only a small fraction of the cells that become sigmoidal intermediate cell progress onwards to full differentiation. But this does not match the trajectories observed in the real-time analysis for uninfected cultured lymph glands (Fig 7A'-D'). marker. Perhaps their algorithm discarded traces from the live imaging in which the differentiation marker did not come up quickly and was thus not analyzed in the trajectories. If my interpretation of the single time point analysis is true, this would argue that the linear path is actually much faster/more fruitful than the sigmoidal one and this would explain why a higher level of total progenitor differentiation infection is the result of infection-inducing more differentiation by the linear path. Otherwise, I don't understand how their data explains that observation.

This work provides a very useful new system for Drosophila immunologists and could provide an important new perspective on the systems-level mechanisms that an organism utilizes to enable increased differentiation of immune cells upon infection.

Reviewer #2 (Public Review):

The study by Povlsen, Bentzen et al. describes certain computational pipelines authors used to analyze the results from a single-cell sequencing experiment of pMHC-multimer stained T cells. DNA-barcoded pMHC multimers and single-cell sequencing technologies provide an opportunity for the high-throughput discovery of novel antigen-specific TCRs and profiling antigen-specific T-cell responses to multiple epitopes in parallel from a single sample. The authors' goal was to develop a computational pipeline that eliminates potential noise in TCR-pMHC assignments from single-cell sequencing data. With several reasonable biological assumptions about underlying data (absence of cross-reactivity between these epitopes, same specificity for different T-cells within a clonotype, more similarity for TCRs recognizing the same epitope, HLA-restriction of T cell response) authors identify the optimal strategy and thresholds to filter out artifacts from their data.

It is not clear If the identified thresholds are optimal for other experiments of this kind, and how the violation of authors' assumptions (for example, inclusion of several highly similar pMHC-multimers recognized by the same clone of cross-reactive T cells) will impact the algorithm performance and threshold selection by the algorithm. The authors do not discuss several recent papers featuring highly similar experimental techniques and the same data filtering challenges:<br /> https://www.science.org/doi/10.1126/sciimmunol.abk3070<br /> https://www.nature.com/articles/s41590-022-01184-4<br /> https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184244/

Unfortunately, I was unable to validate the method on other datasets or apply other approaches to the authors' data because neither code nor raw or processed data were available at the moment of the review.

One of the weaknesses of this study is that the motivation for the experiment and underlying hypothesis is unclear from the manuscript. Why these particular epitopes were selected, why these donors were selected, are any of the donors seropositive for EBV/CMV/influenza is unclear. Without particular research questions, it is hard to evaluate pipeline performance and justify a particular filtering strategy: for some applications, maximum specificity (i.e. no incorrect TCR specificity assignments) is crucial, while for others the main goal is to retain as many cells as possible.

Reviewer #2 (Public Review):

The manuscript "Optimal Cancer Evasion in a Dynamic Immune Microenvironment Generates Diverse Post-Escape Tumor Antigenicity Profiles" by George and Levine describes TEAL - a mathematical model for the dynamics of cancer evolution in response to immune recognition. The authors consider a process in which tumor cells from one clone are characterized by a set of neoantigens that may be recognized by the immune system with a certain probability. In response to the recognition, the tumor may adapt to evade immune recognition, by effective removal of recognizable neoantigens. The authors characterize the statistics of this adaptive process, considering, in particular, the evasion probability parameter, and a possibility of an adaptive strategy when this parameter is optimized in each step of the evolution. The dynamics of the latter process are solved with a dynamic programming approach. In the optimal case, the model captures the tradeoff between a cancer population's need for adaptability in hostile immune microenvironments and the cost of such adaptability to that population. Additionally, immune recognition of neoantigens is incorporated. These two factors, anti-tumor vs pro-tumor IME as quantified by the Beta penalty term, and the level of immune recognition as quantified by the rate q, form the basis of a characterization of tumors as 'hot' or 'cold'.

I think this framework is a valuable attempt to formally characterize the processes and conditions that result in immunologically hot vs cold tumors. The model and the analytical work are sound and potentially interesting to a major audience. However, certain points require clarification for evaluation of the relevance of the model:

1) Tumor clonality

My main concern is about the lack of representation of the evolutionary process in the model and that the heterogeneity of the tumor is just glossed over.

The single mention of the problem occurs in Section 2, p2: "Our focus is on a clonal population, recognizing that subclonal TAA distributions in this model may be studied by considering independent processes in parallel for each clone."

I don't think this assumption resolves the impact of tumor heterogeneity on the immune evasion process. Furthermore, I would claim that the process depicted in Fig 1A is very rare and that cancers rarely lose recognizable neoantigens - typically it would be realized via subclonal evolution, with an already present cancer clone without the neoantigens picking up. Similarly, the adaptation of a tumor clone is an evolutionary process - supposedly the subclones that manage to escape recognition via genetic or epigenetic changes are the ones that persist. It is not clear what the authors assume about the heterogeneity of the adapting/adapted population between different generations, n->(n+1). Is the implicit assumption that the n+1 generation is again clonal, i.e. that the fitness advantage of the resulting subclone was such that the remaining clones were eliminated? Or does the model just focuses on the fittest subclone? A discussion on whether these considerations are relevant to the result would clarify the relevance of the result.

2) Time scales

Section 2, p2: "We assume henceforth that the recognition-evasion pair consists of the T cell repertoire of the adaptive immune system and a cancer cell population, recognizable by a minimal collection of s_n TAAs present on the surface of cancer cells in sufficient abundance for recognition to occur over some time interval n.".

How do the results depend on the duration of interval n? The duration should be long enough to allow for recognition and, up to some limiting duration, proportional to the TAA recognition probability q. However, it should not be so long that the state of the system can change significantly. A clarification on this point is needed.

Reviewer #2 (Public Review):

In this manuscript, investigators explore the m1A modification, an important post-transcriptional regulatory mechanism, in primary normal neuron and OGD/R treated neuron. As far as I know, the regulatory m1A modification remains poorly characterized in neuron. This is an interesting topic in the context of epitranscriptomics. This paper not only provided us with a landscape of m1A modifications in neuron, but also explored the impact of m1A modifications on the biological functions of different RNA (mRNA, lncRNA, circRNA). In addition, the argument that m1A modification affects miRNA binding to other RNAs is of interest to reader, and the authors have performed a dual luciferase validation here to add feasibility to this conclusion.

Reviewer #2 (Public Review):

The function of many proteins depends on posttranslational modifications. Protein glycosylation is widespread and glycosylated proteins are mostly found on the outer surface of cells, where it is frequently implicated in cell-to-cell adhesion. It involves the addition of often complex and branched sugar chains to a protein backbone. Sialic acid is a particular relevant sugar as it is negatively charged and occupies terminal positions at the glycan chain. The enzymatic cascade leading to sialylated proteins is known. Unlike mammals, flies have only one sialyltransferase (SiaT), thus, Drosophila is a particularly well-suited model to study protein sialylation. The penultimate enzymatic steps in sialylation are mediated by N-acetlyneuraminic acid synthetase (NANS) and sialic acid synthetase (CSAS).

Scott et al., start with careful and state-of-the-art dissection of the expression patterns of the relevant genes. They first generated transgenic flies harboring a BAC covering the CSAS gene - which was able to rescue the mutant phenotype. They then replaced the CSAS coding sequence with LexA and demonstrated that LexA expression was sufficient to drive LexAop-CSAS to a full rescue of the CSAS mutant. CSAS-LexA was found to be active only in Repo expressing glial cells. The authors performed further experiments employing another BAC harboring an HA-tagged SiaT gene and found complementary expression in neurons (here I missed a comment on why the endogenously tagged SiaT gene (Repnikova 2010) was not used).

To study cell-type specific requirements UAS-based rescue experiments were conducted. The CSAS mutant phenotype could be rescued not only by panglial expression of CSAS but also by expression exclusively in subperineurial or ensheathing glial cells. Whether astrocytes or cortex glial cells are similarly able to rescue the mutant phenotype has not been addressed. No rescue was observed when CSAS was expressed in neurons, but co-expression of CSAS and NANS led to a partial rescue, further validating the split of the biosynthetic pathway leading to sialylated proteins to glial and neuronal cells.<br /> In addition to the rescue experiment, the authors also performed RNAi-based knockdown experiments for both, CSAS and SiaT which together support the conclusion that sialylation requires a split of the biosynthesis pathway.

In a subsequent mass spec approach, the authors analyzed sialylated proteins in larval brains. Whereas in wild type brains sialylated proteins were barely detected, they could not be seen in SiaT or CSAS mutant brains. However, according to Flybase, the highest expression of both genes is in adult flies. Why not look at these stages? It would also be good to use the cell type-specific knockdown flies for such experiments to fully support the notion that sialylation requires a glia-neuron transfer of intermediates. Possibly, low (and thus undetected) levels of SiaT in glia could be sufficient for function. In this respect it is interesting that the presence of a UAS-SiaT element is sufficient to rescue the SiaT mutant phenotype, suggesting that only very low levels of SiaT are needed for function.

Subsequently, Scott et al., demonstrate that the paralysis phenotype of CSAS mutants is sensitive to gene dose and that CSAS activity protects flies from oxidative stress. Quite interesting, they also demonstrate that sialylation is required - directly or indirectly - to maintain protein expression of the voltage gate sodium ion channel Para.

Reviewer #2 (Public Review):

The authors aim to make a reliable plate-based system for imposing drought stress (which for experiments like this would be better referred to as low water potential stress). This is an admirable goal as a reliable experimental system is key to conducting successful low-water potential experiments and some of the experimental systems in use have problems. They compare several treatments but seem to be unaware that such comparisons need to be based on the measurement of water potential as the fundamental measure of how severe the level of water limitation is. Only by comparing things at the same water potential can one determine if the methods used to impose the low water potential are introducing confounding factors. In this manuscript, they compare several agar-plate-based treatments to what they view as a baseline experiment of plants subjected to soil drying. However, that baseline soil drying (vermiculite drying, to be precise) experiment illustrates many of the problems present in the molecular drought literature in that they give no information on plant or soil water potential or water content. Thus, there is no way to know how severe the drought stress was in that experiment and no way for any other lab to reproduce it. It is directly akin to doing a heat stress experiment and not reporting the actual temperature.

They compare transcriptome data from this soil drying experiment to transcriptome data from agar plates with PEG, mannitol or salt added. However, this comparison is problematic, because none of the treatments being compared are at the same water potential (as mentioned above). Also, the PEG-infused agar plates have limitations in that no buffer is added and it is not clear that anything is done to check or control the pH. Adding PEG to the solution will reduce the pH. Thus, in their unbuffered PEG plates, the plants are almost certainly exposed to low pH stress and this can explain the supposed difference they observe between PEG and other treatments, especially since the plants are left on such stressful pH conditions for a relatively long period. It is also problematic that the comparison between soil drying and plate-based treatments is at different times (5 vs 14 days). They also show an over-reliance on the GO annotations of drought-induced gene expression. This GO annotation is based on experiments using very severe stress for a short time period. It is notorious for not accurately reflecting what happens on longer-term exposure to more moderate levels of low water potential stress. Thus, for example, we would not expect many of the canonical drought regulation genes (RD29A and similar genes) to be upregulated in the longer-term treatments as its expression is induced rapidly but also rapidly declines back to near baseline at the plant acclimates to the low water potential stress.

The authors have not always considered literature that would be relevant to their topic. For example, there is a number of studies that have reported (and deposited in the public database) transcriptome analysis of plants on PEG-plates or plants exposed to well-controlled, moderate severity soil drying assays (for the latter, check the paper of Des Marais et al. and others, for the former, Verslues and colleagues have published a series of studies using PEG-agar plates). They also overlook studies that have recorded growth responses of wild type and a range of mutants on properly prepared PEG plates and found that those results agree well with results when plants are exposed to a controlled, partial soil drying to impose a similar low water potential stress. In short, the authors need to make such comparisons to other data and think more about what may be wrong with their own experimental designs before making any sweeping conclusions about what is suitable or not suitable for imposing low water potential stress.

To solve the problem of using these other systems to impose low water potential stress, the authors propose the seemingly logical (but overly simplistic) idea of adding less water to the same mix of nutrients and agar. Because the increased agar concentration does not substantially influence water potential (the agar polymerizes and thus is not osmotically active), what they are essentially doing is using a concentrated solution of macronutrients in the growth media to impose stress. This is a rediscovery of an old proposal that concentrated macronutrient solutions could be used to study the osmotic component of salt stress (see older papers of Rana Munns). There are also effects of using very hard agar that is of unclear relationship to actual drought stress and low water potential. Thus, I see no reason to think that this would be a better method to impose low water potential.

