Reviewer #3 (Public Review):

Liang and colleagues set out to test whether the human brain uses distance and grid-like codes in social knowledge using a design where participants had to navigate in a two-dimensional social space based on competence and warmth during an fMRI scan. They showed that participants were able to navigate the social space and found distance-based codes as well as grid-like codes in various brain regions, and the grid-like code correlated with behavior (reaction times).

On the whole, the experiment is designed appropriately for testing for distant-based and grid-like codes and is relatively well-powered for this type of study, with a large amount of behavioral training per participant. They revealed that a number of brain regions correlated positively or negatively with distance in the social space, and found grid-like codes in the frontal polar cortex and posterior medial entorhinal cortex, the latter in line with prior findings on grid-like activity in the entorhinal cortex. The current paper seems quite similar conceptually and in design to previous work, most notably by Park et al., 2021, Nature Neuroscience.

Below, I raise a few issues and questions on the evidence presented here for a grid-like code as the basis of navigating abstract social space or social knowledge.

1. The authors claim that this study provides evidence that humans use a spatial / grid code for abstract knowledge like social knowledge.

This data does specifically not add anything new to this argument. As with almost all studies that test for a grid code in a similar "conceptual" space (not only the current study), the problem is that when the space is not a uniform, square/circular space, and 2-dimensional then there is no reason the code will be perfectly grid-like, i.e., show six-fold symmetry. In real-world scenarios of social space (as well as navigation, semantic concepts), it must be higher dimensional - or at least more than two-dimensional. It is unclear if this generalizes to larger spaces where not all part of the space is relevant. Modelling work from Tim Behrens' lab (e.g., Whittington et al., 2020) and Bradley Love's lab (e.g., Mok & Love, 2019) have shown/argued this to be the case. In experimental work, like in mazes from the Mosers' labs (e.g., Derdikman et al., 2009), or trapezoid environments from the O'Keefe lab (Krupic et al., 2015), there are distortions in mEC cells, and would not pass as grid cells in terms of the six-fold symmetry criterion.

The authors briefly discuss the limitations of this at the very end but do not really say how this speaks to the goal of their study and the claim that social space or knowledge is organized as a grid code and if it is in fact used in the brain in their study and beyond. This issue deserves to be discussed in more depth, possibly referring to prior work that addressed this, and raising the issue for future work to address the problem - or if the authors think it is a problem at all.

Data and analysis

2. Concerning the negative correlation of distance with activation in the fusiform gyrus and visual cortex: this is a slightly puzzling but potentially interesting finding. However, could this be related to reaction times? The larger the distance, the longer the reaction times, so the original finding might reflect larger activations with smaller distances.

3. Concerning the correlation of grid-like activity with behavior: is the correlation with reaction time just about how long people took (rather than a task-related neural signal)? The authors have only reported correlations with reaction time. The issue here is that the duration of reaction times also relates to the starting positions of each trial and where participants will navigate to. Considering the speed-accuracy tradeoff, could performance accuracy be negatively correlated with these grid consistency metrics? Or it could be positively correlated, which would suggest the grid signal reflects a good representation of the task.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors report the first evidence of Nav1.5 regulation by a long noncoding RNA, LncRNA-DACH1, and suggest its implication in the reduction in sodium current observed in heart failure. Since no direct interaction is observed between Nav1.5 and the LncRNA, they propose that the regulation is via dystrophin and targeting of Nav1.5 to the plasma membrane.

Strengths:

1. First evidence of Nav1.5 regulation by a long noncoding RNA.<br /> 2. Implication of LncRNA-DACH1 in heart failure and mechanisms of arrhythmias.<br /> 3. Demonstration of LncRNA-DACH1 binding to dystrophin.<br /> 4. Potential rescuing of dystrophin and Nav1.5 strategy.

Weaknesses:

1. Main concern is that the authors do not provide evidence of how LncRNA-DACH1 regulates Nav1.5 protein level. The decrease in total Nav1.5 protein by about 50% seems to be the main consequence of the LncRNA on Nav1.5, but no mechanistic information is provided as to how this occurs.<br /> 2. The fact that the total Nav1.5 protein is reduced by 50% which is similar to the reduction in the membrane reduction questions the main conclusion of the authors implicating dystrophin in the reduced Nav1.5 targeting. The reduction in membrane Nav1.5 could simply be due to the reduction in total protein.

Reviewer #3 (Public Review):

Summary:

The receptor tyrosine kinase Anaplastic Lymphoma Kinase (ALK) in humans is nervous system expressed and plays an important role as an oncogene. A number of groups have been signalling ALK signalling in flies to gain mechanistic insight into its various role. In flies, ALK plays a critical role in development, particularly embryonic development and axon targeting. In addition, ALK also was also shown to regulate adult functions including sleep and memory. In this manuscript, Sukumar et al., used a suite of molecular techniques to identify downstream targets of ALK signalling. They first used targeted DamID, a technique that involves a DNA methylase to RNA polymerase II, so that GATC sites in close proximity to PolII binding sites are marked. They performed these experiments in wild-type and ALK loss of function mutants (using an Alk dominant negative ALkDN), to identify Alk responsive loci. Comparing these loci with a larval single-cell RNAseq dataset identified neuroendocrine cells as an important site of Alk action. They further combined these TaDa hits with data from RNA seq in Alk Loss and Gain of Function manipulations to identify a single novel target of Alk signalling - a neuropeptide precursor they named Sparkly (Spar) for its expression pattern. They generated a mutant allele of Spar, raised an antibody against Spar, and characterised its expression pattern and mutant behavioural phenotypes including defects in sleep and circadian function.

Strengths:

The molecular biology experiments using TaDa and RNAseq were elegant and very convincing. The authors identified a novel gene they named Spar. They also generated a mutant allele of Spar (using CrisprCas technology) and raised an antibody against Spar. These experiments are lovely, and the reagents will be useful to the community. The paper is also well written, and the figures are very nicely laid out making the manuscript a pleasure to read.

Weaknesses:

My main concerns were around the genetics and behavioural characterisation which is incomplete. The authors generated a novel allele of Spar - Spar ΔExon1 and examined sleep and circadian phenotypes of this allele. However, they have only one mutant allele of Spar, and it doesn't appear as if this mutant was outcrossed, making it very difficult to rule out off-target effects. To make this data convincing, it would be better if the authors had a second allele, perhaps they could try RNAi?

Further, the sleep and circadian characterisation could be substantially improved. In Fig 8 E-F it appears as if sleep was averaged over 30 days! This is a little bizarre. They then bin the data as day 1 - 12 and 12-30. This is not terribly helpful either. Sleep in flies, as in humans, undergoes ontogenetic changes - sleep is high in young flies, stabilises between day 3-12, and shows defects by around 3 weeks of age (cf Shaw et al., 2000 PMID 10710313). The standard in the sleep field is to average over 3 days or show one representative day. The authors should reanalyse their data as per this standard, and perhaps show data from 3-10 day old flies, and if they like from 20-30 day old flies. Further, sleep data is usually analysed and presented from lights on to lights on. This allows one to quantify important metrics of sleep consolidation including bout lengths in day and night, and sleep latency. These metrics are of great interest to the community and should be included.

The authors also claim there are defects in circadian anticipatory activity. However, these data, as presented are not solid to me. The standard in the field is to perform eduction analyses and quantify anticipatory activity e.g. using the method of Harrisingh et al. (PMID: 18003827). Further, circadian period could also be evaluated. There are several free software packages to perform these analyses so it should not be hard to do.

Reviewer #3 (Public Review):

A central step in cell division is the formation of midbody abscission that separates two daughter cells at the end of cytokinesis. The ESCRT, endosomal sorting complexes required for transport, plays a critical role in this process. Specifically, the ESCRT-III proteins are actively recruited throughout the cell at the membrane fission sites, and their oligomerization into filaments is necessary to constrict the cell membranes to the fission point. Fundamental structural elements in ESCRT-III interactome are the so-called MIT-interacting motifs (MIMs) located at the protein's C terminal portion. Recently, Sundquist and co-workers (eLife 2022) identified several cofactors interacting with ESCRT-III subunits directly implicated in abscission. Among those cofactors, they identified Calpain-7, a cysteine protease whose function is still unclear. Calpain-7 comprises two MIT domains that target ESCRT-II subunit IST1. Here, the authors use structural methods and cell assays to characterize the interactions between Calpain-7 and IST1. For the structural studies, they constructed a minimalistic system in which MT1 and MT2 domains of Calpain-7 interact with the two MIMs localized in the IST1 construct. The truncated constructs interact with high affinity, recapitulating the strength of interaction expected for the full-length constructs in the cell. Using fluorescence polarization anisotropy binding isotherms, these researchers obtained solid binding data, showing a dissociation constant of 0.09 uM for the construct containing both MIMs, ~2 uM for the second MIM domain, and 100 uM for the first MIM. These data suggest a synergistic binding mechanism between the two MIM domains. The authors expressed and purified these constructs in recombinant systems and obtained purified isotopically labeled proteins to study by NMR. To characterize the binding by NMR, the authors studied the IST1 constructs with the two MIMs in the absence and presence of Calpain-7. The IST1 construct displays a well-resolved NMR fingerprint, with most resonances assigned to specific residues. Upon addition of the Calpain-7 construct, the resonances of the residues involved in the binding either broaden beyond detection or shift significantly, which supports the fluorescence binding studies. Given the high affinity, these authors were able to crystallize these complexes and identify the binding interfaces that parallel the solution NMR studies. Mutational studies confirm the hot spots for the interactions, and the authors concluded that the MIT:MIM binding interface is responsible for the association of the full-length constructs of Calpain-7 and IST1 in the cell. Using localization experiments, the authors concluded that IST1 is responsible for recruiting Calpain-7 to midbodies, and the presence of both MIT domains of Calpain-7 and MIM domains is required for localization. Taken together, the biophysical characterization of these complexes and the cell assays led the authors to conclude that IST1 binding to Calpain-7 is necessary for its role in abscission and Nocut checkpoint maintenance.<br /> In my opinion, the research is well executed and also supported by their previous finding (see Sundquist 2022 eLife). The paper is succinct and well-written.

Reviewer #3 (Public Review):

The manuscript by Salloum et al., titled "Statin-mediated reduction in mitochondrial cholesterol primes an anti-inflammatory response in macrophages by upregulating JMJD3" reports an extensive characterization of the mechanisms underlying the anti-inflammatory role of statins using different in vitro studies. Based on these approaches, the authors observed that cholesterol reduction in response to statin treatment alters mitochondrial function and they identify JMJD3 as a potential critical driver of macrophage anti-inflammatory phenotype. Overall, the study is interesting and provides new findings that could shed light on the molecular effects of statins in these cells, but a number of issues remain confusing, and the experimental design is, on some occasions, not rigorous enough to support the drawn conclusions.

Major issues:

1. Focus on JMJD3 is justified by the authors as it was among the 40 genes commonly up-regulated in macrophages exposed to statin or methyl--cyclodextrin (MCD) by RNA-Seq analysis. However, this analysis has not been presented in the manuscript and it is unclear what genes (apart from JMJD3) might play an important role in the response of these cells. A detailed characterization of both up- and down-regulated genes in these experimental conditions and a better justification for JMJD3 are required to fully support further analysis.<br /> 2. In the same line, Figures 6A and B fail to fully describe the changes found by ATAC-seq and RNA-seq. A more comprehensive analysis of these three datasets (together with previous RNA-seq studies) would help to obtain a better understanding of overlapping dysregulated genes (not only those found up-regulated) and what other epigenetic modifying factors might be involved.<br /> 3. In Figure 6C and Supplementary Figure 7, it would be noteworthy to also measure the gene expression of Kdm6a/UTX homolog Kdm6c/UTY, as it has been shown to lack demethylate H3K27me3 demethylase activity due to mutations in the catalytic site of the Jumomji-C-domain.<br /> 4. The use of rather unspecific treatments such as MG-132 (proteasome inhibitor) and GSKj4 (inhibitor of both JMJD3 and UTX) may distort the results observed and might elude their correct interpretation. To avoid this limitation, additional silencing and/or overexpression experiments are currently needed.<br /> 5. Figure 3 and Supplementary Figure 3 seem to be duplicated, please correct them. Moreover, for a better representation of these data, please include representative Seahorse profile figures of each experimental condition in these figures.<br /> 6. As stated by the authors, macrophage phenotype is much more complex than M1/M2 polarization. In this view, assessing a very limited set of genes (i.e, Il-1, IL-10, TNF, IL-6, IL-12, Arg1, Ym1, Mrc1) appears to be inappropriate. A meaningful number of markers must be added.<br /> 7. For accurate quantification of H3K27me3 global levels, please add immunoblotting against histone H3 in Supplementary Figure 1.

Reviewer #3 (Public Review):

The autocatalytic replication mechanism of misfolded Prion-like proteins (PrP) into amyloid aggregates is associated with a plethora of deleterious neurodegenerative diseases. Despite of the huge amount of research, the underlying molecular events of self-replication and identification of the toxic species are not fully understood. Many recent studies have indicated that non-fibrillar oligomeric intermediates could be more neurotoxic compared to the Prion fibrils. Various cellular factors, like the participation of other proteins and chaperone activity, also play an important role in PrP misfolding, aggregation, and neurotoxicity. The present work focuses on understanding the PrP aggregation mechanism with the identification of the associated toxic species and cellular factors. One of the significant strengths of the work is performing the aggregation assay in near-native conditions. In contrast, most in vitro studies use harsh conditions (such as high temperature, denaturant, detergent, low pH, etc.) to promote protein aggregation. The authors successfully observed the well-known seeding property of the PrP in this aggregation assay that bypasses the primary nucleation during aggregation. Moreover, the authors have shown that syntaxin 6 (Stx6), a known risk factor in prion-mediated Creutzfeldt-Jakob disease, delays fibril formation and prolongs the persistence of toxic intermediates, thus playing an anti-chaperone activity. This study will contribute to understanding the molecular mechanism of PrP aggregation and neurotoxicity. However, further studies are required to identify and characterize the toxic intermediate in the near future precisely.

Reviewer #3 (Public Review):

In this manuscript by Lu et al., the authors cloned TPC1 from Vicia faba (VfTPC1) and characterized its channel properties by patching the vacuoles isolated from VfRPC1 expressing TPC1-loss-of-function Arabidopsis mutant tpc1-2. They found that VfTPC1 displayed faster kinetics, higher voltage dependence, and less sensitivity to luminal calcium than its Arabidopsis orthologue (AtTPC1). Mutating three luminal residues (E457, E605 and D606) in AtTPC1 to the corresponding ones in VfTPC1 converted the channel into one that resembles VfTPC1: hyperactive and desensitized to luminal Ca2+. By constructing a VfTPC1 model based on the published Ca2+-bound AtTPC1-D454N (fou2) cryo-EM structures, the authors proposed a Ca2+-dependent interaction between the E605/D606 motif and a Ca2+ coordination site at the luminal entrance of the selectivity filter (D269/E637; in VfTPC1, D271/E639). Finally, they showed that vacuoles with VfTPC1 or AtTPC1- triple mutant were hyperexcitable. Overall, this is an interesting study that might have both evolutional and functional implications.

A statement of the common "one idea per card" (or per note). He doesn't state it, but links to an article whose title is "One Thought Per Note".

Who else has use this or similar phrasing in the historical record? - Beatrice Webb certainly came pretty close. - Others?

First reference I've seen for someone suggesting using half size 3 x 5" index cards so that they could use commercially available Magic: The Gathering (MTG) boxes.

Oxford and possibly other manufacturers already make 1/2 size 3 x 5" index cards.

Reviewer #3 (Public Review):

The authors investigate the role of commensal microbes and molecules in the antigen presentation pathway in the development and phenotype of CD8 T cells specific for the Qa-1b-restricted peptide FL9 (QFL). The studies track both endogenous QFL-specific T cells and utilize a recently generated TCR transgenic model. The authors confirm that QFL-specific T cells in the spleen and small intestine intraepithelial lymphocyte (IEL) pool show an antigen-experienced phenotype as well as unique phenotypic and innate-like functional traits, especially among CD8+ T cells expressing Va3.2+ TCRs. They find that deficiency in the TAP transporter leads to almost complete loss of QFL-specific T cells but that loss of either Qa1 or the ERAAP aminopeptidase does not impact QFL+ T cell numbers but does cause them to maintain a more conventional, naïve-like phenotype. In germ-free (GF) mice, the QFL-specific T cells are present at similar numbers and with a similar phenotype to SPF animals, but in older animals (>18w) there is a notable loss of IEL QFL-specific cells. This drop can be avoided by neonatal colonization of GF mice with the commensal microbe Pediococcus pentosaceus but not a different commensal, Lactobacillus johnsonii, and the authors show that P. pentosaceus encodes a peptide that weakly stimulates QFL-specific T cells, while the homologous peptide from L. johnsonii does not stimulate such cells.

This study provides new insights into the way in which the differentiation, phenotype, and function of CD8+ T cells specific for Qa-1b/FL9 is regulated by peptide processing and Qa1 expression, and by interactions with the microbiota. The approaches are well designed, the data compelling, and the interpretation, for the most part, appropriate. There are a few relatively minor concerns.

1) For most of the report, the authors use a set of phenotypic traits to highlight the unique features of QFL-specific CD8+ T cells - specifically, CD44high, CD8aa+ve, CD8ab-ve. In Supp. Fig. 4, however, completely distinct phenotypic characteristics are presented, indicating that IEL QFL-specific T cells are CD5low, Thy-1low. No explanation is provided in the text about whether this is a previously reported phenotype, whether any elements of this phenotype are shared with splenic QFL T cells, what significance the authors ascribe to this phenotype (and to the fact that Qa1-deficiency leads to a more conventional Thy-1+ve, CD5+ve phenotype), and whether this altered phenotype is also seen in ERAAP-deficient mice. At least some explanation for this abrupt shift in focus and integration with prior published work is needed. On a related note, CD5 expression is measured in splenic QFL-specific CD8+ T cells from GF vs SPF mice (Supp. Fig. 9), to indicate that there is no phenotypic impact in the GF mice - but from Supp. Fig. 4, it would seem more appropriate to report CD5 expression in QFL-specific cells from the IEL, not the spleen.

2) The authors suggest the finding that QFL-specific cells from ERAAP-deficient mice have a more "conventional" phenotype indicates some form of negative selection of high-affinity clones (this result being somewhat unexpected since ERAAP loss was previously shown to increase the presentation of Qa-1b loaded with FL9, confirmed in this report). It is not clear how this argument aligns with the data presented, however, since the authors convincingly show no significant reduction in the number of QFL-specific cells in ERAAP-knockout mice (Fig. 3a), and their own data (e.g. Fig. 2a) do not suggest that CD44 expression correlates with QFL-multimer staining (as a surrogate for TCR affinity/avidity). Is there some experimental basis for suggesting that ERAAP-deficient lacks a subset of high-affinity QFL-specific cells?

3) The rationale for designing FL9 mutants, and for using these data to screen the proteomes of various commensal bacteria needs further explanation. The authors propose P4 and P6 of FL9 are likely to be "critical" but do not explain whether they predict these to be TCR or Qa-1b contact sites. Published data (e.g., PMID: 10974028) suggest that multiple residues contribute to Qa-1b binding, so while the authors find that P4A completely lost the ability to stimulate a QFL-specific hybridoma, it is unclear whether this is due to the loss of a TCR- or a Qa-1-contact site (or, possibly, both). This could easily be tested - e.g., by determining whether P4A can act as a competitive inhibitor for FL9-induced stimulation of BEko8Z (and, ideally, other Qa-1b-restricted cells, specific for distinct peptides). Without such information, it is unclear exactly what is being selected in the authors' screening strategy of commensal bacterial proteomes. This, of course, does not lessen the importance of finding the peptide from P. pentosaceus that can (albeit weakly) stimulate QFL-specific cells, and the finding that association with this microbe can sustain IEL QFL cells.

Reviewer #3 (Public Review):

In this study, Ruan et al. investigate the role of the IQCH gene in spermatogenesis, focusing on its interaction with calmodulin and its regulation of RNA-binding proteins. The authors examined sperm from a male infertility patient with an inherited IQCH mutation as well as IQCH CRISPR knockout mice. The authors found that both human and mouse sperm exhibited structural and morphogenetic defects in multiple structures, leading to reduced fertility in ICHQ-knockout male mice. Molecular analyses such as mass spectrometry and immunoprecipitation indicated that RNA-binding proteins are likely targets of IQCH, with the authors focusing on the RNA-binding protein HNRPAB as a critical regulator of testicular mRNAs. The authors used in vitro cell culture models to demonstrate an interaction between IQCH and calmodulin, in addition to showing that this interaction via the IQ motif of IQCH is required for IQCH's function in promoting HNRPAB expression. In sum, the authors concluded that IQCH promotes male fertility by binding to calmodulin and controlling HNRPAB expression to regulate the expression of essential mRNAs for spermatogenesis. These findings provide new insight into molecular mechanisms underlying spermatogenesis and how important factors for sperm morphogenesis and function are regulated.

The strengths of the study include the use of mouse and human samples, which demonstrate a likely relevance of the mouse model to humans; the use of multiple biochemical techniques to address the molecular mechanisms involved; the development of a new CRISPR mouse model; ample controls; and clearly displayed results. There are some minor weaknesses in that more background details could be provided to the reader regarding the proteins involved; some assays could benefit from more rigorous quantification; some of the mouse testis images and analyses could be improved; and larger sample sizes, especially for the male mouse breeding tests, could be increased. Overall, the claims made by the authors in this manuscript are well-supported by the data provided and there are only minor technical issues that could increase the robustness and rigor of the study.

1. More background details are needed regarding the proteins involved, in particular IQ proteins and calmodulin. The authors state that IQ proteins are not well-represented in the literature, but do not state how many IQ proteins are encoded in the genome. They also do not provide specifics regarding which calmodulins are involved, since there are at least 5 family members in mice and humans. This information could help provide more granular details about the mechanism to the reader and help place the findings in context.

2. The mouse fertility tests could be improved with more depth and rigor. There was no data regarding copulatory plug rate; data was unclear regarding how many WT females were used for the male breeding tests and how many litters were generated; the general methodology used for the breeding tests in the Methods section was not very explicitly or clearly described; the sample size of n=3 for the male breeding tests is rather small for that type of assay; and, given that ICHQ appears to be expressed in testicular interstitial cells (Fig. S10) and somewhat in other organs (Fig. S2), another important parameter of male fertility that should be addressed is reproductive hormone levels (e.g., LH, FSH, and testosterone).

3. The Western blots in Figure 6 should be rigorously quantified from multiple independent experiments so that there is stronger evidence supporting claims based on those assays.

4. Some of the mouse testis images could be improved. For example, the PNA and PLCz images in Figure S7 are difficult to interpret in that the tubules do not appear to be stage-matched, and since the authors claimed that testicular histology is unaffected in knockout testes, it should be feasible to stage-match control and knockout samples. Also, the anti-ICHQ and CaM immunofluorescence in Figure S10 would benefit from some cell-type-specific co-stains to more rigorously define their expression patterns, and they should also be stage-matched.

Reviewer #3 (Public Review):

Summary. This study sought to clarify the connection between inositol pyrophosphates (IPPs) and their regulation of phosphate homeostasis in the yeast Saccharomyces cerevisiae to answer the question of whether any of the IPPs (1-IP7, 5-IP7, and IP8) or only particular IPPs are involved in regulation. IPPs bind to SPX domains in proteins to affect their activity, and there are several key proteins in the PHO pathway that have an SPX domain, including Pho81. The authors use the latest methodology, capillary electrophoresis and mass spectrometry (CE-MS), to examine the cytosolic concentrations of PP-IPs in wild-type and strains carrying mutations in the enzymes that metabolize these compounds in rich medium and during a phosphate starvation time-course for the wild-type.

Major strengths and weaknesses. The authors have strong premises for performing these experiments: clarifying the regulatory molecule(s) in yeast and providing a unifying mechanism across eukaryotes. They use the latest methodologies and a variety of approaches including genetics, biochemistry, cell biology and protein structure to examine phosphate regulation. Their experiments are rigorous and well controlled, and the story is clearly told. The consideration of physiological levels of IPPs throughout the study was critical to interpretation of the data and a strength of the manuscript. The investigation of the structure of Pho81, its regulation by IPPs, and its interactions with Pho80 provide a vivid model for regulation.

Appraisal. The authors achieved their goal of determining the mechanistic details for phosphate regulation, revising the prior model with new insights. Additionally, they provided strong support for the idea that IP8 regulates phosphate metabolism across eukaryotes - including animals and plants in addition to fungi.

Impact. This study is likely to have broad impact because it addresses prior findings that are inconsistent with current understanding, and they provide good reasoning as to how older methods were inadequate.

Reviewer #3 (Public Review):

This study investigated cognitive mechanisms underlying approach-avoidance behavior using a novel reinforcement learning task and computational modelling. Participants could select a risky "conflict" option (latent, fluctuating probabilities of monetary reward and/or unpleasant sound [punishment]) or a safe option (separate, generally lower probability of reward). Overall, participant choices were skewed towards more rewarded options, but were also repelled by increasing probability of punishment. Individual patterns of behavior were well-captured by a reinforcement learning model that included parameters for reward and punishment sensitivity, and learning rates for reward and punishment. This is a nice replication of existing findings suggesting reward and punishment have opposing effects on behavior through dissociated sensitivity to reward versus punishment.

Interestingly, avoidance of the conflict option was predicted by self-reported task-induced anxiety. Importantly, when a subset of participants were retested over 1 week later, most behavioral tendencies and model parameters were recapitulated, suggesting the task may capture stable traits relevant to approach-avoidance decision-making.

The revised paper commendably adds important additional information and analyses to support these claims. The initial concern that not accounting for participant control over punisher intensity confounded interpretation of effects has been largely addressed in follow-up analyses and discussion.

This study complements and sits within a broad translational literature investigating interactions between reward/punishers and psychological processes in approach-avoidance decisions.

Reviewer #3 (Public Review):

The authors of this study have designed a novel screening pipeline to detect DNA motif spacing preferences between TF partners using publicly available data. They were able to recapitulate previously known composite elements, such as the AP-1/IRF4 composite elements (AICE) and predict many composite elements that are expected to be very useful to the community of researchers interested in dissecting the regulatory logic of mammalian enhancers and promoters. The authors then focus on a novel, SPICE predicted interaction between JUN and IKZF1, and show that under LPS and IL-21 treatment, JUN and IKZF1 in B cells have significant overlap in their genomic localization. Next, to know whether the two TFs physically interact, a co-immunoprecipitation experiment was performed. While JUN immunoprecipitated with an anti-IKZF1 antibody, curiously IKZF1 did not immunoprecipitate with an anti-JUN antibody. Finally, EMSA and luciferase experiments were performed to show that the two TFs bind cooperatively at an IL20 upstream probe.

Major strengths:<br /> 1. SPICE was able to recapitulate previously known composite elements, such as the AP-1/IRF4 composite elements (AICE).<br /> 2. Under LPS and IL-21 treatment, JUN and IKZF1 in B cells have significant overlap in their genomic localization. This is very good supporting evidence for the efficacy of SPICE in detecting TF partners.

Major weaknesses:<br /> 1. The authors fail to convincingly show that IKZF1 and Jun physically interact. A quantitative measurement of their interaction strength would have been ideal.<br /> 2. The super-shift experiment to show that the proteins bound to their EMSA probe were indeed IKZF1 and JUN are not very convincing and would benefit from efforts to quantify the shift (Figure 3E). Nuclear extracts from cells with single or double CRISPR knock outs of the two TFs would have been ideal.<br /> 3. There is a second band beneath the more prominent band in the EMSA experiment with recombinant IKZF1 and JUN (Figure 4C). This second band is most probably bound by IKZF1 because it becomes weaker when the IKZF1 site is mutated and is completely absent when only JUN is added. This is completely ignored by the authors. Therefore, experiments with EMSA fail to convincingly show that IKZF1 and Jun bind cooperatively. They could just as well bind independently to the two sites.

Reviewer #3 (Public Review):

Laham et al. present a manuscript investigating the function of adult-born granule cells (abGCs) projecting to the CA2 region of the hippocampus during social memory. It should be noted that no function for the general DG to CA2 projection has been proposed yet. The authors use targeted ablation, chemogenetic silencing, and in vivo ephys to demonstrate that the abGCs to CA2 projection is necessary for the retrieval of remote social memories such as the memory of one's mother. They also use in vivo ephys to show that abGCs are necessary for differential CA2 network activity, including theta-gamma coupling and sharp wave-ripples, in response to novel versus familiar social stimuli.

The question investigated is important since the function of DG to CA2 projection remained elusive a decade after its discovery. Overall, the results are interesting but focused on the social memory of the mother, and their description in the manuscript and figures is too cursory. For example, raw interaction times must be shown before their difference. The assumption that mice exhibit social preference between familiar or novel individuals such as mother and non-mother based on social memory formation, consolidation, and retrieval should be better explained throughout the manuscript. Thus, when describing the results, the authors should comment on changes in preference and how this can be interpreted as a change in social memory retrieval. Several critical experimental details such as the total time of presentation to the mother and non-mother stimulus mice are also lacking in the manuscript. The in vivo e-phys results are interesting as well but even more succinct with no proposed mechanism as to how abGCs could regulate SWR and PAC in CA2.

The manuscript is well-written with the appropriate references. The choice of the behavioral test is somewhat debatable, however. It is surprising that the authors chose to use a direct presentation test (presentation of the mother and non-mother in alternation) instead of the classical 3-chamber test which is particularly appropriate to investigate social preference. Since the authors focused exclusively on this preference, the 3-chamber test would have been more adequate in my opinion. It would greatly strengthen the results if the authors could repeat a key experiment from their investigation using such a test. In addition, the authors only impaired the mother's memory. An additional experiment showing that disruption of the abGCs to CA2 circuit impairs social memory retrieval would allow us to generalize the findings to social memories in general. As the manuscript stands, the authors can only conclude the importance of this circuit for the memory of the mother. Developmental memory implies the memory of familiar kin as well.

The in vivo ephys section (Figure 3) is interesting but even more minimalistic and it is unclear how abGCs projection to CA2 can contribute to SWR and theta-gamma PAC. In Figure 1, the authors suggest that abGCs project preferentially to PV+ neurons in CA2. At a minimum, the authors should discuss how the abGCs to PV+ neurons to CA2 pyramidal neurons circuit can facilitate SWR and theta-gamma PAC.

Finally, proposing a function for 4-6-week-old abGCs projecting to CA2 begs two questions: What are abGCs doing once they mature further, and more generally, what is the function of the DG to CA2 projection? It would be interesting for the authors to comment on these questions in the discussion.

Reviewer #3 (Public Review):

The potential for sexual selection and the extent of sexual dimorphism in gene expression have been studied in great detail in animals, but hardly examined in plants so far. In this context, the study by Zhao, Zhou et al. al represents a welcome addition to the literature.

Relative to the previous studies in Angiosperms, the dataset is interesting in that it focuses on reproductive rather than somatic tissues (which makes sense to investigate sexual selection), and includes more than a single developmental stage (buds + mature flowers).

