Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

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

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

Reviewer #3 (Public Review):

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

Comments:

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

In this manuscript by Douglas et al., the authors used a functional genomics approach to understand how Staphylococcus aureus survives in the bloodstream to cause bacteraemia. They identified seven novel genes that affect serum survival. The study focused on tcaA, a gene associated with resistance to the antibiotic teicoplanin and is activated when exposed to serum and plays a role in producing a critical virulence factor called wall teichoic acids (WTA) in the cell envelope. This protein affects the bacteria's sensitivity to cell wall attacking agents, human defense fatty acids, and antibiotics, as well as autolytic activity and lysostaphin sensitivity. The data in this study suggested that TcaA play a role in the ligation or retention of WTA within the cell wall. However, more work is needed to clarify that part. Interestingly, despite making the bacteria more vulnerable to serum killing, tcaA contributes to S. aureus virulence by altering the cell wall architecture, as demonstrated by the wild type strain outcompeting the tcaA mutant in a Mouse Co-infection model. The study raises an important point that TcaA in S. aureus may represent a system balancing two scenarios: it makes the bacteria more susceptible to serum killing, potentially limiting bacteraemia and providing long-term benefits between hosts; however, once established in the bloodstream, the bacteria survive and thrive, causing successful bacteraemia, as per the short-sighted evolution of virulence hypothesis. This duality highlights the complex interplay between within-host and between-host fitness in bacterial evolution. I strongly suggest creating a graphical abstract to illustrate the complex relationship between within-host and between-host fitness scenarios involving TcaA. Having this visual representation in the discussion will enhance comprehension and provide a concise summary of the complex system for the reader.

In this manuscript, the authors achieved their aims, and the results support their conclusions. This work will be important for understanding this complex system and for developing novel therapeutics and vaccines for S. aureus.

Reviewer #3 (Public Review):

The regulation of transporters in many physiological systems is poorly known. Here, Forster and colleagues describe how activity of an inorganic carbon transporter, SbtA, in the bacterial carbon concentrating mechanism is regulated by the PII protein SbtB. Although there is now significant structural knowledge of the system and many potential SbtB-regulating small molecule effectors are known, Forster and colleagues clarify, how the adenylate charge in the cell, rather than any single metabolite, is the important regulatory effector. This is critical for the endogenous function, as the cyanobacterial host undergoes dramatic changes in adenylate charge over the course of a diurnal cycle and this result explains how the channel is regulated to efficiently function in CO2 assimilation. The manuscript is generally clear and the data generally supportive of the conclusions as written. However, there are several instances where additional clarification and/or experiments are needed to confirm the major findings of the paper.

Reviewer #3 (Public Review):

In this manuscript, the authors use behavior, calcium imaging, and circuit modulation (DREADDs etc) to assess dopamine regulation of prefrontal cortical circuits in the mouse. The authors have previously established that activation of dopamine inputs to prefrontal cortex during adolescence can drive increases in mPFC DA bouton number and enhanced mPFC activity in WT mice. Here the authors use two mouse models - one with a reporter replacing the Arc gene, and another with knockout of the schizophrenia-associated gene Disc1, both of which are thought to have reduced prefrontal cortical activity. First they trained mice on a Y-maze and showed impaired performance in the Arc knockout. Then they demonstrated selective disruption of neuronal firing with calcium imaging at the time of the decision in the task. The Arc mice were found to have reduced dopamine bouton density, and adolescent activation of the DA neurons corrected this as well as the PFC firing and the behavior. Similar data were shown in the Disc1 KO. The data are well controlled and the authors use a number of leading edge methods.

Reviewer #3 (Public Review):

As a follow up from a manuscript previously published (Ruby et al. 2018), the authors use basic survival analysis methods to estimate hazard rates on an extended dataset of naked mole rats. They conclude that naked mole rats do not show the common exponential increase in mortality that has been typified in most mammals.

In fact, this species has attracted great interest due to their extreme longevity, and the physiological mechanisms that have been associated with slower aging. As the authors show, this species shows unprecedented longevity, particularly considering their body size and phylogenetic location.

However, the data available and the methods used cannot support the conclusion of an absence of increase in mortality for adults. As the authors show, the survivorship curves, calculated using Kaplan-Meier estimators, do not reach below values of 0.5. In short, nothing can be said about hazard rates after the age of median life expectancy. What the authors show is that, up to a certain age (when at least 50% of the individuals are still alive), the hazard rate is relatively constant. Beyond that age, the authors cannot draw any conclusions.

In addition, here is a summary of the methodological limitations I could find based on their limited description: 1) their survivorships do not go below 0.5 and thus cannot make any statements about actuarial senescence; 2) ignoring this last, to test whether the hazards follow a Gompertz mortality it would be more appropriate to use maximum likelihood and test alternative models (e.g., exponential, Siler), and not visually as they show in fig 1; 3) they seem to be confusing left-censoring with left-truncation; 4) given the left-truncation, they should be using product limit estimators and not Kaplan-Meier estimators (which they might, but it's not possible to know based on the limited description of the methods); 5) their treatment of the effects of colony size, breeding status, and body weight should be at least by means of a proportional hazards, not a simple visual inspection on arbitrary age intervals.