Reviewer #2 (Public Review):

Cryptococcus neoformans is an important human pathogen, particularly in immunocompromised individuals. Like many fungal pathogens, resistance to antifungal drugs can emerge quickly in Cryptococcus. Understanding the mechanisms by which fungi develop resistance to antifungals will support new treatment strategies and, potentially, identify new drug targets. In this manuscript, Meng et al. describe a novel role for the conserved ATP-dependent chromatin remodeling factor, Imitation Switch (Isw1) in responding to antifungals in Cryptococcus. The authors first find that loss of Isw1 increases resistance to multiple antifungals and changes expression levels of genes potentially involved in antifungal resistance using functional genetics and cell growth assays. Next, the authors use mass spectrometry data (data generated in this study and public data) to identify ubiquitinated and acetylated sites of Isw1. The authors use this information to carry out an extensive series of western blot experiments using point mutations and chemical perturbations to dissect the contribution of specific modified sites of Isw1. Here, they identify important roles for the acetylation of K97 and ubiquitination of K113 and K441 in Isw1 stability. Lastly, the authors present evidence that clinical isolates of Cryptococcus that have increased antifungal resistance may have defects in Isw1 stability and that overexpressing ISW1 reduces antifungal resistance.

Strengths:

The authors present novel data that Isw1 is involved in responding to antifungals and that changes in Isw1 stability may lead to antifungal resistance. These results are of particular interest to the fungal pathogen research community and add to the general understanding of antifungal resistance.

The authors present exciting data on post-translation modification (i.e., acetylation and ubiquitination) of Isw1, how those modifications contribute to Isw1 stability, and the regulatory interplay between modifications. Considering that Isw1 is broadly conserved across eukaryotes, these results are, potentially, of broad interest and raise questions outside of pathogen biology to be addressed in future research. For example, are the residues characterized in this study conserved in other Isw1 homologs, are they similarly modified, and is regulating the stability of Isw1 (or other chromatin remodeling factors) a general strategy for responding to external signals?

Weaknesses:

The authors demonstrate that Isw1 has a role in responding to antifungals in Cryptococcus. However, it is not clear if changes in Isw1 stability represent a general response to stress. This study would have benefited from experiments to test: (1) if levels of Isw1 change in response to other stressors (e.g., heat, osmotic, or oxidative stress) and (2) if loss of Isw1 impacts resistance to other stressors.

The authors demonstrate a critical role in the acetylation of K97 and ubiquitination of K441 in regulating Isw1 stability. Additionally, this study shows that K113 is also likely involved in this process. However, it appears that K113 can be either acetylated or ubiquitinated, and it is, thus, less clear if one of the two modifications or both modifications is critical at this residue. Additional experiments may be required to answer this question. This study would have benefited from an additional discussion on the results related to the modification of K113.

The authors demonstrate that overexpression of ISW1 in select clinical isolates of Cryptococcus increases sensitivity to antifungals. However, these experiments would have benefited from additional controls, such as including overexpression of ISW1 in the wild-type strain (H99) and antifungal-sensitive isolate (CDLC120).

Reviewer #2 (Public Review):

The role of the family IV polymerases in mycobacteria is only partly understood. In this work, the authors investigate the role of the M. smegmatis DinB2 and DinB3 polymerases by a combination of biochemical analysis of enzyme activity in vitro and mutational and phenotypic characterization of M. smegmatis strains during induced over-expression of these proteins. They show both polymerases to be mutagenic and uncover a distinct role for DinB2 in slippage on homopolymeric tracts that is dependent on manganese.

Previous work showed that DinB1 overexpression resulted in SOS induction. This work shows that DinB2 and DinB3 similarly increase RecA levels. Previous work also showed that DinB1 overexpression resulted in growth inhibition and loss of viability which was independent of its polymerase activity. In this work, overexpression on DinB2 but not DinB3 inhibits growth along with a loss in viability but in contrast to DinB1, this inhibitory effect is only seen with a polymerase-proficient enzyme and is even more enhanced in a steric gate mutant. Overexpression of DinB3 and DinB2 increases the frequency of Rif-resistant mutants independent of the SOS response and DnaE2. The mutation spectrum in DinB2-overexpressing cells was distinct from that caused by DinB1 or DinB3 overexpression. In vitro and in vivo experiments clearly demonstrate that DinB2 catalyzes frameshift mutagenesis on substrates with homopolymeric nucleotide stretches demonstrating enhanced slippage compared to the recent data with DinB1. Remarkably, this slippage is enhanced on homopolymeric runs of purines than pyrimidines in vitro. In vivo slippage by DinB2 was not enhanced by long G runs. The slippage in vitro was only evident in its DNA-dependent DNA polymerase mode and not during ribonucleotide incorporation. In addition, while magnesium alone was associated with mis-addition, the presence of manganese shifted the enzyme to slippage mode in vitro. The detrimental effect of DnaB2 over-expression on viability is, however, not related to its slippage activity since conditions that enhance slippage in vitro (specifically manganese) are associated with a greater detrimental effect on viability in vivo despite a lack of evidence of slippage using reporter constructs.

Reviewer #2 (Public Review):

This manuscript illustrates a vascular network in the postnatal developing and adult epididymis using high-resolution three-dimensional (3D) imaging and organ clearing coupled with multiplex immunodetections of lymphatic and blood markers.

Strengths:<br /> The cutting-edge imaging technique to visualize the three-dimensional vascular network.<br /> The images and videos were of great quality.<br /> The authors were very cautious and careful when interpreting the results of marker immunostaining.

Weaknesses:<br /> 1. Although the images and videos were of great quality, the results derived from them provided little new knowledge and few conceptual insights into male reproductive tract biology and basically confirmed what has been published using traditional methods. For example, the high intensity of the vascular network in the initial segment was previously reported by Abe in 1984 and Suzuki in 1982; the pattern of the major lymphatic vessel and drainage was beautifully depicted by Perez-Clavier, 1982.

2. The authors were very cautious when interpreting the results of marker immunostaining however these markers were not specific for a definite cell type. For example, as the authors stated, VEGFR3 marks both lymphatic vessels and fenestrated blood vessels. how could the authors claim the VEGFR3+ network was lymphatic? The authors claimed that they used three markers for the lymphatic vessel. But staining results of the networks were very different. How could the author make conclusions about the network of lymphatic vessels in the epididymis?

3. To understand the vascular network development in the epididymis, would the authors please look at the fetal stage when the vascular network is established in the first place? Wolffian duct tissues are much smaller and thinner and would be amenable for 3D imaging probably even without clearing.

4. Immunofluorescence staining of VEGF factors was not convincing. As a secreted factor, VEGF will be secreted out of the cells, would it be detected more in the interstitium? I am always skeptical about the results of immunostaining secreted growth factors. Would it be possible to perform in situ or RNAscope to confirm the spatial expression pattern of VEGFs?

5. The study is descriptive and does not provide functional and mechanistic insights. Maybe, the combination of 3D imaging with lineage tracing of endothelium cells or ligation study (removal/ligation of the certain vessel) would help better understand how the vascular network is established and their functional significance.

6. Immune response is among many physiological processes in which vascular networks play significant roles. Discussion would be needed in other physiological processes, such as tissue metabolism and stem/progenitor cell niche microenvironment.

7. How could the author determine the Cd-A labeled vessel in Fig 1 was an artery, not a vein? This leads to another critical question. Would it be possible to stain with artery and vein markers to help illustrate the blood flow directions of the vessel?

Reviewer #2 (Public Review):

This manuscript reassesses the strength of evidence for rapid human germline mutation spectrum evolution, using high coverage whole genome sequencing data and paying particular attention to the potential impact of confounders like biased gene conversion. The authors also refute some recently published arguments that historical changes in the age of reproduction might explain the existence of such mutation spectrum changes. My overall impression is that the paper presents a useful new angle for studying mutation spectrum evolution, and the analysis is nicely suited to addressing whether a particular model such as the parental age model can explain a set of observed polymorphism data. My main criticism is that the paper overstates certain weaknesses of previously published papers on mutation spectrum evolution as well as the generation time hypothesis; correcting these oversimplifications would more accurately capture what the paper's new analyses add to the state of knowledge in these areas.

As part of the motivation for the current study, the introduction states in lines 97-99 that "it thus remains unclear if the numerous observed [mutation spectrum] differences across human populations stem from rapid evolution of the mutation process itself, other evolutionary processes, or technical factors." This seems to overstate the uncertainty that existed prior to this study, given that Speidel, et al. 2021 found elevated TCC>TTC fractions in ancient genomes from a specific ancient European population, which seems like pretty airtight evidence that this historical mutation rate increase really happened. In addition, earlier papers (Harris 2015, Mathieson & Reich 2016, Harris & Pritchard 2017) already presented analyses rejecting the hypothesis that biased gene conversion or genetic drift could explain the reported patterns-in fact, the Mathieson & Reich paper reports one mutation spectrum difference between populations that they conclude is an artifact caused by the Native American population bottleneck, but they conclude that other mutation spectrum differences appear more robust. As the authors acknowledge in the discussion of their own results, biased gene conversion and non-equilibrium demography are difficult confounders to deal with, and neither previous papers nor the current paper are able to do this in a way that is 100% foolproof. The current manuscript makes a valuable contribution by presenting new ways of dealing with these issues, particularly since previous papers' work on this topic was often confined to supplementary material, but it seems appropriate to acknowledge that earlier papers discussed the potential impacts of biased gene conversion and demographic complexity and presented their own analyses arguing that these phenomena were poor explanations for the existence of mutation spectrum differences between populations.

For the most part, I found the paper's introduction to be a useful summary of previous work, but there are a few additional places where the limitations of previous work could be described more clearly. I'd suggest noting that the data artifacts discovered by Anderson-Trocmé, et al. were restricted to a few old samples and that the large differences the current manuscript focuses on were never implicated as potential cell line artifacts. In addition, when the authors mention that their new approach includes "minimiz[ing] confounding effects of selection by removing constrained regions and known targets of selection" (lines 106-107), they should note that earlier papers like Harris & Pritchard 2017 also excluded conserved regions and exons.

One innovative aspect of the current paper's approach is the use of allele ages inferred by Relate, which certainly has advantages over using allele frequencies as a proxy for allele age. Though the authors of Relate previously used this approach to study mutation spectrum evolution, they did not perform such a thorough investigation of ancient alleles and collapsed mutation type ratios. I like the authors' approach of building uncertainty into the use of Relate's age estimates, but I wonder about the validity of assuming that the allele age posterior probability is distributed uniformly between the upper and lower confidence bounds. Can the authors address why this is more appropriate than some kind of peaked distribution like a beta distribution?<br /> I would also argue that the statement on line 104 about Relate's reliability is not yet supported by data-there is certainly value in using Relate ages to investigate mutation spectrum change over time and compare this to what has been seen using allele frequencies, but I don't think we know enough yet to say that the Relate ages are definitely more reliable. Relate's estimates might be biased by the same processes like selection and demography that make allele frequencies challenging to interpret. The paper's statements about the limitations of allele frequencies are fair, but there is always a tradeoff between the clear drawbacks of simple summary statistics and the more cryptic possible blind spots of complicated "black box" algorithms (in the case of Relate, an MCMC that needs to converge properly). DeWitt, et al. 2021 noted that the demographic history inferred by Relate doesn't accurately predict the underlying data's site frequency spectrum, indicating that the associated allele ages might have some problems that need to be better characterized. While testing Relate for biases is beyond the scope of this work, the introduction should acknowledge that the accuracy and precision of its time estimates are still somewhat uncertain.

The paper's results on C>T mutations in Europeans versus Africans are a nice confirmation of previous results, including the observation from Mathieson & Reich that neither SBS7 nor SBS11 is a good match for the mutational signature at play. More novel is the ancient mutational signature enriched in Africa and the interrogation of the ability of parental age to explain the observed patterns. I just have a few minor suggestions regarding these analyses:

1. I like the idea of using maternal age C>G hotspots to test the plausibility of the maternal age as an explanatory factor, but I think this would be more convincing with the addition of a power analysis. Given two populations that have average maternal ages of 20 and 40, and the same population sample sizes available from 1000 Genomes, can the authors calculate whether the results they'd predict are any different from what is observed (i.e. no significant differences within the maternal hotspots and significant differences outside of these regions)?