The main limitation of the study is the very low number of samples analyzed, with only three replicate individuals per sex (i.e. the whole study is built on six individuals only). This provides low power to detect differential expression. Along the same line, only three species were used to evaluate the rates of non-synonymous to synonymous substitutions, which also represents a very limited dataset, in particular when trying to fit parameter-rich models such as those implemented here.

A third limitation relates to the absence of a reference genome for the species, making the use of a de novo transcriptome assembly necessary, which is likely to lead to a large number of incorrectly assembled transcripts. Of course, the production of a reference transcriptome in this non-model species is already a useful resource, but this point should at least be acknowledged somewhere in the manuscript.

Each of these shortcomings is relatively important, and together they strongly limit the scope of the conclusions that can be made, and they should at least be acknowledged more prominently. The study is valuable in spite of these limitations and the topic remains grossly understudied, so I think the study will be of interest to researchers in the field, and hopefully inspire further, more comprehensive analyses.

Reviewer #3 (Public Review):

More than 80 million people live at high altitude. This impacts health outcomes, including those related to pregnancy. Longer-lived populations at high altitudes, such as the Tibetan and Andean populations show partial protection against the negative health effects of high altitude. The paper by Yue sought to determine the mechanisms by which the placenta of Tibetans may have adapted to minimise the negative effect of high altitude on fetal growth outcomes. It compared placentas from pregnancies from Tibetans to those from the Han Chinese. It employed RNAseq profiling of different regions of the placenta and fetal membranes, with some follow-up of histological changes in umbilical cord structure and placental structure. The study also explored the contribution of fetal sex in these phenotypic outcomes.

A key strength of the study is the large sample sizes for the RNAseq analysis, the analysis of different parts of the placenta and fetal membranes, and the assessment of fetal sex differences.

A main weakness is that this study, and its conclusions, largely rely on transcriptomic changes informed by RNAseq. Changes in genes and pathways identified through bioinformatic analysis were not verified by alternate methods, such as by western blotting, which would add weight to the strength of the data and its interpretations. There is also a lack of description of patient characteristics, so the reader is unable to make their own judgments on how placental changes may link to pregnancy outcomes. Another weakness is that the histological analyses were performed on n=5 per group and were rudimentary in nature.

Reviewer #3 (Public Review):

The manuscript by Egan and coworkers investigates how Caspase-1 and Caspase-4 mediated cell death affects replication of Salmonella in human THP-1 macrophages in vitro.

Overall evaluation:

Strength of the study include the use of human cells, which exhibit notable differences (e.g., Caspase 11 vs Caspase-4/5) compared to commonly used murine models. Furthermore, the study combines inhibitors with host and bacterial genetics to elucidate mechanistic links.

The main weaknesses of the study are the inherent limitations of tissue culture models. For example, to study interaction of Salmonella with host cells in vitro, it is necessary to kill extracellular bacteria using gentamicin. However, since Salmonella-induced macrophage cell death damages the cytosolic membrane, gentamicin can reach intracellular bacteria and contribute to changes in CFU observed in tissue culture models (major point 1). This can result in tissue culture "artefacts" (i.e., observations/conclusions that cannot be recapitulated in vivo). For example, intracellular replication of Salmonella in murine macrophages requires T3SS-2 in vitro, but T3SS-2 is dispensable for replication in macrophages of the spleen in vivo (Grant et al., 2012).

Major comments:

In Figure 1: are increased CFU in WT vs CASP1-deficient THP-1 cells due to Caspase 1 restricting intracellular replication or due to Caspase-1 causing pore formation to allow gentamicin to enter the cytosol thereby restricting bacterial replication? The same question arises about Caspase-4 in Figure 2, where differences in CFU are observed only at 24h when differences in cell death also become apparent. The idea that gentamicin entering the cytosol through pores is responsible for controlling intracellular Salmonella replication is also consistent with the finding that GSDMD-mediated pore formation is required for restricting intracellular Salmonella replication (Figure 3). Similarly, the finding that inflammasome responses primarily control Salmonella replication in the cytosol could be explained by an intact SCV membrane protecting Salmonella from gentamicin (Figure 5).

Reviewer #3 (Public Review):

Strengths:

NanoPDLIM2, nanotechnologies that efficiently deliver lentivirus overcomes resistance to chemotherapy and anti-PD-1 immunotherapy. This is a new strategy for enhancing the efficiency of immune checkpoint inhibitors. This finding is important from a clinical translation perspective, but I have several minor concerns.

Weaknesses:

1. Please describe the mechanism of increased MHC class I and PD-L1 by PDLIM2.<br /> 2. Please describe the mechanism of decreased MDR1, nuclear RelA and STAT3 by PDLIM2.<br /> 3. Please determine whether PDLIM2 expression directly impacts immune cells (function and number)?<br /> 4. What is the efficiency of PDLIM2 delivery? Does delivery efficiency determine anti-tumor effect?<br /> 5. Authors used a non-immunogenic tumor model. Can you demonstrate the combination effect with PDLIM2 in immunogenic lung cancer models to determine whether the combination of PDLIM2 with anti-PD-1 Ab confers a synergistic effect without chemotherapy?<br /> 6. On page 11, % change can make one over-interpret data.<br /> 7. In Figure 5, what is the difference between 5A and 5D?<br /> 8. It is unclear whether PDLIM2 confers an additive or a synergistic effect with anti-PD-1/chemo.<br /> 9. Have the authors tested any toxicity in normal lungs?

Reviewer #3 (Public Review):

I very much like this approach and the idea of incorporating hypervariable markers. The method is intriguing, and the ability to e.g. estimate recombination rates, the size of DMRs, etc. is a really nice plus. I am not able to comment on the details of the statistical inference, but from what I can evaluate it seems sound and reasonable. This is an exciting new avenue for thinking about inference from genomic data. I have a few concerns about the presentation and then also questions about the use of empirical methylation data sets.

I think a more detailed description of demographic accuracy is warranted. For example, in L245 MSMC2 identifies the bottleneck (albeit smoothed) and only slightly overestimates recent size. In the same analysis the authors' approach with unknown mu infers a nonexistent population increase by an order of magnitude that is not mentioned.

Similarly, it seems problematic that (L556) the approach requiring estimation of site and region parameters (as would presumably be needed in most empirical systems like endangered nonmodel species mentioned in the introduction) does no better than using only SNPs. Overall, I think a more objective and perhaps quantitative comparison of approaches is warranted.

The authors simulate methylated markers at 2% (and in some places up to 20%). In many plant genomes a large proportion of cytosines are methylated (e.g. 70% in maize: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496265/). I don't know what % of these may be polymorphic, but this leads to an order of magnitude more methylated cytosines than there are SNPs. Couldn't this mean that any appreciable error in estimating methylation threatens to be of a similar order of magnitude to the SNP data? I would welcome the authors' thoughts here.

A few points of discussion about the biology of methylation might be worth including. For example, methylation can differ among cell types or cells within a tissue, yet sequencing approaches evaluate a pool of cells. This results in a reasonable fraction of sites having methylation rates not clearly 0 or 1. How does this variation affect the method? Similarly, while the authors cite literature about the stable inheritance of methylation, a sentence or so more about the time scale over which this occurs would be helpful. Finally, in some species methylated cytosines have mutation rates an order of magnitude higher than other nucleotides. The authors mention they assume independence, but how would violation of this assumption affect their inference?

Reviewer #3 (Public Review):

In this manuscript, Lewis et al. investigate the role of tetraspanins in the formation of discs - the key structure of vertebrate photoreceptors essential for light reception. Two tetraspanin proteins play a role in this process: PRPH2 and ROM1. The critical contribution of PRPH2 has been well established and loss of its function is not tolerated and results in gross anatomical pathology and degeneration in both mice and humans. However, the role of ROM1 is much less understood and has been considered somewhat redundant. This paper provides a definitive answer about the long-standing uncertainty regarding the contribution of ROM1 firmly establishing its role in outer segment morphogenesis. First, using an ingenious quantitative proteomic technique the authors show PRPH2 compensatory increase in ROM1 knockout explaining the redundancy of its function. Second, they uncover that despite this compensation, ROM1 is still needed, and its loss delays disc enclosure and results in the failure to form incisures. Third, the authors used a transgenic mouse model and show that deficits seen in ROM1 KO could be completely compensated by the overexpression of PRPH2. Finally, they analyzed yet another mouse model based on double manipulation with both ROM1 loss and expression of PRPH2 mutant unable to form dimerizing disulfide bonds further arguing that PRPH2-ROM1 interactions are not required for disc enclosure. To top it off the authors complement their in vivo studies by a series of biochemical assays done upon reconstitution of tetraspanins in transfected cultured cells as well as fractionations of native retinas. This report is timely, addresses significant questions in cell biology of photoreceptors, and pushes the field forward in a classical area of photoreceptor biology and mechanics of membrane structure as well. The manuscript is executed at the top level of technical standard, exceptionally well written, and does not leave much more to desire. It also pushes standards of the field- one such domain is the quantitative approach to analysis of the EM images which is notoriously open to alternative interpretations - yet this study does an exceptional job unbiasing this approach.

According to my expertise in photoreceptor biology, there is nothing wrong with this manuscript either technically or conceptually and I have no concerns to express.

Reviewer #3 (Public Review):

Hon et al. investigated the role of BNST CRF signaling in modulating phasic and sustained fear in male and female mice. They found that partial and full fear conditioning had similar effects in both sexes during conditioning and during recall. However, males in the partially reinforced fear conditioning group showed enhanced acoustic startle, compared to the fully reinforced fear conditioning group, an effect not seen in females. Using fiber photometry to record calcium activity in all BNST neurons, the authors show that the BNST was responsive to foot shock in both sexes and both conditioning groups. Shock response increased over the session in males in the fully conditioned fear group, an effect not observed in the partially conditioned fear group. This effect was not observed in females. Additionally, tone onset resulted in increased BNST activity in both male groups, with the tone response increasing over time in the fully conditioned fear group. This effect was less pronounced in females, with partially conditioned females exhibiting a larger BNST response. During recall in males, BNST activity was suppressed below baseline during tone presentations and was significantly greater in the partially conditioned fear group. Both female groups showed an enhanced BNST response to the tone that slowly decayed over time. Next, they knocked CRF in the BNST to examine its effect on fear conditioning, recall and anxiety-like behavior after fear. They found no effect of the knockdown in either sex or group during fear conditioning. During fear recall, BNST CRF knockdown lead to an increase in freezing in only the partially conditioned females. In the anxiety-like behavior tasks, BNST CRF knockdown lead to increased anxiolysis in the partially reinforced fear male, but not in females. Surprisingly, BNST CRF knockdown increased startle response in fully conditioned, but not partially conditioned males. An effect not observed in either female group. In a final set of experiments, the authors single photon calcium imaging to record BNST CRF cell activity during fear conditioning and recall. Approximately, 1/3 of BNST CRF cells were excited by shock in both sexes, with the rest inhibited and no differences were observed between sexes or group during fear conditioning. During recall, BNST CRF activity decreased in both sexes, an effect pronounced in male and female fully conditioned fear groups.

Overall, these data provide novel, intriguing evidence in how BNST CRF neurons may encode phasic and sustained fear differentially in males and females. The experiments were rigorous.

Reviewer #3 (Public Review):

Summary:<br /> The manuscript Kroon et al. described two algorithms, which when combined achieve high throughput automation of "martinizing" protein structures with selected protonation states and post-translational modifications.

Strengths:<br /> A large scale protein simulation was attempted, showing strong evidence that authors' algorithms work smoothly.

The authors described the algorithms in detail and shared the open-source code under Apache 2.0 license on GitHub. This allows both reproducibility of extended usefulness within the field. These algorithms are potentially impactful if the authors can address some of the issues listed below.

Weaknesses:<br /> One major caveat of the manuscript is that the authors claim their algorithms aim to "process any type of molecule or polymer, be it linear, cyclic,<br /> branched, or dendrimeric, and mixtures thereof" and "enable researchers to prepare simulation input files for arbitrary (bio)polymers". However, the examples provided by the manuscript only support one type of biopolymer, i.e. proteins. Despite the authors' recommendation of using polyply along with martinize2/vermouth, no concrete evidence has been provided to support the authors' claim. Therefore, the manuscript must be modified to either remove these claims or include new evidence.

Method descriptions on Martinize2 and graph algorithms in SI should be core content of the manuscript. I argue that Figure S1 and Figure S2 are more important than Figure 3 (protonation state). I recommend the authors can make a workflow chart combining Figure S1 and S2 to explain Martinize2 and graph algorithms in main text.

In Figure 3 (protonation state), the figure itself and the captions are ambiguous about whether at the end the residue is simply renamed from HIS to HIP, or if hydrogen is removed from HIP to recover HIS.

In "Incorporating a Ligand small-molecule Database", the authors are calling for a community effort to build a small-molecule database. Some guidance on when the current database/algorithm combination does or does not work will help the community in contributing.

A speed comparison is needed to compare Martinize2 and Martinize.

Reviewer #3 (Public Review):

To analyze the circuit mechanisms leading to the habituation of the O-bed responses upon repeated dark flashes (DFs), the authors performed 2-photon Ca2+ imaging in larvae expressing nuclear-targeted GCaMP7f pan-neuronally panning the majority of the midbrain, hindbrain, pretectum, and thalamus. They found that while the majority of neurons across the brain depress their responsiveness during habituation, a smaller population of neurons in the dorsal regions of the brain, including the torus longitudinalis, cerebellum, and dorsal hindbrain, showed the opposite pattern, suggesting that motor-related brain regions contain non-depressed signals, and therefore likely contribute to habituation plasticity.

Further analysis using affinity propagation clustering identified 12 clusters that differed both in their adaptation to repeated DFs, as well as the shape of their response to the DF.

Next by the pharmacological screening of 1953 small molecule compounds with known targets in conjunction with the high-throughput assay, they found that 176 compounds significantly altered some aspects of measured behavior. Among them, they sought to identify the compounds that 1) have minimal effects on the naive response to DFs, but strong effects during the training and/or memory retention periods, 2) have minimal effects on other aspects of behaviors, 3) show similar behavioral effects to other compounds tested in the same molecular pathway, and identified the GABAA/C Receptor antagonists Bicuculline, Amoxapine, and Picrotoxinin (PTX). As partial antagonism of GABAAR and/or GABACR is sufficient to strongly suppress habituation but not generalized behavioral excitability, they concluded that GABA plays a very prominent role in habituation. They also identified multiple agonists of both Melatonin and Estrogen receptors, indicating that hormonal signalling may also play a prominent role in habituation response.

To integrate the results of the Ca2+ imaging experiments with the pharmacological screening results, the authors compared the Ca2+ activity patterns after treatment with vehicle, PTX, or Melatonin in the tethered larvae. The behavioral effects of PTX and Melatonin were much smaller compared with the very strong behavioral effects in freely-swimming animals, but the authors assumed that the difference was significant enough to continue further experiments. Based on the hypothesis that Melatonin and GABA cooperate during habituation, they expected PTX and Melatonin to have opposite effects. This was not the case in their results: for example, the size of the 12(Pot, M) neuron population was increased by both PTX and Melatonin, suggesting that pharmacological manipulations that affect habituation behavior manifest in complex functional alterations in the circuit, making capturing these effects by a simple difficult.

Since the 12(𝑃𝑜𝑡, 𝑀) neurons potentiate their responses and thus could act to progressively depress the responses of other neuronal classes, they examined the identity of these neurons with GABA neurons. However, GABAergic neurons in the habituating circuit are not characterized by their Adaptation Profile, suggesting that global manipulations of GABAergic signalling through PTX have complex manifestations in the functional properties of neurons.

Overall, the authors have performed an admirably large amount of work both in whole-brain neural activity imaging and pharmacological screening.

Reviewer #3 (Public Review):

In this paper, Toschi et al. performed dMRI to in vivo estimate axon diameter in the brain and demonstrated that multi-compartmental modeling (AxCaliber) is sensitive to microstructural axonal damage in rats and axon caliber increase in demyelinating lesions in MS patients, suggesting that axon diameter mapping provides a potential biomarker to bridge the gap between medical imaging contrasts and biological microstructure. In particular, authors injected ibotenic acid (IBO) and saline in the left and right rat hippocampus, respectively, and compared in vivo estimated axon diameter and ex vivo neurofilament staining in left and right fimbria. The axon size estimation was larger in the fimbria of IBO injection side, where the neurofilament intensity is higher. Correlation of axon size estimation and neurofilament intensity was observed in both injection sides. Further, higher axon diameter estimation was observed in normal appearing white matter (NAWM) of MS patients, compared with the healthy subjects. The axon size estimation increased in hypointense lesions of T1 weighted contrast, but not in isointense lesions. Through the comparison of dMRI-estimated axon size and histology-based fluorescence intensity, authors indirectly validated the sensitivity of axon diameter mapping to the tissue microstructure in the rat brain, and further explored the axon size change in the brain of MS patients. However, the dMRI protocol and biophysical modeling in this study were not fully optimized to maximize the sensitivity to axon size estimation, and the dMRI-estimated axon size (4.4-5.4 micron) was much larger than values reported in previous histological studies (0.5-3 micron) [Barazany et al., Brain 2009]. Finally, although the modified AxCaliber model incorporated two fiber bundles in different directions, the fiber dispersion in each bundle was not considered (c.f. fiber dispersion ~20-30 degree in corpus callosum), potentially leading to overestimated axon diameter.

The conclusions in this study are supported by experimental results. However, the dMRI protocol and biophysical model could be further optimized and validated:<br /> 1. To in vivo estimate the axon diameter ~1 micron using dMRI, strong diffusion weighting (b-value) should be applied to maximize the signal decay due to intra-axonal restricted diffusion and minimize the signal contribution of extra-cellular hindered diffusion. However, authors only applied maximal b-value = 4000 s/mm2, much smaller than values ~15,000-20,000 s/mm2 in previous studies [Assaf et al., MRM 2008; Huang et al., BSAF 2020, 225:1277]. The use of low diffusion weighting in this study leads to a lower bound ~4-6 micron for accurate diameter estimation, the so-called resolution limit in [Nilsson et al., NMR Biomed 2017, 30:e3711]. In other words, the estimated axon diameter is potentially overestimated and related with the imaging protocol and image quality, confounding the biological interpretation.<br /> 2. In this study, the positive correlation of dMRI-estimated axon size and neurofilament fluorescence intensity is indeed an encouraging result, and yet this validation is indirect since it relies on the positive correlation between neurofilament intensity and axon diameter in histology.<br /> 3. Authors did not consider the fiber dispersion in the proposed dMRI model. This can lead to overestimated axon diameter, even in the highly aligned WM, such as corpus callosum with ~20-30 degree dispersion in histology [Ronen et al., BSAF 2014, 219:1773; Leergaard et all, PLoS One 2010, 5(1), e8595] and MRI [Dhital et al., NeuroImage 2019, 189, 543; Novikov et al., NeuroImage 2018, 174:518].

Reviewer #3 (Public Review):

This study examined the changes in fear response, as measured by the flight initiation distances (FID), of birds living in urban areas. The authors examined the FIDs of birds during the pandemic (COVID-19 lockdown restrictions) compared to FIDs measured before the pandemic (mostly in 2018 & 2019). The main study justification was that human presence changed drastically during the pandemic lockdowns and the change in human presence might have influenced the fear response of birds as a result of changing the "landscape of fear". Human presence was quantified using a 'stringency' index (government-mandated restrictions). Urban areas were selected from within five different cities, which included four European cities (Czech Republic - Prague, Finland - Rovaniemi, Hungary - Budapest, Poland - Poznan), and one city in the global south (Australia - Melbourne). Using 6369 flight initiation distances across 147 different bird species, the authors found that FIDs were not significantly different before the pandemic versus during the pandemic, nor was the variation in FID explained by the level of 'stringency'.

Major strengths: There are several strengths to this study that allows for understanding the variety of factors that influence a bird's response to fear (measured as flight initiation distances). This study also demonstrates that FIDs are highly variable between species and regions.<br /> Specifically,<br /> 1) One of the major strengths of this paper is the focus on birds living in urban areas, a habitat type that is hypothesized to have changed drastically in the 'landscape of fear' experienced by animals during the pandemic lockdown restrictions (due to the presumed decrease in human presence and densities). Maintaining the focus on urban birds allowed for a deeper examination of the effect of human behaviour changes on bird behaviour in urban habitats, which are at the interface of human-wildlife interactions.<br /> 2) This study accounted for several variables that are predicted to influence flight initiation distances in birds including species, genus, region (country), variability between years, pandemic year (pre- versus during), the strictness of government-mandated lockdown measures, and ecological factors such as the human observer starting distance, flock size, species-specific body size, ambient air temperature (also a proxy of the timing during the breeding season), time of day, date of data collection (timing within the regional [Europe or Australia] breeding season), and categorization of urban site type (e.g. park, cemetery, city centre).<br /> 3) This study examined FIDs in two years previous to the pandemic (mostly 2018 and 2019, one site was 2014) which would account for some of the within- and between-year FID variation exhibited prior to the pandemic.<br /> 4) This study uses strong statistical approaches (mixed effect models) which allows for repeat sampling, and a post hoc analysis testing for a phylogenetic signal.

Major weaknesses: The authors used government 'stringency' as a proxy for human presence and densities, however, this may not have been an accurate measure of actual human presence at the study sites and during measurements of FIDs. Furthermore, although the authors accounted for many factors that are predicted to influence fear response and FIDs in birds, there are several other factors that may have contributed to the high level of variation and patterns in FIDS observed during this study, thus resulting in the authors' conclusion that FIDs did not vary between pre- and during pandemic years.<br /> Specifically,<br /> 1) The authors used "government stringency" as a measure of change in human activity, which makes the assumption that the higher the level of 'stringency', the fewer humans in urban areas where birds are living. However, the association between "stringency" and actual human presence at the study sites was not measured, nor was 'stringency' compared to other measures of human presence such as human mobility.<br /> 2) There was considerable variation in FID measurements, which can be seen in the figures, indicating that most of the variation in FID was not accounted for in the authors' models. Factors that may have contributed to variation in FIDs that were not accounted for in this study are as follows:<br /> a. The authors accounted for the date of data collection using the 'day' since the start of the general region's breeding season (Europe: Day 1 = 1 April; Australia: Day 1 = 15 August). Using 'day' since the breeding season started probably was an attempt to quantify the effect of the breeding stage (e.g. territory establishment, nest young, fledgling) on FIDs. However, breeding stages vary both within- and between species, as well as between sub-regions (e.g. Finland vs. Hungary). As different species respond to predation or human presence differently depending on the stage during their breeding cycle, more specificity in the breeding cycle stage may allow for explaining the observed variation and patterns in FID.<br /> b. Variation in species-specific FIDs may also vary with habitat features within urban sites, such as the proximity of trees and other protective structures (e.g. perches and cover), the openness of the area, and the level of stressors present (e.g. noise pollution, distance to roads). Perhaps accounting for this habitat heterogeneity would account for the FID variation measured in this study.<br /> c. The authors accounted for species and genus within their models, however, FIDs may vary with other species-specific (or even specific populations of a species) characteristics such as whether the species/population is neophobic versus neophilic, precocial versus altricial, and the level of behavioural plasticity exhibited. These variables were not accounted for in the analysis.<br /> d. Three different methods of measuring the distances between flight and the observer location were used, and FIDs were only measured once per bird, such that there were no measures of repeatability for a test subject. Thus, variation surrounding the measurement of FIDs would have contributed to the variation in FIDs seen during this study.<br /> 3) The sample design of this study may have influenced the FID variability associated with specific species, and specific populations of species. A different number of species were sampled across the time periods of interest; 68 species were sampled before the pandemic versus 135 species after the pandemic. However, the authors do not appear to have directly compared the FIDs for the same species before the pandemic compared to during the pandemic (e.g. the FIDs of Eurasian blackbirds before the pandemic versus during the pandemic). Furthermore, within the same country-city, it is unclear whether the species observed before the pandemic were observed at the same location (e.g. same habitat type such as the same park) during the pandemic. As a species' FID response may be influenced by population characteristics and features specific to each site (e.g. habitat openness), these factors may have influenced the variability in FID measurements in this study.<br /> 4) The models in this study accounted for many factors predicted to affect FIDs (see the section on major strengths), however, the number of fixed and random factors are large in number compared to the total sample size (N =6369), such that models may have been over-extended.

Overarching main conclusion<br /> Overall, this study examines factors influencing FIDs in a variety of bird species and concludes that FIDs did not differ during the pandemic lockdowns compared to before the pandemic (2019 and earlier). Furthermore, FIDs were not influenced by the strictness of government-mandated restrictions. Although the authors accounted for many factors influencing the measurement of FIDs in birds, the authors did not achieve their aim of disentangling the effects of pandemic-specific ecological effects from ecological effects unrelated to the pandemic (such as habitat heterogeneity). Their findings indicate that FIDs are highly variable both within- and between- species, but do not strongly support the conclusion that FIDs did not change in urban species during the pandemic lockdown. Therefore, this study is of limited impact on our understanding of how a drastic change in human behaviour may impact bird behaviour in urban habitats. Overall, the study demonstrates the challenges in using FIDs as a general fear response in birds, even during a pandemic lockdown when fewer humans are presumably present, and this study illustrates the large degree of variation in FIDs in response to a human observer.

Reviewer #3 (Public Review):

A big open question in evolutionary biology is how single cells become multicellular organisms, capable of adaptation as a collective. Many cells form groups, but adaptation at the level of the group tends to be inefficient (especially in comparison to cells). Theoretically, it has been proposed that groups formed by clonal development (cells remain attached to each other after division) can more readily lead to group-level adaptation than groups coming together through the aggregation of different cells post-division. To evaluate empirically the plausibility of this hypothesis, the authors compared adaptation in two lines of yeast that differ only in a couple of mutations determining their mechanism of group formation. Ace2 mutants develop through staying together, and Floc mutants through aggregation. They performed a form of size selection (through settling) as a way to select for multicellularity (this selection regime has been used before to obtain multicellular phenotypes). This selective regime has two components: growing (largely due to differences between cells) and settling (largely due to differences between groups). Thus, the authors assume that increases in fitness through growth are due mostly to adaptation at the single-cell level, whereas increases in fitness through settling are mostly due to adaptation at the multicellular level. They find that adaptation in clonal groups is mostly through settling and that aggregative groups adapt more through growth (despite getting bigger).

Overall this assumption makes sense (especially in a positive way) but growth, in this case, is also selecting against groups in the snowflake case and less strongly so in the floc case in which cells aggregate and disaggregate with some probability, and therefore cells can keep growing. That is, in addition to assortment the result is somewhat expected because there is less of a trade-off between growth and settling in floc: having a higher density in floc probably leads to higher aggregation and indirectly benefits settling, whereas in the clonal case, larger groups mean that a larger proportion of cells is not growing.

The main result of the paper holds true: clonal development favors multicellular adaptation relative to aggregative multicellularity, but the reason is not exclusively a difference in the distribution of variation, but a difference in the trade-off between single cell and multicellular traits.

In the second part of the paper, the authors beautifully show that the mechanisms of group formation affect evolutionary processes. Clonal aggregation leads to a decrease in the effective population size (because the descendants of mutants are likely to be in the same group, and therefore be selected together). This result shows that the mode of development can affect evolution!

Reviewer #3 (Public Review):

Summary:<br /> Smith-Magenis syndrome (SMS) is associated with obesity and is caused by deletion or mutations in one copy of the Rai1 gene which encodes a transcriptional regulator. Previous studies have shown that Bdnf gene expression is reduced in the hypothalamus of Rai1 heterozygous mice. This manuscript by Javed et al. further links SMS-associated obesity with reduced Bdnf gene expression in the PVH.

Strengths:<br /> The authors show that deletion of the Rai1 gene in all BDNF-expressing cells or just in the PVH BDNF neurons postnatally caused obesity. Interestingly, mutant mice displayed sexual dimorphism in the cause for the obesity phenotype. Overall, the data are well presented and convincing except the data from LM22A-4.

Weaknesses:<br /> 1. The most serious concern is about data from LM22A-4 administration experiments (Figure 5 and associated supplemental figures). A rigorous study has demonstrated that LM22A-4 does not activate TrkB (Boltaev et al., Science Signaling, 2017), which is consistent with unpublished results from many labs in the neurotrophin field. It is tricky to interpret body weight data from pharmacological studies because compounds always have some side effects, which can reduce body weight non-specifically.

2. The resolution of all figures are poor, and thus I could not judge the quality of the micrographs.

3. Citation of the literature is not precise. The study by An et al. (2015) shows that deletion of the Bdnf gene in the PVH leads to obesity due to increased food intake and reduced energy expenditure (not just hyperphagic obesity; Line 72). Furthermore, the study by Unger et al. (2017) carried out Bdnf deletion in the VMH and DMH using AAV-Cre and did not discuss SF1 neurons at all (Line 354). The two studies by Yang et al. (Mol Endocrinol, 2016) and Kamitakahara et al. (Mol Metab, 2015) did use SF1-Cre to delete the Bdnf gene and did not observe any obesity phenotype.

4. Animal number is not described in many figure legends.

Reviewer #3 (Public Review):

This paper considers a challenging motor control task - the critical stability task (CST) - that can be performed equally well by humans and macaque monkeys. This task is of considerable interest since it is rich enough to potentially yield important novel insights into the neural basis of behavior in more complex tasks that point-to-point reaching. Yet it is also simple enough to allow parallel investigation in humans and monkeys, and is also easily amenable to computational modeling. The paper makes a compelling argument for the importance of this type of parallel investigation and the suitability of the CST for doing so.

Behavior in monkeys and in human subjects suggests that behavior seems to cluster into different regimes that seem to either oscillate about the center of the screen, or drift more slowly in one direction. The authors show that these two behavioral regimes can be reliably reproduced by instructing human participants to either maintain the cursor in the center of the screen (position control objective), or keep the cursor still anywhere in the screen (velocity control objective) - as opposed to the usual 'instruction' to just not let the cursor leave the screen. A computational model based on optimal feedback control can similarly reproduce the two control regimes when the costs are varied

Overall, this is a creative study that successfully leverages experiments in humans and computational modeling to gain insight into the nature of individual differences in behavior across monkeys (and people). The approach does work and successfully solves the core problem the authors set out to address. I do think that more comprehensive approaches might be possible that might involve, e.g. using a richer set of behavioral features to classify behavior, fitting a parametric class of control objectives rather than assuming a binary classification, and exploring the reliability of the inference process in more detail.

In addition, the authors do fully establish that varying control objectives is the only way to obtain the different behavioral phenotypes observed. It may, for instance, be possible that some other underlying differences (e.g. the sensitivity to effort costs or the extent of signal-dependent noise) might also lead to a similar range of behaviors as varying the position versus velocity costs.

