Reviewer #3 (Public Review):

This work investigates the use of extracellular vesicles (EVs) in blood as a noninvasive 'liquid biopsy' to aid in the differentiation of patients with pancreatic cancer (PDAC) from those with benign pancreatic disease and healthy controls, an important clinical question where biopsies are frequently non-diagnostic. The use of extracellular vesicles as biomarkers of disease has been gaining interest in recent history, with a variety of published methods and techniques, looking at a variety of different compositions ('the molecular cargo') of EVs particularly in cancer diagnosis (Shah R, et al, N Engl J Med 2018; 379:958-966).

This study adds to the growing body of evidence in using EVs for earlier detection of pancreatic cancer, identifying both new and known proteins of interest. Limitations in studying EVs, in general, include dealing with low concentrations in circulation and identifying the most relevant molecular cargo. This study provides validation of assaying EVs using the novel EVtrap method (Extracellular Vesicles Total Recovery And Purification), which the authors show to be more efficient than current standard techniques and potentially more scalable for larger clinical studies.

The strength of this study is in its numbers - the authors worked with a cohort of 124 cases, 93 of them which were PDAC samples, which are considered large for an EV study (Jia, E et al. BMC Cancer 22, 573 (2022)). The benign disease group (n=20, between chronic pancreatitis and IPMNs) and healthy control groups (n=11) were relatively small, but the authors were not only able to identify candidate biomarkers for diagnosis that clearly stood out in the PDAC cohort, but also validate it in an independent cohort of 36 new subjects.

Proteins they have identified as associated with pancreatic cancer over benign disease included PDCD6IP, SERPINA12, and RUVBL2. They were even able to identify a set of EV proteins associated with metastasis and poorer prognosis, which include the proteins PSMB4, RUVBL2 and ANKAR and CRP, RALB and CD55. Their 7-EV protein signature yielded an 89% prediction accuracy for the diagnosis of PDAC against a background of benign pancreatic diseases that is compelling and comparable to other studies in the literature (Jia, E. et al. BMC Cancer 22, 573 (2022)).

The limitations of this study are its containment within a single institution - further studies are warranted to apply the authors' 7-EV protein PRAC panel to multiple other cases at other institutions in a larger cohort.

Reviewer #3 (Public Review):

Cancer is a disease of many faces and in particular, the ability of cancers cells to change their phenotypes and cell behaviors - cancer cell plasticity - is a major contributor to cancer lethality and therapeutic challenge of treating this disease. In this study, Nasir, Pearson et al., investigate tumor cell plasticity through the lens of invasive heterogeneity, and in particular in models of triple-negative breast cancer (TNBC), a subtype of breast cancer with particularly poor clinical prognosis and more limited treatment modalities. Using organoid models in a variety of matrix systems, microscopy, and signaling pathway inhibitors, they find that invading TNBC breast tumors, primarily in the C31-Tag genetically engineered mouse model of TNBC, are composed of heterogeneous invasive/"trailblazer" type tumor cells that in many cases express vimentin, a classical intermediate filament marker of epithelial-mesenchymal transition, and reduced keratin-14, another filament marker of basal epithelial cells associated with collective invasion in different breast cancer models. Supportive genetic and pharmacologic evidence is provided that generation of these cells is TGF-beta signaling pathway driven, likely in vivo from the surrounding tumor microenvironment, in accord with published studies in this space. Another important aspect of this study is the good transcriptional evidence for multiple migratory states showing differing degrees of partial overlap with canonical EMT programs, dependent on TGF-beta, and suggestive but at present incomplete understanding of a parallel program involving Egfr/Fra-1 mediated effects on invasion. When taken in context with other recent studies (Grasset et al. Science Translational Medicine 2022), these data are broadly supportive of concept of targeting vimentin-dependent invasion programs in TNBC tumors.

The core conclusions of this paper are generally supported by the data, but there are some conceptual and technical considerations that should be taken into account when interpreting this study. Specific comments:

1) The contribution of the different vimentin-positive trailblazer cells to distant metastasis was not directly confirmed in vivo in this study. Given the limited proliferative potential of many fully EMT'd cells and in light of recent studies indicating that invasion can be uncoupled from metastatic potential, it seems important to directly test whether the different C31-tag isolates, varying in invasive potential in this study, produce metastases and if so do metastases abundance correlate with the invasive potential in 3D culture. The collection of lungs at 34 days post injection described in methods is too short to evaluate metastatic frequency.

2) The invasion of cancer cells is dependent on 3D matrix composition. In other studies, collective cancer invasion is performed in exclusively collagen type 1 gels or in other instances entirely in 3D reconstituted basement membrane gel, e.g. lung cancer invasion studies. In this study, the authors use a mixture composed of both matrices. Given the invasion suppressive effects of matrigel, particularly for epithelial type cells, further studies would be important to determine whether the invasion phenotypes seen in this study are generalizable across matrix environments.

3) TGF-beta is well known to induce EMT. Although this study identifies potential transcriptional mediators of the invasion/trailblazer program, is this program reversible?

Reviewer #3 (Public Review):

Wu et al. present cryo-EM structures of the potassium channel Kv1.2 in open, C-type inactivated, toxin-blocked and presumably sodium-bound states at 3.2 Å, 2.5 Å, 2.8 Å, and 2.9 Å. The work builds on a large body of structural work on Kv1.2 and related voltage-gated potassium channels. The manuscript presents a large quantity of structural work on the Kv1.2 channel, and the authors should be commended on the breadth of the studies. The structural studies seem well-executed (this is hard to fully evaluate because the current manuscript is missing a data collection and refinement statistics table). The findings are mostly confirmatory, but they do add to the body of work on this and related channels. Notably, the authors present structures of DTX-bound Kv1.2 and of Kv1.2 in a low concentration of potassium (with presumably sodium ions bound within the selectivity filter). These two structures add new information, but the studies seem somewhat underdeveloped - they would be strengthened by accompanying functional studies and further structural analyses. Overall, the manuscript is well-written and a nice addition to the field.

Reviewer #3 (Public Review):

In this study, the authors investigate the effects of Notch pathway inactivation on the termination of Drosophila neuroblasts at the end of development. They find that termination is delayed, while temporal patterning progression is slowed down. Forcing temporal patterning progression in a Notch pathway mutant restores the correct timing of neuroblast elimination. Finally, they show that Imp, an early temporal patterning factor promotes Delta expression in neuroblast lineages. This indicates that feedback loops between temporal patterning and lineage-intrinsic Notch activity fine tunes timing of early to late temporal transitions and is important to schedule NB termination at the end of development.<br /> The study adds another layer of regulation that finetunes temporal progression in Drosophila neural stem cells. This mechanism appears to be mainly lineage intrinsic - Delta being expressed from NBs and their progeny, but also partly niche-mediated - Delta being also expressed in glia but with a minor influence. Together with a recent study (PMID: 36040415), this work suggests that Notch signaling is a key player in promoting temporal progression in various temporal patterning system. As such it is of broad interest for the neuro-developmental community.

Strengths<br /> The data are based on genetic experiments which are clearly described and mostly convincing. The study is interesting, adding another layer of regulation that finetunes temporal progression in Drosophila neural stem cells. This mechanism appears to be mainly lineage intrinsic - Delta being expressed from NBs and their progeny, but also partly niche-mediated - Delta being also expressed in glia but with a minor influence. A similar mechanism has been recently described, although in a different temporal patterning system (medulla neuroblasts of the optic lobe - PMID: 36040415). It is overall of broad interest for the neuro-developmental community.

Weaknesses<br /> The mechanisms by which Notch signaling regulates temporal patterning progression are not investigated in details. For example, it is not clear whether Notch signaling directly regulates temporal patterning genes, or whether the phenotypes observed are indirect (for example through the regulation of the cell-cycle speed). The authors could have investigated whether temporal patterning genes are directly regulated by the Notch pathway via ChIP-seq of Su(H) or the identification of potential binding sites for Su(H) in enhancers. A similar approach has been recently undertaken by the lab of Dr Xin Li, to show that Notch signaling regulates sequential expression of temporal patterning factors in optic lobes neuroblasts (PMID: 36040415), which exhibit a different temporal patterning system than central brain neuroblasts in the present study. As such, the mechanistic insights of the study are limited.

Reviewer #3 (Public Review):

In this study, Gray and coworkers use a transposon mutant library in order to define: (i) essential genes for K. pneumoniae growth in LB medium, (ii) genes required for growth in urine, (iii) genes required for resistance to serum, and complement-mediated killing. Although there are previous studies, using a similar strategy, to describe essential genes for K. pneumoniae growth and genes required for serum resistance, this is the first work to perform such a study in urine. This is important because these types of pathogens can cause urinary tract infections. Moreover, the authors performed the work using a highly saturated library of mutants, which makes the results more robust, and use a clinically relevant strain from a pathotype for which similar studies have not been performed yet. Besides applying the transposon mutant library coupled with high-throughput sequencing, the authors validate some of the most relevant genes required for each condition using targeted mutagenesis. This is clearly an important step to confirm that the results obtained from the library are reliable. Moreover, in vitro experiments involving complementation of urine with iron provide additional support to the results obtained with the mutants suggesting the importance of genes required for iron acquisition in a limiting-iron environment such as urine. Overall, the study is well-designed and written, and the methodology and analysis performed are adequate. The study would have benefited from in vivo experiments, including a mouse model of bacterial sepsis or urinary tract infections which could have demonstrated the role of the identified genes in the infection process. Nevertheless, the results obtained are informative for the scientific community in order to understand which genes are potentially more relevant in infections caused by K. pneumoniae. The identified genes could represent future targets for developing new therapies against a type of pathogen that is acquiring resistance to all available antibiotics. Below I include several comments regarding potential weaknesses in the methodology used:

- The study was done with biological duplicates. In vitro studies usually require 3 samples for performing statistical robust analysis. Thus, are two duplicates enough to reach reproducible results? This is important because many genes are analyzed which could lead to false positives. That said, I acknowledge that genes that were confirmed through targeted mutagenesis led to similar phenotypic results. However, what about all those genes with higher p and q values that were not confirmed? Will those differences be real or represent false positives? Could this explain the differences obtained between this and other studies?

- Two approaches are performed to investigate genes required for K.pneumoniae resistance to serum. In the first approach, the resistance to complement in serum is investigated. And here a total of 356 genes were identified to be relevant. In contrast, when genes required for overall resistance to serum are studied, only 52 genes seem to be involved. In principle, one would expect to see more genes required for overall resistance to serum and within them identify the genes required for resistance to complement. So this result is unexpected. In addition, it seems unlikely that 356 genes are involved in resistance to complement. Thus, is it possible false positives account for some of the results obtained?

Reviewer #3 (Public Review):

Eyraud and colleagues examine how fibrocytes and CD8 cells can interact with each other to promote COPD. The key findings include that CD8 cells and fibrocytes are found to exist in close proximity to each other in COPD lungs using histopathological analysis of patient samples. The authors leverage pre-existing transcriptomic data on CD8 cells to focus on chemokine release by CD8 cells as a potential pathogenic mechanism by which they could affect fibrocyte migration. In vitro studies using peripheral blood-derived CD8 cells and fibrocytes confirm increased fibrocyte migration in the presence of CD8 cells. as drivers of COPD progression. Conversely, in vitro studies show that fibrocytes exert a pro-proliferative effect on CD8 cells. The authors also use a computational model to assess how these interactions could promote the development of fibrocyte-CD8 clusters as COPD progresses over the course of 20 years.

The strengths of the study include:

1. The multi-faceted research approach that integrates histopathology from clinical COPD lung sections, in vitro co-culture studies, and computational modeling.

2. Applying computational modeling to determine how cell-cell interactions of migration and proliferation can result in distribution patterns within the lung that approximate what is found in actual clinical samples

3. Propose a feedback loop of CD8 cells and fibrocytes that could become a potential therapeutic target to interrupt a vicious cycle that promotes COPD

However, there are also some weaknesses:

1. Specificity of the role of CD8 cells: While much of the focus is on the proximity of and interactions between CD8 cells and fibrocytes, it is not clear whether other cells similarly interact with fibrocytes. For example, CD4 cells, dendritic cells, or interstitial macrophages may similarly interact with fibrocytes as several of these also release chemokines. In the absence of a more comprehensive assessment, it becomes difficult to parse out how specific and relevant the fibrocyte-CD8 cell interactions are for COPD progression when compared to other putative interactions.

2. The transcriptomic analysis which in many ways sets the stage for the chemokine studies uses a pre-existing dataset of COPD and non-COPD samples with only n=2. The robustness of such a sample size is limited and the narrow focus on chemokines or adhesion receptors of CD8 cells in this limited sample size does not provide a more comprehensive analysis that would require larger samples sizes, studying the transcriptomes of other cell types and a broader analysis of which pathways are the most likely to be dysregulated in the cells that surround fibrocytes.

3. Specificity of the findings for COPD: The in vitro studies use circulating cells which are different from lung cells and this is appropriately acknowledged by the authors. However, it appears from the description that the cells are all from COPD patients. It is therefore not clear whether these interactions between fibrocytes and CD8 cells are unique to COPD, whether they also occur between control CD8 and fibrocytes, or only in cells obtained from patients with inflammatory/pulmonary diseases. This limitation is appropriately acknowledged in the manuscript.

Reviewer #3 (Public Review):

The authors provide a detailed analysis of the sulcal and sutural imprints preserved on the natural endocast and associated cranial vault fragments of the KNM-ER3732 early Homo specimen. The analyses indicate a primitive ape-like organization of this specimen's frontal cortex. Given the geological age of around 1.9 million years, this is the earliest well-documented evidence of a primitive brain organization in African Homo.

In the discussion, the authors re-assess one of the central questions regarding the evolution of early Homo: was there species diversity, and if yes, how can we ascertain it? The specimen KNM-ER1470 has assumed a central role in this debate because it purportedly shows a more advanced organization of the frontal cortex compared to other largely coeval specimens (Falk, 1983). However, as outlined in Ponce de León et al. 2021 (Supplementary Materials), the imprints on the ER1470 endocranium are unlikely to represent sulcal structures and are more likely to reflect taphonomic fracturing and distortion. Dean Falk, the author of the 1983 study, basically shares this view (personal communication). Overall, I agree with the authors that the hypothesis to be tested is the following: did early Homo populations with primitive versus derived frontal lobe organizations coexist in Africa, and did they represent distinct species?

I greatly appreciate that the authors make available the 3D surface data of this interesting endocast.

Reviewer #3 (Public Review):

By popular single-cell RNA-seq, the authors identified FOXC2 as an undifferentiated spermatogonia-specific expressed gene. The FOXC2+-SSCs can sufficiently initiate and sustain spermatogenesis, the ablation of this subgroup results in the depletion of the uSPG pool. The authors provide further evidence to show that this gene is essential for SSCs maintenance by negatively regulating the cell cycle in adult mice, thus well-established FOXC2 as a key regulator of SSCs quiescent state.

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 #3 (Public Review):

The researchers aim add to the literature on faculty career pathways with particular attention to how gender disparities persist in the career and funding opportunities of researchers. The researchers also examine aspects of institutional prestige that can further amplify funding and career disparities. While some factors about individuals' pathways to faculty lines are known, including the prospects of certain K award recipients, the current study provides the only known examination of the K99/R00 awardees and their pathways.

Strengths:

The authors establish a clear overview of the institutional locations of K99 and R00 awardees and the pathways for K99-to-R00 researchers and the gendered and institutional patterns of such pathways. For example, there's a clear institutional hierarchy of hiring for K99/R00 researchers that echo previous research on the rigid faculty hiring networks across fields, and a pivotal difference in the time between awards that can impact faculty careers. Moreover, there's regional clusters of hiring in certain parts of the US where multiple research universities are located. Moreover, documenting the pathways of HBCU faculty is an important extension of the Wapman et al. study (among others from that research group), and provides a more nuanced look at the pathways of faculty beyond the oft-discussed high status institutions. (However, there is a need for more refinement in this segment of the analyses as discussed further below.). Also, the authors provide important caveats throughout the manuscript about the study's findings that show careful attention to the complexity of these patterns and attempting to limit misinterpretations of readers.

Weaknesses:

The authors reference institutional prestige in relation to some of the findings, but there's no specific measure of institutional prestige included in the analyses. If being identified as a top 25 NIH-funded institution is the proximate measure for prestige in the study, then more justification of how that relates to previous studies' measures of institutional prestige and status are needed to further clarify the interpretations offered in the manuscript.

The identification of institutional funding disparities impacting HBCUs is an important finding and highlights another aspect of how faculty at these institutions are under resourced and arguably undervalued in their research contributions. However, a lingering question exists: why compare HBCUs with Harvard? What are the theoretical and/or methodological justifications for such comparisons? This comparison lends itself to reifying the status hierarchy of institutions that perpetuate funding and career inequalities at the heart of the current manuscript. If aggregating all HBCU faculty together, then a comparable grouping for comparison is needed, not just one institution. Perhaps looking at the top 25 NIH funded institutions could be one way of providing a clearer comparison. Related to this point is the confusing inclusion of Gallaudet in Figure 6 as it is not an officially identified HBCU. Was this institution also included in the HBCU-related calculations?

There is a clear connection that is missed in the current iteration of the manuscript derived from the work of Robert Merton and others about cumulative advantages in science and the "Matthew effect." While aspects of this connection are noted in the manuscript such as well-resourced institutions (those with the most NIH funding in this circumstance) hire each others' K99/R00 awardees, elaborating on these connections are important for readers to understand the central processes of how a rigid hierarchy of funding and career opportunities exist around these pathways. The work the authors build on from Daniel Larremore, Aaron Clauset, and their colleagues have also incorporated these important theoretical connections from the sociology of knowledge and science, and it would provide a more interdisciplinary lens and further depth to understanding the faculty career inequalities documented in the current study.

Reviewer #3 (Public Review):

Dekraker and colleagues previously developed a new computational tool that creates a "surface representation" of the hippocampal subfields. This surface representation was previously constructed using histology from a single case. However, it was previously unclear how to best register and compare these surface-based representations to other cases with different morphology.

In the current manuscript, Dekraker and colleagues have demonstrated the ability to align hippocampal subfield parcellations across disparate 3D histology samples that differ in contrast, resolution, and processing/staining methods. In doing so, they validated the previously generated Big-Brain atlas by comparing seven different ground-truth subfield definitions. This is an impressive and valuable effort that provides important groundwork for future in vivo multi-atlas methods.

Reviewer #3 (Public Review):

Scheer and Bargmann use a combination of computational and experimental approaches in C. elegans to investigate the neuronal mechanisms underlying the regulation of foraging decisions by the state of arousal. They showed that, in C. elegans, the decision to leave food substrates is linked to a high arousal state, roaming, and that an increase in speed at different timescale preceded the food leaving decisions. They found that mutants that exhibit increased roaming also leave food substrates more frequently and that both behaviors can be triggered if food intake is inhibited. They further identify a set of chemosensory neurons that express the transduction channel tax-4 that couple the roaming state and the food-leaving decisions. The authors postulate that these neurons integrate foraging decisions with behavioral states and internal feeding cues.

The strength of the paper relies on using quantitative and detailed behavioral analysis over multiple time scales in combination with the manipulation of genes and neurons to tackle the state-dependent control of behavioral decisions in C. elegans. The evidence is convincing, the analysis rigorous, and the writing is clear and to the point.

Reviewer #3 (Public Review):

In this manuscript, a cytosolic extract of porcine oocytes is prepared. To this end, the authors have aspirated follicles from ovaries obtained by first maturing oocytes to meiose 2 metaphase stage (one polar body) from the slaughterhouse. Cumulus cells (hyaluronidase treatment) and the zona pellucida (pronase treatment) were removed and the resulting naked mature oocytes (1000 per portion) were extracted in a buffer containing divalent cation chelator, beta-mercaptoethanol, protease inhibitors, and a creatine kinase phosphocreatine cocktail for energy regeneration which was subsequently triple frozen/thawed in liquid nitrogen and crushed by 16 kG centrifugation. The supernatant (1.5 mL) was harvested and 10 microliters of it were used for interaction with 10,000 permeabilized boar sperm per 10 microliter extract (which thus represents the cytosol fraction of 6.67 oocytes).

The sperm were in this assay treated with DTT and lysoPC to prime the sperm's mitochondrial sheath.

After incubation and washing these preps were used for Western blot for Fluorescence microscopy and for proteomic identification of proteins. I am very positive about the porcine cell-free assay approach and the results presented here.

Reviewer #3 (Public Review):

In the present study by Boyle et al., the function of NPY expressing spinal neurons in pain and itch perception is studied. While the function of these neurons has been addressed previously, the difference to previous studies is the combinatorial use of AAV encoded effectors and cre transgenic mice whereas previous studies relied on cre transgenic mice and reporter mice encoding the effector or only viruses. Boyle at al. demonstrate that their strategy enabled them to restrict the analysis to only those neurons expressing NPY in the adult mouse compared to a more heterogenous population that had been studied before. By using a combination of morphology, electrophysiology and behavioral paradigms they convincingly show that NPY neurons impact pruritoception via inhibiting GRPR neurons. Furthermore, they indicate a role of NPY neurons also in nociception as activation attenuates not only responses to acute nociceptive stimuli but also blocks inflammation or nerve injury induced mechanical and heat hypersensitivity. Selectively activating NPY neurons in vivo may therefore be a promising strategy to treat neuropathic pain.

The result of this study extends and partially contrast previous studies. The authors argue that contrasting results may be due to the different experimental strategies (e.g. only neurons expressing NPY adult in the present study versus a more heterogeneous population before).

Overall, the experiments are convincing, and the quality of the data/figures is exceptionally high.

Reviewer #3 (Public Review):

This study investigated the role of Zn2+ on the maintenance of Ca2+ oscillation upon fertilization. TPEN was used to reduce the level of available Zn2+ in fertilized oocytes and different inhibitors were used to pinpoint the mechanistic involvement of intracellular Zn2+ on the maintenance of Ca2+ oscillation. As also stated in the manuscript, previous studies have demonstrated the role of Zn2+ for the successful completion of meiosis/fertilization. However, the mechanistic actions of Zn2+ on the hallmark of fertilization processes such as Ca2+ oscillation has not been elucidated. A previous publication used TPEN to cease Ca2+ oscillation, but the study was not focused on the involvement of Zn2+ signal. The manuscript expands our understanding of fertilization process by describing how the level of Zn2+ regulates Ca2+ channels and stores. The manuscript is well-organized and the topic is important in early embryo development fields.

Reviewer #3 (Public Review):

In this manuscript, Haubrich and Nader investigated the difference between mild and strong fear memory mechanisms at the circuit levels. Previous studies have shown the difference in mechanisms and functions of mild and strong fear memory. Interestingly, memory retrieval induces reconsolidation of mild fear memory, but not always strong fear memory; retrieved mild fear memory is disrupted by protein synthesis inhibition, whereas retrieved fear memory is more immune to this inhibition compared to mild memory. The authors measured c-fos expression following retrieval of mild and strong fear memories and compared functional connectivity of brain regions associated with retrieval of them using computation analyses. The authors suggested that mind and strong fear memories differ in neural networks at the circuit levels.<br /> These are interesting findings.

Major concerns:

1) Previous studies including Karim's lab have shown that protein synthesis in the hippocampus is required for the reconsolidation of contextual fear memory and that the retrieval of contextual fear memory activates gene expression such as c-fos in the hippocampus. However, the authors failed to confirm this observation. This may be due to the small number of rats or some technical problems.

2) The author's computation analyses suggested differences in neural networks activated by the retrieval of mild and strong fear memories. The results of computer analysis are interesting. However, it is not clear whether such results are actually occurring in vivo. At this moment, the author's findings are not a conclusion, but rather a suggestion or hypothesis. Therefore, it is also important to conduct interventional experiments to evaluate the validity of the authors' findings. Specifically, the authors' results could be validated by analyzing the effects of inhibition of specific brain regions on mild and strong fear memories retrieval using such as DREADD and other methods. These experiments seem hard, but would greatly improve the quality of the manuscript.

Reviewer #3 (Public Review):

This is an interesting manuscript that addresses a longstanding debate in evolutionary biology - whether social or ecological factors are primarily responsible for the evolution of the large human brain. To address this, the authors examine the relationship between the size of two prefrontal regions involved in metacognition and working memory (DLPFC and FP) and socioecological variables across 16 primate species. I recommend major revisions to this manuscript due to: 1) a lack of clarity surrounding model construction; and 2) an inappropriate treatment of the relative importance of different predictors (due to a lack of scaling/normalization of predictor variables prior to analysis). My comments are organized by section below:

Introduction:<br /> • Well written and thorough, but the questions presented could use restructuring.

Methods:<br /> • It is unclear which combinations of models were compared or why only population density and distance travelled tested appear to have been included.<br /> • Brain size (vs. body size) should be used as a predictor in the models.<br /> • It is not appropriate to compare the impact of different predictors using their coefficients if the variables were not scaled prior to analysis.

Reviewer #3 (Public Review):

Schnell and colleagues trained rats on a visual categorization task. They found that rats could discriminate objects across various image transformations. Rat performance correlated best with late convolutional layers of an artificial neural network. In contrast, human performance showed the strongest correlation with higher, fully connected layers, indicating that rats employed simpler strategies to accomplish this task as compared to humans. This is a methodologically rigorous study. The authors tested a substantial number of rats across a large variety of stimuli. One notable strength is the use of neural networks to generate stimuli with varying levels of complexity. This approach shows significant potential as a principled model for conducting studies on object recognition and other related visual behavioral phenomena. The data strongly support the conclusion that rats and humans rely on different visual features for discrimination tasks. Overall, this is a valuable study that provides novel, important insights into the visual capabilities of rats. However, some aspects of the study need further clarification. The study does not provide clear insights into the visual features that enable rats to perform these discriminations. The relationship between neural network layers and specific aspects of visual behavior is not well-defined, representing a key limitation of the current work. Further, the current analyses do not adequately address the consistency of visual behaviors across different rats or whether different rats rely on the same visual features to accomplish the task. Lastly, rodent performance was substantially lower compared to humans and generally worse than neural network classification. The factors contributing to this disparity are unclear.

Reviewer #3 (Public Review):

Nitta et al. establish a fly model of autosomal dominant optic atrophy, of which hundreds of different OPA1 mutations are the cause with wide phenotypic variance. It has long been hypothesized that missense OPA1 mutations affecting the GTPase domain, which are associated with more severe optic atrophy and extra-ophthalmic neurologic conditions such as sensorineural hearing loss (DOA plus), impart their effects through a dominant negative mechanism, but no clear direct evidence for this exists particularly in an animal model. The authors execute a well-designed study to establish their model, demonstrating a clear mitochondrial phenotype with multiple clinical analogs including optic atrophy measured as axonal degeneration. They then show that hOPA1 mitigates optic atrophy with the same efficacy as dOPA1, setting up the utility of their model to test disease-causing hOPA1 variants. Finally, they leverage this model to provide the first direct evidence for a dominant negative mechanism for 2 mutations causing DOA plus by expressing these variants in the background of a full hOPA1 complement.

Strengths of the paper include well-motivated objectives and hypotheses, overall solid design and execution, and a generally clear and thorough interpretation of their results. The results technically support their primary conclusions with caveats. The first is that both dOPA1 and hOPA1 fail to fully restore optic axonal integrity, yet the authors fail to acknowledge that this only constitutes a partial rescue nor do they discuss how this fact might influence our interpretation of their subsequent results. The second caveat is that their effect sizes are small. Statistically, the results indeed support a dominant negative effect of DOA plus-associated variants, yet the data show a marginal impact on axonal degeneration for these variants. The authors might have considered exploring the impact of these variants on other mitochondrial outcome measures they established earlier on. They might also consider providing some functional context for this marginal difference in axonal optic nerve degeneration.

