Reviewer #1 (Public Review):

This study investigates the role of microtubules in regulating insulin secretion from pancreatic islet beta cells. This is of great importance considering that controlled secretion of insulin is essential to prevent diabetes. Previously, it has been shown that KIF5B plays an essential role in insulin secretion by transporting insulin granules to the plasma membrane. High glucose activates KIF5B to increase insulin secretion resulting in the cellular uptake of glucose. In order to prevent hypoglycemia, insulin secretion needs to be tightly controlled. Notably, it is known that KIF5B plays a role in microtubule sliding. This is important, as the authors described previously that beta cells establish a peripheral sub-membrane microtubule array, which is critical for the withdrawal of excessive insulin granules from the secretion sites. At high glucose, the sub-membrane microtubule array is destabilized to allow for robust insulin secretion. Here the authors aim to answer the question of how the peripheral array is formed. Based on the previously published data the authors hypothesize that KIF5B organizes the sub-membrane microtubule array via microtubule sliding.

General comment:<br /> This manuscript provides data that indicate that KIF5B, like in many other cells, mediates microtubule sliding in beta cells. This study is limited to in vitro assays and one cell line. Furthermore, the authors provide no link to insulin secretion and glucose uptake and the overall effects described are moderate. Finally, the overall effect of microtubule sliding upon glucose stimulation is surprisingly low considering the tight regulation of insulin secretion. Moreover, the authors state "the amount of MT polymer on every glucose stimulation changes only slightly, often undetectable.....In fact, we observe a prominent effect of peripheral MT loss only after a long-term kinesin depletion (three-four days)". This challenges the view that a KIF5B-dependent mechanism regulating microtubule sliding plays a major role in controlling insulin secretion.

Specific comments:<br /> 1) Notably, the authors have previously reported that high glucose-induced remodeling of microtubule networks facilitates robust glucose-stimulated insulin secretion. This remodeling involves the disassembly of old microtubules and the nucleation of new microtubules. Using real-time imaging of photoconverted microtubules, they report that high levels of glucose induce rapid microtubule disassembly preferentially in the periphery of individual β-cells, and this process is mediated by the phosphorylation of microtubule-associated protein tau. Here, they state that the sub-membrane microtubule array is destabilized via microtubule sliding. What is the relevance of the different processes?

2) On one hand the authors describe how KIF5B depletion prevents sliding and the transport of microtubules to the plasma membrane to form the sub-membrane microtubule array. This indicates KIF5B is required to form this structure. On the other hand, they describe that at high glucose concentration, KIF5B promotes microtubule sliding to destabilize the sub-membrane microtubule array to allow robust insulin secretion. This appears contradictory.

3) Previously, it has been shown that KIF5B induces tubulin incorporation along the microtubule shaft in a concentration-dependent manner. Moreover, running KIF5B increases microtubule rescue frequency and unlimited growth of microtubules. Notably, KIF5B regulates microtubule network mass and organization in cells (PMID: 34883065). Consequently, it appears possible that the here observed phenomena of changes in the microtubule network might be due to alterations in these processes.

4) The authors provide data that indicate that microtubule sliding is enhanced upon glucose stimulation. They conclude that these data indicate that microtubule sliding is an integral part of glucose-triggered microtubule remodeling. Yet, the authors fail to provide any evidence that this process plays a role in insulin secretion or glucose uptake.

5) The authors speculate that the sub-membrane microtubule array prevents the over-secretion of insulin. Would one not expect in this case a change in the distribution of insulin granules at the plasma membrane when this array is affected? Or after glucose stimulation? Notably, it has been reported that "the defects of β-cell function in KIF5B mutant mice were not coupled with observable changes in islet morphology, islet cell composition, or β-cell size" and "the subcellular localization of insulin vesicles was found to not be affected significantly by the decreased Kif5b level. The cytoplasm of both wild-type and mutant β-cells was filled with insulin vesicles. Insulin vesicle numbers per square μm were determined by counting all insulin vesicles in randomly photographed β-cells. More insulin granules were found in Kif5b knockout β-cells compared with control cells. This phenomenon is consistent with the observation that insulin secretion by β-cells is affected" whereby "Insulin vesicles (arrowheads) were distributed evenly in both mutant and control cells" (PMID: 20870970).

6) Does the sub-membrane microtubule array exist in primary beta cells (in vitro and/or in vivo) and how it is affected in KIF5B knockout mice?

Reviewer #1 (Public Review):

Recently discovered extrachromosomal DNA (ecDNA) provides an alternative non-chromosomal means for oncogene amplification and a potent substrate for selective evolution of tumors. The current work aims to identify key genes whose expression distinguishes ecDNA+ and ecDNA- tumors and the associated processes to shed light on the biological mechanisms underlying ecDNA genesis and their oncogenic effects. While this is clearly an important question, the analysis and the evidence supporting the claims are weak. The specific machine learning approach seems unnecessarily convoluted, insufficiently justified and explained, and the language used by the authors conflates correlation with causality. This work points to specific GO processes associated (up and down) with ecDNA+ tumors, many of which are expected but some seem intriguing, such as association with DSB pathways. My specific comments are listed below.

A. The claim of identifying genes required to 'maintain' ecDNA+ status is not justified - predictive features are not necessarily causal.<br /> B. The methods and procedures to identify the key genes is hyperparameterized and convoluted and casts doubt on the robustness of the findings given the size and heterogeneity of the data.<br /> a. In the first two paragraphs of Boruta Analysis Methods section, authors describe an iterative procedure where in each iteration, a binomial p-value is computed for each gene based on number of iterations thus far in which the gene was selected (higher GINI index than max of shadow features). But then in the third paragraph they simply perform Random Forest in 200 random 80% of samples and pick a gene if it is selected in at least 10/200. It is ultimately not clear what was done. Why 10/200? Also "the probability that a gene is a "hit" or "non-hit" in each iteration is 0.5" is unclear. That probability is of a gene achieving GINI index higher than the max of shadow features. How can it be 0.5?<br /> b. The approach of combining genes with clusters is arbitrary. Why not start with clusters and evaluate each cluster (using some gene set summary score) for their ability to discriminate? Ultimately, one needs additional information to disambiguate correlated genes (i.e. in a co-expression cluster) in terms of causality.<br /> c. The cross-validation procedure is not clear at all. There is a mention of 80-20 split but exactly how/if the evaluation is done on the 20% is muddled. The way precision-recall procedure is also a bit convoluted - why not simply use the area under the PR curve?<br /> d. The claim is that Boruta genes are different from differentially expressed genes but the differential expression seems to be estimated without regards to cancer type, which would certainly be highly biased and misleading. Why not do a simple regression of gene expression by ecDNA status, cancer type and select the genes that show significant coefficient for ecDNA status?<br /> C. After identifying key features (which the authors inappropriate imply to be causal) they perform a series of enrichment/correlative analysis.<br /> a. It is known that ecDNA status associates with poor survival, and so are cell cycle related signal. Then the association between Boruta genes and those processes is entirely expected. Is it not? The same goes for downregulation of immune processes.<br /> b. The association with DSB specifically is interesting. Further analysis or discussion of why this should be would strengthen the work.<br /> c. On page 15, second paragraph, when providing the up versus down CorEx genes, please also provide up versus down for non-CorEx genes as well to get a sense of magnitude.<br /> d. The finding that Boruta genes are associated with high mutation burden is intriguing because in general mutation burden is associated with better survival and immunotherapy response. This counter-intuitive result should be scrutinized more to strengthen the work.<br /> e. On page 17 "12 of the 47 genes not specifically enriching any known GO biological Process" is confusing. How can individual gene enrich for a GO process?

Reviewer #1 (Public Review):

Summary:<br /> In this study, the authors generate a Drosophila model to assess disease-linked allelic variants in the UBA5 gene. In humans, variants in UBA5 have been associated with DEE44, characterized by developmental delay, seizures, and encephalopathy. Here, the authors set out to characterize the relationship between 12 disease-linked variants in UBA5 using a variety of assays in their Drosophila Uba5 model. They first show that human UBA5 can substitute all essential functions of the Drosophila Uba5 ortholog, and then assess phenotypes in flies expressing the various disease variants. Using these assays, the authors classify the alleles into mild, intermediate, and severe loss-of-function alleles. Further, the authors establish several important in vitro assays to determine the impacts of the disease alleles on Uba5 stability and function. Together, they find a relatively close correlation between in vivo and in vitro relationships between Uba5 alleles and establish a new Drosophila model to probe the etiology of Uba5-related disorders.

Strengths:<br /> Overall, this is a convincing and well-executed study. There is clearly a need to assess disease-associated allelic variants to better understand human disorders, particularly for rare diseases, and this humanized fly model of Uba5 is a powerful system to rapidly evaluate variants and relationships to various phenotypes. The manuscript is well written, and the experiments are appropriately controlled.

Reviewer #1 (Public Review):

This work describes the induction of SIV-specific NAb responses in rhesus macaques infected with SIVmac239, a neutralization-resistant virus. Typically, host NAb responses are not detected in animals infected with SIVmac239. In this work, seventy SIVmac239-infected macaques were retrospectively screened for NAb responses and a subset of nine animals were identified as NAb-inducers. The viral genomes from 7/9 animals that induced NAb responses were found to encode nonsynonymous mutation in the Nef gene (amino acid G63E). In contrast, Nef G63E mutation was found only in 2/19 NAb non-inducers - implicating that the Nef G63E mutation is selected in NAb inducers. Measurement of Nef G63E frequencies in plasma viruses suggested that Nef G63E selection preceded NAb induction. Nef G63E mutation was found to mediate escape from Nef-specific CD8+ T-cell responses. To examine the functional phenotype of Nef G63E mutant, its effect on downmodulation of Nef-interacting host proteins was examined. Infection of rhesus and cynomolgus macaque CD4+ T cell lines with WT or Nef G63E mutant SIV suggested that Nef mutant reduces S473 phosphorylation of AKT. Using flow cytometry-based proximity ligation assay, it was shown that Nef G63E mutation reduced binding of Nef to PI3K p85/p110 and mTORC2 GβL/mLST8 and MTOR components - kinase complex responsible AKT-S473 phosphorylation. In vitro B-cell Nef invasion and in vivo imaging/flow cytometry-based assays were employed to suggest that Nef from infected cells can target Env-specific B cells. Lastly, it was determined that NAb inducers have significantly higher Env-specific B-cells responses after Nef G63E selection when compared to NAb non-inducers. Finally, a corollary was drawn between the Nef G63E-associated B-cell/NAb induction phenotype and activated PI3K delta syndrome (APDS), which is caused by activating GOF mutations in PI3K, to suggest that Nef G63E-meidated induction of NAb response is reciprocal to APDS.

Strengths:<br /> This study aims to understand the viral-host interaction that governs NAb induction in SIVmac239-infected macaques - this could enable identification of determinants important for induction of NAb responses against hard-to-neutralize tier-2/3 HIV variants. The finding that SIV-specific B-cell responses are induced following Nef G63E CD8+ T-cell escape mutant selection argue for an evolutionary trade-off between CTL escape and NAb induction. Exploitation of such a cellular-humoral immune axis could be important for HIV/AIDS vaccine efforts.

Although more validation and mechanistic basis are needed, the corollary between PI3K hyperactive signaling during autoimmune disorders and Nef-mediated abrogated PI3K signaling could help identify novel targets and modalities for targeting immune disorders and viral infections.

Weaknesses:<br /> Although the paper does have strengths in principle, the weaknesses of the paper are that the mechanistic basis of Nef-mediated induction of NAb responses are not directly examined. For example, it remains unclear whether SIVmac239 with engineered G63E mutation in Nef would induce faster and potent NAb responses. A macaque challenge study is needed to address this point.

As presented, the central premise of the paper involves infected cell-generated Nef (WT or G63E mutant) being targeted to adjacent Env-specific B cells. However, it remains unclear how this is transfer takes place. A direct evidence demonstrating CD4+ T cell-associated and/or cell-free Nef being transferred to B-cell is needed to address this concern.

The interaction between Nef and PI3K signaling components (p85, p110, GβL/mLST8, and MTOR) has been explored using PLA assay, however, this requires validation using additional biochemical and/or immunoprecipitation-based approaches. For example, is Nef (WT or mutant form) sufficient to affect PI3K-induced phosphorylation of Akt in an in vitro kinase assay? Moreover, the details regarding the binding events of WT vs mutant Nef with PI3K signaling components is lacking in this study. Lastly, it is unclear whether the interaction of Nef with PI3K signaling components is a conserved function of all primate lentiviruses or is this SIV-specific phenotype.

It has been previously reported that the region of Nef encoding glycine at position 63 is not conserved in HIV-1 (Schindler et al, Journal of Virology 2004). Thus, does HIV-1 Nef also function in induction of NAb responses in humans? or the observed phenotype specific to SIV?

Joint Public Review

In this manuscript, Karl et al. explore mechanisms underlying the activation of the receptor tyrosine kinase FGFR1 and stimulation of intracellular signaling pathways in response to FGF4, FGF8, or FGF9 binding to the extracellular domain of FGFR1. Quantitative binding experiments presented in the manuscript demonstrate that FGF4, FGF8, and FGF9 exhibit distinct binding affinities towards FGFRs. It is also proposed that FGF8 exhibits "biased ligand" characteristics that is manifested via binding and activation FGFR1 mediated by "structural differences in the FGF- FGFR1 dimers, which impact the interactions of the FGFR1 trans membrane helices, leading to differential recruitment and activation of the downstream signaling adapter FRS2".

In the absence of any structural experimental data of different forms of FGFR dimers stimulated by FGF ligands the model presents in the manuscript is speculative and misleading.

Reviewer #1 (Public Review):

The present study examined the physiological mechanisms through which impaired TG storage capacity in adipose tissues affects systemic energy homeostasis in mice. To accomplish this, the authors deleted DGAT1 and DGAT2, crucial enzymes for TG synthesis, in an adipocyte-specific manner. The authors found that ADGAT DKO mice substantially lost the adipose tissues and developed hypothermia when fasted; however, surprisingly, ADGAT KO mice were metabolically healthy on a high-fat diet. The authors found that it was accompanied by elevated energy expenditure, enhanced glucose uptake by the BAT, and enhanced browning of white adipose tissues. This unique animal model provided exciting opportunities to identify new mechanisms to maintain systemic energy homeostasis even in a compromised energy storage capacity. Overall, the data are compelling and support the conclusions of the paper. The manuscript is also clearly written.

Reviewer #1 (Public Review):

The manuscript presents a framework for studying biomechanical principles and their links to morphology and provides interesting insights into a particular question regarding terrestrial locomotion and speed. The goal of the paper is to derive general principles of directed terrestrial locomotion, speed, and symmetry.

Major strengths:<br /> The manuscript is a unique and creative work that explores performance spaces of a complicated question through computational modeling. Overall, the paper is well written and well crafted and was a pleasure to read.

The methods presented here (variable agents used to represent ultra-simplified body configurations that are not inherently constrained) are interesting and there's significant potential in them for a properly constrained question. For the data that is present here their hypotheses (while they can be anticipated from first principles) are very well validated and serve as a robust validation of these expectations and can help.

Of particular interest was the discussion of the transferability of morphologies designed under one system and moving to another. From a deep-time perspective, of particular interest is the transition from subaqueous to terrestrial locomotion which we know was a major earth life transition. The results of this study show that the best-suited morphologies for subaqueous movement are ill-suited (from a locomotor speed standpoint at least) to fully terrestrial locomotion which begs the question of if there are a suite of forms that have balanced performance in both and how that would differ from aquatic morphologies.

Major weaknesses:<br /> 1. There is a major disagreement between the target and parameters.

From a biomechanics perspective the target of this study, Directed Locomotion, is a fairly broad behavioral mode. However, what the authors are ultimately evaluating their model organisms on is a single performance parameter (speed, or distance traveled after 30s). Statements such as "bilateral symmetry showed to be a law-like pattern in animal evolution for efficient directed locomotion purposes" (p 12 line 365-366) are problematic for this reason.

Attaining the highest possible speed is a relevant but limited subset of ways one might interpret performance for directed locomotion. Efficiency, power generation, and limb loading/strain are equally relevant components.

The focus on speed coupled with selection for only the highest performing morphologies, rather than setting a minimum performance threshold, fundamentally restricts the dynamics of the system in a way that is not representative of their specified target and pulls the simulations toward a specific, anticipatable, result.

Locomotor efficiency is alluded to later in the manuscript as one of the observed outcomes, but speed is not equivalent to locomotor efficiency (in much the same way that it is not the sole metric for describing performance with respect to directed locomotion). Energy/work/power have not been accounted for in the manuscript so this is not a parameter this study weighs in on.

The data and analyses the others present do show an interesting validation of these methods in assessing first-order questions relating the shape of a single performance surface to a theoretical morphology, which has significant potential value.

2. There is a significant population and/or sample size and biasing.

Thirty simulations of a population of 101 morphologies seems small for a study of this kind, particularly looking to investigate such a broad question at an abstract level. Particularly when the top 50% of morphologies are chosen to mutate. It would be very easy for artificial biases to rapidly propagate through this system depending on the parameters bounding the formation of the initial generation.

This strong selection choosing the best 50 morphologies and mutating them enforces an aggressive effect that simulates an even more potent phylogenetic inertia than one might anticipate for an actual evolutionary history (it's no surprise then that all of the simulations were able to successfully retrieve a suite of morphotypes that recovered the performance peak for this system within 1500 generations).

Similarly, why is it that a 4^3 voxel limit was chosen? One can imagine that an increase in this voxel limit would allow for the development of more extreme geometries, which might be successful. It is likely that there might be computational resource constraints involved in this, it would be useful for the authors to add additional context here.

Reviewer #1 (Public Review):

Tomasi et al. performed a combination of bioinformatic, next-generation tRNA sequencing experiments to predict the set of tRNA modifications and their corresponding genes in the tRNAs of the pathogenic bacteria Mycobacterium tuberculosis. Long known to be important for translation accuracy and efficiency, tRNA modifications are now emerging as having regulatory roles. However, the basic knowledge of the position and nature of the modifications present in a given organism is very sparse beyond a handful of model organisms. Studies that can generate the tRNA modification maps in different organisms along the tree of life are good starting points for further studies. The focus here on a major human pathogen that is studied by a large community raises the general interest of the study. Finally, deletion of the gene mnmA responsible for the insertion of s2U at position 34 revealed defects in growth in macrophage but in test tubes suggesting regulatory roles that will warrant further studies. The conclusions of the paper are mostly supported by the data but the partial nature of the bioinformatic analysis and absence of Mass-Spectrometry data make it incomplete. The authors do not take advantage of the Mass spec data that is published for Mycobacterium bovis (PMID: 27834374) to discuss what they find.

Reviewer #1 (Public Review):

This manuscript represents an elegant bioinformatics approach to addressing causal pathways in vascular and liver tissue related to atherosclerosis/coronary artery disease, including those shared by humans and mice and those that are specific to only one of these species. The authors constructed co-expression networks using bulk transcriptome data from human (aorta, coronary) and mouse (aorta) vascular and liver tissue. They mapped human CAD GWAS data onto these modules, mapped GWAS SNPs to putatively causal genes, identified pathways and modules enriched in CAD GWAS hits, assessed those shared between vascular and liver tissues and between humans and mice, determined key driver genes in CAD-associated supersets, and used mouse single-cell transcriptome data to infer the roles of specific vascular and liver cell types. The overall approach used by the authors is rigorous and provides new insights into potentially causal pathways in vascular tissue and liver involved in atherosclerosis/CAD that are shared between humans and mice as well as those that are species-specific. This approach could be applied to a variety of other common complex conditions.

The conclusions are largely supported by the analyses. Some specific comments:

1. It appears that GWAS SNPs were mapped to genes solely through the use of eQTLs. Current methods involve a number of other complementary approaches to map GWAS SNPs to effector genes/transcripts and there is the thought that eQTLs may not necessarily be the best way to map causal genes.<br /> 2. Given the critical causal role of circulating apoB lipoproteins in atherosclerosis in both mice and humans and the central role of the liver in regulating their levels, perhaps the authors could use the 'metabolism of lipids and lipoproteins' network in Fig 3B as a kind of 'positive control' to illustrate the overlap between mice and humans and the driver genes for this network.<br /> 3. Is it possible to infer the directionality of effect of key driver genes and pathways from these analyses? For example, ACADM was found to be a KD gene for a human-specific liver CAD superset pathway involving BCAA degradation. Are the authors able to determine or predict the effect of genetically increased expression of ACADM on BCAA metabolism and on CAD? Or the directionality of the effect of the hepatic KD gene OIT3 on hepatic and plasma lipids and atherosclerosis.<br /> 4. While likely beyond the scope of this manuscript, the substantial amount of publicly available plasma proteomic and metabolomic data could be incorporated into this multiomic bioinformatic analysis. Many of the pathways involve secreted proteins or metabolites that would further inform the biology and the understanding of how these pathways relate to atherosclerosis.

The findings here will motivate the community of atherosclerosis investigators to pursue additional potential causal genes and pathways through computational and experimental approaches. It will also influence the approach around the use of the mouse model to test specific pathways and therapeutic approaches in atherosclerosis. In addition, the computational approach is robust and could (and likely will) be applied to a variety of other common complex conditions.

Reviewer #1 (Public Review):

First, I agree with the authors of this manuscript that conformational changes in the XFEL structures with 2.8 A resolution are not reliable enough for demonstrating the subtle changes in the electron transfer events in this bacterial photosynthesis system. Actually, the data statistics in the paper by Dods et al. showed that the high-resolution range of some of the XFEL datasets may include pretty high noise (low CC1/2 and high Rsplit) so the comparison of the subtle conformational changes of the structures is problematic.

The manuscript by Gai Nishikawa investigated time-dependent changes in the energetics of the electron transfer pathway based on the structures by Dods et al. by calculating redox potential of the active and inactive branches in the structures and found no clear link between the time-dependent structural changes and the electron transfer events in the XFEL structures published by Dods, R.et al. (2021). This study provided validation for the interpretation of the structures of those electron-transferring proteins.

The paper was well prepared.

Reviewer #1 (Public Review):

In this manuscript, Kubori and colleagues characterized the manipulation of the host cell GTPase Rab10 by several Legionella effector proteins, specifically members of the SidE and SidC family. They show that Rab10 undergoes both conventional ubiquitination and noncanonical phosphoribose-ubiquitination, and that this posttranslational modification contributes to the retention of Rab10 around Legionella vacuoles.

Strengths<br /> Legionella is an emerging pathogen of increasing importance, and dissecting its virulence mechanisms allows us to better prevent and treat infections with this organism. How Legionella and related pathogens exploit the function of host cell vesicle transport GTPases of the Rab family is a topic of great interest to the microbial pathogenesis field. This manuscript investigates the molecular processes underlying Rab10 GTPase manipulation by several Legionella effector proteins, most notably members of the SidE and SidC families. The finding that MavC conjugates ubiquitin to SdcB to regulate its function is novel, and sheds further light into the complex network of ubiquitin-related effectors from Lp. The manuscript is well written, and the experiments were performed carefully and examined meticulously.

Weaknesses<br /> Unfortunately, in its current form this manuscript offers only little additional insight into the role of effector-mediated ubiquitination during Lp infection beyond what has already been published. The enzymatic activities of the SidC and SidE family members were already known prior to this study, as was the importance of Rab10 for optimal Lp virulence. Likewise, it had previously been shown that SidE and SidC family members ubiquitinate various host Rab GTPases, like Rab33 and Rab1. The main contribution of this study is to show that Rab10 is also a substrate of the SidE and SidC family of effectors. What remains unclear is if Rab10 is indeed the main biological target of SdcB (not just 'a' target), and how exactly Rab10 modification with ubiquitin benefits Lp infection.

Reviewer #1 (Public Review):

This manuscript describes the transient proteolysis of several Nups during myogenesis due to activation of caspase 3, and how this "trimming" leads to defects in nuclear export. The authors show the NPC-related course of events during cellular differentiation and suggest mechanistic insights into exactly why this limited proteolysis is needed for myogenesis. In addition, the authors introduce a novel concept for caspase cellular function that might be worth investigating in the future. Overall, the authors present an elegant and interesting piece of work, performed at the usual superb quality of this group, and indeed the figures throughout the manuscript clearly show a very high level of experimental expertise.