Specific Comments:<br /> The simulations convincingly show that varying the control objective via the cost function can reproduce the different observed behavioral regimes. However, in principle, the differences in behavior among the monkeys and among the humans in Experiment 1 might not necessarily be due to difference in other aspects of the model. For instance, for a fixed cost function, differences in motor execution noise might perhaps lead the model to favor a position-like strategy or a velocity-like strategy. Or differences in the relative effort cost might alter the behavioral phenotype. Given that the narrative is about inferring control objectives, it seems important to rule out more systematically that some other factor might not potentially dictate each individual's style of performing the task. One approach to rule this out might be to try to formally fit the parameters of the model (or at least a subset of them) under a fixed cost function (e.g. velocity-based), and check whether the model might still recover the different regimes of behavior when parameters *other than the cost function* are varied.

The approach to the classification problem is somewhat ad hoc and based on fairly simplistic, hand-picked features (RMS position and RMS velocity). I do wonder whether a more comprehensive set of behavioral features might enable a clearer separation between strategies, or might even reveal that the uninstructed subjects were doing something qualitatively different still from the instructed groups. Different control objectives ought to predict meaningfully different control policies - that is, different ways of updating hand position based on current state of the cursor and hand - e.g. the hand/cursor gain, which does clearly differ across instructed strategies. Would it be possible to distinguish control strategies more accurately based on this level of analysis, rather than based on gross task metrics? Might this point to possible experimental interventions (e.g. target jumps) that might validate the inferred objective?

It seems that the classification problem cannot be solved perfectly, at least on a single-trial level. Although it works out that the classification can recover which participants were given which instructions, it's not clear how robust this classification is. It should be straightforward to estimate the reliability of the strategy classification by simulating participants and deriving a "confusion matrix", i.e. calculating how often e.g. data generated under a velocity-control objective gets mis-classified as following a position-control objective. It's not clear how this kind of metric relates to the decision confidence outputted by the classifier.

The problem of inferring the control objective is framed as a dichotomy between position control and velocity control. In reality, however, it may be a continuum of possible objectives, based on the relative cost for position and velocity. How would the problem differ if the cost function is framed as estimating a parameter, rather than as a classification problem?

https://www.attorneyatwork.com/analog-attorney-5-best-index-cards/

Article about general usefulness of index cards written by a lawyer and for them, though not specific to them as a subgroup.

Makes not of Nock's Dot-Dash cards which were apparently 3 x 5" dash gridded cards similar to Midori's grid notebooks. The website for the company is no longer active. Archived site: https://web.archive.org/web/20171007102414/https://nockco.com/paper/dotdash-3-x-5-note-cards

Reviewer #3 (Public Review):

This manuscript describes the use of scRNA-seq to decipher the cellular heterogeneity, molecular dynamics and signaling interactions during fibrocartilaginous enthesis formation. They delineate the enthesis growth and the temporal atlas from embryonic stage to postnatal stage by scRNA-seq, compared the development pattern of enthesis origins with tendon and articular cartilage, then demonstrated the cellular complexity and heterogeneity of postnatal enthesis growth and revealed the molecular dynamics and signaling networks during enthesis formation.

This manuscript used appropriate and validated methodology in line with current state-of-the-art, and the conclusions of this paper are mostly well supported by data, more in vitro or in vivo experiments are encouraged to verify the key molecular dynamics and signaling networks revealed by scRNA-seq during enthesis formation.

This manuscript facilitates better understand of the enthesis development, which will benefit the important field of enthesis research.

Reviewer #3 (Public Review):

The major claim from the paper is the dependence of two factors that determine the polymerization of MreB from a Gram-positive, thermophilic bacteria 1) The role of nucleotide hydrolysis in driving the polymerization. 2) Lipid bilayer as a facilitator/scaffold that is required for hydrolysis-dependent polymerization. These two conclusions are contrasting with what has been known until now for the MreB proteins that have been characterized in vitro. The experiments performed in the paper do not completely justify these claims as elaborated below.

Major comments:

1. No observation of filaments in the absence of lipid monolayer can also be accounted due to the higher critical concentration of polymerization for MreBGS in that condition. It is seen that all the negative staining without lipid monolayer condition has been performed at a concentration of 0.05 mg/mL. It is important to check for polymerization of the MreBGS at higher concentration ranges as well, in order to conclusively state the requirement of lipids for polymerization.

2. The absence of filaments for the non-hydrolysable conditions in the lipid layer could also be because the filaments that might have formed are not binding to the planar lipid layer, and not necessarily because of their inability to polymerize.

3. Given the ATPase activity measurements, it is not very convincing that ATP rather than ADP will be present in the structure. The ATP should have been hydrolysed to ADP within the structure. The structure is now suggestive that MreB is not capable of hydrolysis, which is contradictory to the ATP hydrolysis data.

Reviewer #3 (Public Review):

In this study, Ye et al investigated how a peptide that binds to the transmembrane (TM) domain of the T cell receptor (TCR) subunits affects TCR activation. The objective was to test the allosteric relaxation model of TCR activation. To this end, the authors leveraged their previously established strategy of designing TM-targeting peptides and studied how such peptide alters the TCR activation and downstream signaling cascades in Jurkat T cells. The authors found that the TM-targeting peptide inhibited phosphorylation of the TCR submits, phosphorylation of downstream signaling proteins such as ZAP70, and calcium influx in T cells. Using immunoprecipitation experiments, the authors proposed that the peptide binds into the membrane gap between CD3 and CD3 subunits in the TCR complex. The authors conclude that their data support the allosteric TCR activation model, in which allosteric changes in the TM bundle in the TCR complex determine the receptor signaling.

The use of pH-responsive TM-targeting peptides, which the authors previously developed, is a novel aspect of this study. Those peptides can be quite powerful for understanding molecular mechanisms of receptor signaling, such as the allosteric activation model as tested in this study. The manuscript contains several interesting approaches and observations, but there are concerns about the experimental design and interpretation of the results. More importantly, the authors' primary conclusion that the allosteric changes in the TM bundles determine TCR activation is not fully supported by the data presented. For example:

1. The authors provided confocal fluorescence images showing the colocalization of fluorescently labeled peptides and TCR subunits. Based on the data, they concluded that "PITCR is able to bind to TCR". This is misleading, because given the spatial resolution of the imaging technique, "colocalization" does not indicate binding or interaction between molecules. Because the peptide binding to the TM region is the pillar of the primary finding of this study, direct evidence supporting the peptide-TM binding or interaction is essential.<br /> 2. In calcium response experiments, the authors compared calcium influx (indicated by Indo-1 ratio) under different cell activation conditions (Figure 2). There are some concerns about how the authors interpreted the data: (1) The calcium plots from OKT3 activation in A-C panels are inconsistent. The plot in (A) showed a calcium peak after activation, which is not present in the plots shown in (B) and (C). There is no explanation or discussion on this inconsistency. (2) What is more concerning is that this prominent calcium peak in (A) was used to draw the conclusion that the designer peptide inhibitor effectively reduces calcium response. However, inconsistent with that conclusion, the calcium plots are indistinguishable for the three conditions: with PITCR (peptide inhibitor), with PITCRG41P (negative control that should not affect TCR activation), or no peptide. All three plots have similar magnetite and fluctuations. This does not support the authors' conclusion that the PITCR (peptide inhibitor) reduces calcium response in T cells.<br /> 3. Different types of T cells were used for separate measurements: E6-1 Jurkat T cells were used for calcium influx experiments, J. OT.hCD8+ Jurkat cells were used for CD69 measurements, and primary murine CD4+ T cells were used for colocalization imaging experiments. Rationales for the choices of cells in different measurements are also unclear. This is different from the common practice where different cell types are used in repeated experiments to test the generality of a finding. Here, they were used for different experiments, and findings were lumped together as "T cells", without further evidence/discussion on how translatable the findings from different cell types are.<br /> 4. The authors set out to test the model that TCR activation by pMHC occurs through allosteric changes in the TM region, but in most experiments, they activated Jurkat T cells by anti-CD3 antibody, not by antigen peptides. The anti-CD3 antibody activates TCR signaling through clustering. It is unclear whether TCR activation by anti-CD3 leads to the same allosteric changes in the TM region as activation by pMHC.

As such, the main claim of the paper, namely that the designer peptide affects TCR signaling by disrupting the allosteric changes in the TM region, remains insufficiently supported by the data presented.

Reviewer #3 (Public Review):

Here the authors use high-parameter flow cytometry to address expression patterns of inhibitory receptors and concordant functional responses in CD8+ T cells from people living with HIV (PLWH) during early vs. long-term ART treatment in order to understand the potential evolution of exhausted T cells in HIV infection. High-dimensional bioinformatic analysis is employed to uncover different subsets of CD8+ T cells expressing TIM-3, TIGIT, PD1, LAG3, and CD39. Stimulation assays were further conducted to assess polyclonal T cell responses (superantigen) or HIV-gag-specific CD8+ T cells, and whether the responding cells displayed inhibitory receptors. Finally, inhibitory receptor blockade was used (focusing on TIGIT and TIM-3 only) to examine the potential reversal of exhaustion. The authors found that CD107a+ degranulating central memory T cells apparently were sensitive to TIGIT blockade, yielding increased responses in cells from ART-treated PLWH.

Methods and Results Major Strengths: Sample size and data density. Longitudinal samples from long-term treated PLWH. Mechanistic studies to assess inhibitory receptor blockade.

Methods and Results Major Weaknesses: Lack of clarity on flow cytometric analysis and statistical methodology, including correction for multiple comparisons. Clustering density in tSNE analysis is unjustified, leading to potentially spurious outcomes. Insufficient raw flow cytometry data presented on inhibitory receptor expression in the various contexts of the study to allow determination of whether the subsequent bioinformatic analysis was merited due to the very low expression of 3/5 markers examined. Unclear whether differences observed are biologically meaningful (despite statistical differences). Finally, although the longitudinal samples are a distinct strength of the study, changes over time within individuals are unfortunately not assessed.

Aims and conclusions: The authors do find differences between the cohorts as described in the manuscript; however, the biological relevance of the findings is questionable due to an absence of direct studies on the cell populations found to be different. The use of unbiased clustering analysis is both a strength and a weakness. Specifically, the algorithm uncovers potential cell clusters that might be missed; however, the clustering program requires pre-set inputs on the expected number of clusters to be found, leading to possible irrelevant subsets being identified. The conclusions of the study are appropriately limited in scope.

Impact: There have been numerous studies of CD8+ T cell inhibitory receptor expression and T cell exhaustion in the context of HIV infection. It is well-accepted that T-cell exhaustion is a hallmark of progressive infection. This study contributes to the current knowledge in this area specifically through the examination of very long-term ART-treated PLWH. Unfortunately, it is not clear that several of the examined inhibitory receptors could be adequately detected, limiting the interpretation of the findings. Finally, it is unclear that this study justifies the potential use of TIGIT blockade to improve T cell function given the unclear biological relevance of the differential populations of CD8+ T cells observed.

Reviewer #3 (Public Review):

Henault et al. address the important open question of whether hybridization could trigger TE mobilization. To do this they analysed MA lines derived from crosses of Saccharomyces paradoxus and Saccharomyces cerevisiae using long-read sequencing. These MA lines were already analysed in a previous publication using Illumina short-read data but the novelty of this work is the long-read sequencing data, which may reveal previously missed information. It is an interesting message of this study that hybridization between the two species did not lead to much TE activity. Due to this low activity, the authors performed an additional TE activity assay in vivo to measure transposition rates in hybrid backgrounds. The study is well written and I cannot spot any major problems. The study provides some important messages (like the influence of the genotype and mitochondrial DNA on transposition rates).

Major comments<br /> - What I miss the most in this work is the perspective of the host defence against TEs in Saccharmoces. Based on such a mechanistic perspective, why do the authors think that hybridization could lead to a TE reactivation? For example, in Drosophila small RNAs important for the defence against a TE, are solely maternally transmitted. Hybrid offspring will thus solely have small-RNAs complementary to the TEs of the mother but not to the TEs of the father, therefore a reactivation of the paternal TEs may be expected. I was thus wondering, what is the situation in yeast. Why would we expect an upregulation of TEs? Without such a mechanistic explanation the hypothesis that TEs should be upregulated in hybrids is a bit vague, based on a hunch.

Reviewer #3 (Public Review):

In this manuscript, Gustison et al., describe the development of an automated whole-brain mapping pipeline, including the first 3D histological atlas of the prairie vole, and then use that pipeline to quantify Fos immunohistochemistry as a measure of neural activity during mating and pair bonding in male and female prairie voles. Prairie voles have become a useful animal model for examining the neural bases of social bonding due to their socially monogamous mating strategy. Prior studies have focused on identifying the role of a few neuromodulators (oxytocin, vasopressin, dopamine) acting in a limited number of brain regions. The authors use this unbiased approach to determine which areas become activated during mating, cohabitation, and pair bonding in both sexes to identify 68 brain regions clustered in seven brain-wide neuronal circuits that are activated over the course of pair bonding. This is an important study because i) it generates a valuable tool and analysis pipeline for other investigators in the prairie vole research community and ii) it highlights the potential involvement of many brain regions in regulating sexual behavior, social engagement, and pair bonding that have not been previously investigated.

Strengths of the study include the unbiased assessment of neural activity using the automated whole brain activity mapped onto the 3D histological atlas. The design of the behavioral aspect of the study is also a strength. Brains were collected at baseline and 2.5, 6 and 22 hrs after cohabitation with either a sibling or opposite-sex partner. These times were strategically chosen to correspond to milestones in pair bond development. Behavior was also quantified during epochs over the 22 hr period providing useful information on the progression of behaviors (e.g. mating) during pair bonding and relating Fos activation to specific behaviors (e.g. sex vs bonding). The sibling co-housed group provided an important control, enabling the identification of areas specifically activated by sex and bond formation. The analyses of the data were rigorous, resulting in convincing conclusions. While there was nothing particularly surprising in terms of the structures that were identified to be active during the mating and cohabitation, the statistical analysis revealed interesting relationships in terms of interactions of the various clusters, and also some level of synchrony in brain activation between partners. Furthermore, ejaculation was found to be the strongest predictor of Fos activation in both males and females. The sex differences identified in the study were subtle and less than the authors expected, which is interesting.

While the study provides a potentially useful tool and approach that may be of general use to the prairie vole community and identifies in an anatomically precise manner areas that may be important for mating or pair bond formation, there are some weaknesses as well. The study is largely descriptive. It is impossible to determine whether the activated areas are simply involved in sex or in the pair bond process itself. In other words, the authors did not use the Fos data to inform functional testing of circuits in pair bonding or mating behaviors. However, that is likely beyond the scope of this paper in which the goal was more to describe the automated, unbiased approach. This weakness is offset by the value of the comprehensive and detailed analysis of the Fos activation data providing temporal and precise anatomical relationships between brain clusters and in relation to behavior. The manuscript concludes with some speculative interpretations of the data, but these speculations may be valuable for guiding future investigations.

Reviewer #3 (Public Review):

Summary:<br /> The study by Karaś et al. reveals how multi-protein systems can evolve into single-protein equivalents, shedding light on the molecular events enabling gene loss during evolution. This work is valuable for researchers in evolutionary fields and offers potential applications in protein and organism engineering. While the findings lack broader appeal and societal implications, the evidence presented supports the proposed molecular mechanism. Using computational methods and biochemical analysis, the authors traced the evolutionary simplification of bacterial small heat shock proteins, linking specific mutations to functional changes. The study's strength lies in its vertical approach, identifying functional residues, but it does not introduce new techniques, limiting its novelty and significance.

Strengths:<br /> 1) Experimental Approach<br /> The research question was clearly outlined and the author's approach to answering it was systematic. In particular, their model system was highly suitable to address the research question. The authors employed appropriate experimental and computational techniques, and their 'vertical approach' was beneficial in that it allowed them to discover functional residues in the sHsp system which may not have been possible otherwise. Overall, their approach to this study was solid.

2) Reproducibility<br /> The results were presented well. The number of experimental repeats was suitable, as well as their analysis of the data. The values for standard deviation were reasonable, and their results using the alternative ancestors for the substrate aggregation assays helped support the robustness of their observations.

Weaknesses:<br /> During the mutational experiments, the authors examined seven potential substitutions identified through ASR and measured their impact on protein disaggregation activity. Positions 66 and 109 exhibited a significant decrease in luciferase refolding stimulation. To explore the combined effect of these mutations, the authors created the double mutant AncA0. However, predicting the most impactful combination of mutations due to epistatic effects is challenging. A more effective strategy would be to test various combinations of mutations to identify the double mutant with the greatest decrease in luciferase refolding stimulation and/or alternatively perform a co-evolutionary study to try to understand any epistatic effects between the mutations.

Reviewer #3 (Public Review):

Summary:<br /> This study aimed to understand the neural correlates of memory recall over short (1-day) and long (14-days) intervals in children (5-7 years old) relative to young adults. The results show that children recall less than young adults and that this is accompanied by less activation (relative to young adults) in brain networks associated with memory retrieval.

Strengths:<br /> This paper is one of few investigating long-term memory (multiple days) in a developmental population, an important gap in the field. Also, the authors apply a representational similarity analysis to understand how specific memories evolve over time. This analysis shows how the specificity of memories decreases over time in children relative to adults. This is an interesting finding.

Weaknesses:<br /> Overall, these results are consistent with what we already know: recall is worse in children relative to adults (e.g., Cycowicz et al., 2001) and children activate memory retrieval networks to a lesser extent than adults (Bauer et al, 2017).

It seems that the reduced activation in memory recall networks is likely associated with less depth of memory encoding in children due to inattentiveness, reduced motivation, and documented differences in memory strategies. In regards to this, there was consideration of IQ, sex, and handedness but these were not included as covariates as they were not significant although I note p<.16 suggests there was some level of association nonetheless. Also, IQ is measured differently for the children and adults so it's not clear these can be directly contrasted. The authors suggest the instructed elaborative encoding strategy is effective for children and adults but the reference in support of this (Craik & Tulving, 1975) does not seem to support this point.

Reviewer #3 (Public Review):

Overall, this is a strong manuscript that uses multiple current techniques to provide specific mechanistic insight into prior discoveries of the contributions of the Bcl11b transcription factor to mossy fiber synapses of dentate gyrus granule cells. The authors employ an adult deletion of Bcl11b via Tamoxifen-inducible Cre and use immunohistochemical, electron microscopy, and electrophysiological studies of synaptic plasticity, together with viral rescue of C1ql2, a direct transcriptional target of Bcl11b or Nrxn3, to construct a molecular cascade downstream of Bcl11b for DG mossy fiber synapse development. They find that C1ql2 re-expression in Bcl11b cKOs can rescue the synaptic vesicle docking phenotype and the impairments in MF-LTP of these mutants. They also show that C1ql2 knockdown in DG neurons can phenocopy the vesicle docking and plasticity phenotypes of the Bcl11b cKO. They also use artificial synapse formation assays to suggest that C1ql2 functions together with a specific Nrxn3 splice isoform in mediating MF axon development, extending these data with a C1ql2-K262E mutant that purports to specifically disrupt interactions with Nrxn3. All of the molecules involved in this cascade are disease-associated and this study provides an excellent blueprint for uncovering downstream mediators of transcription factor disruption. Together this makes this work of great interest to the field. Strengths are the sophisticated use of viral replacement and multi-level phenotypic analysis while weaknesses include the linkage of C1ql2 with a specific Nrxn3 splice variant in mediating these effects.

Here is an appraisal of the main claims and conclusions:

1. C1ql2 is a downstream target of Bcl11b which mediates the synaptic vesicle recruitment and synaptic plasticity phenotypes seen in these cKOs. This is supported by the clear rescue phenotypes of synapse anatomy (Fig.2) and MF synaptic plasticity (Fig.3). One weakness here is the absence of a control assessing over-expression phenotypes of C1ql2. It's clear from Fig.1D that viral rescue is often greater than WT expression (totally expected). In the case where you are trying to suppress a LoF phenotype, it is important to make sure that enhanced expression of C1ql2 in a WT background does not cause your rescue phenotype. A strong overexpression phenotype in WT would weaken the claim that C1ql2 is the main mediator of the Bcl11b phenotype for MF synapse phenotypes.

2. Knockdown of C1ql2 via 4 shRNAs is sufficient to produce the synaptic vesicle recruitment and MF-LTP phenotypes. This is supported by clear effects in the shRNA-C1ql2 groups as compared to nonsense-EGFP controls. One concern (particularly given the use of 4 distinct shRNAs) is the potential for off-target effects, which is best controlled for by a rescue experiment with RNA-insensitive C1ql2 cDNA as opposed to nonsense sequences, which may not elicit the same off-target effects.

3. C1ql2 interacts with Nrxn3(25b+) to facilitate MF terminal SV clustering. This claim is theoretically supported by the HEK cell artificial synapse formation assay (Fig.5), the inability of the K262-C1ql2 mutation to rescue the Bcl11b phenotype (Fig.6), and the altered localization of C1ql2 in the Nrxn1-3 deletion mice (Fig.7). Each of these lines of experimental evidence has caveats that should be acknowledged and addressed. Given the hypothesis that C1ql2 and Nrxn3b(25b) are expressed in DG neurons and work together, the heterologous co-culture experiment seems strange. Up till now, the authors are looking at pre-synaptic function of C1ql2 since they are re-expressing it in DGNs. The phenotypes they are seeing are also pre-synaptic and/or consistent with pre-synaptic dysfunction. In Fig.5, they are testing whether C1ql2 can induce pre-synaptic differentiation in trans, i.e. theoretically being released from the 293 cells "post-synaptically". But the post-synaptic ligands (Nlgn1 and and GluKs) are not present in the 293 cells, so a heterologous synapse assay doesn't really make sense here. The effect that the authors are seeing likely reflects the fact that C1ql2 and Nrxn3 do bind to each other, so C1ql2 is acting as an artificial post-synaptic ligand, in that it can cluster Nrxn3 which in turn clusters synaptic vesicles. But this does not test the model that the authors propose (i.e. C1ql2 and Nrxn3 are both expressed in MF terminals). Perhaps a heterologous assay where GluK2 is put into HEK cells and the C1ql2 and Nrxn3 are simultaneously or individually manipulated in DG neurons?

4. K262-C1ql2 mutation blocks the normal rescue through a Nrxn3(25b) mechanism (Fig.6). The strength of this experiment rests upon the specificity of this mutation for disrupting Nrxn3b binding (presynaptic) as opposed to any of the known postsynaptic C1ql2 ligands such as GluK2. While this is not relevant for interpreting the heterologous assay (Fig.5), it is relevant for the in vivo phenotypes in Fig.6. Similar approaches as employed in this paper can test whether binding to other known postsynaptic targets is altered by this point mutation.

5. Altered localization of C1ql2 in Nrxn1-3 cKOs. These data are presented to suggest that Nrx3(25b) is important for localizing C1ql2 to the SL of CA3. Weaknesses of this data include both the lack of Nrxn specificity in the triple a/b KOs as well as the profound effects of Nrxn LoF on the total levels of C1ql2 protein. Some measure that isn't biased by this large difference in C1ql2 levels should be attempted (something like in Fig.1F).

Reviewer #3 (Public Review):

This study demonstrates that from fish to mammals CIB2/3 is required for hearing, revealing the high degree of conservation of CIB2/3 function in vertebrate sensory hair cells. The modeling data reveal how CIB2/3 may affect the conductance of the TMC1/2 channels that mediate mechanotransduction, which is the process of converting mechanical energy into an electrical signal in sensory receptors. This work will likely impact future studies of how mechanotransduction varies in different hair cell types.

One caveat is that the experiments with the mouse mutants are confirmatory in nature with regard to a previous study by Wang et al., and the authors use lower resolution tools in terms of function and morphological changes. Another is that the modeling data is not supported by electrophysiological experiments, however, as mentioned above, future experiments may address this weakness.

Reviewer #3 (Public Review):

Summary:<br /> Using ex vivo electrophysiology and morphological analysis, Boi et al. investigate the electrophysiological and morphological properties of serotonergic and dopaminergic subpopulations in the dorsal raphe nucleus (DRN). They performed labor-intensive and rigorous electrophysiology with posthoc immunohistochemistry and neuronal reconstruction to delineate the two major cell classes in the DRN: DRN-DA and DRN-5HT, named according to their primary neurotransmitter machinery. They find that the dopaminergic (DRN-DA) and serotonergic (DRN-5HT) neurons are electrophysiologically and morphologically distinct, and are altered following striatal injection of the toxin 6-OHDA. However, these alterations were largely prevented in DRN-5HT neurons by pre-treatment with desipramine. These findings suggest an important interplay between catecholaminergic systems in healthy and parkinsonian conditions, as well as a relationship between neuronal structure and function.

Strengths:<br /> A large, well-validated dataset that will be a resource for others.<br /> Complementary electrophysiological and anatomical characterizations.<br /> Conclusions are justified by the data.<br /> Relevant for basic scientists interested in DRN cell types and physiology.<br /> Relevant for those interested in serotonin and/or DRN neurons in Parkinson's Disease.

Weaknesses:<br /> Given the scope of the author's questions and hypotheses, I did not identify any major weaknesses.

Reviewer #3 (Public Review):

Ghasemahmad et al. examined behavioral and neurochemical responses of male and female mice to vocalizations associated with mating and restraint. The authors made two significant and exciting discoveries. They revealed that the affective content of vocalizations modulated both behavioral responses and the release of acetylcholine (ACh) and dopamine (DA) but not serotonin (5-HIAA) in the basolateral amygdala (BLA) of male and female mice. Moreover, the results show sex-based differences in behavioral responses to vocalizations associated with mating. The authors conclude that behavior and neurochemical responses in male and female mice are experience-dependent and are altered by vocalizations associated with restraint and mating. The findings suggest that ACh and DA release may shape behavioral responses to context-dependent vocalizations. The study has the potential to significantly advance our understanding of how neuromodulators provide internal-state signals to the BLA while an animal listens to social vocalizations; however, multiple concerns must be addressed to substantiate their conclusions.

Major concerns:

1. The authors normalized all neurochemical data to the background level obtained from a single pre-stimulus sample immediately preceding playback. The percentage change from the background level was calculated based on a formula, and the underlying concentrations were not reported. The authors should report the sample and background concentrations to make the results and analyses more transparent. The authors stated that NE and 5-HT had low recovery from the mouse brain and hence could not be tracked in the experiment. The authors could be more specific here by relating the concentrations to ACh, DA, and 5-HIAA included in the analyses.

2. For the EXP group, the authors stated that each animal underwent 90-min sessions on two consecutive days that provided mating and restraint experiences. Did the authors record mating or copulation during these experiments? If yes, what was the frequency of copulation? What other behaviors were recorded during these experiences? Did the experiment encompass other courtship behaviors along with mating experiences? Was the female mouse in estrus during the experience sessions?

3. For the mating playback, the authors stated that the mating stimulus blocks contained five exemplars of vocal sequences emitted during mating interactions. The authors should clarify whether the vocal sequences were emitted while animals were mating/copulating or when the male and female mice were inside the test box. If the latter was the case, it might be better to call the playback "courtship playback" instead of "mating playback".

4. Since most differences that the authors reported in Figure 3 were observed in Stim 1 and not in Stim 2, it might be better to perform a temporal analysis - looking at behaviors and neurochemicals over time instead of dividing them into two 10-minute bins. The temporal analysis will provide a more accurate representation of changes in behavior and neurochemicals over time.

5. In Figures 2 and 3, the authors show the correlation between Flinching behavior and ACh concentration. The authors should report correlations between concentrations of all neurochemicals (not just ACh) and all behaviors recorded (not just Flinching), even if they are insignificant. The analyses performed for the stim 1 data should also be performed on the stim 2 data. Reporting these findings would benefit the field.

6. The mice used in the study were between p90 - p180. Although CBA/CaJ mice display normal hearing, sexual behaviors, and social behaviors for at least 1 year (Ohlemiller, Dahl, and Gagnon, JARO 11: 605-623, 2010), the age of the mice covers a range of 90 days. It would strengthen the authors' argument that the affective content of vocalizations modulated both behavioral responses and the release of acetylcholine (ACh) and dopamine (DA) but not serotonin (5-HIAA) in the basolateral amygdala (BLA) of male and female mice if there were no correlations between the magnitude of the neural responses and age.

7. The authors reported neurochemical levels estimated as the animals listened to the sounds played back. What about the sustained effects of changes in neurochemicals? Are there any potential long-term effects of social vocalizations on behavior and neurochemical levels? The authors might consider discussing long-term effects.

8. Histology from a single recording was shown in supplementary figure 1. It would benefit the readers if additional histology was shown for all the animals, not just the colored schematics summarizing the recording probe locations. Further explanation of the track location is also needed to help the readers. Make it clear for the readers which dextran-fluorescein labeling image is associated with which track in the schematic.

9. The authors did not control for the sounds being played back with a speaker. This control may be necessary since the effects are more pronounced in Stim 1 than in Stim 2. Playing white noise rather than restraint or courtship vocalizations would be an excellent control. However, the authors could perform a permutation analysis and computationally break the relationship between what sound is playing and the neurochemical data. This control would allow the authors to show that the actual neurochemical levels are above or below chance.

10. The authors indicated that each animal's post-vocalization session was also recorded. No data in the manuscript related to the post-vocalization playback period was included. This omission was a missed opportunity to show that the neurochemical levels returned to baseline, and the results were not dependent on the normalization process described in major concern #1. The data should be included in the manuscript and analyzed. It would add further support for the model described in Figure 6.

11. The authors could use a predictive model, such as a binary classifier trained on the CSF sampling data, to predict the type of vocalizations played back. The predictive model could support the conclusions and provide additional support for the model in Figure 6.

Reviewer #3 (Public Review):

Using the zebrafish model, this paper by Kraus A. et al., described the anti-virus response in the Olfactory bulb (OB) neurons and microglia. This paper used the behavioral test, neuron calcium imaging, and single-cell transcriptomic analysis. Importantly, this paper discovered that following IHNV infection, the OB neuron increased Pacap expression, which likely protects the neuron cells and mediates the anti-viral defense response. Overall, the findings presented in this paper are quite interesting.

Major strength:<br /> (1) The author demonstrated for the first time that zebrafish OSN neurons sense the IHNV viruses and transmit the viral signal to OB neurons. The zebrafish can be used as a new system to investigate the viral-neuron interaction and understand the mechanisms of how the neurons in the CNS to viral infection through the peripheral chemosensory system.

(2) This paper generated the first zebrafish OB sc-RNA sequencing data. The sc-RNA sequencing data generated in this paper will also help other zebrafish researchers who study the OB neurons.

Major weakness:<br /> The experiment results presented in this paper are not well-integrated. For example, it is unclear how the behavioral phenotype is connected to the neuronal calcium phenotype. It is also unclear how the behavioral or neuronal calcium imaging results is connected to the scRNA sequencing result.

Reviewer #3 (Public Review):

This manuscript aims to exploit experimental measurements of the extracellular voltages produced by colliding action potentials to adjust a simplified model of action potential propagation that is then used to predict the extracellular fields at axon terminals. The overall rationale is that when solving the cable equation (which forms the substrate for models of action potential propagation in axons), the solution for a cable with a closed end can be obtained by a technique of superposition: a spatially reflected solution is added to that for an infinite cable and this ensures by symmetry that no axial current flows at the closed boundary. By this method, the authors calculate the expected extracellular fields for axon terminals in different situations. These fields are of potential interest because, according to the authors, their magnitude can be larger than that of a propagating action potential and may be involved in ephaptic signalling. The authors perform direct measurements of colliding action potentials, in the earthworm giant axon, to parameterise and test their model.