In light of these limitations, I would rank the significance of the study as not more than useful, and the strength of evidence inadequate. Still, and as I've stated above, this species is of great interest for ageing research, and the extensive work that the authors have done maintaining this captive colony is to be commended.

Reviewer #3 (Public Review):

Fission yeast is an important model organism and studies on fission yeast have provided many key insights into the understanding of genes and biological pathways. However, even in such a well-studied model organism, there are still many genes without known functions.

In this work, the authors took advantage of the availability of genome-wide fission yeast deletion mutants to systematically analyze the mutant phenotypes under 131 different conditions. This effort generated a genotype-phenotype dataset larger than the currently curated genotype-phenotype dataset, which is derived from studies over many decades by hundreds of fission yeast laboratories. The authors used the dataset to construct gene clusters that provide functional clues for many genes without previously known functions, including ones conserved in humans. This rich resource will surely be highly useful to the fission yeast community and beyond.

In addition, the authors also used machine learning to generate functional predictions of fission yeast genes and yield novel understandings, which are validated by experimental analysis of new ageing-related genes.

Overall, this study provides unprecedented and highly valuable resources for understanding fission yeast gene functions.

Reviewer #3 (Public Review):

The authors present a pipeline for generating strain-specific genome-scale metabolic models for bacteria using Klebsiella spp. as the demonstrative data. The proposed improvement of performance and accuracy in this process holds great value. However, the demonstrated evidence, justification, and validation methods require further discussion.

Apart from the claim to quickly and accurately produce strain-specific models, the manuscript highlights the need to create pan-metabolic models from manually curated models, which are relatively time-consuming and can only be done with well-established organisms. Therefore, claims to speed up the process are redundant.

The justification and evaluation of the generated models are inadequate and one-dimensional. The authors only focus on statistics such as the number of reactions and genes in the models, which does not accurately depict the completeness of the model.

Furthermore, the authors solely compare their results with the performance of the previously published CraveMe packages, and the results do not clearly demonstrate the superior performance of the Bactabolize tool that they developed.

The authors have not provided evidence or discussion on the accuracy of any metabolic fluxes, which are considered to be crucial for reconstructing metabolic models. Additionally, the authors have not mentioned the importance of non-growth associated maintenance and the criticality of biomass composition analysis, both of which significantly determine the fluxes in the system.

Overall, the work holds potential for direct application in certain specific aims and fields. However, the cryptic details and critical points of the justification regarding the completeness of the models require further discussion. A detailed discussion on the importance of manually curated models and the potential future direction of incorporating machine learning into the process would significantly enhance the quality of the manuscript.

Reviewer #3 (Public Review):

In their manuscript, Schneider et al. aim to develop voyAGEr, a web-based tool that enables the exploration of gene expression changes over age in a tissue- and sex-specific manner. The authors achieved this goal by calculating the significance of gene expression alterations within a sliding window, using their unique algorithm, Shifting Age Range Pipeline for Linear Modelling (ShARP-LM), as well as tissue-level summaries that calculated the significance of the proportion of differentially expressed genes by the windows and calculated enrichments of pathways for showing biological relevance. Furthermore, the authors examined the enrichment of cell types, pathways, and diseases by defining the co-expressed gene modules in four selected tissues. The voyAGEr was developed as a discovery tool, providing researchers with easy access to the vast amount of transcriptome data from the GTEx project. Overall, the research design is unique and well-performed, with interesting results that provide useful resources for the field of human genetics of aging. I have a few questions and comments, which I hope the authors can address.

1. In the gene-centric analyses section of the result, to improve this manuscript and database, linear regression tests accounting for the entire range of age should be added. The authors' algorithm, ShARP-LM, tests locally within a 16-year window which makes it has lower power than the linear regression test with the whole ages. I suspect that the power reduction is strongly affected in the younger age range since a larger number of GTEx donors are enriched in old age. By adding the results from the lm tests, readers would gain more insight and evidence into how significantly their interest genes change with age.<br /> 2. In line with the ShARP-LM test results, it is not clear which criterion was used to define the significant genes and the following enrichment analyses. I assume that the criterion is P < 0.05, but it should be clearly noted. Additionally, the authors should apply adjusted p-values for multiple-test correction. The ideal criterion is an adjusted P < 0.05. However, if none or only a handful of genes were found to be significant, the authors could relax the criteria, such as using a regular P < 0.01 or 0.05.<br /> 3. In the gene-centric analyses section, authors should provide a full list of donor conditions and a summary table of conditions as supplementary.<br /> 4. The tissue-specific assessment section has poor sub-titles. Every title has to contain information.<br /> 5. I have an issue understanding the meaning of NES from GSEA in the tissue-specific assessment section. The authors performed GSEA for the DEGs against the background genes ordered by t-statistics (from positive to negative) calculated from the linear model. I understand the p-value was two-tailed, which means that both positive and negative NES are meaningful as they represent up-regulated expression direction (positive coefficient) and down-regulated expression direction (negative coefficient) with age, respectively, within a window. However, in the GSEA section of Methods, authors were not fully elaborate on this directionality but stated, "The NES for each pathway was used in subsequent analyses as a metric of its over- or down-representation in the Peak". The authors should clearly elaborate on how to interpret the NES from their results.<br /> 6. In the Modules of co-expressed genes section, the authors did not explain how or why they selected the four tissues: brain, skeletal muscle, heart (left ventricle), and whole blood. This should be elaborated on.<br /> 7. In the modules of the co-expressed genes section, the authors did not provide an explanation of the "diseases-manual" sub-tab of the "Pathway" tab of the voyAGEr tool. It would be helpful for readers to understand how the candidate disease list was prepared and what the results represent.