Reviewer #1 (Public Review):

The authors revealed that spermatogonia-related genes (e.g., Plzf, Id4, Setdb1, Stra8, Tial1/Tiar, Bcas2, Ddx5, Srsf10, Uhrf1, and Bud31) were bound by SRSF1 in the mouse testes by Crosslinking immunoprecipitation and sequencing (CLIP-seq). Using Vasa-cre mouse line, the authors successfully evidenced that SRSF1 in the testis is essential for homing and self-renewal in mouse spermatogonial stem cells. Further evidence showed that SRSF1 directly binds and regulates the expression of Tial1/Tiar via AS to implement SSC homing and self-renewal. Immunoprecipitation mass spectrometry (IP-MS) data showed that the AS of SSC is regulated by SRSF1 coordinated with other RNA splicing-related proteins (e.g., SRSF10, SART1, RBM15, SRRM2, SF3B6, and SF3A2). The authors revealed the critical role of SRSF1-mediated AS in SSC homing and self-renewal, which may provide a framework to elucidate the molecular mechanisms of the posttranscriptional network underlying the formation of SSC pools and the establishment of niches. The experiments are well-designed and conducted, the overall conclusions are convincing. This work will be of interest to stem cell and reproductive biologists.

Reviewer #1 (Public Review):

The authors assess the accuracy of AlphaFold2 (AF2) structures for small molecule ligand pose prediction versus the accuracy with traditional template-based homology models and experimental crystal structures (with a different ligand). They take a careful, rigorous approach leveraging the wealth of structural data around the GPCR protein family and using state-of-the-art docking methods. They find that binding sites are significantly more accurately modeled by AF2 compared to traditional template-based approaches, but this does not translate to greater accuracy in small-molecule docking pose prediction. The important findings around this conclusion have broad implications for the use of AF2 models in ligand binding pose prediction for proteins and drug design.

Strengths:<br /> The strength of the work is the rigor and careful, thoughtful comparison that cleverly leverages the cut-off date of April 30th, 2018 used in the training of AF2. While the authors list their limited number of docking methods as a caveat, the fact that they use three state-of-the-art ligand docking methods is a strength of the work; many studies use just one. The rigorous analysis of the binding site RMSD and docked ligand pose RMSD is novel to my knowledge and is particularly insightful.

Weaknesses:<br /> The authors are rigorous in their approach by using state-of-the-art workflows that are high-throughput in nature. However, human expert-refined models and expert selection from multiple models could improve the results of ligand pose prediction when using protein models. The authors alluded to this for traditional models but this can also be true when starting from AF2 models. This is difficult to test systematically and rigorously, however. One possible experiment is to explore whether using multiple AF2 models (there are five by default) would have an effect on pose accuracy, perhaps for selected examples such as NK1R, 5HT2A, and DRD1 to help build out the discussion further. Another possible weakness is that the authors focus only on GPCRs for reasons they state but make a good argument as to why the conclusions are likely to extend to other protein classes.

Context:<br /> One of the most common and impactful uses of protein structures is in small molecule therapeutic chemical tool design. When no experimental structure is available, models are frequently used and such models include traditional template-based homology models and, more recently, AF2 models. AF2 is widely recognized as an inflection point in protein structure prediction due to the unprecedented accuracy of the protein structure models produced automatically. However, understanding whether this unprecedented accuracy translates to better small molecule ligand pose prediction has been an open question, and this study directly addresses the question in a systematic, rigorous approach.

Reviewer #1 (Public Review):

In this work, the authors set out to ask whether the MYRF family of transcription factors, represented by myrf-1 and myrf-2 in C. elegans, have a role in the temporally controlled expression of the miRNA lin-4. The precisely timed onset of lin-4 expression in the late L1 stage is known to be a critical step in the developmental timing ("heterochronic") pathway, allowing worms to move from the L1 to the L2 stage of development. Despite the importance of this step of the pathway, the mechanisms that control the onset of lin-4 expression are not well understood.

Overall, the paper provides convincing evidence that MYRF factors have a role in the regulation of lin-4 expression. However, some of the details of this role remain speculative, and some of the authors' conclusions are not fully supported by the studies shown. These limitations arise from three concerns. First, the authors rely heavily on a transcriptional reporter (maIs134) that is known not to contain all of the regulatory elements relevant for lin-4 expression. Second, the authors use mutant alleles with unusual properties that have not been completely characterized, making a definitive interpretation of the results difficult. Third, some conclusions are drawn from circumstantial or indirect evidence that does not use field-standard methods.

The authors convincingly demonstrate that the cytoplasmic-to-nuclear translocation of MYRF-1 coincides with the activation of lin-4 expression, making MYRF-1 a good candidate for mediating this activation. However, the evidence that MYRF-1 is required for the activation of lin-4 is somewhat incomplete. The authors provide convincing evidence that lin-4 activation fails in animals carrying the unusual mutation myrf-1(ju1121), which the authors describe as disrupting both myrf-1 and myrf-2 activity. The concern here is that it is difficult to rule out that ju1121 is not also disrupting the activity of other factors, and it does not disentangle the roles of myrf-1 and myrf-2. Partially alleviating this issue, they also find that expression from the maIs134 reporter is disrupted in putative myrf-1 null alleles, but making inferences from maIs134 about the regulation of endogenous lin-4 is problematic. Helpfully, an endogenous Crispr-generated lin-4 reporter allele is used in some studies, but only using the ju1121 allele. Together, these findings provide solid evidence that MYRF factors probably do have a role in lin-4 activation, but the exact roles of myrf-1 and myrf-2 remain unclear because of limitations of the unusual ju1121 allele and the use of the maIs134 reporter. The creative use of a conditional myrf-1 alleles (floxed and using the AID system) partially overcomes these concerns, providing strong evidence that myrf-1 acts cell-autonomously to regulate lin-4, though again, these key experiments are only carried out with the maIs134 transgene.

A second important question asked by the authors is whether MYRF activity is sufficient to activate lin-4 expression. The authors provide evidence that supports this idea, but this support is somewhat incomplete, because the authors rely partially on the maIs104 array and, more importantly, on mutant alleles of MYRF-1 that they propose are constitutively active but are not completely characterized here.

The authors also approach the question of whether MYRF-1 regulates lin-4 via direct interaction with its promoter. The evidence presented here is consistent with this idea, but it relies on indirect evidence involving genetic interactions between myrf-1 and the presence of multiple copies of the lin-4 promoter, as well as the detection of nuclear foci of MYRF-1::GFP in the presence of multiple copies of the lin-4 promoter. This is not the field-standard approach for testing this kind of hypothesis, and the positive control presented (using the TetR/TetO interaction) is unconvincing. Thus, the evidence here is consistent with the authors' hypothesis, but the studies shown are incomplete and do not represent a rigorous test of this possibility.

Finally, the authors ask whether MYRF factors have a role in the regulation of other miRNAs. The evidence provided (RNAseq experiments, validated by several reporter transgenes) solidly supports this idea, with the provision that it is not completely clear that ju1121 is disrupting only the activity of myrf-1 and myrf-2.

Reviewer #1 (Public Review):

In this paper, the authors investigated the localization and function of the protein Kelch 13 (K13) in Plasmodium falciparum. Mutations of K13 confer parasite resistance to artemisinin derivatives, the first-line treatment for malaria. Previous studies have shown that K13 is located at the cytostome - a structure that the parasite uses to take up hemoglobin - and that K13 mutations confer artemisinin resistance by dampening hemoglobin endocytosis. Digestion of host hemoglobin is thought to be essential for artemisinin activation through the production of haem. However, the exact function of K13 is currently unknown, and direct evidence for a role of K13 in the production of haem (and artemisinin activation) is missing.

The authors used fluorescent dextran to visualize endocytosis, and show an accumulation of dextran-positive structures colocalizing with GFP-tagged K13. They confirm the localization of K13 to cytostomes by immune-electron microscopy, showing that the protein is localized to the cytostomal collar. Using a genetic knock-sideways strategy, the authors show that mislocalization of K13 results in defects in cytostome formation and morphology, with the disappearance of the electron-dense cytostomal collar, as evidenced by serial block face scanning electron microscopy and transmission electron tomography. Finally, they provide direct evidence that K13 mislocalization leads to a decrease in haemoglobin digestion products, haem and hemozoin.

The paper is very well written and the work is very well performed, relying on a validated genetic approach and high-quality imaging. While conceptually the study does not bring many novel insights, the confirmation of K13 localization and, most importantly, the demonstration that K13 is required for cytostome formation and function constitute important pieces that consolidate the current model of artemisinin resistance. However, the exact role of K13 at the cytostomal collar remains undefined. Whether other proteins of the K13 compartments also play a role in cytostome formation remains to be determined. In addition, the study does not address whether the formation of abnormal cytostomes is also seen in artemisinin-resistant K13 mutant field isolates and is a general mechanism underlying resistance to artemisinin.

Reviewer #1 (Public Review):

This is a clear and rigorous study of intracranial EEG signals in the prefrontal cortex during a visual awareness task. The results are convincing and worthwhile, and strengths include the use of several complementary analysis methods and clear results. The only methodological weakness is the relatively small sample size of only 6 participants compared to other studies in the field. Interpretation weaknesses that can easily be addressed are claims that their task removes the confound of report (it does not), and claims of primacy in showing early prefrontal cortical involvement in visual perception using intracranial EEG (several studies already have shown this). Also the shorter reaction times for perceived vs not perceived stimuli (confident vs not confident responses) has been described many times previously and is not a new result.

Reviewer #1 (Public Review):

During the Covid19 pandemic, most cervical cancer screening programs were temporarily put on hold. The authors describe how Swedish health authorities dealt with this situation by implementing primary self-sampling and by launching a campaign with concomitant vaccination and screening. Besides, they show that the coverage of the screening program was one year after the start of the pandemic at pre-pandemic levels.

Strengths of the paper are the clear presentation of the steps taken by the Swedish health authorities and the high quality of the presented screening coverage data which could be obtained directly from the screening registry. However, the paper would benefit from more in-depth analyses because the presented data raise questions. The number of invitations was >30 percent lower in the first year of the pandemic (Figure 1), but the screening coverage was only 4-5 percent lower. In the second year of the pandemic (year 2021), coverage was back at pre-pandemic levels, but the role of primary self-sampling in restoring screening coverage is a bit unclear. It is obvious that primary self-sampling made it possible to invite women again for screening during the pandemic, but there is no data on acceptance of primary self-sampling. Besides, the increase in coverage in year 2021 was only 4% and it is not clear whether such a modest increase could also have been achieved without primary self-sampling. In addition to self-sampling, the authors describe the launch of a concomitant vaccination and screening campaign. This is an interesting initiative but the authors do not show data on the coverage of this campaign in the target age range.

Reviewer #1 (Public Review):

The study by Ramesh et al identifies key components that support presynaptic plasticity (PHP) at Drosophila glutamatergic synapses: an accepted model for their mammalian equivalents. Specifically, they identify that PHP relies on the antagonism between Spinophilin (Spn) and Syd-1 (a Rho GTPase activating protein) to dynamically alter F-actin (de)polymerisation to facilitate increased synaptic vesicle release, thus supporting PHP. A pull-down of Spn identifies additional proteins including Mical, the over-expression of which is sufficient to rescue the excessive actin stabilisation present in an Spn loss-of-function mutant. The studies relate the mechanistic understanding of Spn to aversive mid-term olfactory memory formation formed in the mushroom bodies.

Collectively, this study represents an important addition to the understanding of PHP and its involvement in the formation of memory. The experiments presented are carefully done and the conclusions drawn are appropriate. A potential criticism is that the study spans two big areas (PHP and memory) and that each may have been better considered as separate studies. However, this is a stylistic concern and not one that influences the insights presented by this study.

Reviewer #1 (Public Review):

Moises and Harel develop an impressive set of novel molecular tools in African turquoise killifish, which include hormone tagging by a self-cleavable fluorescent reporter, intramuscular electroporation for ectopic transgene expression and a doxycycline-inducible system. All these tools are per se fundamental technological innovations in killifish. The authors apply their advanced techniques to modulate growth and gonad development in killifish, showing that the methods work and that it is possible to modulate these fundamental developmental milestones through the use of their molecular tools.

Strengths:

The tools developed are effective, convincing and will likely be adopted as a reference for future work, beyond the field of peptide hormones. I congratulate the authors for their ingenuity and resourcefulness. The figures are clear and of high standard.

Weaknesses:

The manuscript does not obviously follow a question-driven flow and the authors do not make a compelling case about the necessity of developing such platform.<br /> The manuscript should be framed as a tool/resource, showcasing the interventions with gh and fshb to support the tool.

The manuscript is not thoroughly edited and the authors should check and review extensively for improvements to the use of English. Overall, I find a disconnect between the way in which the manuscript is written and the quality of the figures. While the figures have a very high quality standard, the Abstract, Introduction and Discussion are not doing justice to the work done.

Reviewer #1 (Public Review):

The usual strategy to combat antimicrobial drug resistance is to administer a combination of two drugs with distinct mechanisms. An alternative, however, would be to use two drugs that attack the same target, if resistance to one is incompatible with resistance to the other. The authors previously studied parasites resistant to the dihydroorotate dehydrogenase (DHODH) inhibitor DSM265 through an E182D mutation and found that resistance to another inhibitor, IDI-6273, resulted in a reversion to wild-type. Here, they screened various other inhibitors and found that TCMDC-125334 is more active on DSM265-resistant parasites than the wild-type. In this case, however, it was possible for the parasites to become resistant to both inhibitors, either by increasing the copy number of DSM-265-resistant DHODH genes (with a C276Y mutation) or by the emergence of a different mutation. The selection of wild-type parasites with both compounds resulted in resistance but this took considerably longer than for either compound alone. (The actual frequency of double resistance emergence was not measured.)

Overall the results suggest that for DHODH, when pre-existing resistant parasites are selected with another inhibitor, the results will depend on both the initial mutation and the new inhibitor. The data are solid and convincing and suggest that DHODH has considerable scope for resistance development. The observations do have relevance for other inhibitors and/or enzyme drug targets. However from the data so far, the sweeping statements that the authors make concerning double resistance, in general, are not supported.

The formatting of the Figures requires some improvement and in some cases, more details of the statistical analyses are needed.

Reviewer #1 (Public Review):

This study applies state-of-the-art single-cell transcriptome analysis to investigate the nature of drug tolerance, a phenomenon distinct from drug resistance, and a problem of considerable importance in the treatment of C. albicans infections. The authors first show that their transcriptomics platform can reveal sub-populations of untreated cells that display distinct transcription profiles related to metabolic and stress responses that are coupled with cell cycle regulation. They note the consistency of these findings with previous work indicating connections between cell cycle phase and expression of genes related to stress responses and metabolism and argue that this validates their experimental approach, which relies on a complex statistical analysis of sparse data from a relatively small number of single cells. They then proceed to analyze drug-treated cells, mostly focusing on fluconazole (FCZ; which targets ERG11, thus disrupting sphingolipid biosynthesis and membrane integrity) and examining individual cells at 2-, 3-, and 6-days following treatment. Their primary finding is the identification of two major classes of cells, one of which they call the α response, characterized by high ribosomal protein (RP) gene expression and the absence of either heat shock or hyperosmotic stress gene expression as well as low expression of glycolytic, carbohydrate reserve pathway, and histone genes. The second survival state on day 2 (called the β response) instead displays low RP gene expression and high heat-shock stress response. Interestingly, the proportion of β cells clearly increases on day 3. In addition, responses to caspofungin (CSP) and rapamycin (RAPA) are examined and compared to FCZ or untreated cells. The main conclusion that the authors draw from their data is that the initial α response transitions to the β response, which is similar to a recently characterized ribosome assembly stress response (RASTR) in the budding yeast S. cerevisiae. They argue that the transcriptional state in α cells provokes the transition to the β state.

This manuscript presents an enormous amount of complex data whose significance will be difficult to evaluate for those (e.g., this reviewer) not immersed in the specialized analytical techniques used here. Taken at face value, however, the experimental findings are consistent with the authors' main conclusions. Nevertheless, and consistent with the complexity of the responses observed, there are many findings that remain to be explored in mechanistic detail and for which conclusions are less precise.

Reviewer #1 (Public Review):

The authors first show in simulated data that differences in the speed of the HRF are reflected in the power spectra of the BOLD signal obtained during oscillatory stimulation at different frequencies. They then identified voxels that were fast or slow responders in data obtained from the primary visual cortex and LGN during visual stimulation and found that the fast and slow groups exhibited the same differences in power spectra observed in the simulations. Moreover, resting state data obtained separately from the same areas also exhibited these spectral differences. In contrast, the onset time of a response to a breath hold was less able to differentiate between fast and slow voxels.

The combination of simulations and experiments in this work provides evidence that power spectra from rs-fMRI can provide information about the HRF in different locations across the brain. However, the simulated HRFs differ in amplitude and duration as well as latency, and all of these features can affect the power spectrum. The authors show that differences remain in the power spectra for amplitude-normalized HRFs, which strengthens their work. However, the entire premise of the work is that the actual HRFs in the brain can be modeled using the range of shapes that were simulated. As the authors point out, we know little about the actual HRF in much of the brain, and it may be that this model does not adequately represent HRFs in other regions. At a minimum, it would be useful to disentangle the effects of latency and duration of the response, in addition to amplitude, because with the current model early onset voxels also have shorter response durations. It is not hard to imagine that a brain area might have a rapid onset but a long duration of HRF, and the power spectrum in this case may look more like that of a slow responder. The current approach was validated in the visual system, which has been the basis for much of what we know about HRFs, and it may not be as accurate in other areas of the brain. This is admittedly a difficult issue to address, but merits consideration as a limitation.

Despite my skepticism of the general applicability of the technique, it remains a significant advance in understanding the variability of HRFs in the brain. The authors make a strong case that cerebrovascular reactivity as measured in response to a breath hold does not accurately capture all of the aspects of neurovascular coupling, an important finding. The work also clearly shows that differences in fALFF or other power-based metrics can reflect differences in neurovascular coupling rather than neural activity, something that is widely suspected but commonly ignored in the interpretation of fALFF data. We still have far to go to fully understand neurovascular coupling throughout the brain and under various conditions, and this manuscript contributes to our knowledge of how two investigative tools perform at the task.

Reviewer #1 (Public Review):

This careful study reports the importance of Rab12 for Parkinson's disease associated LRRK2 kinase activity in cells. The authors carried out a targeted siRNA screen of Rab substrates and found lower pRab10 levels in cells depleted of Rab12. It has previously been reported that LLOME treatment of cells breaks lysosomes and with time, leads to major activation of LRRK2 kinase. Here they show that LLOME-induced kinase activation requires Rab12 and does not require Rab12 phosphorylation to show the effect.

1. Throughout the text, the authors claim that "Rab12 is required for LRRK2 dependent phosphorylation" (Page 4 line 78; Page 9 line 153; Page 22 line 421). This is not correct according to Figure 1 Figure Supp 1B - there is still pRab10. It is correct only in relation to the LLOME activation. Please correct this error.

2. The authors conclude that Rab12 recruitment precedes that of LRRK2 but the rate of recruitment (slopes of curves in 3F and G) is actually faster for LRRK2 than for Rab12 with no proof that Rab12 is faster-please modify the text-it looks more like coordinated recruitment.

3. The title is misleading because the authors do not show that Rab12 promotes LRRK2 membrane association. This would require Rab12 to be sufficient to localize LRRK2 to a mislocalized Rab12. The authors DO show that Rab12 is needed for the massive LLOME activation at lysosomes. Please re-word the title.

Reviewer #1 (Public Review):

This manuscript provides novel and intriguing experiments that aim to elucidate the mechanical properties of the Reissner fiber (RF) and to probe its interactions with the motile cilia in the central canal of the spinal cord. Using in vivo imaging in larval zebrafish, the authors show that the RF is under tension and oscillates dorsoventrally. Importantly, ablation of the RF triggered retraction and relaxation of the fiber cut ends. The retraction speed depends on where the fiber was ablated, with fastest retraction in the rostral side, indicating that tension in the RF builds up rostrally. The authors, based on observations from live imaging of intact and ablated RF and central canal, conjecture that numerous ependymal motile monocilia, that are tilted caudally and interact frequently with the RF, contribute to RF heterogenous tension via weak interactions.

The work is important. The experiments are thorough and intricate. The findings are fascinating and open up the prospect for future investigations and models. I'm particularly curious as to what future experiments can be used to test the hypothesis put forward by the authors about the role of cilia-fiber interactions in the RF mechanical properties and function.

Reviewer #1 (Public Review):

The potential role of the CaMKII holoenzyme in synaptic information processing, storage, and spread has fascinated neuroscientists ever since it has been described that self-phosphorylation of CaMKII at T286 (pT286) can maintain the kinase in an activated state beyond the initial Ca2+ stimulus that induced kinase activation and pT286. The current study by Lučić et al utilizes biochemical and biophysical methods to re-examine two pT286 mechanisms and finds:<br /> (1) that a previously proposed activation-induced subunit exchange within the holoenzyme can not provide pT286 maintenance or propagation; and<br /> (2) that pT286 can occur not only within a holoenzyme but also between two holoenzymes, at least at sufficiently high concentrations.

For the observation regarding the subunit exchange, the authors go above and beyond to demonstrate that a previously proposed activation-induced subunit exchange does not actually occur in their hands and that the previous appearance of such a subunit exchange may instead be due to activation-induced interactions between the kinase domains of separate holoenzymes. This provides important clarification, as the imagination about the possible functions of this subunit exchange has been running wild in the literature.

By contrast, pT286 between holoenzymes at sufficiently high concentrations was largely predicted by the previously reported concentration-dependence of pT286 between monomeric truncated CaMKII (although these previous experiments did not rule out that such pT286 could have been excluded for intact full-length holoenzymes). Notably, the reaction rate reported here for pT286 between two holoenzymes is more than two orders of magnitude slower compared to the previously described rate of the pT286 reaction within a holoenzyme.

In summary, this study contains two somewhat disparate parts: (1) one technical tour-de-force to provide evidence that argues against activation-induced subunit exchange, which was a tremendous effort that provides influential novel information, and (2) another set of experiments showing the somewhat predictable potential for pT286 between holoenzymes, but without indication for the functional relevance of this rather slow reaction. Unfortunately, in the current/initial title of the manuscript, the authors chose to emphasize the weaker part of their findings.

Reviewer #1 (Public Review):

During NonREM sleep, two major oscillations, the slow oscillation (SO) and the sleep spindle, have been shown to interact, putatively to support memory consolidation. These oscillations and their interrelation have been shown to change during development. The authors reanalyse two datasets in children and adolescents. One is longitudinal, assessed at 8-11 years and 14-18 years, the other is cross-sectional, assessed at 5-6 years. The manuscript reports several interesting findings. They identify three types of spindles, canonical slow and fast spindles as well as "age adjusted" fast spindles. They show that fast spindles are modulated by the slow oscillation more in the older children and relate this improved modulation to a sleep-spindle maturation index. The authors use many highly complex data analysis tools and apply them to different transformations of the data, which they explain in great detail. The manuscript is written clearly although it is at times very technical. The findings could be highly interesting to the field of sleep research, as they nicely examine the developmental trajectory of spindles and their coupling to the SO. Although the manuscript makes use of two adequate samples of children and adolescents, they do not compare their findings to adults. In addition, the maturation index is not well justified and the authors could do more to show that the "age adjusted" fast spindles actually develop into fast spindles. The analysis also does not take sex into account, which could be affecting findings in puberty. In general, there are some analyses that could be added to make the findings clearer. For example, it would be great to show averages of the detected spindles to show how they may or may not differ. More descriptive data in form of figures would also help readers understand the complex analyses that are reported (i.e., spectrogram and SO phase locked activity in the spindle bands). Finally, children have been reported to have superior declarative memory consolidation, which itself has been closely linked to spindle-SO coupling. It would be great to have a more broad discussion, how the current findings are related to other developmental changes in the field of sleep (and memory).

Reviewer #1 (Public Review):

The authors used pan-cancer Standardized Incidence Ratio analyses and Mendelian Randomization analysis to reach the causal relationships between first primary cancers and second primary cancers, proving that a primary cancer may cause another type of primary cancer. The results supported that pharynx cancer, ovary cancer, kidney cancer may cause non-Hodgkin lymphoma, soft tissue cancer, lung cancer and myeloma, respectively. This research provides a useful direction for further elucidation of profound mechanisms of secondary primary tumors, and guide the community to attach importance to the prevention of secondary primary tumors. According to previous researches, the number of patients with multiple primary cancers is growing rapidly and second solid tumors are a leading cause of mortality among several populations of long-term survivors, which shed light on the significance of this work.