Although simplified models can be useful and the trick of exploiting the collision condition is interesting, I believe there are several significant problems with the rationale, presentation, and application, such that the validity and potential utility of the approach is not established.

Simplified model vs. Hogdkin and Huxley<br /> The authors employ a simplified model that incorporates a two-state membrane (in essence resting and excited states) and adds a recovery mechanism. This generates a propagating wave of excitation and key observables such as propagation speed and action potential width (in space) can be adjusted using a small number of parameters. However, even if a Hodgkin-Huxley model does contain a much larger number of parameters that may be less easy to adjust directly, the basic formalism is known to be accurate and typical modifications of the kinetic parameters are very well understood, even if no direct characterisations already exist or cannot be obtained. I am therefore unconvinced by the utility of abandoning the Hodgkin-Huxley version.

In several places in the manuscript, the simplified model fits the data well whereas the Hodgkin-Huxley model deviates strongly (e.g. Fig. 3CD). This is unsatisfying because it seems unlikely that the phenomenon could not be modelled accurately using the HH formulation. If the authors really wish to assert that it is "not suitable to predict the effects caused by AP [collision]" (p9) they need to provide a good deal more analysis to establish the mechanism of failure.

(In)applicability of the superposition principle<br /> The reflecting boundary at the terminal is implemented using the symmetry of the collision of action potentials. However, at a closed cable there is no reflecting boundary in the extracellular space and this implied assumption is particularly inappropriate where the extracellular field is one objective of the modelling, as here. I believe this assumption is not problematic for the calculation of the intracellular voltage, because extracellular voltage gradients can usually be neglected, but the authors need to explain how the issue was dealt with for the calculation of the extracellular fields of terminals. I assume they were calculated from the membrane currents of one-half of the collision solution, but this does not seem to be explained. It might be worth showing a spatial profile of the calculated field.

Missing demonstrations<br /> Central analytical results are stated rather brusquely, notably equations (3) and (4) and the relation between them. These merit an expanded explanation at the least. A better explanation of the need for the collision measurements in parameterising the models should also be provided.

Adjusted parameters<br /> I am uncomfortable that the parameters adjusted to fit the model are the membrane capacitance and intracellular resistance. These have a physical reality and could easily be measured or estimated quite accurately. With a variation of more than 20-fold reported between the different models in Appendix 2 we can be sure that some of the models are based upon quite unrealistic physical assumptions, which in turn undermines confidence in their generality.

p8 the values of both the extracellular (100 Ohm m) and intracellular resistivity (1 Ohm m) appear to be in error, especially the former.

(In)applicability to axon terminals<br /> The rationale of the application of the collision formalism to axon terminals is somewhat undermined by the fact that they tend not to be excitable. There is experimental evidence for this in the Calyx of Held and the cerebellar pinceau. The solution found via collision is therefore not directly applicable in these cases.

Comparison with experimental data<br /> More effort should be made to compare the modelling with the extracellular terminal fields that have been reported in the literature.

Choice of term "annihilation"<br /> The term annihilation does not seem wholly appropriate to me. The dictionary definitions are something along the lines of complete destruction by an external force or mutual destruction, for example of an electron and a positron. I don't think either applies exactly here. I suggest retaining the notion of collision which is well understood in this context.

Reviewer #3 (Public Review):

The authors utilize chimpanzee-human hybrid cell lines to assess cis-regulatory evolution. These hybrid cell lines offer a well-controlled environment, enabling clear differentiation between cis-regulatory effects and environmental or other trans effects.<br /> In their research, Wang et al. expand the range of chimpanzee-human hybrid cell lines to encompass six new developmental cell types derived from all three germ layers. This expansion allows them to discern cell type-specific cis-regulatory changes between species from more pleiotropic ones. Although the study investigates only two iPSC clones, the RNA- and ATAC-seq data produced for this paper is a valuable resource.

The authors begin their analysis by examining the relationship between allele-specific expression (ASE) as a measure of species divergence and cell type specificity. They find that cell-type-specific genes exhibit more divergent expression. By integrating this data with measures of constraint within human populations, the authors conclude that the increased divergence of tissue-specific genes is, at least in part, attributable to positive selection. A similar pattern emerges when assessing allele-specific chromatin accessibility (ASCA) as a measure of divergence of cis-regulatory elements (CREs) in the same cell lines.

By correlating these two measures, the authors identify 95 CRE-gene pairs where tissue-specific ASE aligns with tissue-specific ASCA. Among these pairs, the authors select two genes of interest for further investigation. Notably, the authors employ an intriguing machine-learning approach in which they compare the inferred chromatin state of the human sequence with that of the chimpanzee sequence to pinpoint putatively causal variants.

Overall, this study delves into the examination of gene expression and chromatin accessibility within hybrid cell lines, showcasing how this data can be leveraged to identify potential causal sequence differences underlying between-species expression changes.

I have three major concerns regarding this study:

1. The only evidence that the cells are indeed differentiated in the right direction is the expression of one prominent marker gene per cell type. Especially for the comparison of conservation between the differentiated cell types, it would be beneficial to describe the cell type diversity and the differentiation success in more detail.

2. Check for a potential confounding effect of sequence similarity on the power to detect ASE or ASCA.

3. In the last part the authors showcase 2 examples for which the log2 fold changes in chromatin state scores as inferred by the machine learning model Sei are used. This is an interesting and creative approach, however, more sanity checks on this application are necessary.

Reviewer #3 (Public Review):

The manuscript by Bimai et al describes a structural and functional characterization of an anaerobic ribonucleotide reductase (RNR) enzyme from the human microbe, P. copri. More specifically, the authors aimed to characterize the mechanism by how (d)ATP modulates nucleotide reduction in this anaerobic RNR, using a combination of enzyme kinetics, binding thermodynamics, and cryo-EM structural determination. One of the principal findings of this paper is the ordering of a NxN 'flap' in the presence of ATP that promotes RNR catalysis and the disordering of both this flap and the glycyl radical domain (GRD) when the inhibitory effector, dATP, binds. The latter is correlated with a loss of substrate binding, which is the likely mechanism for dATP inhibition. It is important to note that the GRD is remote (>30 Ang) from the binding site of the dATP molecule, suggesting long-range communication of the structural (dis)ordering. The authors also present evidence for a shift in oligomerization in the presence of dATP. The work does provide evidence for new insights/views into the subtle differences of nucleotide modulation (allostery) of RNR through long-range interactions.

The strengths of the work are the impressive, in-depth structural analysis of the various regulated forms of PcRNR by (d)ATP using cryo-EM. The authors present seven different models in total, with striking differences in oligomerization and (dis)ordering of select structural features, including the GRD that is integral to catalysis. The authors present several, complementary biochemical experiments (ITC, MST, EPR, kinetics) aimed at resolving the binding and regulatory mechanism of the enzyme by various nucleotides. The authors present a good breadth of the literature in which the focus of allosteric regulation of RNRs has been on the aerobic orthologues.

Given the resolution of some of the structures in the remote regions that appear to be of importance, the rigor of the work could have been improved by complementing this experimental studies with molecular dynamics (MD) simulations to reveal the dynamics of the GRD and loops/flaps at the active site. The biochemical data supporting the loss of substrate binding with dATP association is compelling, but the binding studies of the (d)ATP regulatory molecules are not; the authors noted less-than-unity binding stoichiometries for the effectors. Also, the work would benefit from additional support for oligomerization changes using an additional biochemical/biophysical approach.

Overall, the authors have mostly achieved their overall aims of the manuscript. With focused modifications, including additional control experiments, the manuscript should be a welcomed addition to the RNR field.

Reviewer #3 (Public Review):

Light harvesting (LH) associated with photosynthesis, photoprotection, and the formation of useful pigment-protein complexes are all major functions of carotenoid (Car) pigments. However, the connections between quinone exchange, prokaryotic reaction center (RC)-LH complex formation, and Car depletion in the LH are not entirely understood. This article examined the native RC-LH (nRC-LH) and Car-depleted RC-LH (dRC-LH) complexes in the filamentous anoxygenic phototroph Roseiflexus castenholzii. The authors show with a high degree of detail using crystallography and Cryo-EM complemented with biophysical techniques important results of a new conformation of a LH. They could assigned the amino acid sequences of subunit X and two hypothetical proteins, Y and Z, that formed the quinone channel and maintained the RC-LH connections. This study identifies a new architectural basis for the regulation of bacterial RC-LH complex and quinone exchange by Cars assembly, which is distinct from the well known purple bacteria. These findings represent a significant advancement of diversity and development of bacterial photosynthetic machinery.

Reviewer #3 (Public Review):

The present paper uncovers evidence of the coordination of two brain areas involved in a two-step learning process in birdsong plasticity. Indeed, songbirds can modify their song based on an error-correction mechanism that involves a motor bias expressed by a basal ganglia-thalamo-cortical loop. After training (hundreds or a few thousands of renditions), the motor bias necessary to correct vocal errors becomes independent of the BG-thalamo-cortical loop and is transferred into the long-term motor program stored in a primary motor network. Current understanding claims that the output nucleus of the BG-thalamo-cortical loop, LMAN, trains the primary motor networks (in area RA) to drive the learning transfer. However, no clear evidence for such entrainment was available until now. In the present study, the authors elegantly show that correlations in trial-by-trial fluctuations in the premotor activity in LMAN and RA are present spontaneously (in multi-unit electrophysiological recordings) and are increased during a lab-induced plasticity protocol. The change in correlation is specific to the syllable that undergoes plasticity. Moreover, perturbing LMAN activity through low-intensity and spatially broad electrical stimulation of LMAN during the premotor window prevents behavioral adaptation. Altogether, their results convincingly show that the entrainment of RA neural populations by LMAN neurons is present during baseline, strengthened during plasticity in a syllable-specific manner, and necessary for song plasticity.

This study thus provides important validation of the current model for the 2-step learning process underlying song learning and plasticity, where a BG-thalamo-cortical network drive motor bias to correct vocal errors based on a reinforcement learning mechanism, while the song motor engram is updated slowly through the adjustment of song-related activity in the primary motor areas. Beyond the songbird field, these results will be of importance to all studying sensorimotor learning and adaptation, and more broadly the formation of memory through a two-step learning process.

The authors present the context for their hypothesis clearly, state their hypothesis precisely, and conduct a thorough investigation of the posed question. The conclusions are well supported by data.

In particular, the statistical evaluation of the covariance of LMAN and RA activity in the premotor window is adequate and the interpretation of the results is therefore well backed by their analysis. The methods used here to assess covariation between LMAN and RA activity during singing set the ground for future studies looking at the coordination between brain areas during behavior.

Reviewer #3 (Public Review):

Jie Yang et al. investigated the transgenerational behavioral modification of a high-sugar diet (HSD) in Drosophila and revealed the underlying molecular and neural mechanisms. It has been reported that HSD exposure decreases sweet sensitivity in gustatory sensory neurons, resulting in reduced sugar response (Proboscis extension reflex, PER) in flies. The current study reports that this effect can be transmitted across generations through the maternal germline. Furthermore, the authors show that H3K27me3 modification is enhanced in the first-generation progenies of HSD-treated flies (F1), and genetical or pharmacological disruption of PCL-PRC2 complex blocks the behavioral change and restores the sweet sensitivity in the Gr5a+ sweet sensory neurons. The authors further analyze the differentially expressed genes in the F1 flies. Among H3K27me3 hypermethylated regions, they focus on homeobox genes and find a transcription factor Caudal (Cad), which shows decreased expression in the F1 flies. Knocking down Cad in Gr5a+ neurons results in decreased PER response to sucrose.

Transgenerational changes in physiology and metabolism have been broadly studied, while inherited changes at the behavioral level are much less investigated. This work provides convincing evidence for transgenerational modification of feeding behavior and digs out the underlying molecular and neural mechanisms. However, there still are several concerns that need to be clarified.

1) The epigenetic regulator PCR2 has been found to play an essential role in the 7d-HSD-induced modification of the PER response. In this study, it's important to clarify for the transgenerational change, whether epigenetic modification is required in the flies exposed to HSD (F0), the progenies (F1), or both. It would be very helpful for better interpretation if the procedures of HSD treatment in RNAi experiments and the drug treatments were stated in more detail. In addition, the F0 flies should be examined as the control.<br /> 2) The information on the drug treatment period is also missing for imaging experiments (Fig.4C). Moreover, the response curve is very different from those recorded in the same neurons in previous studies. What's the reason? Please also provide a representative image showing which part of the Gr5a neurons is recorded.<br /> 3) It's unclear whether the decreased Cad expression upon HSD treatment specifically occurred in Gr5a+ neurons or a lot of cells. If the change in gene expression is significant in the qPCR test, it should occur in a large number of cells, most likely including different types of gustatory sensory neurons. If lower cad expression led to lower neural response and thereby lower behavioral response, how to specifically decrease the PER response to sucrose but not to other tastes? --whether HSD-induced desensitization is specific to sucrose in the offspring?<br /> 4) In Fig.2D, data are sorted for genomic regions showing an up-regulated modification of H3K27me. It's unclear whether similar sorting was performed in panel C. This needs to be clarified.

Reviewer #3 (Public Review):

The authors aim to gain a more comprehensive understanding of the role of FIKK4.1 in parasite biology. To achieve this, they used a novel approach termed PerTurboID that allows them to map changes in the conformational and interaction environment of proteins that are in close proximity of the tagged gene of interest. Here the authors focus on two proteins KHARP and PTP4 who are known targets of FIKK4.1 and assessed the impact of the genetic disruption of the kinase on the interaction environment of these proteins. The experimental strategy identifies a range of changes that indicate that changes go beyond the direct targets of FIKK4.1 and therefore creates new insights of interaction networks that are regulated by this specific kinase.

The strength of this approach is not only that it can identify new interaction networks relating to FIKK4.1 but that serves as a proof of concept that can be used for a wide range of applications in parasite biology. At the same time as the authors have noted themselves the extent of the biotin pulse is important and most likely needs to be calibrated for every specific application. In addition, this approach is only suitable for proteins that can be tagged without impacting their function.

The authors present very convincing evidence that the PerTurboID is suitable to study FIKK kinases in parasites and have used this to shed new light on how FIKK4.1 is involved directly or indirectly in a wider range of biological activities in the parasite.

The main impact of this work is that it provides a wider understanding of the relationship between a specific kinase and structural as well as biological consequences. The methodology is also very powerful and will have a wide range of applications.

Reviewer #3 (Public Review):

Summary:<br /> The authors present a biophysically detailed model of the basolateral amygdala (BLA) that is capable of fear learning through a depression-dominated spike-timing dependent plasticity (STDP) mechanism. Furthermore, the model also replicates experimentally measured rhythmic signatures of baseline amygdala activity and changes of these signatures during and after fear learning. The authors furthermore carefully dissect the contributions of the three different types of interneurons (parvalbumin-positive (PV), somatostatin-positive (SOM), and vaso-active peptide-positive (VIP) interneurons) in regulating network activity to allow for the association between conditioned and unconditioned stimuli.

Strengths:<br /> The biophysical detail of the model allows the authors to go beyond a simple modelling of the fear learning process in terms of spiking activity of the principal cells and to link the associative learning to several oscillatory rhythms in the BLA, namely high and low theta and gamma rhythms. This provides an understanding of the generation and function of these rhythms in the baseline amygdala circuit as well as of the functional consequences of alterations of these rhythms during and after the fear learning process. This offers a new and uniquely detailed insight into the mechanistic level.

Weaknesses:<br /> The main weakness of the approach is the lack of experimental data from the BLA to constrain the biophysical models. This forces the authors to use models based on other brain regions and leaves open the question of whether the model really faithfully represents the basolateral amygdala circuitry. Furthermore, the authors chose to use model neurons without a representation of the morphology. However, given that PV and SOM cells are known to preferentially target different parts of pyramidal cells and given that the model relies on a strong inhibition form SOM to silence pyramidal cells, the question arises whether SOM inhibition at the apical dendrite in a model representing pyramidal cell morphology would still be sufficient to provide enough inhibition to silence pyramidal firing. Lastly, the fear learning relies on the presentation of the unconditioned stimulus over a long period of time (40 seconds). The authors justify this long-lasting input as reflecting not only the stimulus itself but as a memory of the US that is present over this extended time period. However, the experimental evidence for this presented in the paper is only very weak.

The authors achieved the aim of constructing a biophysically detailed model of the BLA not only capable of fear learning but also showing spectral signatures seen in vivo. The presented results support the conclusions with the exception of a potential alternative circuit mechanism demonstrating fear learning based on a classical Hebbian (i.e. non-depression-dominated) plasticity rule, which would not require the intricate interplay between the inhibitory interneurons. This alternative circuit is mentioned but a more detailed comparison between it and the proposed circuitry is warranted.

The presented model demonstrates how the complex interplay between different types of interneurons is able to precisely control neural activity to enable learning to happen. Furthermore, the presented work shows this interactive control of activity by the interneurons gives rise to specific oscillatory signatures. Since the three types of interneurons considered here are found throughout the brain, the findings will likely have a big impact on other studies of interneuron function and learning in general.

Reviewer #3 (Public Review):

In the present study, Iversen et al investigate the effect of middle cerebral artery occlusion (MCAo) on penumbral capillary blood flow in rat brains. Using Laser Speckle Contrast imaging and two-photon microscopy, they found that during MCAo the red blood cell dynamics become chaotic in penumbral capillaries despite an apparent constant residual blood flow. They further conclude that these disturbances would cause decreases in steady-state cerebral metabolic rate of oxygen (CMRO2), and tissue oxygen tension (PtO2) using a post hoc biophysical model for oxygen extraction. Interestingly, the authors present data excluding a role for pericytes in altering capillary blood flow. From this observation, the study raises potentially interesting questions on the origin of the disturbance but fails to address them by not investigating the upstream arteriolar behavior. Increased vasomotion, palpability, or intermittent vasospasm may trigger capillary blood flow disturbances without necessarily impacting residual blood flow resting as measured by Laser Speckle Contrast imaging. Furthermore, the data are very poorly presented, here are some examples:<br /> Fig 1b is incorrectly labeled and, assuming this is the "first" 1f panel, the scale bar shows 500 µm while the legend says 200.<br /> Fig 1d is poorly convincing as pink or grey, as detailed in the legend, are not visible. It also looks like there is a second core and penumbra on the more rostral left part of the brain.<br /> Line 219 time is misspelled.<br /> Fig 2, what does "percent of alle capillaries" on the y axes mean? 2d is presented before 2c in the text.<br /> What is the rationale for presenting the statistics from Fig 3 in Fig 4? Panels 4e and 4f are not discussed. The reference in the Fig 4 legend is not formatted.<br /> Fig 6 is presented before Fig 5.<br /> The overall lack of a central hypothesis combined with the aforementioned weaknesses prevents the study from achieving its proposed goal "to characterize microvascular flow disturbances in penumbral tissue in a rat model of acute ischemic stroke".

Reviewer #3 (Public Review):

Summary:<br /> Bidirectional transsynaptic signaling via cell adhesion molecules and cell surface receptors contributes to the remarkable specificity of synaptic connectivity in the brain. Zaman et al., investigate how the receptor tyrosine kinase Kit and its trans-cellular kit ligand regulate molecular layer interneuron (MLI)- Purkinje cell (PC) connectivity in the cerebellum. Presynaptic Kit is specific for MLIs, and forms a trans-synaptic complex with Kit ligand in postsynaptic PC cells. The authors begin by generating Kit cKOs via an EUCOMM allele to enable cell-type specific Kit deletion. They cross this Kit cKO to the MLI-specific driver Pax2-Cre and conduct validation via Kit IHC and immunoblotting. Using this system to examine the functional consequences of presynaptic MLI Kit deletion onto postsynaptic PC cells, they record spontaneous and miniature synaptic currents from PC cells and find a selective reduction in IPSC frequency. Deletion of Kit ligand from postsynaptic PC cells also results in reduced IPSC frequency, together supporting that this trans-synaptic complex regulates GABAergic synaptic formation or maturation. The authors then show that sparse Kit ligand overexpression in PCs decreases neighboring uninfected control sIPSCs in a potentially competitive manner.

Strengths:<br /> Overall, the study addresses an important open question, the data largely support the authors' conclusions, the experiments appear well-performed, and the manuscript is well-written. I just have a few suggestions to help shore up the author's interpretations and improve the study.

Weaknesses:<br /> The strong decrease in sIPSC frequency and amplitude in control uninfected cells in Figure 4 is surprising and puzzling. The competition model proposed is one possibility, and I think the authors need to do additional experiments to help support or refute this model. The authors can conduct similar synaptic staining experiments as in Fig S4 but in their sparse infection paradigm, comparing synapses on infected and uninfected cells. Additional electrophysiological parameters in the sparse injection paradigm, such as mIPSCs or evoked IPSCs, would also help support their conclusions.

The authors should validate KL overexpression and increased cell surface levels using their virus to support their overexpression conclusions.

Reviewer #3 (Public Review):

This manuscript provides a more or less quantitative analysis of protein synthesis in lymphocytes. I have no issue with the data as presented, as I'm sure all measurements have been expertly done. I see no need for additional experimental work, although it would be helpful if the authors could comment on the possibility of measuring the rate of synthesis of a defined protein, say a histone, in cells prior to and after activation. The conclusion the authors leave us with is the idea that the rates of protein synthesis recorded here are incompatible with observed rates of T cell division in vivo. Indeed, in the final paragraph of the discussion, the authors note the mismatch between what they consider a requirement for cell division, and the observed rates of protein synthesis. They then invoke unconventional mechanisms to make up for the shortfall, without -in this reviewer's opinion- discussing in adequate detail the technical limitations of the methodology used.

A key question is the broad interest, novelty, and extension of current knowledge, in comparison with Argüello's (reference 27) 'SunRise' method. It would be helpful for the authors to stake out a clear position as to the similarities and differences with reference 27: what have we learned that is new? The authors could cite reference 27 in the introduction of their manuscript, given the similarity in approach. That said, the findings reported here will generate further discussion.

The manuscript would increase in impact if the authors were to clearly define why a particular measurement is important and then show the actual experiment/result. As an example, it would be helpful to explain to the non-expert why the distinction between monosomes, polysomes, and stalled versions of the same is important, and then explain the rationale of the actual experiment: how can these distinctions be made with confidence, and what are confounding variables? The initial use of human cells, later abandoned in favor of the OT-1 in vitro and in vivo models, requires contextualization. If the goal is to address the relationship between rates of translation and cell division of antigen-activated T cells in vivo, then a lot of the work on the human model and the in vitro experiments becomes more of a distraction, unless properly contextualized. Is there any reason to assume that antigen-specific activation in vivo will impact translation differently than the use of the PMA/ionomycin/IL2 cocktail? The way the work is presented leaves me with the impression that everything that was done is included, regardless of whether it goes to the core of the question(s) of interest.

It would be helpful if the authors made explicit some of the assumptions that underlie their quantitative comparisons. Likewise, the authors should discuss the limitations of their methods and provide alternative interpretations where possible, even if they consider them less/not plausible, with justification. As they themselves note, improvements in the RPM protocols raised the increase in translating ribosomes upon activation from 10-fold to 15-fold. Who's to say that is the best achievable result? What about the reliability/optimization of the other measurements?

The composition of the set of proteins produced upon activation will differ from cell to cell (CD4, CD8, B, resting vs. dividing). Even if analyses are performed on fixed cells, the ability of the monoclonal anti-puromycin antibody to penetrate the matrix of the various fixed cell types may not be equal for all of them, depending on protein composition, susceptibility to fixation etc. Is it possible for puromycin to occupy the ribosome's A site and terminate translation without forming a covalent bond with the nascent chain? This could affect the staining with anti-puromycin antibodies and also underestimate the number of nascent chains.

I believe that the concept of FACS-based quantitation also requires an explanation for the non-expert. For the FACS plots shown, the differences between the highest and lowest RPM scores for cells that divided and that have a similar CFSE score is at least 10-fold. Does that mean that divided cells can differ by that margin in terms of the number of nascent chains present? If I make the assumption that cells stimulated with PMA/ionomycin/IL2 respond more or less synchronously, why would there be a 10-fold difference in absolute fluorescence intensity (anti=puromycin) for randomly chosen cells with similar CFSE values? While the use of MFI values is standard practice in cytofluorimetry, the authors should devote some comments to such variation at the population level.

It is assumed that for cells to complete division, they must have produced a full and complete copy of their proteome and only then divide. What if cells can proceed to divide even when expressing a subset of the proteome of departure (=the threshold set required for initiation of division), only to complete synthesis of the 'missing ' portion once cell division is complete? Would this obviate the requirement for an unusual mechanism of protein acquisition (trogocytosis; other)?

Translation is estimated to proceed at a rate of ~6 amino acids per second, but surely there is variability in this number attributable to inaccuracies of the methods used, in addition to biological variability. Were these so-called standard values determined for a range of different tissues? It stands to reason that there might be variation depending on the availability of initiation/elongation factors, NTPs, aminoacyl tRNAs etc. What is the margin of error in calculating chain elongation rates based on the results shown here?

Reviewer #3 (Public Review):

The authors introduce two new concepts for antimicrobial resistance borrowed from pharmacology, "variant vulnerability" (how susceptible a particular resistance gene variant is across a class of drugs) and "drug applicability" (how useful a particular drug is against multiple allelic variants). They group both terms under an umbrella term "drugability". They demonstrate these features for an important class of antibiotics, the beta-lactams, and allelic variants of TEM-1 beta-lactamase.

The strength of the result is in its conceptual advance and that the concepts seem to work for beta-lactam resistance. However, I do not necessarily see the advance of lumping both terms under "drugability", as this adds an extra layer of complication in my opinion.

I also think that the utility of the terms could be more comprehensively demonstrated by using examples across different antibiotic classes and/or resistance genes. For instance, another good model with published data might have been trimethoprim resistance, which arises through point mutations in the folA gene (although, clinical resistance tends to be instead conferred by a suite of horizontally acquired dihydrofolate reductase genes, which are not so closely related as the TEM variants explored here).

The impact of the work on the field depends on a more comprehensive demonstration of the applicability of these new concepts to other drugs.

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 manuscript describes the development of CRISPR knockouts for gh, fsh and tsh in the fast-aging Nothobranchius furzeri grz strain. CRISPR knockouts have been published before, and the strength of the paper is that here, the authors include a novel, easy and fast way of rescuing the loss of function in the entire body by electroporation in muscle. This offers flexibility in timing and dosage, and leads to intriguing results regarding the role of these hormones in growth and fertility. Finally they also add a conditional doxycycline-dependent overexpression model that would allow even more control over the modalities of the rescue. The phenotypes of the knockouts were not the key message of the paper and remained at times only superficially described. The doxycycline-dependent overexpression was only minimally validated, and here it is not yet clear how robust this system is in terms of overexpression levels, timing, and reversibility.

Overall this study brings a new set of tools in the killifish toolbox that can have much wider applications and will be appreciated also in other teleost models.

Reviewer #3 (Public Review):

The present study used novel data logging devices to record the foraging behavior of wandering albatrosses. Specifically, the authors showed how localized winds and wave heights influence their ability to take off from the sea surface, which is the most expensive behavior they engage in while foraging. There is no better platform for this initial work because these birds are so large, the equipment they can carry without creating significant impact is tremendous.

The results were impressive, presented well, and the paper was generally written in an accessible way to readers with less knowledge. The authors provide a convincing set of results that support the aims and conclusions. The theory and application could be used to inform our understanding of flight behavior in other seabirds.

Although the idea of taking off from the sea surface may sound trivial, it is essential to understand that albatrosses and other soaring seabirds have wings that are adapted for soaring (i.e. long and narrow). The trade off, however, is that powered flight through wing flapping is energetically expensive because the wings have a shallow amplitude and generate less power compared to a shorter, wider wing. Thus, wind is everything and this study shows how wind facilitates the ability of the birds to gain flight using wind and waves. Awesome!

Reviewer #3 (Public Review):

In this work, the authors tried to profile time-dependent changes in gene and protein expression during BMP-induced amnion differentiation from hPSCs. The authors depicted a GATA3 - TFAP2A - ISL1/HAND1 order of amniotic gene activation, which provides a more detailed temporary trajectory of amnion differentiation compared to previous works. As a primary goal of this study, the above temporal gene/protein activation order is amply supported by experimental data. However, the mechanistic insights on amniotic fate decision, as well as the transcriptomic analysis comparing amnion-like cells from this work and other works remain limited. While this work allows us to see more details of amnion differentiation and understand how different transcription factors were turned on in a sequence and might be useful for benchmarking the identity of amnion in ex utero cultured human embryos/embryoids, it provides limited insights on how amnion cells might diverge from primitive streak / mesoderm-like cells, despite some transcriptional similarity they shared, during early development.

Reviewer #3 (Public Review):

In this manuscript by Berrocal and coworkers, the authors do a deep dive into the transcriptional regulation of the eve gene in both an endogenous and ectopic background. The idea is that by looking at eve expression under non-native conditions, one might infer how enhancers control transcriptional bursting. The main conclusion is that eve enhancers have not evolved to have specific behaviors in the eve stripes, but rather the same rates in the telegraph model are utilized as control rates even under ectopic or 'de novo' conditions. For example, they achieve ectopic expression (outside of the canonical eve stripes) through a BAC construct where the binding sites for the TF Giant are disrupted along with one of the eve enhancers. Perhaps the most general conclusion is that burst duration is largely constant throughout at ~ 1 - 2 min. This conclusion is consistent with work in human cell lines that enhancers mostly control frequency and that burst duration is largely conserved across genes, pointing to an underlying mechanistic basis that has yet to be determined.

Reviewer #3 (Public Review):

Strengths:

On the positive side, I thought the use of ChatGPT to score the sentiment of text was novel and interesting, and I was largely convinced by the parts of the methods which illustrate that the AI provides broadly similar sentiment and politeness scores to humans who were asked to rank a sub-set of the reviews. The paper is mostly clear and well-written, and tackles a question of importance and broad interest (i.e. the potential for bias in the peer review process, and the objectivity of peer review).

Weaknesses:

The sample size and scope of the paper are a bit limited, and I have concerns covering diverse aspects including statistical/inferential issues, missing references, and suggestions for other material that could be included that would greatly increase the usefulness of the paper. A major limitation is that the paper focuses on published papers, and thus is a biased sample of all the reviews that were written, which prevents the paper properly answering the questions that it sets out to answer (e.g. is peer review repeatable, fair and objective).

Reviewer #3 (Public Review):

This paper reports a considerable technical achievement: the optogenetic activation of single retinal ganglion cells in vivo in monkeys. As clearly specified in the paper, this is an important step towards causal tests of the role of specific ganglion cell types in visual perception. Yet this methodological advance is not described currently in sufficient detail to replicate or evaluate. The paper could be improved substantially by including additional methodological details. Some specific suggestions follow.