2. Is it possible that the T>C/T>G ratio is elevated in all variants above a certain age but shows up as an African-specific signal because the African population retains more segregating variation in this age range, whereas non-African populations have fixed or lost more of this variation? Since Durvasula & Sankararaman identified putative tracts of of super-archaic introgression within Africans, is it possible to test whether the mutation spectrum signal is enriched within those tracts?

3. Although Coll Macià, et al. argued that generation time is capable of explaining all mutation spectrum differences between populations, including the excess of TCC>TTC in Europeans, Wang et al. argue something slightly different. They exclude TCC>TTC and the other major components of the European signature from their analysis and then argue that parental age can explain the rest of the differences between populations. I think the analysis in this paper convincingly refutes the Coll Macià, et al. argument, but refuting the Wang, et al. version would require excluding the same mutation types that are excluded in that paper.

Reviewer #2 (Public Review):

In these studies, the authors make the observation that macrophages transfer their mitochondria to cancer cells. The authors claim that these mitochondria are dysfunctional and release reactive oxygen species (ROS) in the recipient cancer cells. Further, the authors illustrate that the mitochondrial-derived ROS activates proliferative ERK signaling. Macrophage mitochondria exhibit fragmentation, the extent of which promotes their transfer to cancer cells resulting in a functional increase in cancer cell proliferation. The authors initiated this work based on their previous findings where they illustrated the ability of macrophages to transfer cytosolic contents to recipient cancer cells.

The observations made in this manuscript, if further substantiated, are of interest in the field of cancer immunotherapy, metabolism, and basic cancer biology.

Reviewer #2 (Public Review):

This study identifies the neural circuits inhibited by activation of opioid receptors using complex experimental approaches such as electrophysiology, pharmacology, and optogenetics and combined them with retrograde and anterograde tracings. The authors characterize two key regions of the brainstem, the preBötzinger Complex, and the Kolliker-Fuse, and how these neuronal populations interact. Understanding the interactions of these circuits substantially increases our understanding of the neural circuits sensitive to opioid drugs which are critical to understand how opioids act on breathing and potentially design new therapies.

Major strengths.<br /> This study maps the excitatory projections from the Kolliker-Fuse to the preBötzinger Complex and rostral ventral respiratory group and shows that these projections are inhibited by opioid drugs. These Kolliker-Fuse neurons express FoxP2, but not the calcitonin gene-related peptide, which distinguishes them from parabrachial neurons. In addition, the preBötzinger Complex is also hyperpolarized by opioid drugs. The experiments performed by the authors are challenging, complex, and the most appropriate types of approaches to understanding pre- and post-synaptic mechanisms, which cannot be studied in vivo. These experiments also used complex tracing methods using adenoassociated virus and cre-lox recombinase approaches.

Limitations.<br /> (1) The roles of the mechanisms identified in this study have not been established in models recording opioid-induced respiratory depression or respiratory activity. This study does not record, modulate, or assess respiratory activity in-vitro or in-vivo, without or with opioid drugs such as fentanyl or morphine.<br /> (2) Experiments are performed in-vitro which do not mimic the effects of opioids observed in-vivo or in freely-moving animals. However, identification of pre- and post- synaptic mechanisms, as well as projections, cannot be performed in-vivo, so the authors use the right approaches for their experiments.<br /> (3) The type of neurons projecting from KP to preBötzinger Complex or ventral respiratory group have not been identified. Although some of these cells are glutamatergic, optogenetic experiments could have been performed in other cre-expressing cell populations, such as neurokinin-1 receptors.

This study provides new insights into the types of circuits inhibited by opioid drugs, and the site of actions of inhibition, such as pre- or post-synaptic, and proposes how inhibition by opioids acts at multiple sites in the brainstem through various mechanisms.

Although many studies have recently explored the types of neurons and sites in the brain sensitive to opioids, the present study is the first to provide a clear picture of the neuronal mechanism underlying inhibition by opioids. Importantly, it provides a link between two sites known to inhibit breathing when inhibited by opioids. The results provided here combined with a complex methodology support the various conclusions reached by the authors.

Reviewer #2 (Public Review):

This manuscript is focused on the identification and characterization of transcriptional networks that control the major Candida albicans virulence property of filamentation during infection in vivo. Using an intravital imaging assay, the authors have screened a C. albicans transcription factor mutant library to identify factors important for controlling both filament initiation and elongation in vivo. They also perform Nanostring experiments to identify the in vivo transcriptional profiles of genes controlled by specific key factors in the network. Overall, the authors identify three positive and two negative core factors important for the initiation of filamentation and several factors specifically important for filament elongation (including 4 factors whose mutants have no in vitro elongation phenotypes). Target genes associated with filament initiation and elongation were shown to be mostly distinct. Unexpectedly, the authors also show that the main role of Efg1, a major positive regulator of filamentation, is to mediate relief of repression by Nrg1.

Overall, the manuscript is well-written and the data are clearly presented. In addition, the authors clearly appear to have achieved their Aim of identifying and characterizing transcriptional networks that regulate C. albicans morphogenesis during infection in vivo. In general, the conclusions of this paper are well-supported by the results. The results of this study are likely to have a significant impact on the field for several reasons: 1) new and valuable information will be provided about transcriptional networks that control C. albicans filamentation in vivo, 2) this study describes an important distinction between genes associated with filament initiation and elongation and will be the first to systematically analyze C. albicans genes associated with filament elongation, 3) while there are similarities, the authors also observe several important differences between transcriptional networks that control C. albicans filamentation in vivo vs. in vitro, which will help to clarify regulation that actually occurs during infection, 4) as indicated above, a new and surprising role for the C. albicans master regulator of filamentation, Efg1, is reported, 5) because filamentation is an important C. albicans virulence property, several of the target genes of transcription factor networks identified by this study (and the factors themselves) could serve as potential targets for new antifungals. As a consequence, this study is likely to provide information that opens up new and useful lines of research for the field.

Strengths:<br /> 1. Intravital imaging allows for the identification of transcription factors specifically important for C. albicans filamentation during infection.<br /> 2. Distinct sets of C. albicans genes and factors associated with filament initiation vs. elongation are identified.<br /> 3. Key differences between in vivo and in vitro transcriptional regulation of C. albicans filamentation are demonstrated, which in some cases challenge current paradigms. This also highlights the effect of the environment in determining target genes.<br /> 4. Evidence is presented to suggest that Efg1 promotes C. albicans filamentation primarily through relief of Nrg1 repression.

Weaknesses:<br /> 1. Nanostring does not profile the complete set of C. albicans genes, but rather a subset that is pre-selected. Therefore, defining proportions of genes and gene classes controlled by specific transcription factors may not give the complete picture and may not be accurate with respect to the transcriptome as a whole.<br /> 2. As the authors have noticed, transcription factors and target genes associated with C. albicans filamentation may vary significantly depending on the environment. It is therefore unclear whether the in vivo gene expression patterns observed in this study apply to other host niches besides the ear.<br /> 3. Similarly, variations in filamentation-associated transcription factors and target genes may occur in the "in vitro" conditions used by the authors. RPMI + 10% serum is the main "in vitro" condition but many other conditions are known to drive C. albicans filamentation.<br /> 4. Lines 361-366: A clear rationale for additional TFs to study in more detail was not provided.<br /> 5. Post-translational mechanisms, particularly septin phosphorylation, are likely to have an important effect on filament elongation (see work from Yue Wang's lab), which was not discussed.<br /> 6. Many Nrg1 targets are known to also be Tup1 targets (Kadosh & Johnson, 2005), which counters the argument that this corepressor and DNA-binding protein function separately.<br /> 7. While useful, examining genetic interactions using haploinsufficiency has several limitations and certain interactions may escape detection.

Reviewer #2 (Public Review):

Purandare and Mehta investigated the neural activities modulated by continuous and sequential visual stimuli composed of natural images, termed "movie-tuning," measured along the visuo-hippocampal network when the animals passively viewed a movie without any task demand. Neurons selectively responded to some specific parts of the movie, and their activity timescales ranged from tens of milliseconds to seconds and tiled the entire movie with their movie-fields. The movie-tuning was lost in the hippocampus but not in the visual cortices when the image frames were temporally scrambled, implying that the rodent hippocampus encoded the specific sequence of images.

The authors have concluded that the neurons in the thalamo-cortical visual areas and the hippocampus commonly encode continuous visual stimuli with their firing fields spanning the mega-scale, but they respond to different aspects of the visual stimuli (i.e., visual contents of the image versus a sequence of the images). The conclusion of the study is fairly supported by the data, but some remaining concerns should be addressed.

1) Care should be taken in interpreting the results since the animal's behavior was not controlled during the physiological recording. It has been reported that some hippocampal neuronal activities are modulated by locomotion, which may still contribute to some of the results in the current study. Although the authors claimed that the animal's locomotion did not influence the movie-tuning by showing the unaltered proportion of movie-tuned cells with stationary epochs only, the effects of locomotion should be tested in a more specific way (e.g., comparing changes in the strength of movie-tuning under certain locomotion conditions at the single-cell level).

2) The mega-scale spanning of movie-fields needs to be further examined with a more controlled stimulus for reasonable comparison with the traditional place fields. This is because the movie used in the current study consists of a fast-changing first half and a slow-changing second half, and such varying and ununified composition of the movie might have largely affected the formation of movie-fields. According to Fig. 3, the mega-scale spanning appears to be driven by the changes in frame-to-frame correlation within the movie. That is, visual stimuli changing quickly induced several short fields while persisting stimuli with fewer changes elongated the fields. The presentation of persisting visual input for a long time is thought to be similar to staying in one place for a long time, and the hippocampal activities have been reported to manifest in different ways between running and standing still (i.e., theta-modulated vs. sharp wave ripple-based). Therefore, it should be further examined whether the broad movie-fields are broadly tuned to the continuous visual inputs or caused by other brain states.

3) The population activities of the hippocampal movie-tuned cells in Fig. 3a-b look like those of time cells, tiling the movie playback period. It needs to be clarified whether the hippocampal cells are actively coding the visual inputs or just filling the duration. The scrambled condition in which the sequence of the images was randomly permutated made the hippocampal neurons totally lose their selective responses, failing to reconstruct the neural responses to the original sequence by rearrangement of the scrambled sequence. This result indirectly addressed that the substantial portion of the hippocampal cells did not just fill the duration but represented the contents and temporal order of the images. However, it should be directly confirmed whether the tiling pattern disappeared with the population activities in the scrambled condition (as shown in Extended Data Fig. 11, but data were not shown for the hippocampus).

Reviewer #2 (Public Review):

The authors have provided important detailed information on the inflammatory response to live E. coli infection in neonatal and juvenile mouse lungs. They have delineated key distinctions in these two periods and the potential impact on lung development. The study will inform future lines of investigation on the impact of bacterial infections on lung development.

Reviewer #2 (Public Review):

Most neuronal computations require keeping track of the inputs over temporal windows that exceed the typical time scales of single neurons. A standard and relatively well-understood way of obtaining time scales longer than those of the "microscopic" elements (here, the single neurons) is to have appropriate recurrent synaptic connectivity. Another possibility is to have a transient, input-dependent modulation of some neuronal and/or synaptic properties, with the appropriate time scale. Indeed, there is ample experimental evidence that both neurons and synapses modify their dynamics on multiple time scales, depending on the previous history of activation. There is, however, little understanding of the computational implications of these modifications, in particular for short-term memory.

Here, the authors have investigated the suitability of a class of transient synaptic modulations for storing and processing information over short-time scales. They use a purely feed-forward network architecture so that "synaptic modulation" is the only mechanism available for temporarily storing the information. The network is called Multi-Plasticity Network (MPN), in reference to the fact that the synaptic connectivity being transiently modulated is adjusted via standard supervised learning. They find that, in a series of integration-based tasks of varying difficulty, the MPN exhibits performances that are comparable with those of (trained) recurrent neuronal networks (RNNs). Interestingly, the MPN consistently outperforms the RNNs when only the read-out is being learned, that is in a minimal-training condition.

The conclusions of the paper are convincingly supported by the careful numerical experiments and the analysis performed by the authors, mostly to compare the performances of the MPN against various RNN architectures. The results are intriguing from a "classic" neuroscience perspective, providing a computational point of view to rationalize the various synaptic dynamics observed experimentally on largely different time scales, and are of certain interest to the machine learning community.