The methods of the work are logically rigorous, which revealed the incidence relationship among numerous types of cancers using SEER database analyses and further confirmed the causal relationship between first primary cancers and second primary cancers through MR analysis utilizing GWAS as an exposure database and UK Biobank as an outcome database. Then, 2 outlier-detected methods were used and validate the harmonization between SIR and MR analyses, making the results more solid.

Nonetheless, SEER SIR analyses might be affected by confounding factors of screening and did not represent the whole population. In addition, too few SNPs were included in part of cancer types mentioned in the research, such as larynx, stomach and male breast cancer.

Reviewer #1 (Public Review):

For a gaseous therapeutic agent such as NO, delivery to the site and release to the injured area are both required for efficacy. Previous work has focused on hydrogels for delivery. The authors engineered a combined gene/cell therapy plus a pharmaceutical approach to NO delivery. Engineered MSC produced a mutant beta-galactosidase (B-GALH363A) that when a prodrug is administered, will release NO locally.

One can imagine applications involving such a novel concept for gaseous signaling molecule delivery to include other kinds of cells, other prodrugs, other gaseous agents, and other injury types. In this elegant study, the concept has been explored deeply in one potential application, making it a landmark contribution to the field of regenerative medicine.

Limitations of the current study are that the mice utilized were C57/Bl6 females, the most resistant sex and strain to kidney injury. Another limitation is the use of human placental MSCs only; as such we do not know if other MSCs will perform equally well.

Reviewer #1 (Public Review):

The Authors of this study have investigated the consequence of knocking out protein 4.1B on hippocampal interneurons. They observed that in 4.1B KO mice, the myelinization of axons of PV and SST interneurons was altered. In addition, the molecular organization of the nodal, heminodal, and juxtaparanodal parts of the interneuron axons was disrupted in 4.1B KO mice. Further, the authors found some changes in spiking features of SST, but not PV interneurons as well as synaptic inhibition recorded in CA1 pyramidal cells. Lastly, 4.1B KO mice showed some impairment in spatial memory.

Strengths<br /> One of the strengths of this MS is the multilevel approach to the question of how myelinization of interneuron axons can contribute to hippocampal functions. Further, the cell biological results support the claim of the reorganization of channel distributions at axonal nodes.

Weaknesses<br /> 1. Although the authors acknowledge that SST is expressed in different GABAergic cell types in the hippocampus, they claim that OLM cells, which express SST are subject to changes in 4.1B KO mice. However, this claim is not supported by data. Both OLM cells and GABAergic projection cells expressing SST have many long-running axons in the stratum radiatum, where the investigations have been conducted (e.g. Gulyas et al., 2003; Jinno et al., 2007). Thus, the SST axons can originate from any of these cell types. In addition, both these GABAergic cells have a sag in their voltage responses upon negative current injections (e.g. Zemankovics et al., 2010), making it hard to separate these two SST inhibitory cell types based on the single-cell features. In summary, it would be more appropriate to name the sampled interneurons as SST interneurons. Alternatively, the authors may want to label intracellularly individual interneurons to visualize their dendrites and axons, which would allow them to verify that the de-myelinization occurs along the axons of OLM cells, but not SST GABAergic projection neurons.

2. Although both the cellular part and the behavioral part are interesting, there is no link between them at present. The changes observed in spatial memory tests may not be caused by the changes in the axonal de-myelinization of hippocampal interneurons. Such a claim can be made only using rescue experiments, since changes in 4.1B KO mice leading to behavioral alterations may occur i) in other cell types and ii) in other regions, which have not been investigated.

Reviewer #1 (Public Review):

Summary:<br /> In this manuscript, the authors use ATAC-seq to find regions of the genome of rat embryonic striatal neurons in culture that show changes in regulatory element accessibility following stimulation by KCl-mediated membrane depolarization. The authors compare 1hr and 4hr transcriptomes to see both rapid and late response genes. When they look at ATAC-seq data they see no changes in accessibility at 1hr but strong changes at 4hr. The differentially accessible sites were enriched for the AP-1 site, suggesting regulation by Fos-Jun family members, and consistent with the requirement for IEG expression, anisomycin blocked the increase in accessibility. To test the functional importance of this regulation the authors focus on a putative enhancer 45kb upstream of the activity-induced gene encoding the neuromodulator dynorphin (Pdyn). To test the function of this region, the authors recruited CRISPRi to the site, which blocked KCL-dependent Pdyn induction, or CRISPRa, which selectively increased Pdyn expression in the absence of KCl. Finally, the authors reanalyze other human and rat datasets to show cell-type specific function of this enhancer correlated to Pdyn expression.

Strengths:<br /> The idea that stimuli that induce expression of Fos in neurons can change the accessibility of regulatory elements bound from Fos has been shown before, but almost all the data are from hippocampal neurons so it is nice to see the different cell type used here. The most interesting part of the study is the identification of the Pdyn enhancer because of the importance of this gene product in the function of striatal neurons. Overall the conclusions appear to be well supported by the data.

Weaknesses:<br /> The timing and the location of the accessibility changes are meaningfully different from other similar studies, which should be discussed. The authors provide good data for the function of a single enhancer near Pdyn, but could contextualize this with respect to other regulatory elements nearby.

Reviewer #1 (Public Review):

Midbrain dopamine neurons have attracted attention as a part of the brain's reward system. A different line of research, on the other hand, has shown that these neurons are also involved in higher cognitive functions such as short-term memory. However, these neurons are thought not to encode short-term memory itself because they just exhibit a phasic response in short-term memory tasks, which cannot seem to maintain information during the memory period. To understand the role of dopamine neurons in short-term memory, the present study investigated the electrophysiological property of these neurons in rodents performing a T-maze version of short-term memory task, in which a visual cue indicated which arm (left or right) of the T-maze was associated with a reward. The animal needed to maintain this information while they were located between the cue presentation position and the selection position of the T-maze. The authors found that the activity of some dopamine neurons changed depending on the information while the animals were located in the memory position. This dopamine neuron modulation was unable to explain the motivation or motor component of the task. The authors concluded that this modulation reflected the information stored as short-term memory.

I was simply surprised by their finding because these dopamine neurons are similar to neurons in the prefrontal cortex that store memory information with a sustained activity. Dopamine neurons are an evolutionally conserved structure, which is seen even in insects, whereas the prefrontal cortex is developed mainly in the primate. I feel that their findings are novel and would attract much attention from readers in the field. But the authors need to conduct additional analyses to consolidate their conclusion.

Reviewer #1 (Public Review):

Main contributions / strengths

The authors propose a process to improve the ground truth segmentation of fetal brain MRI via a semi-supervised approach based on several iterations of manual refinement of atlas label propagations. This procedure represents an impressive amount of work, likely resulting in a very high-quality ground truth dataset. The corrected labels (obtained from multiple different datasets) are then used to train the final model which performs the brain extraction and tissue segmentation tasks. We also acknowledge the caution paid by the authors regarding the future application of their pipeline to unseen datasets.

The conclusions of this paper are mostly well supported by data, but some aspects of the analysis and validation procedure need to be clarified and extended. In addition, the article would greatly benefit from providing further descriptions of crucial aspects of the study.

Main limitations and potential improvements

1) New nomenclature/atlas not sufficiently described/justified.

The proposed nomenclature and atlas are one of the main contributions of this work. We clearly acknowledge the importance for the community of such a contribution. The definition of any nomenclature implies that decisions were taken regarding the acceptable level of ambiguity in the identification of the boundary between neighboring anatomical structures with respect to the gradient in the intensities in the MRI. It is acceptable (and probably inevitable) to set relatively arbitrary criteria in ambiguous regions, providing that these criteria are explicitly stated. The explicit statement of the decisions taken is essential in particular for better interpretation of residual segmentation inaccuracies in application studies.

As a matter of comparison, the postnatal atlas and nomenclature were based on the Albert protocol, which is described in extensive detail. While such a complete description might fall beyond the scope of this work, we believe that an additional description of the nomenclature and protocol, allowing reproduction the manual segmentation on external datasets is required, at least for most ambiguous junctions between structures. For instance, the boundaries across substructures within the DGM are difficult to visualize on the exemplar subjects shown in Fig. 5 and Fig. 6.

Please provide additional precision on how the following were defined: boundaries between lateral ventricles and cavum; between cavum and CSF; the delineation of 3rd and 4th ventricles; the definition of the vermis, especially its junctions with the cerebellum and the brainstem.<br /> How are these boundaries impacted by the changes in the image intensities related to tissue maturation?

We would also greatly appreciate an extension of the qualitative comparison with the two most commonly used protocols (Albert and FETA), for instance, why didn't the authors isolate the hippocampus/amygdala structure? And then how is the boundary between gray and white matter defined in this region?

2) More detailed comparison with FETA for some structures would be informative despite obvious limitations.

More specifically, the GM should have a very similar definition. In the "Impact of anomalies' section (page 7) the authors compare their results with the dice score from the FETA challenge and conclude that the difference "highlights the advantages of using high-quality consistent ground truth labels for training". The better performances (from ~0.78 to ~0.88) might be mostly due to the improvement of the ground truth (of the test set). This could be confirmed by observing the ground truth from FETA of the GM for a few cases for which the dice shows a strong increase in performance with respect to FETA. Note that the gain in performance is appreciable even if it is due to a better ground truth.

3) Improvement of the ground truth labels is an important contribution of this work, thus we would appreciate a more quantitative description of the impact of the manual correction, such as reporting the change in the dice score induced by the correction.

Quantification of the refinement process would help to better evaluate the relevance of the proposed approach in future studies e.g. introducing a different nomenclature. More specifically, a marked change would be expected after the first training when there is a switch (and refinement) from the registration-propagated labels to the ones predicted by the DL model (as shown in Fig. 5, the changes are quite strong). Again a dice score indicating quantitatively how much improvement results from each iteration would be informative. In the same line, is the last iteration of this process needed or did the authors observe a 'stabilization' (i.e. less manual editing needing to be performed)?

4) The testing / training data-splitting strategy is not sufficiently detailed and difficult to follow. The following points deserve clarification:

a) Why did the authors select only four sites for the test set (out of six studies presented in the 3.1 section)?

b) Data used for training: in the first step the authors selected 200 for label propagation and selected only the best 100. In the second stage, the predictions are computed for all training/validation sets (380) and only 200 are selected. When the process was iterated, why did the authors select only 200 out of the 380? Are the same subjects selected across iterations?<br /> Were the acquisition parameters / gestational age controlled for each selection? If yes please specify the distributions precisely.

Did the authors control the potential imbalanced proportion that is present in the dataset (more subjects from dHCP for instance)? (line 316, 100 subjects were selected from only three centers. Why only three? Did the authors keep the same sub-site for other stages?)

c) "The testing dataset includes 40 randomly selected images from four different acquisition protocols" which shows that attention was paid to variations in the scanning parameters, which is of crucial importance. However, no precision is provided regarding the gestational age of this dataset, which impedes the interpretation since a potential influence of age on the accuracy of the segmentation would be problematic. Indeed, the authors mention that the manual correction deserved special attention for late GA (>34 weeks). Please specify precisely the age distribution across the 10 subjects of each of the four acquisition protocols. In addition, the qualitative results shown in Fig.6 and subsection "Impact of GA at scan" are not sufficient and an additional result table reporting the same population and metrics as in Table 2, but dissociating younger versus older fetuses, would be much more informative to rule out potential bias related to gestational age.

d) The definition of the ground truth labels for the test set is not described.

We understand (from the result) that the ground truth for the test set is defined by manual refinement of the atlas label propagated. This should be explicitly described on page 5 after the "Preparation of training datasets" section.

5) The validation of segmentation accuracy based on the volumetry growth chart is invalid.

In Section "4.3. Growth charts of normal fetal brain development", since manual corrections were involved, the reported results cannot be considered as a validation of the segmentation pipeline. Regarding the validation of the segmentation pipeline, the quantitative and qualitative results provided in Table 2 and the corresponding text and figures seem sufficient to us (providing our concerns above are addressed, especially regarding the impact of the gestational age).

The growth charts are still valuable to support the validity of the nomenclature and segmentation protocol, but then why are the growth charts computed only for some structures? Reporting the growth chart and statistical evaluation of the impact of acquisition settings using ANCOVA for all the substructures from the proposed protocol would be expected here, in particular for the structures for which the delineation might be ambiguous such as the cavum, the vermis, and DGM substructures such as the thalamus.

Finally, please provide further details on the type and amount of manual correction needed for computing the growth charts.

6) MRI data was acquired only on Phillips scanners.

We acknowledge the efforts to maximize heterogeneity in the MRIs,e.g. with both 1.5T and 3T scanners, variations in TE and image resolution, but still, all MRIs included in this study were acquired using the SSTSE sequence on Phillips scanners. The study does not include any MRI acquired on Siemens nor GE scanners, and no image was acquired using the balance-FFE/TRUFISP/FIESTA type sequence. This might limit generalizability.

Reviewer #1 (Public Review):

While there are many models for sequence retrieval, it has been difficult to find models that vary the speed of sequence retrieval dynamically via simple external inputs. While recent works [1,2] have proposed some mechanisms, the authors here propose a different one based on heterogeneous plasticity rules. Temporally symmetric plasticity kernels (that do not distinguish between the order of pre and post spikes, but only their time difference) are expected to give rise to attractor states, asymmetric ones to sequence transitions. The authors incorporate a rate-based, discrete-time analog of these spike-based plasticity rules to learn the connections between neurons (leading to connections similar to Hopfield networks for attractors and sequences). They use either a parametric combination of symmetric and asymmetric learning rules for connections into each neuron, or separate subpopulations having only symmetric or asymmetric learning rules on incoming connections. They find that the latter is conducive to enabling external inputs to control the speed of sequence retrieval.

Strengths:<br /> The authors have expertly characterised the system dynamics using both simulations and theory. How the speed and quality of retrieval varies across phases space has been well-studied. The authors are also able to vary the external inputs to reproduce a preparatory followed by an execution phase of sequence retrieval as seen experimentally in motor control. They also propose a simple reinforcement learning scheme for learning to map the two external inputs to the desired retrieval speed.

Weaknesses:<br /> 1. The authors translate spike-based synaptic plasticity rules to a way to learn/set connections for rate units operating in discrete time, similar to their earlier work in [5]. The bio-plausibility issues of learning in [5] carry over here, for e.g. the authors ignore any input due to the recurrent connectivity during learning and effectively fix the pre and post rates to the desired ones. While the learning itself is not fully bio-plausible, it does lend itself to writing the final connectivity matrix in a manner that is easier to analyze theoretically.

2. While the authors learn to map the set of two external input strengths to speed of retrieval, they still hand-wire one external input to the subpopulation of neurons with temporally symmetric plasticity and the other external input to the other subpopulation with temporally asymmetric plasticity. The authors suggest that these subpopulations might arise due to differences in the parameters of Ca dynamics as in their earlier work [29]. How these two external inputs would connect to neurons differentially based on the plasticity kernel / Ca dynamics parameters of the recurrent connections is still an open question which the authors have not touched upon.

3. The authors require that temporally symmetric and asymmetric learning rules be present in the recurrent connections between subpopulations of neurons in the same brain region, i.e. some neurons in the same brain region should have temporally symmetric kernels, while others should have temporally asymmetric ones. The evidence for this seems thin. Though, in the discussion, the authors clarify 'While this heterogeneity has been found so far across structures or across different regions in the same structure, this heterogeneity could also be present within local networks, as current experimental methods for probing plasticity only have access to a single delay between pre and post-synaptic spikes in each recorded neuron, and would therefore miss this heterogeneity'.

4. An aspect which the authors have not connected to is one of the author's earlier work:<br /> Brunel, N. (2016). Is cortical connectivity optimized for storing information? Nature Neuroscience, 19(5), 749-755. https://doi.org/10.1038/nn.4286<br /> which suggests that the experimentally observed over-representation of symmetric synapses suggests that cortical networks are optimized for attractors rather than sequences.

Despite the above weaknesses, the work is a solid advance in proposing an alternate model for modulating speed of sequence retrieval and extends the use of well-established theoretical tools. This work is expected to spawn further works like extending to a spiking neural network with Dale's law, more realistic learning taking into account recurrent connections during learning, and experimental follow-ups. Thus, I expect this to be an important contribution to the field.

Reviewer #1 (Public Review):

This study explores whether the extreme polygenicity of common traits can be explained in part by competition among genes for limiting molecular resources (such as RNA polymerases) involved in gene regulation. The authors hypothesise that such competition would cause the expression levels of all genes that utilise the same molecular resource to be correlated and could thus, in principle, partly explain weak trans-regulatory effects and the observation of highly polygenic architectures of gene expression. They study this hypothesis under a very simple model where the same molecule binds to regulatory elements of a large number m of genes, and conclude that this gives rise to trans-regulatory effects that scale as 1/m, and which may thus be negligible for large m.

The main limitation of this study lies in the details of the mathematical analysis, which does not adequately account for various small effects, whose magnitude scales inversely with the number m of genes that compete for the limiting molecular resource. In particular, the fraction of "free" molecule (which is unbound to any of the genes) also scales as 1/m, but is not accounted for in the analysis, making it difficult to assess whether the quantitative conclusions are indeed correct. Second, the questions raised in this study are better analysed in the framework of a sensitivity or perturbation analysis, i.e., by asking how *changes* in expression level or binding affinity at one gene (rather than the total expression level or total binding affinity) affect expression level at other genes.

Thus, while the qualitative conclusion that resource competition in itself is unlikely to mediate trans-regulatory effects and explain highly polygenic architectures of gene expression traits probably holds, the mathematical reasoning used to arrive at this conclusion requires more care.

In my opinion, the potential impact of this kind of analysis rests at least partly on the plausibility of the initial hypothesis- namely whether most molecular resources involved in gene regulation are indeed "limiting resources". This is not obvious, and may require a careful assessment of existing evidence, e..g., what is the concentration of bound vs. unbound molecular species (such as RNA polymerases) in various cell types?

Reviewer #1 (Public Review):

This study presents a genetic and molecular analysis of the role of the cytoplasmic ub ligase Deltex (Dx) in regulating the Drosophila Wingless (Wg) pathway in the larval wing disc. The study exploits the strength of the fly system to uncover a series of genetic interactions between dx and wg and fz allele that support a role for Dx upstream of the Wg pathway. These are paired with molecular evidence that dx lof alleles lower Wg protein in 'source' cells at the DV margin, and that Dx associates with Arm and lowers its levels in a manner that can be rescued by pharmacological inhibition of the proteasome. The genetic data are solid but subject to alternative explanations based on the authors' model that Dx both inhibits and activates the pathway, and the published link between Dx and its target Notch, which regulates wg transcription. The molecular data are suggestive but need follow-up tests of the model to prove that Dx mediates poly-ub of Arm, and the degree to which Dx shares this role with the validated Arm E3 ligase Slmb. Overall, the story is very interesting but has mechanistic gaps that lead to speculative models that require more rigorous study to clarify the mechanism. Dx sharing a role in Arm degradation with the Slmb/APC destruction would have important implications for the many Wg/Wnt regulated processes in development and disease.

Reviewer #1 (Public Review):

Wang et al have constructed a comprehensive single nucleus atlas for the gills of the deep sea Bathymodioline mussels, which possess intracellular symbionts that provide a key source of carbon and allow them to live in these extreme environments. They provide annotations of the different cell states within the gills, shedding light on how multiple cell types cooperate to give rise to the emergent functions of the composite tissues and the gills as a whole. They pay special attention to characterizing the bacteriocyte cell populations and identifying sets of genes that may play a role in their interaction with the symbiotes.

Wang et al sample mussels from 3 different environments: animals from their native methane-rich environment, animals transplanted to a methane-poor environment to induce starvation, and animals that have been starved in the methane-poor environment and then moved back to the methane-rich environment. They demonstrated that starvation had the biggest impact on bacteriocyte transcriptomes. They hypothesize that the upregulation of genes associated with lysosomal digestion leads to the digestion of the intracellular symbiont during starvation, while the non-starved and reacclimated groups more readily harvest the nutrients from symbiotes without destroying them.

Strengths:<br /> This paper makes available a high-quality dataset that is of interest to many disciplines of biology. The unique qualities of this non-model organism and the collection of conditions sampled make it of special interest to those studying deep sea adaptation, the impact of environmental perturbation on Bathymodioline mussels populations, and intracellular symbiotes. The authors do an excellent job of making all their data and analysis available, making this not only an important dataset but a readily accessible and understandable one.

The authors also use a diverse array of tools to explore their data. For example, the quality of the data is augmented by the use of in situ hybridizations to validate cluster identity and KEGG analysis provides key insights into how the transcriptomes of bacteriocytes change.

The authors also do a great job of providing diagrams and schematics to help orient non-mussel experts, thereby widening the audience of the paper.

Weaknesses:<br /> One of the main weaknesses of this paper is the lack of coherence between the images and the text, with some parts of the figures never being referenced in the body of the text. This makes it difficult for the reader to interpret how they fit in with the author's discussion and assess confidence in their analysis and interpretation of data. This is especially apparent in the cluster annotation section of the paper.

Another concern is the linking of the transcriptomic shifts associated with starvation with changes in interactions with the symbiotes. Without examining and comparing the symbiote population between the different samples, it cannot be concluded that the transcriptomic shifts correlate with a shift to the 'milking' pathway and not other environmental factors. Without comparing the symbiote abundance between samples, it is difficult to disentangle changes in cell state that are due to their changing interactions with the symbiotes from other environmental factors.

Additionally, conclusions in this area are further complicated by using only snRNA-seq to study intracellular processes. This is limiting since cytoplasmic mRNA is excluded and only nuclear reads are sequenced after the organisms have had several days to acclimate to their environment and major transcriptomic shifts have occurred.

Reviewer #1 (Public Review):

This paper looks at nutrient-responsive Ca++ flux in islet cells of eight genetically diverse mouse strains. The investigators correlate Ca++ flux with insulin secretory capacity, demonstrating that calcium parameters in response to different nutrients are a better predictor of insulin secretory capacity than average calcium. They also correlate Ca++ flux with previously collected islet protein abundance followed by integration with human genome-wide association studies. This integration allows them to identify a sub-set of proteins that are both relevant to human islet function and that may play a causal role in regulating islet Ca++ oscillations. All data have been deposited in a searchable public database. There are many strengths to this paper. To my knowledge, this is the first work to assess the genetics of nutrient-responsive Ca++ flux in islets. Given the importance of Ca++ for beta cell insulin secretion, this work is of high importance. Investigators also use the founders of two powerful genetic mouse models: the diversity outbred and collaborative cross, opening up several avenues of future research into the genetics of Ca++ flux. By looking at multiple parameters of Ca++ flux, investigators are able to start to understand which parameters may be driving low or high insulin secretion. Integration with protein abundance and human GWAS has allowed identification of proteins with known roles in insulin secretory capacity, as well as several novel regulators, again opening up several avenues of future research. Finally, the public database is likely to be useful to multiple investigators interested in following up specific protein targets or in conducting future genetic studies.

Reviewer #1 (Public Review):

This work aims to evaluate the use of pressure insoles for measurements that are traditionally done using force platforms in the assessment of people with knee osteoarthritis and other arthropathies. This is vital for providing an affordable assessment that does not require a fully equipped gait lab as well as utilizing wearable technology for personalized healthcare.

Towards these aims, the authors were able to demonstrate that individual subjects can be identified with high precision using raw sensor data from the insoles and a convolutional neural network model. The authors have done a great job creating the models and combining an already available public dataset of force platform signals and utilizing them for training models with transferable ability to be used with data from pressure insoles. However, there are a few concerns, regarding substantiating some of the goals that this manuscript is trying to achieve.

In addressing these concerns, if the results are further corroborated using the suggestions provided to the authors, this provides an exciting tool for identifying an individual's gait patterns out of a cluster of data, which is extremely useful for providing identifiable labels for personalized healthcare using wearable technologies.