Despite these caveats, the authors provide the first animal model of DOA that also allows for rapid assessment and mechanistic testing of suspected OPA1 variants. The impact of this work in providing the first direct evidence of a dominant negative mechanism is under-stated considering how important this question is in development of genetic treatments for DOA. The authors discuss important points regarding the potential utility of this model in clinical science. Comments on the potential use of this model to investigate variants of unknown significance in clinical diagnosis requires further discussion of whether there is indeed precedent for this in other genetic conditions (since the model is nevertheless so evolutionarily removed from humans).

So this forces Scope 3 in California, even if the rest of the country doesnt go ahead with it

Reviewer #3 (Public Review):

This study of CFTR, its mutants, dynamics, and effects of ATP binding, and drug binding is well written and highly informative. They have employed coarse-grained dynamics that help to interpret the dynamics in useful and highly informative ways. Overall the paper is highly informative and a pleasure to read.

The investigation of the effects of drugs is particularly interesting, but perhaps not fully formed.

Reviewer #3 (Public Review):

This paper presents several eyetracking experiments measuring task-directed reading behavior where subjects read texts and answered questions.<br /> It then models the measured reading times using attention patterns derived from deep-neural network models from the natural language processing literature.<br /> Results are taken to support the theoretical claim that human reading reflects task-optimized attention allocation.

Strengths:

1) The paper leverages modern machine learning to model a high-level behavioral task (reading comprehension). While the claim that human attention reflects optimal behavior is not new, the paper considers a substantially more high-level task in comparison to prior work. The paper leverages recent models from the NLP literature which are known to provide strong performance on such question-answering tasks, and is methodologically well grounded in the NLP literature.

2) The modeling uses text- and question-based features in addition to DNNs, specifically evaluates relevant effects, and compares vanilla pretrained and task-finetuned models. This makes the results more transparent and helps assess the contributions of task optimization. In particular, besides fine-tuned DNNs, the role of the task is further established by directly modeling the question relevance of each word. Specifically, the claim that human reading is predicted better by task-optimized attention distributions rests on (i) a role of question relevance in influencing reading in Expts 1-2 but not 4, and (ii) the fact that fine-tuned DNNs improve prediction of gaze in Expts 1-2 but not 4.

3) The paper conducts experiments on both L2 and L1 speakers.

Weaknesses:

1) The paper aims to show that human gaze is predicted the the DNN-derived task-optimal attention distribution, but the paper does not actually derive a task-optimal attention distribution. Rather, the DNNs are used to extract 144 different attention distributions, which are then put into a regression with coefficients fitted to predict human attention. As a consequence, the model has 144 free parameters without apparent a-priori constraint or theoretical interpretation. In this sense, there is a slight mismatch between what the modeling aims to establish and what it actually does.

2) While Experiment 1 tests questions from different types in blocks, and the paper mentions that this might encourage the development of question-type-specific reading strategies -- indeed, this specifically motivates Experiment 2, and is confirmed indirectly in the comparison of the effects found in the two experiments ("all these results indicated that the readers developed question-type-specific strategies in Experiment 1") -- the paper seems to miss the opportunity to also test whether DNNs fine-tuned for each of the question-types predict specifically the reading times on the respective question types in Experiment 1. Testing not only whether DNN-derived features can differentially predict normal reading vs targeted reading, but also different targeted reading tasks, would be a strong test of the approach.

3) The paper compares the DNN-derived features to word-related features such as frequency and surprisal and reports that the DNN features are predictive even when the others are regressed out (Figure S3). However, these features are operationalized in a way that puts them at an unfair disadvantage when compared to the DNNs: word frequency is estimated from the BNC corpus; surprisal is derived from the same corpus and derived using a trigram model. The BNC corpus contains 100 Million words, whereas BERT was trained on several Billions of words. Relatedly, trigram models are now far surpassed by DNN-based language models. Specifically, it is known that such models do not fit human eyetracking reading times as well as modern DNN-based models (e.g., Figure 2 Dundee in: Wilcox et al, On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior, CogSci 2020). This means that the predictive power of the word-related features is likely to be underestimated and that some residual predictive power is contained in the DNNs, which may implicitly compute quantities related to frequency and surprisal, but were trained on more data. In order to establish that the DNN models are predictive over and above word-related features, and to reliably quantify the predictive power gained by this, the authors could draw on (1) frequency estimated from the corpora used for BERT (BookCorpus + Wikipedia), (2) either train a strong DNN language model, or simply estimate surprisal from a strong off-the-shelf model such as GPT-2.

This concern does not fundamentally cast doubt on the conclusions, since the authors found a clear effect of the task relevance of individual words, which by definition is not contained in those baseline models. However, Figure S3 -- specifically Figure S3C -- is likely to inflate the contribution of the DNN model over and above the text-based features.

The results broadly support the conclusions; however, with the qualification that the paper provides a somewhat indirect test, by testing DNN-derived features without deriving a single task-optimized attention distribution for each task.

The data are likely to be useful as a benchmark in further modeling of eye-movements, an area of interest to computational research on psycholinguistics.<br /> The modeling results contribute to theoretical understanding of human reading behavior, and strengthens a line of research arguing that it reflects task-adaptive behavior.

The theoretical claim, and some basic features of the research, are quite similar to other recent work (Hahn and Keller, Modeling task effects in human reading with neural network-based attention, Cognition, 2023; cited with very little discussion as ref 44), which also considered task-directed reading in a question-answering task and derived task-optimized attention distributions. There are various differences, and the paper under consideration has both weaknesses and strengths when compared to that existing work -- e.g., that paper derived a single attention distribution from task optimization, but the paper under consideration provides more detailed qualitative analysis of the task effects, uses questions requiring more high-level reasoning, and uses more state-of-the-art DNNs.

Reviewer #3 (Public Review):

Artificial neural networks have developed into a new research tool across various disciplines of neuroscience. However, specifically for studying neural control of movement it was extremely difficult to train those models, as they require not only simulating the neural network, but also the body parts one is interested in studying. The authors provide a solution to this problem which is built upon one of the main software packages used for deep learning (Tensorflow). This allows them to make use of state-of-the-art tools for training neural networks.

They show that their toolbox is able to (re-)produce several commonly studied experiments e.g., planar reaching with and without loads. The toolbox is described in sufficient detail to get an overview of the functionality and the current state of what can be done with it. Although the authors state that only a few lines of code can reproduce such an experiment, they unfortunately don't provide any source code to reproduce their results (nor is it given in the respective repository).

The modularity of the presented toolbox makes it easy to exchange or modify single parts of an experiment e.g., the task or the neural network used as a controller. Together with the open-source nature of the toolbox, this will facilitate sharing and reproducibility across research labs.

I can see how this paper can enable a whole set of new studies on neural control of movement and accelerate the turnover time for new ideas or hypotheses, as stated in the first paragraph of the Discussion section. Having such a low effort to run computational experiments will be definitely beneficial for the field of neural control of movement.

Reviewer #3 (Public Review):

The authors have devised a clever experimental design involving the provision of cues to participants, indicating the finger that is more likely to be stimulated in each trial (e.g., ring finger or thumb). Employing fMRI analyses, the authors have leveraged the distinct and well-defined finger representations in the somatosensory cortex to investigate how prior knowledge influences the processing of haptic stimuli in a probability cueing paradigm. The authors successfully replicate key neural phenomena associated with predictive processing, encompassing expectation suppression, the sharpening of expected information representation, and the pre-activation of sensory templates associated with the anticipated stimulus. The methodology employed in this study is straightforward, and the obtained results are convincing.

However, it is worth noting that the cue-finger and finger associations were explicitly conveyed to the participants in this study. Additionally, the inter-stimulus interval (ISI) between the finger-cue and the cue varied randomly across trials, rendering the onset of the cue unpredictable (in time) for the participants. These experimental manipulations lead me to consider that the observed results may not be solely explained by predictive mechanisms but could also involve top-down controlled attention. It would be valuable for the authors to include a task similar to Experiment 2 in Kok et al. (2012), where participants' attention was diverted away from the gratings contrast, yet decoding sharpening for expected but task-irrelevant stimulus orientations was still evident. By incorporating such a task, it would help elucidate whether the authors would replicate similar results when predictive information remains intact but the predicted stimulus feature becomes task-irrelevant.

Furthermore, I have concerns regarding potential issues related to the training of the multivariate decoder. If I understand correctly, instead of using the functional localiser to train the SVM classifier, the authors directly employed the experimental data from the congruent, incongruent, and non-informative conditions together. It is noted that the number of trials used in each training fold was downsampled to achieve an equal number of trials from each condition, controlling for the asymmetry in number of trials between the incongruent and congruent conditions. However, I am concerned that if there are univariate differences between the activity patterns in the training datasets (e.g., congruent < incongruent), the decoder might exhibit a bias towards relying more on the activity of one specific condition, thereby potentially performing better in decoding that particular condition. To address this, I suggest presenting Representational Similarity Analysis (RSA) results using the activity patterns evoked by congruent, incongruent, and non-informative stimuli. This analysis would offer a simpler, more interpretable representation of changes in the representational geometry of the stimuli based on previous predictions (see Blank & Davis, 2016), and might shed some light on whether your results correspond on sharpening or dampening of the expected information.

Reviewer #3 (Public Review):

Goetz, Akl and Dixit investigated the heterogeneity in the fidelity of sensing the environment by individual cells in a population using computational modeling and analysis of experimental data for two important and well-studied mammalian signaling pathways: (insulin-like growth factor) IGF/FoxO and (epidermal growth factor) EFG/EFGR mammalian pathways. They quantified this heterogeneity using the conditional mutual information between the input (eg. level of IGF) and output (eg. level of FoxO in the nucleus), conditioned on the "state" variables which characterize the signaling pathway (such as abundances of key proteins, reaction rates, etc.) First, using a toy stochastic model of a receptor-ligand system - which constitutes the first step of both signaling pathways - they constructed the population average of the mutual information conditioned on the number of receptors and maximized over the input distribution and showed that it is always greater than or equal to the usual or "cell state agnostic" channel capacity. They constructed the probability distribution of cell state dependent mutual information for the two pathways, demonstrating agreement with experimental data in the case of the IGF/FoxO pathway using previously published data. Finally, for the IGF/FoxO pathway, they found the joint distribution of the cell state dependent mutual information and two experimentally accessible state variables: the response range of FoxO and total nuclear FoxO level prior to IGF stimulation. In both cases, the data approximately follow the contour lines of the joint distribution. Interestingly, high nuclear FoxO levels, and therefore lower associated noise in the number of output readout molecules, is not correlated with higher cell state dependent mutual information, as one might expect. This paper contributes to the vibrant body of work on information theoretic characterization of biochemical signaling pathways, using the distribution of cell state dependent mutual information as a metric to highlight the importance of heterogeneity in cell populations. The authors suggest that this metric can be used to infer "bottlenecks" in information transfer in signaling networks, where certain cell state variables have a lower joint distribution with the cell state dependent mutual information.

The utility of a metric based on the conditional mutual information to quantify fidelity of sensing and its heterogeneity (distribution) in a cell population is supported in the comparison with data. Some aspects of the analysis and claims in the main body of the paper and SI need to be clarified and extended.

1) The authors use their previously published (Ref. 32) maximum-entropy based method to extract the probability distribution of cell state variables, which is needed to construct their main result, namely p_CeeMI (I). The salient features of their method, and how it compares with other similar methods of parameter inference should be summarized in the section with this title. In SI 3.3, the Lagrangian, L, and Rm should be defined.<br /> 2) Throughout the text, the authors refer to "low" and "high" values of the channel capacity. For example, a value of 1-1.5 bits is claimed to be "low". The authors need to clarify the context in which this value is low: In some physically realistic cases, the signaling network may need to simply distinguish between the present or absence of a ligand, in which case this value would not be low.<br /> 3) Related to (2), the authors should comment on why in Fig. 3A, I_Cee=3. Importantly, where does the fact that the network is able to distinguish between 23 ligand levels come from? Is this related to the choice (and binning) of the input ligand distribution (described in the SI)?<br /> 4) The authors should justify the choice of the gamma distribution in a number of cases (eg. distribution of ligand, distribution cell state parameters, such as number of receptors, receptor degradation rate, etc.).<br /> 5) Referring to SI Section 2, it is stated that the probability of the response (receptor binding occupancy) conditioned on the input ligand concentration and number of receptors is a Poisson distribution. Indeed this is nicely demonstrated in Fig. S2. Therefore it is the coefficient of variation (std/mean) that decreases with increasing R0, not the noise (which is strictly the standard deviation) as stated in the paper.<br /> 6) In addition to explicitly stating what the input (IGF level) and the output (nuclear GFP-tagged FoxO level) are, it would be helpful if it is also stated what is the vector of state variables, theta, corresponding to the schematic diagram in Fig. 2C.<br /> 7) Related to Fig. 2C, the statement in the caption: "Phosphorylated Akt leads to phosphorylation of FoxO which effectively shuttles it out of the nucleus." needs clarification: From the figure, it appears that pFoxO does not cross the nuclear membrane, in which case it would be less confusing to say that phosphorylation prevents reentry of FoxO into the nucleus.<br /> 8) The explanations for Fig. 2D, E and insets are sparse and therefore not clear. The authors should expand on what is meant by model and experimental I(theta). What is CC input dose? Also in Fig. 2E, the overlap between the blue and pink histograms means that the value of the blue histogram for the final bin - and therefore agreement or lack thereof with the experimental result - is not visible. Also, the significance of the values 3.25 bits and 3 bits in these plots should be discussed in connection with the input distributions.<br /> 9) While the joint distribution of the cell state dependent mutual information and various biochemical parameters is given in Fig. S7, there is no explanation of what these results mean, either in the SI or main text. Related to this, while a central claim of the work is that establishing this joint distribution will allow determination of cell state variables that differentiate between high and low fidelity sensing, this claim would be stronger with more discussion of Figs. 3 and S7.<br /> 10) The related central claim that cell state dependent mutual information leads to higher fidelity sensing at the population level would be made stronger if it can be demonstrated that in the limit of rapidly varying cell state variables, the I_CSA is retrieved.

Reviewer #3 (Public Review):

This manuscript presents a novel fluorescence toolkit designed for investigating the folding states of RNA-binding proteins (RBPs) and their association with molecular chaperones during liquid-liquid phase separation (LLPS) in the formation of nuclear bodies under stress. The strategy is to use SNAP-tag technology including cell lines stably expressing three model proteins fused with SNAP tag and a series of environmentally sensitive fluorophores that can selectively label on the SNAP proteins. The changes in the microenvironment such as microviscosity and micropolarity can be well characterized by these fluorophores to reflect the conformational states of the RBPs.

The strength of this method is that the SNAP protein is smaller than classic fluorescent proteins like GFP and thus its impact on the conformation and behavior of the targeted proteins is much smaller. The experiment is carefully designed and well thought-out. Overall, this work is of very high quality.

This method can thus be adapted by other protein systems to study the LLPS process and thus I believe it will be of great interest to cell biologists and biophysicists.

Reviewer #3 (Public Review):

This paper concerns whether scaling (or homeostatic synaptic plasticity; HSP) occurs similarly at GABA and Glu synapses and comes to the surprising conclusion that these are regulated separately. This is surprising because these were thought to be co-regulated during HSP and in fact, the major mechanisms thought to underlie downscaling (TTX or CNQX driven), retinoic acid and TNF, have been shown to regulate both GABARs and AMPARs directly. (As a side note, it is unclear that the manipulations used in Josesph and Turrigiano represent HSP, and so might not be relevant). Thus the main result, that GABA HSP is dissociable from Glu HSP, is novel and exciting. This suggests either different mechanisms underlie the two processes, or that under certain conditions, another mechanism is engaged that scales one type of synapse and not the other.

However, strong claims require strong evidence, and the results presented here only address GABA HSP, relying on previous work from this lab on Glu HSP (Fong, et al., 2015). But the previous experiments were done in rat cultures, while these experiments are done in mice and at somewhat different ages (DIV). Even identical culture systems can drift over time (possibly due to changes in the components of B27 or other media and supplements). Therefore it is necessary to demonstrate in the same system the dissociation. To be convincing, they need to show the mEPSCs for Fig 4, clearly showing the dissociation. Doing the same for Fig 5 would be great, but I think Fig 4 is the key.

The paper also suggests that only receptor function or spiking could control HSP, and therefore if it is not receptor function then it must be spiking. This seems like a false dichotomy; there are of course other options. Details in the data may suggest that spiking is not the (or the only) homeostat, as TTX and CNQX causes identical changes in mIPSC amplitude but have different effects on spiking. Further, in Fig 5, CTZ had a minimal effect on spiking but a large effect on mIPSCs. Similar issues appear in Fig 6, where the induction of increased spiking is highly variable, with many cells showing control levels or lower spiking rates. Yet the synaptic changes are robust, across all cells. Overall, this is not persuasive that spiking is necessarily the homeostat for GABA synapses.

The paper also suggests that the timing of the GABA changes coincides with the spiking changes, but while they have the time course of the spiking changes and recovery, they only have the 24h time point for synaptic changes. It is impossible to conclude how the time courses align without more data.

Reviewer #3 (Public Review):

This work shows how, in the formation of the immune synapse, the B cell controls the contraction phase, the formation and retraction of actin structures concentrating the antigen (actin foci), and, ultimately, global signal attenuation. The authors use a combination of TIRF microscopy and original image quantification to show that Arp2/3 activated by N-WASP controls a pool of actin concentrated in foci (situated in the synapse), formed and transported centripetally towards the center of the synapse through myosin II mediated contractions. These contractions concentrate the B cell receptors (BCRs) in the center, promote disassembly of the stimulatory kinase Syk as well as the the disassociation from the BCR of the inhibitory phosphatase SHIP, process which entails the attenuation of the BCR signal.

The author prove their claims by mean of thorough image analysis, mainly observing and quantifying the fluorescence and the dynamics of single clusters of antigen and actin foci and analyzing two-colors dynamical images. They perform their observation in control cells, on pharmacologically perturbed cells where the action of Arp2/3 or N-WASP is inhibited, and on modified primary cells (primary derived from genetically engineered mice) to silence N-WASP or WASP. The work is sound and complete, the experiments technically excellent and well explained. Some experiments and discussions are objectively harder to describe, and given the length of the work, the reader might find itself lost some times. A graphical abstract/summary of the main way NWASP ultimately control signal attenuation would solve this minor point.

This work adds an important information to the current view of B cell activation, in particular it links the contraction phase to the actin foci that have been recently characterized. Moreover, the late phase of the immune synapse formation is, in general, poorly investigated, but it is crucial for the fate of the cell: this work provides an explanation for the attenuation of the signal that might lead to the termination of the synapse.

Reviewer #3 (Public Review):

This study investigates the roles of astrocytes in the regulation of synapse development and astrocyte morphology using conditional KO mice carrying mutations of three neuroligins1-3 in astrocytes with the deletion starting at two different time points (P1 and P10/11). The authors use morphological, electrophysiological, and cell-biological approaches and find that there are no differences in synapse formation and astrocyte cytoarchitecture in the mutant hippocampus and visual cortex. These results differ from the previous results (Stogsdill et al., 2017), although the authors make several discussion points on how the differences could have been induced. This study provides important information on how astrocytes and neurons interact with each other to coordinate neural development and function. The experiments were well-designed, and the data are of high quality.

Reviewer #3 (Public Review):

My biggest concern is that I am not convinced that the HIER task is indeed hierarchical. Based on Figure 1B, it seems that the rules of the task can be listed as "Green and same = 2", "Green and different = 4", "Red and same = 1", "Red and different = 3". If so, the hierarchical organisation intended by the authors can be trumped by simply memorising these 4 options. The rote memory explanation is even more likely given that the other, ITER task, clearly required rote memory. Hence the two tasks may vary simply in the amount of difficulty/WM involvement.

Reviewer #3 (Public Review):

The authors first explore structural differences of unbound TCR-CD3 complexes and pMHC-bound TCR-CD3 complexes with coarse-grained simulations. In the simulations with pMHC-bound complexes, the transmembrane (TM) domains of the TCR-CD3 complex and of pMHC are embedded in two opposing membrane patches. In the pMHC membrane patch, a pore is created and stabilised in the simulation setup with the aim to allow water transport in and out of the compartment between the membranes. The authors report a more upright conformation of the TCR extracellular (EC) domain in the simulations in which this EC domain is bound to pMHC, compared to simulations with unbound TCR, and postulate an allosteric signalling model based on these apparent conformational changes and associated changes in TCR-CD3 quaternary arrangements. Subsequently, the authors identify a GxxG motif in the TCRbeta connecting peptide between EC domain and TM domain as putative hinge in allosteric signalling, and explore the effect of double proline and double alanine substitutions in atomistic simulations and experiments.

While these simulation and experimental setups and the addressed questions are of interest in the field, the following weaknesses prevail in my overall assessment of the work:

(1) I am not convinced that the reported coarse-grained simulation results are sound or allow to draw the conclusions stated in the work. In the simulations with a pMHC-bound TCR-CD3 complex, the intermembrane distance in the setup shown in Figure S1 appears excessively large and likely leads to a rather strong force in the membrane-vertical direction and to the reported upright conformation of the TCR EC domain. This upright confirmation thus appears to be a consequence of force from the simulation setup, rather than a consequence of pMHC binding alone as suggested by the authors. While the membrane pore in principle allows water exchange, the relaxation of the intermembrane distance resulting from this water exchange in the 10 microsecond long simulation trajectories is not (but needs to be) addressed. This relaxation eventually would lead to an equilibrated membrane separation, in which essentially no force is exerted on the TCR-pMHC EC complex. However, I suspect that this computationally demanding equilibration is not achieved in the simulations, with the consequence that forces on the TCR-pMHC EC complex in the membrane-vertical direction remain.

In addition, I am not convinced that the Martini force field of the coarse-grained simulations allows a reliable assessment of the quaternary interactions between the TCR and CD3 EC domains. Getting protein structures and interactions right in coarse-grained simulations is notoriously difficult. In simulations with the coarse-grained Martini force field, secondary protein structures are constrained as a standard procedure, and the authors also use a recommended Go-potential procedure, likely to stabilise tertiary protein structures. The quaternary interactions between the TCR EC domain and the pMHC EC domain are modelled by rather strong harmonic constraints to prevent dissociation. While the treatment of the quaternary interactions between the TCR EC domain and the CD3 EC domains in the simulations is not (but needs to be) addressed in detail, I suspect that there are no additional, or only weak constraints to stabilise these interactions. In any case, I think that a faithful representation of these quaternary interactions is beyond the reach of the Martini force field, as is the reported diffusion of the CD3 EC domains around the TCR EC domain, which plays a central role in the allosteric mechanism proposed by the authors (see Fig 2 and 5).

(2) The allosteric model suggested by the authors is motivated in an introduction that appears to omit central controversial aspects in the field, as well as experimental evidence that is not compatible with allosteric conformational changes in the TCR. These aspects are:

- The mechanosensor model is controversial. In original versions of this model, a transversal force has been postulated to be required for T cell activation. However, more recent single-molecule force-sensor experiments reported in J Goehring et al., Nat Commun 12, 1 (2021) provide no evidence for a scenario in which transversal forces beyond 2 pN are associated with T cell activation.

- The role of catch bonds is controversial. Evidence for TCR catch bonds has been mainly obtained in experimental setups using the biomembrane force probe, in which force is applied to TCRs on the surface of T cells, but is not reproduced in experimental setups using isolated TCRs, see e.g. L Limozin et al., PNAS 116, 16943 (2019)

- Ref. 1 of the manuscript prominently discusses the kinetic segregation model of T cell activation, which is not (but needs to be) addressed in the introduction. In this model, exclusion of CD45 from close-contact zones around pMHC-bound TCRs triggers T cell activation. The model is supported by evidence from diverse experiments, see for example M Aramesh et al., PNAS 118, e2107535118 (2021) and Ref. 1. At least part of this evidence is not compatible with mechanosensing or allosteric models of T cell activation.

Reviewer #3 (Public Review):

The Liu, et al. manuscript focuses on the interesting topic of evaluating in an almost genome-wide-scale, the number of transcriptional isoforms and fusion gene are present in single cells across the annotated protein coding genome. They also seek to determine the occurrences of single nucleotide variations/mutations (SNV) in the same isoform molecule emanating from the same gene expressed in normal and normal and hepatocellular carcinoma (HCC) cells. This study has been accomplished using modified LoopSeq long‐read technology (developed by several of the authors) and single cell isolation (10X) technologies. While this effort addresses a timely and important biological question, the reader encounters several issues in their report that are problematic.:

1) Much of the analysis of the evolution of mutations results and the biological effects of the fusion genes is conjecture and is not supported by empirical data. While their conclusions leave the reader with a sense that the results obtained from the LoopSeq has substantive biological implications. However, they are extended interpretations of the data. For example: The fusion protein likely functions as a decoy interference protein that negatively impacts the microtubule organization activity of EML4.(pg 9)... and other statements presented in a similar fashion.

2) LoopSeq has the advantage of using short read sequencing analyses to characterize the exome capture results and thus benefits from low error rate compared to standard long-read sequencing techniques. However, there is no evidence obtained from standard long read sequencing that the isoforms observed with LoopSeq are obtained with parallel technologies such as long read technologies. It is not made clear how much discordance there is in comparing the LoopSeq results are with either PacBio or ONT long read technologies.

3) There is no proteome evidence (empirically derived or present in proteome databases) from the HCC and normal samples that confirms the presence or importance of the identified novel isoforms, nor is there support that indicate that changes in levels HLA genes translate to effects observed at the protein level. Since the stability and transport differences of isoforms from the same gene are often regulated at the post-transcriptional level, the biological importance of the isoform variations is unclear.

4) It is unclear why certain thresholds were chosen for standard deviation (SD) <0.4 (page 5), SD >1.0 (pg 11).

5) HLA is known to accumulate considerable somatic variation. Of the many non-immunological genes determined to have multiple isoforms what are the isoform specific mutation rates in the same isoform molecule? Are the HLA genes unique in the number of mutations occurring in the same isoform?

Reviewer #3 (Public Review):

In this paper by Keramidioti et al, the authors have characterized a polyclonal antibody from rabbit, which was raised against a peptide of the intracellular domain of the Hydra Cadherin. This antibody unexpectedly recognizes presumably all neurons in the Hydra polyp and indeed the specificity of the antibody is not fully convincing. Regardless, the antibody can be used to visualize and study the nerve net under a variety of conditions. The authors find that the endodermal and ectodermal nerve net do not make any contacts through the mesoglea, in contrast to earlier assumptions and data. They show that ectodermal neurons make close contacts to the myoepithelial muscles, in contrast to the endodermal muscles. Furthermore, they show that tentacle endoderm surprisingly does not have any neurons. Finally, a very nice tool to visualize the connections between the neurons is the staining of mosaic nGreen transgenic lines. This showed that the neurites align in parallel forming bundles of neurites over longer stretches, in particular in the ectoderm, which offers a mechanism how new neurons are added laterally to the existing nerve net. This has important implications about the way the neurons might communicate with each other.

Taken together, this paper adds to our knowledge of the Hydra nerve net and provides a new experimental tool. Although most of the study is rather descriptive the pictures are of spectacular quality, providing fascinating new insights into the arrangement and topology of the nerve net.

Reviewer #3 (Public Review):

In this study, a minimalistic setup was used to investigate the selectivity of the nuclear pore complex as a function of its diameter. For this an array of solid-state pores was designed in a free-standing palladium membrane and attached to a PDMS-based fluidic cell, which could be mounted on a confocal microscope. In this way, the frequency of translocation events could be measured in an unbiased manner, i.e., no voltage was applied in this setup to facilitate them as it was done previously (Kowalczyk et al., 2011; Ananth et al, 2018; Fragasso et al., 2021, 2022), and therefore they can be considered as spontaneous. Moreover, the pores exhibited the key properties of the nuclear pore complex: (i) the size of the pore, (ii) disordered FG Nups specifically attached in the central channel; (ii) transport receptors that can shuttle through the central channel by binding to the FG Nups. Additionally, the properties of such minimalistic system could be well controlled. This gave the authors an advantage to monitor the translocation of multiple fluorescently labeled molecules (e.g. Kap95 and BSA) simultaneously, in real time and under well controlled conditions.