On the other hand, the general principle appears (perhaps naively) very general: any stimulus-dependent, sufficiently long-lived change in neuronal/synaptic properties is a potential memory buffer. For instance, one might wonder whether some non-associative form of synaptic plasticity (unlike the Hebbian-like form studied in the paper), such as short-term synaptic plasticity which depends only on the pre-synaptic activity (and is better motivated experimentally), would be equally effective. Or, for that matter, one might wonder whether just neuronal adaptation, in the hidden layer, for instance, would be sufficient. In this sense, a weakness of this work is that there is little attempt at understanding when and how the proposed mechanism fails.

Reviewer #2 (Public Review):

Here I will mainly comment on the biology of adipocytes, which is my specialty.

In this manuscript, it has been very convincingly shown that O-GlcNAc acts as an important regulator of MSC differentiation in mice, and given previous studies in which O-GlcNAc is regulated by aging and nutritional status, it makes sense that this PTM determines differentiation and BM niche.

The point that O-GlcNAc regulates adipocyte differentiation is convincing, but there are already previous studies using 3T3-L1 (e.g., Biochemical and Biophysical Research Communications 417 (2012) 1158-1163), and a more step-by-step demonstration of the molecular mechanism would make this an excellent paper that can be extended to adipocyte research in general, not just BM.

It is somewhat unclear whether or not the authors' in vitro experiments using 10T1/2 cells accurately reflect what is happening in vivo in knockout mice. The PDGFRa+VCAM1+ population of adipocyte progenitors shown by the authors is upregulated by about 30% by knockout of Ogt (Figure 4C). How significant is this difference? Rather, might the expression of Pparg, which indicates lineage commitment, be the underlying mechanism? In any case, this manuscript is highly impactful in the sense that the differentiation of adipocytes forming the BM niche can be controlled using tissue-specific knockouts of the Ogt gene.

Reviewer #2 (Public Review):

The manuscript proposes a theoretical framework for the size scaling of cells. The main predictions are (1) the application of a nested pump-leak model to explain cell size scaling through an osmotic balance, (2) the role of metabolites in maintaining electroneutrality, and (3) the breakdown of this scaling law during specific phases of cell growth and senescence.

Although the overall topic and approach are of significant interest, there are several issues with the presentation and claimed scope, detailed below.

Major comments:

1. The manuscript claims to provide a unified theory of cell size scaling, but quantitative agreement is only shown in a few specific cases (non-dividing yeast cells, mitotic swelling in mammalian cells, nuclear size scaling). Given the significant number of adjustable parameters in the model, the claim of a unified theory seems to be somewhat of a stretch. In addition, many of the approximations used (such as turgor pressure being negligible on p. 5) are valid in mammalian cells, but not in plant or yeast cells. For example, in walled cells, the rate of volume growth is dictated largely by cell-wall synthesis and turgor pressure (Rojas and Huang, 2018).

2. The paper claims to supersede previous work: "Many theoretical papers have assumed a priori a linear phenomenological relation between volume and protein number in order to study cell size [30],[31],[32]. Our results instead emphasize that the proportionality is indirect, only arising from the scaling between amino-acid and protein numbers." However, the conclusions reached (e.g. NC1 in eq. 15) appear to recover those of previous work, at least in certain limiting cases. Moreover, this is not a fully accurate description of the previous work, since in some of the previous works the osmotic balance is given in terms of general macromolecules, not necessarily proteins, and the linear relationship was not assumed but rather derived based on osmotic balance. The authors should carefully explain the relationship of their work to the previous studies.

3. The role of metabolites is an important point that should be further clarified. The authors state that "As a key consequence, we find that the NC ratio would be four times larger in the absence of metabolites". However, the formula obtained in the metabolite-dominated limit for NC1 in eq. 15 recovers previous results which were based solely on osmotic balance, without accounting for electroneutrality via metabolites. Why is electroneutrality violated in the absence of metabolites? Does this remain true if the chromatin and counterions are considered to be polyelectrolytes?

4. Appendix H on the extension to scaling of other organelles contains no comparison to data. Is the size control of all membrane-bound organelles expected to behave according to the same principles, or is the theory applicable to a particular subset of organelles?

5. It is stated several times that the size cell is "tightly regulated by active processes". The authors should define what they mean by "control" and "active" in this context. For example, one interpretation of the NC ratio size scaling result is that it is not under direct control, but rather is a consequence of the ratio of nuclear-bound proteins and is only controlled indirectly. (The authors themselves state that the relationship between volume and protein number is indirect.) If the NC ratio is actively controlled, this suggests that its maintenance at a certain value is important for the proper functioning of the cell. Is there evidence of this, or would the cell continue to function if the nuclear size could hypothetically be perturbed independently of the protein ratio?

Reviewer #2 (Public Review):

A common problem in mutation analysis is that DNA damage (present in one strand) is difficult to separate from real mutations (present in both strands). One of the approaches to solve this problem based on independent tagging of the two strands by different unique molecular identifiers was developed by the authors about 10 years ago. This study summarizes the application of this method to a wide range of mouse tissues, ages, and drug treatment regimes. Much of the results confirm previous conclusions from this laboratory. This involves overall mutational levels of somatic mtDNA mutations (~10-6-10-5), their accumulation with age, the prevalence of GA/CT transitions, and their clonality. Although these results were not new, it is important that these were confirmed in a single study with high confidence in a huge number of independent mutations.

What really sets this study apart from other studies is the detection of a large proportion of transversion mutations, primarily of the C>A/G>T and C>G/G>C types. Transversions are traditionally considered 'persona non grata' in mtDNA mutational spectra and are typically associated with errors of mutational analysis (which they in fact are). The presence of these mutations in both strands of the duplex makes a good case that these mutations are real, rather than converted damage. However, because this is such a novel discovery and because regular controls do not work (I mean, for example, that these mutations never clonally expand. If there is a clonal expansion, then the mutation is real, only real mutation can expand. But in the case of non-expandable C>A/G>T and C>G/G>C this control does not help to validate these mutations), it would be nice to provide extra assurances that this is not some kind of artifact that somehow slipped through the ds sequencing procedure. I would recommend including in the supplement the data on the abundance of single-stranded base changes as detected by ds sequencing (i.e., changes confirmed in one and not in the other strand of a given molecule). An unusually high presence of such single-stranded changes of the C>A/G>T and C>G/G>C type would be a red flag for me. If ratios of single and double-stranded mutations were similar for transitions and transversions - that would reassure me and hopefully the reader.

Furthermore, a similar excess of C>A/G>T and C>G/G>C has been observed in a recent paper by Abascal 2021 (cited in the manuscript). In that paper, a UMI- free, but otherwise very similar ds sequencing approach in nuclear DNA (BotSeqS) was demonstrated to suffer from an artifact causing (among other effects) an excess of C>A/G>T and C>G/G>C transversions. This artifact is related to end repair and nick-translation of DNA fragments during library preparation. Because BotSeqS is very similar to ds sequencing, we expect that same artifact may be taking place in the study under review. We recommend running checks similar to those undertaken by Abascal et al (which include, at the very minimum, checking the distribution of the C>A/G>T and C>G/G>C transversions within the reads (artifacts tend to be concentrated towards the ends of the reads).

Of note, even if transversions detected in this study prove to be artifacts of the Abascal type (likely) they still may reflect real ss damage in mtDNA (not instrumental artifacts, like sequencing errors or in vitro DNA damage). This is supported by the strong variation in the levels of transversions across tissues and as a result of the ameliorating drug intervention. Artifacts, in contrast, would be expected to be at a constant level. This logic, however, does not differentiate between real ds mutations and ss damage. So UMI-based ds sequencing evidence remains the only (though very strong) independent proof. So, in my view, whereas the jury may be still out on whether the observed transversions are true ds mutations or some kind of single-stranded damage, this is a critically important observation. The evidence of ss damage greatly varied between tissues and detected with such precision on a single molecule level is a very important finding as well.

Out of caution, I would recommend mentioning the above-stated uncertainty and noting that more research is needed to fully confirm that C>A/G>T and C>G/G>C changes detected in this study are indeed double-stranded mutations.

Reviewer #2 (Public Review):

This study identifies 110 disease-affected cell types for 714 Mendelian diseases, based on preferential expression of known disease-associated genes in single-cell data. It is likely that many or most of the results are real, and the results are biologically interesting and provide a valuable resource. However, updates to the method are needed to ensure that inference of statistical significance is appropriately stringent and rigorous.

Strengths: a systematic evaluation of disease-affected cell types across Mendelian diseases is a valuable addition to the literature, complementing systematic evaluations of common disease and targeted analyses of individual Mendelian diseases. The validation via excess overlap with disease-cell type pairs from literature co-appearance provides compelling evidence that many or most of the results are real. In addition, many of the results are biologically interesting. In particular, it is interesting that diseases with multiple affected tissues tend to affect similar cell types in the respective tissues.

Limitations: the main limitation of the study is that, although many or most of the results are likely to be real, the criteria for statistical significance is probably not stringent enough, and is not well-justified. For diseases with only 1 disease-associated gene, the threshold is a z-score>2 for preferential expression in the cell type, but this threshold is likely to be often exceeded by chance. (For diseases with many disease-associated genes, the threshold is a median (across genes) z-score>2 for preferential expression in the cell type, which is less likely to occur by chance but still an arbitrary threshold.) Thus, there is a good chance that a sizable proportion of the reported disease-affected cell types might be false positives. The best solution would be to assess statistical significance via empirical comparison with results for non-disease-associated control genes, and assess the statistical significance of the resulting P-values using FDR.

The re-analysis using mouse single-cell data adds an interesting additional dimension to the study, with the small caveat that mouse single-cell data does not provide statistically independent information across genes (for the same reason that adding data from independent human individuals would not provide statistically independent information across genes, given that human and mouse expression are partially correlated).

Reviewer #2 (Public Review):

Fuijino et al. provide interesting data describing the RNA-binding protein, FUS, for its ability to bind the RNA produced from the hexanucleotide repeat expansion of GGGGCC (G4C2). This binding correlates with reductions in the production of toxic dipeptides and reductions in toxic phenotypes seen in (G4C2)30+ expressing Drosophila. Both FUS and G4C2 repeats of >25 are associated with ALS/FTD spectrum disorders. Thus, these data are important for increasing our understanding of potential interactions between multiple disease genes. However, further validation of some aspects of the provided data is needed, especially the expression data.

Some points to consider when reading the work:

The broadly expressed GMR-GAL4 driver leads to variable tissue loss in different genotypes, potentially confounding downstream analyses dependent on viable tissue/mRNA levels.

The relationship between FUS and foci formation is unclear and should be interpreted carefully.

Reviewer #2 (Public Review):

This manuscript reports on the use of Optogenetics to influence endothelial barrier integrity by light. Light-induced membrane recruitment of GTPase GEFs is known to stimulate GTPases and modulate cell shape, and here this principle is used to modulate endothelial barrier function. It shows that Rac and CDc42 activating constructs enhance barrier function and do this even when a major junctional adhesion molecule, VE-cadherin, is blocked. Activation of Rac and Cdc42 enhanced lamellipodia formation and cellular overlaps, which could be the basis for the increase in barrier integrity.

The authors aimed at developing a light-driven technique with which endothelial barrier integrity can be modulated on the basis of activating certain GTPases. They succeeded in using optogenetic tools that recruit GEF exchange domains to membranes upon light induction in endothelial cell monolayers. Similar tools were in principle known before to modulate cell shape/morphology upon light induction but were used here for the first time as regulators of endothelial barrier integrity. In this way, it was shown that the activation of Cdc42 and Rac can increase barrier integrity even if VE-cadherin, a major adhesion molecule of endothelial junctions, is blocked. Although it was shown before that stimulation of the S1P1 receptor or of Tie-2 can enhance endothelial barrier integrity in dependence on Cdc42 or Rac1 and can do this independent of VE-cadherin, the current study shows this with tools directly targeting these GTPases.

Furthermore, this study presents very valuable tools. The immediate and repeatable responses of barrier integrity changes upon light-on and light-off switches are fascinating and impressive. It will be interesting to use these tools in the future in the context of analyzing other mechanisms which also affect endothelial barrier function and modulate the formation of endothelial adherens junctions.

Reviewer #2 (Public Review):

This study presents a dynamic, multi-step model for the activation of Aurora-B kinase through the interaction with INCENP and autophosphorylation. This interaction is critical to the proper execution of chromosome segregation, and key details of the mechanism are not resolved. The study is an advance on previous studies on Aurora-B and the related kinase Aurora-C, primarily because it clarifies the roles of the different phosphorylation sites. However, major differences in the details of the molecular interactions are presented that are not clearly backed up by the evidence due to limitations in the approach, when compared to previous work based on crystal structures.