Reviewer #1 (Public Review):

This is a well-written manuscript addressing a fundamental question regarding the functional organization of spinal circuits controlling the execution of locomotor movements. The authors take advantage of the power of mouse genetics to exploit the expression of Hes2 to study the function of the whole population of V2 interneurons. Previous studies could only focus on either the excitatory V2a or inhibitory V2b subpopulations. Here, by combining two different genetic manipulations based on either silencing or acute ablation of V2 interneurons with rigorous functional analysis the authors showed that V2 interneurons can act together to control interlimb coordination and antagonistically to regulate joint movements. The data are convincing and properly analyzed, the conclusions are in line with the results, and the limitations of the study are appropriately addressed. The discussion nicely frames the work in a conceptual framework that takes into account the current literature on the mode of operation of spinal motor circuits. There are a few weaknesses that should be addressed and would further improve what is already a very nice study.

1) While previous work from the authors has consistently shown the validity and reliability of these neuronal silencing and ablation approaches, the study presents no data showing the efficiency and specificity of these genetic manipulations. These are critical parameters for interpreting the results and should be presented, especially considering that the strategies employed are susceptible to the limitations of a lineage-tracing approach. These data would also be important for the discussion section to interpret the differences between the two genetic models and could address some of the options proposed by the authors, as well as the possibility of incomplete and/or unexpected recombination.

2) The authors suggest that the changes in interlimb coordination are "consistent with mice keeping the limbs closer to the body, limiting forward movements in the attempt to preserve body stability". A common reaction to body instability in quadrupeds is a widening of the limbs to lower the center of gravity: limbs are positioned further away from the body. Not quite sure whether I would be so certain of the interpretation that the observed phenotypes are due to body/postural instability. It is possible that the changes in gait are just a direct consequence of the inactivation of V2 interneurons. To clarify this issue, it could be useful to test whether other features of postural control are affected by perturbation of V2 neurons, for example, swimming and rearing analyses would provide interesting insights.

Reviewer #1 (Public Review):

In this manuscript, Gruber et al perform serial EM sections of the antennal lobe and reconstruct the neurites innervating two types of glomeruli - one that is narrowly tuned to geosmin and one that is broadly tuned to other odours. They quantify and describe various aspects of the innervations of olfactory sensory neurons (OSNs), uniglomerlular projection neurons (uPNs), and the multiglomerular Local interneurons (LNs) and PNs (mPNs). They find that narrowly tuned glomeruli had stronger connectivity from OSNs to PNs and LNs, and considerably more connections between sister OSNs and sister PNs than the broadly tuned glomeruli. They also had less connectivity with the contralateral glomerluli. These observations are suggestive of strong feed-forward information flow with minimal presynaptic inhibition in narrowly tuned gomeruli, which might be ecologically relevant, for example, while making quick decisions such as avoiding a geosmin-laden landing site. In contrast, information flow in more broadly tuned glomeruli show much more lateralisation of connectivity to the contralateral glomerulus, as well as to other ipsilateral glomeruli.

The data are well presented, the manuscript clearly written, and the results will be useful to the olfaction community. I wonder, given the hemibrain and FAFB datasets exist, whether the authors have considered verifying whether the trends they observe in connectivity hold across three brains? Is it stereotypic?

Reviewer #1 (Public Review):

This paper aims to explain recent experimental results that showed deactivating the PPC in rats reduced both the contraction bias and the recent history bias during working memory tasks. The authors propose a two-component attractor model, with a slow PPC area and a faster WM area (perhaps mPFC, but unspecified). Crucially, the PPC memory has slow adaptation that causes it to eventually decay and then suddenly jump to the value of the last stimulus. These discrete jumps lead to an effective sampling of the distribution of stimuli, as opposed to a gradual drift towards the mean that was proposed by other models. Because these jumps are single-trial events, and behavior on single events is binary, various statistical measures are proposed to support this model. To facilitate this comparison, the authors derive a simple probabilistic model that is consistent with both the mechanistic model and behavioral data from humans and rats. The authors show data consistent with model predictions: longer interstimulus intervals (ISIs) increase biases due to a longer effect over the WM, while longer intertrial intervals (ITIs) reduce biases. Finally, they perform new experiments using skewed or bimodal stimulus distributions, in which the new model better fits the data compared to Bayesian models.

The mechanistic proposed model is simple and elegant, and it captures both biases that were previously observed in behavior, and how these are affected by the ISI and ITI (as explained above). Their findings help rethink whether our understanding of contraction bias is correct.

On the other hand, the main proposal - discrete jumps in PPC - is only indirectly verified.

The model predicts a systematic change in bias with inter-trial-interval. Unless I missed it, this is not shown in the experimental data. Perhaps the self-paced nature of the experiments allows to test this?

The data in some of the figures in the paper are hard to read. For instance, Figure 3B might be easier to understand if only the first 20 trials or so are shown with larger spacing. Likewise, Figure 5C contains many overlapping curves that are hard to make out.

There is a gap between the values of tau_PPC and tau_WM. First - is this consistent with reports of slower timescales in PFC compared to other areas? Second - is it important for the model, or is it mostly the adaptation timescale in PPC that matters?<br /> Regarding the relation to other models, the model by Hachen et al (Ref 43) also has two interacting memory systems. It could be useful to better state the connection, if it exists.

Reviewer #1 (Public Review):

In their study, Aman et al. utilized single cell transcriptome analysis to investigate wild-type and mutant zebrafish skin tissues during the post-embryonic growth period. They identified new epidermal cell types, such as ameloblasts, and shed light on the effects of TH on skin morphogenesis. Additionally, they revealed the important role of the hypodermis in supporting pigment cells and adult stripe formation. Overall, I find their figures to be of high quality, their analyses to be appropriate and compelling, and their major claims to be well-supported by additional experiments. Therefore, this study will be an important contribution to the field of vertebrate skin research.

Reviewer #1 (Public Review):

In this manuscript entitled "Hexokinase regulates Mondo-mediated longevity via the PPP and organellar dynamics", Laboy and colleagues investigated upstream regulators of MML-1/Mondo, a key transcription factor that regulates aging and metabolism, using the nematode C. elegans and cultured mammalian cells. By performing a targeted RNAi screen for genes encoding enzymes in glucose metabolism, the authors found that two hexokinases, HXK-1 and HXK-2, regulate nuclear localization of MML-1 in C. elegans. The authors showed that knockdown of hxk-1 and hxk-2 suppressed longevity caused by germline-deficient glp-1 mutations. The authors demonstrated that genetic or pharmacological inhibition of hexokinases decreased nuclear localization of MML-1, via promoting mitochondrial β-oxidation of fatty acids. They found that genetic inhibition of hxk-2 changed the localization of MML-1 from the nucleus to mitochondria and lipid droplets by activating pentose phosphate pathway (PPP). The authors further showed that the inhibition of PPP increased the nuclear localization of mammalian MondoA in cultured human cells under starvation conditions, suggesting the underlying mechanism is evolutionarily conserved. This paper provides compelling evidence for the mechanisms by which novel upstream metabolic pathways regulate MML-1/Mondo, a key transcription factor for longevity and glucose homeostasis, through altering organelle communications, using two different experimental systems, C. elegans and mammalian cells. This paper will be of interest to a broad range of biologists who work on aging, metabolism, and transcriptional regulation.

Reviewer #1 (Public Review):

The main objective of this study is to achieve the development of a synthetic autotroph using adaptive laboratory evolution. To accomplish this, the authors conducted chemostat cultivation of engineered E. coli strains under xylose-limiting conditions and identified autotrophic growth and the causative mutations. Additionally, the mutational mechanisms underlying these causative mutations were also explored with drill down assays. Overall, the authors demonstrated that only a small number of genetic changes were sufficient (i.e., 3) to construct an autotrophic E. coli when additional heterologous genes were added. While natural autotrophic microorganisms typically exhibit low genetic tractability, numerous studies have focused on constructing synthetic autotrophs using platform microorganisms such as E. coli. Consequently, this research will be of interest to synthetic biologists and systems biologists working on the development of synthetic autotrophic microorganisms. The conclusions of this paper are mostly well supported by appropriate experimental methods and logical reasoning. However, further experimental validation of the mutational mechanisms involving rpoB and crp would enhance readers' understanding and provide clearer insights, despite acknowledgement that these genes impact a broad set of additional genes. Additionally, a similar study, 10.1371/journal.pgen.1001186, where pgi was deleted from the E. coli genome and evolved to reveal an rpoB mutation is relevant to this work and should be placed in the context of the presented findings.

The authors addressed rpoB and crp as one unit and performed validation. They cultivated the mutant strain and wild type in a minimal xylose medium with or without formate, comparing their growth and NADH levels. The authors argued that the increased NADH level in the mutant strain might facilitate autotrophic growth. Although these phenotypes appear to be closely related, their relationship cannot be definitively concluded based on the findings presented in this paper alone. Therefore, one recommendation is to explore investigating transcriptomic changes induced by the rpoB and crp mutations. Otherwise, conducting experimental verification to determine whether the NADH level directly causes autotrophic growth would provide further support for the authors' claim.

• for: carbon inequality, w2w, leverage point - climate change, 1%, inequality, wealth tax
• title
  • The role of high-socioeconomic-status people in locking in or rapidly reducing energy-driven greenhouse gas emissions
• authors
  • Kristian S. Nielsen
  • Kimberly A. Nicholas
  • Felix Creutzig
  • Thomas Dietz
  • Paul C. Stern
• date
  • Sept 30, 2021 -source
  • https://www.nature.com/articles/s41560-021-00900-y
• abstract
  • People with high socioeconomic status disproportionally affect energy-driven greenhouse gas emissions directly
    • through their consumption and
    • indirectly through their financial and social resources.
  • However, few climate change mitigation initiatives have targeted this population segment,
    • and the potential of such initiatives remains insufficiently researched.
  • In this Perspective, we analyse key characteristics of high-socioeconomic-status people and explore five roles through which they have a disproportionate impact on energy-driven greenhouse gas emissions and potentially on climate change mitigation, namely as:
    • consumers,
    • investors,
    • role models,
    • organizational participants and
    • citizens.
  • We examine what is known about their disproportionate impact via consumption and
    • explore their potential influence on greenhouse gas emissions through all five roles.
  • We suggest that future research should focus on strategies to reduce greenhouse gas emissions by high-socioeconomic-status people and to align their
    • investments,
    • organizational choices and
    • actions as social and political change agents
  • with climate change mitigation goals.

• for: inequality, carbon inequality, 1%, wealth tax

• for: inequality, 1%, carbon inequality private jets, carbon emissions, patriotic millionaires
• title
  • He’s a millionaire with a private jet. But now he’s selling it for the sake of the environment
• source
  • https://edition.cnn.com/travel/article/millionaire-selling-private-jet/index.html
• date
  • July 13, 2023
• Stephen Prince, vice-chair of the Patriotic Millionaires – a group of wealthy Americans pushing for higher taxes which also contributed to the report – is giving up his Cessna 650 Citation III.

• for: inequality, 1%, elites, carbon inequality, leverage point, leverage point - climate change, oxfam
• quote
  • Richest 1% Took 2/3rds of Global Wealth Since 2020
  • Richest 1% increased wealth in 2020 and 2021 twice as much as 99% of Population Earned

Reviewer #1 (Public Review):

This is an interesting study, covering a future direction for the diagnosis of osteoporosis.

Strength: well validated cohorts, authors are more than experts in the field, use of technology.

Weakness: the approach is still very experimental and far away to be clinically relevant.

The authors have performed a very interesting analysis combining data from different, well designed, cohorts.<br /> Authors are leaders in the field. The topic is of interest, the statistical analysis well designed, and the paper is well written and easy to read even for not experts.

I have a few comments<br /> 1) Although authors are very optimistic about HRpQCT, they should recognize (and acknowledge in the discussion) that their data have a very low clinical impact for the majority of the population. The cost of the machine is still prohibitive for the majority of clinical centers, technology needs more validations out of the reference centers, a lot of controversy on the methodology for cortical porosity. Basically, after 20 years since its introduction, it remains more a research tool than a clinical opportunity. This comment is of course not against the scientific hypothesis or the conduction of the study which remain brilliant<br /> 2) How authors have managed the role of possible secondary causes of osteoporosis? Did they excluded patients with GIOP for example? Are all study subjects treatment naïve?<br /> 3) It would be worth to better describe the role of cortical porosity and the predictive value of this parameter which has been extensively studied by Dr Seeman.

Reviewer #1 (Public Review):

Weinberger et al. use different fate-mapping models, the FIRE model and PLX-diet to follow and target different macrophage populations and combine them with single-cell data to understand their contribution to heart regeneration after I/R injury. This question has already been addressed by other groups in the field using different models. However, the major strength of this manuscript is the usage of the FIRE mouse model that, for the first time, allows specific targeting of only fetal-derived macrophages.<br /> The data show that the absence of resident macrophages is not influencing infarct size but instead is altering the immune cell crosstalk in response to injury, which is in line with the current idea in the field that macrophages of different origins have distinct functions in tissues, especially after an injury.<br /> To fully support the claims of the study, specific targeting of monocyte-derived macrophages or the inhibition of their influx at different stages after injury would be of high interest.<br /> In summary, the study is well done and important for the field of cardiac injury. But it also provides a novel model (FIRE mice + RANK-Cre fate-mapping) for other tissues to study the function of fetal-derived macrophages while monocyte-derived macrophages remain intact.

Reviewer #1 (Public Review):

Privitera et al., provide a comprehensive and rigorous assessment of how noradrenaline (NA) inputs from the locus coeruleus (LC) to the hippocampus regulate stress-induced acute changes in gene expression. They utilize RNA-sequencing with selective activation/inhibition of LC-NA activity using pharmacological, chemogenetic and optogenetic manipulations to identify a great number of reproducible sets of genes impacted by LC activation. It is noteworthy that this study compares transcriptomic changes in the hippocampus induced by stress alone, as compared with selective circuit activation/inhibition. This reveals a small set of genes that were found to be highly reproducible. Further, the publicly available data will be highly useful to the scientific community.

A major strength of the study is the inclusion of both males and females. However, with this aspect of the study also lies the biggest weakness. While the experiments tested males and females, they were not powered for identifying sex differences. There are vast amounts of literature documenting the inherent sex differences, both under resting and stress-evoked conditions, in the LC-NA system and this is a major missed opportunity to better understand if there is an impact of these sex-specific differences at the genetic level in a major LC projection region. There are many instances whereby sex effects are apparent, but do not pass multiple testing correction due to low n's. The authors highlight one of them (Ctla2b) in supplemental figure 6. This gene is only upregulated by stress in females. It is appreciated that the manuscript provides an incredible amount of novel data, making the investigation of sex differences ambitious. Data are publicly available for others to conduct follow up work, and therefore it may be useful if a list of those genes that were different based on targeted interrogation of the dataset be provided with a clear statement that multiple testing corrections failed. This will aid further investigations that are powered to evaluate sex effects.

A major finding of the present study is the involvement of noradrenergic transcriptomic changes occurring in astrocytic genes in the hippocampus. Given the stated importance of this finding within the discussion, it seems that some additional dialogue integrating this with current literature about the role of astrocytes in the hippocampus during stress or fear memory would be important.

The comparison of the candidate genes activated by the LC in the present study (swim) with datasets published by Floriou-Servou et al., 2018 (Novelty, swim, restraint, and footshock) is an interesting and important comparison. Were there other stressors identified in this paper or other publications that do not regulate these candidate genes? Further, can references be added to clarify to the reader, that prior studies have identified that novelty, restraint and footshock all activate LC-NA neurons.

Comparisons are made between chemogenetic studies and yohimbine, stating that fewer genes were activated by chemogenetic activation of LC neurons. There is clear justification for why this may occur, but a caveat may need to be mentioned, that evidence of neuronal activation in the LC by each of these methods were conducted at 90 (yohimbine) versus 45 (hM3Dq) minutes, and therefore it cannot be ruled out that differences in LC-NA activity levels might also contribute.<br /> Please add information about how virus or cannula placement was confirmed in these studies. Were missed placements also analyzed separately?

Time of day for tissue collection used in genetic analysis should be reported for all studies conducted or reanalyzed.

Reviewer #1 (Public Review):

This study uses single-cell genomics and gene pathway analysis to characterize the transcriptional effects of influenza H1N1 infection on hypothalamic cell types. The authors use droplet-based single-nuclei RNA-seq to profile genome-wide RNA expression in adult mouse hypothalamic cells at 3, 7, and 23 days after intranasal infection with the H1N1 influenza virus. Through state-of-the-art and rigorous computational methods, the authors find that many hypothalamic cell types, glia, and especially neurons, are transcriptionally altered by respiratory infection with a non-neurotropic influenza virus and that these alterations can persist for weeks and potentially affect cell type interactions that disrupt function. For instance, microglia shift towards a pro-inflammatory molecular phenotype at 3 days post-infection, while astrocytes and oligodendrocytes significantly alter their expression of oxidoreductase activity genes and transport genes, respectively, at 7 days post-infection. In addition, POMC neurons of the arcuate hypothalamus, which suppress appetite and increase metabolism, appear to be unusually sensitive to H1N1 infection, upregulating more genes than other hypothalamic neurons. The authors' thorough discussion of the findings raises interesting questions and hypotheses about the functional implications of the molecular changes they observed, including the physiological changes that can persist long after acute viral infection. Given the role of the hypothalamus in homeostasis, this work sheds light on potential mechanisms by which the H1N1 virus can disrupt cell function and organismal homeostasis beyond the cells that it directly infects.

Reviewer #1 (Public Review):

The study isolated extracellular vesicles (EV) from healthy controls (HCs) and Parkinson patients (PwP), using plasma from the venous blood of non-fasting people. Such EVs were characterized and validated by the presence of markers, their size, and their morphology. The main aim of the manuscript is to correlate the presence of synaptic proteins, namely SNAP-25, GAP-43, and SYNAPTOTAGMIN-1, normalized with HSP70, with the clinical progression of PwP. Changes in synaptic proteins have been documented in the CSF of Alzheimer's and Parkinson's patients. The demographics of participants are adequately presented. One important limiting, as well as puzzling aspect, is the fact that authors did not find differences between groups at the beginning of the study nor after one year, after age and sex adjustment. Tables in general are hard to follow. Specifically, Table 2 does not convey a clear message nor in the text of the Table itself, and the per 100% of change needs to be explained in the corresponding legend. It is only when PwP were classified as a first quartile that a significantly greater deterioration was found. However, in the case of tremor, the top 25% had values going from 0.46-0.47 to 0.32-0.35, whereas the lower three quarters went from 0.33-0.34 to 0.27-0.28 depending on the protein analyzed. This needs to be clarified in the text. Table 3 is hard to read and some of the values seem repetitive, especially for tremor, AR, and PIGD. It looks as if Figure 2 represents the same information as Table 3. The text and figure legends are not helpful in guiding the reader to understand the presented information.

Reviewer #1 (Public Review):

The authors design an automated 24-well Barnes maze with 2 orienting cues inside the maze, then model what strategies the mice use to reach the goal location across multiple days of learning. They consider a set of models and conclude that one of these models, a combined strategy model, best explains the experimental data.

This study is written concisely and the results presented concisely. The best fit model is reasonably simple and fits the experimental data well (at least the summary measures of the data that were presented).

Major points:

1. One combined strategy (once the goal location is learned) that might seem to be reasonable would be that the animal knows roughly where the goal is, but not exactly where, so it first uses a spatial strategy just to get to the first vestibule, then switches to a serial strategy until it reaches the correct vestibule. How well would such a strategy explain the data for the later sessions? The best combined model presented in the manuscript is one in which the animal starts with a roughly 50-50 chance of a serial (or spatial strategy) from the start vestibule (i.e. by the last session before the reversal the serial and spatial strategies are at ~50-50m in Fig. 5d). Is it the case that even after 15 days of training the animal starts with a serial strategy from its starting point approximately half of the time? The broader point is whether additional examination of the choices made by the animal, combined with consideration of a larger range of possible models, would be able to provide additional insight into the learning and strategies the animal uses.

2. To clarify, in the Fig. 4 simulations, is the "last" vestibule visit of each trial, which is by definition 0, not counted in the plots of Fig. 4b? Otherwise, I would expect that vestibule 0 is overrepresented because a trial always ends with Vi = 0.

Reviewer #1 (Public Review):

Chan et al. attempted to identify the binding sites or pockets for the KCNQ1-KCNE1 activator mefenamic acid. Because the KCNQ1-KCNE1 channel is responsible for cardiac repolarization, genetic impairment of either the KCNQ1 or KCNE1 gene can cause cardiac arrhythmias. Therefore, the development of activators without side effects is highly desired. Since mefenamic acid binding requires both KCNQ1 and KCNE1 subunits, the authors performed drug docking simulations using the KCNQ1-psKCNE1 structural model with substitution of the extracellular five amino acids (R53-Y58) of KCNE3 to D39-A44 of KCNE1. They successfully identified some critical amino acid residues, including W323 of KCNQ1 and K41 and A44 of KCNE1. They then tested these identified amino acid residues by analyzing the point mutants and confirmed that they were critical for the binding of the activator. They also examined another activator, but structurally different DIDS, and reported that DIDS and mefenamic acid share the binding pocket, and concluded that the extracellular region composed of S1, S6, and KCNE1 is a generic binding pocket for the IKS activators.

The limitation of this study is that they had to use the KCNQ1-KCNE3-based structural model for the docking simulation. Although they only focused on the extracellular region substituted by the six amino acid residues of KCNE1, the binding mode or location of KCNE1 might be different from KCNE3. Another weakness is that unbinding may be facilitated in the closed state, whereas they had to use the open channel for the MD simulation. Therefore, their MD simulations do not necessarily reflect the unbinding process in the closed state, which should occur in the comparable electrophysiological experiments. Nevertheless, the data are solid and well support their conclusions. This work should be valuable to the field, not only for future drug design but also for the biophysical understanding of the binding/unbinding of drugs to ion channel complexes.

Reviewer #1 (Public Review):

In this ms, Tejeda-Muñoz and colleagues examine the roles of macropinocytosis in WNT signalling activation in development (Xenopus) and cancer (CRC sections, cell lines and xenograft experiments). Furthermore, they investigate the effect of the inflammation inducer Phorbol-12-myristate-13-acetate (PMA) in WNT signalling activation through macropinocytosis. They propose that macropinocytosis is a key driver of WNT signalling, including upon oncogenic activation, with relevance in cancer progression.

I found the analyses and conclusions of the relevance of macropinocytosis in WNT signalling compelling, notably upon constitutive activation both during development and in CRC. However, I think this manuscript only partially characterises the effects of PMA in WNT signalling, largely due to a lack of an epistatic characterisation of PMA roles in Wnt activation. For example:

1- The authors show that PMA cooperate with 1) GSK3 inhibition in Xenopus to promote WNT activation, and 2) (possibly) with APCmut in SW480 to induce b-cat and FAK accumulation. To sustain a specific functional interaction between WNT and PMA, the effects should be tested through additional epistatic experiments. For example, does PMA cooperate with Wnt8 in axis duplication analyses? Does PMA cooperate with any other WNT alteration in CRC or other cell lines? Importantly, does APC re-introduction in SW480 rescue the effect of PMA? Such analyses could be critical to determine specificity of the functional interactions between WNT and PMA. This question could be addressed by performing classical epistatic analyses in cell lines (CRC or HEK) focusing on WNT activity, and by including rescue experiments targeting the WNT pathway downstream of the effects e.g., dnTCF, APC re- introduction, etc.

2- While the epistatic analyses of WNT and macropinocytosis are clear in frog, the causal link in CRC cells is contained to b-catenin accumulation. While is clear that macropinocytosis reduces spheroid growth in SW480, the lack of rescue experiments with e.g., constitutive active b-catenin or any other WNT perturbation or/and APC re-introduction, limit the conclusions of this experiment.

Minor comments:

3- Different compounds targeting membrane trafficking are used to rescue modes of WNT activation (Wnt8 vs LiCl) in Xenopus.

4- The abstract does not state the results in CRC/xenografts

5- Labels of Figure 2E might be swap

6- Figure 4i,j, 6 and s4 rely on qualitative analyses instead of quantifications, which underscores their evaluation. On the other hand, the detailed quantifications in Figure S3A-D strongly support the images of Figure 5

Reviewer #1 (Public Review):

Despite durable viral suppression by antiretroviral therapy (ART), HIV-1 persists in cellular reservoirs in vivo. The viral reservoir in circulating memory T cells has been well characterized, in part due to the ability to safely obtain blood via peripheral phlebotomy from people living with HIV-1 infection (PWH). Tissue reservoirs in PWH are more difficult to sample and are less well understood. Sun and colleagues describe isolation and genetic characterization of HIV-1 reservoirs from a variety of tissues including the central nervous system (CNS) obtained from three recently deceased individuals at autopsy. They identified clonally expanded proviruses in the CNS in all three individuals.