Strength:<br /> By being able to adjust each system parameter independently, the authors were able to monitor a reciprocal influence of active transporters, such as Kap95, and passive diffusion (using BSA as passive cargo) at different pore sizes and protein concentrations. It was discovered that up to a certain pore size (ca. 50-60 nm, which is close to the diameter of the physiological nuclear pore complex) and the Nsp1 density, Kap95 binding in the pore significantly increases selectivity as it was previously predicted by 'Kap-centric control' model (Kapinos, et al, 2014, Wagner et al, 2015). However, in pores larger than 60 nm, this effect was fading and becoming negligible in very large pores (> 60 nm), showing that the pores could become leaky and less selective due to stretching, as has been previously suggested (Andreu et al., 2022). It was also shown that passive molecules, such as BSA, had no effect on the Kap95 translocation frequency through the pore.

The experimental data were also supported by coarse-grained modelling of Nsp1-coated pores, and the theoretical prediction correlates qualitatively with the experimentally obtained data. These simulations show that there is a relationship between pore diameter and Nsp1 conformation. Based on these simulations, the authors suggest that in small pores (<60 nm) Kap95 increases selectivity by interacting with the Nsp1-FG domains across the pore, whereas this is less likely for larger pore diameters and Kap95 may collapse the Nsp1-FG domains along the pore walls, making them more permeable.

Weaknesses:<br /> However, the simulations did not consider an effect of Kap95 on the conformation of the Nsp1 layer within the pore, which weakens the conclusion of Kap95-induced collapse, even though it seems very plausible.<br /> In addition, there is a discrepancy in the frequency of translocation events in different experimental setups reported in different studies. The authors suggest that this may be due to differences in the sensitivity of detection methods.

Strength of evidence:<br /> However, this does not detract from the results obtained in this work, as the conclusions are based on the relative changes compared to the numerous controls within the same experimental setup and a careful evaluation of all possible sources of error.

Reviewer #3 (Public Review):

Wang et al. investigated the role of acetate production, a byproduct of fatty acid oxidation, in the context of metabolic stressors, including diabetes mellitus and prolonged fasting. Mechanistically, they show the importance of the liver enzymes ACOT8 (peroxisome) and ACOT12 (cytoplasm) in converting FFA-derived acetyl-CA into acetate and CoA. The regeneration of CoA allows for subsequent fatty acid oxidation. Inhibiting the generation of acetate has negative motor and behavioral consequences in streptozocin-treated mice, which are mitigated with acetate injection.

This paper's strengths include using multiple mouse models, metabolic stressors (db/db-/-, streptozocin, and prolonged starvation), numerous cell lines, precise knockout and rescue experiments, and complimentary use of mass spectrometry and nuclear magnetic resonance analytical platforms. The presented data support the conclusions of this paper, but some aspects need to be clarified.

For example, for all animal studies, please list the age and sex of the animals at the time of the experiments. Sex and age are important biological variables that can affect metabolism, and such characteristics are needed when comparing results from different research groups.

In clinical medicine, common ketones that are measured are acetoacetate, beta-hydroxybutyrate, and acetone. However, the data presented here suggest the importance of measuring acetate when patients present with ketoacidosis in uncontrolled diabetes or starvation.

Reviewer #3 (Public Review):

One challenge with this study doing descriptive mosquito and virus work in a remote location is the uncertainly with species identification for both mosquitoes and viruses. It appears that nearly half of the mosquitoes in three of the study sites could not be identified to species. This appears problematic for the estimation of host (mosquito) richness and diversity along the anthropogenic gradient. Viral taxonomy is also complicated and this study is presenting many new viruses which, based on partial or whole sequencing, are putative novel viruses. It is not clear how many of these novel viruses would be accepted by current practices endorsed by the International Committee on Taxonomy of Viruses. The viral taxa uncertainty add complexity for the current analysis. How many of these viral lineages that cluster together are variants of the same virus? How many are unique taxonomic units? This has important consequences on the application of these data to the analyses conducted in this study.

On a related front, many of these viruses the authors are documented are mostly Insect-specific viruses (ISVs). But it also appears that several could be amplified by vertebrate hosts with poorly understood natural history and for the purposes of this study, all of the viral taxa appear to be grouped together. The inclusion of all viruses is therefore somewhat confounding given the very different natural history associated with these viruses. You frequently refer to 'hosts' throughout the MS and for ISVs, the host would likely only be mosquitoes but for arboviruses involving vertebrate amplification hosts, the hosts would be both the mosquitoes and the vertebrates. This study did not quantify any aspect of vertebrate host abundance, diversity, or richness across the gradient. Since most of this study focuses just on the ISVs as a unique system to test the hypotheses, it would be interesting if the authors restricted the analysis to just those viruses with higher probability of being restricted to mosquitoes (e.g. based on phylogenetic placement) to see if the results remain the same.

You report an anthropogenic disturbance gradient from primary forest to village habitat but how was this quantified? How is a village more disturbed than an agricultural field (rice plantation?)? The method to rank these study sites, which becomes important for the analysis, was not described in the methods. Also, along this topic of study sites, it appears you really only had one replicate of each of the study site type. To test these hypotheses on how host communities influence viral communities it would seem prudent to have had multiple replicates of each study area.

Reviewer #3 (Public Review):

This paper investigates the role of the p38g and p38d kinases in the immune response using genetically modified mice that are deficient in p38d and express a kinase-inactive form of p38g. This model avoids the possible confounding effect of the downregulation of the ERK1/2 activator Tpl2, which is observed in mice that are deficient for both p38g and p38d, making it more straightforward to determine the contribution of p38g/p38d to specific phenotypes. The mice that express kinase-inactive p38g and lack p38d show reduced susceptibility to both C. albicans infection and LPS-induced septic shock. Macrophages derived from these mice show dysregulated expression of a number of genes involved in innate immunity. Phospho-proteomics analysis identifies the transcription factor MEF2D as one of the targets of p38g/p38d in macrophages, and in vitro assays show that p38d can phosphorylate several residues of MEF2D including Ser444. Reporter assays provide evidence that a MEF2D-S444A mutant has enhanced transcriptional activity compared with the WT MEF2D, and this is also supported by analyzing the mRNA levels of MEF2D targets in fibroblasts overexpressing both proteins. Taken together, these results support that S444A phosphorylation negatively regulates MEF2D activity.

The manuscript contains a number of interesting observations supporting a role for p38g/p38d in the control of the innate immune response independently of the regulation of the Tpl2-ERK1/2 pathway. It also provides evidence that p38d but not p38g can phosphorylate MEF2D, which inhibits its transcriptional activity, and it is therefore a candidate target for some of the gene expression changes observed. Altogether, the manuscript adds new and exciting information on the functions performed by p38 MAPKs in macrophages and introduces a new mouse model that will be useful for further studies.

Reviewer #3 (Public Review):

This paper enhances our understanding of the evolution of cerebellar size and structure and is a potentially valuable addition to the recent literature on this. The examination of both the correlated evolution and divergent patterns of folding in the cerebellum and cortex may help us to understand what processes are involved and how these relate to the structural organisation at macro- and micro-levels. The study combines careful anatomical measurements based on a curated, publicly available mammalian brain collection, consideration of theoretical explanation of folding patterns, and for the most part a good comparative sample size. However, questions about the sample size arise in the authors' more complex statistical models (see below).

The main issues I have are with the statistical analyses. The authors use a standard phylogenetic approach - Phylogenetic Generalised Least Squares - which is adequate for these questions. I think the authors need to be a bit more cautious in interpreting their results in two respects.

1. The first problem relates to their use of the Ornstein-Uhlenbeck (OU) model: they try fitting three evolutionary models, and conclude that the Ornstein-Uhlenbeck model provides the best fit. However, it has been known for a while that OU models are prone to bias and that the apparent superiority of OU models over Brownian Motion is often an artefact, a problem that increases with smaller sample sizes. (Cooper et al (2016) Biological Journal of the Linnean Society, 2016, 118, 64-77,

2. Second, for the partial correlations (e.g. fig 7) and Principal Components (fig 8) there is a concern about over-fitting: there are 9 variables and only 56 data points (violating the minimal rule of thumb that there should be >10 0bservations per parameter). Added to this, the inclusion of variables lacks a clear theoretical rationale. The high correlations between most variables will be in part because they are to some extent measuring the same things, e.g. the five different measures of cerebellar anatomy which include two measures of folial size. This makes it difficult to separate their effects. I get that the authors are trying to tease apart different aspects of size, but in practice, I think these results (e.g. the presence of negative coefficients in Fig 7) are really hard or impossible to interpret. The partial correlation network looks like a "correlational salad" rather than a theoretically motivated hypothesis test. It isn't clear to me that the PC analyses solve this problem, but it partly depends on the aims of these analyses, which are not made very clear.

The claim of concerted evolution between cortical and cerebellar values (P 11-12) seems to be based on analyses that exclude body size and brain size. It, therefore, seems possible - or even likely - that all these analyses reveal overall size effects that similarly influence the cortex and cerebellum. When the authors state that they performed a second PC analysis with body and brain size removed "to better understand the patterns of neuroanatomical evolution" it isn't clear to me that is what this achieves. A test would be a model something like [cerebellar measure ~ cortical measure + rest of the brain measure], and this would deal with the problem of 'correlation salad' noted below.

It is not quite clear from fig 6a that the result does indeed support isometry between the data sets (predicted 2/3 slope), and no coefficient confidence intervals are provided.

Referencing/discussion/attribution of previous findings<br /> - With respect to the discussion of the relationship between cerebellar architecture and function, and given the emphasis here on correlated evolution with cortex, Ramnani's excellent review paper goes into the issues in considerable detail, which may also help the authors develop their own discussion: Ramnani (2006) The primate cortico-cerebellar system: anatomy and function. Nature Reviews Neuroscience 7, 511-522 (2006<br /> - The result that humans are outliers with a more folded cerebellum than expected is interesting and adds to recent findings highlighting evolutionary changes in the hominin human cerebellum, cerebellar genes, and epigenetics. Whilst Sereno et al (2020) are cited, it would be good to explain that they found that the human cerebellum has 80% of the surface area of the cortex. It would surely also be relevant to highlight some of the molecular work here, such as Harrison & Montgomery (2017). Genetics of Cerebellar and Neocortical Expansion in Anthropoid Primates: A Comparative Approach. Brain Behav Evol. 2017;89(4):274-285. doi: 10.1159/000477432. Epub 2017 (especially since this paper looks at both cerebellar and cortical genes); also Guevara et al (2021) Comparative analysis reveals distinctive epigenetic features of the human cerebellum. PLoS Genet 17(5): e1009506. https://doi.org/10.1371/journal. pgen.1009506. Also relevant here is the complex folding anatomy of the dentate nucleus, which is the largest structure linking cerebellum to cortex: see Sultan et al (2010) The human dentate nucleus: a complex shape untangled. Neuroscience. 2010 Jun 2;167(4):965-8. doi: 10.1016/j.neuroscience.2010.03.007.<br /> - The authors state that results confirm previous findings of a strong relationship between cerebellum and cortex (P 3 and p 16): the earliest reference given is Herculano-Houzel (2010), but this pattern was discovered ten years earlier (Barton & Harvey 2000 Nature 405, 1055-1058. https://doi.org/10.1038/35016580; Fig 1 in Barton 2002 Nature 415, 134-135 (2002). https://doi.org/10.1038/415134a) and elaborated by Whiting & Barton (2003) whose study explored in more detail the relationship between anatomical connections and correlated evolution within the cortico-cerebellar system (this paper is cited later, but only with reference to suggestions about the importance of functions of the cerebellum in the context of conservative structure, which is not its main point). In fact, Herculano-Houzel's analysis, whilst being the first to examine the question in terms of numbers of neurons, was inconclusive on that issue as it did not control for overall size or rest of the brain (A subsequent analysis using her data did, and confirmed the partially correlated evolution - Barton 2012, Philos Trans R Soc Lond B Biol Sci. 367:2097-107. doi: 10.1098/rstb.2012.0112.)

Reviewer #3 (Public Review):

This manuscript provides a high amount of data supporting the author's hypothesis. Serre et al aimed to address the root surface pH and the molecular factors regulating the establishment of the root surface pH pattern important for root growth and gravitropic response. The authors are able to provide solid data on the role of AUX1, AFB1, and CNGC14 in establishing an alkalic patch in the transition zone on the root surface. A weak point in the manuscript is the absence of cellular resolution. The authors mention the technical problems to assess apoplastic pH with previously published tools. They offer Fluorescein106 5-(and-6)-Sulfonic Acid, Trisodium Salt (FS) as an alternative. Even though they were able to generate valuable data with FS, bringing in cellular resolution would increase the quality of the paper even more. Overall, Serre et al provide a solid manuscript with novel data which is of high importance for the field of root and auxin biology.

Reviewer #3 (Public Review):

The comments below focus mainly on ways that the data and analysis as currently present do not to this reviewer compel the conclusions the authors wish to draw. It is possible that further analysis and/or clarification in the presentation would more persuasively bolster the authors' position. It also seems possible that a presentation with more limited conclusions but clarity on exactly what has been demonstrated and where additional future work is needed would make a strong contribution to the literature.

* Fig 3A. It might be worth emphasizing a bit more explicitly that the x-axis (delta S) is the result of a model fit to the data being shown, since this then means that if RNL model fit the data perfectly, all of the thresholds would fall at deltaS = 1. They don't, so I would like to see some evaluation from the authors' experience with this model as to whether they think the deviations (looks like the delta S range is ~0.4 to ~1.6 in Figure 4B) represent important deviations of the data from the model, the non-significant ANOVA notwithstanding. For example, Figure 4B suggests that the sign of the fit deviations is driven by the sign of the UV contrast and that this is systematic, something that would not be picked up by the ANOVA. Quite a bit is made of the deviations below, but that the model doesn't fully account for the data should be brought out here I think. As the authors note elsewhere, deviations of the data from the RNL model indicate that factors other than receptor noise are at play, and reminding the reader of this here at the first point it becomes clear would be helpful.

* Line 217 ff, Figure 4, Supplemental Figure 4). If I'm understanding what the ANOVA is telling us, it is that the deviations of the data across color directions and fish (I think these are the two factors based on line 649) is that the predictions deviate significantly from the data, relative to the inter-fish variability), for the trichromatic models but not the tetrachromatic model. If that's not correct, please interpret this comment to mean that more explanation of the logic of the test would be helpful.

Assuming that the above is right about the nature of the test, then I don't think the fact that the tetrachromatic model has an additional parameter (noise level for the added receptor type) is being taken into account in the model comparison. That is, the trichromatic models are all subsets of the tetrachromatic model, and must necessarily fit the data worse. What we want to know is whether the tetrachromatic model is fitting better because its extra parameter is allowing it to account for measurement noise (overfitting), or whether it is really doing a better job accounting for systematic features of the data. This comparison requires some method of taking the different number of parameters into account, and I don't think the ANOVA is doing that work. If the models being compared were nested linear models, than an F-ratio test could be deployed, but even this doesn't seem like what is being done. And the RNL model is not linear in its parameters, so I don't think that would be the right model comparison test in any case.

Typical model comparison approaches would include a likelihood ratio test, AIC/BIC sorts of comparisons, or a cross-validation approach.

If the authors feel their current method does persuasively handle the model comparison, how it does so needs to be brought out more carefully in the manuscript, since one of the central conclusions of the work hinges at least in part on the appropriateness of such a statistical comparison.

* Also on the general point on conclusions drawn from the model fits, it seems important to note that rejecting a trichromatic version of the RNL model is not the same as rejecting all trichromatic models. For example, a trichromatic model that postulates limiting noise added after a set of opponent transformations will make predictions that are not nested within those of RNL trichromatic models. This point seems particularly important given the systematic failures of even the tetrachromatic version of the RNL model.

* More generally, attempts to decide whether some human observers exhibit tetrachromacy have taught us how hard this is to do. Two issues, beyond the above, are the following. 1) If the properties of a trichromatic visual system vary across the retina, then by imaging stimuli on different parts of the visual field an observer can in principle make tetrachromatic discriminations even though visual system is locally trichromatic at each retinal location. 2) When trying to show that there is no direction in a tetrachromatic receptor space to which the observer is blind, a lot of color directions need to be sampled. Here, 9 directions are studied. Is that enough? How would we know? The following paper may be of interest in this regard: Horiguchi, Hiroshi, Jonathan Winawer, Robert F. Dougherty, and Brian A. Wandell. "Human trichromacy revisited." Proceedings of the National Academy of Sciences 110, no. 3 (2013): E260-E269. Although I'm not suggesting that the authors conduct additional experiments to try to address these points, I do think they need to be discussed.

* Line 277 ff. After reading through the paper several times, I remain unsure about what the authors regard as their compelling evidence that the UV cone has a higher sensitivity or makes an omnibus higher contribution to sensitivity than other cones (as stated in various forms in the title, Lines 37-41, 56-57, 125, 313, 352 and perhaps elsewhere).

At first, I thought they key point was that the receptor noise inferred via the RNL model as slightly lower (0.11) for the UV cone than for the double cones (0.14). And this is the argument made explicitly at line 326 of the discussion. But if this is the argument, what needs to be shown is that the data reject a tetrachromatic version of the RNL model where the noise value of all the cones is locked to be the same (or something similar), with the analysis taking into account the fewer parametric degrees of freedom where the noise parameters are so constrained. That is, a careful model comparison analysis would be needed. Such an analysis is not presented that I see, and I need more convincing that the difference between 0.11 and 0.14 is a real effect driven by the data. Also, I am not sanguine that the parameters of a model that in some systematic ways fails to fit the data should be taken as characterizing properties of the receptors themselves (as sometimes seems to be stated as the conclusion we should draw).

Then, I thought maybe the argument is not that the noise levels differ, but rather that the failures of the model are in the direction of thresholds being under predicted for discriminations that involve UV cone signals. That's what seems to be being argued here at lines 277 ff, and then again at lines 328 ff of the discussion. But then the argument as I read it more detail in both places switches from being about the UV cones per se to being about postive versus negative UV contrast. That's fine, but it's distinct from an argument that favors omnibus enhanced UV sensitivity, since both the UV increments and decrements are conveyed by the UV cone; it's an argument for differential sensitivity for increments versus decrements in UV mediated discriminations. The authors get to this on lines 334 of the discussion, but if the point is an increment/decrement asymmetry the title and many of the terser earlier assertions should be reworked to be consistent with what is shown.

Perhaps the argument with respect to model deviations and UV contrast independent of sign could be elaborated to show more systematically that the way the covariation with the contrasts of the other cone stimulations in the stimulus set goes, the data do favor deviations from the RNL in the direction of enhanced sensitivity to UV cone signals, but if this is the intent I think the authors need to think more about how to present the data in a manner that makes it more compelling than currently, and walk the reader carefully through the argument.

* On this point, if the authors decide to stick with the enhanced UV sensitivity argument in the revision, a bit more care about what is meant by "the UV cone has a comparatively high sensitivity (line 313 and throughout)" needs more unpacking. If it is that these cones have lower inferred noise (in the context of a model that doesn't account for at least some aspects of the data), is this because of properties of the UV cones, or the way that post-receptoral processing handles the signals from these cones mimicking a cone effect in the model. And if it is thought that it is because of properties of the cones, some discussion of what those properties might be would be helpful. As I understand the RNL model, relative numbers of cones of each type are taken into account, so it isn't that. But could it be something as simple as higher photopigment density or larger entrance aperture (thus more quantum catches and higher SNR)?

* Line 288 ff. The fact that the slopes of the psychometric functions differed across color directions is, I think, a failure of the RNL model to describe this aspect of the data, and tells us that a simple summary of what happens for thresholds at delta S = 1 does not generalize across color directions for other performance levels. Since one of the directions where the slope is shallower is the UV direction, this fact would seem to place serious limits on the claim that discrimination in the UV direction is enhanced relative to other directions, but it goes by here without comment along those lines. Some comment here, both about implications for fit of RNL model and about implications for generalizations about efficacy of UV receptor mediated discrimination and UV increment/decrement asymmetries, seems important.

* Line 357 ff. Up until this point, all of the discussion of differences in threshold across stimulus sets has been in terms of sensitivity. Here the authors (correctly) raise the possibility that a difference in "preference" across stimulus sets could drive the difference in thresholds as measured. Although the discussion is interesting and germaine, it does to some extent further undercut the security of conclusions about differential sensitivity across color directions relative to the RNL model predictions, and that should be brought out for the reader here. The authors might also discuss about how a future experiment might differentiate between a preference explanation and a sensitivity explanation of threshold differences.

* RNL model. The paper cites a lot of earlier work that used the RNL model, but I think many readers will not be familiar with it. A bit more descriptive prose would be helpful, and particularly noting that in the full dimensional receptor space, if the limiting noise at the photoreceptors is Gaussian, then the isothreshold contour will be a hyper-ellipsoid with its axes aligned with the receptor directions.

* Use of cone isolating stimuli? For showing that all four cone classes contribute to what the authors call color discrimination, a more direct approach would seem to be to use stimuli that target stimulation of only one class of cone at a time. This might require a modified design in which the distractors and target were shown against a uniform background and approximately matched in their estimated effect on a putative achromatic mechanism. Did the authors consider this approach, and more generally could they discuss what they see as its advantages and disadvantages for future work.

Reviewer #3 (Public Review):

In this manuscript, Mao et al. reported that the two proteases ECS1 and ECS2 participate in both polyspermy block and gamete fusion in Arabidopsis thaliana. The authors could observe polytubey phenotype which has been reported previously and obtain both triparental plants and haploids in ecs1 ecs2 mutants. Therefore they proposed that the triparental plants resulted from the polytubey block defect, whereas the haploids were caused by the gamete fusion defect. Together with two other previous reports, I think it is very interesting to see these two proteases participating in so many different but connected processes. Although they did not provide the molecular mechanism of how ECS participated in polyspermy block and gamete fusion, their findings provide more options for and thus promote plant breeding. The work may have a wide application in the future and will be of broad interest to cell biologists working on gamete fusion and plant breeders. Although most of the conclusions in this paper are well supported by the data, it could be improved with a minor revision including providing clearer data analysis and descriptions, images with higher resolution, and more discussions.

Reviewer #3 (Public Review):

Pentameric ligand-gated ion channels are a class of neurotransmitter receptors playing a key role in cellular communication. Besides their presence in mammalians, a multitude of receptors is found in lower organisms such as bacteria and invertebrates. They display a large diversity of molecular architectures and functions, as exemplified by atypical bacterial channels GLIC, ELIC, STELIC, or DeCLIC that have been characterized at the structural and functional levels. The study of unorthodox receptors, while challenging, is thus fascinating and is expected to give insights into the evolution, as well as the functional and structural divergence occurring in the superfamily.

In this work, authors solve the structure of the orphan receptor Alpo4 from an extreme thermophile worm Alvinella pompejana. Alpo4 is solved in two conformations, Apo and CHAPS-bound, both displaying a closed channel. The structures show several unusual features, in particular in the orthosteric site where, in the Apo, the tryptophan residues at the heart of the site lie in a place usually occupied by the neurotransmitter resembling a "self-liganded" conformation. In addition, the channel is bordered by unusual rings of hydrophobic residues in its upper part, and the protein shows substantial reorganization upon CHAPS binding. Alpo4 was previously investigated by electrophysiology but no agonist was found. Based on the structures, a number of gain-of-function mutants and chimeric constructs have been tested, but unfortunately, none are allowed to observe a ligand-gated ion channel function.

Overall, the paper is written in a very clear and fair manner, presenting the structural architecture and conformational reorganizations but also the limitation of the work concerning the lack of functional identification.

The paper constitutes a substantial amount of work (six cryo-EM structures in total). While it failed to identify an agonist and capture an open-channel conformation, the structure of a member of the family from an extreme thermophile species is novel and interesting for our fundamental knowledge of this important family of receptors.

Reviewer #3 (Public Review):

The paper by Xie et al is a modelling study of the mossy fiber-to-granule cell-to-Purkinje cell network, reporting that the optimal type of representations in the cerebellar granule cell layer depends on the type task. The paper stresses that the findings indicate a higher overall bias towards dense representations than stated in the literature, but it appears the authors have missed parts of the literature that already reported on this. While the modelling and analysis appear mathematically solid, the model is lacking many known constraints of the cerebellar circuitry, which makes the applicability of the findings to the biological counterpart somewhat limited.

I have some concerns with the novelty of the main conclusion, here from the abstract:<br /> 'Here, we generalize theories of cerebellar learning to determine the optimal granule cell representation for tasks beyond random stimulus discrimination, including continuous input-output transformations as required for smooth motor control. We show that for such tasks, the optimal granule cell representation is substantially denser than predicted by classic theories.'<br /> Stated like this, this has in principle already been shown, i.e. for example:<br /> Spanne and Jorntell (2013) Processing of multi-dimensional sensorimotor information in the spinal and cerebellar neuronal circuitry: a new hypothesis. PLoS Comput Biol. 9(3):e1002979.<br /> Indeed, even the 2 DoF arm movement control that is used in the present paper as an application, was used in this previous paper, with similar conclusions with respect to the advantage of continuous input-output transformations and dense coding. Thus, already from the beginning of this paper, the novelty aspect of this paper is questionable. Even the conclusion in the last paragraph of the Introduction: 'We show that, when learning input-output mappings for motor control tasks, the optimal granule cell representation is much denser than predicted by previous analyses.' was in principle already shown by this previous paper.

However, the present paper does add several more specific investigations/characterizations that were not previously explored. Many of the main figures report interesting new model results. However, the model is implemented in a highly generic fashion. Consequently, the model relates better to general neural network theory than to specific interpretations of the function of the cerebellar neuronal circuitry. One good example is the findings reported in Figure 2. These represent an interesting extension to the main conclusion, but they are also partly based on arbitrariness as the type of mossy fiber input described in the random categorization task has not been observed in the mammalian cerebellum under behavior in vivo, whereas in contrast, the type of input for the motor control task does resemble mossy fiber input recorded under behavior (van Kan et al 1993).

The overall conclusion states:<br /> 'Our results....suggest that optimal cerebellar representations are task-dependent.'<br /> This is not a particularly strong or specific conclusion. One could interpret this statement as simply saying: ' if I construct an arbitrary neural network, with arbitrary intrinsic properties in neurons and synapses, I can get outputs that depend on the intensity of the input that I provide to that network.'<br /> Further, the last sentence of the Introduction states: 'More broadly, we show that the sparsity of a neural code has a task-dependent influence on learning...' This is very general and unspecific, and would likely not come as a surprise to anyone interested in the analysis of neural networks. It doesn't pinpoint any specific biological problem but just says that if I change the density of the input to a [generic] network, then the learning will be impacted in one way or another.

The interpretation of the distribution of the mossy fiber inputs to the granule cells, which would have a crucial impact on the results of a study like this, is likely incorrect. First, unlike the papers that the authors cite, there are many studies indicating that there is a topographic organization in the mossy fiber termination, such that mossy fibers from the same inputs, representing similar types of information, are regionally co-localized in the granule cell layer. Hence, there is no support for the model assumption that there is a predominantly random termination of mossy fibers of different origins. This risks invalidating the comparisons that the authors are making, i.e. such as in Figure 3. This is a list of example papers, there are more:<br /> van Kan, Gibson and Houk (1993) Movement-related inputs to intermediate cerebellum of the monkey. Journal of Neurophysiology.<br /> Garwicz et al (1998) Cutaneous receptive fields and topography of mossy fibres and climbing fibres projecting to cat cerebellar C3 zone. The Journal of Physiology.<br /> Brown and Bower (2001) Congruence of mossy fiber and climbing fiber tactile projections in the lateral hemispheres of the rat cerebellum. The Journal of Comparative Neurology.<br /> Na, Sugihara, Shinoda (2019) The entire trajectories of single pontocerebellar axons and their lobular and longitudinal terminal distribution patterns in multiple aldolase C-positive compartments of the rat cerebellar cortex. The Journal of Comparative Neurology.