Strengths. The experimental approach to the analysis of the Aurora-B/INCENP interaction is sound and novel and it is striking example of preparation of proteins in specific phosphorylation states, and of using HDX to characterise localised changes in the structural dynamics of a protein complex. The authors have generated two intermediate phosphorylation states of the complex, enabling them to dissect their contributions to the regulation of structural dynamics and activity of the complex.

Weaknesses. The major weakness of the study is the molecular dynamics simulation. The resulting model of the complex differs from the crystal structure of the Aurora-C/IN-box structure in key details, and these are neither described clearly nor explained. The challenges/limitations of simulation of phosphorylated proteins should be described.

Reviewer #2 (Public Review):

By using elegant optogenetic viral transgenic approaches the authors show that subgroups of neurons located in the preBötzinger region of the brainstem and projecting to the facial nucleus are involved in controlling orofacial activity while being minimally implicated in breathing behavior. The experiments are properly performed, and technically challenging with several physiological parameters measured in vivo allowing the monitoring of several functions simultaneously (breathing, heart rate, blood pressure, orofacial muscle activity). They also demonstrate that the type of anesthetic used and the state of consciousness are important for the effects of their photoinhibition. While this study is particularly interesting for a better understanding of the coordination between breathing and other behaviours controlled by neurons located in the brainstem, the identification of the neurons of interest here as components of the preBötC network requests clarification and the interpretation of the effects of photo-inhibiting both excitatory and inhibitory neurons remain difficult.

Reviewer #2 (Public Review):

This study presents important findings on trade-offs in investment in costly traits related to survival and reproduction. The evidence supporting the claims of the authors is convincing with an exceptional sample size, the inclusion of three species, and measurement of numerous traits. The authors do not incorporate genetics or use experimentation, but they do use an elegant observational approach to glean the likely presence of trade-offs and improve understanding of investment in crucial life-history traits. The work will be of interest to evolutionary biologists, researchers working in the field of animal behavior, and those specializing in sexual selection.

The extent to which individuals should invest in costly traits is an ongoing puzzle to evolutionary biologists. Why is there a limit to investment in traits that enhance survival or mating? Why do some individuals invest so much less than others in traits that should boost fitness? In this manuscript, Dinh and Patek use a strong sample size of snapping shrimp to investigate this question. They examine three species and measure numerous traits. The approach they use to deduce trade-offs is to examine residuals. Specifically, they plot the traits of interest against body size generating a regression for the population. Then, for each individual, they extract a residual value that is how much more or less they invest in a trait for a given body size. For example, some individuals might grow a big claw, but also express a small abdomen relative to others of the same size. The authors measure the extent to which each individual invests in a number of traits to investigate resource allocation trade-offs and reproductive benefits and costs.

This is an elegant and thorough study that thoughtfully examines how animals invest in their bodies and with what potential costs. They even look at male pairing success and the size of his mate to better understand the reproductive benefits of growing a larger claw in snapping shrimp. For females, they examine if growing a larger claw might lead to reduced reproduction because such females cannot care for as many eggs. The strengths of this study are many. It would, of course, be helpful to more thoroughly understand the costs and benefits of investment in claws, but the authors did an excellent job with what was possible. The current version of the manuscript would benefit from a discussion of the pros and cons of their approach of using residuals versus other approaches to measure resource allocation trade-offs.

Overall, this is such a nice study with excellent writing, and it will likely inspire others to examine trait investment in a myriad of other animals. It helps the field of sexual selection better understand the costs and benefits of growing a big (or small) weapon. And, more generally, it addresses the important question of why animals cannot have it all.

Reviewer #2 (Public Review):

In the present article, the author aimed at finding disease-modifier for a disease that still nowadays is incurable. To do so the authors decided to employ a drosophila model of ALS, bearing four mutations on the Ubiquilin gene. The model displays eye and motoneuron phenotypes serving as a valuable platform for genetic screenings. The screening performed in the present work shows many suppressors and enhancers of the toxicity associated with the presence of the 4 Ubiquilin mutations. The authors then strengthen the findings of the screening by validating some hits and by studying more in details one involved in the axon guidance signaling. They found that suppressing Unc5 and DCC leads to a less severe phenotype in the flies. They then suppress the ligand of the Unc5 receptor and found that also this approach relieves the phenotype. They then confirmed this results in iPSCs by creating a new cell line harboring the four mutations. They found that the neurites defects found in the mutated UBQLN iPSC was rescued by suppressing Unc5 and DCC. This study has relevance to the ALS field as many of the findings can be harnessed to develop drugs suited for ALS patient bearing Ubiquilin mutations. I think that the major weaknesses of this paper are (i) the fact that they focus on just one mutation, which is pretty rare, while probably most of findings should be also validated in models of sporadic ALS (iPSCs lines). (ii) The amount of data presented, for as much as it is technically well-performed, does not help the reader to focus the attention of the main point which is Unc5 signaling relevance in Ubiquilin associated ALS.

Reviewer #2 (Public Review):

In this manuscript, the authors identify a critical unmet need for the (structure-based) drug design of human Nav channels, which are of clinical interest. They cleverly rationalized a hybrid strategy for developing target-specific small molecule inhibitors, which integrate binding mechanisms of two drug candidates that act orthogonally on the VSD4 of Nav 1.7. Thus, the authors illustrate a promising outlook on pharmaceutical intervention on Nav channels.

Overall, the cryo-EM structures of the ligand-bound Nav channels are convincing, with a clear indication of the site-specific, distinct density of the small molecules. At the moment, it is difficult to tell how innovative the pipeline is compared to conventional cryo-EM structure determination.

Reviewer #2 (Public Review):

The authors propose a proteome allocation model which includes a ribosomal and metabolic sector (and an additional sector in the case of nutrient upshift or downshift), and they consider the effect of tRNA charging on translation. It appears that the rate of protein generation via translation by ribosomes and the rate of tRNA charging via metabolic proteins are mutually maximized (the so-called "flux-parity regulation"). Based on this principle, one can reproduce many aspects of bacterial growth both in and out of a steady state, without having to consider other processes.

A major strength of this article is that the authors include many different E. coli datasets. From the figures presented, the model appears to agree well with the data. If the model can indeed predict bacterial growth out of a steady state, then it will be useful in understanding how tRNA charging affects the bacterial response to environmental fluctuations.

To improve the manuscript, units and typical values in E. coli should be provided in the main text as parameters are introduced, to give the reader some benchmark numbers and physical intuition. Furthermore, how proteins are assigned to metabolic, ribosomal, or other proteome sectors can be better explained in the main text, i.e. based on the dependence of their respective abundances on the growth rate. It would also help the reader to explicitly state which parameters are being adjusted and which are fixed (four are mentioned in Section 8 of the appendix but there are many others defined in the text). Finally, whether v_max (max metabolic rate) and tau (uncharged-to-charged tRNA ratio) take on physically reasonable values is not clear, e.g. values for v_max span 4 orders of magnitude. These are essential parameters to the model, and without a sense of how they compare to real values, it is difficult to judge the robustness of the results.

Some specific questions follow:

- Are there experimental data to verify the charging sensitivity parameter tau?<br /> - Which molecules, other than charged tRNAs, are considered 'precursors', and are these neglected or accounted for in the model? For example, the other components of the ternary complex, e.g. GTP and EF-Tu, are not mentioned.<br /> - What is the yield coefficient Y in Eqs. 10, 55, Fig. S2,A(iii)? No value appears in the text or supplemental tables.<br /> - Why is the inactive fraction of ribosomes considered a puzzle? Bremer & Dennis and Metzl-Raz et al. have provided polysomal profiling data in E. coli and in S. cerevisiae, respectively. In E. coli it is ~85% but can be considerably lower in S. cerevisiae. Furthermore, it seems unphysical that 100% of ribosomes would be active at all times; it takes time for a ribosome to find and bind to mRNA.<br /> - (p)ppGpp binds to molecules other than tRNAs, e.g. RNA polymerase. Shouldn't this be accounted for in, e.g., Eq. 3?

Reviewer #2 (Public Review):

In this manuscript, Mazanek et al use Rosetta to calculate the relative binding energies of the six distinct PYD/PYD interactions between the pyrin-only proteins (POPs) and the pyrin domains (PYD) of various inflammasome components. Following these calculations, the authors measure the ability of the POPs to disrupt PYD spec formation or disrupt PYD oligomerization. From these experiments the authors propose that the POPs do not simply disrupt ASC oligomerization, but instead that each POP has unique specificity for the various PYDs and can thusly act upstream of ASC filamentation through their direct interactions with the inflammasome PYDs. Furthermore, the authors propose the ability of the POPs to inhibit PYD filament formation is not solely dictated by sequence similarity between the POP and the PYDs, but instead that a combination of both strong and weak interactions between the POP and PYD is required to disrupt PYD filament formation. These observations help to elucidate the individual roles of the different POPs.

In total this manuscript presents a rigorous and careful biochemical analysis of how the POPs act to modulate PYD oligomerization. However, there are several weaknesses that need to be addressed. First, while the authors propose that the combination of strong and weak interactions dictates the ability of the POPs to disrupt PYD oligomerization this hypothesis is not directly tested. Second, while the author's careful examination demonstrates the ability of the POPs to disrupt PYD spec formation in a reconstituted system, they do not confirm that their in vitro measurements correlate with the ability to restrict inflammasome activity in an endogenous system and as such the physiological consequences of their measurements remain unclear.

Reviewer #2 (Public Review):

Sensory hair cells have high metabolic demands and rely on mitochondria to provide energy as well as regulate homeostatic levels of intracellular calcium. Using high-resolution serial block face SEM, the authors examined the influences of both developmental age and hair cell activity on hair cell mitochondrial morphology. They show that hair cell mitochondria develop a regionally specific architecture, with the highest volume mitochondria localized to the basolateral presynaptic region of hair cells. Data obtained from mutants lacking either mechanotransduction or presynaptic calcium influx provide evidence that hair cell activity shapes regional mitochondrial morphology. These observed specializations in mitochondrial morphology may play an important role in mitochondrial function, as mutants showing disrupted hair cell mitochondrial architecture showed depolarized mitochondrial potentials and impaired evoked mitochondrial calcium influx.

This work provides novel and intriguing evidence that mechanotransduction and presynaptic calcium influx play important roles in shaping subcellular mitochondrial morphology in sensory hair cells. Yet there was a lack of consistency in the analysis and presentation of the data which made it difficult to contextualize and interpret the results. This study would be greatly strengthened by i) consistent definitions for hair cell maturation, ii) comparable data analysis of cav1.3a mutant and cdh23 mutant mitochondrial morphologies, and iii) more detailed descriptions and interpretations of the UMAP analysis.

Reviewer #2 (Public Review):

The authors present a manuscript highlighting recent advancements in cryo-focused ion beam/scanning electron microscopy (cryo-FIB) using plasma ion sources as an alternative to positively-charged gallium sources for cryo-FIB milling and volumetric SEM (cryo-FIB/SEM) imaging. The authors benchmark several sources of plasma and determine argon gas is the most suitable source for reducing undesirable curtaining effects during milling. The authors demonstrate that milling with an argon source enables volumetric imaging of vitrified cells and tissue with sufficient contrast to gleam biological insight into the spatial localization of organelles and large macromolecular complexes in both vitrified human cells and in high-pressure frozen mouse brain tissue slices. The authors also show that altering the sample angle from 52 to 90 degrees relative to the SEM beam enhances the contrast and resolution of biological features imaged within the vitrified samples. Importantly, the authors also demonstrate that the resolution of SEM images after serial milling with argon and nitrogen plasma sources does not appear to significantly affect resolution, suggesting that resolution does not vary over an acquisition series. Finally, the authors test and apply a neural network-based approach for mitigating image artifacts caused by charging due to SEM imaging of biological features with high lipid content, such as lipid droplets in yeast, thereby increasing the clarity and interpretability of images of samples susceptible to charging.

Strengths and Weaknesses:<br /> The authors do a fantastic job demonstrating the utility of plasma sources for increased contrast of biological features for cryo-FIB/SEM images. However, they do not specifically address the lingering question of whether or not it is possible to use this plasma source cryo-FIB/SEM volumetric imaging for the specific application of localizing features for downstream cryo-ET imaging and structural analyses. As a reader, I was left wondering whether this technique is ideally suited solely for volumetric imaging of cryogenic samples, or if it can be incorporated as a step in the cellular cryo-ET workflow for localization and perhaps structure determination. Another biorxiv paper (doi.org/10.1101/2022.08.01.502333) from the same group establishes a plasma cryo-FIB milling workflow to generate lamella of sufficient quality to elucidate sub-nanometer reconstructions of cellular ribosomes. However, I anticipate the real impact on the field will be from the synergistic benefits of combining both approaches of volumetric cryo-FIB/SEM imaging to localize regions of interest and cryo-ET imaging for high-resolution structural analyses.