Strengths of the work include the study of human tissues that are under-studied and difficult to access, and the sophisticated near-full length sequencing technique that allows for inferences about genetic intactness and clonality of proviruses. The small sample size (n=3) is a drawback. Furthermore, two individuals were on ART for just one year at the time of autopsy and had T cells compatible with AIDS, and one of these individuals had a low-level detectable viral load (Figure S1). This makes generalizability of these results to PWH who have been on ART for years or decades and have achieved durable viral suppression and immune reconstitution difficult.

While anatomic tissue compartment and CNS region accompany these PCR results, it is unclear which cell types these viruses persist in. As the authors point out, it is possible that these reservoir cells might have been infiltrating T cells from blood present at the time of autopsy tissue sampling. Cell type identification would greatly enhance the impact of this work. Several other groups have undergone similar studies (with similar results) using autopsy samples (links below). These studies included more individuals, but did not make use of the near-full length sequencing described here. In particular, the Last Gift cohort, based at UCSD and led by Sara Gianella and Davey Smith, has established protocols for tissue sampling during autopsy performed soon after death.<br /> https://pubmed.ncbi.nlm.nih.gov/35867351/<br /> https://pubmed.ncbi.nlm.nih.gov/37184401/

Overall, this small, thoughtful study contributes to our understanding of the tissue distribution of persistent HIV-1, and informs the ongoing search for viral eradication.

Reviewer #1 (Public Review):

This is a short but important study. Basically, the authors show that α-synuclein overexpression's negative impact on synaptic vesicle recycling is mediated by its interaction with E-domain containing synapsins. This finding is highly relevant for synuclein function as well as for the pathophysiology of synucleinopathies. While the data is clear, functional analysis is somewhat incomplete.

Reviewer #1 (Public Review):

The aim of this paper is to describe a novel method for genetic labelling of animals or cell populations, using a system of DNA/RNA barcodes.

Strengths:<br /> • The author's attempt at providing a straightforward method for multiplexing Drosophila samples prior to scRNA-seq is commendable. The perspective of being able to load multiple samples on a 10X Chromium without antibody labelling is appealing.<br /> • The authors are generally honest about potential issues in their method, and areas that would benefit from future improvement.<br /> • The article reads well. Graphs and figures are clear and easy to understand.

Weaknesses:<br /> • The usefulness of TaG-EM for phototaxis, egg laying or fecundity experiments is questionable. The behaviours presented here are all easily quantifiable, either manually or using automated image-based quantification, even when they include a relatively large number of groups and replicates. Despite their claims (e.g., L311-313), the authors do not present any real evidence about the cost- or time-effectiveness of their method in comparison to existing quantification methods.<br /> • Behavioural assays presented in this article have clear outcomes, with large effect sizes, and therefore do not really challenge the efficiency of TaG-EM. By showing a T-maze in Fig 1B, the authors suggest that their method could be used to quantify more complex behaviours. Not exploring this possibility in this manuscript seems like a missed opportunity.<br /> • Experiments in Figs S3 and S6 suggest that some tags have a detrimental effect on certain behaviours or on GFP expression. Whereas the authors rightly acknowledge these issues, they do not investigate their causes. Unfortunately, this question the overall suitability of TaG-EM, as other barcodes may also affect certain aspects of the animal's physiology or behaviour. Revising barcode design will be crucial to make sure that sequences with potential regulatory function are excluded.<br /> • For their single-cell experiments, the authors have used the 10X Genomics method, which relies on sequencing just a short segment of each transcript (usually 50-250bp - unknown for this study as read length information was not provided) to enable its identification, with the matching paired-end read providing cell barcode and UMI information (Macosko et al., 2015). With average fragment length after tagmentation usually ranging from 300-700bp, a large number of GFP reads will likely not include the 14bp TaG-EM barcode. When a given cell barcode is not associated with any TaG-EM barcode, then demultiplexing is impossible. This is a major problem, which is particularly visible in Figs 5 and S13. In 5F, BC4 is only detected in a couple of dozen cells, even though the Jon99Ciii marker of enterocytes is present in a much larger population (Fig 5C). Therefore, in this particular case, TaG-EM fails to detect most of the GFP-expressing cells. Similarly, in S13, most cells should express one of the four barcodes, however many of them (maybe up to half - this should be quantified) do not. Therefore, the claim (L277-278) that "the pan-midgut driver were broadly distributed across the cell clusters" is misleading. Moreover, the hypothesis that "low expressing driver lines may result in particularly sparse labelling" (L331-333) is at least partially wrong, as Fig S13 shows that the same Gal4 driver can lead to very different levels of barcode coverage.<br /> • Comparisons between TaG-EM and other, simpler methods for labelling individual cell populations are missing. For example, how would TaG-EM compare with expression of different fluorescent reporters, or a strategy based on the brainbow/flybow principle?<br /> • FACS data is missing throughout the paper. The authors should include data from their comparative flow cytometry experiment of TaG-EM cells with or without additional hexameric GFP, as well as FSC/SSC and fluorescence scatter plots for the FACS steps that they performed prior to scRNA-seq, at least in supplementary figures.<br /> • The authors should show the whole data described in L229, including the cluster that they chose to delete. At least, they should provide more information about how many cells were removed. In any case, the fact that their data still contains a large number of debris and dead cells despite sorting out PI negative cells with FACS and filtering low abundance barcodes with Cellranger is concerning.

Overall, although a method for genetic tagging cell populations prior to multiplexing in single-cell experiments would be extremely useful, the method presented here is inadequate. However, despite all the weaknesses listed above, the idea of barcodes expressed specifically in cells of interest deserves more consideration. If the authors manage to improve their design to resolve the major issues and demonstrate the benefits of their method more clearly, then TaG-EM could become an interesting option for certain applications.

• for: carbon inequality, W2W, leverage point

• five leverage points

  • consumer.
  • investors
  • lobbyist
  • influencer
  • citizen

• for: w2w, elites, top 1%, 1%, leverage point
• title
  • How the rich wreck the climate (and how to stop them)
• source
  • https://www.youtube.com/watch?v=PvPlCr_fPSA

Reviewer #1 (Public Review):

The current manuscript focuses on the adenine phosphoribosyltransferase (Aprt) and how the lack of its function affects nervous system function. It puts it into the context of Lesch-Nyhan disease, a rare hereditary disease linked to hypoxanthine-guanine phosphoribosyltransferase (HGPRT). Since HGPRT appears absent in Drosophila, the study focuses initially on Aprt and shows that aprt mutants have a decreased life-span and altered uric acid levels (the latter can be attenuated by allopurinol treatment). Moreover, aprt mutants show defects in locomotor reactivity behaviors. A comparable phenotype can be observed when specifically knocking down aprt in dopaminergic cells. Interestingly, also glia-specific knock-down caused a similar behavioral defect, which could not be restored when re-expressing UAS-aprt, while neuronal re-expression did restore the mutant phenotype. Moreover, mutants, pan-neuronal and pan-neuronal plus glia RNAi for aprt caused sleep-defects. Based on immunostainings Dopamine levels are increased; UPLC shows that adenosine levels are reduced and PCR showed in increase of Ent2 levels are increased (but not AdoR). Moreover, aprt mutants display seizure-like behaviros, which can be partly restored by purine feeding (adenosine and N6-methyladenosine). Finally, expression of the human HGPRT also causes locomotor defects.

The authors provide a wide range of genetic experimental data to assess behavior and some molecular assessment on how the defects may emerge. It is clearly written, and the arguments follow the experimental evidence that is provided.

The findings provide a new example of how manipulating specific genes in the fruit fly allows the study of fundamental molecular processes that are linked to a human disease.

Reviewer #1 (Public Review):

Cell type deconvolution is one of the early and critical steps in the analysis and integration of spatial omic and single cell gene expression datasets, and there are already many approaches proposed for the analysis. Sang-aram et al. provide an up-to-date benchmark of computational methods for cell type deconvolution.

In doing so, they provide some (perhaps subtle) additional elements that I would say are above the average for a benchmarking study: i) a full Nextflow pipeline to reproduce their analyses; ii) methods implemented in Docker containers (which can be used by others to run their datasets); iii) a fairly decent assessment of their simulator compared to other spatial omics simulators. A key aspect of their results is that they are generally very concordant between real and synthetic datasets. And, it is important that the authors include an appropriate "simpler" baseline method to compare against and surprisingly, several methods performed below this baseline. Overall, this study also has the potential to also set the standard of benchmarks higher, because of these mentioned elements.

The only weakness of this study that I can readily see is that this is a very active area of research and we may see other types of data start to dominate (CosMx, Xenium) and new computational approaches will surely arrive. The Nextflow pipeline will make the prospect of including new reference datasets and new computational methods easier.

Reviewer #1 (Public Review):

In this paper, Hui and colleagues investigate how the predictive accuracy of a polygenic score (PGS) for body mass index (BMI) changes when individuals are stratified by 62 different covariates. After showing that the PGS has different predictive power across strata for 18 out of 62 covariates, they turn to understanding why these differences and seeing if predictive performance could be improved. First, they investigated which types of covariates result in the largest differences in PGS predictive power, finding that covariates with larger "main effects" on the trait and covariates with larger interaction effects (interacting with the PGS to affect the trait) tend to better stratify individuals by PGS performance. The authors then see if including interactions between the PGS and covariates improves predictive accuracy, finding that linear models only result in modest increases in performance but nonlinear models result in more substantial performance gains.

Overall, the results are interesting and well-supported. The results will be broadly interesting to people using and developing PGS methods. Below I list some strengths and minor weaknesses.

Strengths:

A major impediment to the clinical use of PGS is the interaction between the PGS and various other routinely measured covariates, and this work provides a very interesting empirical study along these lines. The problem is interesting, and the work presented here is a convincing empirical study of the problem.

The result that PGS accuracy differs across covariates, but in a way that is not well-captured by linear models with interactions is important for PGS method development.

Weakness:

While arguably outside the scope of this paper, one shortcoming is the lack of a conceptual model explaining the results. It is interesting and empirically useful that PGS prediction accuracy differs across many covariates, but some of the results are hard to reconcile simultaneously. For example, it is interesting that triglyceride levels are associated with PGS performance across cohorts, but it seems like the effect on performance is discordant across datasets (Figure 2). Similarly, many of these effects have discordant (linear) interactions across cohorts (Figure 3). Overall it is surprising that the same covariates would be important but for presumably different reasons in different cohorts. Similarly, it would be good to discuss how the present results relate to the conceptual models in Mostafavi et al. (eLife 2020) and Zhu et al. (Cell Genomics 2023).

Reviewer #1 (Public Review):

The manuscript by Zhao et al describes the identification of RAPSYN, a NEDD8 E3 ligase previously studied for its role in acetylcholine receptor clustering and neuromuscular junction formation, as a factor promoting the stabilisation of the BCR-ABL oncogene in Chronic Myeloid Leukemia (CML) cells. The authors have identified that NEDDylation of BCR-ABL by RAPSYN antagonises its poly-ubiquitin and subsequent proteasome-based degradation. Knocking down RAPSYN with shRNA led to increased poly-ubiquitination and faster turnover of BCR-ABL. Furthermore, they describe that SRC-dependent phosphorylation of RAPSYN facilitates its NEDD8-ligase activity.

The authors' findings are primarily rooted in a series of well-conducted in vitro experiments using two CML cell lines, K562 and MEG-01. While the findings are interesting and novel, further work to corroborate these findings in primary CML samples would have greatly strengthened the potential real-world relevance of these discoveries. The authors appear to have some PBMCs from primary CML patients and a BM sample from a Ph+ ALL in which they performed western blot analyses (Fig 1). Couldn't these samples have been used to at least confirm some of the key discoveries? For example, the neddylation of BCR-ABL, or; sensitivity of primary leukemic cells to RAPSYN knockdown, and/or; phosphorylation of RAPSYN by SRC?

The authors initially interrogated a fairly dated (circa 2009) microarray-based primary dataset to show that the increase in RAPSYN is primarily a post-transcriptional event, as mRNA levels are not different between healthy and CML samples. It would be interesting to see whether differences might be more readily seen in more recent RNA-seq datasets from CML patients, given the well-known differences in sensitivity between the two platforms. Additionally, I wonder if there would be transcriptional signatures of increased NEDDylation (or RAPSYN-induced NEDDylation) that could be interrogated in primary samples? Furthermore, there are proteomics datasets of CML cells made resistant to TKIs (through in vitro selection experiments) that could be interrogated for independent validation of the authors' discoveries. For example: from K562 cells, PMID: 30730747 or PMID: 34922009).

Reviewer #1 (Public Review):

To further understand the plasticity of vestibular compensation, Schenberg et al. sought to characterize the response of the vestibular system to short-term and partial impairment using gaze stabilization behaviors. A transient ototoxic protocol affected type I hair cells and produced gain changes in the vestibulo-ocular reflex and optokinetic response. Interestingly, decreases in vestibular function occurred in coordination with an increase in ocular reflex gain at frequencies where vestibular information is more highly weighted over visual. Moreover, computational approaches revealed unexpected detriment from low reproducibility on combined gaze responses. These results inform the current understanding of visual-vestibular integration especially in the face of dysfunction.

Strengths<br /> The manuscript takes advantage of VOR measurements which can be activated by targeted organs, are used in many species including clinically, and indicate additional adverse effects of vestibular dysfunction.

The authors use a variety of experimental procedures and analysis methods to verify results and consider individual performance effects on the population data.

The conclusions are well-justified by current data and supported by previous research and theories of visuo-vestibular function and plasticity.

Weaknesses<br /> The manuscript describes the methodology as inducing reversible changes (lines 44, 67,) but the data shows a reversible effect only in hair cell histology (Fig 3A-B) not in function as demonstrated by the persistent aVOR gain reduction in week 12 (Fig 1C) and increase of OKR gain in weeks 6-12 (Fig 4C/D).

The manuscript begins with the mention of fluctuating vestibular function clinically, but does not connect this to any specific pathologies nor does it relate its conclusions back to this motivation.

The conclusions of frequency-specific changes in OKR would be stronger if frequency-specific aVOR effects were demonstrated similar to Figure 4D.

Reviewer #1 (Public Review):

The authors provide a large-scale study of 18-month-olds, tested on a battery of tests (7 tasks designed to study attention, 1 to study working memory). Most of these tasks are already well-established, and the authors provide an additional replication.<br /> They further show that the variability in toddler's behavior (in terms of accuracy and reaction time) can be best and most parsimoniously accounted by two variable, one would correspond to attention, and the second one to social attention (i.e., the well-known interest for faces across the lifespan). Additionally, the authors find no evidence for a distinction between endogenous and exogenous attention. One may however argue that it is unclear whether any of the tasks actually tap endogenous attention. More detailed discussion of the cognitive functions involved in each of the tasks would enrich the paper.

Arguably, the working memory task is the task that is most likely to involve endogenous attention, as the behavior being tested is a spontaneous interest for a hidden object, hence attention triggered by an internal mental representation. Unfortunately, this task yielded null results and did not replicate previous findings.

Altogether, these findings provide an interesting method. Its value will be assessed when these behavioral evaluations will be combined with neural data and clinical assessment, as teased by the authors towards the end of the paper.

Reviewer #1 (Public Review):

Force sensing and gating mechanisms of the mechanically activated ion channels is an area of broad interest in the field of mechanotransduction. These channels perform important biological functions by converting mechanical force into electrical signals. To understand their underlying physiological processes, it is important to determine gating mechanisms, especially those mediated by lipids. The authors in this manuscript describe a mechanism for mechanically induced activation of TREK-1 (TWIK-related K+ channel. They propose that force induced disruption of ganglioside (GM1) and cholesterol causes relocation of TREK-1 associated with phospholipase D2 (PLD2) to 4,5-bisphosphate (PIP2) clusters, where PLD2 catalytic activity produces phosphatidic acid that can activate the channel. To test their hypothesis, they use dSTORM to measure TREK-1 and PLD2 colocalization with either GM1 or PIP2. They find that shear stress decreases TREK-1/PLD2 colocalization with GM1 and relocates to cluster with PIP2. These movements are affected by TREK-1 C-terminal or PLD2 mutations suggesting that the interaction is important for channel re-location. The authors then draw a correlation to cholesterol suggesting that TREK-1 movement is cholesterol dependent. It is important to note that this is not the only method of channel activation and that one not involving PLD2 also exists. Overall, the authors conclude that force is sensed by ordered lipids and PLD2 associates with TREK-1 to selectively gate the channel. Although the proposed mechanism is solid, some concerns remain.

1) Most conclusions in the paper heavily depend on the dSTORM data. But the images provided lack resolution. This makes it difficult for the readers to assess the representative images.

2) The experiments in Figure 6 are a bit puzzling. The entire premise of the paper is to establish gating mechanism of TREK-1 mediated by PLD2; however, the motivation behind using flies, which do not express TREK-1 is puzzling. Importantly, data in this figure is not convincing.<br /> -Figure 6B, the image is too blown out and looks over saturated. Unclear whether the resolution in subcellular localization is obvious or not.<br /> -Figure 6C-D, the differences in activity threshold is 1 or less than 1g. Is this physiologically relevant? How does this compare to other conditions in flies that can affect mechanosensitivity, for example?

3) 70mOsm is a high degree of osmotic stress. How confident are the authors that a. cell health is maintained under this condition and b. this does indeed induce membrane stretch? For example, does this stimulation activate TREK-1?

Reviewer #1 (Public Review):

The authors investigated the molecular underpinnings of sleep-related memory consolidation and explored transcriptional changes in memory-related neurons, specifically the α′β′-Kenyon cells in the mushroom bodies (MB) of fruit flies in different states of memory consolidation. Their experiments identified changes in several genes and subsequently conducted functional experiments on sleep and memory. These findings are useful for further characterization of the molecular pathways that may link sleep to long-term memory formation.

The functional characterization of the identified genes revealed that the perturbation of two genes alters sleep: 1) Polr1f knockdown reduces sleep and increases pre-ribosome and translation. 2) In contrast, knockdown of Regnase-1 decreases sleep. Furthermore, Regnase-1 knockdown impairs all forms of appetitive memory. Although the findings are generally interesting, the functional relationship between these genes, sleep, and memory does not entirely become clear from the presented work. Some conclusions are not completely supported by the data, and alternative explanations are not considered.

Reviewer #1 (Public Review):

Randomized clinical trials use experimental blinding and compare active and placebo conditions in their analyses. In this study, Fassi and colleagues explore how individual differences in subjective treatment (i.e., did the participant think they received the active or placebo treatment) influence symptoms and how this is related to objective treatment. The authors address this highly relevant and interesting question using a powerful method by (re-)analyzing data from four published neurostimulation studies and including subjective treatment in statistical models explaining treatment response. The major strengths include the innovative and important research question, the inclusion of four different studies with different techniques and populations to address this question, sound statistical analyses, and findings that are of high interest and relevance to the field.

My main suggestion is that authors reconsider the description of the main conclusion to better integrate and balance all findings. Specifically, the authors conclude that (e.g., in the abstract) "individual differences in subjective treatment can explain variability in outcomes better than the actual treatment", which I believe is not a consistent conclusion across all four studies as it does not appropriately consider important interactions with objective treatment observed in study 2 and 3. In study 2, the greatest improvement was observed in the group that received TMS but believed they received sham. While subjective treatment was associated with improvement regardless of objective active or sham treatment, improvement in the objective active TMS group who believed they received sham suggests the importance of objective treatment regardless of subjective treatment. In Study 3, including objective treatment in the model predicted more treatment variance, further suggesting the predictive value of objective treatment. In addition to updating the conclusions to better reflect this interaction, I suggest authors include the proportion of participants in each subjective treatment group that actually received active or sham treatment to better understand how much of the subjective treatment is explained by objective treatment. I think it is particularly important to better integrate and more precisely communicate this finding, because the conclusions may otherwise be erroneously interpreted as improvements after treatment only being an effect of subjective treatment or sham.

The paper will have significant impact on the field. It will promote further investigation of the effects of sham vs active treatment by the introduction of the terms subjective treatment vs objective treatment and subjective dosage that can be used consistently in the future. The suggestions to assess the expectation of sham vs active earlier on in clinical trials will advance the understanding of subjective treatment in future studies. Overall, I believe the data will substantially contribute to the design and interpretation of future clinical trials by underscoring the importance of subjective treatment.

Reviewer #1 (Public Review):

This study compares visuospatial working memory performance between patients with MS and healthy controls, assessed using analog report tasks that provide continuous measures of recall error. The aim is to advance on previous studies of VWM in MS that have used binary (correct/incorrect) measures of recall, such as from change detection tasks, that are not sensitive to the resolution with which features can be recalled, and to use mixture modelling to potentially disentangle different contributions to overall performance. This aim is met in part, but there are some problems with the authors' interpretation of their findings:

- How can the authors be confident the performance deficits in the patient groups are impairments of working memory and not visual or motor in nature? I appreciate there was some kind of clinical screening, but it seems like there should have been a control condition matched to the experimental tasks with only the memory components removed.<br /> - The participant groups are large, which is definitely a strength, but not particularly well-matched in terms of demographics, with notable differences in age (mean and spread), years of education and gender. These could potentially contribute to differences in performance between groups and tasks.<br /> - The authors interpret the mixture model parameter described as "misbinding error" as reflecting failures of feature binding, and propose a link to hippocampus on that basis, however there is now quite strong evidence that these errors (often called swaps) are explained mostly or entirely by imprecision in memory for the cue feature (bar color in this case), e.g. McMaster et al. (2022), already cited in the ms.

The methodology of the ROC analyses should be described in more detail: it is not clear what measures are being used to classify participants or how.

There are a number of unusual choices of terminology that could potentially confuse or mislead the reader:<br /> - The tasks are not "n-Back" tasks by the usual meaning: they are analog report tasks with sequential presentation.<br /> - The terms recall "error","variability", "precision" and "fidelity" are used idiosyncratically. Variability and precision usually refer to the same thing: they describe the dispersion or spread of errors. The measure described as recall error in the sequential tasks is presumably absolute (or unsigned) error. For the mixture model parameters I suggest describing them more explicitly in terms of the mixture attributes, e.g. "Von Mises SD", "Target proportion", "Non-target proportion" "Uniform proportion".

Reviewer #1 (Public Review):

In this study, the authors attempt to describe alterations in gene expression, protein expression, and protein phosphorylation as a consequence of chronic adenylyl cyclase 8 overexpression in a mouse model. This model is claimed to have resilience to cardiac stress.

Major strengths of the study include 1) the large dataset generated which will have utility for further scientific inquiry for the authors and others in the field, 2) the innovative approach of using cross-analyses linking transcriptomic data to proteomic and phosphoproteomic data. One weakness is the lack of a focused question and clear relevance to human disease. These are all critical biological pathways that the authors are studying and essentially, they have compiled a database that could be surveyed to generate and test future hypotheses.

Reviewer #1 (Public Review):

The authors aim to develop an easy-to-use image analysis tool for the mother machine that is used for single-cell time-lapse imaging. Compared with related software, they tried to make this software more user-friendly for non-experts with a design of "What You Put Is What You Get". This software is implemented as a plugin of Napari, which is an emerging microscopy image analysis platform. The users can interactively adjust the parameters in the pipeline with good visualization and interaction interface.

Strengths:<br /> - Updated platform with great 2D/3D visualization and annotation support.<br /> - Integrated one-stop pipeline for mather machine image processing.<br /> - Interactive user-friendly interface.<br /> - The users can have a visualization of intermediate results and adjust the parameters.

Weaknesses:<br /> - Based on the presentation of the manuscript, it is not clear that the goals are fully achieved.<br /> - Although there is great potential, there is little evidence that this tool has been adopted by other labs.<br /> - The comparison of Otsu and U-Net results does not make much sense to me. The systematic bias could be adjusted by threshold change. The U-Net output is a probability map with floating point numbers. This output is probably thresholded to get a binary mask, which is not mentioned in the manuscript. This threshold could also be adjusted. Actually, Otsu is a segmentation method and U-Net is an image transformation method and they should not be compared together. U-Net output could also be segmented using Otsu.<br /> - The diversity of datasets used in this study is limited.