The nature of the mossy fiber-granule cell recording is also reviewed here:<br /> Gilbert and Miall (2022) How and Why the Cerebellum Recodes Input Signals: An Alternative to Machine Learning. The Neuroscientist<br /> Further, considering the recoding idea, the following paper shows that detailed information, as it is provided by mossy fibers, is transmitted through the granule cells without any evidence of recoding: Jorntell and Ekerot (2006) Journal of Neuroscience; and this paper shows that these granule inputs are powerfully transmitted to the molecular layer even in a decerebrated animal (i.e. where only the ascending sensory pathways remains) Jorntell and Ekerot 2002, Neuron.

I could not find any description of the neuron model used in this paper, so I assume that the neurons are just modelled as linear summators with a threshold (in fact, Figure 5 mentions inhibition, but this appears to be just one big lump inhibition, which basically is an incorrect implementation). In reality, granule cells of course do have specific properties that can impact the input-output transformation, PARTICULARLY with respect to the comparison of sparse versus dense coding, because the low-pass filtering of input that occurs in granule cells (and other neurons) as well as their spike firing stochasticity (Saarinen et al (2008). Stochastic differential equation model for cerebellar granule cell excitability. PLoS Comput. Biol. 4:e1000004) will profoundly complicate these comparisons and make them less straight forward than what is portrayed in this paper. There are also several other factors that would be present in the biological setting but are lacking here, which makes it doubtful how much information in relation to the biological performance that this modelling study provides:<br /> What are the types of activity patterns of the inputs? What are the learning rules? What is the topography? What is the impact of Purkinje cell outputs downstream, as the Purkinje cell output does not have any direct action, it acts on the deep cerebellar nuclear neurons, which in turn act on a complex sensorimotor circuitry to exert their effect, hence predictive coding could only become interpretable after the PC output has been added to the activity in those circuits. Where is the differentiated Golgi cell inhibition?

The problem of these, in my impression, generic, arbitrary settings of the neurons and the network in the model becomes obvious here: 'In contrast to the dense activity in cerebellar granule cells, odor responses in Kenyon cells, the analogs of granule cells in the Drosophila mushroom body, are sparse...' How can this system be interpreted as an analogy to granule cells in the mammalian cerebellum when the model does not address the specifics lined up above? I.e. the 'inductive bias' that the authors speak of, defined as 'the tendency of a network toward learning particular types of input-output mappings', would be highly dependent on the specifics of the network model.

More detailed comments:<br /> Abstract:<br /> 'In these models [Marr-Albus], granule cells form a sparse, combinatorial encoding of diverse sensorimotor inputs. Such sparse representations are optimal for learning to discriminate random stimuli.' Yes, I would agree with the first part, but I contest the second part of this statement. I think what is true for sparse coding is that the learning of random stimuli will be faster, as in a perceptron, but not necessarily better. As the sparsification essentially removes information, it could be argued that the quality of the learning is poorer. So from that perspective, it is not optimal. The authors need to specify from what perspective they consider sparse representations optimal for learning.

Introduction:<br /> 'Indeed, several recent studies have reported dense activity in cerebellar granule cells in response to sensory stimulation or during motor control tasks (Knogler et al., 2017; Wagner et al., 2017; Giovannucci et al., 2017; Badura and De Zeeuw, 2017; Wagner et al., 2019), at odds with classic theories (Marr, 1969; Albus, 1971).' In fact, this was precisely the issue that was addressed already by Jorntell and Ekerot (2006) Journal of Neuroscience. The conclusion was that these actual recordings of granule cells in vivo provided essentially no support for the assumptions in the Marr-Albus theories.

Results:<br /> 1st para: There is no information about how the granule cells are modelled.

2nd para: 'A typical assumption in computational theories of the cerebellar cortex is that inputs are randomly distributed in a high-dimensional space.' Yes, I agree, and this is in fact in conflict with the known topographical organization in the cerebellar cortex (see broader comment above). Mossy fiber inputs coding for closely related inputs are co-localized in the cerebellar cortex. I think for this model to be of interest from the point of view of the mammalian cerebellar cortex, it would need to pay more attention to this organizational feature.

Reviewer #3 (Public Review):

Combining slice physiology and simulation, Combe and colleagues discovered that TRPM4 channels activated by Ca2+ in nanodomains mediate ICAN currents in CA1 pyramidal neurons that drive the cholinergic modulation of firing rate. The finding is novel and interesting.

Strengths:<br /> 1) Identification of TRPM4 channels as the carrier of ICAN currents with independent pharmacological inhibitors and other supporting evidence.<br /> 2) Physiological and simulational verification of physically closely located Ca2+ source and TRPM4 channels required for ICAN activation.

Weaknesses:<br /> 1) The conclusion of the cholinergic role in down-ramp or backward firing shifts is not convincing.

Reviewer #3 (Public Review):

The authors, in their research manuscript, dissected the role of Metformin in bone healing under type-2 diabetics conditions. The authors used three classic bone fracture models to assess the impacts of Metformin in bone healing under hyperglycemic conditions. In all three models, Metformin treatment showed bone formation. At the cellular level, the authors showed the effect of Metformin on promoting bone healing using BMSCs in vitro. The authors in the paper demonstrated that Metformin promotes bone growth only in hyperglycemic conditions. The experiments were appropriately well-defined and carried out to support the role of Metformin in bone healing. The use of three different bone-defective rat models to study the role of Metformin in skeletal tissues is convincing.

Reviewer #3 (Public Review):

Dhekne et al. set out to identify novel activators of the LRRK2 kinase. They developed a flow cytometry assay to separate pools of unmodified and phosphorylated Rab10 (pRab10) from mouse NIH-3T3 cells. They then used this methodology to perform a CRISPR-based genome-wide screen to identify genes responsible for increased pRab10 levels. Candidates were validated with knock-out experiments. As far as we know, LRRK2 is the only kinase that phosphorylates the Switch II motif in Rab10. Therefore, the genes affecting pRab10 levels were classified into positive and negative LRRK2 regulators. Knocking out a positive LRRK2 regulator led to a decrease in pRab10 while knocking out a negative regulator led to an increase in pRab10. The authors found several interesting, previously unknown modulators of LRRK2 activity, including SPTLC2 and CERT1, which are involved in ceramide synthesis.

The major finding of this work is the unexpected effect of Rab12 on pRab10 levels in cells. Knocking out Rab12 resulted in a five-fold decrease in pRab10 levels. This observation was validated in an animal model. Conversely, overexpression of Rab12 led to a ten-fold increase in pRab10 levels. To exclude the possibility that other kinases were responsible for modifying Rab10, the authors overexpressed Rab12 in A549 cells lacking LRRK2; no increase in pRab10 was observed in these cells.

Dhenke et al. then used AlphaFold to model possible interaction between LRRK2 and Rab12 and identified a putative binding site for Rab12 in its Armadillo domain. This is the third Rab binding site in the domain of LRRK2. To validate this interaction, they mutated E240 and S244, both of which are involved in the interface; they observed no changes in pRab10 levels in cells expressing LRRK2 carrying the E240R and S244R mutations. The previously reported site #1 and site #2, both of which also bind Rabs and are involved in feed-forward LRRK2 activation, seem to be unrelated to the binding of Rab12 to site #3. The authors propose that site #3 might open the kinase of LRRK2 to increase its activity.

Finally, the authors point out the important role of Rab12 in lysosomal damage by showing that LLOME- or Nigericin-induced cellular stress increases LRRK2 activity in a Rab12-dependent manner.

Reviewer #3 (Public Review):

In this manuscript Fujino and colleagues used C9-ALS/FTD fly models to demonstrate that FUS modulates the structure of (G4C2) repeat RNA as an RNA chaperone, and regulates RAN translation, resulting in the suppression of neurodegeneration in C9-ALS/FTD. They also confirmed that FUS preferentially binds to and modulates the G-quadruplex structure of (G4C2) repeat RNA, followed by the suppression of RAN translation. The potential significance of these findings is high, since C9ORF72 repeat expansion is the most common genetic cause of ALS/FTD, especially in Caucasian populations and the DPR proteins have been considered the major cause of the neurodegenerations.

1) While the effect of RBP as an RNA chaperone on (G4C2) repeat expansion is supposed to be dose-dependent according to (G4C2)n RNA expression, the first experiment of the screening for RBPs in C9-ALS/FTD flies lacks this concept. It is uncertain if the RBPs of the groups "suppression (weak)" and "no effect" were less or no ability of RNA chaperone or if the expression of the RBP was not sufficient, and if the RBPs of the group "enhancement" exacerbated the toxicity derived from (G4C2)89 RNA or the expression of the RBP was excessive. The optimal dose of any RBPs that bind to (G4C2) repeats may be able to neutralize the toxicity without the reduction of (G4C2)n RNA.

2) In relation to issue 1, the rescue effect of FUS on the fly expressing (G4C2)89 (FUS-4) in Figure 4-figure supplement 1 seems weaker than the other flies expressing both FUS and (G4C2)89 in Figure 1 and Figure 1-figure supplement 2. The expression level of both FUS protein and (G4C2)89 RNA in each line is important from the viewpoint of therapeutic strategy for C9-ALS/FTD.

3) While hallmarks of C9ORF72 are the presence of DPRs and the repeat-containing RNA foci, the loss of function of C9ORF72 is also considered to somehow contribute to neurodegeneration. It is unclear if FUS reduces not only the DPRs but also the protein expression of C9ORF72 itself.

4) In Figure 5E-F, it cannot be distinguished whether FUS binds to GGGGCC repeats or 5' flanking region. Same experiment should be done by using FUS-RRMmut to elucidate whether FUS binding is the major mechanism for this translational control. Authors should show that FUS binding to long GGGGCC repeats is important for RAN translation.

5) It is not possible to conclude, as the authors have, that G-quadruplex-targeting RBPs are generally important for RAN translation (Figure 6), without showing whether RBPs which do not affect to (G4C2)89 RNA levels lead to decreased DPR protein level or RNA foci.

Reviewer #3 (Public Review):

This important work provides convincing evidence that artificial recurrent neural networks can be used to model neural activity during remapping events while an animal is moving along a one-dimensional circular track. This will be of interest to neuroscientists studying the neural dynamics of navigation and memory, as well as the community of researchers seeking to make links between artificial neural networks and the brain.

Low et al. trained artificial recurrent neural networks (RNNs) to keep track of their location during a navigation task and then compared the activity of these model neurons to the firing rates of real neurons recorded while mice performed a similar task. This study shows that a simple set of ingredients, namely, keeping track of spatial location along a one-dimensional circular track, along with storing the memory of a binary variable (representing which of the two spatial maps are currently being used), are enough to obtain model firing rates that reproduce features of real neural recordings during remapping events. This offers both a normative explanation for these neural activity patterns as well as a potential biological implementation.

One advantage of this modeling approach using RNNs is that this gives the authors a complete set of firing rates that can be used to solve the task. This makes analyzing these RNNs easier, and opens the door for analyses that are not always practical with limited neural data. The authors leverage this to study the stable and unstable fixed points of the model. However, in this paper there appear to be a few places where analyses that were performed on the RNNs were not performed on the neural data, missing out on an opportunity to appreciate the similarity, or identify differences and pose challenges for future modeling efforts. For example, in the neural data, what is the distribution of the differences between the true remapping vectors for all position bins and the average remapping vector? What is the dimensionality of the remapping vectors? Do the remapping vectors vary smoothly over position? Do the results based on neural data look similar to the results shown for the RNN models (Figures 2C-E)?

There are many choices that must be made when simulating RNNs and there is a growing awareness that these choices can influence the kinds of solutions RNNs develop. For example, how are the parameters of the RNN initialized? How long is the RNN trained on the task? Are the firing rates encouraged to be small or smoothly varying during training? For the most part these choices are not explored in this paper so I would interpret the authors' results as highlighting a single slice of the solution space while keeping in mind that other potential RNN solutions may exist. For example, the authors note that the RNN and biological data do not appear to solve the 1D navigation and remapping task with the simplest 3-dimensional solution. However, it seems likely that an RNN could also be trained such that it only encodes the task relevant dynamics of this 3-dimensional solution, by training longer or with some regularization on the firing rates. Similarly, a higher-dimensional RNN solution may also be possible and this would likely be necessary to explain the more variable manifold misalignment reported in the experimental data of Low et al. 2021 as opposed to the more tightly aligned distribution for the RNNs in this paper. However, thanks to the modeling work done in this paper, the door has now been opened to these and many other interesting research directions.

This piqued my interest because I wonder how there are so many volunteers on Wikipedia. It raises questions like, why are they willingly patrolling the site and making sure there is no vandalism or inaccurate information? What is in it for them? Since it says volunteers I assume there are so rewards for these people so is it just good morals or boredom? I attached a picture of a chart showing the increase in editors after COVID. I think during COVID many people were bored so they decided to take on volunteering on Wikipedia and afterwards maybe it became a hobby.

Reviewer #3 (Public Review):

Mahlandt et al. report the design and proof of concept of Opto-RhoGEF, a new set of molecular tools to control the activation by light of the three best known members of the Rho GTPase family, RhoA, Rac1 and Cdc42.

The study is based on the optogenetically-controlled activation of chimeric proteins that target to the plasma membrane guanine nucleotide exchange factors (GEFs) domains, which are natural activators specific for each of these three Rho GTPases. Membrane-targeted GEFs encounter and activate endogenous Rho proteins. Further investigation on the effect of these tools on RhoGTPase signaling would have strengthened the report.

These three Opto-RhoGEFs are reversible and enable the precise spatio-temporal control of Rho-regulated processes, such as endothelial barrier function, cell contraction and plasma membrane extension. Hence, these molecular tools will be of broad interest for cell biologists interested in this family of GTPases.

Mahlandt et al. design and characterize three new optogenetic tools to artificially control the activation of the RhoA, Rac1 and Cdc42 by light. These three Rho GTPases are master regulators of the actin cytoskeleton, thereby regulating cell-cell contact stability or actin-mediated contraction and membrane protrusions.

The main strength of this new experimental resource lies in the fact that, to date, few tools controlling Rho activation by reversibly targeting Rho GEFs to the plasma membrane are available. In addition, a comparative analysis of the three Opto-RhoGEFs adds value and further strengthens the results, given the fact that each Opto-GEF produces different (and somehow expected) effects, which suggest specific GTPase activation. The design of the tools is correct, although the membrane targeting could be improved, since the Lck N-terminus used to construct the recombinant proteins contains myristoylation and palmitoylation sites, which has the potential to target the chimeric protein to lipid rafts. As a consequence, this may not evenly translocate these Rho-activating domains.

An additional technical feature that must be highlighted is an elegant method to activate Opto-RhoGEFs in cultured cells, independent of laser and microscopes, by using led strips, which notably expands the possibilities of this resource, potentially allowing biochemical analyses in large numbers of cells.

The experimental evidence clearly indicates that authors have achieved their aim and designed very useful tools. However, they should have taken more advantage of this remarkable technical advance and investigate in further detail the spatiotemporal dynamics of Rho-mediated signaling. Although the manuscript is a "tool and resource", readers may have better grasped the potential benefits of tuning GTPase activity with this tool by learning about some original and quantitative insights of RhoA, Rac1 and Cdc42 function.

One of such insights may have come from the set of data regarding the contribution of adherens junctions. The effect of other endothelial cell-cell junctions, such as tight junctions, may also contribute to barrier function, as well as junctional independent, cell-substratum adhesion. These optogenetic tools will undoubtedly impact on these future studies and help decipher whether these other adhesion events that are important for endothelial barrier integrity are also under control of these three GTPases. Overall, the manuscript is sound and presents new and convincing experimental strategies to apply optogenetics to the field of Rho GTPases.

Reviewer #3 (Public Review):

This paper proposes a computational account for the phenomenon of pattern differentiation (i.e., items having distinct neural representations when they are similar). The computational model relies on a learning mechanism of the nonmonotonic plasticity hypothesis, fast learning rate and inhibitory oscillations. The relatively simple architecture of the model makes its dynamics accessible to the human mind. Furthermore, using similar model parameters, this model produces simulated data consistent with empirical data of pattern differentiation. The authors also provide insightful discussion on the factors contributing to differentiation as opposed to integration. The authors may consider the following to further strengthen this paper:

The model compares different levels of overlap at the hidden layer and reveals that partial overlap seems necessary to lead to differentiation. While I understand this approach from the perspective of modeling, I have concerns about whether this is how the human brain achieves differentiation. Specifically, if we view the hidden layer activation as a conjunctive representation of a pair that is the outcome of encoding, differentiation should precede the formation of the hidden layer activation pattern of the second pair. Instead, the model assumes such pattern already exists before differentiation. Maybe the authors indeed argue that mechanistically differentiation follows initial encoding that does not consider similarity with other memory traces?

Related to the point above, because the simulation setup is different from how differentiation actually occurs, I wonder how valid the prediction of asymmetric reconfiguration of hidden layer connectivity pattern is.

Although as the authors mentioned, there haven't been formal empirical tests of the relationship between learning speed and differentiation/integration, I am also wondering to what degree the prediction of fast learning being necessary for differentiation is consistent with current data. According to Figure 6, the learning rates lead to differentiation in the 2/6 condition achieved differentiation after just one-shot most of the time. On the other hand, For example, Guo et al (2021) showed that humans may need a few blocks of training and test to start showing differentiation.

Related to the point above, the high learning rate prediction also seems to be at odds with the finding that the cortex, which has slow learning (according to the theory of complementary learning systems), also shows differentiation in Wammes et al (2022).

More details about the learning dynamics would be helpful. For example, equation(s) showing how activation, learning rate and the NMPH function work together to change the weight of connections may be added. Without the information, it is unclear how each connection changes its value after each time point.

In the simulation, the NMPH function has two turning points. I wonder if that is necessary. On the right side of the function, strong activation leads to strengthening of the connectivity, which I assume will lead to stronger activation on the next time point. The model has an upper limit of connection strength to prevent connection from strengthening too much. The same idea can be applied to the left side of the function: instead of having two turning points, it can be a linear function such that low activation keeps weakening connection until the lower limit is reached. This way the NMPH function can take a simpler form (e.g., two line-segments if you think the weakening and strengthening take different rates) and may still simulate the data.

Reviewer #3 (Public Review):

Kandola et al. explore the important and difficult question regarding the initiating event that triggers (nucleates) amyloid fibril growth in glutamine-rich domains. The researchers use a fluorescence technique that they developed, dAMFRET, in a yeast system where they can manipulate the expression level over several orders of magnitude, and they can control the length of the polyglutamine domain as well as the insertion of interfering non-glutamine residues. Using flow cytometry, they can interrogate each of these yeast 'reactors' to test for self-assembly, as detected by FRET.

In the introduction, the authors provide a fairly thorough yet succinct review of the relevant literature into the mechanisms of polyglutamine-mediated aggregation over the last two decades. The presentation as well as the illustrations in Figure 1A and 1B are difficult to understand, and unfortunately, there is no clear description of the experimental technique that would allow the reader to connect the hypothetical illustrations to the measurement outcomes. The authors do not explain what the FRET signal specifically indicates or what its intensity is correlated to. FRET measures distance between donor and acceptor, but can it be reliably taken as an indicator of a specific beta-sheet conformation and of amyloid? Does the signal increase with both nucleation and with elongation, and is the signal intensity the same if, e.g., there were 5 aggregates of 10 monomers each versus 50 monomeric nuclei? Is there a reason why the AmFRET signal intensity decreases at longer Q even though the number of cells with positive signal increases? Does the number of positive cells increase with time? The authors state later that 'non-amyloid containing cells lacked AmFRET altogether', but this seems to be a tautology - isn't the lack of AmFRET taken as a proof of lack of amyloid? Overall, a clearer description of the experimental method and what is actually measured (and validation of the quantitative interpretation of the FRET signal) would greatly assist the reader in understanding and interpreting the data.

The authors demonstrate that their assay shows that the fraction of cells with AmFRET signal increases strongly with an increase in polyQ length, with a 'threshold around 50-60 glutamines. This roughly correlates with the Q-length dependence of disease. The experiments in which asparagine or other amino acids are inserted at variable positions in the glutamine repeat are creative and thorough, and the data along with the simulations provide compelling support for the proposed Q zipper model. The experiments shown in Figure 5 are strongly supportive of a model where formation of the beta-sheet nucleus is within a monomer. This is a potentially important result, as there are conflicting data in the literature as to whether the nucleus in polyQ is monomer.

I did not find the argument, that their data shows the Q zipper grows in two dimensions, compelling; there are more direct experimental methods to answer this question. I was also confused by the section that Q zippers poison themselves. It would be easier for the reader to follow if the authors first presented their results without interpretation. The data seem more consistent with an argument that, at high concentrations, non-structured polyQ oligomers form which interfere with elongation into structured amyloid assemblies - but such oligomers would not be zippers.

Although some speculation or hypothesizing is perfectly appropriate in the discussion, overall the authors stretch this beyond what can be supported by the results. A couple of examples: The conclusion that toxicity arises from 'self-poisoned polymer crystals' is not warranted, as there is no relevant data presented in this manuscript. The authors refer to findings 'that kinetically arrested aggregates emerge from the same nucleating event responsible for amyloid formation', but I cannot recall any evidence for this statement in the results section.

Reviewer #3 (Public Review):

The manuscript by Kairouani et al. investigates the function of a small family of plant RNA binding proteins with similarity to the well-studied Musashi protein in animals, and, therefore, called MUSASHI-LIKE1-4 (MSL1-4). Studies on the biological importance of post-transcriptional control of gene expression via RNA-binding proteins in plants are not numerous, and advances in this important field are much needed. The thorough work presented in this manuscript is such an advance.

The central observations of the paper are:

- Knockout of any MSL gene alone does not produce a phenotype.<br /> It is of note that basic characterization of knockout mutations is really well done - for example, the authors have taken care to raise specific antibodies to each of the MSL proteins and use them to demonstrate that each of the T-DNA insertion mutants used actually does knock out protein production from the corresponding gene.

- Knockout of MSL2/4 (but no other double mutant) produces a clear leaf phenotype, and a remarkable stem phenotype in which the mutants collapse as they are unable to support upright growth

- The phenotypes of knockout mutants persist in point mutants defective in RNA-binding, indicating that RNA-binding is required for biological activity. Consistent with this, and associate physically with other RNA-binding proteins and translation factors.

- MSL proteins are cytoplasmic

- The msl2/4 mutants present multiple defects in secondary cell wall composition and structure, probably explaining their inability to grow upright. I did not examine the cell wall analyses in detail as I am no specialist in this field.

- Msl2/4 mutants show transcriptomic changes with at large two big categories of differentially expressed genes compared to wild type.<br /> (1) Genes related to cell wall metabolism<br /> (2) Genes associated with defense against herbivores and pathogens

- Two of the mRNAs encoding cell wall factors with significant upregulation in msl2/4 mutants compared to wild type also associate physically with MSL4 as judged by RNA-immunoprecipitation-RT-PCR assays, and this physical association is abrogated in the RNA-binding deficient MSL4 mutant.

Altogether, the study shows clear biological relevance of the MSL family of RNA-binding proteins, and provides good arguments that the underlying mechanism is control of mRNAs encoding enzymes involved in secondary cell wall metabolism (although concluding on translational control in the abstract is perhaps saying too much - post-transcriptional control will do given the evidence presented). One observation reported in the study makes it vulnerable to alternative interpretation, however, and I think this should be explicitly treated in the discussion:

The fact that immune responses are switched on in msl2/4 mutants could also mean that MSL2/4 have biological functions unrelated to cell wall metabolism in wild type plants, and that cell wall defects arise solely as an indirect effect of immune activation (that is known to involve changes in expression of many cell wall-modifying enzymes and components such as pectin methylesterases, xyloglucan endotransglycosylases, arabinogalactan proteins etc. Indeed, the literature is rich in examples of gene functions that have been misinterpreted on the basis of knockout studies because constitutive defense activation mediated by immune receptors was not taken into account (see for example Lolle et al., 2017, Cell Host & Microbe 21, 518-529).

With the evidence presented here, I am actually close to being convinced that the primary defect of msl2/msl4 mutants is directly related to altered cell wall metabolism, and that defense responses arise as a consequence of that, not the other way round. But I do not think that the reverse scenario can be formally excluded with the evidence at hand, and a discussion listing arguments in favor of the direct effect proposed here would be appropriate. Elements that the authors could consider to include would be the isolation of a cellulose synthase mutant as a constitutive expressor of jasmonic acid responses (cev1) as a clear example that a primary defect in cell wall metabolism can produce defense activation as secondary effect. The interaction of MSL4 with GXM1/3 mRNAs is also helpful to argue for a direct effect, and it would strengthen the argument if more examples of this kind could be included.

Reviewer #3 (Public Review):

The manuscript by Daly et al examines endosomal signaling of the vasopressin type 2 receptors using engineered mini G protein (mG proteins) and a number of novel techniques to address if sustained G protein signaling in the endosomal compartment is enhanced by β arrestin. Employing these interesting techniques they have how V2R could activates Gαs and Gα in the endosomal compartments and how this modulation could occur in arrestin dependent and independent manner. Although the phenomenon of endosomal signaling is complex to address the authors have tried their best to examine these using a number of well controlled set of experiments.

https://www.imdb.com/title/tt1568150/

Based on having watched the documentary Joan Rivers: A Piece of Work and the depictions of Rivers' card index in the film and using her hands and a lateral file for scale, her cards seem to have been 3 x 5" index cards.

cross reference: https://hypothes.is/a/RvLTZjCQEe2uuaNwpTBNuA

Reviewer #3 (Public Review):

The spindle checkpoint ensures the accuracy of chromosome segregation by sensing unattached kinetochores during mitosis and meiosis and delays the onset of anaphase. Unattached kinetochores catalyze the conformational activation of the latent open MAD2 (O-MAD2) to the active closed MAD2 (C-MAD2). C-MAD2 is then incorporated into the mitotic checkpoint complex (MCC), which inhibits the anaphase-promoting complex or cyclosome (APC/C) to delay anaphase. When all kinetochores are properly unattached, the MAD2-binding protein p31comet and the ATPase TRIP13 extract C-MAD2 from the MCC, leading to MCC disassembly and the conversion of C-MAD2 back to O-MAD2. This action turns off the spindle checkpoint, resulting in APC/C activation and anaphase onset. Cells deficient in p31comet exhibit mitotic delays.

In the current study, Huang et al. have linked p31comet mutations to female infertility. Biallelic loss-of-function alleles of p31comet cause delays in the exiting metaphase of meiosis I and polar body extrusion. The p31comet mutant proteins contain C-terminal truncations and fail to bind to MAD2. Reintroducing full-length p31comet into patient oocytes can bypass the metaphase arrest. Together with a previous study that showed biallelic mutations of TRIP13 caused female infertility, this work established a critical role of the p31comet-TRIP13 module in regulating meiotic progression during oogenesis. As such, this is a significant study.

Reviewer #3 (Public Review):

In this work, Eccleston et. al. use a computational method involving the Rosetta (Flex ddG) suite to infer epistasis in binding free energy changes for combinatorial sets of mutations in the DHFR gene and the drug pyrimethamine. They use this to estimate the most likely path of stepwise mutation accumulation in the evolution of antimalarial drug resistance. The authors also infer likely pathways from different geographical regions from isolated data using a method based on mutation frequencies. They report that these results are broadly consistent with their computational predictions as well.