Another weakness is the lack of demonstration that the contrast gained from plasma cryo-FIB/SEM is sufficient to apply neural network-based approaches for automated segmentation of biological features. The ability to image vitrified samples with enhanced contrast is huge, but our interpretation of these reconstructions is still fundamentally limited in our ability to efficiently analyze subcellular architecture.

Reviewer #2 (Public Review):

Charme is a long non-coding RNA reported by the authors in their previous studies. Their previous work, mainly using skeletal muscles as a model, showed the functional relevance of Charme, and presented data demonstrating its nuclear role, primarily via modulating the sub-nuclear localization of Matrin 3 (MATR3). Their data from skeletal muscles suggested that loss of the intronic region of Charme affects the local 3D genome organization, affecting MATR3 occupancy and this gene expression. Loss of Charme in vivo leads to cardiac defects. In this manuscript, they characterize the cardiac developmental defects and present molecular data supporting how the loss of Charme affects the cardiac transcriptome repertoire. Specifically, by performing whole transcriptome analysis in E12.5 hearts, they identify gene expression changes affected in developing hearts due to loss of Charme. Based on their previous study in skeletal muscles, they assume that Charme regulates cardiac gene expression primarily via MATR3 also in developing cardiomyocytes. They provide CLIP-seq data for MATR3 (transcriptome-wide footprinting of MATR3) in wild-type E15.5 hearts and connect the binding of MATR3 to gene expression changes observed in Charme knockout hearts. I credit the authors for providing CLIP seq data from in vivo embryonic samples, which is technically demanding.

Major strengths:

Although, as previously indicated by the authors in Charme knockout mice, the major strength is the effect of Charme on cardiac development. While the phenotype might be subtle, the functional data indicate that the role of Charme is essential for cardiac development and function. The combinatorial analysis of MATR3 CLIP-seq and transcriptional changes in the absence of Charme suggests a role of Charme that could be dependent on MATR3.

Weakness:

(i) Nuclear lncRNAs often affect local gene expression by influencing the local chromatin. Charme locus is in close proximity to MYBPC2, which is essential for cardiac function, sarcomerogenesis, and sarcomere maintenance. It is important to rule out that the cardiac-specific developmental defects due to Charme loss are not due to (a) the influence of Charme on MYBPC2 or, of that matter, other neighboring genes, (b) local chromatin changes or enhancer-promoter contacts of MYBPC2 and other immediate neighbors (both aspects in the developmental time window when Charme expression is prominent in the heart, ideally from E11 to E15)

(ii) The authors provide data indicating cardiac developmental defects in Charme knockouts. Detailed developmental phenotyping is missing, which is necessary to pinpoint the exact developmental milestones affected by Charme. This is critical when reporting the cell type/ organ-specific developmental function of a newly identified regulator.

(iii) Along the same line, at the molecular level, the authors provide evidence indicating a change in the expression of genes involved in cardiogenesis and cardiac function. Based on changes in mRNA levels of the genes affected due to loss of Charme and based on immunofluorescence analysis of a handful of markers, they propose a role of Charme in cell cycle and maturation. Such claims could be toned down or warrant detailed experimental validation.

(iv) Authors extrapolate the mechanistic finding in skeletal muscle they reported for Charme to the developing heart. While the data support this hypothesis, it falls short in extending the mechanistic understanding of Charme beyond the papers previously published by the authors. CLIP-seq data is a step in the right direction. MATR3 is a relatively abundant RBP, binding transcriptome-wide, mainly in the intronic region, based on currently available CLIP-seq data, as well as shown by the authors' own CLIP seq in cardiomyocytes. It is also shown to regulate pre-mRNA splicing/ alternative splicing along with PTB (PMID: 25599992) and 3D genome organization (PMID: 34716321). In addition, the authors propose a MATR3 depending molecular function for Charme primarily dependent on the intronic region of Charme and due to the binding of MATR3. Answering the following question would enable a better mechanistic understanding of how Charme controls cardiac development. (i) what are the proximal genomic regions in the 3D space to Charme locus in embryonic cardiomyocytes? Authors can re-analysis published Hi-C data sets from embryonic cardiomyocytes or perform a 4-C experiment using Charme locus for this purpose. (ii) does the loss of Charme affect the splicing landscape of MATR3 bound pre-mRNAs in E12.5 ventricles in general and those arising from the NCTC region specifically? (iii) MATR3 binds DNA, as also shown by authors in previous studies. Is the MATR3 genomic binding altered by Charme loss in cardiomyocytes globally, as well as on the loci differentially expressed in Charme knockout heart? Overlapping MATR3 genomic binding changes and transcriptome binding changes to differentially expressed genes in the absence of Charme would better clarify the MATR3-centric mechanisms proposed here. Further connecting that to 3D genome changes due to Charme loss could provide needed clarity to the mechanistic model proposed here.

Reviewer #2 (Public Review):

The manuscript by Abdirahman I. Abdi et al. examines markers of host immunity and metabolism and markers of the malaria parasite (Plasmodium falciparum) growth and transmission. As the transmission of the malaria disease is governed by the sexual forms, (gametocytes), understating the commitment process represents a major step towards the global elimination of malaria. While the study focuses on a sound, very important topic in malaria research, its findings are partially based on rather weak evidence. In particular, in some parts there is a lack of adequate correlations, inaccurate statistics and misleading statistical tests. Moreover, these analyses are poorly explained, to a degree that some conclusions seem a bit enforced. In addition, the multitude of terms used makes it hard for the reader to follow the text. The appeal of this study lies in its potential relevance to the global public health drive to eliminate malaria.

Reviewer #2 (Public Review):

The goal of the work described in this paper is to comprehensively describe the contribution of Neanderthal-informative mutations (NIMs) to complex traits in modern human populations. There are some known challenges in studying these variants, namely that they are often uncommon, and have unusually long haplotype structures. To overcome these, the authors customized a genotyping array to specifically assay putative Neanderthal haplotypes, and used a recent method of estimating heritability that can explicitly account for differences in MAF and LD.

This study is well thought-out, and the ability to specifically target the genotyping array to the variants in question and then use that information to properly control for population structure is a massive benefit. The methodology also allowed them to include rarer alleles that were generally excluded from previous studies. The simulations are thorough and convincingly show the importance of accounting for both MAF and LD in addition to ancestry. The fine-mapping done to disentangle effects between actual Neanderthal variants and Modern human ones on the same haplotype also seems reasonable. They also strike a good balance between highlighting potentially interesting examples of Neanderthal variants having an effect on phenotype without overinterpreting association-based findings.

The main weakness of the paper is in its description of the work, not the work itself. The paper currently places a lot of emphasis on comparing these results to prior studies, particularly on its disagreement with McArthur, et al. (2021), a study on introgressed variant heritability that was also done primarily in UK Biobank. While they do show that the method used in that study (LDSR) does not account for MAF and LD as effectively as this analysis, this work does not support the conclusion that this is a major problem with previous heritability studies. McArthur et al. in fact largely replicate these results that Neanderthal variants (and more generally regions with Neanderthal variants) are depleted of heritability, and agree with the interpretation that this is likely due to selection against Neanderthal alleles. I actually find this a reassuring point, given the differences between the variant sets and methods used by the two studies, but it isn't mentioned in the text. Where the two studies differ is in specifics, mainly which loci have some association with human phenotypes; McArthur et al. also identified a couple groups of traits that were exceptions to the general rule of depleted heritability. While this work shows that not accounting for MAF and LD can lead to underestimating NIM heritability, I don't follow the logic behind the claim that this could lead to a false positive in heritability enrichment (a false negative would be more likely, surely?). There are also more differences between this and previous heritability studies than just the method used to estimate heritability, and the comparisons done here do not sufficiently account for these. A more detailed discussion to reconcile how, despite its weaknesses, LDSR picks up similar broad patterns while disagreeing in specifics is merited.

In general this work agrees with the growing consensus in the field that introgressed Neanderthal variants were selected against, such that those that still remain in human populations do not generally have large effects on phenotypes. There are exceptions to this, but for the most part observed phenotypic associations depend on the exact set of variants being considered, and, like those highlighted in this study, still lack more concrete validation. While this paper does not make a significant advance in this general understanding of introgressed regions in modern populations, it does increase our knowledge in how best to study them, and makes a good attempt at addressing issues that are often just mentioned as caveats in other studies. It includes a nice quantification of how important these variables are in interpreting heritability estimates, and will be useful for heritability studies going forward.

Reviewer #2 (Public Review):

Centriole satellites are membraneless granules that surround the centrosome. Some proteins localize exclusively to centriole satellites, while others are present at both satellites and the centrosome. The function of centriole satellites is somewhat mysterious, but they have been implicated in ciliogenesis, autophagy, and mediating cellular stress responses. PCM1 is a core scaffolding protein essential for the assembly of centriole satellite and many studies have examined the role of centriole satellites in PCM1 depleted cell lines. However, the role of centrosome satellites at the organismal level has not been examined, and it remains unclear if the effects observed in cell lines are present across diverse cell types found in vivo.

In this manuscript, Hall et al., examine the effect of PCM1 knockout in mice. Surprisingly, Pcm1-/- mice are viable but exhibit increased perinatal lethality. Mice lacking PCM1 also have many interesting phenotypes, including dwarfism, male infertility, hydrocephaly, and hydronephrosis. These phenotypes are consistent with defects occurring in both primary and motile cilia. The ciliogenesis deficits in Pcm1-/- mice must be relatively mild, as severe defects in cilia assembly result in embryonic lethality. Thus, centriole satellites are not required for cilia assembly in most cell types. Consistently, the authors show that Pcm1-/- MEFs have no apparent phenotypes in cilia assembly. Pcm1-/- multiciliated ependymal cells have a delay in ciliogenesis and defects in cilia beating. Surprisingly, given the array of interesting phenotypes to examine in the mice, the authors switch to characterizing PCM1-/- RPE1 cells. Unlike primary MEFs, PCM1-/- RPE1 cells show reduced ciliogenesis. The authors show that in RPE1 cells, PCM1 promotes the recruitment of preciliary vesicles to the mother centriole and helps remove the CP110/CEP97 centriole capping complex. The authors propose that CP110 and CEP97 are transported away from mother centrioles by centriole satellites. However, Pcm1-/- MEFs also fail to remove CP110 from the mother centriole, despite having no defects in ciliogenesis. Thus, CP110 removal is not universally required for ciliogenesis.

This is an excellent manuscript that thoroughly examines the role of PCM1 both in vivo and in vitro. In my view, the major strength of this work lies in the examination of the impact of PCM1 loss in vivo. As a result, I was a little surprised the authors didn't focus more attention on the interesting phenotypes that arise in the Pcm1-/- mouse. The switch over to RPE1 cells is abrupt. Moreover, the phenotypes observed in this cell line are likely not occurring in most cell types in vivo, or else the expected organismal phenotypes would probably be even more severe. That notwithstanding, the RPE1 cell biology is rigorous, high quality, and the conclusions are well-justified. Overall, the work will be of broad interest to the centrosome/cilia community.

Reviewer #2 (Public Review):

Overall, the greatest value of this article lies in the discovery and statistics of the inhibitory components that increased in response to continuous repetitive visual stimuli and suppressed responses of the critical neurons that transmit looming information to elicit escape. Although the author proposes a possible mechanism for visual habituation in larva zebrafish, there are still some shortcomings in the circuitry level proof and data interpretation, most conclusions in Figures 1-5 have been drawn in other work and lack certain innovations. In general, the overall logic of this article is relatively complete and the content is substantial, many data are very interesting and worth further interpretation.

Reviewer #2 (Public Review):

TPP is critical for regulating the mRNA abundance of proinflammatory cytokines. Sara Scinicariello et al., identified ubiquitin E3 ligase HUWE1 function as a key regulator of the TPP degradation, which could direct the related immune responses. However, the physiological importance and their major conclusions were not fully clarified or supported by the experimental data.

Reviewer #2 (Public Review):

The authors convincingly show that their reconstructed ancestral nitrogenases are active both in vivo and in vitro, and show similar inhibitory effects as extant/wild-type enzymes.