The start of the results needs a paragraph or more to outline how you got to Figure 1. Figure 1 itself lacks scale bars, and it is unclear, for example, that the ganglion cells targeted are in the foveal slope.

The text mentions the potential difficulties targeting ganglion cells at larger eccentricities where the soma density increases. If this is something that you have tried it would be nice to include some of that data (whether or not selective activation was possible). Related to this point, it would be helpful to include a summary of the ganglion cell density in monkey retina.

Related to the point in the previous paragraph - do you have any experiments in which you systematically moved the stimulation spot away from the target ganglion cell to directly test the dependence of stimulation on distance? This would be a valuable addition to the paper.

The activity in Figure 1 recovers from activation very slowly - much more slowly than the light response of these cells, and much more slowly than the activity elicited in most optogenetic studies. Can you quantify this time course and comment on why it might be so slow?

Traces from non-targeted cells should be shown in Figure 1 along with those of targeted cells.

Reviewer #3 (Public Review):

a) Important findings<br /> - This study confirms that Gr28 subfamily members are expressed in distinct sets of taste neurons in Drosophila larvae, supporting previous findings (e.g., Kwon et al., 2011).<br /> - Neurons expressing different members of the Gr28 family exhibit distinct behavioral responses when chemically activated with capsaicin.<br /> - Silencing experiments reveal that neurons expressing Gr28bc are necessary for larval avoidance of four bitter compounds, whereas neurons expressing Gr28be are necessary for avoiding lobeline and caffeine.<br /> - Inserting either Gr28ba or Gr28bc into the GR28 mutant line restored larval avoidance of denatonium.<br /> - Calcium imaging experiments show that Gr28ba and Gr28bc are involved in sensing denatonium, while none of the GR28 family members are involved in detecting quinine.

b) Caveats<br /> - The authors did not acknowledge that neurons expressing members of the GR28 family also express other Gr family members, which could potentially contribute to the detection and behavioral responses to the tested bitter compounds.<br /> - Gal4 lines from various studies exhibit varying expression patterns, highlighting the necessity for improved reagents. These findings also suggest the importance of employing different Gal4 lines for each receptor to validate the results of the current study.<br /> - Activating or silencing neurons pertains to the function of the neurons rather than the receptors.<br /> - Inconsistency is observed in the use of different reagents across the experiments. Specifically, all six Gal4 lines were utilized in the Chemical Activation experiments, while only two lines were employed in the silencing experiments.<br /> - The Alphafold structure prediction is exciting but lacks conclusive evidence.

Reviewer #3 (Public Review):

Leeds et al. employ elegant in vitro experiments and sophisticated numerical modeling to investigate the ability of mechanical coupling to coordinate the growth of individual microtubules within microtubule bundles, specifically k-fibers. While individual microtubules naturally polymerize at varying rates, their growth must be tightly regulated to function as a cohesive unit during chromosome segregation. Although this coordination could potentially be achieved biochemically through selective binding of polymerases and depolymerases, the authors demonstrate, using a novel dual laser trap assay, that mechanical coupling alone can also coordinate the growth of in vitro microtubule pairs.

By reanalyzing recordings of single microtubules growing under constant force (data from their own previous work), the authors investigate the stochastic kinetics of pausing and show that pausing is suppressed by tension. Using a constant shared load, the authors then show that filament growth is tightly coordinated when pairs of microtubules are mechanically coupled by a material with sufficient stiffness. In addition, the authors develop a theoretical model to describe both the natural variability and force dependence of growth, using no freely adjustable parameters. Simulations based on this model, which accounts for stochastic force-dependent pausing and intrinsic variability in microtubule growth rate, fit the dual-trap data well.

Overall, this study illuminates the potential of mechanical coupling in coordinating microtubule growth and offers a framework for modeling k-fibers under shared loads. The research exhibits meticulous technical rigor and is presented with exceptional clarity. It provides compelling evidence that a minimal, reconstituted biological system can exhibit complex behavior. As it currently stands, the paper is highly informative and valuable to the field.

To provide further clarity regarding the implications of their study, the authors may wish to address the following points in more detail:

- Considering the authors' understanding of the quantitative relationship between forces, microtubule growth, and coordination, is the dual trap assay necessary to demonstrate this coordination? What advantages does the (semi)experimental system offer compared to a purely in silico treatment?

- What are the limitations of studying a system comprising only two individual microtubules? How might the presence of crosslinkers, which are typically present in vivo between microtubules, influence their behavior in this context?

- How dependent are the results on the chosen segmentation algorithm? Can the distributions of pause and run durations truly be fitted by "simple" Gaussians, as indicated in Figure S5-2? Given the inherent limitations in accurately measuring short durations and the application of threshold durations, it is likely that the first bins in the histograms underestimate events. Cumulative plots could potentially address this issue.

Reviewer #3 (Public Review):

The authors investigated the initial steps involved in angiogenesis. Using appropriate experimental tools they associated engineered vasculature models with a strong mathematical analysis. The study provides a dynamic view of the early steps involved in angiogenesis. It shows significant fluctuations in the onset of angiogenesis that suggest transitions between order and disorder in cell organization. The data obtained strongly support the hypothesis and support the conclusion of the study. This work brings new insights into the comprehension of the complex processes involved in the onset of angiogenesis and it provides a strong model to predict how VEGF will activate the delta-NOTCH signaling. Nevertheless, it would be important to describe in more detail how the current study can be used for a better understanding of the angiogenesis process in physiological and in pathological situations.

Reviewer #3 (Public Review):

Prior work from the Kaverina lab and others had determined that beta-cells build a microtubule network that differs from the canonical radial organization typical in most mammalian cell types and that this organization facilitates the regulated secretion of insulin-containing secretory granules (IGs). In this manuscript, the authors tested the hypothesis that kinesin-driven microtubule sliding is an underlying mechanism that establishes a sub-membranous microtubule array that regulates IG secretion. They employed knock-down and dominant-negative strategies to convincingly show microtubule sliding does, in fact, drive the assembly of the sub-membranous microtubule band. They also used live cell imaging assays to demonstrate that kinesin-mediated microtubule sliding in beta-cells is triggered by extracellular high glucose. Overall, this is an interesting and important study that relates microtubule dynamics to an important physiological process. The experiments were rigorous and well-controlled.

Reviewer #3 (Public Review):

Summary:<br /> Using a combination of approaches, including automated feature selection and hierarchical clustering, the author identified a set of genes persistently associated with extrachromosomal DNA (ecDNA) presence across cancer types. The authors further validated the gene set identified using gene ontology enrichment analysis and identified that upregulated genes in extrachromosomal DNA-containing tumors are enriched in biological processes like DNA damage and cell proliferation, whereas downregulated genes are enriched in immune response processes.

Major comments:<br /> 1. The authors presented a solid comparative analysis of ecDNA-containing and ecDNA-free tumors. An established automated feature selection approach, Boruta, was used to select differentially expressed genes (DEG) in ecDNA(+) and ecDNA(-) TCGA tumor samples, and the iterative selection process and two-tier multiple hypothesis testing ensured the selection of reliable DEGs. The author showed that the DEG selected using Boruta has stronger predictive power than genes with top log-fold changes.

2. The author performed a thorough interpretation of the findings with GO enrichment analysis of biological processes enriched in the identified DEG set, and presented interesting findings, including the enrichment in DNA damage process among the genes upregulated in ecDNA(+) tumors.

3. Overall, the authors achieved their aims with solid data mining and analysis approaches applied to public data tumor data sets.

4. While it may not be the scope of this study, it will be interesting to at least have some justification for choosing Boruta over other feature selection methods, such as Recursive Feature Elimination (RFE) and backward stepwise selection.

5. The authors showed that DESEQ-selected DEGs with top log-fold changes have less strong predictive power and speculated that this may be due to the fact that genes with top log-fold changes (LFC) are confined only to a small subset of samples. It will be interesting to select DEGs with top log-fold changes after first partitioning the tumor samples. For example, randomly partition the tumor samples, identify the DEGs with top LFC, combine the DEGs identified from each partition, then evaluate the predictive power of these DEGs against the Boruta-selected DEGs.

6. While the authors showed that the presence of mutations was not able to classify ecDNA(+) and (-) tumor samples, it will be interesting to see if variant allele frequencies of the genes containing these mutations have predictive power.

Reviewer #3 (Public Review):

Summary:<br /> Variants in the UBA5 gene are associated with rare developmental and epileptic encephalopathy, DEE44. This research developed a system to assess in vivo and in vitro genotype-phenotype relationships between UBA5 allele series by humanized UBA5 fly models and biochemical activity assays. This study provides a basis for evaluating current and future individuals afflicted with this rare disease.

Strengths:<br /> The authors developed a method to measure the enzymatic reaction activity of UBA5 mutants over time by applying the UbiReal method, which can monitor each reaction step of ubiquitination in real time using fluorescence polarization. They also classified fruit fly carrying humanized UBA5 variants into groups based on phenotype. They found a correlation between biochemical UBA5 activity and phenotype severity.

Weaknesses:<br /> In the case of human DEE44, compound heterozygotes with both loss-of-function and hypomorphic forms (e.g., p.Ala371Thr, p.Asp389Gly, p.Asp389Tyr) may cause disease states. The presented models have failed to evaluate such cases.

Reviewer #3 (Public Review):

The work presented in the manuscript tries to identify tRNA modifications present in Mycobacterium tuberculosis (Mtb) using reverse transcription-derived error signatures with tRNA-seq. The study identified enzyme homologs and correlates them with presence of respective tRNA modifications in Mtb. The study used several chemical treatments (IAA and alkali treatment) to further enhance the reverse transcription signals and confirms the presence of modifications in the bases. tRNA modifications by two enzymes TruB and MnmA were established by doing tRNA-seq of respective deletion mutants. Ultimately, authors show that MnmA-dependent tRNA modification is important for intracellular growth of Mtb. Overall, this report identifies multiple tRNA modifications and discuss their implication in Mtb infection.

Reviewer #3 (Public Review):

In this study, the authors sought to test the hypothesis that blocking triglyceride storage in adipose tissue by knockout of DGAT1 and DGAT2 in adipocytes would lead to ectopic lipid deposition, lipodystrophy, and impaired glucose homeostasis. Surprisingly, the authors found the opposite result, with DGAT1/2 DKO in adipocytes leading to increased energy expenditure, minimal ectopic lipid deposition, and improved glucose homeostasis with HFD feeding. These metabolic improvements were largely attributed to increased beiging of the white fat and increased brown adipose tissue activity. This study provides an interesting new paradigm whereby impairing fat storage, the major function of adipose tissue, does not lead to severe metabolic disease, but rather improves it. The authors provide a comprehensive assessment of the metabolism of these DKO mice under chow and HFD conditions, which support their claims. The study lacks in mechanistic insight, which would strengthen the study, but does not detract from the authors' major conclusions.

Reviewer #3 (Public Review):

In this study, Sun et al examine the role of the splicing factor SRSF1 in spermatogenesis in mice. Alternative splicing is important for spermatogenic development, but its regulation and major developmental roles during spermatogenesis are not well understood. The authors set out to better define both SRSF1 function in testes and the contribution of alternative splicing. They collect several large 'omics datasets to define SRSF1 targets in testis, including RNA interactions by CLIP-seq in whole testis, protein interactions by IP-mass spec in whole testis, and RNA sequencing to detect expression levels and splice variants. They also examine the phenotype of germline conditional knockouts (cKO) for Srsf1, using the early-acting Vasa-Cre, and find a severe depletion of germ cells starting at 7 days post partum (dpp) and culminating with a lack of germ cells (Sertoli Cell Only Syndrome) by adulthood. They detect differences in gene expression as well as differences in splicing between control and knockout, including 9 genes that are downregulated, experience alternative splicing, and whose transcripts are also bound by SRSF1, and identify the Tial1/Tiar transcript as one of these targets. They conclude that SRSF1 is required for homing and self-renewal of spermatogonial stem cells, at least in part through its regulation of Tial1/Tiar splicing.

Strengths of the paper include detailed phenotyping of the Srsf1 cKO, which convincingly supports the Sertoli Cell Only phenotype, establishes the timing of the first appearance of the spermatogonial defect, and provides new insight into the role of splicing factors and SRSF1 specifically in spermatogenesis. Another strength is the generation of CLIP-seq, IP-MS, and RNA-seq datasets which will be a useful resource for the field of germ cell development. Major weaknesses include a lack of robust support for two major claims: first, there is inadequate support for the claim of defects in either "homing" or "self-renewal" of spermatogonia in the cKO, and second, there is inadequate support for the claim that altered splicing of the Tial1 transcript mediates the effect of SRSF1 loss. A moderate weakness is the superficial discussion of the CLIP, RNA-seq, and IP-MS datasets, limiting their otherwise high utility for other researchers. Overall, the paper as it stands will have a moderate impact on the field of male reproductive biology. Specific points that should be addressed to improve support for the claims are below.

Major comments

1) In Fig 1D, it appears that SRSF1 is expressed most strongly in spermatogonia by immunofluorescence, but this is inconsistent with the sharp rise in expression detected by RT-qPCR at 20 days post partum (dpp) (Fig. 1B), which is when round spermatids are first added; this discrepancy should be explained or addressed.

2) It is important to provide a more comprehensive basic description of the CLIP-seq datasets beyond what is shown in the tracks shown in Fig. 2B. This would allow a better assessment of the data quality and would also provide information about the transcriptome-wide patterns of SRSF1 binding. No information or quality metrics are provided about the libraries, and it is not stated how replicates are handled to maximize the robustness of the analysis. The distribution of peaks across exons, introns, and other genomic elements should also be shown.

3) The claim that SRSF1 is required for "homing and self-renewal" of SSCs is made in multiple places in the manuscript. However, neither homing nor self-renewal is ever directly tested. A single image is shown in Fig. 5E of a spermatogonium at 5dpp that does not appropriately sit on the basal membrane, potentially indicating a homing defect, but this is not quantified or followed up. There is good evidence for depletion of spermatogonia starting at 7 dpp, but no further explanation of how homing and/or self-renewal fit into the phenotype.

4) In Fig. 6A (lines 258-260) very few genes downregulated in the cKO are bound by SRSF1 and undergo abnormal splicing. The small handful that falls into this overlap could simply be noise. A much larger fraction of differentially spliced genes are CLIP-seq targets (~33%), which is potentially interesting, but this set of genes is not explored.

5) The background gene set for Gene Ontology analyses is not specified. If these were done with the whole transcriptome as background, one would expect enrichment of spermatogenesis genes simply because they are expressed in testes. The more appropriate set of genes to use as background in these analyses is the total set of genes that are expressed in testis.

6) In general, the model is over-claimed: aside from interactions by IP-MS, little is demonstrated in this study about how SRSF1 affects alternative splicing in spermatogenesis, or how alternative splicing of TIAL1 specifically would result in the phenotype shown. It is not clear why Tial1/Tiar is selected as a candidate mediator of SRSF1 function from among the nine genes that are downregulated in the cKO, are bound by SRSF1, and undergo abnormal splicing. Although TIAL1 levels are reduced in cKO testes by Western blot (Fig. 7J), this could be due just be due to a depletion of germ cells from whole testis. The reported splicing difference for Tial1 seems very subtle and the ratio of isoforms does not look different in the Western blot image.

Reviewer #3 (Public Review):<br /> <br /> Tutor et al. present their work on Kelch13/K13 from Plasmodium falciparum, the causative agent of malaria. This protein is involved in resistance against artemisinin (ART), one of the most commonly used drugs to treat malaria. Despite having identified the mutation in K13 that leads to resistance to ART, the exact molecular mechanism, function of K13, and impact of the K13 mutations still need to be elucidated. This is where the authors step in to investigate the relationship between endocytosis and K13, as well as the impact of depleting the protein using knock-sideway (KS). Using light microscopy, the authors demonstrate how K13-YFP forms a pore associated with fluorescently labeled dextran, which is taken up into tubules that move toward the digestive vacuole. This tubule formation is not sensitive to jasplakinolide (JAS) treatment. Using electron microscopy, they show that K13 is localized at the dark contrast border of the cytostome, and knocking down K13 leads to the disruption of the cytostome structure. Upon removal of K13, the structure changes, and the opening enlarges. The impact of KS induction on the cytostome was quantified using TEM and tomography. The authors also provide reconstructions of the cytostome in both induced and non-induced parasites. Finally, they measure the impact of KS on haem degradation. These data provide clear information on the function of K13 in cytostome formation and the implication of this structure in endocytosis for Plasmodium falciparum.

The conclusions of this paper are well supported by the data, but some data analysis should be clarified and extended, and some complementary experiments would further strengthen the authors' claims.

Reviewer #3 (Public Review):

The authors report a study in which they use intracranial recordings to dissociate subjectively aware and subjectively unaware stimuli, focusing mainly on prefrontal cortex. Although this paper reports some interesting findings (the videos are very nice and informative!) the interpretation of the data is unfortunately problematic for several reasons. I will detail my main comments below. If the authors address these comments well, I believe the paper may provide an interesting contribution to further specifying the neural mechanisms important for conscious access (in line with Gaillard et al., Plos Biology 2009).

The main problem with the interpretation of the data is that the authors have NOT used a so-called "no-report paradigm". The idea of no report paradigms is that subjects passively view a certain stimulus without the instruction to "do something with it", e.g., detect the stimulus, immediately or later in time. Because of the confusion of this term, specifically being related to the "act of reporting", some have argued we should use the term no-cognition paradigm instead (Block, TiCS, 2019, see also Pitts et al., Phil Trans B 2018). The crucial aspect is that, in these types of paradigms, the critical stimulus should be task-irrelevant and thus not be associated with any task (immediately or later). Because in this experiment subjects were instructed to detect the gratings when cued 600 ms later in time, the stimuli are task relevant, they have to be reported about later and therefore trigger all kinds of (known and potentially unknown) cognitive processes at the moment the stimuli are detected in real-time (so stimulus-locked). You could argue that the setup of this delayed response task excludes some very specific report related processes (e.g., the preparation of an eye-movement), which is good, however this is usually not considered the main issue. For example when comparing masked versus unmasked stimuli (Gaillard et al., 2009 Plos Biology), these conditions usually also both contain responses but these response related processes are "averaged out" in the specific contrasts (unmasked > masked). In this paper, RT differences between conditions (that are present in this dataset) are taken care of by using this delayed response in this paper, which is a nice feature for that and is not the case for the above example set-up.

Given the task instructions, and this being merely a delayed-response task, it is to be expected that prefrontal cortex shows stronger activity for subjectively aware versus subjectively unaware stimuli. Unfortunately, given the nature of this task, the novelty of the findings is severely reduced. The authors cannot claim that prefrontal cortex is associated with "visual awareness", or what people have called phenomenal consciousness (this is the goal of using no-cognition paradigms). The only conclusion that can be drawn is that prefrontal cortex activity is associated with accessing sensory input: and hence conscious access. This less novel observation has been shown many times before and there is also little disagreement about this issue between different theories of consciousness (e.g., global workspace theory and local recurrency theories both agree on this).

The best solution at this point seems to rewrite the paper entirely in light of this. My advice would be to state in the introduction that the authors investigate conscious access using iEEG and then not refer too much to no-cognition paradigm or maybe highlight some different strategies about using task-irrelevant stimuli (see Canales-Johnson et al., Plos Biology 2023; Hesse et al., eLife 2020; Hatamimajoumerd et al Curr Bio 2022; Alilovic et al., Plos Biology 2023; Pitts et al., Frontiers 2014; Dwarakanth et al., Neuron 2023 and more). Obviously, the authors should then also not claim that their results solve debates about theories regarding visual awareness (in the "no-cognition" sense, or phenomenal consciousness), for example in relation to the debate about the "front or the back of the brain", because the data do not inform that discussion. Basically, the authors can just discuss their results in detail (related to timing, frequency, synchronization etc) and relate the different signatures that they have observed to conscious access.

I think the authors have to discuss the Gaillard et al PLOS Biology 2009 paper in much more detail. Gaillard et al also report a study related to conscious access contrasting unmasked and masked stimuli using iEEG. In this paper they also report ERP, time frequency and phase synchronization results (and even Granger causality). Because of the similarities in approach, I think it would be important to directly compare the results presented in that paper with results presented here and highlight the commonalities and discrepancies in the Discussion.

In the Gaillard paper they report a figure plotting the percentage of significant frontal electrodes across time (figure 4A) in which it can be seen that significant electrodes emerge after approximately 250 ms in PFC as well. It would be great if the authors could make a similar figure to compare results. In the current paper there are much more frontal electrode contacts than in the Gaillard paper, so that is interesting in itself.

In my opinion, some of the most interesting results are not highlighted: the findings that subjectively unaware stimuli show increased activations in the prefrontal cortex as compared to stimulus absent trials (e.g., Figure 4D). Previous work has shown PFC activations to masked stimuli (e.g., van Gaal et al., J Neuroscience 2008, 2010; Lau and Passigngham J Neurosci 2007) as well as PFC activations to subjectively unaware stimuli (e.g., King, Pescetelli, and Dehaene, Neuron 2016) and this is a very nice illustration of that with methods having more detailed spatial precision. Although potentially interesting, I wonder about the objective detection performance of the stimuli in this task. So please report objective detection performance for the patients and the healthy subjects, using signal detection theoretic d'. This gives the reader an idea of how good subjects were in detecting the presence/absence of the gratings. Likely, this reveals far above chance detection performance and in that case I would interpret these findings as "PFC activation to stimuli indicated as subjectively unaware" and not unconscious stimuli. See Stein et al., Plos Biology 2021 for a direct comparison of subjectively and objectively unaware stimuli.

In Figure 7 of the paper the authors want to make the case that the contrast does not differ between subjectively aware stimuli and subjectively unaware stimuli. However so far they've done the majority of their analyses across subjects, and for this analysis the authors only performed within-subject tests, which is not a fair comparison imo. Because several P values are very close to significance I anticipate that a test across subjects will clearly show that the contrast level of the subjectively aware stimuli is higher than of the subjectively unaware stimuli, at the group level. A solution to this would be to subselect trials from one condition (NA) to match the contrast of the other condition (NU), and thereby create two conditions that are matched in contrast levels of the stimuli included. Then do all the analyses on the matched conditions.

Related, Figure 7B is confusing and the results are puzzling. Why is there such a strong below chance decoding on the diagonal? (also even before stimulus onset) Please clarify the goal and approach of this analysis and also discuss/explain better what they mean.

I was somewhat surprised by several statements in the paper and it felt that the authors may not be aware of several intricacies in the field of consciousness. For example a statement like the following "Consciousness, as a high-level cognitive function of the brain, should have some similar effects as other cognitive functions on behavior (for example, saccadic reaction time). With this question in mind, we carefully searched the literature about the relationship between consciousness and behavior; surprisingly, we failed to find any relevant literature." This is rather problematic for at least two reasons. First, not everyone would agree that consciousness is a high-level cognitive function and second there are many papers arguing for a certain relationship between consciousness and behavior (Dehaene and Naccache, 2001 Cognition; van Gaal et al., 2012, Frontiers in Neuroscience; Block 1995, BBS; Lamme, Frontiers in Psychology, 2020; Seth, 2008 and many more). Further, the explanation for the reaction time differences in this specific case is likely related to the fact that subjects' confidence in that decision is much higher in the aware trials than in the unaware trials, hence the speeded response for the first. This is a phenomenon that is often observed if one explores the "confidence literature". Although the authors have not measured confidence I would not make too much out of this RT difference.

I would be interested in a lateralized analysis, in which the authors compare the PFC responses and connectivity profiles using PLV as a factor of stimulus location (thus comparing electrodes contralateral to the presented stimulus and electrodes ipsilateral to the presented stimulus). If possible this may give interesting insights in the mechanism of global ignition (global broadcasting), supposing that for contralateral electrodes information does not have to cross from one hemisphere to another, whereas for ipsilateral electrodes that is the case (which may take time). Gaillard et al refer to this issue as well in their paper, and this issue is sometimes discussed regarding to Global workspace theory. This would add novelty to the findings of the paper in my opinion.

Reviewer #3 (Public Review):

The authors report on the nature of interventions that were applied to aid and improve engagement in cervical screening, brought about by the SARS CoV Pandemic in Sweden.

I appreciate that the impact of these interventions, given that they are recent, will take some time to quantify but the description (and reach) of the policy changes that occurred in a short amount of time is of significant interest to the screening community. The piece on HPV Even Faster is particularly novel; I am not aware of another example of where this has been enacted within a routine programme.

The authors make reference to (15) where the reader can find greater details relating to the population who received the offer of self sampling (and the nature of the device). However I was a little confused (in this stand alone piece) as to who the self sampling group constituted exactly. Did this group not include pregnant women, women invited for first screen or women on non routine recall?

The authors state that "the most likely explanation for the large increase in population coverage seen is that the sending of self-sampling kits resulted in improved attendance in particular among previously non-attending women" - why is this written as speculation at this stage (?) is it not possible to attribute directly the contribution made by self sampling, or is this in hand?

While self sampling is certainly an option that can support uptake and enfranchisement in cervical screening - its overall performance is fundamentally contingent on the number of women who then comply with follow up should the HPV test be positive; it is not simply about who returns the sample. It would have been of interest to see the proportion of women who did comply with follow up.

Reviewer #3 (Public Review):

'Collateral sensitivity' occurs when drug-resistance mutations render a drug target more sensitive to inhibition by another drug, which has been previously described in some detail for malaria parasite dihydroorotate dehydrogenase (DHODH - see refs 36, 46, and 47, for example). Although it has been suggested that combinations of such drugs could potentially suppress the emergence of resistance, cross-resistance-associated mutation (or copy-number variation, CNV) could render such combination strategies ineffective. In the current study, the authors assess a new pairing of DHODH-targeting drugs. Cross-resistant parasites with DHODH mutation or CNV arise following either sequential or combined drug selection, suggesting that the drug combination described would likely fail to effectively suppress the emergence of resistance.

The strength of the study is that it describes, for a particular drug combination, different mutations associated either with collateral sensitivity or with cross-resistance, and the authors conclude that "combination treatment with DSM265 and TCMDC-125334 failed to suppress resistance". They go on to say that this "brings into question the usefulness of pursuing further DHODH inhibitors." More specific interpretations and implications of the study are as follows:<br /> a. Other combinations may also fail but there may be combinations that can effectively suppress resistance. A more exhaustive analysis of mutational space will likely be required to determine which combinations if any, would be predicted to succeed in a clinical setting.<br /> b. It was previously reported that "a combination of [DHODH] wild-type and mutant-type selective inhibitors led to resistance far less often than either drug alone. ... Comparative growth assays demonstrated that two mutant parasites grew less robustly than their wild-type parent, and the purified protein of those mutants showed a decrease in catalytic efficiency, thereby suggesting a reason for the diminished growth rate" (Ref 46). Also, "selection with a combination of Genz-669178, a wild-type PfDHODH inhibitor, and IDI-6273, a mutant-selective PfDHODH inhibitor, did not yield resistant parasites" (Ref 36). It is possible that these previously tested combinations would also yield cross-resistant mutants if selected further.<br /> c. Although increased DHODH copy number "confers only moderately reduced susceptibility" to the drug used for selection and although these clones were not assessed here for cross-resistance, it seems likely that CNV may represent a general mechanism that could undermine other collateral resistance strategies.

Reviewer #3 (Public Review):

The authors described their extensive single-cell analysis of Candida undergoing (sub-inhibitory) antibiotic treatment versus no treatment. To do so, the authors used a microfluidics platform they had previously developed, and they optimized, characterized, and validated it for this particular application. Their findings included: (a) the transcription of untreated cells is driven mostly by cell cycle phase, (b) treated cells can be clustered into several major groups and a few outlier groups that the authors termed comets, (c) cells undergoing FCZ treatment can adopt one of two different states (possibly bistability). I found the results interesting and the approach to be sound, and much of the results confirmed my prior expectations. The authors provide a detailed depiction of what is going on in the transcriptome during sub-inhibitory treatment, although this did not always lead to a mechanistic explanation. The clinical relevance was unclear to me beyond a proof of concept application for single-cell transcriptomics. In my opinion, an interesting follow-up would be to follow the transcriptional trajectory of lineages undergoing antimicrobial switching (on and off). The main issues I identified were the author's use of the term tolerance versus resistance, interpretation of "comets", clustering approach, description of fitness, and comparison between time points.

Reviewer #3 (Public Review):

Increased LRRK2 kinase activity is known to confer Parkinson's disease risk. While much is known about disease-causing LRRK2 mutations that increase LRRK2 kinase activity, the normal cellular mechanisms of LRRK2 activation are less well understood. Rab GTPases are known to play a role in LRRK2 activation and to be substrates for the kinase activity of LRRK2. However, much of the data on Rabs in LRRK2 activation comes from over-expression studies and the contributions of endogenously expressed Rabs to LRRK2 activation are less clear. To address this problem, Bondar and colleagues tested the impact of systematically depleting candidate Rab GTPases on LRRK2 activity as measured by its ability to phosphorylate Rab10 in the human A549 type 2 pneumocyte cell line. This resulted in the identification of a major role for Rab12 in controlling LRRK2 activity towards Rab10 in this model system. Follow-up studies show that this role for Rab12 is of particular importance for the phosphorylation of Rab10 by LRRK2 at damaged lysosomes. Increases in LRRK2 activity in cells harboring disease-causing mutants of LRRK2 and VPS35 also depend (at least partially) on Rab12. Confidence in the role of Rab12 in supporting LRRK2 activity is strengthened by parallel experiments showing that either siRNA-mediated depletion of Rab12 or CRISPR-mediated Rab12 KO both have similar effects on LRRK2 activity. Collectively, these results demonstrate a novel role for Rab12 in supporting LRRK2 activation in A549 cells. It is likely that this effect is generalizable to other cell types. However, this remains to be established. It is also likely that lysosomes are the subcellular site where Rab12-dependent activation of LRRK2 occurs. Independent validation of these conclusions with additional experiments would strengthen this conclusion and help to address some concerns that much of the data supporting a lysosome localization for Rab12-dependent activation of LRRK2 comes from a single method (LysoIP). Furthermore, there is a discrepancy between panel 4A versus 4D in the effect of LLoMe-induced lysosome damage on LRRK2 recruitment to lysosomes that will need to be addressed to strengthen confidence in conclusions about lysosomes as sites of LRRK2 activation by Rab12.

Reviewer #3 (Public Review):

This manuscript by Bellegarda et al. examined the in vivo dynamic behavior of the Reissner fiber and its interactions with cilia and sensory neurons in the central canal of zebrafish larvae. The authors accomplished this by performing live imaging with a transgenic reporter zebrafish line in which the fiber is GFP-tagged and by finely tracking the movement of the fiber. Interestingly, they discovered that the fiber undergoes a dynamic vibratory-like movement along the dorsoventral axis. The authors then utilized a pulsed laser to precisely cut the fiber, which frequently resulted in a fast retraction behavior and a loss of calcium activity in sensory neurons in the central canal called CSF-CNs. Mechanical modeling of the elastic properties of the fiber indicated that the fiber is a soft elastic rod with graded tension along the rostrocaudal axis. Finally, by performing live imaging of motile cilia and the fiber in the central canal, they found that the two interact in close proximity and that cilia motility is affected when the fiber was cut. The authors concluded that the Reissner fiber is a dynamic structure under tension that interacts with sensory neurons and cilia in the central canal.