In contrast to machine learning approaches, the Rosetta Flex ddG method uses physical models at the atomic scale to compute various macromolecular properties. The present paper, therefore, uses atomic-scale molecular properties to make predictions at the population level. As acknowledged by the authors, their method has the limitation that chemical factors other than the free energy changes are largely ignored, as are complications arising from complex population dynamics. Nonetheless, there is reasonable agreement between their predictions and the experimental data, especially at high drug concentrations.

The authors also infer likely trajectories of mutation acquisition from isolate data from various parts of the world. The inference method is based on a simple ranking scheme of mutation frequencies. It is difficult to gauge the reliability of this method, given the complexity of infectious disease dynamics, including confounding factors introduced by varied drug treatment regimens. However, predictions from the computational method are still able to capture some of the general trends in the inferred pathways from isolates, inspiring some confidence in both approaches. The authors emphasize the importance of geographic variation in evolutionary pathways, but their computational method is limited in its ability to provide quantitative insights into the origins of such variation.

A few limitations of the work should be mentioned. It suffers from a lack of summary metrics that quantify the performance of its computational method, which is important for a clearer understanding of its accuracy. While the work is a useful indicator of the potential usefulness of the Rosetta Flex ddG method in enabling evolutionary predictions through macromolecular modeling, the method is applied to a well-studied system and the work remains limited in the novelty of the insights it generates into the dynamics of the evolution of antimalarial drug resistance.

Reviewer #3 (Public Review):

The authors of this study have examined which cation channels specifically confer to ventral tegmental area dopaminergic neurons their autonomic (spontaneous) firing properties. Having brought evidence for the key role played by NALCN and TRPC6 channels therein, the authors aimed at measuring whether these channels play some role in so-called depression-like (but see below) behaviors triggered by chronic exposure to different stressors. Following evidence for a down-regulation of TRPC6 protein expression in ventral tegmental area dopaminergic cells of stressed animals, the authors provide evidence through viral expression protocols for a causal link between such a down-regulation and so-called depression-like behaviors. The main strength of this study lies on a comprehensive bottom-up approach ranging from patch-clamp recordings to behavioral tasks. However, the interpretation of the results gathered from these behavioral tasks might also be considered one main weakness of the abovementioned approach. Thus, the authors make a confusion (widely observed in numerous publications) with regard to the use of paradigms (forced swim test, tail suspension test) initially aimed (and hence validated) at detecting the antidepressant effects of drugs and which by no means provide clues on "depression" in their subjects. Indeed, in their hands, the authors report that stress elicits changes in these tests which are opposed to those theoretically seen after antidepressant medication. However, these results do not imply that these changes reflect "depression" but rather that the individuals under scrutiny simply show different responses from those seen in nonstressed animals. These limits are even more valid in nonstressed animals injected with TRPC6 shRNAs (how can 5-min tests be compared to a complex and chronic pathological state such as depression?). With regard to anxiety, as investigated with the elevated plus-maze and the open field, the data, as reported, do not allow to check the author's interpretation as anxiety indices are either not correctly provided (e.g. absolute open arm data instead of percents of open arm visits without mention of closed arm behaviors) or subjected to possible biases (lack of distinction between central and peripheral components of the apparatus).

Reviewer #3 (Public Review):

In this study, authors used the Drosophila model to characterize molecular details underlying traumatic brain injury (TBI). The authors used the transcriptomic analysis of astrocytes collected by FACS sorting of cells derived from Drosophila heads following brain injury and identified upregulation of multiple genes, such as Pvr receptor, Jun, Fos, and MMP1. Additional studies identified that Pvr positively activates AP-1 transciption factor (TF) complex consisting of Jun and Fos, of which activation leads to the induction of MMP1. Finally, authors found that disruption of endocytosis and endocytotic trafficking facilitates Pvr signaling and subsequently leads to induction of AP-1 and MMP1.

Overall, this study provides important clues to understanding molecular mechanisms underlying TBI. The identified molecules linked to TBI in astrocytes could be potential targets for developing effective therapeutics. The obtained data from transcriptional profiling of astrocytes will be useful for future follow-up studies. The manuscript is well-organized and easy to read. However, I would like to request the authors to address the following issue to improve the quality of their study.

It is unclear why the authors did not explore the involvement of the JNK pathway in their study. While they described the potential involvement of the JNK pathway based on previous literature, they did not include any evidence on the JNK pathway in their own study.

It is important to note that the mechanism by which JNK activates AP-1 is primarily through phosphorylation, not the quantitative control of amounts, as much as I know. This raises questions about the authors' proposed hierarchical relationship between Pvr and AP-1 and the potential involvement of the JNK pathway in mediating this relationship.

Given the significance of the mechanistic link between Pvr and AP-1 in solidifying the authors' conclusion, it would have been beneficial for them to explore the involvement of the JNK pathway in their study, even if only minimally. The lack of such exploration may weaken the overall strength of their findings and the potential implications for understanding TBI.

a person who breaks a law or moral rule:

in a way that proudly refuses to obey authority 對抗地；對立地；違抗地

an act that is criminal or bad 違法行為；罪行；不端行為

a formal agreement or promise between two or more people盟約；契約；協定；承諾

the act of having sex 性交，交媾

a wheeled vehicle for living or travelling in, especially for holidays, that contains beds and cooking equipment and can be pulled by a car （尤指度假時使用，由汽車拖曳的）宿營拖車，旅行拖車

to make something appear by magic, or as if by magic 變戲法；用魔法變出；像變魔術般變出

a man who takes care of a large group of animals of the same type 放牧人

If an animal such as a dog fawns on/upon you, it is very friendly towards you and rubs itself against you.（狗等動物）向…搖尾乞憐

a feeling of great pleasure and attraction, especially because something is very beautiful 陶醉，入迷

Reviewer #3 (Public Review):

This work provides new insights into the regulation of the intracellular effector protein Calcineurin B homologous protein 3 (CHP3). The authors precisely delineate how intracellular calcium signals and myristoylation affect the binding of CHP3 to lipid membranes and the sodium/proton exchanger NHE1. Different mechanisms are known to trigger the exposure of the myristoyl-moiety in the calcium-binding protein family and CHP3 was proposed to use a "calcium-myristoyl switch", which leads to exposure of the myristoyl group due to conformational changes in the protein triggered by calcium-binding. Becker and Fuchs et al. now demonstrate that CHP3 uses a novel mechanism, in which not calcium-binding but binding to the target protein NHE1 triggers exposure of its myristoyl-group. This paper represents a detailed functional characterization of CHP3 and the maximum level of mechanistic interpretation that can be achieved without high-resolution structural information.

The conclusions of this paper are fully supported by the data.

Strengths<br /> The protein biochemistry is of an exceptionally high level, both with respect to the quality of the material and the stringency with which the authors assess and assure the protein quality. The authors purify CHP3 without any affinity tags, and thus in its most representative relevant state. Their validations indicate that complete myristoylation of CHP3 is achieved and that all protein is functional with respect to calcium binding.

The authors go to extensive lengths to convince themselves of the quality of their data and their interpretation. They use an extensive amount of replicates, including both biological and technical replicates. Assays and experimental procedures are verified using model proteins, such as Recoverin. In addition, the authors employ an extensive set of complementary approaches to assure their observations are universal.

Weaknesses<br /> A small weakness is the fact that the interpretation in terms of mechanistic insights contributed by some of the assays employed is rather limited, resulting in comparably unprecise descriptions of the state of the protein such as "affects the conformation and/or flexibility of CHP3" or the "open" and "closed" conformations. As indicated by the authors, structural studies are required to precisely detail the conformational states and delineate their mechanism of action.

The authors imply that the major form of CHP3 is the myristoylated state. However, it remains unclear whether the source of the biological material, which appears to be membrane-only, already implies a significant experimental bias that only allows (or highly favors) the identification of myristoylated CHP3. Without a calcium-signal, unmyristoylated CHP may not associate with membranes, or be less strong, resulting in its depletion upon isolation of the vesicles.

Reviewer #3 (Public Review):

The authors present a study of 13000, Salmonella Typhi genomes from across the globe. Here, they present an overview of the global genomic epidemiology of Salmonella Typhi, in the context of the evolution of antimicrobial resistance. The authors present the temporal trends in the prevalence of Salmonella Typhi genotypes in select regions/ countries as well as the prevalence and antimicrobial resistance. The authors cite travel isolates of Salmonella Typhi as a useful proxy for surveillance in high burden settings where there exists a paucity of genomic data. While the authors acknowledge the limitations of their study, there remain major concerns over sampling bias and representativeness that question the generalizability of their findings.

Based on the methods section, the authors did not make mention of adjusting their prevalence estimates for outbreak investigations. When conducting a population analysis, including outbreak samples can lead to an overestimation of the prevalence of the outbreak strain. First, outbreaks tend to be sampled more densely than isolates from routine surveillance of endemic disease, secondly in an outbreak, you are essentially sampling the same strain multiple times. This needs to be taken into consideration when estimating the prevalence of genotypes in the population. Treating outbreak investigations and routine surveillance equally in calculating prevalence can be misleading if the proportion of outbreak isolates sequenced is greater than the proportion of isolates in the surveillance area that are sequenced.

There are concerns regarding the validity of the results presented in Figures 1-3. These results require a nuanced assessment of the factors that are likely to influence genotypic diversity including type of study, duration of sampling and total number of genomes sequenced. In big Countries like Nigeria and India, where can be heterogeneity in different regions of the country and this needs to also be considered in inferring the prevalence of genotypes.

This heterogeneity in prevalence of genotypes was observed in countries with multiple laboratories. In India for example, the prevalence of lineage 4.3.1.2 ranged from 39% to 82%, in different cities/ regions. The authors have not provided sufficient context on the underlying source of this variation in prevalence. In order to understand the reason for observing these differences there needs to be a discussion around when the samples in each place/ region were conducted, how long the study was conducted, how many isolates were collected and whether this was a routine surveillance, outbreak investigation or other type of study. Similar variability is observed in Nigeria where most isolates were from Abuja (Zankli Medical Center, n=105, 2010-2013) and other sources included Ibadan (University of Ibadan, n=14, 2017-2018), and reference laboratories in England (n=15, 2015-2019) and the USA (n=10, 2016-2019). Given the small sample sizes and the fact that the time periods for sample collection varied, using this dataset to get a snapshot of the prevalence of genotypes in Nigeria can be potentially misleading.

Moreover, the authors cite that 70% of cases in Pakistan are caused by XDR. Is this based on the proportions of isolates that are XDR in this dataset? Klemm et al 2018 sequenced primarily XDR isolates, therefore that dataset is not representative of the wider population. Rasheed et al included on 27 genomes, which were isolated at hospitals. Hospitals isolates may give an overestimated XDR burden because susceptible isolates are likely to get treated successfully with antibiotics alleviating the need for hospitalization. Similarly, Yousafzai et al 2019 was an investigation into an outbreak of ceftriaxone-resistant Salmonella Typhi in Hyderabad, which is a densely sample dataset and not necessarily a representation of the wider population. Aggregating these data may lead to an accumulation of bias that gives a distorted snap shot of the diversity on genotypes. Also, it is unclear whether the number of isolates collected from each of these studies was consistent with time. Thus, changes in the prevalence may be representative of a change in the proportion of genomes that were sampled from individual studies.

One of the major recommendations from this study was that travel associated isolates can be a proxy for surveillance in high burden regions where there is paucity of data. The authors have not demonstrated a rigorous test for representativeness of the travel associated samples. The test conducted by the authors looked at how well the travel isolates correlated with the isolates from other studies conducted in the source population. However, they have not factored in potential biases associated with the studies conducted in the host countries. Also, travel is more likely to encompass a specific socio-economic demography of people who can afford to travel. This leads to underrepresentation of low income individuals and communities, especially in low-income countries. Moreover, the authors have not shown that the phylogenetic placement of the travel isolates supports the claim that they originated from that country. Conclusions drawn from travel associated isolates need to be tempered, while it can be a useful tool for early detection of potentially virulent lineages or lineages that have novel resistance mechanisms, using it to determine prevalence can be misleading.

Other minor observations include:

Introduction needs to trim significantly to be more concise. The authors can demonstrate that Salmonella typhi accumulates resistance genotypes over time and as new antibiotics are introduced resistance mutations become selected for and fixed in the population.

Figure 4 is very similar to Figure 1 of Klemm et al 2018, does not add any new insights.

Reviewer #3 (Public Review):

The authors study the spatio-temporal dynamics of gap and pair-rule pattern formation in the Tribolium embryo. Their main contributions are (1) to perform DNA accessibility profiles at multiple time points and in three domains along the A/P axis, (2) to establish a reporter gene system to examine reporter gene expression driven by candidate enhancers (including live imaging), (3) identify at least three new enhancers, and (4) provide some evidence in favor of the "Enhancer Switching" model.

This is an interesting study that marks solid progress towards an organizing principle of pattern formation. The two practical contributions of the work are impactful: (1) germband region-specific accessibility profiling provides a novel view of the epigenome, especially when combined with profiling of temporal variation. (2) the live imaging system has been powerful in Drosophila studies and this work establishes this system for Tribolium, which has certain advantages as a model.

I have two major concerns: First, the claim about differential accessibility being related to enhancer activity is not really established from the presented data, in my view. This needs to be clarified. (I do believe in the claim to some extent, but not based on presented evidence.) Second, the evidence in support of the Enhancer Switching model for runt should be accompanied by identification of and spatiotemporal profiling of the "speed regulator", if this is not established yet. In addition to these two concerns, the simulations of the Enhancer Switching model need to be described, at least in the outline, in the Methods section.

Reviewer #3 (Public Review):

This work attempts to introduce a new attribute of the receptor- efficiency, a fraction of an agonist binding energy consumed by conformational transition of the receptor from resting to active (open) states. Furthermore, the authors use an impressive set of experimental data (single channel recordings with 23 agonists and 53 mutations) to measure the efficiency for each agonist and mutant receptor. All the estimated efficiencies fall into a few groups and inside each of the efficiency groups there is a strong correlation between agonist affinity and receptor opening efficacy.

The main finding in this study is that estimated efficiencies fall into 5 groups. There is no clear description of the method how the efficiencies were allocated into different groups. Most importantly, it is not clear if the method used takes into account the uncertainty of the efficiency estimate. The study does not show any statistical metrics of the efficiency estimates as well as any other calculated variable such as dissociation equilibrium constants to resting or open states. Surely, the uncertainty of the efficiency should matter especially considering how near the efficiency group values are (eg. difference about 10% between 0.51 and 0.56 or 0.41 and 0.45).

All the tested agonists fell into groups according to the efficiency value attributed to them. It is difficult to see why some of the agonists belong to the same group. For example, it is not obvious at all why such agonists as epibatidine, decamethonium and TMP are in the same group. The question, I guess, arises if this grouping based on efficiency has any predictability value. Furthermore, if a series of mutations with the same agonist fall into different groups, the prediction power of this approach is very limited if one attempts to design a new agonist or look for a new mutation.

Reviewer #3 (Public Review):

My general assessment of the paper is that the analyses done after they find the model are exemplary and show some interesting results. However, the method they use to find the number of states (Calinski-Harabasz score instead of log-likelihood), the model they use generally (HMM), and the fact that they don't show how they find the number of states on HCP, with the Schaeffer atlas, and do not report their R^2 on a test set is a little concerning. I don't think this perse impedes their results, but it is something that they can improve. They argue that the states they find align with long-standing ideas about the functional organization of the brain and align with other research, but they can improve their selection for their model.

Strengths:

- Use multiple datasets, multiple ROIs, and multiple analyses to validate their results<br /> - Figures are convincing in the sense that patterns clearly synchronize between participants<br /> - Authors select the number of states using the optimal model fit (although this turns out to be a little more questionable due to what they quantify as 'optimal model fit')<br /> - Replication with Schaeffer atlas makes results more convincing<br /> - The analyses around the fact that the base state acts as a flexible hub are well done and well explained<br /> - Their comparison of synchrony is well-done and comparing it to resting-state, which does not have any significant synchrony among participants is obvious, but still good to compare against.<br /> - Their results with respect to similar narrative engagement being correlated with similar neural state dynamics are well done and interesting.<br /> - Their results on event boundaries are compelling and well done. However, I do not find their Chang et al. results convincing (Figure 4B), it could just be because it is a different medium that explains differences in DMN response, but to me, it seems like these are just altogether different patterns that can not 100% be explained by their method/results.<br /> - Their results that when there is no event, transition into the DMN state comes from the base state is 50% is interesting and a strong result. However, it is unclear if this is just for the sitcom or also for Chang et al.'s data.<br /> - The involvement of the base state as being highly engaged during the comedy sitcom and the movie are interesting results that warrant further study into the base state theory they pose in this work.<br /> - It is good that they make sure SM states are not just because of head motion (P 12).<br /> - Their comparison between functional gradient and neural states is good, and their results are generally well-supported, intuitive, and interesting enough to warrant further research into them. Their findings on the context-specificity of their DMN and DAN state are interesting and relate well to the antagonistic relationship in resting-state data.

Weaknesses:

- Authors should train the model on part of the data and validate on another<br /> - Comparison with just PCA/functional gradients is weak in establishing whether HMMs are good models of the timeseries. Especially given that the HMM does not explain a lot of variance in the signal (~0.5 R^2 for only 27 brain regions) for PCA. I think they don't report their own R^2 of the timeseries<br /> - Authors do not specify whether they also did cross-validation for the HCP dataset to find 4 clusters<br /> - One of their main contributions is the base state but the correlation between the base state in their Song dataset and the HCP dataset is only 0.399<br /> - Figure 1B: Parcellation is quite big but there seems to be a gradient within regions<br /> - Figure 1D: Why are the DMNs further apart between SONG and HCP than the other states<br /> - Page 5 paragraph starting at L25: Their hypothesis that functional gradients explain large variance in neural dynamics needs to be explained more, is non-trivial especially because their R^2 scores are so low (Fig 1. Supplement 8) for PCA<br /> - Generally, I do not find the PCA analysis convincing and believe they should also compare to something like ICA or a different model of dynamics. They do not explain their reasoning behind assuming an HMM, which is an extremely simplified idea of brain dynamics meaning they only change based on the previous state.<br /> - For the 25- ROI replication it seems like they again do not try multiple K values for the number of states to validate that 4 states are in fact the correct number.<br /> - Fig 2B: Colorbar goes from -0.05 to 0.05 but values are up to 0.87<br /> - P 16 L4 near-critical, authors need to be more specific in their terminology here especially since they talk about dynamic systems, where near-criticality has a specific definition. It is unclear which definition they are looking for here.<br /> - P16 L13-L17 unnecessary<br /> - I think this paper is solid, but my main issue is with using an HMM, never explaining why, not showing inference results on test data, not reporting an R^2 score for it, and not comparing it to other models. Secondly, they use the Calinski-Harabasz score to determine the number of states, but not the log-likelihood of the fit. This clearly creates a bias in what types of states you will find, namely states that are far away from each other, which likely also leads to the functional gradient and PCA results they have. Where they specifically talk about how their states are far away from each other in the functional gradient space and correlated to (orthogonal) components. It is completely unclear to me why they used this measure because it also seems to be one of many scores you could use with respect to clustering (with potentially different results), and even odd in the presence of a log-likelihood fit to the data and with the model they use (which does not perform clustering).<br /> - Grammatical error: P24 L29 rendering seems to have gone wrong

Questions:

- Comment on subject differences, it seems like they potentially found group dynamics based on stimuli, but interesting to see individual differences in large-scale dynamics, and do they believe the states they find mostly explain global linear dynamics?<br /> - P19 L40 why did the authors interpolate incorrect or no-responses for the gradCPT runs? It seems more logical to correct their results for these responses or to throw them out since interpolation can induce huge biases in these cases because the data is likely not missing at completely random.

Reviewer #3 (Public Review):

Rajan et al. used scRNAseq to identify transcription factors responsible for fine-tuning stemness gene expression in neural stem cells (neuroblasts), identifying Fruitless (fru) as a putative regulator of this process. Specifically, loss of the fru isoform C (fruc) results in increased stemness gene expression, while its overexpression leads to the opposite effect. Consistently, overexpression of fruc in a brat-null neuroblast-tumor background is sufficient to partially restore differentiation. Furthermore, by performing extensive genome-wide binding studies, the authors show that Fruc preferentially binds to cis-regulatory elements of stemness genes, with evidence that this transcription factor regulates the Notch-pathway via co-binding with Notch-target genes. The overall impact of FruC on transcription was not assessed.

Their data also shows that instead of regulating the deposition of histone marks associated with active transcription, such as H3K27ac or H3K4me3, loss of fruc results in decreased levels of the repressive mark H3K27me3, namely in the Notch locus or in Notch downstream effector genes, indicating that FruC fine-tunes the expression of their bound genes through maintenance of low-levels of repressive marks at cis-regulatory elements of its target genes. Given the extensive binding profile of FruC the effects promoted by its misexpression in neuroblasts are likely multifactorial.

In addition, the authors also show that PRC2 subunits, Caf1 and Su(z)12, the multisubunit complex responsible for catalyzing H3k27me3 deposition, (1) co-localize with Fru in Fruc-bound regions and (2) their loss partially phenocopies the previous results obtained for fruc depletion. The authors propose a model in which Fruc, via synergistic work with PRC2, is capable of fine-tuning the expression of stemness genes, in particular, Notch and Notch targets in neuroblasts by promoting low levels of transcriptionally repressive histone marks at their target cis-regulatory elements. If FruC and PRC2 functionally interact, and if the recruitment of one factor affects the binding of the other remains unknown.

The authors present an assortment of results that will be useful for those working in transcription and chromatin regulation, namely in the field of Drosophila neural stem cells (neuroblasts). Specifically, the authors provide robust single-cell RNA sequencing results and analysis that can be used by researchers interested in trying to understand the transcriptional state of neuroblasts and their progeny. Additionally, genome-wide binding studies for FruC or PRC2 subunits, together with the profiling of active/repressive histone marks, offer new insights regarding transcription factor or transcriptional repressor binding and the respective read-out in terms of histone modifications. Moreover, the authors propose an interesting model via which transcription regulation of Notch and Notch downstream effectors is rendered via fine-tuning of the transcriptional output. Hence, FruC restrains and limits the levels of its target genes within neuroblasts, avoiding the segregation of high levels of stemness-associated proteins to the progeny, which would incur in fate and differentiation defects. The model proposed here highlights how transcription regulation by histone marks is much more dynamic and layered, other than being dictated only by the mutually exclusive presence of either active or repressive marks.

Reviewer #3 (Public Review):

Previously, it has been shown the essential role of IDA peptide and HAESA receptor families in driving various cell separation processes such as abscission of flowers as a natural developmental process, of leaves as a defense mechanism when plants are under pathogenic attack or at the lateral root emergence and root tip cell sloughing. In this work, Olsson et al. show for the first time the possible role of IDA peptide in triggering plant innate immunity after the cell separation process occurred. Such an event has been previously proposed to take place in order to seal open remaining tissue after cell separation to avoid creating an entry point for opportunistic pathogens. The elegant experiments in this work demonstrate that IDA peptide is triggering the defense-associated marker genes together with immune specific responses including release of ROS and intracellular CA2+. Thus, the work highlights an intriguing direct link between endogenous cell wall remodeling and plant immunity. Moreover, the upregulation of IDA in response to abiotic and especially biotic stimuli are providing a valuable indication for potential involvement of HAE/IDA signalling in other processes than plant development.

Strengths:<br /> The various methods and different approaches chosen by the authors consolidates the additional new role for a hormone-peptide such as IDA. The involvement of IDA in triggering of the immunity complex process represents a further step in understanding what happens after cell separation occurs. The Ca2+ and ROS imaging and measurements together with using the haehsl2 and haehsl2 p35S::HAE-YFP genotypes provide a robust quantification of defense responses activation. While Ca2+ and ROS can be detected after applying the IDA treatment after the occurrence of cell separation it is adequately shown that the enzymes responsible for ROS production, RBOHD and RBOHF, are not implicated in the floral abscission.<br /> Furthermore, IDA production is triggered by biotic and abiotic factors such as flg22, a bacterial elicitor, fungi, mannitol or salt, while the mature IDA is activating the production of FRK1, MYB51 and PEP3, genes known for being part of plant defense process.

Weaknesses:<br /> Even though there is shown a clear involvement of IDA in activating the after-cell separation immune system, the use of p35S:HAE-YFP line represent a weak point in the scientific demonstration. The mentioned line is driving the HAE receptor by a constitutive promoter, capable of loading the plant with HAE protein without discriminating on a specific tissue. Since it is known that IDA family consist of more members distributed in various tissues, it is very difficult to fully differentiate the effects of HAE present ubiquitously.<br /> The co-localization of HAE/HSL2 and FLS2 receptors is a valuable point to address since in the present work, the marker lines presented do not get activated in the same cell types of the root tissues which renders the idea of nanodomains co-localization (as hypothetically written in the discussion) rather unlikely.

Reviewer #3 (Public Review):

Abstract:

The paper follows a recent study by the same team (Jaroenlak et al Plos Pathogens 2020), which documented the dramatic ejection dynamics of the polar tube (PT) in microsporidia using live-imaging and scanning electron microscopy. Although several key observations were reported in this paper (the 3D architecture of the PT within the spore, the speed and extent of the ejection process, the translocation dynamics of the nucleus during germination), the precise geometry of the PT during ejection remain inaccessible to imaging, making it difficult to physically understand the phenomenon.

This paper aims to fill this gap with an indirect "data-driven" approach. By modeling the hydrodynamic dissipation for different unfolding mechanisms identified in the literature and by comparing the predictions with experiments of ejection in media of various viscosities, authors shows that data are compatible with an eversion (caterpillar-like) mechanism but not compatible with a "jack-in-the-box" scenario. In addition, the authors observe that most germinated spores exhibit an inward bulge, which they attribute to buckling due to internal negative pressure and which they suggest may be a mean of pushing the nucleus out of the PT during the final stage of ejection.

Major strengths:

Probably the most impressive aspect of the study is the experimental analysis of the ejection dynamics (velocity, ejection length) in medium of various viscosities over 3 orders of magnitudes, which, combined with a modeling of the viscous drag of the PT tube, provides very convincing evidence that the unfolding mechanism is not a global displacement of the tube but rather an apical extension mechanism, where the motion is localized at the end of the tube. The systematic classification of the different unfolding scenarios, consistent with the previous literature, and their confrontation with data in terms of energy, pressure and velocity also constitute an original approach in microbiology where in-situ and real time geometry is often difficult to access.

Major weaknesses:

1) While the experimental part of the paper is clear, I had (and still have) a hard time understanding the modeling part. Overall, the different unfolding mechanisms should be much better explained, with much more informative sketches to justify the dissipation and pressure terms, magnifying the different areas where dissipation occurs, showing the velocity field and pressure field, etc. In particular, a key parameter of eversion models is the geometry of the lubrication layers inside and outside the spore (h_sheath, h_slip). Where do the values of h_sheath and h_slip come from? What is the physical process that selects these parameters? For clarity, the figures showing the unfolding mechanics in the different scenario should be in the main text, not in the supplemental materials.

2) The authors compute and discuss in several places "the pressure" required to ejection, but no pressure is indicated in the various sketches and no general "ejection mechanism" involving this pressure is mentioned in the paper. What is this "required pressure" and to what element does it apply? I understand that the article focuses on the dissipation required to the deployment of the PT but I find it difficult to discuss the unfolding mechanism without having any idea on the driving mechanism of the movement. How could eversion be initiated and sustained?

3) Finally, the authors do not explain how pressure, which appears to be a positive, driving quantity at the beginning of the process, can become negative to induce buckling at the end of ejection. Although the hypothesis of rapid translocation induced by buckling is interesting, a much better mechanistic description of the process is needed to support it.