- There is some ambiguity in the main point of this manuscript, the title and figures illustrate a complete pipeline, including imaging, image segmentation, and analysis. While the abstract focus only on the software MM3. If only MM3 is the focus and contribution of this manuscript, more presentations should focus on this software tool. It is also not clear whether the analysis features are also integrated with MM3 or not.

- The impact of this work depends on the adoption of the software MM3. Napari is a promising platform with expanding community. With good software user experience and long-term support, there is a good chance that this tool could be widely adopted in the mother machine image analysis community.<br /> - The data analysis in this manuscript is used as a demo of MM3 features, rather than scientific research.

Joint Public Review:

The objectives of the study:

This paper aims to characterize the dynamics that drive allostery of the adenosine A1 receptor (A1R) via computational analysis of its activation free energy landscape and measurements of the appropriate geometrical parameters. This is done by focusing on the allosteric signaling pathways in different activation states, from inactive to active states via intermediate and pre-active ones, as well as the characterization of putative drug-binding pockets. The long-term objectives are to eventually be able to aid drug discovery efforts for this therapeutically important<br /> GPCR.

Key findings and major conclusions:

Conventional MD does not enable the sampling of the complete conformational landscape of receptor activation. Instead, enhanced sampling MD simulations are required to achieve this. Using metadynamics, the authors decipher the activation pathway of A1R, decode the allosteric networks and identify transient pockets. The protein energy networks computed throughout the inactive, intermediate active, pre-active and active conformational states unravel the extra and intracellular allosteric centers and the communication pathways that couple them, whereby the pathways are reinforced in the activated state. These conformations primarily differ in the dynamics of the ionic lock motif that couples TM3 to TM6 in the inactive conformation and reveal that G-proteins are required to fully stabilize the active conformation. Support for these findings comes from prior mutagenesis work on the A1R that identified key allosteric residues that in many cases map to identified communication nodes. Finally, the authors identified allosteric pockets throughout the A1R in four different conformational states that support prior experimental and MD studies on the mechanism of the positive allosteric modulator MIPS521 and which could be targeted for the design of new modulators. This indicates how energy networks are enhanced and redistributed by allosteric modulators and how this might explain their effect on receptor activity. Overall, these findings provide complementary support to a structure-based mechanism of activation and allosteric modulation of A1R, and extend the findings to incorporate dynamics across the full activation pathway.

The perceived strengths and weaknesses:

This preprint employs a combination of computational techniques to successfully reconstruct and analyze the conformational ensemble of the A1R activation. The metadynamics simulations supported the aim of the study, the results are clearly presented, and the work is very well written. The authors provide a valuable discussion of how the protein energy network analysis can contribute to the rational design of specific A1R modulators with desired mode of action. The employed computational approach does not capture communication pathways that involve water-mediated connections or interactions between ligands and residues. Moreover, full convergence of the free energy landscapes is not guaranteed. Overall, A1R is a good choice as the target for this study as there is existing structural and pharmacological data to support preliminary findings. Moreover, the framework presented herein could be adapted and scaled to other GPCRs with structural templates, which might enable comparison of allosteric pathways across families and classes.

Reviewer #1 (Public Review):

The manuscript investigates the role of membrane contact sites (MCSs) and sphingolipid metabolism in regulating vacuolar morphology in the yeast Saccharomyces cerevisiae. The authors show that tricalbin (1-3) deletion leads to vacuolar fragmentation and the accumulation of the sphingolipid phytosphingosine (PHS). They propose that PHS triggers vacuole division through MCSs and the nuclear-vacuolar junction (NVJ). The study presents some solid data and proposes potential mechanisms underlying vacuolar fragmentation driven by this pathway. However, there are some concerns regarding the strength and interpretation of their lipid data, and the robustness of some conclusions. The manuscript would benefit from addressing these concerns and providing more conclusive evidence to support the proposed conclusions. Overall, the study provides valuable insights into the connection between MCSs, lipid metabolism, and vacuole dynamics, but further clarification will be highly valuable to strengthen the conclusions.

Reviewer #1 (Public Review):

DMRT1 is essential in testis development in different species. While Dmrt1 is the testis-determining factor in chicken and deletion encompassing this gene lead to gonadal dysgenesis in human, the role of DMRT1 in testis development remains to be clarified. Despite an early expression of Dmrt1 in the mouse gonad and a potential function as a pioneer factor, DMRT1 is only required for the maintenance of the Sertoli cell identity in the postnatal testis. The use of a new animal model could provide new insights into the role of this factor in humans. Here the authors have generated a knockout model of DMRT1 in rabbits. They show that the XY mutant gonads differentiate as ovary indicating that DMRT1 is required for testis differentiation in rabbits. In addition, most of the germ cells remain pluripotent as evidenced by the maintenance of POU5F1 in both XY and XX mutant gonads. These are very important results potentially explaining gonadal dysgenesis associated with the DMRT1 locus in disorders of sex development in humans.

The experiments are meticulous and convincing. I find the arguments of the authors about the role of DMRT1 in germ cells in addition to its function in Sertoli cell differentiation, both comprehensible and compelling. Clearly, this is an important insight in sex determination and gametogenesis.

Reviewer #1 (Public Review):

Summary<br /> While DNA sequence divergence, differential expression, and differential methylation analysis have been conducted between humans and the great apes to study changes that "make us human", the role of lncRNAs and their impact on the human genome and biology has not been fully explored. In this study, the authors computationally predict HSlncRNAs as well as their DNA Binding sites using a method they have developed previously and then examine these predicted regions with different types of enrichment analyses. Broadly, the analysis is straightforward and after identifying these regions/HSlncRNAs the authors examined their effects using different external datasets.

Strengths/weaknesses<br /> By and large, the analysis performed is dependent on their ability to identify HSlncRNAs and their DBS. I think that they have done a good job of showing the performance metrics of their methods in previous publications. Thereafter, they perform a series of enrichment-type analyses that have been used in the field for quite a while now to look at tissue-specific enrichment, or region-specific enrichment, or functional enrichment, and I think these have been carried out well. The authors achieved the aims of their work. I think one of the biggest contributions that this paper brings to the field is their annotation of these HSlncRNAs. Thus a major revisionary effort could be spent on applying their method to the latest genomes that have been released so that the community could get a clean annotation of newly identified HSlncRNAs (see comment 2).

Comments<br /> 1) Though some of their results about certain HSlncRNAs having DBSs in all genes is rather surprising/suspicious, I think that broadly their process to identify and validate DBSs is robust, they have multiple lines of checks to identify such regions, including functional validation. These predictions are bound to have some level of false positive/negative rate and it might be nice to restate those here and on what experiment/validation data these were conducted. However, the rest of their analysis comprises different types of enrichment analysis which shouldn't be affected by outlier HSlncRNAs if indeed their FPR/FNR are low.

2) There are now several new genomes available as part of the Zoonomia consortium and 240 Primate consortium papers released. These papers have re-examined some annotations such as Human Accelerated Regions (HARs) and found with a larger dataset as well as better reference genomes, that a large fraction of HARs were actually incorrectly annotated - that is that they were also seen in other lineages outside of just the great apes. If these papers have not already examined HSlncRNAs, the authors should try and re-run the computational predictions with this updated set and then identify HSlncRNAs there. This might help to clarify their signal and remove lncRNAs that might be present in other primates but are somehow missing in the great apes. This might also help to mitigate some results that they see in section 3 of their paper in comparing DBS distances between archaics and humans.

3) The differences between the archaic hominins in their DBS distances to modern humans are a bit concerning. At some level, we expect these to be roughly similar when examining African modern humans and perhaps the Denisovan being larger when examining Europeans and Asians, but they seem to have distances that aren't expected given the demography. In addition, from their text for section 3, they begin by stating that they are computing two types of distances but then I lost track of which distance they were discussing in paragraph 3 of section 3. Explicitly stating which of the two distances in the text would be helpful for the reader.

4) Isn't the correct control to examine whether eQTLs are more enriched in HSlncRNA DBSs a set of transcription factor binding sites? I don't think using just promoter regions is a reasonable control here. This does not take away from the broader point however that eQTLs are found in DBSs and I think they can perform this alternate test.

5) In the discussion, they highlight the evolution of sugar intake, which I'm not sure is appropriate. This comes not from GO enrichment but rather from a few genes that are found at the tail of their distribution. While these signals may be real, the evolution of traits is often highly polygenic and they don't see this signal in their functional enrichment. I suggest removing that line. Moreover, HSlncRNAs are ones that are unique across a much longer time frame than the transition to agriculture which is when sugar intake rose greatly. Thus, it's unlikely to see enrichment for something that arose in the past 6000-7000 years would in the annotation that is designed to detect human-chimp or human-neanderthal level divergence.

Reviewer #1 (Public Review):

In order to find small molecules capable of enhancing regenerative repair, this study employed a high throughput YAP-activity screen method to query the ReFRAME library, identifying CLK2 inhibitor as one of the hits. Further studies showed that CLK2 inhibition leads to AMOTL2 exon skipping, rendering it unable to suppress YAP.

The novelty of the study is that it showed that inhibition of a kinase not previously associated with the HIPPO pathway can influence YAP activity through modification of mRNA splicing. The major arguments appear solid.

There are several noteworthy points when assessing the results. In Figure S1C, 100nM drug was toxic to cells at 72 hours and 1nM drug suppressed cell proliferation by 60%. Yet such concentrations were used in Figure 1B and C to argue CLK2 inhibition liberates YAP activity (which one would assume will increase cellular proliferation). In Figure 1C it appears that 1nM drug treatment led to some kind of cellular stress, as cells are visibly enlarged. In Figure 1D, 1nM drug, which would have suppressed cell growth by 60%, did not affect YAP phosphorylation. Taken together, it appears even though CLK2 inhibitor (at high concentrations) liberates YAP activity, its toxicity may override the potential use of this drug as a YAP-activator to salve tissue regenerative repair, which was one of the goals hinted in the background section.

In Figure 2D, at 100nM concentration, the drug did not appear to affect AMOTL2 splicing. Even though at higher concentrations it did, this potentially put into question whether YAP activity liberated by this drug at 1nM (Fig 2A), 10-50nM (Fig 2C) concentrations is caused by altered AMOTL2 splicing. Discussions should be provided on the difference in drug concentrations in these experiments. Does the drug decay very fast, and is that why later studies required higher dose?

Likely impact of the work on the field: this study presented a high throughput screen method for YAP activators and showed that such an approach works. The hit compound found from ReFRAME library, a CLK2 inhibitor, may not be actually useful as a YAP activator, given its clear toxicity. Applying this screen method on other large compound libraries may help find a YAP activator that helps regenerative repair. The finding that CLK2 inhibition could alter AMOTL2 splicing to affect HIPPO pathway could bring a new angle to understanding the regulation of HIPPO pathway.

Reviewer #1 (Public Review):

As a pathogen, S. aureus has evolved strategies to evade the host's immune system. It effectively remains 'under the radar' in the host until it reaches high population densities, at which point it triggers virulence mechanisms, enabling it to spread within the host. The agr quorum sensing system is central to this process, as it coordinates the pathogen's virulence network in response to its cell density. When a threshold cell density (quorum) is reached, individual cells in the population detect the quorum peptide (AgrD). This activates the Agr two-component system comprising of a histidine kinase (AgrC) and response regulator (AgrA), leading to the expression of exoenzymes and toxins that facilitate the pathogenicity of S. aureus.<br /> However, previous research has indicated that oxidative stress can possibly bypass agr quorum signaling and inhibit agr-dependent gene expression. Specifically, when exposed to high concentrations of hydrogen peroxide (H2O2), the redox-sensitive cysteines in AgrA can form an intramolecular disulfide bond, preventing AgrA from binding to DNA and initiating gene expression. Moreover, another study has shown that cells which have responded to the quorum peptide are vulnerable to oxidative damage. This damage is mediated by PSM toxins that are produced by quorum peptide responders. Consequently, this oxidative stress leads to the selection of agr mutants that are better adapted to growth in oxygen-rich environments and can exploit the benefits of the products released by the quorum-responding cells.

Given this sensitivity of the agr system to oxidative modification and the fitness cost arising from PSM expression, it raises a pertinent question: how do cells with a functional agr quorum sensing system persist within the population under conditions of oxidative stress without being overtaken by agr mutants? The current study by Podkowik et al. offers a plausible explanation. It suggests that cells that respond to the quorum peptide may be primed against oxidative stress by activating intrinsic mechanisms that reduce not only the endogenous production of harmful ROS but also mitigate their adverse effects on the cell, thus providing a unique benefit to cells that maintain an active agr system. Interestingly, these protective mechanisms are long-lived, and safeguard the cells against external oxidative stressors such as H2O2, even after the agr system has been deactivated in the population.

In their study, the authors present compelling evidence that supports the role of agr in shielding S. aureus from lethal H2O2 stress, and they establish that this protection is connected to the activation of agr-dependent RNAIII and the subsequent block of Rot translation. Importantly, the protective mechanisms that are activated persist throughout growth and provide S. aureus with defense against the host's ROS in a murine intraperitoneal infection model.

However, the study falls short in elucidating the specific intrinsic mechanisms responsible for this long-lasting protection against external ROS. While the authors infer that agr mutants, which are more vulnerable to external H2O2, display an increased respiratory activity and gene expression profile associated with aerobic fermentation, it remains ambiguous whether controlling these mechanisms alone can confer extended protection from external H2O2 in an aerobic environment. Further research is needed to confirm this hypothesis.

In summary, this study reveals that the agr quorum sensing system's role in relation to ROS is multifaceted and more complex than previously thought, as it orchestrates a balance between mechanisms that both induce and mitigate endogenous ROS, ultimately contributing to the pathogenesis of S. aureus.

Reviewer #1 (Public Review):

Sun and co-authors have determined the crystal structures of EHEP with/without phlorotannin analog, TNA, and akuBGL. Using the akuBGL apo structure, they also constructed model structures of akuBGL with phlorotannins (inhibitor) and laminarins (substrate) by docking calculation. They clearly showed the effects of TNA on akuBGL activity with/without EHEP and resolubilization of the EHEP-phlorotannin (eckol) precipitate under alkaline conditions (pH >8). Based on this knowledge, they propose the molecular mechanism of the akuBGL-phlorotannin/laminarin-EHEP system at the atomic level. Their proposed mechanism is useful for further understanding of the defensive-offensive association between algae and herbivores. However, there are several concerns, especially about structural information, that authors should address.

1. TNA binding to EHEP<br /> The electron densities could not show the exact conformations of the five gallic acids of TNA, as the authors mentioned in the manuscript. On the other hand, the authors describe and discuss the detailed interaction between EHEP and TNA based on structural information. The above seems contradictory. In addition, the orientation of TNA, especially the core part, in Fig. 4 and PDB (8IN6) coordinates seem inconsistent. The authors should redraw Fig. 4 and revise the description accordingly to be slightly more qualitative.

2. Two domains of akuBGL<br /> The authors concluded that only the GH1D2 domain affects its catalytic activity from a detailed structural comparison and the activity of recombinant GH1D1. That conclusion is probably reasonable. However, the recombinant GH1D2 (or GH1D1+GH1D2) and inactive mutants are essential to reliably substantiate conclusions. The authors failed to overexpress recombinant GH1D2 using the E. coli expression system. Have the authors tried GH1D1+GH1D2 expression and/or other expression systems?

3. Inhibitor binding of akuBGL<br /> The authors constructed the docking structure of GH1D2 with TNA, phloroglucinol, and eckol because they could not determine complex structures by crystallography. The molecular weight of akuBGL would also allow structure determination by cryo-EM, but have the authors tried it? In addition, the authors describe and discuss the detailed interaction between GH1D2 and TNA/phloroglucinol/eckol based on docking structures. The authors should describe the accuracy of the docking structures in more detail, or in more qualitative terms if difficult.

Reviewer #1 (Public Review):

This manuscript tried to answer a long-standing question in an important research topic. I read it with great interest. The quality of the science is high, and the text is clearly written. The conclusion is exciting. However, I feel that the phenotype of the transgenic line may be explained by an alternative idea. At least, the results should be more carefully discussed.

Specific comments:

1) Stability or activity (Fv/Fm) was not affected in PSII with the W14F mutation in D1. If W14F really represents the status of PSII with oxidized D1, what is the reason for the degradation of almost normal D1?

2) To focus on the PSII in which W14 is oxidized, this research depends on the W14F mutant lines. It is critical how exactly the W-to-F substitution mimics the oxidized W. The authors tried to show it in Figure 5. Because of the technical difficulty, it may be unfair to request more evidence. But the paper would be more convincing with the results directly monitoring the oxidized D1 to be recognized by FtsH.

3) Figure 3. If the F14 mimics the oxidized W14 and is sensed by FtsH, I would expect the degradation of D1 even under the growth light. The actual result suggests that W14F mutation partially modifies the structure of D1 under high light and this structural modification of D1 is sensed by FtsH. Namely, high light may induce another event which is recognized by FtsH. The W14F is just an enhancer.

Reviewer #1 (Public Review):

In this study, the authors aimed to investigate how cells respond to dynamic combinations of two stresses compared to dynamic inputs of a single stress. They applied the two stresses - carbon stress and hyperosmotic stress - either in or out of phase, adding and removing glucose and sorbitol.

Both a strength and a weakness, as well as the main discovery, is that the cells' hyperosmotic response strongly requires glucose. For in-phase stress, cells are exposed to hyperosmotic shock without glucose, limiting their ability to respond with the well-studied HOG pathway; for anti-phase stress, cells do have glucose when hyperosmotically shocked, but experience a hypo-osmotic shock when both glucose and sorbitol are simultaneously removed. Responding with the HOG pathway and so amassing intracellular glycerol amplifies the impact of this hypo-osmotic shock. Counterintuitively then, it is the presence of glucose rather than the stress of its absence that is deleterious for the cells.

The bulk of the paper supports these conclusions with clean, compelling time-lapse microscopy, including extensive analysis of gene deletions in the HOG network and measurements of both division and death rates. The methodology the authors develop is powerful and widely applicable.

Some discussion of the value of applying periodic inputs would be helpful. Cells are unlikely to have previously seen such inputs, and periodic stimuli may reveal behaviours that are rarely relevant to selection.

The authors' findings demonstrate the tight links that can exist between metabolism and the ability to respond to stress. Their study appears to have parted somewhat from their original aim because of the HOG pathway's reliance on glucose. It would be interesting to see if the cells behaviour is simpler in periodically varying sorbitol and a stress where there is little known connection to the HOG network, such as nitrogen stress.

Reviewer #1 (Public Review):

Li, Fan et al. designed and evaluated a reinforcement learning (RL) based model to automate the planning of an optimal path for the collection of data for single particle cryo-electron microscopy. The goal was to maximize the quality of the data while minimizing the time required for acquisition. They use a deep regressor (DR) to rank all the targets in the grid based on their quality as predicted from low-magnification images. In the cryo-RL model, the prediction of the DR is modified by the result of a deep Q-network (DQN) driven by a reward based on the real-time assessment of newly acquired images and a penalty based on the time required to move the microscope stage to explore new areas of the specimen. The DR and the DQN are trained on a set of low-magnification preview images and their corresponding high-magnification recordings labeled based on the quality of fit of the contrast transfer function (the CTFMaxRes parameter). The distribution of quality of a series of non-ranked trajectories was used as a snowball baseline (SB). Importantly, all tests in this paper were performed on four datasets collected by an exhaustive sampling of the grid. Thus, all data is available to all protocols.

When trained on a subset of squares from the same grid, DR+DQN outperforms DR which in turn outperforms SB. To improve transferability between specimens, both DR and DQN were trained with a large dataset sourced from a variety of samples and grid types imaged at the Cianfrocco Lab. Comparison of the performance of Cryo-RL (DR+DQN), DR, SB and of human subjects with different levels of expertise indicates shows that Cryo-RL yields the most high-resolution images in the shortest time. Further, the quality of the maps obtained from subsets of data selected using Cryo-RL is on par with the best datasets collected manually, although the latter showed marked variability.

The demonstration that a low-magnification image contains sufficient information to predict the quality of high magnification counterpart is very encouraging. However, the authors show that this translates into a high-resolution structure for one of the four datasets. The use of CTFMaxRes, although prevalent in the field, is an incomplete estimator of the quality of micrographs. Even though both the DQN and DR can be trained using different criteria, it is not clear how strong a correlation between alternative parameters and the low-magnification images would be.

This study concentrates on three "well-behaved" samples that tend to distribute evenly in the holes. The behavior of many macromolecules, e.g. orientation bias and stability, correlates with ice thickness in convoluted ways. Since ice thickness can vary drastically throughout a single hole, the overall appearance may not be sufficient to ensure a recording of the region where "good particles" concentrate. In these cases, sub-hole characterization from the low-magnification images will be necessary to target the appropriate areas. However, the feasibility of such an approach is yet to be determined. All that said, this is a timely publication that is likely to have a positive impact on the efficiency of data collection for cryo-EM.

Reviewer #1 (Public Review):

The Hedgehog (HH) protein family is important for embryonic development and adult tissue maintenance. Deregulation or even temporal imbalances in the activity of one of the main players in the HH field, sonic hedgehog (SHH), can lead to a variety of human diseases, ranging from congenital brain disorders to diverse forms of cancers. SHH activates the GLI family of transcription factors, yet the mechanisms underlying GLI activation remain poorly understood. Modification and activation of one of the main SHH signalling mediators, GLI2, depends on its localization to the tip of the primary cilium. In a previous study the lab had provided evidence that SHH activates GLI2 by stimulating its phosphorylation on conserved sites through Unc-51-like kinase 3 (ULK3) and another ULK family member, STK36 (Han et al., 2019). Recently, another ULK family member, ULK4, was identified as a modulator of the SHH pathway (Mecklenburg et al. 2021). However, the underlying mechanisms by which ULK4 enhances SHH signalling remained unknown. To address this question, the authors employed complex biochemistry-based approaches and localization studies in cell culture to examine the mode of ULK4 activity in the primary cilium in response to SHH. The study by Zhou et al. demonstrates that ULK4, in conjunction with STK36, promotes GLI2 phosphorylation and thereby SHH pathway activation. Further experiments were conducted to investigate how ULK4 interacts with SHH pathway components in the primary cilium. The authors show that ULK4 interacts with a complex formed between STK36 and GLI2 and hypothesize that ULK4 functions as a scaffold to facilitate STK36 and GLI2 interaction and thereby GLI2 phosphorylation by STK36. Furthermore, the authors provide evidence that ULK4 and STK36 co-localize with GLI2 at the ciliary tip of NIH 3T3 cells, and that ULK4 and STK36 depend on each other for their ciliary tip accumulation. Overall, the described ULK4-mediated mechanism of SHH pathway modulation is based on detailed and rigorous Co-IP experiments and kinase assays as well as confocal imaging localization studies. The authors used various mutated and wild-type constructs of STK36 and ULK4 to decipher the mechanisms underlying GLI2 phosphorylation at the tip of the primary cilium. These novel results on SHH pathway activation add valuable insight into the complexity of SHH pathway regulation. The data also provide possible new strategies for interfering with SHH signalling which has implications in drug development (e.g., cancer drugs).

However, it will be necessary to explore additional model systems, besides NIH3T3, HEK293 and MEF cell cultures, to conclude on the universality of the mechanisms described in this study. Ultimately, it needs to be addressed whether ULK4 modulates SHH pathway activity in vivo. Is there evidence that genetic ablation of ULK4 in animal models leads to less efficient SHH pathway induction? It also remains to be resolved how ULK3 and ULK4 act in distinct or common manners to promote SHH signalling. Another remaining question is, whether cell type- and tissue-specific features exist, that play a role in ULK3- versus ULK4-dependent SHH pathway modulation. In particular for the studies on ciliary tip localization of factors, relevant for SHH pathway transduction, a higher temporal resolution will be needed in the future as well as a deeper insight into tissue/ cell type-specific mechanisms. These caveats, mentioned here, don't have to be addressed in new experiments for the revision of this manuscript but could be discussed.