Strengths:<br /> 1. The study utilizes state-of-the-art microscopy techniques and beautiful transgenic zebrafish tools to characterize the in vivo behavior of the Reissner fiber and found that it exhibits surprising dynamic movements along the dorsal-ventral axis. This observation has important implications for the physiology and function of the Reissner fiber.

2. By performing a series of clever laser cutting experiments, the authors reveal that the Reissner fiber is under tension in the central canal of zebrafish. This finding provides direct experimental evidence to support the hypothesis that the Reissner fiber functions in a biomechanical manner during spinal cord development and body axis straightening.

3. By developing a mechanical model of the Reissner fiber and its retraction behavior, the authors estimate the elastic properties of the fiber and found that it is more akin to an elastic polymer rather than a stiff rod. This is a useful finding that illuminates the biophysical properties of the fiber.

4. Through calcium and cilia imaging studies, the authors demonstrate that the Reissner fiber likely interacts with motile cilia and regulates the activity of ciliated sensory neurons (CSF-CNs). The authors propose a model in which fiber-cilia interactions may occur via weak interactions or frictional forces. This model is plausible and opens several new doors for additional investigation.

Weaknesses:<br /> 1. All the live imaging experiments appear to be performed with animals paralyzed via the injection of a chemical agent (bungarotoxin). Does paralysis and/or bungarotoxin negatively impact the behavior of the Reissner fiber? Some data from non-paralyzed animals would ameliorate this concern.

2. Although the authors convincingly demonstrate that the Reissner fiber is under graded tension, it remains unclear what is the relevance and function of tension on this structure. The photoablation data presented do not delineate between the relevance of the fiber being intact or tension on the fiber as cutting the fiber impacts both. Is fiber tension required for body straightening? At the site of fiber photoablation, does a spinal curvature develop? If cultured, do the ablated animals exhibit a scoliotic phenotype?

3. One of the most potentially impactful conclusions of the paper is that the Reissner fiber interacts with cilia, but the evidence is insufficient to support this. Although some motile cilia are near the fiber (Figure 3A), many cilia are not near the fiber. The provided images and videos do not clearly demonstrate that cilia physically contact or influence the behavior of the Reissner fiber. Further, the data is lacking to conclude that the Reissner fiber directly impacts cilia motility as they did not observe an overall statistically significant difference before and after ablation (Supplemental Figure 1A). Higher magnification, higher resolution, higher acquisition rate and/or colocalization analyses of fiber-cilia interactions could alleviate this concern.

4. Similarly, how does the Reissner fiber interact with CSF-CN sensory neurons? The authors suggest that the fiber interacts with CSF-CN sensory neurons by modulating their spontaneous calcium activity via weak interactions or frictional forces from motile ciliated ependymal radial glial cells. While the calcium imaging data of the CSF-CNs is convincing and sound, the exact nature of the fiber-neuron interaction is unclear. Do cilia or apical extensions on CSF-CN sensory neurons sense the fiber or forces through a mechanosensing or chemosensing mechanism? There is some additional confusion as the authors appear to focus their cilia experiments on ependymal radial glial cells in section 4, rather than CSF-CNs. The addition of an illustrative cartoon would add clarity.

Overall, the conclusions of the study are well supported by the data presented. However, the strength of the conclusions could be enhanced by additional controls, alternative experimental approaches and clarifications.

This manuscript is an important contribution to the fields of spinal cord development and body axis development, which are fundamental questions in neurobiology, developmental biology, and musculoskeletal biology. In recent years, the Reissner fiber and motile cilia function have been linked to cerebrospinal fluid flow signaling and body straightening, but the precise form and function of the fiber remain unclear. This study provides new insight into the dynamic and biophysical properties of the Reissner fiber in vivo in zebrafish and proposes a model in which the fiber interacts with cilia and sensory neurons. This study provides novel insight into the cellular mechanisms that underlie the pathogenesis of disorders such as idiopathic scoliosis.

Reviewer #3 (Public Review):

CaMKII is a multimeric kinase of great biologic interest due to its crucial roles in long-term memory, cardiac pacemaking, and fertilization. CaMKII subunits organize into holoenzymes comprised of 12-14 subunits, adopting a donut-like, double-ringed structure. In this manuscript, Lucic et al challenge two models in the CaMKII field, which are somewhat related. The first is a longstanding topic in the field about whether the autophosphorylation of a crucial residue, Thr286, can be phosphorylated between intact holoenzymes (inter-holoenzyme phosphorylation). The second is a more recent biochemical finding, which tested the long-running theory that CaMKII exchanges subunits between holoenzymes to create mixed oligomers. These two models are connected by the idea that subunit exchange could facilitate phosphorylation between subunits of different holoenzymes by allowing subunits to integrate into a different holoenzyme and driving transphosphorylation within the CaMKII ring. Here, the authors attempt to show that one intact holoenzyme phosphorylates another intact holoenzyme at Thr286. The authors also provide evidence suggesting that subunit exchange is not occurring under their conditions, and therefore not driving this phosphorylation event. The authors propose a model where instead of exchanging subunits, two holoenzymes interact via their kinase domains to enable transphosphorylation at Thr286 without integrating into the holoenzyme structure. In order for the authors to successfully convince readers of all three facets of this new model, they need to provide evidence that 1) transphosphorylation at Thr286 happens when subunit exchange is blocked, 2) subunit exchange does not occur under their conditions, and 3) there are interactions between kinases of different holoenzymes that lead to productive autophosphorylation at Thr286.

Strengths:<br /> The authors have designed and performed a battery of cleverly designed and orthogonal experiments to test these models. Using mutagenesis, they mixed a kinase-dead mutant with an active kinase to ask whether transphosphorylation occurs. They observe phosphorylation of the kinase-dead variant in this experiment, which indicates that the active kinase must have phosphorylated it. A few key questions arise here: 1) whether this phosphorylation occurred within a single CaMKII holoenzyme ring (which is the canonical mechanism for Thr286 phosphorylation), 2) whether the phosphorylation occurred between two separate holoenzyme rings, and 3) why was this not observed in previous literature? To address questions 1 and 2, the authors implemented an innovative strategy introducing a genetically-encoded photocrosslinker in the oligomerization domain, which when crosslinked using UV light, should lock the holoenzyme in place. The rate of phosphorylation was the same when comparing uncrosslinked and crosslinked CaMKII variants, indicating that phosphorylation is occurring between holoenzymes, rather than through a subunit exchange mechanism that would require some type of disassembly and reassembly (presumably blocked by crosslinking). The 3rd question remains as to why this has not been previously observed, as it has not been for lack of effort. The authors mention low temperature and low concentration as culprits, however, Bradshaw et al, JBC v. 277, 2002 carry out a series of careful experiments that indicated that autophosphorylation at T286 is not concentration-dependent (meaning that the majority of phosphorylation occurs via intra-holoenzyme), and this is done over a concentration and temperature range. It is possible that due to the mutants used in the current manuscript, it allows for the different behavior of the kinase-dead domains, which will have an empty nucleotide-binding pocket. Further studies will need to elucidate these details, and importantly, understand what physiological conditions facilitate this mechanism.

The most convincing data that subunit exchange does not occur is from the crosslinking mass spectrometry experiment. The authors created mixtures of 'light' and 'heavy' CaMKII holoenzymes, either activated or not and then used a Lys-Lys crosslinker (DSS) to trap the enzyme in its final state. The results of this experiment indicate that subunit exchange is not occurring under their conditions. A caveat here is that there are not many lysines at hub-hub interfaces, which is the crux of this experiment. If there is no subunit exchange under their conditions, how does transphosphorylation occur between holoenzymes? The authors show very nice mass photometry data indicating that there are populations of 24-mers, which corresponds to a double-holoenzyme. Paired with the data from their crosslinking mass spectrometry which shows crosslinks between kinase domains of different holoenzymes, this indicates that perhaps kinases between holoenzymes do interact, and they do so in a competent manner to allow transphosphorylation to occur.

Weaknesses:<br /> The authors should be commended for performing three orthogonal experiments to test whether CaMKII holoenzymes exchange subunits to form heterooligomers. However, there are technical issues that dampen the strength of the results shown here. For simplicity, let's consider that CaMKII holoenzymes are comprised of two stacked hexameric rings. It has been proposed that the stable unit of CaMKII assembly and perhaps also disassembly and subunit exchange is a vertical dimer unit (comprised of one subunit from each hexameric ring). In the UV crosslinking data shown in this paper, the authors have a significant number of monomers, some crosslinked dimers (of which there are two populations), and fewer higher-order oligomers. To effectively block subunit exchange, robust crosslinking into hexamers is necessary, which the authors have not done. Incomplete crosslinking results in smaller species that can still exchange (and/or dissociate), confounding the results of this experiment. In addition, Figure 3 shows a trapping experiment, where if the exchange was occurring, there would be an oligomeric band in Lane 8, which is visible and highlighted with a blue arrow by the authors. This result is explained by nonspecific UV effects, however by eye it is not clear if there is an equivalent band in lane 10. The overall issue here is inefficient crosslinking.

The authors also employ a single-molecule TIRF experiment to further interrogate subunit exchange. Upon inspection of the TIRF images, it is not clear that the authors are achieving single molecule resolution (there are evident overlapping and distorted particles). The analysis employed here is Pearson's correlation coefficient, which is not sufficient for single molecule analysis and would not account for particle overlap, particles that are too bright, and/or particles that are too dim. For example, an alternative explanation for the authors' results is that activation results in aggregation (high correlation), and subsequent EGTA treatment leads to dissociation at these low concentrations (low correlation). However, further experimentation and analysis are necessary.

Taken together, the authors have provided important food for thought regarding inter-holoenzyme phosphorylation and subunit exchange. However, given the shortcomings discussed here, it remains unclear exactly what mechanisms are at play within and between CaMKII holoenzymes once activated.

Reviewer #3 (Public Review):

Joechner and their co-authors performed an extensive analysis of two existing datasets from sleeping children aged between 5 to 18 years. By identifying discrete events of slow oscillations (SOs) and (fast) sleep spindles they examined not only the developmental changes of these distinct sleep grapho-elements. They also took a closer look at their interplay, e.g., to what extend sleep spindles are co-occurring with slow oscillation up-states, as this coupling is thought to underlie sleep-dependent memory consolidation.

The authors found that both sleep spindles and slow oscillation undergo a change across the young age, e.g., while sleep spindles increased in frequency approaching the typical 12-16 Hz range found in adults, slow oscillation showed a shift in occurrence patterns from posterior to anterior sites. Likewise, the coupling of fast spindles within slow oscillation up-states manifested with age, which is almost non-existing in 5- to 6-year-old children. However, and most intriguingly, a coupling analysis based on the adult-like 12-16 Hz range revealed an already existing SO-spindle phase-relation across all age ranges. Altogether, this data nicely demonstrates the trajectory of sleep spindles and SOs in children and highlights the almost inherent coupling between SOs and "adult" sleep spindles. In my view, these results not only provide a good overview of a healthy development but also interesting food for thought regarding the function of SO-spindle coupling in healthy or clinical development.

Overall, this work is well-written, and the performed analyses are well conceptualized. Hence, there are one general and a few minor aspects that could be addressed to hopefully strengthen this manuscript a bit further.

The biggest aspect that was striking is the shear amount of data reported, e.g., a supplement with 28 tables is too extensive. The authors should consider reducing a few aspects.<br /> For example, the authors employ a linear mixed effects model and report coefficient etc. in the supplement. However, in the main text, the authors mainly report ANOVA-based results. Obviously, a LMM and an ANOVA are equivalent, however, focusing on one approach could streamline everything.<br /> Another example is the assessment of spindle frequency via the discrete events: First spindle peak frequency is derived via power spectra. Using the then individually identified peaks, discrete events are detected. Shouldn't it be obvious that these events show the same behavior with regard to their frequency?<br /> As a final example, the authors first report changes in fast spindle properties across age and, e.g., find an increase in frequency towards 12-16 Hz adult range. They then repeat the whole analysis in the 12-16 Hz range and examine the "distance" to the individualized results. It should again be obvious that this approach comes to the same conclusion, a smaller distance in older children. Even more obvious is the conclusion "Hence, it appears as if fast centro-parietal SPs become more dominant and adult-like in their frequency and amplitude characteristics in older children" because it describes a normal development of a healthy child. Altogether, the authors could streamline a few aspects by removing hidden redundancies and focus on the - in my view - central aspect of an inherent 12-16 Hz coupling across all ages.

Reviewer #3 (Public Review):

Mesenchymal stem cells have been shown to have potent immunomodulatory and regenerative properties and have been tested and tried in kidney transplantation. In a previous paper, the authors of this paper reviewed the beneficial actions of nitric oxide (NO) on the beneficial action of MSC. In this manuscript, they describe a method to generate NO in the therapeutic MSC. While NO donors like the short-acting nitrates have been used for angina pectoris patients few therapeutic approaches have been published aiming at the local delivery of NO to specific tissues or organs like the kidney. Gene therapy with adenoviral vectors, overexpressing the eNOS gene itself failed due to the fact that the eNOS enzyme, when overexpressed quickly runs out of sufficient co-factors like BH4. As a consequence, the enzyme uncouples and becomes cytotoxic due to the generation of peroxynitrate. Hence, the current strategy to generate NO in the MSC itself is novel and interesting.

The authors first describe the cryoprotective effects and antioxidant effects of NO generation in MSC in vitro and subsequently in vivo in a mouse model of ischemia-reperfusion injury that may reflect acute kidney injury (or ischemia associated with kidney transplantation) in patients. While the MSC are transplanted intracortical on a local position in the kidneys, the manuscript describes surprising effectivity on serum creatinine, ureum, casts, and protection of brush border. Also, upon immunohistochemical analyses, fibrosis, and kidney injury markers decrease. Most likely there is a strong paracrine effect. It is unfortunate that the control "PBS + MGP" is lacking to exclude some low-grade background conversion of the compound with subsequent release of NO. MGP only is tested however, studies in kidney sections with state-of-the-art EPR, give the authors the wanted control.

The paper provides an interesting proof of concept for a novel therapeutic approach. However, in the clinical arena, some questions remain involving the survival of the MSC after transplantation and the introduction of novel antigens associated with the engineered cells

Reviewer #3 (Public Review):

Pinatel and colleagues addressed a currently understudied topic in neurobiology, namely, the architecture and function of myelination in subsets of Parvalbumin (PV)- and Somatostatin (SST)-positive GABAergic hippocampal interneurons and its dependence on juxtaparanodal organizer proteins. In order to elucidate the structural and functional implications of interneuron myelination, the authors visualized inhibitory neurons by utilizing a Lhx2-tdTomato reporter line in combination with crucial cytoskeletal linker proteins such as Contactin2/TAG-1, Caspr2, and Protein 4.1B. They then applied a comprehensive set of histological, electrophysiological, and behavioral experiments to dissect the role these proteins play in proper myelination and function of PV- and SST-interneurons.

The bulk of the study's data is based on immunofluorescence, which is presented in a number of figures comprised of high-quality images. As much as this is a strength of the study, the underlying image analysis as described in the methods falls short. All structural data rely on the measurements of physical parameters such as length of internodes, the distance between (juxta)paranode and node, the distance between node and myelin sheath, length of the axon initial segment (AIS), etc. In light of this, and considering the small physical dimensions of the nodal region in general, the methods remain unclear about the depth of 3D reconstruction/deconvolution applied to the samples. Measurements presented in the results show significant differences in sub-micrometer dimension, which at least according to the stated methods, are unlikely to be precise given that the confocal imaging parameters do not seem to reach Nyquist conditions. For a study in which a third of all data is aimed at elucidating (sub)micrometer changes, this is crucial and the study would benefit from a more rigorous method description by the authors.

Another methodological weakness is the somewhat small n, and its incoherence across the experiments and therefore, the statistics performed in some of the experiments. Statistics are based on either n for animals, or n for individual data points from several animals. Why is not all data represented as mean/animal? Also, the sampling in general with n = 3 animals is borderline acceptable; in some cases, it seems that only 2 animals were used, and in others, no number is given at all (please refer to author comments for details). This needs to be addressed, either by explaining why so few animals were used, or by adding more data from individual animals. Assigning structures (AIS, nodes) as n results in overstating effects, since especially for AIS, there is significant heterogeneity in the length across neurons from the same type, and this is masked when 100 AIS are considered as individual n instead 100 AIS per animal, and the animal is (correctly) the n. Since the study seems to switch back and forth between these assignments, it would be helpful to level these data across all experiments unless there are specific reasons not to do so, which then need to be explained. As outlined in the methods, all values are given as means {plus minus} SEM; this needs to be corrected for those cases where the standard deviation is the appropriate choice (e.g. all graphs showing n = individual structure, and not the mean of an animal).

As far as the analysis of geometrical AIS changes is concerned, the method section should be extended to address how, if at all, AIS length and position were analyzed in 3D, also considering the somewhat "spotty" immunosignal outlined in Fig. 8D. The observed AIS length change is then discussed in the context of a study conducted in a pharmacological model of myelin loss, however, that particular study (Hamada & Kole, 2015) found not only a length change but a position change after cuprizone-induced AIS plasticity. The authors should therefore discuss this finding in a bit more detail than simply stating "Adaptation of the AIS has been reported in the cuprizone chemical model of demyelination" (p. 14, ll. 512).

Similarly to the points made about structural data above, the data from electrophysiological recordings should be presented in such a way that e.g. the number of cells and/or animals is readily accessible from the graph or legend. In its current form, this information - while available - needs to be pieced together from in-text information supplemented by figure legends. Sometimes, the authors do not include the number of animals behind individual cell data (for details please see author comments). Please carefully review all figures and edit accordingly.

The behavioral data presented in the study is interesting, but the conclusions drawn are not supported by the data presented, as many unknown factors remain in place that could contribute to the observed phenotype.

Reviewer #3 (Public Review):

This work contributes to the literature characterizing early and late waves of transcription and associated chromatin remodeling following neuronal depolarization, here in cultured embryonic striatum. While they find IEG transcription 1h after depolarization, they find chromatin remodeling is slower (opening at the 4h time point). This may be due to chromatin at IEG regulatory regions already being open (in embryonic striatum), although previous work has found remodeling occurring at the 1h time point (in adult dentate gyrus). The authors next show that the chromatin remodeling that occurs at the late (4h) stage is largely in putative regulatory regions of the genome (rather than gene bodies), and is dependent on translation, which validates and extends the prior literature. The authors then transition from genome-wide basic neuroscience to focus on a specific gene of interest, prodynorphin (Pdyn), and a putative enhancer they identify from their chromatin analysis. They target CRISPR-activating and -inhibiting complexes to the putative enhancer and demonstrate that accessibility of this locus is necessary and sufficient for Pdyn transcription. They then show that at least one PDYN enhancer is conserved from rodents to humans, and is only activity-regulated in human GABAergic but not glutamatergic neurons. Finally, the authors generate snATAC-seq and show Pdyn gene and enhancer activity are also cell-type-specific in the rat striatum. The Pdyn work in particular is thorough and novel.

Strengths:<br /> This work integrates multiple cutting-edge methods (multiple forms of genome-wide sequencing, combining new and published data across species, applying new forms of bioinformatic analysis, and targeted epigenome editing) to repeatedly and convincingly demonstrate these waves of chromatin remodeling and transcription. The figures and visual representations of data in particular set a new standard for the field. Although several findings within this paper are not novel, this paper ties previous findings all together in one place and goes on to show potential relevance for neuropsychiatric disorders beyond basic cellular neuroscience. The conclusions are mostly supported by the data.

Results and conclusions that would benefit from clarification/extension.<br /> 1. Throughout the paper, the authors emphasize a "temporal decoupling" of transcriptional and chromatin response to depolarization, based on a lack of significant chromatin changes at 1h, despite IEG transcription. However, previous publications show significant chromatin remodeling at 1h (e.g. Su et al., NN 2017 in adult dentate gyrus) or 2h (Kim et al., Nature 2010; Malik et al., NN 2014 in cultured embryonic cortical neurons). The discussion briefly mentions this contrast, but it remains difficult to conclude decisively whether there is temporal decoupling when such decoupling is not found consistently. If one is to make broad conclusions about basic neural chromatin response to depolarization, it would be ideal to know under which conditions there is temporal decoupling, or if this is a region-specific phenomenon.

2. The UMAP analysis is a novel way to probe transcription factor enrichment, but it's unclear what this is actually showing. The authors sought to ask whether "DARs could be separated based on transcription factor motifs in these regions." However, the motifs present in any genomic stretch are fixed based on genomic sequence, so it seems like this analysis might be asking whether certain motifs are more likely to be physically clustered together in the genome, in activity-regulated regions (rather than certain transcription factors acting in concert, as is implied in discussion). While still potentially interesting, this analysis does not seem to give much additional insight into activity-dependent chromatin remodeling beyond the motif enrichment analysis already performed. Nevertheless, to draw stronger conclusions, it would be necessary to compare clustering to a random set of genomic regions of the same length/size to interpret the clustering here. It would also be useful to know whether the ISL1 motif is also enriched in ubiquitously accessible genomic regions in the striatum (and not just DARs).

3. The authors identify late-response gene enhancers by 3 criteria. However, only Pdyn was highlighted thereafter. How many putative DARs met these three criteria in striatum? Only Pdyn?

Reviewer #3 (Public Review):

This work provides a novel framework for semi-automatic segmentation and parcellation of brain tissues from fetal magnetic resonance imaging (MRI) by fusing an advanced deep learning technique and manual correction by experts. Over the broad age spectrum spanning newborns to adults, several fully-automatic segmentation/parcellation techniques have been proposed, showing robust, reliable performance across MR images with varying imaging quality. Unlike other age groups, however, scanning of the fetal brain is conducted in the womb; thus, there are additional and unique challenges, such as ambiguous positioning of the fetal brain, the surrounding maternal tissue in the fetal MRI, and fetal and maternal motion. These challenges in fetal MRI have collectively served as important bottlenecks in developing robust, reliable automatic segmentation/parcellation frameworks to date. This paper proposes a methodological framework for the segmentation and parcellation of fetal MRI scans using a two-step deep learning model, each for segmentation and parcellation. It is also noteworthy that the validity of the proposed framework has been extensively tested over different datasets with different image quality and different recording parameters, so the robust generalizability of the framework over other fetal MRI datasets is clearly suggested.

Strengths:

In general, a novel design framework, with separation of segmentation and parcellation schemes under each deep learning model, provides ample room for improving the model performance, as suggested by the results of this study. In addition, thanks to the flexibility in the model design (e.g., the choice of deep learning model) and parameters (e.g., manual correction step during training), an identical or similar framework can be easily extended to other datasets for different age groups or diagnostic groups/brain disorders. Another strength is the minimal requirement of human interaction after the training stage as significant time and effort of manual correction is often required following the automatic segmentation of fetal MR images. Lastly, thorough investigation of the inter-dataset generalizability of the proposed segmentation/parcellation framework will be well-received by the fetal neuroscience community.

Weakness:

The main weakness of this paper is the vague definition of the scientific novelty. By design, this paper is a technical study. The technical advancement claimed by the authors is a novel design of deep learning and a two-step deep-learning framework; each for segmentation and parcellation. There have been, however, other deep learning studies, and some share nearly identical model architecture to the one published by Asis-Cruz et al. (Frontiers in Neuroscience, 2022). As such the conceptual improvement in terms of deep learning model architecture is overstated. Regarding the separate framework for segmentation and parcellation, the conventional preprocessing protocol (e.g., Draw-EM; Makropoulos et al. IEEE Transactions on Medical Imaging, 2014) already presented a similar concept. Overall, it is unclear what unique technical advances have been made in the current paper.

A second weakness of the work is the insufficient comparison to other conventional published methods. While the authors' claim that there is no "universally accepted" protocol for fetal brain segmentation/parcellation is at least partially true, Draw-EM, which was originally designed for neonatal brain segmentation, has been widely and successfully utilized in many fetal MRI studies, as discussed by the authors. Instead of a direct comparison to Draw-EM, the authors only performed a descriptive comparison using two exemplar MRI scans. It is unclear whether the superior performance of the proposed framework in these selected scans would be generalizable to others. Similarly, the authors claim that the proposed deep-learning-based segmentation/parcellation framework required minimal time for manual post-preprocessing refinement (1-3 mins), compared to 1-3 hours in another study using Draw-EM (Story et al. Neuroimage: Clinical, 2021). Again, this may not represent a fair comparison considering that the intensity/precision of manual refinement may differ depending on the different goals/objectives of other studies.

Reviewer #2 (Public Review):

In this study, the authors propose the possibility that some neurons in the enteric nervous system (ENS) originate postnatally from a non-ectodermal source. This possibility is investigated using a combination of transgenic lines, single cell RNA-sequencing (scRNA-seq), and immunofluorescence. Initially the authors identify a subset of neurons within myenteric enteric ganglia that are not lineage-labeled by canonical neural-crest derived cre-LoxP strategies. In their analysis, the group seeks to show that these neurons have an origin distinct from neural crest-derived progenitors that are known to initially colonize the developing gut. The team uses multiple cre lines (both Wnt1-cre and Pax3-cre) as well as several distinct reporter lines (ROSA-tdTomato, ROSA-EGFP, Hprt-tdTomato) to demonstrate that the lack of labeling by neural crest cre transgenes is consistent across several tools and not due to any transgene or reporter line artifact. Based on prior analysis that suggests some neurons in the ENS might be arising from a mesodermal lineage, the authors evaluate the possibility that mesoderm could contribute neurons to the ENS by evaluating expression of Tek-cre and Mesp1-cre tagged cell types in myenteric ganglia. The work with transgenic lines is convincing that some ENS neurons originate from an alternative source in the postnatal intestine and that this population increases in aging mice.

The authors apply single cell RNA-sequencing to identify additional markers of these non-neural crest enteric neurons. They rely on dissociation of laminar gut muscle preparations, stripped from the outside of the adult intestine, that contain many cell types including smooth muscle, vasculature, and enteric ganglia. In the analysis of this scRNA-seq data, the authors focus on a cluster of cells in the resulting UMAP plots as being the MENs cluster based on labeling of this cluster with three genes (Calcb (CGRP), Met, and Cdh3). Based on expression of these marker genes there are a very large number of MENs and very few neural crest-derived enteric neurons (NENs) seen in the UMAPs. It is not clear why this difference in cell numbers has occurred. The early lineage tracing data shown with cre transgenes (Figures 1 and 2) shows relatively equal numbers of NENs and MENs in confocal imaging studies, yet in the RNA-seq UMAPs thousands of MENs are displayed while very few NENs are present. There is the possibility that the authors have identified a cell cluster as MENs that does not coincide with the Mesp1-cre or Tek-cre lineage labeled neurons observed within enteric ganglia of the laminar gut muscle preparations. The authors state that they have "used the single cell transcriptomics to both confirm the presence of MENs and identify more MEN-specific markers", however there is not a direct relationship made in this study between the MENs imaged and the cells profiled by single cell RNA-sequencing.

In their analysis the authors note a difference in the percentage of enteric neurons labeled by the neural crest lineage tracer line, Wnt1-cre, relative to the total neurons labeled by the pan-neuronal marker HuC/D with age of the mice studied. They undertake a temporal analysis of the percentage of Wnt1-cre labeled neurons over total HuC/D neurons over the lifespan and note a decrease of Wnt1-cre labeled neurons with age. Further, the team assessed levels of growth factors that are known to promote proliferation and survival of NENs (GDNF-Ret signaling) versus factors known to promote growth of mesoderm (HGF) with age and document a decrease in GDNF-Ret signaling while HGF levels increase with age. The authors propose that the balance between these two signaling pathways is responsible for the shift in proportions of NENs versus MENs in aging animals.

Some of the conclusions of this paper are supported, but several additional analyses are needed to reach the outcomes that the authors infer:

1) Because the scRNA-seq data generated in this study derives from mixed cell populations present in laminar gut muscle preparations, there is a gap between the image data shown for the mesodermal cre lineage tracing and the MENs clusters the authors have selected in their single cell RNA-seq analysis. The absence of direct transcriptional profiling of cells labeled by Mesp1-cre or Tek1-cre expression prevents the authors from definitively connecting their in situ lineage labeling with specific clusters in the single cell RNA-seq analysis.

2) Differential gene expression is the standard approach for identifying markers of a particular cluster and yet this is lacking in this study, and the rationale for why some genes were prioritized as markers of MENs is missing from the manuscript. Reanalysis of the authors posted single cell RNA-seq data found that genes integral to calling MENs (marker genes) were detectable in the data. Met, Cdh3, Calcb, Elavl2, Hand2, Pde10a, Vsnl1, Tubb2b, Stmn2, Stx3, and Gpr88 were all expressed in very few cells and at low levels. Given this, how were these genes chosen to be marker genes for MENs, especially given the low sequencing depth utilized?

3) The authors rely on Phox2b as a marker for all ENS cells, including MENs. However, reprocessing of the authors posted single cell RNA-seq data finds that Phox2b is not detected in any of the cells in the MENs cluster and it's only expressed in very few cells of the neuroglia cluster. This discrepancy between the data the authors have generated and what is widely known about Phox2b expression in the ENS field must be explained as the absence of Phox2b message suggests there is an issue with reliance on low-depth scRNA-seq data for reaching the stated conclusions.

4) The authors have not considered potential similarities between their MENs and other developing ENS lineages, like enteric mesothelial fibroblasts reported by Zeisel et al. 2018, and further analysis is needed to show that MENs are indeed a distinct cell type. Top marker genes of the author's MENs clusters were expressed more often in the clusters that were left out of Morarach et al 2021's E15.5 and E18.5 datasets because those clusters were mostly Phox2b-negative on UMAPs. This lack of Phox2b expression matches the characteristic of the MENs clusters' Phox2b-negative status in the authors single cell dataset. It is important to note that the Morarach dataset consists of Wnt1-cre lineage labeled (originating from neural crest) flow sorted cells. This is of import as it implies that Phox2b-negative cells ARE present within the Wnt1-cre lineage labeled population, an aspect that is relevant to this study's data analysis.

5) Upon reprocessing of the authors MENs-genesis dataset with integration by sample as the authors describe, Met expression is evident within the cluster of NENs on the resulting UMAP plot and yet the authors rely on this gene as a marker of MENs. Whether Met expression is restricted to MENs should be resolved because the authors state it is exclusive to MENs and they subsequently investigate this gene across lifespan. Because it is not clear that Met is absent from neural crest derived enteric neurons this caveat complicates the interpretations of the present study.

6) The authors apply MHCst immunofluorescence to mark MENs, but do not show any RNA expression for the MHCst transcripts in their single cell data. How did the authors come to the conclusion that MHCst IHC would be an appropriate marker for MENs? This rationale is missing from the text.