Reviewer #3 (Public Review):

In this study, the authors identified homozygous ZMYND12 variants in four unrelated patients. In sperm cells from these individuals, immunofluorescence revealed altered localization of DNAH1, DNALI1, WDR66, and TTC29. Axonemal localization of ZMYND12 ortholog TbTAX-1 was confirmed using the Trypanosoma brucei model. RNAi knock-down of TbTAX-1 dramatically affected flagellar motility, with a phenotype similar to ZMYND12-variant-bearing human sperm. Co-immunoprecipitation and ultrastructure expansion microscopy in T. brucei revealed TbTAX-1 to form a complex with TTC29. Comparative proteomics with samples from Trypanosoma and Ttc29 KO mice identified a third member of this complex: DNAH1. The data presented revealed that ZMYND12 is part of the same axonemal complex as TTC29 and DNAH1, which is critical for flagellum function and assembly in humans, and Trypanosoma. The manuscript is informative for the clinical and basic research in the field of spermatogenesis and male infertility.

Reviewer #3 (Public Review):

The study from Grechi et al showed that emerging environmental microplastics (MPs) are present in both human and bovine follicular fluid. Moreover, based on the characterization and quantification data, authors treated bovine oocytes with environmentally relevant levels of polystyrene (PS) MPs and found that PS MPs interfered with oocyte maturation in vitro. This study is novel, particularly the first part of MP characterization and quantification, and for the first time confirms the presence of MPs in follicular fluid of humans and large farm animals. These results provide a possible mechanism by which the female infertility rate has been increasing in both humans and large farm animals. The session of exposing MPs to bovine and related oocyte health evaluation can be further improved. For example, authors examined the morphology of the oocyte zona pellucida (ZP) and degeneration and stained oocyte DNA to determine the meiotic maturation status. However, a much more comprehensive oocyte health evaluation can be performed including but not limited to the examination of oocyte spindle morphology, meiotic division, fertilization, early embryo development, mitochondria, and accumulation of ROS. These additional endpoints can provide more robust evidence to determine the impact of MPs on oocyte health. While the oocyte proteomic analysis identified altered proteins, more functional studies and causation experiments can be performed. In addition, authors exposed cumulus-oocyte-complexes (COCs) but not denuded oocytes with MPs, it is crucial to determine whether MPs accumulate in cumulus cells or oocytes or both as well as the compromised oocyte quality is caused by the direct effect of MPs or the indirect impact on somatic cumulus cells to cause a secondary effect on the oocytes.

Reviewer #3 (Public Review):

The authors collected BALF samples from lung cancer patients newly diagnosed with PCP, DI-ILD or ICI-ILD. CyTOF was performed on these samples, using two different panels (T-cell and B-cell/myeloid cell panels). Results were collected, cleaned-up, manually gated and pre-processed prior to visualisation with manifold learning approaches t-SNE (in the form of viSNE) or UMAP, and analysed by CITRUS (hierarchical clustering followed by feature selection and regression) for population identification - all using Cytobank implementation - in an attempt to identify possible biomarkers for these disease states. By comparing cell abundances from CITRUS results and qualitative inspection of a small number of marker expressions, the authors claimed to have identified an expansion of CD16+ T-cell population in PCP cases and an increase in CD57+ CD8+ T-cells, FCRL5+ B-cells and CCR2+ CCR5+ CD14+ monocytes in ICI-ILD cases.

By the authors' own admission, there is an absence of healthy donor samples and, perhaps as a result of retrospective experimental design, also an absence of pre-treatment samples. The entire analysis effectively compares three yet-established disease states with no common baseline - what really constitutes a "biomarker" in such cases? The introduction asserts that "y characterizing the cellular and molecular changes in BAL from patients with these complications, we aim to improve our understanding of their pathogenesis and identify potential therapeutic targets" (lines 82-84). Given these obvious omissions, no real "changes" have been studied in the paper. These are very limited comparisons among three, and only these three, states.

Even assuming more thorough experimental design, the data analysis is unfortunately too shallow and has not managed to explore the wealth of information that could potentially be extracted from the results. CITRUS is accessible and convenient, but also make a couple of big assumptions which could affect data analysis - 1) Is it justified to concatenate all FCS files to analyse the data in one batch / small batches? Could there be batch effects or otherwise other biological events that could confuse the algorithm? 2) With a relatively small number of samples, and after internal feature selection of CITRUS, is the regression model suitable for population identification or would it be too crude and miss out rare populations? There are plenty of other established methods that could be used instead. Have those methods been considered?

Colouring t-SNE or UMAP (e.g. Figure 6C) plots by marker expression is useful for quick identification of cell populations but it is not a quantitative analysis. In a CyTOF analysis like this, it is common to work out fold changes of marker expressions between conditions. It is inadequate to judge expression levels and infer differences simply by looking at colours.

The relatively small number of samples also mean that most results presented in the paper are not statistical significant. Whilst it is understandable that it is not always possible to collect a large number of patient samples for studies like this, having several entire major figures showing "n.s." (e.g. Figures 3A, 4B and 5C), together with limitations in the comparisons themselves and inadequate analysis, make the observations difficult to be convincing, and even less so for the single fatal PCP case where N = 1.

It would also be good scientific practice to show evidence of sample data quality control. Were individual FCS files examined? Did the staining work? Some indication of QC would also be great.

This dataset generated and studied by the authors have the potential to address the question they set out to answer and thus potentially be useful for the field. However, in the current state of presentation, more evidence and more thorough data analysis are needed to draw any conclusions, or correlations, as the authors would like to frame them.

Reviewer #3 (Public Review):

This manuscript by Fisher and colleagues documents the change in clinical activity in English general practices during the COVID-19 pandemic according to a set of indicators of clinical activity. The indicators include measures of clinical reviews (e.g. blood pressure, asthma, chronic obstructive pulmonary disease, medication, and cardiovascular risk reviews), blood tests (e.g. cholesterol, liver function, thyroid function, full blood counts, diabetes monitoring blood tests, and kidney function). All these measures saw a drop during the pandemic, to a varying degree, and some recovered afterwards but others did not.

Clinical activity was measured using SNOMED CT codes, which are standard codes used for recording clinical events in UK GP records.

Strengths:

This is a large and comprehensive study including data from 99% of general practices in England. The indicators are clinically relevant, cover a broad range of disease areas, and have been chosen in a sensible manner, involving relevant stakeholders such as GPs, pharmacists, and pathologists.

The OpenSAFELY platform has the ability to enable federated analyses to be run on raw coded data of almost all patients registered with a GP in England.

The study demonstrates the value of OpenSAFELY in being able to monitor clinical activity in general practice at a detailed level, which is essential for planning and improving health services. The statistical methodology is broadly sound.

Weaknesses:

The measures are all related to chronic physical diseases in adults, with a particular focus on cardiometabolic and respiratory conditions. There are no measures related to mental health, maternal or child health.

The description of the measures does not distinguish between different types of clinical activity e.g. lab tests, clinical measurements, or diagnoses, and all are lumped together as 'codes'. This is a peculiarity of the way that information is recorded in GP systems - many different types of clinical information (such as diagnoses and lab tests) are recorded using a SNOMED CT 'code', and only the exact code differentiates what type of information is in the record.

The codelists were broad and comprehensive, but it is unclear how necessary this is because for some measures e.g. lab tests, laboratories typically record a particular type of test using a single standardised code. Instead of using a broad set of codes in the analysis, the authors could have initially verified which codes are associated with the clinical activity being measured (e.g. a numerical value of a blood pressure measurement) in all practices, as I would expect the same single or small number of codes would be used in all practices. This would have provided a smaller and simpler final codelist.

Reviewer #3 (Public Review):

In this manuscript, Fang and colleagues found that IQGAP1 interacts with TNFAIP2, which activates Rac1 to promote drug resistance in TNBC. Furthermore, they found that ITGB4 could interact with TNFAIP2 to promote TNBC drug resistance via the TNFAIP2/IQGAP1/Rac1 axis by promoting DNA damage repair.

This work has good innovation and high potential clinical significance.

Reviewer #3 (Public Review):

The authors explore an important question concerning the underlying mechanism of representational drift, which despite intense recent interest remains obscure. The paper explores the intriguing hypothesis that drift may reflect changes in the intrinsic excitability of neurons. The authors set out to provide theoretical insight into this potential mechanism.

They construct a rate model with all-to-all recurrent connectivity, in which recurrent synapses are governed by a standard Hebbian plasticity rule. This network receives a global input, constant across all neurons, which can be varied with time. Each neuron also is driven by an "intrinsic excitability" bias term, which does vary across cells. The authors study how activity in the network evolves as this intrinsic excitability term is changed.

They find that after initial stimulation of the network, those neurons where the excitability term is set high become more strongly connected and are in turn more responsive to the input. Each day the subset of neurons with high intrinsic excitability is changed, and the network's recurrent synaptic connectivity and responsiveness gradually shift, such that the new high intrinsic excitability subset becomes both more strongly activated by the global input and also more strongly recurrently connected. These changes result in drift, reflected by a gradual decrease across time in the correlation of the neuronal population vector response to the stimulus.

The authors are able to build a classifier that decodes the "day" (i.e. which subset of neurons had high intrinsic excitability) with perfect accuracy. This is despite the fact that the excitability bias during decoding is set to 0 for all neurons, and so the decoder is really detecting those neurons with strong recurrent connectivity, and in turn strong responses to the input. The authors show that it is also possible to decode the order in which different subsets of neurons were given high intrinsic excitability on previous "days". This second result depends on the extent by which intrinsic excitability was increased: if the increase in intrinsic excitability was either too high or too low, it was not possible to read out any information about past ordering of excitability changes.

Finally, using another Hebbian learning rule, the authors show that an output neuron, whose activity is a weighted sum of the activity of all neurons in the network, is able to read out the activity of the network. What this means specifically, is that although the set of neurons most active in the network changes, the output neuron always maintains a higher firing rate than a neuron with randomly shuffled synaptic weights, because the output neuron continuously updates its weights to sample from the highly active population at any given moment. Thus, the output neuron can readout a stable memory despite drift.

Strengths:<br /> The authors are clear in their description of the network they construct and in their results. They convincingly show that when they change their "intrinsic excitability term", upon stimulation, the Hebbian synapses in their network gradually evolve, and the combined synaptic connectivity and altered excitability result in drifting patterns of activity in response to an unchanging input (Fig. 1, Fig. 2a). Furthermore, their classification analyses (Fig. 2) show that information is preserved in the network, and their readout neuron successfully tracks the active cells (Fig. 3). Finally, the observation that only a specific range of excitability bias values permits decoding of the temporal structure of the history of intrinsic excitabililty (Fig. 2f and Figure S1) is interesting, and as the authors point out, not trivial.

Weaknesses:<br /> 1) The way the network is constructed, there is no formal difference between what the authors call "input", Δ(t), and what they call "intrinsic excitability" Ɛ_i(t) (see Equation 3). These are two separate terms that are summed (Eq. 3) to define the rate dynamics of the network. The authors could have switched the names of these terms: Δ(t) could have been considered a global "intrinsic excitability term" that varied with time and Ɛ_i(t) could have been the external input received by each neuron i in the network. In that case, the paper would have considered the consequence of "slow fluctuations of external input" rather than "slow fluctuations of intrinsic excitability", but the results would have been the same. The difference is therefore semantic. The consequence is that this paper is not necessarily about "intrinsic excitability", rather it considers how a Hebbian network responds to changes in excitatory drive, regardless of whether those drives are labeled "input" or "intrinsic excitability".

2) Given how the learning rule that defines input to the readout neuron is constructed, it is trivial that this unit responds to the most active neurons in the network, more so than a neuron assigned random weights. What would happen if the network included more than one "memory"? Would it be possible to construct a readout neuron that could classify two distinct patterns? Along these lines, what if there were multiple, distinct stimuli used to drive this network, rather than the global input the authors employ here? Does the system, as constructed, have the capacity to provide two distinct patterns of activity in response to two distinct inputs?

Impact:<br /> Defining the potential role of changes in intrinsic excitability in drift is fundamental. Thus, this paper represents a potentially important contribution. Unfortunately, given the way the network employed here is constructed, it is difficult to tease apart the specific contribution of changing excitability from changing input. This limits the interpretability and applicability of the results.

Reviewer #3 (Public Review):

Swallowing is an essential daily activity for survival, and pharyngo-laryngeal sensory function is critical for safe swallowing. In Drosophila, it has been reported that the mechanical property of food (e.g. Viscosity) can modulate swallowing. However, how mechanical expansion of the pharynx or fluid content sense and control swallowing was elusive. Qin et al. showed that a group of pharyngeal mechanosensory neurons, as well as mechanosensory channels (nompC, Tmc, and Piezo), respond to these mechanical forces for regulation of swallowing in Drosophila melanogaster.

Strengths:<br /> There are many reports on the effect of chemical properties of foods on feeding in fruit flies, but only limited studies reported how physical properties of food affect feeding especially pharyngeal mechanosensory neurons. First, they found that mechanosensory mutants, including nompC, Tmc, and Piezo, showed impaired swallowing, mainly the emptying process. Next, they identified cibarium multidendritic mechanosensory neurons (md-C) are responsible for controlling swallowing by regulating motor neuron (MN) 12 and 11, which control filling and emptying, respectively.

Weaknesses:<br /> While the involvement of md-C and mechanosensory channels in controlling swallowing is convincing, it is not yet clear which stimuli activate md-C. Can it be an expansion of cibarium or food viscosity, or both? In addition, if rhythmic and coordinated contraction of muscles 11 and 12 is essential for swallowing, how can simultaneous activation of MN 11 and 12 by md-C achieve this? Finally, previous reports showed that food viscosity mainly affects the filling rather than the emptying process, which seems different from their finding.

Reviewer #3 (Public Review):

The manuscript by Zhang and colleagues attempts to combine genetically barcoded rabies viruses with spatial transcriptomics in order to genetically identify connected pairs. The major shortcoming with the application of a barcoded rabies virus, as reported by 2 groups prior, is that with the high dropout rate inherent in single cell procedures, it is difficult to definitively identify connected pairs. By combining the two methods, they are able to establish a platform for doing that, and provide insight into connectivity, as well as pros and cons of their method, which is well thought out and balanced.

Overall the manuscript is well-done, but I have a few minor considerations about tone and accuracy of statements, as well as some limitations in how experiments were done. First, the idea of using rabies to obtain broader tropism than AAVs isn't really accurate - each virus has its own set of tropisms, and it isn't clear that rabies is broader (or can be made to be broader). Second, rabies does not label all neurons that project to a target site - it labels some fraction of them. Third, the high rate of rabies virus mutation should be considered - if it is, or is not a problem in detecting barcodes with high fidelity, this should be noted. Fourth, there are a number of implicit assumptions in this manuscript, not all of which are equally backed up by data. For example, it is not clear that all rabies virus transmission is synaptic-specific; in fact, quite a few studies argue that it is not (e.g., detection of rabies transcripts in glial cells). Thus, arguments about lost-source networks and the idea that if a cell is lost from the network, that will stop synaptic transmission, is not clear. There is also the very real propensity that, the sicker a starter cell gets, the more non-specific spread of virus (e.g., via necrosis) occurs. Fifth, in the experiments performed in Figure 5, the authors used a FLEx-TVA expressed via a retrograde Cre, and followed this by injection of their rabies virus library. The issue here is that there will be many (potentially thousands) of local infection events near the injection site that TVA-mediated but are Cre-dependent (=off-target expression of TVA in the absence of Cre). This is a major confound in interpreting the labeling of these cells. They may express very low levels of TVA, but still have infection be mediated by TVA. The authors did not clearly explore how expression of TVA related to rabies virus infection of cells near the rabies injection site. A modified version of TVA, such as 66T, should have been used to mitigate this issue. Otherwise, it is impossible to determine connectivity locally. The authors do not go to great lengths to interpret the findings of these observations, so I am not sure this is a critical issue, but it should be pointed out by the authors as a caveat to their dataset. Sixth, the authors are making estimates of rabies spread by comparison to a set of experiments that was performed quite differently. In the two studies cited (Liu et al., done the standard way, and Wertz et al., tracing from a single cell), the authors were likely infecting with a rabies virus using a high multiplicity of infection, which likely yields higher rates of viral expression in these starter cells and higher levels of input labeling. However, in these experiments, the authors need to infect with a low MOI, and explicitly exclude cells with >1 barcode. Having only a single virion trigger infection of starter cells will likely reduce the #s of inputs relative to starter neurons. Thus, the stringent criteria for excluding small networks may not be entirely warranted. If the authors wish to only explore larger networks, this caveat should be explicitly noted.

Overall, if the caveats above are noted and more nuance is added to some of the interpretation and discussion of results, this would greatly help the manuscript, as readers will be looking to the authors as the authority on how to use this technology.

Reviewer #3 (Public Review):

The fundamental question that the authors address in this work is how our brain encodes when two events occur in the same context as against two events occurring in different contexts. Often in life, we encounter situations where it is difficult to alter the memory specifically associated with a place, for e.g. when we try to find our favourite brand of soap after the shopkeeper rearranges the shelves. Here the authors hypothesise that the acquisition of new memory, bound by a context /space, results in extensive remapping. Presumably, such remapping is manifested as an increased difficulty in acquiring new memories that are linked through space, especially when we have to remember both the old and the new. Using a combination of a modified behavioural task and in vivo imaging of neuronal activity, the authors test this hypothesis in mice. The spatial task requires the animal to learn two navigational rules of when to make a turn. Rule 1 requires the animal to make a turn with respect to itself (turn right), and Rule 2 requires the animal to turn with respect to the outside world (turn East). This is achieved by training the mice in two distinct contexts (mazes). Having trained the mice, they acquire the neuronal activation data and analysis through i) correlation matrices and ii) population vectors they test and show that the hypothesis is true. The manuscript is well-written and easy to follow in general. One of the important aspects of this manuscript is the clarity and detail with which the methods are described. The descriptions are unambiguous and complete in detail. This needs to be appreciated.

One of the soft spots of the study is the following: The animal learns to perform the task in two different contexts. It could also be interpreted as a change in context triggering the change in rule rather than a specific context predicting a specific rule as interpreted. I would like to know the authors' views on this. Additionally, the data is from one experiment with six mice, and the data is analyzed through different frameworks to glean information. This is both the boon and bane of the study. Independent/additional cross-validation of the overall effect would be nice to establish the observed phenomena. For e.g., the use of IEGs to identify the ensembles across the two scenarios, and/or inactivation of CA1 to show that rule change is affected or the first memory is also affected.

Reviewer #3 (Public Review):

Flagella are crucial for bacterial motility and virulence of pathogens. They represent large molecular machines that require strict hierarchical expression control of their components. So far, mainly transcriptional control mechanisms have been described to control flagella biogenesis. While several sRNAs have been reported that are environmentally controlled and regulate motility mainly via control of flagella master regulators, less is known about sRNAs that are co-regulated with flagella genes and control later steps of flagella biogenesis.

In this carefully designed and well-written study, the authors explore the role of four E. coli σ28-dependent 3' or 5' sUTR-derived sRNA in regulating flagella biogenesis. UhpU and MotR sRNAs are generated from their own σ28(FliA)-dependent promoter, while FliX and FlgO sRNAs are processed from the 3'UTRs of flagella genes under control of FliA. The authors provide an impressive amount of data and different experiments, including phenotypic analyses, genomics approaches as well as in-vitro and in-vivo target identification and validation methods, to demonstrate varied effects of three of these sRNAs (UhpU, FliX and MotR) on flagella biogenesis and motility. For example, they show different and for some sRNAs opposing phenotypes upon overexpression: While UhpU sRNA increases flagella number and motility, FliX has the opposite effect. MotR sRNA also increases the number of flagella, with minor effects on motility.

While the mechanisms and functions of the fourth sRNA, FlgO, remain elusive, the authors provide convincing experiments demonstrating that the three sRNAs directly act on different targets (identified through the analysis of previous RIL-seq datasets), with a variety of mechanisms. The authors demonstrate, UhpU sRNA, which derives from the 3´UTR of a metabolic gene, downregulates LrhA, a transcriptional repressor of the flhDC operon encoding the early genes that activate the flagellar cascade. According to their RIL-seq data analyses, UhpU has hundreds of additional potential targets, including multiple genes involved in carbon metabolism. Due to the focus on flagellar biogenesis, these are not further investigated in this study and the authors further characterize the two other flagella-associated sRNAs, FliX and MotR. Interestingly, they found that these sRNAs seem to target coding sequences rather than acting via canonical targeting of ribosome binding sites. The authors show FliX sRNA represses flagellin expression by interacting with the CDS of the fliC mRNA. Both FliX and MotR sRNA turn out to modulate the levels of ribosomal proteins of the S10 operon with opposite effects. MotR, which is expressed earlier, interacts with the leader and the CDS of rpsJ mRNA, leading to increased S10 protein levels and S10-NusB complex mediated anti-termination, promoting readthrough of long flagellar operons. FliX interacts with the CDSs of rplC, rpsQ, rpsS-rplV, repressing the production of the encoded ribosomal proteins. The authors also uncover MotR and FliX affect transcription selected representative flagellar genes, with an unknown mechanism.

Overall, this comprehensive study expands the repertoire of characterized UTR derived sRNAs and integrate new layers of post-transcriptional regulation into the highly complex flagellar regulatory cascade. Moreover, these new flagella regulators (MotR, FliX) act non-canonically, and impact protein expression of their target genes by base-pairing with the CDS of the transcripts. Their findings directly connect flagella biosynthesis and motility, highly energy consuming processes, to ribosome production (MotR and FliX) and possibly to carbon metabolism (UhpU).

Specific points to be considered:

- The authors use a crl- hyper-motile strain as WT strain for the study and sometimes also a crl+ strain is used. Can the authors comment on potential reasons why some phenotypes (e.g., UhpU and MotR effects on motility) are only detectable in the crl+ strain or vice versa? Is σS regulation important for the function of these sRNAs?

- In several experiments, a variant of MotR sRNA, MotR* that harbors a 3 nt mutation upstream of the seed sequence is used and seems to mediate stronger phenotypes (impact on flagellar number) upon overexpression compared to WT or phenotypes not retrieved for WT MotR (increased flagellin expression). It would be helpful to have some more clarification throughout the text, why this variant was used, even when OE of WT MotR already has impact on the target and how these three mutated nucleotides impact target regulation. For example, does MotR* show increased RNA stability or Hfq binding compared to MotR? Does the mutation in MotR* impact MotR structure (e.g., based on secondary structure predictions) or increase the complementarity with selected targets at potential secondary binding sites (e.g., based on target predictions)? For example, Fig. S7 shows additional regions of interaction between MotR and fliC mRNA beside the seed sequence. It is also suggested that MotR might have multiple interaction sites on rpsJ mRNA. Additional structure probing or biocomputational predictions could clarify these points.

- It is suggested that UphU impacts on motility via regulation of LrhA, which represses transcription of flhDC, and therefore the flagellar cascade. While LhrA-mediated regulation by UphU is validated based on reporter genes, the effect of UhpU OE on FlhDC levels is not directly examined (Fig. 3). Furthermore, as deletion of LrhA de-represses the flagellar cascade and UhpU was also shown to increase motility, the conclusions could be further strengthened by examining flhDC levels and/or the effect of ∆UhpU (if the sRNA part can be deleted) on motility (reduction) due to relieved down-regulation of LrhA.

-This study provides many opportunities for future follow-work. Now that the four sRNAs and some of their targets and opposing effects on flagella biogenesis have been identified, it will be interesting to see how the sRNAs themselves are temporally regulated throughout the flagella biogenesis cascade and which other targets are regulated by them. Future studies could also provide insights into the mechanism and function of FlgO sRNA, which seems to act via a different mechanism than base-pairing to target RNAs, as well as the global effects of regulation of ribosomal genes via FliX and MotR.

Reviewer #3 (Public Review):

This work focuses on the important problem of how to access the highly polymorphic var gene family using short-read sequence data. The approach that was most successful, and utilized for all subsequent analyses, employed a different assembler from their prior pipeline, and impressively, more than doubles the N50 metric.

The authors then endeavor to utilize these improved assemblies to assess differential RNA expression of ex vivo and short-term cultured samples, and conclude that their results "cast doubt on the validity" of using short-term cultured parasites to infer in vivo characteristics. Readers should be aware that the various approaches to assess differential expression lack statistical clarity and appear to be contradictory. Unfortunately there is no attempt to describe the rationale for the different approaches and how they might inform one another.

It is unclear whether adjusting for life-cycle stage as reported is appropriate for the var-only expression models. The methods do not appear to describe what type of correction variable (continuous/categorical) was used in each model, and there is no discussion of the impact on var vs. core transcriptome results.

Reviewer #3 (Public Review):

In this study, the authors investigate the role of the Notch signalling regulator RBP-J on Ly6Clow monocyte biology starting with the observation that RBP-J-deficient mice have increased circulating Ly6low monocytes. Using myeloid specific conditional mouse models, the authors investigate how RBP-J deficiency effects circulating monocytes and lung interstitial macrophages.

A major strength of this study is that it describes RBP-J as a novel, critical factor regulating Ly6Clow monocyte cell frequency in the blood. The authors demonstrate that RBP-J deficiency leads to increased Ly6Clow monocytes in the blood and lung and CD16.2+ interstitial macrophages in steady state. The authors use a number of different techniques to confirm this finding including bone marrow transplantation experiments and parabiosis.

There are several critical weaknesses that need to be assessed to improve the manuscript, in summary the data presented in the current manuscript are highly descriptive and without mechanistic insight. The inclusion of more mechanistic insight would greatly improve the manuscript.

The authors begin to explore the potential mechanism underlying why Ly6Clow monocytes are increased in the absence of RBP-J - is it through increased survival, increased conversion from Ly6C+ monocytes, increased proliferation or increased egress from the bone marrow. The majority of the data they present here is negative. Whilst I applaud the authors for including negative data, I think that their exploration into how RBP-J leads to increased monocytes does not go far enough and it is critical to understand the mechanism by which RBP-J increases circulating monocytes. Low n-numbers in multiple figures mean that the claims made are not fully supported.

The current title of the paper "RBP-J regulates homeostasis and function of circulating Ly6Clo monocytes" does not fully reflect the manuscript in its current form - there is no exploration of Ly6Clow monocyte functionality in the paper as it stands.<br /> Given that targeting monocytes and macrophages in a range of inflammatory diseases is an attractive yet elusive therapeutic option, understanding the underlying biology that regulates monocyte biology are critically important. This manuscript has the potential to add to our current knowledge of how Ly6Clow monocyte biology is regulated and potentially opens novel avenues for preferentially enhancing Ly6Clow monocytes without influencing Ly6C+ monocytes. This is an attractive proposition for many inflammatory conditions however, considerably more in-depth analysis is required to understand the role of RBP-J in monocyte biology.

Reviewer #3 (Public Review):

Mizukami et al. compare the structure of the coronary arteries in multiple species of amniotes, amphibians, and fish. By selecting species from each of these taxa, the authors were able to evaluate modifications to the coronary arteries during key evolutionary transitions. In mice and quail, they show two populations of vessels that are visible on the developing heart-true coronary arteries on the ventricle and a second population of vessels on the outflow tract known as the ASV., They found that in amphibians, outflow tract vessels were present but ventricular coronary arteries were completely absent. In zebrafish (a more ancestral species) an arterial branch off the rostral section of the hypobranchial artery was shown to have similar anatomical features to outflow tract vessels found in higher organisms. These zebrafish outflow tract arteries also appeared conserved in several chondriichthyes specimens. The authors conclude that rearrangement of the outflow tract vasculature or hypobranchial arteries in fish during evolution, could be homologous to the ASV population of coronary arteries in amphibians and amniotes. These data give new insight into the evolutionary origins of the coronary vasculature.