The conclusion that, evolutionarily, there is a "single available mechanism for dinitrogen reduction" is not well explored in the paper. This suggests a limitation of using ancestral sequence reconstruction in this instance.

Reviewer #2 (Public Review):

In this paper, Kliesmete et al. analyze the protein and regulatory evolution of TRNP1, linking it to the evolution of brain size in mammals. We feel that this is very interesting and the conclusions are generally supported, with one concern.

The comparison of dN/dS (omega) values to 125 control proteins is helpful, but an important factor was not controlled. The fraction of a protein in an intrinsically disordered region (IDR) is potentially even more important in affecting dN/dS than the protein length or number of exons. We suggest comparing dN/dS of TRNP1 to another control set, preferably at least ~500 proteins, which have similar % IDR.

Reviewer #2 (Public Review):

Nutrigenomics has advanced in recent years, with studies identifying how the food environment influences gene expression in multiple model organisms. The molecular mechanisms mediating these food-gene interactions are poorly understood. Previous work identified the enzyme O-GlcNAC (OGT) in mediating the decreased sensitivity in sweet-taste cells when exposed to a high-sugar diet. The present study, using fly gustatory neurons as a model, provides mechanistic insight into how nutrigenomic signaling encodes nutritional information into cellular changes. The authors expand previous work by showing that OGT is associated with neural chromatin at introns and transcriptional start sites, and that diet-induced changes in chromatin accessibility were amplified at loci with presence of both OGT and PRC2.1. The work also identifies Mitogen Activated Kinase as a critical mediator in this pathway. This is an elegant group of experiments revealing mechanisms for how nutrigenomic signaling triggers cellular responses to nutrients.

Reviewer #2 (Public Review):

The authors address a very old question: what is the mechanism that controls genetic exchanges (crossovers) between the maternal and paternal chromosomes during sexual reproduction (meiosis). Specifically, what could account for two crucial aspects of the non-random distribution of crossovers: the lower-than-expected rate of non-exchange chromosomes, and the larger-than-expected distance between adjacent crossovers on the same chromosome. Despite the great progress that was made in the last few decades in understanding the molecular details crossover formation, the mechanism accounting for their non-random distribution remains a matter of heated debate. Hence, an ability to provide new insight into this question will be of interest to the wide chromosome biology community.

In this work, the authors combine two important findings/resources. The first is their own modeling of a biophysical framework called 'coarsening'. Coarsening relates to the well-described behavior of liquid compartments, which tend to get larger with time, at the expense of smaller compartments. As the authors note, their coarsening work builds on research by many labs, and on the recent understanding of the role of condensates in cell biology in general, and the liquid nature of the synaptonemal complex - a conserved meiotic chromosomal interface. In their previous paper, the authors found that coarsening could account for multiple cytological aspects of crucial regulators of crossovers - a conserved protein called HEI10. Their modeling was able to recapitulate temporal changes in HEI10 distribution and to account for changes that occur upon changes to HEI10 expression levels (halving of expression and over-expression). The second is the recent analysis of plant strains lacking the synaptonemal complex (zyp1). In that mutant, crossovers do occur (this is different than in some organisms), but the non-random distribution of crossovers is mostly lost: both crossover interference and the paucity of non-exchange chromosomes fit mostly random distribution.

Here, the authors combine these resources and adjust their modeling to account for the lack of the synaptonemal complex. A crucial difference is that instead of diffusing inside the SC (which spans each chromosome pair end-to-end), HEI10 now diffuses in the nucleoplasm. With this modified simulation they mostly account for crossover distribution in zyp1 mutants, using both published and new data they have acquired.

Despite the very limited amount of new data included in this manuscript, the clever combination of these two sources of data manages to add yet another layer of evidence to the idea that coarsening can explain crossover distribution. The main concern regarding the manuscript is that most of the aspects of crossover distribution that the model reproduces are quite trivial - for example, the resulting random distribution of the number of crossovers per chromosome. Some of the non-trivial aspects of the distribution - for example, the telomere enrichment - were built into the simulation as an explicit parameter. The only aspect that would be considered truly non-trivial is the narrower-than-expected number of total crossovers, despite the random distribution of crossovers per chromosome (Fig. 2A). Indeed, the modeling recapitulates this parameter, albeit to a much stronger degree than the in vivo data.

The ability of the model to recreate one non-trivial aspect of the crossover distribution is not sufficient to rule out other possible models, which would be necessary to consider this work a significant advance. However, if the authors are able to provide additional, non-trivial predictions relating to this and to other experimental conditions, this would dramatically elevate their ability to claim that a coarsening-based mechanism is indeed the most plausible one to explain crossover distribution. Some of these conditions could involve experimental perturbation of key parameters in the model: HEI10 levels, the number of DSBs or recombination intermediates (the 'substrate' that ends up resulting in crossovers), the length of time coarsening is allowed to proceed, or the volume of the nucleus.

Reviewer #2 (Public Review):

There is a lot of interest in how cells transfer materials (proteins, RNA, organelles) by extracellular vesicles (EV) and tunneling nanotubes (TNTs). Here, Zhang and Schekman developed quantitative assays, based on two different reporters, to measure EV and direct contact-dependent mediated transfer. The first assay is based on transfer of Cas9, which then edits a luciferase gene, whose enzymatic activity is then measured. The second assay is based on a split-GFP system. The experiments on EV trafficking convincingly show that purified exosomes, or any other diffusible agent, are unable to transfer functional Cas9 (either EV-tethered or untethered) and induce significant luciferase activity in acceptor cells. The authors suggest a plausible model by which Cas9 (with the gRNA?) gets "stuck" in such vesicles and is thus unable to enter the nucleus to edit the gene.

To test alternative pathways of transfer, e.g. by direct cell-cell contact, the authors co-cultured donor and acceptor cells and detect significant luciferase activity. The split GFP assay also showed successful transfer. The authors further characterize this process by biochemical, genetic and imaging approaches. They conclude that a small percentage of cells in the population produce open-ended membrane tubules (which are wider and distinct from TNTs) that can transfer material between cells. This process depends on actin polymerization but not endocytosis or trogocytosis. The process also seems to depend on endogenously expressed Syncytin proteins - fusogens which could be responsible for the membrane fusion leading to the open ends of the tubules.

The paper provides additional solid evidence to what is already known about the inefficiency of EV-mediated protein transport. Importantly, it provides an interesting new mechanism for contact-dependent transport of cellular material and assigns valuable new information about the possible function of Syncytins. However, the evidence that the proteins and vesicles transfer through the tubules is incomplete and a few more experiments are required. In addition, certain inconsistencies within the paper and with previous literature need to be resolved. Finally, some parts of the text, methods and the figures require re-writing or additional information for clarity.

Major comments<br /> 1. In Figure 1F, the authors compare the function of exosome-transported SBP-Cas9-GFP vs. transient transfection of SBP-Cas9-GFP. It is not clear if the cells in the transiently transfected culture also express the myc-str-CD63 and were treated with biotin. It is important to determine if CD63-tethering itself affects Cas9 function.<br /> 2. The authors do not rule out that TNTs are a mode of transfer in any of their experiments. Their actin polymerization inhibition experiments are also in-line with a TNT role in transfer. This possibility is not discussed in the discussion section.<br /> 3. Issues with the Split GFP assay:<br /> a. On page 4, line 176, the authors claim that "A mixture of cells before co-culture should not exhibit a GFP signal". However, this result is not presented.<br /> b. The authors show in Figure 2C and F that in MBA/HEK co-culture or only HEK293T co-culture, there are dual-labeled, CFP-mCherry, cells. First - what is the % of this sub-population? Second, the authors dismiss this population as cell adhesion (Page 5, line 192) - but in the methods section they claim they gated for single particles (page 17, line 642), supposedly excluding such events. There is a simple way to resolve this - sort these dual labeled cells and visualize under the microscope. Finally - why do the authors think that the GFP halves can transfer but not the mature CFP or mCherry?<br /> c. In the Cas9 experiments - the authors detect an increase in Nluc activity similar in order of magnitude that that of transient transfection with the Cas9 plasmid - suggesting most acceptor cells now express Nluc. However, only 6% of the cells are GFP positive in the split-GFP assay. Can the authors explain why the rate is so low in the split-GFP assay? One possibility (related to item #2 above) is that the split-GFP is transferred by TNTs.<br /> 4. The membrane tubules, the membrane fusion and the transfer process are not well characterized:<br /> a. The suggested tubules are distinct from TNTs by diameter and (I presume, based on the images) that they are still attached to the surface - whereas TNTs are detached. However, how are these structures different from filopodia except that they (rarely) fuse?<br /> b. Figure 5E shows that the acceptor cells send out a tubule of its own to meet and fuse. Is this the case in all 8 open-ended tubules that were imaged? Is this structure absent in the closed-ended tubules (e.g. as seen in Figures 6 & 8)?<br /> c. The authors suggest a model for transport of the proteins tethered to vesicles (via CD63 tethering). However, the data is incomplete.<br /> i. They show only a single example of this type of transport, without quantification. How frequent is this event?<br /> ii. Furthermore, the labeling does not conclusively show that these are vesicles and not protein aggregates. Labeling of the vesicle - by dye or protein marker will be useful to determine if these are indeed vesicles, and which type.<br /> iii. The data from Figure 2 suggest (if I understand correctly) transfer of the CD63-tethered half-GFP, further strengthening the idea of vesicular transfer. However, the authors also show efficient transfer of untethered Cas9 protein (Figure 2A and other figures). Does this mean that free protein can diffuse through these tubules? The Cas9 has an NLS so the un-tethered versions should be concentrated in the nucleus of donor cells. How, then, do they transfer? The authors do not provide visual evidence for this and I think it is important they would.<br /> iv. In Figures 6 & 8, where transfer is diminished, there are still red granules in acceptors cells (representing CD63-mcherry). Does this mean that vesicles do transfer, just not those with Cas9-GFP? Is this background of the imaging? The latter case would suggest that the red granule moving from donor to acceptor cells in figure 4 could also be "background". This matter needs to be resolved.<br /> 5. Why do HEK293T do not transfer to HEK293T?<br /> a. A major inexplicable result is that HEK293T express high levels of both Syncytin proteins (Figure 7 - supp figure 1A) yet ectopic expression of mouse Syncytin increases transfer (Figure 7E). Why would that be? In addition, Fig 3A shows high transfer rates to A549 cells - which express the least amount of Syncytin. The authors suggest in the discussion that Syncytin in HEK293T might not be functional without real evidence.<br /> b. In addition - previous publications (e.g. PMID: 35596004; 31735710) show that over expression of syncytin-1 or -2 in HEK293T cells causes massive cell-cell fusion. The authors do not provide images of the cells, to rule out cell-cell fusion in this particular case.

Reviewer #2 (Public Review):

To explore their dataset, the authors first identify all eligible women (n = 4673) in the database queried and use propensity score matching (PSM) to match group A (not infected by HPV) with group B (infected by HPV) for several covariates thought to affect bone mineral density (e.g.: age, smoking, alcohol). After PSM, no significant difference for selected covariates can be detected between the two groups.

Because they add matched their groups for relevant covariates possibly affecting bone mineral density, the authors then use Welch two-sample t-test to compare bone mineral densities of leg and lumbar spine between group A and group B, and detect significantly lower bone mineral densities for participants infected by HPV, group B. Here, the statistical approach chose by the author seems limited, and although PSM had been applied to match group earlier in the analysis pipeline, the reader could expect the statistical approach to be more robust, i.e. accounting for other covariates, like a linear mixed model.

Then, the authors analyse each HPV subtype independently and use Kendall's tau-b correlation test to estimate a correlation between a given HPV subtype and bone mineral density. To apply this test, the authors had to transform the bone mineral density to a binary variable, i.e. greater or equal to 1. Here again, the statistical approach does not control for any of the bone mineral density potentially affecting covariates. Also, the authors' study performed 32 Kendall's tau-b correlation tests and did not seem to correct for multiple testing.

Finally, the authors use the Restricted cubic spline model to establish a non-linear relationship between the number of infected HPV subtypes and bone mineral density.

The authors had set the aim to explore the association between HPV and bone mineral density. Unfortunately, due to possibly not high enough robustness of statistical approaches used in this manuscript, it does not seem sufficient to establish a clear association between HPV infection status and a lower bone mineral density. However, given the database the authors have created, it is believed that they have all the tools needed to pursue their aim.