Reviewer #3 (Public Review):

Human complex traits including common diseases are highly polygenic (influenced by thousands of loci). This observation is in need of an explanation. The authors of this manuscript propose a model that competition for a single global resource (such as RNA polymerase II) may lead to a highly polygenic architecture of traits. Following an analytical examination, the authors reject their hypothesis. This work is of clear interest to the field. It remains to be seen if the model covers the variety of possible competition models.

Reviewer #3 (Public Review):

Wang et al. explored the unique biology of the deep-sea mussel Gigantidas platifrons to understand the fundamental principles of animal-symbiont relationships. They used single-nucleus RNA sequencing and validation and visualization of many of the important cellular and molecular players that allow these organisms to survive in the deep sea. They demonstrate that a diversity of cell types that support the structure and function of the gill including bacteriocytes, specialized epithelial cells that host sulfur-oxidizing or methane-oxidizing symbionts as well as a suite of other cell types including supportive cells, ciliary, and smooth muscle cells. By performing experiments of transplanting mussels from one habitat which is rich in methane to methane-limited environments, the authors showed that starved mussels may consume endosymbionts versus in methane-rich environments upregulated genes involved in glutamate synthesis. These data add to the growing body of literature that organisms control their endosymbionts in response to environmental change.

The conclusions of the data are well supported. The authors adapted a technique that would have been technically impossible in their field environment by preserving the tissue and then performing nuclear isolation after the fact. The use of single-nucleus sequencing opens the possibility of new cellular and molecular biology that is not possible to study in the field. Additionally, the in-situ data (both WISH and FISH) are high-quality and easy to interpret. The use of cell-type-specific markers along with a symbiont-specific probe was effective. Finally, the SEM and TEM were used convincingly for specific purposes in the case of showing the cilia that may support water movement.

The one particular area for clarification and improvement surrounds the concept of a proliferative progenitor population within the gill. The authors imply that three types of proliferative cells within gills have long been known, but their study may be the first to recover molecular markers for these putative populations. The markers the authors present for gill posterior end budding zone cells (PEBZCs) and dorsal end proliferation cells (DEPCs) are not intuitively associated with cell proliferation and some additional exploration of the data could be performed to strengthen the argument that these are indeed proliferative cells. The authors do utilize a trajectory analysis tool called Slingshot which they claim may suggest that PEBZCs could be the origin of all gill epithelial cells, however, one of the assumptions of this analysis is that differentiated cells are developed from the same precursor PEBZC population.

However, these conclusions do not detract from the overall significance of the work of identifying the relationship between symbionts and bacteriocytes and how these host bacteriocytes modulate their gene expression in response to environmental change. It will be interesting to see how similar or different these data are across animal phyla. For instance, the work of symbiosis in cnidarians may converge on similar principles or there may be independent ways in which organisms have been able to solve these problems.

Reviewer #3 (Public Review):

In this manuscript, the authors describe the development of a machine-learning model to be used for gait assessment using insole data. They first developed a machine learning model using an existing, large data set of ground reaction forces collected during walking with force plates in a lab, from healthy adults and a group of people with knee injuries. Subsequently, they tested this model on ground reaction forces derived from insoles worn by a group of 19 healthy adults and a group of n=44 people with knee osteoarthritis (OA). The model was able to accurately identify individuals belonging to the knee OA group or the healthy group using the ground reaction forces during walking. Note: I do not have expertise on machine learning and will therefore refrain from reviewing the ML methods that were applied in this paper.

Strengths: The authors successfully externally validated the trained model for GRF on insole data. Insole data carries potentially rich information, including the path of the CoP during the stance phase. The additional value of insoles over force plates in itself is clear, as insoles can be used independently of laboratory facilities. Moreover, insoles provide information on the COP path, which can have added value over other mobile assessment methods such as inertial sensors.

Limitations: The second ML model, using only insole data to identify knee arthropathy from healthy subjects, was trained on a small sample of subjects. Although I have no background in ML, I can imagine that external validation in an independent and larger sample is needed to support the current findings.

Gait speed has a major influence on the majority of gait-related outcomes. Slow or more cautious gait, due to pain or other causes, is reflected in vertical GRF's with less pronounced peaks. A difference in gait speed between people with pain in their knee (due to injury) and healthy subjects can be expected. This raises the question of what the added value of a model to estimate vertical GRF is over a simpler output (e.g. gait speed itself). Moreover, the paper does not elucidate what the added value of machine learning is over a simpler statistical model.

In line with this issue, the current analyses are not strongly convincing me that the model described resulted in an identification of knee arthropathy-specific signature. Only knee arthropathy vs healthy (relatively young) subjects was compared, and we cannot rule out that this group only reflects general cautious, slow, or antalgic gait. As such, the data does not provide any evidence that the tool might be valuable to identify people with more or less severity of symptoms, or that the tool can be used to discriminate knee osteoarthritis from hip, or ankle osteoarthritis, or even to discriminate between people with musculoskeletal diseases and people with neurological gait disorders. This substantially limits the relevance for clinical (research) practice. In short, the output of the model seems to be restricted to "something is going on here", without further specification. Further development towards more specific aims using the insole data may substantially amplify clinical relevance.

Reviewer #3 (Public Review):

Hayashi et al., investigate the role of spinal neurons derived from the V2 progenitor domain. They identify a molecular marker, Hes2, specific to the V2 lineage in the spinal cord. The authors use this result to generate a new mouse line allowing specific access to the Hes2 lineage and show that this lineage is composed of excitatory V2a and inhibitory V2b spinal interneurons plus some populations of supraspinal neurons. Taking advantage of this new tool, they demonstrate that the developmental silencing of the Hes2 lineage leads to a disruption of mouse locomotor gait characterized by shorter strides and an increased cadence with no alteration of the alternation between flexion and extension. In addition, the authors show that the silencing of the Hes2 lineage also leads to an alteration of the interlimb coordination and a decreased capacity of the mice to achieve complex motor tasks. Using an intersectional genetic approach, the authors further demonstrate that the selective ablation of spinal V2 neurons in adult mice recapitulates the festination phenotype as well as the altered execution of complex motor tasks.

By identifying a novel marker of the V2 lineage in the spinal cord and using this finding to generate a new mouse line Hayashi and colleagues suggest an intriguing interplay between excitatory and inhibitory V2 spinal neurons modulating differentially, multiple facets of motor behavior.

The conclusions of this study are for the vast majority well supported by data. However, a few additional validations of the mouse model that is used and clarification about the methods of statistical analysis would improve the quality of this manuscript.

1) Additional validations of the Hes2iCre mouse line generated and used in this study would improve the quality of the manuscript as well as shed light on the potential value of the use of the Hes2iCre mouse line for future investigations.

- When reporting the cell population labeled by GFP in Hes2iCre; R26LSL-Sun1-GFP the authors need to report the number of animals on which these quantifications were performed to strengthen their conclusions (Figure 3C-E). Similarly, when showing the number of Hes2+, Chx10+ (V2a) and GATA3+ (V2b) neurons in Hes2iCre heterozygous vs homozygous the number of animals should be reported (Figure 3G; Figure S2E-F).

- The numbers of Hes2+, Chx10+ (V2a) and GATA3+ (V2b) neurons in Hes2iCre heterozygous vs homozygous is reported. However, it would improve the validation of the mouse line, if the authors could provide a quantification of the numbers of Chx10+ and GATA3+ cells in heterogygous Hes2WT/iCre animals versus littermates lacking the Cre.

- Although the study focus on spinal V2 neurons and the intersectional approach used in the last part of the paper is compelling, a better description of the supraspinal neurons that are part of the Hes2 lineage would give a better insight into the potential contribution of supraspinal Hes2 lineage to the motor phenotype described in Hes2-silenced mouse. In particular, an experiment showing if V2 (especially Chx10+ V2a) neurons from the medullary reticular nucleus are part of the Hes2 lineage would allow us to get a better grasp on the potential supraspinal effect of Hes2 neurons silencing.

2) Adding a part in the methods explaining the statistical analysis applied is needed. In this part, the choices of the statistical analysis performed should be clearly explained and the assumptions stated. Although the intersectional genetic approach is challenging and does not allow for obtaining numerous animals, the use of parametric Student's t-tests on groups with only 4 animals is discussable and at least needs to be justified in the methods (results presented in Figure 6 and Figure S5). When the number of statistical units allows it, the normality of the distributions and the homoscedasticity should be tested prior to the use a parametric test. In some instances, tests taking into account the hierarchical structure of the data could be used. Furthermore, running statistical analysis on what seems to be a group of n=2 statistical units (Figure S3L) is not appropriate.

3) Although this decision belongs to the authors, the use of the term "synergy" in the title and abstract might be misleading and might lead to confusion regarding the important outcome of this study. The authors show compelling evidence that the spinal ablation of the V2 lineage leads to a disruption of the ipsilateral coordination of body movements. However, as well explained by the authors, prior studies ablating individual V2a and V2b populations did not show any abnormal ipsilateral body coordination. This rather suggests a redundant or complementary function of inhibitory and excitatory V2 spinal neurons in spinal circuits, with the possibility for one individual population to compensate for the effect on the ipsilateral coordination following the ablation of the other population. Alternatively, "synergy" may suggest a simultaneous activity of V2a and V2b neurons that is not in the scope of this work.

Reviewer #3 (Public Review):

The authors previously showed that expressing formate dehydrogenase, rubisco, carbonic anhydrase, and phosphoribulokinase in Escherichia coli, followed by experimental evolution, led to the generation of strains that can metabolise CO2. Using two rounds of experimental evolution, the authors identify mutations in three genes - pgi, rpoB, and crp - that allow cells to metabolise CO2 in their engineered strain background. The authors make a strong case that mutations in pgi are loss-of-function mutations that prevent metabolic efflux from the reductive pentose phosphate autocatalytic cycle. The authors also argue that mutations in crp and rpoB lead to an increase in the NADH/NAD+ ratio, which would increase the concentration of the electron donor for carbon fixation. While this may explain the role of the crp and rpoB mutations, there is good reason to think that the two mutations have independent effects, and that the change in NADH/NAD+ ratio may not be the major reason for their importance in the CO2-metabolising strain.

Specific comments:

1. Deleting pgi rather than using a point mutation would allow the authors to more rigorously test whether loss-off-function mutants are being selected for in their experimental evolution pipeline. The same argument applies to crp.

2. Page 10, lines 10-11, the authors state "Since Crp and RpoB are known to physically interact in the cell (26-28), we address them as one unit, as it is hard to decouple the effect of one from the other". CRP and RpoB are connected, but the authors' description of them is misleading. CRP activates transcription by interacting with RNA polymerase holoenzyme, of which the Beta subunit (encoded by rpoB) is a part. The specific interaction of CRP is with a different RNA polymerase subunit. The functions of CRP and RpoB, while both related to transcription, are otherwise very different. The mutations in crp and rpoB are unlikely to be directly functionally connected. Hence, they should be considered separately.

3. A Beta-galactosidase assay would provide a very simple test of CRP H22N activity. There are also simple in vivo and in vitro assays for transcription activation (two different modes of activation) and DNA-binding. H22 is not near the DNA-binding domain, but may impact overall protein structure.

4. There are many high-resolution structures of both CRP and RpoB (in the context of RNA polymerase). The authors should compare the position of the sites of mutation of these proteins to known functional regions, assuming H22N is not a loss-of-function mutation in crp.

5. RNA-seq would provide a simple assay for the effects of the crp and rpoB mutations. While the precise effect of the rpoB mutation on RNA polymerase function may be hard to discern, the overall impact on gene expression would likely be informative.

Reviewer #3 (Public Review):

This work presents a novel approach for predicting fracture risk from high-resolution peripheral quantitative computed tomography (HR-pQCT): by training a deep learning model to predict five-year fracture risk where the sole input is the full 3D HR-pQCT image. Prior studies have developed models, of varying complexity, to predict fracture risk from HR-pQCT. However, this study is novel in that neither the typical manual efforts required for HR-pQCT image analysis nor additional biomarker collection are required, simplifying potential clinical implementation. The authors show that their model predicts fracture within five years with greater sensitivity than FRAX (with an assumed diagnostic threshold of FRAX > 20% or T-score < -2.5 SD), albeit with reduced specificity. The authors further investigate how their model output, the structural fragility score derived by artificial intelligence (SFS-AI), is correlated with two microarchitectural parameters that can be measured with HR-pQCT, demonstrating that their model captures many relevant characteristics of a patient's bone quality that cannot be captured by the standard clinical tools used to diagnose osteoporosis, and thus to identify patients at elevated risk of fracture.

Strengths

The authors use a very large dataset and a combination of state-of-the-art methods for training and validating their fracture prediction model: k-fold cross-validation is used for training and a held-out external test dataset is used to evaluate ensembled model predictions compared to the current clinical standard for fracture screening. The results with the test dataset show that the model can identify women at risk of fracture in the next five years with greater sensitivity than both FRAX with BMD and BMD alone.

Because the model takes only a full 3D HR-pQCT image as input, the feasibility of clinical implementation is maximized. Standard morphological analysis with HR-pQCT is semi-automated and the labour required for the manual portions of analysis poses a significant barrier to clinical implementation. There is mounting evidence for the clinical utility of HR-pQCT (see Gazzotti et al. Br. J. Radiol. 2023) and fully automated models such as the one presented in this work will be critical for making clinical applications of HR-pQCT feasible.

The authors quantify the contributions to the variance of the model output and examine activation maps overlaid on the HR-pQCT images. These sub-analyses indicate that the model is identifying relevant characteristics of hierarchical bone structure for fracture prediction that are not available from aBMD measurements from DXA and thus are not accounted for in the current standard clinical diagnostic tool.

Weaknesses

The authors make the claim that SFS-AI outperforms FRAX with BMD and BMD in terms of sensitivity and specificity of predicting fragility fractures within 5 years. This claim is supported by looking at the ROCs in figure 1, but the specific comparison made in the discussion is not completely fair as currently presented in the article. The thresholds of FRAX > 20% and T-score < -2.5SD were selected by the authors for binary comparison. FRAX and BMD achieve specificities of ~95% at these thresholds, while SFS-AI achieves a specificity of only 77% at the selected threshold, SFS-AI > 0.5. Conversely, SFS-AI achieves a sensitivity of 50% to 60% while FRAX and BMD achieve very poor sensitivities, between 4% and 16%. The authors have not justified their choice of binarization thresholds for FRAX or BMD by citing literature or clinical guidelines, nor have they motivated their choice of any of the thresholds with a discussion of how clinical considerations could influence the sensitivity-specificity trade-off. It is difficult to directly compare the prognosticative performance of SFS-AI to that of FRAX or BMD when the thresholds for FRAX and BMD are at such different locations on the respective ROCs when compared to where the threshold for SFS-AI places it on the ROC. The authors have also not compared their estimates of the sensitivity and specificity of FRAX and BMD to literature to provide important context for the comparison to SFS-AI. An additional unacknowledged limitation is that the FRAX tool is designed to predict 10-year fracture risk, while the outcome used to train the SFS-AI model and to compare to FRAX was 5-year fracture risk.

Direct comparison may be impossible due to differences in study design or reported performance metrics, but the authors have not at all discussed the quantitative performance of prior models for fracture prediction or discrimination that use HR-pQCT (see Lu et al. Bone 2023 or Whittier et al. JBMR 2023) to contextualize the performance of their novel model. While the model presented in this article has the advantage that it does not require the typical expertise and manual effort needed for HR-pQCT image analysis, it is still important to acknowledge the potential trade-off of ease of implementation vs performance. Models that incorporate additional clinical data or that use standard HR-pQCT analysis outputs rather than raw images may perform well enough to justify the increase in the difficulty of clinical implementation or to motivate further work on fully automating microarchitectural analysis with HR-pQCT images.

Finally, the article does not indicate that either the code used for model training or the trained model itself will be made publicly available. This limits the ability of future researchers to replicate and build on the results presented in the article.

Reviewer #3 (Public Review):

Royer et al. present a fully automated variant of the Barnes maze to reduce experimenter interference and ensure consistency across trials and subjects. They train mice in this maze over several days and analyze the progression of mouse search strategies during the course of the training. By fitting models involving stochastic processes, they demonstrate that a model combined of the random, spatial, and serial processes can best account for the observed changes in mice's search patterns. Their findings suggest that across training days the spatial strategy (using local landmarks) was progressively employed, mostly at the expense of the random strategy, while the serial strategy (consecutive nearby vestibule check) is reinforced from the early stages of training. Finally, they discuss potential mechanistic underpinnings within brain systems that could explain such behavioral adaptation and flexibility.

Strength:<br /> The development of an automated Barnes maze allows for more naturalistic and uninterrupted behavior, facilitating the study of spatial learning and memory, as well as the analysis of the brain's neural networks during behavior when combined with neurophysiological techniques. The system's design has been thoughtfully considered, encompassing numerous intricate details. These details include the incorporation of flexible options for selecting start, goal, and proximal landmark positions, the inclusion of a rotating platform to prevent the accumulation of olfactory cues, and careful attention given to atomization, taking into account specific considerations such as the rotation of the maze without causing wire shortage or breakage. When combined with neurophysiological manipulations or recordings, the system provides a powerful tool for studying spatial navigation system.<br /> The behavioral experiment protocols, along with the analysis of animal behavior, are conducted with care, and the development of behavioral modeling to capture the animal's search strategy is thoughtfully executed. It is intriguing to observe how the integration of these innovative stochastic models can elucidate the evolution of mice's search strategy within a variant of the Barnes maze.

Weakness:<br /> 1. The development of the well-thought-out automated Barnes maze may attract the interest of researchers exploring spatial learning and memory. However, this aspect of the paper lacks significance due to insufficient coverage of the materials and methods required for readers to replicate the behavioral methodology for their own research inquiries.<br /> Moreover, as discussed by the authors, the methodology favors specialists who utilize wired recordings or manipulations (e.g. optogenetics) in awake, behaving rodents. However, it remains unclear how the current maze design, which involves trapping mice in start and goal positions and incorporating angled vestibules resulting in the addition of numerous corners, can be effectively adapted for animals with wired implants.

2. Novelty: In its current format, the main axis of the paper falls on the analysis of animal behavior and the development of behavioral modeling. In this respect, while it is interesting to see how thoughtfully designed models can explain the evolution of mice search strategy in a maze, the conclusions offer limited novel findings that align with the existing body of research and prior predictions.

3. Scalability and accessibility: While the approach may be intriguing to experts who have an interest in or are familiar with the Barnes maze, its presentation seems to primarily target this specific audience. Therefore, there is a lack of clarity and discussion regarding the scalability of behavioral modeling to experiments involving other search strategies (such as sequence or episodic learning), other animal models, or the potential for translational applications. The scalability of the method would greatly benefit a broader scientific community. In line with this view, the paper's conclusions heavily rely on the development of new models using custom-made codes. Therefore, it would be advantageous to make these codes readily available, and if possible, provide access to the processed data as well. This could enhance comprehension and enable a larger audience to benefit from the methodology.

4. Cross-validation of models: The authors have not implemented any measures to mitigate the risk of overfitting in their modeling. It would have been beneficial to include at least some form of cross-validation with stochastic models to address this concern. Additionally, the paper lacks the presence of analytics or measures that assess and compare the performance of the models.

5. Quantification of inter-animal variations in strategy development: It is important to investigate, and address the argument concerning the possibility that not all animals recruit and develop the three processes (random, spatial, and serial) in a similar manner over days of training. It would be valuable to quantify the transition in strategy across days for each individual mouse and analyze how the population average, reflecting data from individual mice, corresponds to these findings. Currently, there is a lack of such quantification and analysis in the paper.

Reviewer #3 (Public Review):

The authors identified the mefenamic (Mef) binding site and DIDS binding site on the KCNQ1 KCNE1 complex. The authors also identified the mechanism of interactions using electrophysiological recording, calculating V1/2 of different mutants, and looking at the instantaneous and tail currents. The contribution of each residue within the binding pocket was analysed using GBSA and PBSA and traditional molecular dynamics simulation.

The manuscript has been substantially revised from the previous version with a greater depth of computational analysis.

Reviewer #3 (Public Review):

The manuscript by Tejeda-Munoz examines signaling by Wnt and macropinocytosis in Xenopus embryos and colon cancer cells. A major problem with the study is the extensive use of pleiotropic inhibitors as "specific" inhibitors of macropinocytosis in embryos. It is true that BafA and EIPA block macropinocytosis, but they do many other things as well. A major target of EIPA is the NheI Na+/proton transporter, which also regulates invasive structures (podosomes, invadopodia) which could have major roles in development. Similarly, Baf1 will disrupt lysosomes and the endocytic system, which secondary effects on mTOR signaling and growth factor receptor trafficking. The authors cannot assume that processes inhibited by these drugs demonstrate a role of macropinocytosis. While correlations in tumor samples between increased expression of PAK1 and V0a3 and decreased expression of GSK3 are consistent with a link between macropinocytosis and Wnt-driven malignancy, the cell and embryo-based experiments do not convincingly make this connection. Finally, the data on FAK and TES are not well integrated with the rest of the manuscript.

1. The data in Fig. 1A do not convincingly demonstrate macropinocytosis - it is impossible to tell what is being labeled by the dextran.

2. The data in Fig. 2 do not make sense. LiCL2 bypasses the WNT activation pathway by inhibiting GSK3. If subsequent treatment with BafA blocks the effects of GSK3 inhibition, then BafrA is doing something unrelated to Wnt activation, whose target is the inhibition/sequestration of GSK3. While BafA might block GSK3 sequestration by inhibiting MVB function, it should have no effect on the inhibition of GSK3 by LiCl2.

3. The effect of EHT on MP in SW480 cells is not clearly related to what is happening in the embryos. The nearly total loss of staining for Rac and -catenin after overnight EIPA does not implicate MP in protein stability - critical controls for cell viability and overall protein turnover are absent. Inhibition of WNT signaling might be expected to enhance -catenin turnover, but the effect on Rac1 is surprising. A more quantitative analysis by western blotting is required.

4. The data on FAK inhibition and TES trafficking are poorly integrated with the rest of the paper.

Reviewer #3 (Public Review):

The work addresses challenges in linking anatomical information to transcriptomic data in single-cell sequencing. It proposes a method called Targeted Genetically-Encoded Multiplexing (TaG-EM), which uses genetic barcoding in Drosophila to label specific cell populations in vivo. By inserting a DNA barcode near the polyadenylation site in a UAS-GFP construct, cells of interest can be identified during single-cell sequencing. TaG-EM enables various applications, including cell type identification, multiplet droplet detection, and barcoding experimental parameters. The study demonstrates that TaG-EM barcodes can be decoded using next-generation sequencing for large-scale behavioral measurements. Overall, the results are solid in supporting the claims and will be useful for a broader fly community. I have only a few comments below:

Specific comments:

1. The authors mentioned that the results of structure pool tests in Fig. 2 showed a high level of quantitative accuracy in detecting the TaG-EM barcode abundance. Although the data were generally consistent with the input values in most cases, there were some obvious exceptions such as barcode 1 (under-represented) and barcodes 15, 20 (over-represented). It would be great if the authors could comment on these and provide a guideline for choosing the appropriate barcode lines when implementing this TaG-EM method.

2. In Supplemental Figure 6, the authors showed GFP antibody staining data with 20 different TaG-EM barcode lines. The variability in GFP antibody staining results among these different TaG-EM barcode lines concerns the use of these TaG-EM barcode lines for sequencing followed by FACS sorting of native GFP. I expected the native GFP expression would be weaker and much more variable than the GFP antibody staining results shown in Supplemental Figure 6. If this is the case, variation of tissue-specific expression of TaG-EM barcode lines will likely be a confounding factor.

3. As the authors mentioned in the manuscript, multiple barcodes for one experimental condition would be a better experimental design. Could the authors suggest a recommended number of barcodes for each experiential condition? 3? 4? Or more? Also, it would be great if the authors could provide a short discussion on the cost of such TaG-EM method. For example, for the phototaxis assay, if it is much more expensive to perform TaG-EM as compared to manually scoring the preference index by videotaping, what would be the practical considerations or benefits of doing TaG-EM over manual scoring?

Reviewer #3 (Public Review):

The study attempts to develop a Drosophila model for the human disease of LND. The issue here, and the main weakness of this study, is that Drosophila does not express the enzyme, HGPRT, which when mutated causes LND. The authors, instead, mutate the functionally-related Drosophila Aprt enzyme. However, it is unknown whether Aprt is also a structural homologue. Because of this, it will likely not be possible to identify pharmacological compounds that rescue HGPRT activity via a direct interaction (unless modelling predicts high conservation of substrate binding pocket between the two enzymes, etc). An additional weakness is that the study does not identify a molecule that may act as a lead compound for further development for treating LND. Rather, the various rescues reported are selective for only a subset of the disease-associated phenotypes. Thus, whilst informative, this first section of the study does not meet the study ambitions.

The second approach adopted is to express a 'humanised mutated' form of HGPRT in Drosophila, which holds more promise for the development of a pharmacological screen. In particular, the locomotor defect is recapitulated but the seizure-like activity, whilst reported as being recapitulated, is debatable. A recovery time of 2.3 seconds is very much less than timings for typical seizure mutants. Nevertheless, the SING behaviour could be sufficient to screen against. However, this is not explored.

In summary, this is a largely descriptive study reporting the behavioural effects of an Aprt loss-of-function mutation. RNAi KD and rescue expression studies suggest that a mix of neuronal (particularly dopaminergic and possibly adenosinergic signalling pathways) and glia are involved in the behavioural phenotypes affecting locomotion, sleep and seizure. There is insufficient evidence to have confidence that the Arpt fly model will prove valuable for understanding / treating LND.

Reviewer #3 (Public Review):

The authors thoroughly evaluate the performance and scalability of existing cell-type deconvolution methods. The paper builds on the existing knowledge by considering the suitability of deconvolution algorithms in the context of more challenging analyses where rare cell types are present or when dealing with unmatched references or noise introduced by a highly abundant cell type within the data. The paper also presents a new simulation framework for spatial transcriptomics data to support their benchmarking effort.

● Major strengths and weaknesses of the methods and results.

While most of the benchmarking studies rely on publicly available spatial transcriptomics datasets, one of the major strengths of the paper is the additional evidence support from their silver standard datasets. Leveraging computational processes synthspot, the authors generated abundant synthetic spatial transcriptomics data with replicates. In addition, the data generation process also accounts for 9 different biological patterns to stay close to real data quality. The authors also communicated with the original authors of each benchmarked method to ensure correct implementation and optimal performance. Figure 2 provides a clear and concise summary of the benchmark results, which will be of great assistance to users who are contemplating conducting deconvolution analysis.

The simulation setup has a significant weakness in the selection of reference single-cell RNAseq datasets used for generating synthetic spots. It is unclear why a mix of mouse and human scRNA-seq datasets were chosen, as this does not reflect a realistic biological scenario. This could call into question the findings of the "detecting rare cell types remains challenging even for top-performing methods" section of the paper, as the true "rare cell types" would not be as distinct as human skin cells in a mouse brain setting as simulated here. Furthermore, it is unclear why the authors developed Synthspot when other similar frameworks, such as SRTsim, exist. Have the authors explored other simulation frameworks? Finally, we would have appreciated the inclusion of tissue samples with more complex structures, such as those from tumors, where there may be more intricate mixing between cell types and spot types.

The authors have effectively accomplished their objectives in benchmarking deconvolution methods by thoughtfully designing the experiments and selecting appropriate evaluation metrics. This paper will be highly beneficial for the community.

This paper can provide guidance for selecting the most proper deconvolution methods under user-decided scenarios of the interests. Synthspot, allows for generating more realistic artificial tissue data with specific spatial patterns and is integrated as part of an easy-to-use and adaptable Nextflow pipeline. It might be worthwhile to clearly differentiate this work from previous work either in the benchmarking area or SRT data simulation area.

Reviewer #3 (Public Review):

Polygenic scores (PGS), constructed based on genetic effect sizes estimated in genome-wide association studies (GWAS) and used to predict phenotypes in humans have attracted considerable recent interest in human and evolutionary genetics, and in the social sciences. Recent work, however, has shown that PGSs have limited portability across ancestry groups, and that even within an ancestry group, their predictive accuracy varies markedly depending on characteristics such as the socio-economic status, age, and sex of the individuals in the samples used to construct them and to which they are applied. This study takes further steps in investigating and addressing the later problem, focusing on body mass index, a phenotype of substantial biomedical interest. Specifically, it quantifies the effects of a large number of co-variates and of interactions between these covariates and the PGS on prediction accuracy; it also examines the utility of including such covariates and interaction in the construction of predictors using both standard methods and artificial neural networks. This study would be of interest to investigators that develop and apply PGSs.

I should add that I have not worked on PGSs and am not a statistician, and apologize in advance if this has led to some misunderstandings.

Strengths:

- The paper presents a much more comprehensive assessment of the effects of covariates than previous studies. It finds many covariates to have a substantial effect, which further highlights the importance of this problem to the development and application of PGSs for BMI and more generally.<br /> - The findings re the relationships between the effects of covariates and interactions between covariates and PGSs are, to the best of my knowledge, novel and interesting.<br /> - The development of predictors that account for multiple covariates and their interaction with the PGS are, to the best of my knowledge, novel and may prove useful in future efforts to produce reliable PGSs.<br /> - The improvement offered by the predictors that account for PGS and covariates using neural networks highlights the importance of non-linear interactions that are not addressed by standard methods, which is both interesting and likely to be of future utility.

Weaknesses:

- The paper would benefit substantially from extensive editing. It also uses terminology that is specific to recent literature on PGSs, thus limiting accessibility to a broader readership.<br /> - The potential meaning of most of the results is not explored. Some examples are provided below:<br /> • the paper emphasizes that 18/62 covariates examined show significant effects, but this result clearly depends on the covariates included. It would be helpful to provide more detail on how these covariates were chosen. Moreover, many of these covariates are likely to be correlated, making this result more difficult to interpret. Could these questions at least be partially addressed using the predictors constructed using all covariates and their interactions jointly (i.e., with LASSO)? In that regard, it would be helpful to know how many of the covariates and interactions were used in this predictor (I apologize if I missed that).<br /> • While the relationship between covariate effects and covariate-PGS interaction effects is intriguing, it is difficult to interpret without articulating what one would expect, i.e., what would be an appropriate null.<br /> • The finding that using artificial neural networks substantially improves prediction over more standard methods is especially intriguing, and highlights the potential importance of non-linear relationships between PGSs and covariates. These relationships remain hidden in a black box, however. Even fairly straightforward analyses, based on using different combinations of the PGS and/or covariates may shed some light on these relationships. For example, analyzing which covariates have a substantial effect on the prediction or varying one covariate at a time for different values of the PGS, etc.<br /> - The relationship to previous work should be discussed in greater detail.

Reviewer #3 (Public Review):

Braithwaite et al. present data from a comprehensive large-scale study of 18-month-old's visual attention. The authors leverage a battery of well-known visual attention tasks to replicate canonical effects found in the literature and assess the latent structure of these tasks. They find that, while controlling for eye tracking precision and accuracy, two factors best fit the data - attention to social and non-social stimuli.