Major Points

1. The manuscript presents important data on the coronary vascular structure of several different species. However, these data alone do not conclusively demonstrate whether the developmental origins of ASV like vessels are homologous. Therefore, care should be taken when concluding that the outflow tract vessels found in all different species are conserved features. While this is a reasonable hypothesis and should be presented, the manuscript could be improved by also discussing alternate explanations. For example, ASVs in mice originate during embryonic development, while in fish and amphibians outflow tract vessels are formed only in mature animals.

2. Figure 3 A-D: The authors state that "the ASV ran through the outflow tract, then entered the aortic root before reaching the ventricle to form a secondary orifice". Do the authors have serial sections to conclude that the vessel branching off the carotid runs the length of the aorta and is continuous with an orifice at the aortic root? The endothelial projection off the aorta in panel C could reasonably be an independent projection. For example, Chen et al., described similar looking projections in the base of the aorta that were not attached to external vessels. A whole mount approach would be the most convincing to show the attachments of the ASV vessel.

3. Figure 3E: Similar as above, how is it concluded that the orifice is continuous with the ASV and that this projection is not the coronary artery stem?

4. The discussion section could be improved by making some statements more consistent, using more precise or appropriate terminology accepted in the field, and being more cognizant of how the authors' findings fit within the history of the field. For example, when referring to coronary arteries, please clarify whether this refers to ASV/ outflow tract coronary arteries, or true ventricular coronary arteries. In addition, the first sentence of the discussion makes it seem like the origins of coronary arteries were unknown prior to this study, however, their origins have been described in multiple papers previously. The authors could revise their statement to acknowledge these previous findings.

Reviewer #3 (Public Review):

The authors develop and analyze a novel model of microbial communities that considers both space and chemical mediator dynamics explicitly, with the goal of understanding the impact of spatial structure on coexistence. The authors' primary method for assessing the impact of space is to compare numerical simulations of their spatial model to simulations of an equivalent well-mixed model. They explore how spatial structure changes coexistence over a wide range of parameter space, varying parameters such as the ratio of facilitative to inhibitory interactions and the degree of mediator diffusion. They find that spatial structure can have variable effects on richness (the number of cell types within a community), in contrast to existing intuition in the field that spatial structure increases diversity.

Overall, I think the approach that the authors have taken is sound. A very interesting aspect of this model is that the diffusion of mediators and microbes can occur at different rates. In other spatial systems, such as the classic Turing model of pattern formation, differences in diffusion timescales are the key ingredient needed for interesting spatial dynamics. However, while the authors have thoroughly characterized the impact of model parameters on ecological richness, their focus on this single metric provides a somewhat limited view of coexistence in their models. For example, richness considers neither the population composition nor the spatial patterns of coexistence emerging from the model. I also have some concerns about the implementation of the carrying capacity in the model, which in its current form may lead to non-physical outcomes in a small part of the phase space.

Reviewer #3 (Public Review):

Modi et al. developed a novel data-driven computational framework to investigate interactions between multiple brain oscillations and validated this approach in hippocampal CA1 utilizing well-studied changes in oscillations across CA1 layers. This approach provides a new way to investigate complex interactions between diverse neural oscillations during different behaviors. In contrast to standard approaches that classify LFP recordings into a few different oscillatory states which simplify patterns in the LFP, this approach maps a complex state space. The essential idea behind the method is novel and interesting with the potential to expand to other studies of other brain regions or interactions between regions. The authors provide a comprehensive analysis showing how this state space relates to traditional oscillatory states (like delta, theta, and gamma). Among the reported results, it is sometimes unclear what is a validation of their approach versus a novel scientific finding (in the context of the larger literature) and the significance of the finding. Although the overall results seem convincing, the paper is a lacking a demonstration that shows why this approach is of high physiological significance. Furthermore, more evidence showing the specific advantages of using this method in LFP data from a single CA1 layer would make this approach more readily adoptable for the community.

Major concerns:<br /> 1. My primary concern is to provide clear evidence that this approach will provide key insights of high physiological significance, especially for readers who may think the traditional approaches are advantageous (for example due to their simplicity). I think the authors' findings of distinct sleep state signatures or altered organization of the NLG3-KO mouse could serve this purpose. However, right now the physiological significance of these results is unclear. For example, do these sleep state signatures predict later behavior performance, or is altered organization related to other functional impairments in the disease model? Do neurons with distinct sleep state signatures form distinct ensembles and code for related information?<br /> 2. For cells with different mean firing rates during exploration: is that because they are putative fast-spiking interneurons and pyramidal cells? From the reported mean firing rates, I think some of these cells are interneurons. Since mean firing rates are well known to vary with cell type, this should be addressed. For example, the sleep state signatures may be distinct for different putative pyramidal cells and interneurons. This would be somewhat expected considering prior work that has shown different cell types have different oscillatory coupling characteristics. I think it would be more interesting to determine if pyramidal cells had distinct sleep state signatures and, if so, whether pyramidal cells from the same sleep state signature have similar properties like they code for similar things or commonly fire together in an ensemble. It seems the number of cells in Fig. 8 may be limited for this analysis. The authors could use the hc-11 data in addition, which was also tested in this work.<br /> 3. Example traces are needed to show how LFPs change over the state-space. Example traces should be included for key parts of the state-space in Figures 2 and 3.<br /> 4. What is the primary rationale for 200ms time bins? Is this time scale sufficient to capture the slow dynamics of delta rhythm (1-5Hz) with a maximum of 1s duration?<br /> 5. Since oscillatory frequency and power are highly associated with running speed, how does speed vary over the state space. Is the relationship between speed and state-space similar to the results of previous studies for theta (Slawinska and Kasicki, Brain Res 1998; Maurer et al, Hippocampus 2005) and gamma oscillations (Ahmed and Mehta J. Neurosci 2012; Kemere et al PLOS ONE 2013), or does it provide novel insights?<br /> 6. The separation of 9 states (Fig. 6ABC) seems arbitrary, where state 1 (bin 1) is never visited. I suggest plotting the density distribution of the data in Fig. 2A or Fig. 6A to better determine how many states are there within the state space. For example, five peaks in such a density plot might suggest five states. Alternately, clustering methods could be useful to determine how the number of states.<br /> 7. The results in Fig. 4G are very interesting and suggest more variation of sub-states during nonREM periods in sleep1 than in sleep2. What might explain this difference? Was it associated with more frequent ripple events occurring in sleep2?<br /> 8. The state transition results in Fig. 6 are confusing because they include two fundamentally different timescales: fast transitions between oscillatory states and slow dynamics of sleep states. I recommend clarifying the description in the results and the figure caption. Furthermore, how can an animal transition between the same sleep state (Fig. 6EF)? Would they both be in a single sleep state?

Reviewer #3 (Public Review):

Bohannon and colleagues demonstrate that aromatic PUFA analogues positively modulate delayed rectifier potassium channel (Iks) currents, identifying new compounds that could be useful for the treatment of long QT syndrome. The data suggest that aromatic PUFA analogues have two modulatory effects that occur by distinct mechanisms involving hydrogen bonds and ionic interactions. However, the exact determinants of these molecular interactions remain unclear.

Strengths of the study include the following:<br /> 1) By examining a large panel of aromatic PUFA analogues, the study provides a thorough understanding of the relationship between the structure of these analogues and the modulatory effect. Of note, these aromatic PUFA analogues are more efficacious than previously characterized PUFAs such as DHA and N-AT. This knowledge will be important for the design of PUFA analogues for the modulation of IKs current, which could be a strategy for the treatment of long QT syndrome.<br /> 2) By examining the effect of mutations previously shown to disrupt two mechanisms of PUFA modulation, the results suggest that aromatic PUFAs act through the same mechanisms. Furthermore, the effects of the different analogues shed light on the determinants of these binding sites such as the presence of additional hydrogen bonds and electrostatic interactions between the aromatic PUFAs and ion channels.

One limitation of the study is that the structure-activity relationships and effects of the mutations do not provide a complete molecular understanding of how the aromatic PUFA analogues are interacting with the channel. This understanding will require additional studies to examine PUFA analogue binding combined with more extensive mutagenesis. Specifically, the model in Figure 5 suggests that the effect of aromatic PUFAs on the voltage dependence of activation depends on an electrostatic interaction with R231 and a hydrogen bond interaction possibly with T224. Similarly, the effect on channel conductance depends on an electrostatic interaction with K326 through the carboxylate anion of the aromatic PUFA as well as an additional electrostatic interaction with some other part of the protein. It is unclear what residues mediate these interactions. Additionally, the authors propose that T224 is forming a hydrogen bond interaction with the hydroxyl group of NALT, but there appears to be a relatively similar effect of the T224V mutation on NAL-phe, only that the spread in the data makes this effect statistically insignificant. Therefore, the conclusion that T224 mediates NALT action by forming a hydrogen bond with the hydroxyl group (a chemical moiety that is absent in NAL-phe) is not fully supported by the data. A structural model to indicate that T224 is well-positioned to form a hydrogen bond with NALT when it is also interacting with R231 would strengthen this model.

Reviewer #3 (Public Review):

In animals, several recent studies have revealed a substantial role for non-replicative mutagenic processes such as DNA damage and repair rather than replicative error as was previously believed. Much less is known about how mutation operates in plants, with only a handful of studies devoted to the topic. Authors Satake et al. aimed to address this gap in our understanding by comparing the rates and patterns of somatic mutation in a pair of tropical tree species, slow-growing Shorea lavis and fast-growing S. leprosula. They find that the yearly somatic mutation rates in the two species is highly similar despite their difference in growth rates. The authors further find that the mutation spectrum is enriched for signatures of spontaneous mutation and that a model of mutation arising from different sources is consistent with a large input of mutation from sources uncorrelated with cell division. The authors conclude that somatic mutation rates in these plants appears to be dictated by time, not cell division numbers, a finding that is in line with other eukaryotes studied so far.

In general, this work shows careful consideration and study design, and the multiple lines of evidence presented provide good support for the authors' conclusions. In particular, they use a sound approach to identify rare somatic mutations in the sampled trees including biological replicates, multiple SNP-callers and thresholds, and without presumption of a branching pattern. By applying these methods consistently across both species, the authors provide confidence in the comparative mutation rate results. Further steps could be taken to ensure the validity of the results; however, these issues are relatively minor and should minimally impact the overall findings.

Some of the identified somatic mutations (primarily those in individual F1) appear to require two mutation events-one on each chromosome-to be generated and should be either removed or accounted for. Also, while the authors provide estimates of their false positive rate at different filtering thresholds, an assessment of the false negative rate is absent and would help assure readers that the differing number of somatic mutations found is not due to differences in statistical power.

The authors compare the mutation rate per meter of growth, demonstrating that the rate is higher in slow-growing S. laevis: a key piece of evidence in favor of the authors' conclusion that somatic mutations track absolute time rather than cell division. To estimate the mutation rate per unit distance, they regress the per base-pair rate of mutations found between all pairwise branch tips against the physical distance separating the tips (Fig. 2a). While a regression approach is appropriate, the narrowness of the confidence interval is overstated as the points are not statistically independent: internal branches are represented multiple times. (For example, all pairwise comparisons involving a cambium sample will include the mutations arising along the lower trunk.) Regressing rates and lengths of distinct branches might be more appropriate. Judging from the data presented, however, the point estimates seem unlikely to change much.

The most obvious drawback of this study is the low sample size with only two individuals of each species sequenced. To eliminate lingering doubts, it would be helpful to include a more in-depth discussion about stray factors that might affect the authors' conclusions. For example: Could an error in estimation of the trees' ages affect the yearly mutation rate comparisons? If mutations are replicatively driven, could the 30% species difference in the number of cell divisions per meter be sufficient to explain the results?

This work deepens our understanding of how mutation operates at the cellular level by adding plants to the list of eukaryotes in which many mutations appear to derive from non-replicative sources. Given these results, it is intriguing to consider whether there is a fundamental mechanism linking mutation across distantly related species. Plants, generally, present a unique opportunity in the study of mutation as the germline is not sequestered, as it is in animals, and thus the forces of both mutation and selection acting throughout an individual plant's life could in principle affect the mutations transmitted to seed. The authors touch on this aspect, finding no evidence for a reduction in non-synonymous somatic mutations relative to the background rate, but more work-both experimental and observational-is needed to understand the dynamics of mutation and cell-competition within an individual plant. Overall, these results open the door to several intriguing questions in plant mutation. For example, is somatic mutation age-dependent in other species, and do other tropical plants harbor a high mutation rate relative to temperate genera? Any future inquiries on this topic would benefit from modeling their approach for identifying somatic mutations on the methods laid out here.

Reviewer #3 (Public Review):

Ding et al. address the experimental question of whether the microbially derived I3A can exert pro-metabolic effects in an experimental model of diet induced obesity/hepatic steatosis. This was based on previous findings by the authors that high fat diet alters levels of I3A, and that I3A can exert anti-steatotic and anti-inflammatory effects in vitro. The data are robust and the authors provide a plethora of omics-based platforms including proteomics and metabolomics under a variety of treatment paradigms. By performing these studies in vivo in mouse liver tissue, these atlases of proteomic and metabolomic datasets would be of interest to the field of metabolism for future analysis. However, there are several weaknesses identified within this manuscript. Primarily, weaknesses in the interpretation and organization of presented data overshadow the robust data presented and make it difficult for the reader to draw any new biological conclusions. Specifically, this manuscript in its current form is primarily of descriptive nature and does not distill any of the complex datasets presented into digestable conclusions that shed new insight into regulation of hepatic metabolism and inflammation by I3A. In essence, this manuscript in its current form is an in vivo extension to the author's previous in vitro assessment of I3A on liver function. Finally, there is a flaw in the model presented (Supplemental Fig. 9) with regards to the authors linking the anti-inflammatory effects of I3A with the metabolic effects. In fact, the authors present data (Fig. 1&2) that show the opposite of this interpretation in which inflammation is uncoupled from the metabolic effects of I3A in the low dose treatment group. While the authors achieved their main goal of addressing the metabolic effects of I3A in vivo, the organization and interpretation of the data presented in its current form is likely to result in a modest impact on the field.

Reviewer #3 (Public Review):

The manuscript entitled, "Uremic toxin indoxyl sulfate induces trained immunity via the AhR-dependent arachidonic acid pathway in ESRD" demonstrates that indoxyl sulfate (IS) induces trained immunity in monocytes via epigenetic and metabolic reprogramming, resulting in augmented cytokine production. The authors conducted well-designed experiments to show that the aryl hydrocarbon receptor (AhR) contributes to IS-trained immunity by enhancing the expression of arachidonic acid (AA) metabolism-related genes such as arachidonate 5-lipoxygenase (ALOX5) and ALOX5 activating protein (ALOX5AP). Overall, this is a very interesting study that highlights that IS mediated trained immunity may have deleterious outcomes in augmented immune responses to the secondary insult in ESRD. Key findings would help to understand accelerated inflammation in CKD or RSRD.

Reviewer #3 (Public Review):

The study focuses on in vivo and in vitro cellular responses intranasal instillation of glycoforms and mutants of SARS-CoV2 spike trimer or spike bearing VLP. Collectively, the experiments suggest that SARS-CoV2 spike has pro-inflammatory roles through increase M1 macrophage associated cytokines and induction of neutrophil netosis, a proinflammatory cell death mechanisms. These effects seem largely independent of hACE2 interaction and partly depend upon interactions with scavenger receptors on macrophages and neutrophils. A strength of the study is that a number sophisticated methods are used, including intravital microscopy in the cramaster and liver as well as acute lung slice models, to look at uptake of the spike proteins and immune cell dynamics. The weakness is that some of the reagents maybe contaminated with uncharacterized glycoforms and some important controls, such as control spike protein and control VLP are unevenly applied or not included. Given the breadth of the studies, it would be ideal for the authors to prioritise strengthening the most important in vivo results in the best animal models with the strongest controls to be able to realise the full impact of the results.

Reviewer #3 (Public Review):

The manuscript entitled "SMARCAD1 and TOPBP1 contribute to heterochromatin maintenance at the transition from the 2C-like to the pluripotent state" by Sebastian-Perez et al. adopted the iPOTD method to compare the chromatin-bound proteome in ESCs and 2C-like cells generated by Dux overexpression. The authors identified 397 chromatin-bound proteins enriched only in ESC and 2C- cells, among which they further investigated TOPBP1 due to its potential role in controlling chromocenter reorganization. SMARCD1, a known interacting protein of TOPBP1, was also investigated in parallel. The authors observed increased size and decreased number of H3K9me3-heterochromatin foci in Dux-induced 2C+ cells. Interestingly, depletion of TOPBP1 or SMARCD1 also led to increased size and decreased number of H3K9me3 foci. However, depletion of these proteins did not affect entry into or exit from the 2C-like state. Nevertheless, the authors showed that both TOPBP1 and SMARCD1 are required for early embryonic development.

Although this manuscript provides new insights into the features of 2C-like cells regarding H3K9me3-heterochromatin reorganization, it remains largely descriptive at this stage. It does not provide new insights into the following important aspects: 1) how SMARCD1 associates with H3K9me3 and contributes to heterochromatin maintenance, 2) how TOPBP1 regulates the expression of SMARCD1 and facilitates its localization in heterochromatin foci, 3) whether the remodelling of chromocenter is causally related to the mutual transitions between ESCs and 2C-like cells. Furthermore, some results are over-interpreted. Additional experiments and analyses are needed to increase the strength of mechanistic insights and to support all claims in the manuscript.

Reviewer #3 (Public Review):

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with progressive and irreversible deterioration of respiratory functions that lacks curative therapies. The authors investigated a new therapeutic approach to treat idiopathic pulmonary fibrosis by targeting P2RX7/IL-18/IFNG axis.

The current data are mainly based on P2RX7 activator HEI3090 and genetic experiments are lacking to support the primary claim that activation P2RX7/IL-18/IFNG axis is beneficial for IPF.

- Parenteral systemic administration of IFN-γ failed in clinical trials (INSPIRE; NCT00075998). However, this study used i.p. administration of P2RX7 activator HEI3090 to activate P2RX7/IL-18/IFNG axis.

- Activation of P2RX7 NLRP3 inflammasome triggers cell death and the current experiments do not explore IL-18 as a potential therapy that would avoid harmful cell death as a consequence of P2RX7/NLRP3 inflammasome activation.

- Reciprocal bone marrow chimera model is needed to demonstrate the requirement of a hematopoietic compartment for HEI3090's antifibrotic effect.

- There is no evidence to show whether P2RX7 interferes with bleomycin during the generation of the IPF model. Independent IPF models would validate the therapeutic effect of P2RX7.

https://docdrop.org/pdf/Larson---1998---Schenkerian-analysis-of-modern-jazz-Question-p5tbw.pdf/

Schenkerian Analysis of Modern Jazz: Questions about Method Larson, S 1998

Reviewer #3 (Public Review):

"Continuous, long-term crawling behavior characterized by a robotic transport system" by Yu et al. presents their new robotic device to track, reposition, and feed Drosophila larvae as they crawl on an arena. By using a water droplet (or if necessary, suction) to transport larvae from the edge of the arena to the middle, long behavior trajectories can be recorded without losing larvae from the arena or camera field of view. The picker robot is also able to dispense small amounts of apple juice at precise locations to keep larvae alive for extended periods although the food was not sufficient to trigger molting and the development to the next instar stage.

The approach is interesting, but the authors could provide more details on why the approach is necessary for non-expert readers. For example, what are the advantages of using the robot picker compared to simply confining larvae in a closed arena? It's not obvious (to me) that being picked back to the center of the arena is a smaller perturbation compared to running into a chamber wall and changing direction.

The first paragraph of the introduction emphasizes the multiple time scales that are relevant for behavior from rapid stimulus response up to developmental times. This is to set the context of the authors' contribution but I'm not sure it's a fair representation of the state of the art. For example, the authors state that high-bandwidth measurement over long times is prohibitive and cite three Drosophila papers, but there are home-cage monitoring systems that allow continuous recording of mouse behavior over long times with high resolution. At the other end of the spectrum, there have been some long-term behaviour experiments done on worm behaviour with reasonably high time resolution (e.g Stern et al. 10.1016/j.cell.2017.10.041).

The authors train a neural network to segment and track the larvae, however, little information is given on the training process and I don't think it would be possible to reproduce the model based on the description. More details of the network, hyperparameters, and training data would be required to evaluate it.

The authors also state several times that larval identity is maintained throughout the recording, but this isn't quantified. It's not clear whether identity is maintained across collisions of two or more animals by the tracking algorithm or whether these collisions simply don't happen in their data because density is low.

The environment is nominally isotropic, but once larvae have been crawling on the surface for hours, including periodic feeding, there will likely be multiple gradients the larvae may sense. This may not be observable in the data, but should perhaps be mentioned in the text.

The authors show that the picking action results in a small but detectable increase in speed. The degree of perturbation overall depends on the picking frequency so some quantification of the inter-pick time interval would help to interpret whether this perturbation is relevant for a particular experiment. Is there a difference in excitation when larvae are picked successfully on the first try compared to when multiple tries or suction are required?

From the reconstructed trajectory in Figure 4, this interval looks very long compared to speed increase after picking. When reconstructing the trajectory, how are the segments joined? Is it simply by resetting the xy position or also updating rotating to match the previous direction of travel? (I'm guessing the larva can rotate during transport?)

The authors present a simple model in Figure 6 to illustrate the differences between individuals that can be hidden when looking at population distributions. However, the differences they show in the simulation don't seem relevant to the differences they observe in the experiments. Specifically, Fig. 6A and B show a contrast between individuals with similar mean speeds compared to individuals with different (but still unimodal) mean speeds. In contrast, the experimental data in Fig. D shows individual distributions that are quite similar but that are bimodal. So, there is indeed a difference between the individual distributions that is obscured in the population distribution, but is there evidence of larval personality types (line 444)? Similarly, the sentence beginning line 381 doesn't seem right either.

Reviewer #3 (Public Review):

Bushy cells are one of the principal neurons in the cochlear nucleus that provide temporal information to higher auditory nuclei to compare sound signals from both ears. One prominent feature in the auditory processing of bushy cells is that they show enhanced temporal responses compared to the auditory nerve (AN) inputs, thus providing a better temporal representation of the acoustic signals. Another feature of the AN-bushy cell circuit is that AN fibers form large synapses termed "endbulbs" around the soma of bushy cells. Scientists have proposed that the temporal enhancement can be due to the coincidence detection of subthreshold convergent AN inputs, or a first-spike latency-based detection of convergent supra-threshold inputs. However, testing these hypotheses requires knowledge of the detailed anatomical arrangement of the AN inputs onto bushy cells. This paper provides a first look at the 3-D organizations of the pre- and postsynaptic structures of mouse bushy cells at a nanoscale resolution. Furthermore, the authors create a morphology-constrained biophysical model to examine how these structures may affect synaptic integration and auditory processing.

The main finding of the paper is that the authors found two input motifs in the AN-bushy cell circuit: one with all small, subthreshold endbulb inputs (all < 180 um2), and the other with 1-2 large, suprathreshold endbulb inputs (> 180 um2) plus other smaller endbulb inputs. Using modeling, the authors argue that the former group correlates with a physiological phenotype of "coincidence detection", and the latter correlates with a phenotype termed "mixed-mode detection". "Coincidence detection" cells require the coincident firing of many subthreshold presynaptic inputs to evoke a postsynaptic spike; "mixed-mode" cells can either have postsynaptic spikes evoked by the largest input(s) alone, or by the coincident firing of small (plus large) inputs. Interestingly, the authors found that even though the large inputs alone can trigger spikes in the "mixed mode" cells, smaller inputs can further enhance the temporal precision of the spikes. The structural data are of very high quality and clearly show the endbulb inputs comprise various sizes. Whether these inputs are really supra/sub-threshold remains to be explored physiologically, but nevertheless, the model provides a hypothesis for the functional roles of the endbulb of different sizes.

In addition to the finding of "two convergent motifs', the authors also report a first complete map of synaptic inputs to a single bushy cell, and structures that have not been observed before, such as synapses at axon-hillock and axon initial segment, dendritic "hub", "braided" dendrites, non-innervated dendrites, etc. These data, like the previous "two input motifs" observation, are also of very high quality and can be useful resources for the ultrastructural study of the bushy cells.

Strengths:<br /> The strengths of this paper are that the authors obtained unprecedented high-resolution 3-D images of the AN-bushy cell circuit, and they implemented a biophysical model to simulate the neural processing of AN inputs based on these structural data. The 3-D reconstruction of the pre- (input organization) and post- (dendrites and axons) synaptic structures of bushy cells are of high quality, as exemplified by the high-resolution figures and animations. The biophysical modeling, although lacking comparison with in vivo physiological data due to the chosen species (mice), is also solid and well documented. The combination of high-resolution imaging and structure-based modeling, together with the detailed documentation, provides rich information for not only auditory scientists but non-auditory scientists who want to use similar techniques to explore neural circuits.

Weaknesses:<br /> Despite the high quality of the data, the paper is marred by the species they chose: there are very few published in vivo single-unit results from mouse bushy cells, so it is hard to evaluate how well the model predictions fit the real-world data, and how the structural findings address the "fundamental questions" in physiology. If we look at data from other animals such as cats and gerbils, it is true that high-frequency (globular) bushy cells show envelope phase locking, but compared to ANs they are at best only moderately enhanced (gerbils: Frisina et al. 1990: Fig 7 and 10; cats: Joris and Yin 1998 Fig 4); the most prominent enhancement is actually to the temporal fine structures of low-frequency bushy cells (cells tuned to < 1 kHz), which mice lack. Furthermore, the temporal modulation transfer function (tMTF, i.e. the vector strengths vs modulation frequency plots in Fig 7O of the paper) of (globular) bushy cells are mostly low-pass filtered, with a cutoff frequency close to 1 kHz, and the highest vector strength rarely surpasses 0.9 (cats: Rhode 1994 Fig 9, 16, Rhode 2008 Fig 8G, Joris and Yin 1998 Fig 7; and there's one report from mice: Kopp-Scheinpflug et al 2003 Fig 8). Thus, the band-pass tMTFs tuned to 100-200 Hz with vector strengths > 0.9 or 0.95 in this paper (Fig 7O, Fig 8M) do not really match known physiology (in non-mouse species). Again, we know very little about in vivo physiology of mouse (globular) bushy cells and there is of course a possibility that responses in mice may be closer to the predictions of this paper. No rationale (e.g. use of molecular tools or in vitro physiology) is given why the authors focus on the mouse. It seems that the analyses provided here could as well have done on a species with good low-frequency hearing, which may have provided a much more interesting case for understanding the spectacular temporal transformation performed by bushy cells.

Reviewer #3 (Public Review):

In this paper, Ichinose et al. examine mechanisms that contribute to building inhibitory synapses through differential protein release from microtubules. They find that tenurin-2 plays a role in this process in cultured hippocampal neurons via EB1 using a variety of genetic and imaging methods. Overall, the experiments are generally designed well, but it is unclear whether their findings offer a significant advance. The experimental logic flow and rational difficult for readers to follow in the manuscript's current form.

Strengths:<br /> 1) The experiments are generally well designed overall, and appropriate to the questions posed.<br /> 2) Several experimental methods are combined to validate key results.<br /> 3) Use of cutting-edge technologies (i.e. STORM imaging) to help answer key questions in the paper.