Reviewer #1 (Public Review):

Summary:<br /> Shibl et al., studied the possible role of dicarboxylate metabolite azelaic acid (Aze) in modulating the response of different bacteria, it was used as a carbon source by Phycobacter and possibly toxic for Alteromonas. The experiments were well conducted using transcriptomics, transcriptional factor coexpression networks, uptake experiments, and chemical methods to unravel the uptake, catabolism, and toxicity of Aze on these two bacteria. They identified a putative Aze TRAP transporter in bacteria and showed that Aze is assimilated through fatty acid degradation in Phycobacter. Meanwhile, in Alteromonas it is suggested that Aze inhibits the ribosome and/or protein synthesis, and that efflux pumps shuttles Aze outside the cytoplasm. Further on, they demonstrate that seawater amended with Aze selects for microbes that can catabolize Aze.

Major strengths:<br /> The manuscript is well written and very clear. Through the combination of gene expression, transcriptional factor co-expression networks, uptake experiments, and chemical methods Shibl et al., showed that Aze has a different response in two bacteria.

Major weakness:<br /> There is no confirmation of the Aze TRAP transporters through mutagenesis.

Impact on the field:<br /> Metabolites exert a significant influence on microbial communities in the ocean, playing a crucial role in their composition, dynamics, and biogeochemical cycles. This research highlights the intriguing capacity of a single metabolite to induce contrasting responses in distinct bacterial species, underscoring its role in shaping microbial interactions and ecosystem functions.

Reviewer #1 (Public Review):

The authors have conducted lots of field work, lab work and statistical analysis to explore the effect of brumation on individual tissue investments, the evolutionary links between the relative costly tissue sizes, and the complex non-dependent processes of brain and reproductive evolution in anuran. The topic fits well within the scope of the journal and the manuscript is generally written well. The different parameters used in the present study will attract a board readership across ecology, zoology, evolution biology, and global change biology.

Reviewer #1 (Public Review):

In this work, the authors examine the mechanism of action of MOTS-c and its impact on monocyte-derived macrophages. In the first part of the study, they show that MOTS-c acts as a host defense peptide with direct antibacterial activity. In the second part of the study, the authors aim to demonstrate that MOTS-c influences monocyte differentiation into macrophages via transcriptional regulation.

Major strengths. Methods used to study the bactericidal activity of MOTS-c are appropriate and the results are convincing.

Major weaknesses. Methods used to study the impact on monocyte differentiation are inappropriate and the conclusions are not supported by the data shown. A major issue is the use of the THP-1 cell line, a transformed monocytic line which does not mimic physiological monocyte biology. In particular, THP-1 differentiation is induced by PMA, which is a completely artificial system and conclusions from this approach cannot be generalized to monocyte differentiation. The authors would need to perform this series of experiments using freshly isolated monocytes, either from mouse or human. The read-out used for macrophage differentiation (adherence to plastic) is also not very robust, and the authors would need to analyze other parameters such as cell surface markers. It is also not clear whether MOTS-c could act in a cell-intrinsic fashion, as the authors have exposed cells to exogenous MOTS-c in all their experiments. The authors did not perform complementary experiments using MOTS-c deficient monocytes. The authors have also analyzed the transcriptomic changes induced by MOTS-c exposure in macrophages derived from young or old mice. While the results are potentially interesting, the differences observed seem independent from MOTS-c and mainly related to age, therefore the conclusions from this figure are not clear. Another concern is the reproducibility of the experiments, as the authors do not indicate the number of biological replicates analyzed nor the number of independent experiments performed.

The different parts of the manuscript do not appear well connected and it is not clear what the main message from the manuscript would be. The physiological relevance of this study is also unclear.

Reviewer #1 (Public Review):

In this manuscript, the authors propose a new codon adaptation metric, Codon Adaptation Index of Species (CAIS), which they present as an easily obtainable proxy for effective population size. To permit between-species comparisons, they control for both amino acid frequencies and genomic GC content, which distinguishes their approach from existing ones. Having confirmed that CAIS negatively correlates with vertebrate body mass, as would be expected if small-bodied species with larger effective populations experience more efficient selection on codon usage, they then examine the relationship between CAIS and intrinsic structural disorder in proteins.

The idea of a robust species-level measure of codon adaptation is interesting. If CAIS is indeed a reliable proxy for the effectiveness of selection, it could be useful to analyze species without reliable life history- or mutation rate data (which will apply to many of the genomes becoming available in the near future).

A key question is whether CAIS, in fact, measures adaptation at the codon level. Unfortunately, CAIS is only validated indirectly by confirming a negative correlation with body mass. As a result, the observations about structural disorder are difficult to evaluate.

A potential problem is that differences in GC between species are not independent of life history. Effective population size can drive compositional differences due to the effects of GC-biased gene conversion (gBGC). As noted by Galtier et al. (2018), genomic GC correlates negatively with body mass in mammals and birds. It would therefore be important to examine how gBGC might affect CAIS, and to what extent it could explain the relationship between CAIS and body mass.

Suppose that gBGC drives an increase in GC that is most pronounced at 3rd codon positions in high-recombination regions in small-bodied species. In this case, could observed codon usage depart more strongly from expectations calculated from overall genomic GC in small vertebrates compared to large ones? The authors also report that correcting for local intergenic GC was unsuccessful, based on the lack of a significant negative relationship with body mass (Figure 3D). In principle, this could also be consistent with local GC providing a relatively more appropriate baseline in regions with high recombination rates. Considering these scenarios would clarify what exactly CAIS is capturing.

Given claims about "exquisitely adapted species", the case for using CAIS as a measure of codon adaptation would also be stronger if a relationship with gene expression could be demonstrated. RSCU is expected to be higher in highly expressed genes. Is there any evidence that the equivalent GC-controlled measure behaves similarly?

The manuscript is overall easy to follow, though some additional context may be helpful for the general reader. A more detailed discussion of how this work compares to the approach taken by Galtier et al. (2018), which accounted for GC content and gBGC when examining codon preferences, would be appropriate, for example. In addition, it would have been useful to mention past work that has attempted to explicitly quantify selection on codon usage.

Reviewer #1 (Public Review):

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

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

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

Reviewer #1 (Public Review):

Kainate receptors play various important roles in synaptic transmission. The receptors can be divided into low affinity kainate receptors (GluK1-3) and high affinity kainate receptos (GluK4-5). The receptors can assemble as homomers (GluK1-3) or low-high affinity heteromers (GluK4-5). The functional diversity is further increased by RNA splicing. Previous studies have investigated C-terminal splice variants of GluK1, but GluK1 N-terminal (exon 9) insertions have not been previously characterized. In this study Dhingra et al investigate the functional implications of a GluK1 splice variant that inserts a 15 amino acid segment into the extracellular N-terminal region of the protein using whole-cell and excised outside-out electrophysiology.<br /> The authors produce solid data to show that the insertion profoundly impacts the function of GluK1-1a - the channels that have the insertion are slower to desensitize. The data also shows that the insertion changes the modulatory effects of Neto proteins, resulting in altered rates of desensitization and recovery from desensitization. To determine the mechanism by which the insertion exerts these functional effects, the authors perform pull-down assays of Neto proteins, and extensive mutagenesis on the insert.

The electrophysiological part of the study is very rigorous and meticulous.

The biggest weakness of the manuscript is the structural work. Due to issues with preferred orientation (a common problem in cryo-EM), the 3D reconstructions are at a low resolution (in the 5-8 Å range) and cannot offer much mechanistic insight into the effects of the insertion. Based on the available data, the authors posit that the insertion does not change the arrangement of the subunits in the desensitized state. However, there is no comparison with a structure that does not contain the insertion, so while the statement may well be true, no data is shown to support it.

Overall, the cryo-EM contributes little and distracts from the good parts of the manuscript.

Another part that does not contribute much is the RNAseq data that has been pulled from a database and analyzed for the paper. It is being used to show that the exon 9 insertion variant is predominantly expressed in the cerebellar cortex at early stages of brain development. The methods do not describe in detail how the data has been analyzed (e.g., is the data scaled per sample/gene or globally?) so it is hard to know what we can compare in the heat plots. In Figure 1- supplement 1 there aren't striking differences in expression (at least not obviously visible in the current illustration).

Despite these weaknesses, the study is an important contribution to the field because it characterizes a GluK1 variant that has not been studied before and highlights the functional diversity that exists within the kainate receptor family.

Reviewer #1 (Public Review):

The study provides a complete comparative interactome analysis of α-arrestin in both humans and drosophila. The authors have presented interactomes of six humans and twelve Drosophila α-arrestins using affinity purification/mass spectrometry (AP/MS). The constructed interactomes helped to find α-arrestins binding partners through common protein motifs. The authors have used bioinformatic tools and experimental data in human cells to identify the roles of TXNIP and ARRDC5: TXNIP-HADC2 interaction and ARRDC5-V-type ATPase interaction. The study reveals the PPI network for α-arrestins and examines the functions of α-arrestins in both humans and Drosophila.

Comments<br /> I will like to congratulate the authors and the corresponding authors of this manuscript for bringing together such an elaborate study on α-arrestin and conducting a comparative study in drosophila and humans.

Introduction:<br /> The introduction provides a rationale behind why the comparison between humans and Drosophila is carried out.<br /> - Even though this is a research manuscript, including existing literature on similar comparison of α-arrestin from other articles will invite a wide readership.

Results:<br /> The results cover all the necessary points concluded from the experiments and computational analysis.<br /> • The authors could point out the similarity of the α-arrestin in both humans and Drosophila.<br /> • Citing the direct connecting genes from the network in the text will invite citations and a wider readership.

Figures:<br /> The images are elaborate and well-made.<br /> • The authors could use a direct connected gene-gene network that pointing interactions. This can be used by other readers working on the same topic and ensure reproducibility and citations.<br /> • The blot/gel images can be of higher resolution.

Discussion:<br /> The authors have utilized and discussed the conclusion they draw from their study. But could highlight more on ARRDCs and why it was selected out of the other arrestins. The authors have provided future work directions associated with their work.

Supplementary figures:<br /> The authors have a rigorous amount of work added together for the success of this manuscript.

Reviewer #1 (Public Review):

The manuscript by Sejour et al. is testing "translational ramp" model described previously by Tuller et al. in S. cerevisiae. Authors are using bioinformatics and reporter based experimental approaches to test whether "rare codons" in the first 40 codons of the gene coding sequences increase translation efficiency and regulate abundance of translation products in yeast cells. Authors conclude that "translation ramp" model does not have support using new set of reporters and bioinformatics analyses. The strength of bioinformatic evidence and experimental analyses of the rare codons insertion in the reporter make compelling case for authors claims. However major weakness of the manuscript is that authors do not take in account other confounding effects in their analyses as well as multiple previous studies that argue with "translation ramp" model. The existence of the early elongation ramp with "rare codons" was previously contested with local mRNA structure at the start codon, peptidyl-tRNA drop-off or interactions of the nascent peptide chain with exit channel of the ribosome models. All of these effects are not considered or discussed in the manuscript at this point. Such an authors approach makes the manuscript rather biased and short on discussing multiple other possible conclusions on reasons of slow translation elongation at the beginning of the protein synthesis.

• The richest 1 percent grabbed nearly two-thirds of all new wealth worth $42 trillion created since 2020,
  • almost twice as much money as the bottom 99 percent of the world’s population,

Reviewer #1 (Public Review):

In the current study, the authors employed C elegans transgenic line of sfGFP::Abeta worms to investigate molecules implicated in Abeta aggregation and clearance. They conduct siRNA knockdown, RNA deletion, and overexpression experiments to demonstrate that collagens and ADM-2 play critical roles in aggregate formation and clearance, respectively. Basically, the data support the main claims and key conclusions. However, the impact and significance of the findings are considered average at this time. Additional work is necessary for strengthening the research and supporting the major conclusions. For instance, it remains unclear how ADM-2 removes extracellular aggregates. The work is also missing studies to assess whether collagen escalation increases aggregate formation. These two biological processes are critical for understanding the balance in Abeta aggregate formation.

Reviewer #1 (Public Review):

Meiosis uses distinct cohesin complexes for chromosome morphogenesis and segregation such as cohesins with meiosis-specific REC-8 and COH-3/4 in the nematode. In this important paper, by using stage-specific depletion of the cohesin component, the authors nicely showed that REC-8-cohesin stably binds to meiotic chromosomes and plays an essential role in sister chromatid cohesion in diakinesis and meiosis I. Moreover, COH-3/4-cohesin, whose chromosome binding is stabilized by the SCC-2 cohesin regulator, is more dynamic than Rec8-cohesin in prophase I and plays a role in loop-axis formation.

Reviewer #1 (Public Review):

The manuscript describes a multi-ancestry meta-analysis of genome-wide association studies of tuberculosis risk from case-control cohorts across several European, Asian, and African countries.

A main finding is that there is substantial common variant heritability of tuberculosis risk is well established. However, this analysis needs to be adjusted for differing case-control ratios in order to put the heritability estimates onto the liability scale so that variation across countries/cohorts can be properly assessed.

The authors find the strongest statistical evidence for association at a HLA locus. However, because of the complexity of this region and the diversity across ancestries, interpretation of this association is difficult.

This manuscript shows that there is potential to identify heritable sources of tuberculosis risk across ancestries. However, better genotyping of the HLA region and larger sample sizes will be needed to make further progress.

Reviewer #1 (Public Review):

Liu et al present a very interesting manuscript investigating whether there are distinct mechanisms of learning in children with ASD. What they found was that children with ASD showed comparable learning to typically developing children, but that there was a difference in learning strategy, with less plasticity and more stable learning representations in children with ASD. In other words, children with ASD showed similar learning performance to typically developing children but were more likely to use different learning rules to get there. Interestingly greater fMRI-measured brain plasticity was associated with learning gains in typically developing children, whereas more stable (less plasticity) neural patterns were associated with learning gains in autistic children. This was mediated by insistence on sameness (from the RRIB) in the ASD group.

This is a good paper, well reasoned and with strong methods. The biggest issue is related to subject numbers and possibly the conceptualization of ASD. With n=35 it is only possible to make a generalized statement about autism. For example, take the following statement from the results: "while most TD children used the memory-based strategy most frequently following training, nearly half of the children with ASD used rule-based strategies most frequently for trained problems." Is this the heterogeneity of autism at play, or the noisiness of the task and measures? Conceptually, is it realistic to expect a unitary learning strategy in all of autism? Lastly, the task itself can only be solved in a subset of autistic children and therefore presents a limited view of the condition.

Reviewer #1 (Public Review):

As part of a special issue on COVID-19 and cancer, Fuzzell and colleagues report findings from their mixed method study on the impact of the pandemic on cervical cancer screening and colposcopies, consisting of a national (United States) survey (March-August 2021) of 1251 clinicians (675 perform colposcopy) and qualitative interviews (June-December 2021) with 55 of these clinicians. The study looked specifically at perceived pandemic-related practice changes and disruptions over one year into the pandemic after the lockdowns had been lifted.

The overall focus is on three pandemic-related questions (impact on cervical cancer screening practice, colposcopy practice, ability to provide LEEP) that were asked as part of a larger survey related to cervical cancer screening and management of abnormal results, details of which are however not fully described in terms of the survey's general aim and items, but seem to have been designed within the context of adherence to guidelines (following Cabana's Guideline Based Practice Improvement Framework).

Reviewer #1 (Public Review):

The strength of this work is the quality and quantity of data, which identify a critical histidine residue, His12 of SsrB, that is responsible for the allosteric, pH-dependent conformational change in SsrB and for phosphorylation of SsrB. That is the fundamental question to the field: the low pH response when Salmonella invades host cells and utilizes acidification within a Salmonella-containing vacuole as a signal to initiate the expression of virulence genes from the Salmonella pathogenicity island 2 (SPI-2) suite of virulence genes, which encode specific effector proteins and a unique secretion injectisome that has, to date, eluded purification. The SsrB protein will activate the transcription of non-SPI-2 genes at neutral pH in the regulation of biofilm formation. The low pH, phosphorylated SsrB structure allows for cooperative binding to DNA that is necessary for SPI-2 gene activation. Remarkably, the substitution of the single His12 residue of SsrB is enough to eliminate its activity at acidic pH, but not at normal pH. The authors employ a clever and exceptional single-molecule DNA unzipping assay for their DNA affinity measurements. Another major strength of this work is the logical flow of the results section and the lucidity of the written presentation. This work will guide the field in allowing for the expression of SPI-2 in the lab for mechanistic studies that would be otherwise impossible to do within a vacuole.

The first chapter of the results section includes the demonstration that acid pH increases SsrB affinity for SPI-2 promoter DNA. The authors employed a sophisticated single molecule DNA unzipping to measure the effects of pH on SsrB affinity to the DNA target. The DAN affinity was ~32-fold higher at acid pH (6.1) than at neutral pH (7.4). At both acidic and neutral pH conditions DNA binding was highly cooperative.

In the second results chapter, the authors investigated whether the DAN binding domain of SsrB was responsible for low pH-stimulated DNA binding. SsrB is a classic two-component regulatory protein with an N-terminal receiver domain that gets phosphorylated during activation and a C-terminal DNA binding domain to affect the regulation of gene expression in response to phosphorylation. Again, the single molecule DNA unzipping assay was employed to characterize pH effects on just the C-terminal binding domain (SsrB-C). The isolated C-terminal domain bound DNA with a 4-fold lower affinity as compared to the full-length protein. Cooperativity was also reduced. SsrB-C was shown to be unable to support acid-stimulation of SPI-2 transcription using both in vivo and in vitro transcriptional assays. The data is quite solid.

The third results chapter is a comparison of SsrB to other members of the NarL/FixJ subfamily of response regulators. SsrB is the only member to have known pH dependence on its activity. The authors found SsrB to have the highest pI of the subfamily and the second-greatest number of histidine residues. Of four histidine residues in the receiver domain His12 was conserved in the subfamily, while His28, His34, and His 72 were unique to SsrB and thus initially investigated. Since histidine residues are known to play a role in pH sensing, the three histidine residues in the receiver domain were extensively characterized for a potential role in pH-dependent transcriptional activation. The experiments ruled out the role of the three unique histidine residues in the SsrB receiver domain in pH sensing.

The fourth research chapter demonstrated that it is the conserved His14 of SsrB that is responsible for pH sensing. A striking result was the finding that the H12Q substitution retained full DNA binding activity at neutral pH, but at acidic pH, the H12Q allele was unable to activate SPI-2 transcription. Further analysis showed the mutant allele was defective in subunit cooperativity.

The fifth research chapter characterized other amino acid substitutions at His12 of SsrB. Positively charged substitutions were employed to mimic the protonated state of His12 and aromatic substitutions were chosen to mimic the aromatic nature of the imidazole ring of histidine. H12Y and H12F substitutions had substantially reduced activity but retained pH sensing. Charged substitutions were defective for both binding and pH sensing. These results support the conclusion that the aromatic nature of the histidine imidazole role was important for pH sensing.

In the final research chapter, the authors characterized His 12 substitutions for effects on SsrB phosphorylation at Asp56. The results of these assays showed that substitution at His12 reduced both SsrB phosphorylation at neutral pH and abolished pH-dependent changes in SsrB phosphorylation consistent with conformational changes in SsrB as a result of substitution at His12.

Overall, a solid study that defines the essential role of His12 in SsrB activation at low pH. His12 is critical for pH sensing, SsrB phosphorylation, SsrB oligomerization, and in vivo Salmonella virulence.

Reviewer #1 (Public Review):

This study uses electrophysiological techniques in vitro to address the role of the Na+ leak channel NALCN in various physiological functions in cartwheel interneurons of the dorsal cochlear nucleus. Comparing wild type and glycinergic neuron-specific knockout mice for NALCN, the authors show that these channels 1) are required for spontaneous firing, 2) are modulated by noradrenaline (NA, via alpha2 receptors) and GABA (through GABAB receptors), 3) how the modulation by NA enhances IPSCs in these neurons.

This work builds on previous results from the Trussell's lab in terms of the physiology of cartwheel cells, and from other labs in terms of the role of NALCN channels, that have been characterized in more and more brain areas somewhat recently; for this reason, this study could be of interest for researchers that work in other preparations as well. The general conclusions are strongly supported by results that are clearly and elegantly presented.

I have a few comments that, in my opinion, might help clarify some aspects of the manuscript.

1) It is mentioned throughout the manuscript, including the abstract, that the results suggest a closed apposition of NALCN channels and alpha2 and GABAB receptors. From what I understand, this conclusion comes from the fact that GABAB receptors activate GIRK channels through a membrane-delimited mechanism. Is it possible that these receptors converge on other effectors, for example adenylate cyclase (see https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374141/).

2) In Figure 2G, the neurons from NALCN KO mice appear to reach a significantly higher frequency than those from WT (figure 2E, 110 vs. 70 spikes/s). Was this higher frequency a feature of all experiments? The results mention a rundown of peak firing rate due to whole-cell dialysis, but, from what I understand, the control conditions should be similar for all experiments.

3) Also in Figure 2, the firing patterns for neurons from WT and NALCN KO mice appear to be quite different, with spikes appearing to be generated during the hyperpolarization of the bursts in the second half of the current step for WT neurons but always during the depolarization in KO neurons. Was this always the case? If so, could NALCN channels be involved in this type of firing? Along these lines, it would be interesting to show an example of a firing pattern of neurons from WT mice in the presence of NA, which inhibits NALCN channels.

4) It might be interesting to discuss how the hyperpolarization induced by the activation of GIRK channels and inhibition of NALCN channels could have different consequences due to their opposite effect on the input resistance.

Reviewer #1 (Public Review):

Wang and colleagues recently demonstrated the essential role of RBM24 (RNA-binding motif protein 24a) in the development of mouse hair cells (source: https://doi.org/10.1002/jcp.31003). In this study, they further expand on their findings by revealing that Rbm24 expression is absent in Pou4f3 mutant mice but not in Gfi1 mutant mice. This observation suggests that POU4F3 acts as an upstream regulator of Rbm24. The researchers effectively demonstrate that POU4F3 can bind to and regulate Rbm24 through three distant enhancers, which are located in open chromatin regions and are bound by POU4F3. Lastly, Wang and colleagues discovered that ectopic expression of Rbm24 was unable to prevent the degeneration of POU4F3 null hair cells.

The findings in this manuscript hold great significance as they provide additional insights into the transcriptional cascades crucial for hair cell development. The discovery of enhancers capable of driving transgene expression specifically in hair cells holds promising therapeutic implications. The figures presented in the study are of excellent quality, the employed techniques are state-of-the-art, the data are accurately represented without exaggeration, and the study demonstrates a high level of rigor.

Reviewer #1 (Public Review):

This manuscript by Kelly et al. reports results from single-cell transcriptomic analysis of spinal neurons in zebrafish. The work builds on a strong foundation of literature and the objective, to discern gene expression patterns specializing on functionally distinct motor circuits, is well rationalized. Specifically, they compared the transcriptomes in the escape and swimming circuits.

The authors discovered, in the motor neurons of the escape circuit, two functional groups or "cassettes" of genes related to excitability and vesicle release, respectively. Expression of these genes makes sense for a "fast" circuit. This finding will be important to the field and form the basis for subsequent studies differentiating the escape circuit from others.

Unfortunately, efforts to identify a counterpart cassette in the SMns of the swimming pathway were unsuccessful. Instead, they found an abundance of transcription factors and ribosomal proteins; 1/3 were reported as other proteins, although it wasn't clear whether those were genes mediating excitability or transmitter release. Further analysis was not reported, and the authors speculate that the neurons in that pathway may not yet be born.

Reviewer #1 (Public Review):

SUMMARY:

In this paper, Luppi et al., apply the recently developed integrated information decomposition to the question how the architecture of information processing changes when consciousness is lost. They explore fMRI data from two different populations: healthy volunteers undergoing reversible anesthesia, as well as from patients who have long-term disorders of consciousness. They show that, in both populations, synergistic integration of information is disrupted in common ways. These results are interpreted in the context of the SAPHIRE model (recently proposed by this same group), that describes information processing in the brain as being composed of several distinct steps: 1) gatekeeping (where gateway regions introduce sensory information to the global synergistic workspace where 2) it is integrated or "processed" before 3) by broadcast back to to the brain.

I think that this paper is an excellent addition to the literature on information theory in neuroscience, and consciousness science specifically. The writing is clear, the figures are informative, and the authors do a good job of engaging with existing literature. While I do have some questions about the interpretations of the various information-theoretic measures, all in all, I think this is a significant piece of science that I am glad to see added to the literature.