Strengths:<br /> The current study represents what amounts to years of hard work collecting data from a population that is challenging to work with - young children. The authors have diligently attended to data cleaning and sample size throughout the manuscript. Not only do they provide a large-scale replication of several well-known tasks, but they use advanced statistical modeling to discover the structure of visual attention in these 18-month-olds. Overall, this is a valuable contribution to the literature and provides a useful framework for studying visual attention development.

Weaknesses:<br /> While the study is clearly a valuable addition to the extant literature, I have several concerns that might be addressed to improve the manuscript. These primarily center around clarity and conciseness. First, the introduction seems to lack clarity at times. For example, the first paragraph seems to introduce several ideas (e.g., brain and cognitive development, direct and indirect measures of cognition, eyetracking, etc) that make it hard to understand where the paper is going. The authors might consider homing in on 2 main points to motivate eye tracking as a tool. Second, there are many different eye tracking measures may make it difficult for the reader to track which measures were used for each task and which were relevant for the larger model. This may be remedied by adding a section to the methods that briefly describes how each measure was calculated and perhaps a table that lists each task, the measure, and how it was calculated. Third, the results are exciting but hard to visualize in the supplementary figures. I commend them on using raincloud plots to visualize the individual data, but I would strongly encourage the authors to rethink how they display the data. For example, I find the supplementary images hard to see and as a result the effects reported are hard to discern in the image. Fourth, I believe the current data warrant a deeper discussion of what these findings mean. For example, given the developmental nature of the current study, it would be valuable for the authors to discuss how the structure visual attention might change or stay the same across development. For example, do the authors believe the current two factor model would replicate in older children, or would exogenous and endogenous attention emerge as separable components? How do these predictions relate to the extensive research in the adult literature?

Reviewer #3 (Public Review):

The manuscript "Mechanical activation of TWIK-related potassium channel by nanoscopic movement and second messenger signaling" presents a new mechanism for the activation of TREK-1 channel. The mechanism suggests that TREK1 is activated by phosphatidic acids that are produced via a mechanosensitive motion of PLD2 to PIP2-enriched domains. Overall, I found the topic interesting, but several typos and unclarities reduced the readability of the manuscript. Additionally, I have several major concerns on the interpretation of the results. Therefore, the proposed mechanism is not fully supported by the presented data. Lastly, the mechanism is based on several previous studies from the Hansen lab, however, the novelty of the current manuscript is not clearly stated. For example, in the 2nd result section, the authors stated, "fluid shear causes PLD2 to move from cholesterol dependent GM1 clusters to PIP2 clusters and this activated the enzyme". However, this is also presented as a new finding in section 3 "Mechanism of PLD2 activation by shear."

For PLD2 dependent TREK-1 activation. Overall, I found the results compelling. However, two key results are missing.<br /> 1. Does HEK cells have endogenous PLD2? If so, it's hard to claim that the authors can measure PLD2-independent TREK1 activation.<br /> 2. Does the plasma membrane trafficking of TREK1 remain the same under different conditions (PLD2 overexpression, truncation)? From Figure S2, the truncated TREK1 seem to have very poor trafficking. The change of trafficking could significantly contribute to the interpretation of the data in Figure 1.

For shear-induced movement of TREK1 between nanodomains. The section is convincing, however I'm not an expert on super-resolution imaging. Also, it would be helpful to clarify whether the shear stress was maintained during fixation. If not, what is the time gap between reduced shear and the fixed state. lastly, it's unclear why shear flow changes the level of TREK1 and PIP2.

For the mechanism of PLD2 activation by shear. I found this section not convincing. Therefore, the question of how does PLD2 sense mechanical force on the membrane is not fully addressed. Particularly, it's hard to imagine an acute 25% decrease cholesterol level by shear - where did the cholesterol go? Details on the measurements of free cholesterol level is unclear and additional/alternative experiments are needed to prove the reduction in cholesterol by shear.<br /> Importantly, there is no direct evidence for "shear thinning" of the membrane and the authors should avoid claiming shear thinning in the abstract and summary of the manuscript.

The authors should also be aware that hypotonic shock is a very dirty assay for stretching the cell membrane. Often, there is only a transient increase in membrane tension, accompanied by many biochemical changes in the cells (including acidification, changes of concentration etc). Therefore, I would not consider this as definitive proof that PLD2 can be activated by stretching membrane.

Reviewer #3 (Public Review):

A landmark work (Chouhan et al., 2022) from the Sehgal group previously investigated the relationship between sleep and long-term memory formation by dissecting the role of mushroom body intrinsic neurons, extrinsic neurons, and output neurons during sleep-dependent and sleep-independent memory consolidation. In this manuscript, Li et al., profiled transcriptome in the anterior-posterior (ap) α'/β' neurons and identified genes that are differentially expressed after training in fed condition, which supports sleep-dependent memory formation. By knocking down candidate genes systematically, the authors identified Polr1F and Regnase-1 as two important hits that play potential roles in sleep and memory formation. What is the function of sleep and how to create a memory are two long-standing questions in science. The present study used a creative approach to identify novel components that may link sleep and memory consolidation in a specific type of neuron. Importantly, these components implicated that RNA processing may play a role in these processes.

While I am enthusiastic about the innovative approach employed to identify RNA processing genes involved in sleep regulation and memory consolidation, I feel that the data presented in the manuscript is insufficient to support the claim that these two genes establish a definitive link between sleep and memory consolidation. First, the developmental role of Regnase-1 in reducing sleep remains unclear because knocking down Regnase-1 using the GeneSwitch system produced neither acute nor chronic sleep loss phenotype. To address potential confounding issues caused by the GeneSwtich system, I would suggest considering alternative methods, such as Gal80ts, to restrict the RNAi knockdown to adulthood. In addition, QPCR or other expression-measuring methods should be used to validate the specificity and efficiency of the knockdown. Further testing of additional RNAi fly lines and conducting overexpression experiments would also lend credibility to the phenotypes. Second, while constitutive Regnase-1 knockdown produced robust phenotypes for both sleep-dependent and sleep-independent memory, it also led to a severe short-term memory phenotype. This raises the possibility that flies with constitutive Regnase-1 knockdown are poor learners, thereby having little memory to consolidate. The defect in learning could be simply caused by chronic sleep loss before training. Thus, this set of results does not substantiate a strong link between sleep and long-term memory consolidation. Lastly, the discussion on the sequential function of training, sleep, and RNA processing on memory consolidation appears to be speculative based on the present data. While the novel approach did provide novel candidate genes with functions in sleep, memory, and potentially their link, the manuscript would greatly benefit from carefully adjusting the conclusions and incorporating rigorous validations for the RNAi knockdown experiments.

Reviewer #3 (Public Review):

In this manuscript, the authors investigated the effects of deletion of the ER-plasma membrane/Golgi tethering proteins tricalbins (Tcb1-3) on vacuolar morphology to demonstrate the role of membrane contact sites (MCSs) in regulating vacuolar morphology in Saccharomyces cerevisiae. Their data show that tricalbin deletion causes vacuolar fragmentation possibly in parallel with TORC1 pathway. In addition, their data reveal that levels of various lipids including ceramides, long-chain base (LCB)-1P and phytosphingosine (PHS) are increased in tricalbin-deleted cells. The authors find that exogenously added PHS can induce vacuole fragmentation and by performing analyses of genes involved in sphingolipid metabolism, they conclude that vacuolar fragmentation in tricalbin-deleted cells is due to the accumulated PHS in these cells. Importantly, exogenous PHS- or tricalbin deletion-induced vacuole fragmentation was suppressed by loss of the nucleus vacuole junction (NVJ), suggesting the possibility that PHS transported from the ER to vacuoles via the NVJ triggers vacuole fission.

This work provides valuable insights into the relationship between MCS-mediated sphingolipid metabolism and vacuole morphology. The conclusions of this paper are mostly supported by their results, but there is concern about physiological roles of tricalbins and PHS in regulating vacuole morphology under known vacuole fission-inducing conditions. That is, in this paper it is not addressed whether the functions of tricalbins and PHS levels are controlled in response to osmotic shock, nutrient status, or ER stress.

There is another weakness in their claim that the transmembrane domain of Tcb3 contributes to the formation of the tricalbin complex which is sufficient for tethering ER to the plasma membrane and the Golgi complex. Their claim is based only on the structural simulation, but not on biochemical experiments such as co-immunoprecipitation and pull-down.

Reviewer #3 (Public Review):

This manuscript deals with the sex-related gene, DMRT1, showing that it has a testis-promoting function in the rabbit. In loss-of-function studies in the mouse and human, DMRT1 has a role in testis maintenance after birth, although forced expression in the mouse can induce testis formation.

The authors used CRISPR/Cas9 genome editing to generate DMRT1-/- rabbit embryos. The gonads of these embryos developed as ovaries. Interestingly, in addition Y-linked SRY, DMRT1 is required for timely up-regulation of SOX9 during Sertoli cell differentiation in the male gonad. This is quite different to the situation in mice, where Dmrt1 is not required in the testis until after birth (and Sry induced up-regulation of Sox9 hence does not require Dmrt1).

The work adds to the field of sex determination by further broadening our understanding of the DMRT1 gene and the evolution of gonadal sex determination.

In the Discussion section, it is suggested that DMRT1 could act as a pioneering factor to allow SRY action upon Sox9 in the rabbit model. The data show that DMRT1 may be more central to testis formation in mammals than previously considered. The work supports the notion that our understanding that the genetics of gonadal development (and indeed development more generally) should not rest solely on findings in the mouse.

Reviewer #3 (Public Review):

This study on drug repurposing presents the identification of potent activators of the Hippo pathway. The authors successfully screen a drug library and identify two CLK kinase inhibitors as YAP activators, with SM04690 targeting specifically CLK2. They further investigate the molecular basis of SM04690-induced YAP activity and identify splicing events in AMOTL2 as strongly affected by CLK2 inhibition. Exon skipping within AMOTL2 decreases the interactions with membrane bound proteins and is sufficient to induce YAP target gene expression. Overall the study is well designed, the conclusions are supported by sufficient data and represent an exciting connection between alternative splicing and the HIPPO pathway. The specificity of the inhibitor towards CLK2 and the mode of action via AMOTL2 could be supported by further data:

1. The inconsistent inhibitor concentrations and varying results reported in the paper can be distracting. For instance, the response of endogenous targets to 100 nM concentration is described as a >5-fold increase in Figure 2B, whereas it is reported as a 1-1.5-fold response to 1000 nM in Figure 2D. This inconsistency should be addressed and clarified to provide a more accurate and reliable representation of the findings.<br /> 2. In the absence of a strong inhibitor induced YAP target gene expression (Figure 2D), it is difficult to conclude the dependency on YAP expression, as investigated by siRNA mediated knockdown. In a similar experiment, the dependency of the inhibitor on CLK2 expression could be confirmed<br /> 3. To further support the conclusion that CLK2 is the direct target of SM04690, it would be informative to investigate the effects of CLK1/4 inhibition on AMOTL2 exons (for example within RNA-seq data). If CLK1/4 inhibitors do not induce changes in AMOTL2 exons, it would strengthen the evidence for CLK2's role as the direct target. Including the results in the discussion would enhance the comprehensiveness of the study.<br /> 4. It would be important to determine the specific dose of SM04690 required to induce changes in AMOTL2 splicing. The authors observe that AMOTL2 protein levels appear unaffected at doses below 50 nM in Figure 3D, while YAP target genes are already affected at 20 nM in Figure 3G. Although Western blotting may not be the most sensitive method to detect minor changes in splicing, performing PCR experiments at lower doses could provide more insight into the splicing changes. Therefore, it is suggested that the authors include PCR experiments at lower doses to determine if changes in splicing are visible and to better establish the relationship between splicing and gene expression changes.

Reviewer #3 (Public Review):

The manuscript by Sun et al. reveals several crystal structures that help underpin the offensive-defensive relationship between the sea slug Aplysia kurodai and algae. These centre on TNA (a algal glycosyl hydrolase inhibitor), EHEP (a slug protein that protects against TNA and like compounds) and BGL (a glycosyl hydrolase that helps digest algae). The hypotheses generated from the crystal structures herein are supported by biochemical assays.

The crystal structures of apo and TNA-bound EHEP reveals the binding (and thus protection) mechanism. The authors then demonstrate that the precipitated EHEP-TNA complex can be resolubilised at an alkaline pH, potentially highlighting a mechanism for EHEP recycling in the A. kurodai midgut. The authors also present the crystal structures of akuBGL, a beta-glucosidase utilised by Aplysia kurodai to digest laminarin in algae into glucose. The structure revealed that akuBGL is composed of two GH1 domains, with only one GH1 domain having the necessary residue arrangement for catalytic activity, which was confirmed via hydrolytic activity assays. Docking was used to assess binding of the substrate laminaritetraose and the inhibitors TNA, eckol and phloroglucinol to akuBGL. The docking studies revealed that the inhibitors bound akuBGL at the glycone-binding suggesting a competitive inhibition mechanism. Overall, most of the claims made in this work are supported by the data presented.

Reviewer #3 (Public Review):

Light energy drives photosynthesis. However, excessive light can damage (i.e., photo-damage) and thus inactivate the photosynthetic process. A major target site of photo-damage is photosystem II (PSII). In particular, one component of PSII, the reaction center protein, D1, is very suspectable to photo-damage, however, this protein is maintained efficiently by an elaborate multi-step PSII-D1 turnover/repair cycle. Two proteases, FtsH and Deg, are known to contribute to this process, respectively, by efficient degradation of photo-damaged D1 protein processively and endoproteolytically. In this manuscript, Kato et al., propose an additional step (an early step) in the D1 degradation/repair pathway. They propose that "Tryptophan oxidation" at the N-terminus of D1 may be one of the key oxidations in the PSII repair, leading to processive degradation of D1 by FtsH. Both, their data and arguments are very compelling.

The D1 protein repair/degradation pathway in its simplest form can be defined essentially by five steps: (1) migration of damaged PSII core complex to the stroma thylakoid, (2) partial PSII disassembly of the PSII core monomer, (3) access of protease degrading damaged D1, (4) concomitant D1 synthesis, and (5) reassembly of PSII into grana thylakoid. An enormous amount of work has already been done to define and characterize these various steps. Kato et al., in this manuscript, are proposing a very early yet novel critical step in D1 protein turnover in which Tryptophan(Trp) oxidation in PSII core proteins influences D1 degradation mediated by FtsH.

Using a variety of approaches, such as mass-spectrometry (Table 1), site-directed mutagenesis (Figures 2-4), D1 degradation assays (Figures 3, and 4), and simulation modeling (Figure 5), Kato et al., provide both strong evidence and reasonable arguments that an N-terminal Trp oxidation may be likely to be a 'key' oxidative post-translational modification (OPTM) that is involved in triggering D1 degradation and thus activating the PSII repair pathway. Consequently, from their accumulated data, the authors propose a scenario in which the unraveling of the N-terminal of the D1 protein facilitated by Trp oxidation plays a critical 'recognition' role in alerting the plant that the D1 protein is photo-damaged and thus to kick start the processive degradation pathway initiated possibly by FtsH. Coincidently, Forsman and Eaton-Rye (Biochemistry 2021, 60, 1, 53-63), while working with the thermophilic cyanobacterium, Thermosynechococcus vulcanus, showed that when the N-terminal DE-loop of the D1 protein is photo-damaged a disruption of the interaction between the PsbT subunit and D1 occurs which may serve as a signal for PSII to undergo repair following photodamage. While the activation of the processive degradation pathways in Chlamydomonas versus Thermosynechococcus vulcanus have significant mechanistic differences, it's interesting to note and speculate that the stability of the N-terminal of their respective D1 proteins seems to play a critical role in 'signaling' the PSII repair system to be activated and initiate repair. But it's complicated. For instance, significant Trp oxidation also occurs on the lumen side of other PSII subunits which may also play a significant role in activating the repair processes as well. Indeed, Kato et al.,( Photosynthesis Research volume 126, pages 409-416 (2015)) proposed a two-step model whereby the primary event is disruption of a Mn-cluster in PSII on the lumen side. A secondary event is damage to D1 caused by energy that is absorbed by chlorophyll. But models adapt, change, and get updated. And the data provided by Kato et al., in this manuscript, gives us a unique glimpse/snapshot into the importance of the stability of the N-terminal during photo-damage and its role in D1-turnover. For instance, the author's use site-directed mutagenesis of Trp residues undergoing OPTM in the D1 protein coupled with their D1 degradation assays (Figure 3 and 4), provides evidence that Trp oxidation (in particular the oxidation of Trp14) in coordination with FtsH results in the degradation of D1 protein. Indeed, their D1 degradation assays coupled with the use of a ftsh mutant provide further significant support that Trp14 oxidation and FtsH activity are strongly linked. But for FstH to degrade D1 protein it needs to gain access to photo-damaged D1. FtsH access to D1 is achieved by having CP43 partially dissociate from the PSII complex. Hence, the authors also addressed the possibility that Trp oxidation may also play a role in CP43 disassembly from the PSII complex thereby giving FtsH access to D1. Using a site-directed mutagenesis approach, they showed that Trp oxidation in CP43 appeared to have little impact on the PSII repair (Supplemental Figure S6). This result shows that D1-Trp14 oxidation appears to be playing a role in D1 turnover that occurs after CP43 disassembly from the PSII complex. Alternatively, the authors cannot exclude the possibility that D1-Trp14 oxidation in some way facilitates CP43 dissociation. Further investigation is needed on this point. However, D1-Trp14 oxidation is causing an internal disruption of the D1 protein possibly at the N-terminus of the protein. Consequently, the role of Trp14 oxidation in disrupting the stability of the N-terminal domain of the D1 protein was analyzed computationally. Using a molecular dynamics approach (Figure 5), the authors attempted to create a mechanistic model to explain why when D1 protein Trp14 undergoes oxidation the N-terminal domain of D1protein becomes unraveled. Specifically, the authors propose that the interaction between D1 protein Trp14 with PsbI Ser25 becomes disrupted upon oxidation of Trp14. Consequently, the authors concluded from their molecular dynamics simulation analysis that " the increased fluctuation of the first α-helix of D1 would give a chance to recognize the photo-damaged D1 by FtsH protease". Hence, the author's experimental and computational approaches employed here develop a compelling early-stage repair model that integrates 1) Trp14 oxidation, 2) FtsH activation and 3) D1- turnover being initiated at its N-terminal domain. However, a word of caution should be emphasized here. This model is just a snapshot of the very early stages of the D1 protein turnover process. The data presented here gives us just a small glimpse into the unique relationship between Trp oxidation of the D1 protein which may trigger significant N-terminal structural changes of the D1 protein that both signals and provides an opportunity for FstH to begin protease digestion of the D1 protein. However, the authors go to great lengths in their discussion section to not overstate solely the role of Trp14 oxidation in the complicated process of D1 turnover. The authors certainly recognize that there are a lot of moving parts involved in D1 turnover. And while Trp14 oxidation is the major focus of this paper, the authors show in Supplemental Fig S4 the structural positions of various additional oxidized Trp residues in the Thermosynecoccocus vulcans PSII core proteins. Indeed, this figure shows that the majority of oxidized Trps are located on the luminal side of PSII complex clustered around the oxygen-evolving complex. So, while oxidized Trp14 may be involved in the early stages of D1 turnover certainly oxidized Trps on the lumen side are also more than likely playing a role in D1 turnover as well. To untangle this complex process will require additional research.

Nevertheless, identifying and characterizing the role of oxidative modification of tryptophan (Trp) residues, in particular, Trp14, in the PSII core provides another critical step in an already intricate multi-step process of D1 protein turnover during photo-damage.

Reviewer #3 (Public Review):

The data presented suggest that their algorithm can replace a human operator, which is a strong enough reason to publish and disseminate the technology. At the same time, aspects of the methods and results could benefit from a clearer explication. For example, the reported R^2 values for their model's performance are less than 0.5, (0.191, 0.2, 0.345, 0.467). I take this to mean the model's predictions are better than the mean value but that it will probably not generalize well for data it hasn't seen yet. Please comment.

Did the authors partition their data into a training set, a validation set, and a test set? From the manuscript, it wasn't obvious to me they withheld a test set (a set of data never seen by the model, which they used to evaluate the performance of the model selected based on the validation set). From Extended Data Figures 1 and 2, I inferred that the number of samples in the confusion matrix matches the validation size (n=2341). So, are they reporting validation results and not test results? Please explain.

Reviewer #3 (Public Review):

In this manuscript, Zhou et al. demonstrate that the pseudokinase ULK4 has an important role in Hedgehog signaling by scaffolding the active kinase Stk36 and the transcription factor Gli2, enabling Gli2 to be phosphorylated and activated.<br /> Through nice biochemistry experiments, they show convincingly that the N-terminal pseudokinase domain of ULK4 binds Stk36 and the C-terminal Heat repeats bind Gli2.

Lastly, they show that upon Sonic Hedgehog signaling, ULK4 localizes to the cilia and is needed to localize Stk36 and Gli2 for proper activation.

This manuscript is very solid and methodically shows the role of ULK4 and STK36 throughout the whole paper, with well controlled experiments. The phosphomimetic and incapable mutations are very convincing as well.<br /> I think this manuscript is strong and stands as is, and there is no need for additional experiments.

Overall, the strengths are the rigor of the methods, and the convincing case they bring for the formation of the ULK4-Gli2-Stk36 complex. There are no weaknesses noted. I think a little additional context for what is being observed in the immunofluorescence might benefit readers who are not familiar with these cell types and structures.

Reviewer #3 (Public Review):

The study by Thommen et al. sought to identify the native role of the Plasmodium falciparum FKBP35 protein, which has been identified as a potential drug target due to the antiplasmodial activity of the immunosuppressant FK506. This compound has multiple binding proteins in many organisms; however, only one FKBP exists in P. falciparum (FKBP35). Using genetically-modified parasites and mass spectrometry-based cellular thermal shift assays (CETSA), the authors suggest that this protein is in involved in ribosome homeostasis and that the antiplasmodial activity of FK506 is separate from its activity on the FKBP35 protein. The authors first created a conditional knockdown using the destruction domain/shield system, which demonstrated no change in asexual blood stage parasites. A conditional knockout was then generated using the DiCre system. FKBP35KO parasites survived the first generation but died in the second generation. The authors called this "a delayed death phenotype", although it was not secondary to drug treatment, so this may be a misnomer. This slow death was unrelated to apicoplast dysfunction, as demonstrated by lack of alterations in sensitivity to apicoplast inhibitors. Quantitative proteomics on the FKBP35KO vs FKBP35WT parasites demonstrated enrichment of proteins involved in pre-ribosome development and the nucleolus. Interestingly, the KO parasites were not more susceptible to cycloheximide, a translation inhibitor, in the first generation (G1), suggesting that mature ribosomes still exist at this point. The SunSET technique, which incorporates puromycin into nascent peptide chains, also showed that in G1 the FKBP35KO parasites were still able to synthesize proteins. But in the second generation (G2), there was a significant decrease in protein synthesis. Transcriptomics were also performed at multiple time points. The effects of knockout of FKBP35 were transcriptionally silent in G1, and the parasites then slowed their cell cycles as compared to the FKBP35WT parasites.

The authors next sought to evaluate whether killing by FK506 was dependent upon the inhibition of PfKBP35. Interestingly, both FKBP35KO and FKBP35WT parasites were equally susceptible to FK506. This suggested that the antiplasmodial activity of FK506 was related to activity targeting essential functions in the parasite separate from binding to FKBP35. To identify these potential targets, the authors used MS-CETSA on lysates to test for thermal stabilization of proteins after exposure to drug, which suggests drug-protein interactions. As expected, FK506 bound FKBP35 at low nM concentrations. However, given that the parasite IC50 of this compound is in the uM range, the authors searched for proteins stabilized at these concentrations as putative secondary targets. Using live cell MS-CETSA, FK506 bound FKBP35 at low nM concentrations; however, in these experiments over 50 ribosomal proteins were stabilized by the drug at higher concentrations. Of note, there was also an increase in soluble ribosomal factors in the absence of denaturing conditions. The authors suggested that the drug itself led to these smaller factors disengaging from a larger ribosomal complex, leading to an increase in soluble factors. Ultimately, the authors conclude that the native function of FKBP35 is involved in ribosome homeostasis and that the antiplasmodial activity of FK506 is not related to the binding of FKBP35, but instead results from inhibition of essential functions of secondary targets.

Strengths<br /> This study has many strengths. It addresses an important gap in parasite biology and drug development, by addressing the native role of the potential antiplasmodial drug target FKBP35 and whether the compound FK506 works through inhibition of that putative target. The knockout data provide compelling evidence that the KBP35 protein is essential for asexual parasite growth after one growth cycle. Analysis of the FKBP35KO line also provides evidence that the effects of FK506 are likely not solely due to inhibition of that protein, but instead must have secondary targets whose function is essential. These data are important in the field of drug development as they may guide development away from structure-based FK506 analogs that bind more specifically to the FKBP35 protein.

Weaknesses:<br /> There are also a few notable weaknesses in the evidence that call into question the conclusion in the article title that FKBP35 is definitely involved in ribosomal homeostasis. While the proteomics supports alterations in ribosome biogenesis factors, it is unclear whether this is a direct role of the loss of the FKBP35 protein or is more related to non-specific downstream effects of knocking down the protein. The CETSA data clearly demonstrate that FK506 binds PfKB35 at low nM concentrations, which is different than the IC50 noted in the parasite; however, the evidence that the proteins stabilized by uM concentrations of drug are actual targets is not completely convincing. Especially, given the high uM amounts of drug required to stabilize these proteins. This section of the manuscript would benefit from validation of a least one or two of the putative candidates noted in the text. In the live cell CETSA, it is noted that >50 ribosomal components are stabilized in drug treated but not lysate controls. Similarly, the authors suggest that the -soluble fraction of ribosomal components increases in drug-exposed parasites even at 37{degree sign}C and suggests that this is likely from smaller ribosomal proteins disengaging from larger ribosomal complexes. While the evidence is convincing that this protein may play a role in ribosome homeostasis in some capacity, it is not sure that the title of the paper "FKBP secures ribosome homeostasis" holds true given the lack of mechanistic data. A minor weakness, but one that should nonetheless be addressed, is the use of the term "delayed death phenotype" with regards to the knockout parasite killing. This term is most frequently used in a very specific setting of apicoplast drugs that inhibit apicoplast ribosomes, so the term is misleading. It is also possible that the parasites are able to go through a normal cycle because of the kinetics of the knockout and that the time needed for protein clearance in the parasite to a level that is lethal.

Overall, the authors set out to identify the native role of FKB35 in the P. falciparum parasites and to identify whether this is, in fact, the target of FK506. The data clearly demonstrate that FKBP35 is essential for parasite growth and provide evidence that alterations in its levels have proteomic but not transcriptional changes. However, the conclusion that FKBP35 actually stabilizes ribosomal complexes remains intermediate. The data are also very compelling that FK506 has secondary targets in the parasite aside from FKBP35; however, the high uM concentrations of the drug needed to attain results and the lack of biological validation of the CETSA hits makes it difficult to know whether any of these are actually the target of the compound or instead are nonspecific downstream consequences of treatment.

Reviewer #3 (Public Review):

GluK1 forms glutamate-gated ion channels with an important function in synaptic transmission and neuron excitability. Particularly, a GluK1 splice-variant (Gluk1-1) with significant expression in different regions of the brain has not been characterized before. The paper of Dhingra et al. aims to evaluate the role of the Exon 9 splice insert in GluK1 on channel function. This study relies mainly on electrophysiological approaches to determine the effect of the splice insert on GluK1 gating properties, including desensitization, agonist efficacy, recovery, and rectification. Overall, this work provides two major milestones: 1) the first functional characterization of the Gluk1-1a variant and 2) the first structure of this channel. The functional data supporting the role of the insert on channel properties are solid, although the current data does not provide significant insights about the mechanisms behind this. Also, the little information associated with the resolved structure precludes providing further insights about the structural basis that account for the impact of the insert on channel function. Overall, I consider this an interesting paper that represents an important advance in the study of glutamate receptors.

Reviewer #3 (Public Review):

Lee, Kyungtae and colleagues have discovered and mapped out alpha-arrestin interactomes in both human and Drosophila through the affinity purification/mass spectrometry and the SAINTexpress method. They found the high confident interactomes, consisting of 390 protein-protein interactions (PPIs) between six human alpha-arrestins and 307 preproteins, as well as 740 PPIs between twelve Drosophila alpha-arrestins and 467 prey proteins. To define and characterize these identified alpha-arrestin interactomes, the team employed a variety of widely recognized bioinformatics tools. These included protein domain enrichment analysis, PANTHER for protein class enrichment, DAVID for subcellular localization analysis, COMPLEAT for the identification of functional complexes, and DIOPT to identify evolutionary conserved interactomes. Through these analyses, they confirmed known alpha-arrestin interactors' role and associated functions such as ubiquitin ligase and protease. Furthermore, they found unexpected biological functions in the newly discovered interactomes, including RNA splicing and helicase, GTPase-activating proteins, ATP synthase. The authors carried out further study into the role of human TXNIP in transcription and epigenetic regulation, as well as the role of ARRDC5 in osteoclast differentiation. This study holds important value as the newly identified alpha-arrestin interactomes are likely aiding functional studies of this group of proteins. Despite the overall support from data for the paper's conclusions, certain elements related to data quantification, interpretation, and presentation demand more detailed explanation and clarification.

1) In Figure 1B, it is shown that human alpha-arrestins were N-GFP tagged (N-terminal) and Drosophila alpha-arrestins were C-GFP (C-terminal). However, the rationale of why the authors used different tags for human and fly proteins was not explained in the main text and methods.<br /> 2) In Figure 2A, there seems to be an error for labeling the GAL4p/GAL80p complex that includes NOTCH2, NOTCH1 and TSC2.<br /> 3) In Figure 5, given that knockdown of TXNIP did not affect the levels and nuclear localization of HDAC2, the authors suggest that TXNIP might modulate HDAC2 activity. However, the ChiP assay suggest a different model - TXNIP-HDAC2 interaction might inhibit the chromatin occupancy of HDAC2, reducing histone deacetylation and increasing global chromatin accessibly. The authors need to propose a model consistent with these sets of all data.<br /> 4) The authors showed that ectopic expression of ARRDC5 increased osteoclast differentiation and function. Does loss of ARDDC5 lead to defects in osteoclast function and fate determination?<br /> 5) From Figure 6D, the authors argued that ARRDC5 overexpression resulted in more V-ATPase signals: however, there is no quantification. Quantification of the confocal images will foster the conclusion. Also, western blots for V-ATPase proteins will provide an alternative way to determine the effects of ARRDC5.<br /> 6) The results from Figure 6D did not support the authors' argument that ARRDC5 might control the membrane localization of the V-ATPase, as bafilomycin is the V-ATPase inhibitor. ARRDC5 knockdown experiments will help to determine whether ARRDC5 can control the membrane localization of the V-ATPase in osteoclast.

• food supply chain vulnerability
• most US cities would run out of food in 3 days if there was a major food supply chain disruption