Weakness:<br /> 1) Simplifying the text and story line would go a long way to ensure the study results are more effectively communicated. Additional specific suggestions are provided in the recommendations for the authors.<br /> 2) The introduction overall would benefit from simplification so that the reader is given only the information they need to know to understand the question at hand.<br /> 3) MT dynamics are important for paper results, but the background in the paper does not appropriately introduce this topic.<br /> 4) It is a bit unclear from the abstract and introduction how the findings of this paper have significantly advanced the field or taught something fundamentally new about how inhibitory synapses are regulated.<br /> 5) Figure 1 - Line 109, it is obscure why "it was found appropriate" to divide the data into three clusters. This section would better justified by starting with cellular functions and then basing the clusters on these functions.<br /> 6) The proteomic screen and candidate selection is not well justified and the logic steps for arriving at TEN2 are a bit weak. Again, less is more here.<br /> 7) Fig. 2 - The authors should consider whether EB1 overexpression would have functional consequences that alter the results and colocalization.<br /> 8) Fig. 3 - Is immobilization of COS cells using HA tag antibodies a relevant system for study of these questions?<br /> 9) Fig. 4 - The authors should confirm post-synaptic localization in vivo (brain).<br /> 10) Figure 4D-E - The way the STORM results are presented is confusing. The authors state is shows that TEN2 is postsynaptic but before this say that the Abs are the same size as the synaptic cleft so that the results cannot be considered conclusive. This issue should be resolved.<br /> 11) Figure 5 -The authors should examine the levels of gephyrin relative to the levels of knockdown given the knockdown variability.<br /> 12) Functional validation of a reduction in inhibition following TEN2 manipulation would elevate the paper.<br /> 13) Figure 6E - The expression levels of TEN2TM and TEN2NL are important to the outcome of these experiments. How did the authors ensure that the levels of two proteins were the same to begin with?

Reviewer #3 (Public Review):

In this study, the authors examined the expression of GPR110 in a HFD-fed mouse model and validated their findings in human samples. They then performed both gain- and loss-of-function studies on the cellular and systemic metabolic effects of manipulating the levels of GPR110. They further demonstrated that SCD-1 was a downstream effector of GPR110, and the effects of GPR110 could be mediated by SCD-1. This study provides a novel target in NAFLD. Overall the data and analyses well performed and convincing. As the GPR110-SCD1-lipid metabolic phenotype axis is a central theme of the study, I would suggest that the authors further discuss the connection between GPR110 and SCD1, especially the persistent upregulation of SCD1 at late stage of HFD-fed mice (obese mouse model) when GPR110 is very low, for example, whether another regulator plays a more relevant role at this time point.

Reviewer #3 (Public Review):

The manuscript by Shin et al, "Aerobic exercise reverses aging-induced depth-dependent decline in cerebral microcirculation", addresses fundamental questions on the mechanism by which aerobic exercise can reverse several age-related dysfunctions in the cerebral vasculature. This work is solid as they use a wide range of complementary in vivo imaging modalities including two-photon fluorescent imaging, optical coherence tomography, and measurements of PO2 as well as behavioral tests. The experiments specifically examined region-specific differences in the young and aged vasculature and the response to aerobic exercise in superficial cortical areas and importantly in deeper white matter areas. This is a solid contribution because it provides additional understanding of age-related changes in the white matter microcirculation, a brain region where our understanding is incomplete. This work effectively sets the stage to further examine aging-related white matter degeneration, how aerobic exercise ameliorates the vascular decline in aging, and will potentially lead to novel interventions targeting the white matter.

• definition
  • the three inevitables

• the third inevitable

  • bad things will happen

• comment

  • progress traps are the right framework to describe the AI problem

Reviewer #3 (Public Review):

In the work presented in "A label-free method to track individuals and lineages of budding cells", Pietsch et al. use multiple machine learning approaches to identify, delineate, and track yeast cells in microscopy images.

I commend the authors for putting a lot of work into this manuscript and coming up with many new ideas to solve their problem of interest. However, throughout the manuscript, I felt that this manuscript does not work well as a 'methods' paper. Maybe it should have been a paper about the biology, which I find very interesting. My main reason for finding this manuscript not well-suited for a methods paper is that their approach as well as the goal are so specific that it may not be readily adopted by others. I would like to list the number of limitations and particularities of their set-up to support this conclusion:

- The whole problem of small cells not being in focus in a single plane is to a large part due to the high ceiling of the authors' microfluidic chips (6 um according to Crane et al.). Other microfluidic chips have much lower ceilings, keeping cells essentially in 2D. If Pietsch et al. used a lower ceiling, small cells would presumably not be out of focus so frequently nor appear to overlap with other cells, and the usual single z-stack approach would suffice. (Another configuration in which cells appear to overlap is in wells, e.g., 96-well plates, which are similarly not ideal for imaging.) Thus, for the problem of interest to Pietsch et al., I would have used a different chip first and then seen what remains of the identification, segmentation, and tracking problem.

- The method requires a number of z-stacks (although I read somewhere how many z-stacks the method needs, I now cannot find that information any more, which highlights a general problem with the presentation, which I will get to below). This means that the already large amount of data that needs to be acquired with regular 2D images now is multiplied by "n" for each z-stack. More importantly, initially, z-stacks have to be individually labeled for training the neural network. That is n times what other segmentation methods require. So, one would presumably only invest this amount of work if one really cared about the tiniest buds because that is, from what I understand, the main selling point of the method. But how many labs do care about this question and going about it in the exact same way as Pietsch et al.? For example, to just find the exact time a bud appears, most people could just extrapolate the size of a new bud in time to zero or simply use a fluorescent budneck marker. Somebody would have to want to measure the growth rates of the smallest buds without fluorescent labels, which the authors do in this present work. But unless someone wants to repeat this exact measurement, say, with other mutants, I do not see who else would invest a large amount of time and resources for this. Other quantities such as fluorescent protein levels cannot be measured with this approach anyway, i.e., by going through z-stacks with a widefield microscope. One would presumably have to use a confocal microscope.

- Could the problem have been simplified by taking z-stacks but analyzing each as a regular 2D image with existing segmentation methods? If a new bud is detected in any of the z-stacks, it is counted as a new cell. This would allow one to use existing 2D training sets and methods and only add a few images of one's own, whether taken in a single z-stack or not. It would only involve tweaking or augmenting existing methods slightly.

- While a 3D image needs to be fed to the neural network, ultimately, all measurements in this manuscript are 2D measurements, e.g., all growth rates are in units of um^2/h. (Somewhat unexpectedly, the authors use a Myo1-GFP construct to identify the budded phase of cells in Fig. 4, i.e., exactly what this method was designed to avoid.) Thus, the effort of going to 3D is only to make the identification of buds more accurate. So, we are not really dealing with a method that goes from 2D to 3D and reconstructs, for example, the shape of cells in 3D. So, while z-stacks go in, it is not 3D annotations that come out.

- The authors may argue that they want to use their high-ceiling chips because they want to follow aging cells. Or, they may argue that indeed, this method is going to be used more widely because people want to study the growth rates of tiny buds in various mutants. However, then the limitation of their method to convex shapes or shapes that can be represented in cylindrical coordinates is a problem since old cells and many mutants can have strange shapes. In this way, the authors have gone a step back methodologically for reasons that I do not understand.

- Given that the method is tailored to detecting small buds, I also do not understand why the authors do not use a higher magnification objective, e.g., a 100x objective instead of 60x? Maybe the problem becomes much easier that way?

- It is unclear how well the tracking method generalizes for other configurations. Here, the tracking problem is somewhat special because there are only a few cell in and around the traps and frequently cells are washed away. For a method paper, the tracking method would need to be compared and contrasted with others for different kinds of experiments. Since tracking is in the title of the manuscript, it is presumably an important selling point of the manuscript.

- The same applies to the segmentation problem. The traps in the authors' microfluidic chips only keep a small number of cells, avoiding problems that emerge when many cells of similar sizes abut.

Reviewer #3 (Public Review):

Lee et al. identify the Stranded at second (Sas) cell surface protein as an extracellular vesicle (EV) component in Drosophila. They first show that different isoforms of Sas exhibit differential tissue distribution in vivo, with the EV-enriched full-length Sas isoform exhibiting distribution at distant sites away from its cells of origin. They show that Sas is present in EVs purified from Drosophila S2 cells, as assessed using exosome isolation kits and via immuno-electron microscopy. Their data suggest that Sas-bearing EVs preferentially target cells expressing Ptp10D, a receptor tyrosine phosphatase that is a known binding partner of Sas, both in the context of S2 cells and imaginal discs engineered to overexpress Ptp10D and the endocytosis regulatory protein Numb. Through immunoprecipitation (IP) of Sas from S2 cell EVs, as well as validation co-IPs and peptide binding assays, the authors found that Sas can interact with the dArc1 protein (i.e. the orthologue of mammalian Arc, which has the ability to form capsid-like structures) via a conserved protein motif of Sas. Finally, they show that Sas increases the transfer of dArc1 protein and mRNA from Sas-expressing cells to Ptp10D-enriched tissues in vivo. The authors conclude that Sas facilitates the delivery of dArc1 capsids that carry dArc1 mRNA to recipient cells that express Ptp10D.

General Strengths: The in vivo and in vitro data conveying the selective targeting of the full-length Sas isoform to EVs, and the impact on the delivery of dArc1 to distant Ptp10D-expressing cells, are generally strong and supportive of the proposed model. The authors also show convincing data confirming the interaction of Sas with dArc1 by IP-MS and binding assays.

General Weakness: It is not clear if the major biological function of the endogenous Sas-Ptp10D interaction is mediated via EVs. The inclusion of additional data evaluating dArc1 mRNA EV-mediated transfer to the trachea in Sas and/or Ptp10D null mutant flies would strongly enhance the paper and support the role of these proteins in tissue-specific EV targeting in vivo. Moreover, throughout the paper, there are several controls and quantifications missing that would be required in order to strengthen the general conclusions and proposed regulatory model. For instance, it is not clear to what extent Sas and dArc1 proteins are co-enriched within purified EV specimens. Immuno-EM studies or nanoparticle analysis strategies should be implemented to address this aspect. Several of the IF- and FISH-based labeling experiments lacked controls. Also, there are few if any quantifications provided as to the number of tissue specimens that were examined in the various assays as a basis for making specific conclusions.

Reviewer #3 (Public Review):

In this manuscript, Yang et al. claimed the creation of a single-cell atlas of the human anterior cruciate ligament (ACL) using scRNA-seq, spRNA-seq, and transcriptomic profiling. Upon analysis of about 25K cells from healthy and degenerated human ACL, the authors reported the existence of fibroblasts, endothelial cells, pericytes, and immune cells in healthy ACL. Their ratios altered in the degenerative ACL, featuring an increase in fibroblasts and immune cells, as demonstrated by the UMAP. Further characterization revealed the presence of subclusters in each of the four major types of cells. The evolution trajectory, spatial transcriptome, and signaling pathways that may contribute to biphasic ACL degeneration were also explored. These data are valuable, to some extent, in improving the current knowledge regarding ACL cellular heterogeneity, homeostasis, and ligamental degeneration. However, the abovementioned findings are purely derived from computational modeling; the authors haven't validated any of them experimentally in vitro and in vivo, particularly regarding whether there are multiple fibroblast subclusters in the ACL with distinct biology. The spatial transcriptomic analysis is also superficial, and few novel insights were generated. The reported work seems like a window show of fancy technologies rather than a hypothesis-driven investigation. Some figures were not clearly labeled, and figure legends were too brief to follow up the studies. Therefore, the significance of this work and its value as a cell atlas of ACL are compromised.

Reviewer #3 (Public Review):

Macrophages play an important role during heart regeneration. This has been shown in the mouse and zebrafish for example by treating the animals with clodronate liposomes to eliminate phagocytic cells.<br /> The manuscript follows up on a previous observation by the authors performing these experiments in the zebrafish (Lai et al eLife 2017). When comparing regenerative vs non-regenerative teleosts zebrafish resp Medaka they found that macrophages and neutrophils were the cell types more differentially responding in these two species to a cardiac injury.

Here the authors anaylse in extenso neutrophil and macrophage populations using single-cell RNA-seq at different stages of regeneration. They perform FAC sorting of the two populations using specific reporter lines. They also assess the change in these populations upon clodronate treatment. They find that clodronate treatment affects the gene expression profiles of different subsets of macrophages and neutrophils as well as their abundance.

They also show that chlodronate treatment performed several days before cryoinjury depleted macrophages from the heart but after injury overall macrophage number recovers. However, heart regeneration does not. Cardiomyocyte is the only parameter that is not affected, but vasculogenesis and scar resolution is impaired.

The authors conclude that (1) there are different subsets of macrophages and neutrophils, (2) that they interact with each other during regeneration through specific ligand and receptor pairs, and (3) that a cardiac resident population rather than a circulating macrophage population is important for heart regeneration.

The transcriptomic characterization of the two immune cell populations is very exhaustive and rigorous. No functional validation of subpopulation marker genes was performed, but the data as it stands will already be of great value to the community. The figure quality is outstanding.

Reviewer #3 (Public Review):

The authors examined mechanotransductive feedback dynamics that govern endothelial cell motility and vascular morphogenesis. They investigated endothelial cell morphology, migration speed, cell shape, cytoskeletal and focal adhesion maturation in human derived ECFC. To substantiate their in vitro data set, they imaged intersegmental vessel development in zebrafish embryos treated with various inhibitors of translation and acto-myosin remodelling . They conclude that the transcriptional regulators, YAP and TAZ, are activated by mechanical cues to transcriptionally limit cytoskeletal and focal adhesion maturation, forming a conserved mechanotransductive feedback loop that mediates endothelial cell motility. Mechanistically, YAP and TAZ induced transcriptional suppression of myosin II activity to maintain dynamic cytoskeletal equilibria. Such transcriptional feedback loop may be necessary for persistent endothelial cell migration and vascular morphogenesis. The authors addressed an interesting aspect of vascular development and I have some comments and suggestions that are listed below.

Comments:

The authors used ECFC - endothelial colony forming cells (circulating endothelial cells that activate in response to vascular injury).

Q: did the authors characterize these cells and made sure that they are truly endothelial cells - for example examine specific endothelial markers, arterial-venous identity markers & Notch signalling status, overall morphology etc prior to the start of the experiment. How were ECFC isolated from human individuals, are these "healthy" volunteers - any underlying CVD risk factors, cells from one patient or from pooled samples, what injury where these humans exposed to trigger the release of the ECPFs into the circulation, etc. The materials & methods on ECFC should be expanded.

The authors suggest that loss of YAP/TAZ phenocopies actinomycin-D inhibition - "both transcription inhibition and YAP/TAZ depletion impaired polarization, and induced robust ventral stress fiber formation and peripheral focal adhesion maturation". However, the cell size of actinomycin-D treated cells (Fig. 1B, top right panel), differs from the endothelial cell size upon siYAP/TAZ (Fig. 1E, top right panel) - and vinculin staining seems more pronounced in actinomyocin-D treated cells (B, bottom right) when compared to siYAP/TAZ group. Cell shape is defined by acto-myosin tension.

Q: besides Fraction of focal adhesion >1um; focal adhesion number did the authors measure additional parameters related to cytoskeleton remodelling / focal adhesions that can substantiate their statement on similarity between loss of YAP/TAZ and actinomycin-D treatment. Would it be possible to make a more specific genetic intervention (besides YAP/TAZ) interfering with the focal adhesion pathway as opposed to the broad spectrum inhibitor actinomyocin-D.<br /> Q: does the actinomycin-D treatment affect responsiveness to Vegf? induce apoptosis or reduce survival of the ECFC?<br /> Q: Which mechanism links ECM stiffness with endothelial surface area in the authors scenario. In zebrafish, activity of endothelial guanine exchange factor Trio specifically at endothelial cell junctions (Klems, Nat Comms, 2020) and endoglin in response to hemodynamic factors (Siekmann, Nat Cell Biol 2017) have been show to control EC shape/surface area - do these factors play a role in the scenario proposed by the authors.<br /> Q: the authors report that EC migrate faster on stiff substrate, and concomitantly these cells have a larger surface area. What is the physiological rationale behind these observations. Did the authors observe such behaviors in their zebrafish ISV model? How do these observation integrate with the tip - stalk cell shuffling model (Jakobsson&Gerhardt, Nat Cell Biol, 2011) and Notch activity in developing ISVs.

The authors examined the formation of arterial intersegmental vessels in the trunk of developing zebrafish embryos in vivo. They used a variety of pharmacological inhibitors of transcription and acto-myosin remodelling and linked the observed morphological changes in ISV morphogenesis with changes in endothelial cell motility.<br /> Q: reduced formation and dorsal extension of ISVs may have several reasons, including reduced EC migration and proliferation. The Tg(fli1a:EGFP) reporter however is not the most suitable line to monitor migration of individual endothelial cells. Can the authors repeat the experiments in Tg(fli1a:nEGFP); Tg(kdrl:HRAS-mCherry) double transgenics to visualise movement-migration of the individual endothelial cells and EC proliferation events, in the different treatment regimes.<br /> ISV formation is furthermore affected by Notch signalling status and a series of (repulsive) guidance cues.<br /> Q: Does de novo blockade of gene expression with Actinomycin D affect Notch signalling status, expression of PlexinD - sFlt1, netrin1 or arterial-venous identify genes.

Remark: the authors may want to consider using the Tg(fli1:LIFEACT-GFP) reporter for in vivo imaging of actin remodelling events.

Remark: the authors report "As with broad transcription inhibition, in situ depletion of YAP and TAZ by RNAi arrested cell motility, illustrated here by live-migration sparklines over 10 hours: siControl: , siYAP/TAZ: (25 μm scale-bar: -)". Can the authors make a separate figure panel for this, how many cells were measured?<br /> Remark: in the wash-out experiments, exposure to the inhibitors is not the same in the different scenarios - could it be that the longer exposure time induces "toxic" side effect that cannot be "washed out" when compared to the short treatment regimes?

Reviewer #3 (Public Review):

This study focuses on defining the specific importance of HSP90 isoforms in stress-resistance. Specifically, addressing the importance of the two HSP90 isoforms alpha and beta in adapting cells to chronic stress. Noting that chronic stresses of different types can induce increases of cellular size, the authors investigated the role of HSP90a/b in this process. Intriguingly, they found that KO of either of these isoforms did not influence chronic stress-dependent increases of cell size. However, they did find that HSF1 plays an important role in this process through undefined mechanisms. The authors go on to show that this increase in cell size appears to be correlated with enhanced protein synthesis during conditions of stress, which allow cells to maintain protein density in the enlarged cell. Intriguingly, this correlation is disrupted in HSP90a/b KO cells, where cell size increases, but there is a deficiency in recovery of protein synthesis following the initial insult. This appears to involve sustained ISR signaling that does not resolve in HSP90-deficient cells. Using a number of different compounds that increase (e.g., CDKi) or inhibit (e.g., rapamycin) cell size changes, the authors demonstrate that protection against chronic stress correlates with cell size and protein density, linking cell expansion to stress resistance.

Overall, this is an observational study that heavily relies on correlation to define a proposed stress responsive signaling mechanism termed the 'rewiring stress response' to explain the coordinated increase in cell size and protein translation in protection against chronic stress.

Due to the reliance on correlation, there remains many questions unanswered related to this work. For example. What is the specific role for HSP90a/b in regulating protein translation during chronic stress through the ISR or related pathways? The authors indicate that the induction of the eIF2a phosphatase GADD34 is not impacted in HSP90-deficient cells, so what role does HSP90 have in this process. Is HSP90 required for proper folding of GADD34? Would you see similar effects in protein translation recovery if other ISR activators are used in HSP90-deficient cells? Addressing this central unanswered question that would significantly enhance the current study. While the authors are undoubtedly pursuing this in subsequent studies, it is difficult to fully gauge the impact of this work without more clarity on that point specifically.

Along the same lines, another critical unanswered question is 'Are similar effects observed in non-dividing cells?' Does chronic stress lead to increases of size and regulation of protein translation in primary cell models that are not undergoing division.

Ultimately, this is an interesting study that does a good job of establishing correlations between increases in cell size and protein translation, but does not get to the really intriguing questions related to this coordination. As this study is extended through either revisions to this manuscript or subsequent papers, the importance of this rewiring stress response in the context of cellular stress and pathologic conditions (e.g., age-associated disease) will become increasing apparent.

Reviewer #3 (Public Review):

In this study, Wang et al extend on their previous finding of a novel quality control pathway, the MAGIC pathway. This pathway allows misfolded cytosolic proteins to become imported into mitochondria and there they are degraded by the LON protease. Using a screen, they identify Snf1 as a player that regulates MAGIC. Snf1 inhibits mitochondrial protein import via the transcription factor Hap4 via an unknown pathway. This allows cells to adapt to metabolic changes, upon high glucose levels, misfolded proteins an become imported and degraded, while during low glucose growth conditions, import of these proteins is prevented, and instead import of mitochondrial proteins is preferred.

This is a nice and well-structured manuscript reporting on important findings about a regulatory mechanism of a quality control pathway. The findings are obtained by a combination of mostly fluorescent protein-based assays. Findings from these assays support the claims well.

While this study convincingly describes the mechanisms of a mitochondria-associated import pathway using mainly model substrates, my major concern is that the physiological relevance of this pathway remains unclear: what are endogenous substrates of the pathway, to which extend are they imported and degraded, i.e. how much does MAGIC contribute to overall misfolded protein removal (none of the experiments reports quantitative "flux" information). Lastly, it remains unclear by which mechanism Snf1 impacts on MAGIC or whether it is "only" about being outcompeted by mitochondrial precursors.

Reviewer #3 (Public Review):

This study performs in vivo recordings of neurons in the mouse superior colliculus and their afferents from the retina, retinal ganglion cells (RGCs). Building on a preparation they previously published, this study adds the use of optogenetic identification of inhibitory neurons (aka optotagging) to compare RGC connectivity to excitatory and inhibitory neurons in SC. Using this approach, the authors characterize connection probability, strength, and response correlation between RGCs and their target neurons in SC, finding several differences from what is observed in the retina-thalamus-visual cortex pathway. As such, this may be a useful dataset for efforts to understand retinocollicular connectivity and computations.

Reviewer #3 (Public Review):

This work provides a new approach to simultaneously control elbow and wrist degrees of freedom using movement based inputs, and demonstrate performance in a virtual reality environment. The work is also demonstrated using a proof-of-concept physical system. This control algorithm is in contrast to prior approaches which electrophysiological signals, such as EMG, which do have limitations as described by the authors. In this work, the movements of proximal joints (eg shoulder), which generally remain under voluntary control after limb amputation, are used as input to neural networks to predict limb orientation. The results are tested by several participants within a virtual environment, and preliminary demonstrated using a physical device, albeit without it being physically attached to the user.

Strengths:<br /> Overall, the work has several interesting aspects. Perhaps the most interesting aspect of the work is that the approach worked well without requiring user calibration, meaning that users could use pre-trained networks to complete the tasks as requested. This could provide important benefits, and if successfully incorporated into a physical prosthesis allow the user to focus on completing functional tasks immediately. The work was also tested with a reasonable number of subjects, including those with limb-loss. Even with the limitations (see below) the approach could be used to help complete meaningful functional activities of daily living that require semi-consistent movements, such as feeding and grooming.

Weaknesses:<br /> While interesting, the work does have several limitations. In this reviewer's opinion, main limitations are: the number of 'movements' or tasks that would be required to train a controller that generalized across more tasks and limb-postures. The authors did a nice job spanning the workspace, but the unconstrained nature of reaches could make restoring additional activities problematic. This remains to be tested.

The weight of a device attached to a user will impact the shoulder movements that can be reliably generated. Testing with a physical prosthesis will need to ensure that the full desired workspace can be obtained when the limb is attached, and if not, then a procedure to scale inputs will need to be refined.

The reliance on target position is a complicating factor in deploying this technology. It would be interesting to see what performance may be achieved by simply using the input target positions to the controller and exclude the joint angles from the tracking devices (eg train with the target positions as input to the network to predict the desired angles).

Treating the humeral rotation degree of freedom is tricky, but for some subjects, such as those with OI, this would not be as large of an issue. Otherwise, the device would be constructed that allowed this movement.

Overall, this is an interesting preliminary study with some interesting aspects. Care must be taken to systematically evaluate the method to ensure clinical impact.

Reviewer #3 (Public Review):

Drougard et al. explore microglial detection of a switch to high-fat diet and a subsequent metabolic response that benefits memory. The findings are both surprising and novel in the context of acute high-fat intake, with convincing evidence of increased CSF palmitate after 3 days of HFD. While the authors demonstrate compelling signs of microglial activation in multiple brain regions and unique metabolite release in tracing studies, they should address the following areas prior to acceptance of this manuscript.

Major Points:<br /> 1. It appears that the authors perform key metabolic assays in vitro/ex vivo using primary microglia from either neonatal or adult mice, which should be more clearly delineated especially for the 13C-palmitate tracing. In the case of experiments using primary microglia derived from mixed glial cultures stimulated with M-CSF, this system relies on neonatal mice. This is understandable given the greater potential yield from neonatal mice, but the metabolic state and energetic demands of neonatal and adult microglia differ as their functional roles change across the lifespan. The authors should either show that the metabolic pathways they implicate in neonatal microglia are also representative of adult microglia or perform additional experiments using microglia pooled from adult mice, especially because they link metabolites derived from neonatal microglia (presumably not under the effects of acute HFD) to improved performance in behavioral assays that utilize adult mice.

2. The authors demonstrate that 3 days of HFD increases circulating palmitate by CSF metabolomics and that microglia can readily metabolize palmitate, but the causal link between palmitate metabolism specifically by microglia and improved performance in behavioral paradigms remains unclear. A previous body of research, alluded to by the authors, suggests that astrocyte shuttling of lactate to neurons improves long-term and spatial memory. The authors should account for palmitate that also could be derived from astrocyte secretion into CSF, and the relative contribution compared to microglia-derived palmitate. Specifically, although microglia can metabolize the palmitate in circulation, there is no direct evidence that the palmitate from the HFD is directly shuttled to microglia and not, for example, to astrocytes (which also express CX3CR1). Thus, the Barnes Maze results could be attributed to multiple cell types. Furthermore, the evidence provided in Figure 5J is insufficient to claim a microglia-dependent mechanism without showing data from mice on HFD with and without microglia depletion (analogous to the third and fourth bars in panel K).

3. Given the emphasis on improved cognitive function, there is minimal discussion of the actual behavioral outcomes in both the results and discussion sections. The data that HFD-treated animals outperform controls should be presented in more detail both in the figure and in the text. For example, data from all days/trials of the Barnes Maze should be shown, including the day(s) HFD mice outperform controls. Furthermore, the authors should either cite additional literature or provide experimental evidence supporting the notion that microglia release of TCA-associated substrates into the extracellular milieu after HFD specifically benefits neuronal function cellularly or regionally in the brain, which could translate to improved performance in classical behavioral paradigms. The single reference included is a bit obscure, given the study found that increased lactate enhances fear memory which is a neural circuit not studied in the current manuscript. Are there no additional studies on more relevant metabolites (e.g., itaconate, succinate)?

Minor Points:<br /> 1. In Figure 5J the latency to find the hole was noticeably higher (mean around 150s) than the latency in panel K (mean around 100s for controls, and 60s for Drp1MGWT on HFD). This suggests high variability between experiments using this modified version of the Barnes Maze, despite the authors' assertion that a "standard" Barnes Maze was employed and the results were reproducible at multiple institutions. Why do Drp1MGWT mice on control diet find the escape hole significantly faster than WT mice on control diet in panel J? Given the emphasis on cognitive improvement following acute HFD as a novel finding, the authors should explain this discrepancy.

2. The authors highlight in the graphical abstract and again in Figure 4A the formation of lipid droplets following palmitate exposure as evidence of that microglia can process fatty acids. They later suggest that a lack of substantial induction of lipid droplet accumulation suggests that microglia are metabolically wired to release carbon substrates to neighboring cells. Clarification as to the role of lipid droplet formation/accumulation in explaining the results would eliminate any possible confusion.

3. In many bar graphs showing relatively modest effects, it would be helpful to use symbols to also show the distribution of sample and animal replicates (especially behavioral paradigms).