One specific question I have is that I am still a little unsure about what "synergy" really is in this context. From the methods, it is defined as that part of the joint mutual information that is greater than the maximum marginal mutual information. While this is a perfectly fine mathematical measure, it is not clear to me what that means for a squishy organ like the brain. What should these results mean to a neuro-biologist or clinician?

Right now the discussion is very high level, equating synergy to "information processing" or "integrated information", but it might be helpful for readers not steeped in multivariate information theory to have some kind of toy model that gets worked out in detail. On page 15, the logical XOR is presented in the context of the single-target PID, but 1) the XOR is discrete, while the data analyzed here are continuous BOLD signals w/ Gaussian assumptions and 2) the XOR gate is a single-target system, while the power of the Phi-ID approach is the multi-target generality. Is there a Gaussian analog of the single-target XOR gate that could be presented? Or some multi-target, Gaussian toy model with enough synergy to be interesting?

I think this would go a long way to making this work more accessible to the kind of interdisciplinary readership that this kind of article with inevitably attract.

STRENGTHS

The authors have a very strong collection of datasets with which to explore their topic of interest. By comparing fMRI scans from patients with disorders of consciousness, healthy resting state, and various stages of propofol anesthesia, the authors have a very robust sample of the various ways consciousness can be perturbed, or lost. Consequently, it is difficult to imagine that the observed effects are merely a quirk of some biophysical effect of propofol specifically, or a particular consequence of long-term brain injury, but do in fact reflect some global property related to consciousness. The data and analyses themselves are well-described, have been previously validated, and are generally strong. I have no reason to doubt the technical validity of the presented results.

The discussion and interpretation of these results is also very nice, bringing together ideas from the two leading neurocognitive theories of consciousness (Global Workspace and Integrated Information Theory) in a way that feels natural. The SAPHIRE model seems plausible and amenable to future research. The authors discuss this in the paper, but I think that future work on less radical interventions (e.g. movie watching, cognitive tasks, etc) could be very helpful in refining the SAPHIRE approach.

Finally, the analogy between the PID terms and the information provided by each eye redundantly, uniquely, and synergistically is superb. I will definitely be referencing this intuition pump in future discussions of multivariate information sharing.

WEAKNESSES

I have some concerns about the way "information processing" is used in this study. The data analyzed, fMRI BOLD data is extremely coarse, both in spatial and temporal terms. I am not sure I am convinced that this is the natural scale at which to talk about information "processing" or "integration" in the brain. In contrast to measures like sample entropy or Lempel-Ziv complexity (which just describe the statistics of BOLD activity), synergy and Phi are presented here as quasi-causal measures: as if they "cause" or "represent" phenomenological consciousness. While the theoretical arguments linking integration to consciousness are compelling, is this is right data set to explore them in?

For example, the work by Newman, Beggs, and Sherril (nee Faber), synergy is associated with "computation" performed in individual neurons: the information about the future state of a target neuron that is only accessible when knowing both inputs (analogous to the synergy in computing the sum of two dice). Whether one thinks that this is a good approach neural computation or not, it fits within the commonly accepted causal model of neural spiking activity: neurons receive inputs from multiple upstream neurons, integrate those inputs and change their firing behavior accordingly.

In contrast, here, we are looking at BOLD data, which is a proxy measure for gross-scale regional neural activity, which itself is a coarse-graining of millions of individual neurons to a uni-dimensional spectrum that runs from "inactive to active." It feels as though a lot of inferences are being made from very coarse data.

REFERENCES:

1. Newman, E. L., Varley, T. F., Parakkattu, V. K., Sherrill, S. P. & Beggs, J. M. Revealing the Dynamics of Neural Information Processing with Multivariate Information Decomposition. Entropy 24, 930 (2022).

Reviewer #1 (Public Review):

In this study, the authors investigate where and when brain activity is modulated by incoming linguistic cues during sentence comprehension. Sentence stimuli were designed such that incoming words had varying degrees of constraint on the sentence's structural interpretation as participants listened to them unfolding, i.e. due to varying degrees of verb transitivity and the noun's likelihood of assuming a specific thematic role. Word-by-word "online" structural interpretations for each sentence were extracted from a deep neural network model trained to reproduce language statistics. The authors relate the various metrics of word-by-word predicted sentence structure to brain data through a standard RSA approach at three distinct points of time throughout sentence presentation. The data provide convincing evidence that brain activity reflects preceding linguistic constraints as well as integration difficulty immediately after word onset of disambiguating material.

The authors confirm that their sentence stimuli vary in degree of constraint on sentence structure through independent behavioral data from a sentence continuation task. They also show a compelling correlation of these behavioral data with the online structure metric extracted from the deep neural network, which seems to pick up on the variation in constraints. In the introduction, the authors argue for the potential benefits of using deep neural network-derived metrics given that it has "historically been challenging to model the dynamic interplay between various types of linguistic and nonlinguistic information". Similarly, they later conclude that "future DLMs (...) may provide new insights into the neural implementation of the various incremental processing operations(...)".

By incorporating structural probing of a deep neural network, a technique developed in the field of natural language processing, into the analysis pipeline for investigating brain data, the authors indeed take an important step towards establishing advanced machine learning techniques for researching the neurobiology of language. However, given the popularity of deep neural networks, an argument for their utility should be carefully evidenced. However, the data presented here don't directly test how large the benefit provided by this tool really is. In fact, the authors show compelling correlations of the neural network-derived metrics with both the behavioral cloze-test data as well as several (corpus-)derived metrics. While this is a convincing illustration of how deep language models can be made more interpretable, it is in itself not novel. The correlation with behavioral data and corpus statistics also raises the question of what is the additional benefit of the computational model? Is it simply saving us the step of not having to collect the behavioral data, not having to compute the corpus statistics or does the model potentially uncover a more nuanced representation of the online comprehension process? This remains unclear because we are lacking a direct comparison of how much variance in the neural data is explained by the neural network-derived metrics beyond those other metrics (for example the main verb probability or the corpus-derived "active index" following the prepositional phrase).

With regards to the neural data, the authors show convincing evidence for early modulations of brain activity by linguistic constraints on sentence structure and importantly early modulation by the coherence between multiple constraints to be integrated. Those modulations can be observed across bilateral frontal and temporal areas as well as parts of the default mode network. The methods used are clear and rigorous and allow for a detailed exploration of how multiple linguistic cues are neurally encoded and dynamically shape the final representation of a sentence in the brain. However, at times the consequences of the RSA results remain somewhat vague with regard to the motivation behind different metrics and how they differ from each other. Therefore, some results seem surprising and warrant further discussion, for example:

Why does the neural network-derived parse depth metric fit neural data before the V1 uniqueness point if the sentence pairs begin with the same noun phrase? This suggests that the lexical information preceding V1, is driving the results. However, given the additional results, we can already exclude an influence of subject likelihood for a specific thematic role as this did not model the neural data in the V1 epoch to a significant degree. Relatedly, In Fig 2C it seems there are systematic differences between HiTrans and LoTrans sentences regarding the parse depth of determiner and subject noun according to the neural network model, while this is not expected according to the context-free parse.

"The degree of this mismatch is proportional to the evidence for or against the two interpretations (...). Besides these two measures based on the entire incremental input, we also focused on Verb1 since the potential structural ambiguity lies in whether Verb1 is interpreted as a passive verb or the main verb."

The neural data fits in V1 epoch differ in their temporal profile for the mismatch metrics and the Verb 1 depth respectively. I understand the "degree of mismatch" to be a measure of how strongly the neural network's hidden representations align with the parse depth of an active or passive sentence structure. If this is correct, then it is not clear from the text how far this measure differs from the Verb 1 depth alone, which is also indicating either an active or passive structure.

In previous studies, differences in neural activity related to distinct amounts of open nodes in the parse tree have been interpreted in terms of distinct working memory demands (Nelson et al. pnas 2017, Udden et al tics 2020). It seems that some of the metrics, for example the neural network-derived parse depth or the V1 depth may be similarly interpreted in the light of working memory demands. After all, during V1 epoch, the sentences do not only differ with respect to predicted sentence structure, but also in the amount of open nodes that need to be maintained. In the discussion, however, the authors interpret these results as "neural representations of an unfolding sentence's structure".

Reviewer #1 (Public Review):

The authors' aim was to test to what extent atypical organization of language is associated with a mirrored brain organization of other cognitive functions. In particular, they focused on the inferior frontal gyri (IFG) by studying the inhibitory control network. This allowed them to directly test the support for the Causal hypothesis of hemispheric specialization, arguing for fast sequences of cognitive processes being better performed by a single hemisphere, versus the Statistical hypothesis of lateralization, postulating an independent lateralization of each cognitive function.

Previous studies on this topic did not focus on functions involving homotopic language regions. This limitation is bypassed in this study by assessing inhibition with a Stop-Signal Task which also engages the IFG in the contralateral site to the verb generation task. By studying a combination of structural and functional information, in addition to the activation contrasts, the authors are able to test whether atypical organization is accompanied by stronger interhemispheric connectivity. Although relying mainly on correlations and lacking important methodological information that may be critical to understand the reported effects, the results are quite straightforward. However the bilingual/monolingual status and gender of the participants is not reported which might affect the relationship between language and inhibitory control.

The conclusions of the paper are supported by the data. With their design, the authors observed that, as a group, individuals with atypical organization show a mirror organization of the whole inhibitory network to the contralateral site, supporting the Causal hypothesis at the group level. However, individual data support the Statistical hypothesis, since the segregation between language and inhibition was not observed in all individuals and a variety of configurations in bilateral and bilateral organisation of language and inhibition were also observed.

The results of this study have important implications for our understanding of the independence between different cognitive functions which is crucial when addressing brain damage and rehabilitation. This aspect also indirectly speaks to researchers interested in evolution and in bilingualism and its relation to cognitive control. These aspects are not discussed but incorporating them would broaden the interest of the paper beyond the current implications mentioned.

Reviewer #1 (Public Review):

This study provides a novel in vitro model for the study of retinol transport across the human BBB by pairing iPSC-derived BMECs with the use of recombinant vitamin A serum transport proteins, RBP and TTR. Key findings of the paper include 1) the observation that the delivery mode of retinol affects its intracellular accumulation at the BBB but not its permeation across the BBB, 2) further highlighting that intracellular concentrations of retinol are also ensured by its efflux via its receptor STRA6 and 3) a potential novel role for TTR in retinol transport by upregulating LRAT mRNA expression, independently of RBP. Notably, the model appears to be more accurate than ones previously used (primary porcine BMECs) to study retinol delivery at the BBB, and could be used to study the retinol dysregulation at the BBB in neurodegenerative diseases (e.g. by using iPSC lines from NDD patients), something that miss in the paper.

Indeed, the major disappointment of this work is the clinical relevance that was highlighted in the Introduction but was never really studied in the end. iPSC from patients could be added to the study.

As a general comment, the study is well done however the introduction and the discussion as a bit long and do not get to the point of the work easily. Even sometimes losing the reader in many details (necessary here?). Less abbreviations would be appreciated for general readers.

Reviewer #1 (Public Review):

This study provides insights into the early detection of malignancies with noninvasive methods. The study contained a large sample size with external validation cohort, which raises the credibility and universality of this model. The new model achieved high levels of AUC in discriminating malignancies from healthy controls, as well as the ability to distinguish tumor of origin. Based on these findings, prospective studies are needed to further confirm its predictive capacity.

However, there are several concerns about the manuscript, which needs to be clarified or modified.

First, the use of "multimodal model" will definitely increase workload of the testing. From the results of this manuscript, the integration of multimodal data did not significantly outperform the EM-based model. Is this kind of integration necessary? Is that tool really cost-effective? The authors did not convince me of its necessity, advantages, and clinical application.

Second, the baseline characteristics of part of the enrolled patients are not clear. It seems that some of the cancer patients were diagnosed only by imaging examinations. The manuscript described "staging information was not available for 25.7% of cancer patients, who were confirmed by specialized clinicians to have non-metastatic tumors". I have no idea how did this confirmation make? According to clinicians' experience only?

Third, it seems that one of the important advantages of this new model is the low depth coverage in comparing to previous screening models for cancer. The authors should discuss more on the reason why the new model could achieve comparable predictive accuracy with an obviously lower sequencing depth.

Lastly, the readability of this manuscript needs to be improved. The focus of the background section is not clear, with too much detail of other studies and few purposeful summaries. You need to explain the goals and clinical significance of your study. In addition, the results section is too long, and needs to be shortened and simplified. Move some of the inessential results and sentences to supplementary materials or methods.

Reviewer #1 (Public Review):

The manuscript addresses a fundamental question about how different types of communication signals differentially affect brain states and neurochemistry. In addition, the manuscript highlights the various processes that modulate brain responses to communication signals, including prior experience, sex, and hormonal status. Overall, the manuscript is well-written and the research is appropriately contextualized. The authors are thoughtful about their quantitative approaches and interpretations of the data.

That being said, the authors need to work on justifying some of their analytical approaches (e.g., normalization of neurochemical data, dividing the experimental period into two periods (as opposed to just analyzing the entire experimental period as a whole)) and should provide a greater discussion of how their data also demonstrate dissociations between neurochemical release in the basolateral amygdala and behavior (e.g., neurochemical differences during both of the experimental periods but behavioral differences only during the first half of the experimental period). The normalization of neurochemical data seems unnecessary given the repeated-measures design of their analysis and could be problematic; by normalizing all data to the baseline data (p. 24), one artificially creates a baseline period with minimal variation (all are "0"; Figures 2, 3 & 5) that could inflate statistical power.

The Introduction could benefit from a priori predictions about the differential release of specific neuromodulators based on previous literature.

The manuscript would also benefit from a description of space use and locomotion in response to different valence vocalizations.

Nevertheless, the current manuscript seems to provide some compelling support for how positive and negative valence vocalizations differentially affect behavior and the release of acetylcholine and dopamine in the basolateral amygdala. The research is relevant to broad fields of neuroscience and has implications for the neural circuits underlying social behavior.

Reviewer #1 (Public Review):

In this manuscript, the authors described a computational method catELMo for embedding TCR CDR3 sequences into numeric vectors using a deep-learning-based approach, ELMo. The authors applied catELMo to two applications: supervised TCR-epitope binding affinity prediction and unsupervised epitope-specific TCR clustering. In both applications, the authors showed that catELMo generated significantly better binding prediction and clustering performance than other established TCR embedding methods. However, there are a few major concerns that need to be addressed.

1. There are other TCR CDR3 embedding methods in addition to TCRBert. The authors may consider incorporating a few more methods in the evaluation, such as TESSA (PMCID: PMC7799492), DeepTCR (PMCID: PMC7952906) and the embedding method in ATM-TCR (reference 10 in the manuscript). TESSA is also the embedding method in pMTnet, which is another TCR-epitope binding prediction method and is the reference 12 mentioned in this manuscript.

2. The TCR training data for catELMo is obtained from ImmunoSEQ platform, including SARS-CoV2, EBV, CMV, and other disease samples. Meanwhile, antigens related to these diseases and their associated TCRs are extensively annotated in databases VDJdb, IEDB and McPAS-TCR. The authors then utilized the curated TCR-epitope pairs from these databases to conduct the evaluations for eptitope binding prediction and TCR clustering. Therefore, the training data for TCR embedding may already be implicitly tuned for better representations of the TCRs used in the evaluations. This seems to be true based on Table 4, as BERT-Base-TCR outperformed TCRBert. Could catELMo be trained on PIRD as TCRBert to demonstrate catELMo's embedding for TCRs targeting unseen diseases/epitopes?

3. In the application of TCR-epitope binding prediction, the authors mentioned that the model for embedding epitope sequences was catElMo, but how about for other methods, such as TCRBert? Do the other methods also use catELMo-embedded epitope sequences as part of the binding prediction model, or use their own model to embed the epitope sequences? Since the manuscript focuses on TCR embedding, it would be nice for other methods to be evaluated on the same epitope embedding (maybe adjusted to the same embedded vector length). Furthermore, the authors found that catELMo requires less training data to achieve better performance. So one would think the other methods could not learn a reasonable epitope embedding with limited epitope data, and catELMo's better performance in binding prediction is mainly due to better epitope representation.

4. In the epitope binding prediction evaluation, the authors generated the test data using TCR-epitope pairs from VDJdb, IEDB, McPAS, which may be dominated by epitopes from CMV. Could the authors show accuracy categorized by epitope types, i.e. the accuracy for TCR-CMV pair and accuracy for TCR-SARs-CoV2 separately?

5. In the unsupervised TCR clustering evaluation, since GIANA and TCRdist direct outputs the clustering result, so they should not be affected by hierarchical clusters. Why did the curves of GIANA and TCRdist change in Figure 4 when relaxing the hierarchical clustering threshold?

6. In the unsupervised TCR clustering evaluation, the authors examined the TCR related to the top eight epitopes. However, there are much more epitopes curated in VDJdb, IEDB and McPAS-TCR. In real application, the potential epitopes is also more complex than just eight epitopes. Could the authors evaluate the clustering result using all the TCR data from the databases?

7. In addition to NMI, it is important to know how specific each TCR cluster is. Could the authors add the fraction of pure clusters in the results? Pure cluster means all the TCRs in the cluster are binding to the same epitope, and is a metric used in the method GIANA.

Joint Public Review

The manuscript by Mitra and coworkers analyses the functional role of Orai in the excitability of central dopaminergic neurons in Drosophila. The authors show that a dominant-negative mutant of Orai (OraiE180A) significantly alters the gene expression profile of flight-promoting dopaminergic neurons (fpDANs). Among them, OraiE180A attenuates the expression of Set2 and enhances that of E(z) shifting the level of epigenetic signatures that modulate gene expression. The present results also demonstrate that Set2 expression via Orai involves the transcription factor Trl. The Orai-Trl-Set1 pathway modulates the expression of VGCC, which, in turn, are involved in dopamine release. The topic investigated is interesting and timely and the study is carefully performed and technically sound; however, there are several major concerns that need to be addressed:

1- In Figure S2E, STIM is overexpressed in the absence of Set2 and this leads to rescue. It is presumed that STIM overexpression causes excess SOCE, yet this is rarely the case. Perhaps the bigger concern, however, is how excess SOCE might overcome the loss of SET2 if SET2 mediates SOCE-induced development of flight. These data are more consistent with something other than SET2 mediating this function.

2- In Figure 3, data is provided linking SET2 expression and Cch-induced Ca2+ responses. The presentation of these data is confusing. In addition, the results may be a simple side effect of SET2-dependent expression of IP3R. Given that this article is about SOCE, why isn't SOCE shown here? More generally, there are no measurements of SOCE in this entire article. Measuring SOCE (not what is measured in response to Cch) could help eliminate some of this confusion.

3- A significant gap in the study relates to the conclusion that trl is a SOCE-regulated transcription factor. This conclusion is entirely based on genetic analysis of STIMKO heterozygous flies in which a copy of the trl13C hypomorph allele is introduced. While these results suggest a genetic interaction between the expression of the two genes, the evidence that expression translates into a functional interaction that places trl immediately downstream of SOCE is not rigorous or convincing. All that can be said is that the double mutant shows a defect in flight which could arise from an interruption of the circuit. Further, it is not clear whether the trl13C hypomorph is only introduced during the critical 72-96 hour time window when the Orai1E180E phenotype shows up. The same applies to the over-expression of Set2 and the other genes. If the expression is not temporally controlled, then the phenotype could be due to the blockade of an entirely different aspect of flight neuron function.

4- In Figure 4, data is shown that SOCE compensates for the loss of Trl, the presumed mediator of SOCE-dependent flight. The fact that flight deficits are rescued by raising SOCE in the absence of Trl is very inconsistent with this conclusion.

5- In Figure 5 (A-C), data is provided that Trl transcripts are unaffected by loss of SOCE and that overexpression cannot rescue flightlessness. From this, the authors conclude that this gene "must" be calcium responsive. While that is one possibility, it is also possible that these genes are not functionally linked.

6- There is no characterization of SOCE in fpDANs from flies expressing native Orai or the dominant negative OraiE180A mutant. While the authors refer to previous studies, as the manuscript is essentially based on Orai function thapsigargin-induced SOCE should be tested using the Ca2+ add-back protocol in order to assess the release of Ca2+ from the ER in response to thapsigargin as well as the subsequent SOCE.

7- In the experiments performed to rescue flight duration in Set2RNAi individuals the authors overexpress STIM and attribute the effect to "Excess STIM presumably drives higher SOCE sufficient to rescue flight bout durations caused by deficient Set2 levels.". This should be experimentally tested as the STIM:Orai stoichiometry has been demonstrated as essential for SOCE.

8- The authors show that overexpression of OraiE108A results in Stim downregulation at a mRNA level. What about the protein level? And more important, how does OraiE108A downregulate Stim expression? Does it promote Stim degradation? Does it inhibit Stim expression?

9- Lines 271-273, the authors state "whereas overexpression of a transgene encoding Set2 in THD' neurons either with loss of SOCE (OraiE180A) or with knockdown of the IP3R (itprRNAi), lead to significant rescue of the Ca2+ response". This is attributed to a positive effect of Set2 expression on IP3R expression and the authors show a positive correlation between these two parameters; however, there is no demonstration that Set2 expression can rescue IP3R expression in cells where the IP3R is knocked down (itprRNAi). This should be further demonstrated.

10- The data presented in Figure 3E should be functionally demonstrated by analyzing the ability of CCh to release Ca2+ from the intracellular stores in the absence of extracellular Ca2+.

11- The conclusion that SOCE regulates the neuronal excitability threshold is based entirely on either partial behavioral rescue of flight, or measurements of KCl-induced Ca2+ rises monitored by GCaMP6m in DAN neurons. The threshold for neuronal excitability is a precise parameter based on rheobase measurements of action potentials in current-clamp. Measurements of slow calcium signals using a slow dye such as GCaMp6m should not be equated with neuronal excitability. What is measured is a loss of the calcium response in high K depolarization experiments, which occurs due to the loss of expression of Cav channels. Hence, the use of this term is not accurate and will confuse readers. The use of terms referring to neuronal excitability needs to be changed throughout the manuscript. As such, the conclusions regarding neuronal excitability should be strongly tempered and the data reinterpreted as there are no true measurements of neuronal excitability in the manuscript. All that can be said is that expression of certain ion channel genes is suppressed. Since both Na+ channels and K+ channel expression is down-regulated, it is hard to say precisely how membrane excitability is altered without action potential analysis.

12- Related, since trl does not contain any molecular domains that could be regulated by Ca2+ signaling, it is unclear whether trl is directly regulated by SOCE or the regulation is highly indirect. Reporter assays evaluating trl activation upon Ca2+ rises would provide much stronger and more direct evidence for the conclusion that trl is a SOCE-regulated TF. As such the evidence is entirely based on RNAi downregulation of trl which indicates that trl is essential but has no bearing on exactly what point of the signaling cascade it is involved.

13- Are NFAT levels altered in the Orai1 loss of function mutant? If not, this should be explicitly stated. It would seem based on previous literature that some gene regulation may be related to the downregulation of this established Ca2+-dependent transcription factor. Same for NFkb.

14- Does over-expression of Set2 restore ion channel expression especially those of the VGCCs? This would provide rigorous, direct evidence that SOCE-mediated regulation of VGCCs through Set2 controls voltage-gated calcium channel signaling.

15- All 6 representative panels from Figure 3B are duplicated in Figure 4G. Likewise, 2 representative panels from Figure 5H are duplicated in Figure 6D. Although these panels all represent the results from control experiments, the relevant experiments were likely not conducted at the same time and under the same conditions. Thus, control images from other experiments should not be used simply because they correspond to controls. This situation should be clarified.

16- The figures are unusually busy and difficult to follow. In part this is because they usually have many panels (Fig. 1: A-I; Fig. 2, A-J, etc) but also because the arrangement of the panels is not consistent: sometimes the following panel is found to the right, other times it is below. It would help the reader to make the order of the panels consistent, and, if possible, reduce the number of panels and/or move some of the panels to new figures.

17- As a final recommendation, the reviewers suggest that the authors a- Reword the text that refers to membrane excitability since membrane excitability was not directly measured here. b-Explain why STIM1 rescues the partial loss of flight in Set2 RNAi flies (Fig. S2E); and c- Explain how/why trl is calcium regulated and test using luciferase (or other) reporter assays whether Orai activation leads to trl activation.