Reviewer #2 (Public Review):

This study investigates whether frequency tuning in the avian auditory midbrain is changed by the reliability of a key sound localization cue (Interaural Time Differences, ITDs) during development. It tests whether auditory neurons become more sensitive to sound frequencies that provide more reliable information about ITDs.

To manipulate the reliability of ITDs in a frequency-specific way, the authors removed the facial ruff of barn owls during development, which alters the acoustical input available to the animal in a number of important ways. When these animals reached adulthood, electrophysiological recordings were performed in the external nucleus of the inferior colliculus (ICx). Compared to control animals, these recordings revealed a weaker relationship between the best-frequency and best-ITD of individual neurons. A similarly weak relationship was observed in young animals whose ruff had not yet fully developed.

These results arise partly because animals without a facial ruff possess neurons with a best ITD of 0 that are tuned to unusually low frequencies. Having considered a number of possible explanations, the authors argue that this occurs because facial ruff removal reduces the reliability of high-frequency ITDs for frontal locations. Consequently, neurons tuned to frontal locations shift their frequency sensitivity to lower frequencies, which provides more reliable information about ITD. This shift toward lower frequencies is also thought to partly explain changes in tuning width that are observed in the absence of a facial ruff.

The study concludes that these results collectively provide evidence that the brain learns to implement probabilistic coding of sound location during development. However, although the study clearly shows changes in neural tuning in the absence of a fully developed facial ruff, the causal link with ITD reliability is complicated by a number of technical issues. The most important of these include a tendency to ignore the rear hemifield for some analyses but not others, the complex acoustical effects of facial ruff removal, and a model of IPD reliability that may or may not accurately reflect real-world listening. Nevertheless, the study presents an interesting set of results and shows an innovative approach in a number of places.

ACOUSTICS: A key strength of the study is its attempt to quantify the reliability of ITDs, which forms the foundation for the rest of the study. However, it is not entirely clear whether the method used for calculating ITD reliability is the most appropriate, and the way the data are presented raises a number of questions.<br /> 1) Why is IPD variability plotted instead of ITD variability (or indeed spatial reliability)? The relationship between these measures is likely to vary across frequency, which makes it difficult to compare ITD variability across frequency when IPDs are plotted. Normalizing data across frequencies also makes it difficult to compare different locations and acoustical conditions. For example, in Fig.1a and Fig.1b, the data shown for 3 kHz at ~160 degrees seems quantitatively and visually quite different, but the difference (in Fig.1c) appears to be negligible.

2) How well do the measures of ITD reliability used reflect real-world listening? For example, the model used to calculate ITD reliability appears to assume the same (flat) spectral profile for targets and distractors, which are presented simultaneously with the same temporal envelope, and a uniform spatial distribution of sounds across space. It is therefore unclear how robust the study's results are to violations of these assumptions.

3) Does facial ruff removal produce an isolated effect on ITD variability or does it also produce changes in directional gain, and the relationship between spatial cues and sound location? Although the study considers this issue in some places (e.g. Fig.2, Fig.5), a clearer presentation of the acoustical effects of facial ruff removal and their implications (for all locations, not just those to the front), as well as an attempt to understand how these acoustical changes lead to the observed changes in ITD reliability, would greatly strengthen the study. In addition, Fig.1 shows average ITD reliability across owls, but it would be helpful to know how consistent these measures are across owls, given individual variability in Head-Related Transfer Functions (HRTFs). This potentially has implications for the electrophysiological experiments, if the HRTFs of those animals were not measured. One specific question that is potentially very relevant is whether the facial ruff attenuates sounds presented behind the animal and whether it does so in a frequency-dependent way. In addition, if facial ruff removal enables ILDs to be used for azimuth, then ITDs may also become less necessary at higher frequencies, even if their reliability remains unchanged.

ELECTROPHYSIOLOGY: The electrophysiological recordings in young owls are impressive, particularly since they were done longitudinally (although the follow-up data in adults is not shown). The decision to look at the relationship between different tuning properties following different types of developmental experience (e.g. relationship between best ITD and best frequency in the absence/presence of a fully developed facial ruff) is also a major strength, particularly in light of the very interesting results observed. The authors have succeeded in identifying clear evidence for the importance of acoustical input for determining frequency-tuning properties in the auditory midbrain. However, a number of points remain unclear.

1) It is unclear why some analyses (Fig.5, Fig.7) are focused on frontal locations and frontally-tuned neurons. It is also unclear why neurons with a best ITDs of 0 are described as frontally tuned since locations behind the animal produce an ITD of 0 also. Related to this, in Fig.1, facial ruff removal appears to reduce IPD variability at low frequencies for locations to the rear (~160 degrees), where the ITD is likely to be close to 0. Neurons with a best ITD of 0 might therefore be expected to adjust their frequency tuning in opposite directions depending on whether they are tuned to frontal or rearward locations.

2) The study suggests that information about high-frequency ITDs is not passed on to the ICX if the ICX does not contain neurons that have a high best frequency. However, neurons might be sensitive to ITDs at frequencies other than the best frequency, particularly if their frequency tuning is broader. It is also unclear whether the best frequency of a neuron always corresponds to the frequency that provides the most reliable ITD information, which the study implicitly assumes.

Reviewer #2 (Public Review):

Octopuses are known for their abilities in solving complex tasks and numerous apparently complex cognitive behaviours such as astonishment at octopuses learning how to open jars by watching others and the mind-boggling camouflage. They are very clever molluscs. The octopus shows the famously advanced brain plan but it is one that has little research progress due to its large size and structural complexity. This was originally recognised by the work of BB Boycott, JZ Young, EG Gray, and others in mid last century. Since then, however, little progress has been achieved towards a modern-day description of the octopus neural network particularly in the higher-order brain lobe, despite intense interest and indeed research progress concerning their complex behavioural and cognitive abilities.

This study applied a combination of EM-based imaging, neural tracing, and analyses to start revealing a further detailed view of a part of the lateral gyrus of the vertical lobe (learning and memory centre) of the common European octopus. It is a long overdue contribution and starts to bring octopus neuroscience a step close to the details of some vertebrates achieved. The new findings of neurons and the associated network provide new insights into this very complex but unfamiliar brain, allowing to propose a functional network that may link to the octopus memory formation. Also, this work could be of potential interest to a broad audience of neuroscientists and marine biologists as well as those in bio-imaging and deep-learning fields.

Strengths:<br /> Current knowledge of the neuroanatomy and the associating network of the octopus vertical lobe (learning and memory centre) remains largely based on the pioneering neuroanatomical studies in the '70s, this work indeed provides a rich and new dataset using modern-day imaging technology and reveals numerous previously-unknown neuron types and the resulting further complex network than we thought before. This new dataset reveals hundreds of cell processes from seven types of neurons located in one gyrus of the vertical lobe and can be useful for planning further approaches for advanced microscopy and other approaches including electrophysiological and molecular studies.<br /> Another strength of this study is to apply the current fashion of the deep learning technique to accelerate the imaging process on this octopus complex neural network. This could trigger some inventions to develop new algorithms for further applications on those non-model animals.

Weakness/limitations:<br /> In an effort to match the key claims of the first connectome of the octopus vertical lobe, mapping up an entire vertical lobe is essential. However, also understandably, given challenges in imaging a large-sized brain region, this study managed to image a very small proportion of the anterior part of the lateral gyrus. Along with the current limited dataset, a partially reconstructed neural network of one gyrus, it is unclear whether the wiring pattern found in this study would appear as a similar arrangement throughout an entire lateral gyrus. Furthermore, it is also unknown if another 4 gyri might keep a similar pattern of neural network as it found in the lateral gyrus. Considering some recent immunochemistry evidence that showed distinct different signals in different gyri in terms of heterogeneity of neuron types amongst gryi, to assume this newly-discovered network can represent the wiring pattern across an entire 5-gyrus vertical lobe is inadequate. As this study is the first big step to reveal the complex network in the octopus vertical lobe system, the title may be changed to "Toward connectomics of the Octopus vulgaris vertical lobe - new insights of memory acquisition network".

Reviewer #2 (Public Review):

The chemosensory systems of vertebrates and insects share a lot of structural and functional similarities. However, looking deeper into their molecular components reveals that these similarities likely represent remarkable examples of convergent evolution. For instance, receptor molecules that detect odors are unrelated between vertebrates and insects - vertebrates use G-protein coupled receptors while insects use ligand-gated ion channels. The latter was long regarded as specific to insects, but later studies identified putative homologs in other animals, (but not in vertebrates), some unicellular eukaryotes, and plants, raising the possibility that it is an ancient family. Still, the evolution of this protein family is notoriously difficult to analyze due to a high degree of sequence divergence between the genes despite the shared structural features of the proteins they encode. Here, the authors make use of the recent explosion of high-quality structural predictions produced by AlphaFold to conduct a deep search for previously undiscovered homologs of insect odorant and gustatory receptors.

The study describes two major findings:<br /> 1. In contrast to the previous idea that vertebrates lack any homologs of the insect receptors, two proteins in vertebrates turn out to display a similar structure (Fig. 2B).<br /> 2. The authors describe a previously uncharacterized family of Drosophila "gustatory receptor-like" proteins with a putative function in chemoreception as suggested by expression data (Fig 3A, G).

All analyses are extremely thorough, the logic of the narrative is very clear, and I find all conclusions well supported by data. The authors clearly favor a hypothesis that the family that includes insect odorant and gustatory receptors has a very deep evolutionary origin, and the homologous genes in other animals and non-animals have strongly diverged at the level of the sequence but retained detectable structural homology. However, they also acknowledge the limitations of some of their arguments and they discuss an alternative whereby the observed structural similarity is the result of convergence (which would be equally interesting). Overall, this study represents a major advance in our understanding of protein evolution and opens several avenues of research into the question of how functional demands steer the preservation of structural features of proteins while allowing their amino acid sequences to diverge.

Reviewer #2 (Public Review):

In this foundational article, the authors conduct an ancient DNA characterization of maize unearthed in archaeological contexts from Paredones and Huaca Prieta in the Chicama river valley of Peru. These maize specimens were recovered by painstakingly controlled excavation. Their context would appear to be beyond reproach though the individual radiocarbon determinations should be subject to further scrutiny.

Radiocarbon determination for at least one of the maize cobs analyzed for aDNA is not a direct date, but dates associated material. The authors should provide a table of the direct dates on the specimens that were analyzed for ancient DNA. They should also specify the type and quantity of material sent and whether the cob, glumes, pith, or husks were submitted for dates. Include δ13C determinations for each cob with laboratory analysis numbers because there is justifiable concern that at least one of these cob dates has a δ13C value suggesting the material dated is not maize. Generally, the δ13C for maize ranges from -14 to -7. One or more of the specimens subjected to ancient DNA analysis in this paper have δ13C values far outside of this confidence interval.

From the perspective of future scientists being able to repeat the analyses performed here, I would hope that all details of specimen treatment, extraction methods, read length and quality would need to be assiduously described. Routine analytical results should be reported so that comparisons with earlier and future results are facilitated, and not made difficult to decipher or search for.

The aDNA analysis may or may not be affected by the anomalous δ13C values but one would anticipate that standard aDNA extraction and analysis protocols would provide a means by which the specimen's preservation of the specimens could be ascertained, for example, perhaps deamination and fragmentation rates could be compared or average read length evaluated with modern-contemporary materials so that preservation of the Paredones samples relative to that of maize in the CIMMYT germplasm bank and the San Marcos specimens investigated by the same researchers can be evaluated.

The size and shape of the cobs depicted are similar to specimens occurring much later in Mesoamerican assemblages. For example, the approximate rachis diameter of the San Marcos specimens depicted by Valle-Bueno et al. (2016: Fig.1) averages less than 0.5cm while the specimens depicted in Valle-Bueno et al. (this manuscript) average 1.0 cm. The former - San Marcos - specimens are dated at 5300-4970 BP cal while the larger - Paredones - specimens date roughly 6777 - 5324 BP cal. The considerable disparity among the smaller more recent specimens compared to the very much larger putatively older specimens suggests the Paredones specimen's radiocarbon determinations are equivocal. The authors point this out but repeatedly state these cobs are the most ancient; a conundrum that should be resolved.

I would suggest the authors consider redating these three specimens and if they do, hope that they will prepare the laboratory personnel with depositional environment information. MacNeish was skeptical about late dates on maize at Tehuacan, at first. Adovasio was initially certain about maize's associated dates from Meadowcroft. One would prefer to be reasonably certain the foundation this article creates is solid; the author's repeated reference to these cobs as the most ancient in the Americas should be reaffirmed so retraction will not be necessary.