Reviewer #3 (Public Review):

This study is well designed and executed and provides new and important insights into the role of two TFs during the maturation of female gametocytes and fertilization in the mosquito midgut. However, it would benefit from a more thorough characterization of the phenotype to understand at which step of development these factors are required.

The gene at the center of this study (PBANKA_0902300) was identified in an earlier genetic screen by Russell et al. as being a female specific gene with essential role in transmission and named Fd2 (for female-defective 2). Since this name entered the literature first and is equally descriptive, the Fd2 name should be used instead of PFG to maintain clarity and avoid unnecessary confusion.

This study is well designed and executed and provides new and important insights into the role of two TFs during the maturation of female gametocytes and fertilization in the mosquito midgut. However, it would benefit from a more thorough characterization of the phenotype to understand at which step of development these factors are required.

The gene at the center of this study (PBANKA_0902300) was identified in an earlier genetic screen by Russell et al. as being a female specific gene with essential role in transmission and named Fd2 (for female-defective 2). Since this name entered the literature first and is equally descriptive, the Fd2 name should be used instead of PFG to maintain clarity and avoid unnecessary confusion.

Reviewer #3 (Public Review):

In this study, the authors developed and tested a novel framework for extracting muscle synergies. The approach aims at removing some limitations and constrains typical of previous approaches used in the field. In particular, the authors propose a mathematical formulation that removes constrains of linearity and couple the synergies to their motor outcome, supporting the concept of functional synergies and distinguishing the task-related performance related to each synergy. While some concepts behind this work were already introduced in recent work in the field, the methodology provided here encapsulates all these features in an original formulation providing a step forward with respect to the currently available algorithms. The authors also successfully demonstrated the applicability of their method to previously available datasets of multi-joint movements.

Preliminary results positively support the scientific soundness of the presented approach and its potential. The added values of the method should be documented more in future work to understand how the presented formulation relates to previous approaches and what novel insights can be achieved in practical scenarios and confirm/exploit the potential of the theoretical findings.

Strengths:

This work proposes a novel framework that addresses physiologically non-verified hypothesis of standard muscle synergy methods: it removes restrictive model assumptions (e.g. linearity, same mixing coefficients) and the reliance on variance-accounted-for (VAF) metrics.

The method is solid and achieves the prescribed objectives at a computational level and in preliminary laboratory data.

A toolbox is available for testing the methods on a larger scale.

The paper is well written and shows a high level of innovation, original content and analysis

Weaknesses:

Task performance variables could be specified in more quantitative definition in future work (e.g.: articular angles rather than a generic starting point- end point).

The paper does not show a comparison with previous approaches (e.g.: NMF) or recently developed approaches (such as MMF).

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.

In this work, the effort of the authors aimed at developing the field is clear. It is fundamental to develop novel frameworks for synergy extraction and use them to make them more interpretable and applicable to real scenarios, as well as more adherent to recent findings achieved in motor control and neuroscience that are not reflected in the standard models. At the same time, muscle synergies are being used more and more in research but their impact in practical scenarios is still limited, probably because synergies have rarely been analyzed in a functional context. This paper shows a very in-depth analysis and a novel framework to interpret data that links to the task space from a functional perspective. I also found that the results on the datasets are very well commented but could expand more to show why using this framework is advantageous.

There are some key points for discussion that follow from this paper which can be described more, maybe in future work, and that might contribute to major developments in the field, including:

The understanding of how the separation between relevant (redundant and synergistic) and irrelevant synergies impact on synergy analysis in practical works;

Interpreting how different synergistic organizations described in this work allows to better describe data from real scenarios (e.g.: motor recovery of patients after neurological diseases);

Discussing in detail how the presented findings compare with standard algorithms such as NMF to determine the added value provided with this approach;

Describe how redundant synergies reflect real neural organization and - if their "existence" is confirmed - how they contribute to redesign the concept of muscle synergies and of modular/synergistic control in general.

Reviewer #3 (Public Review):

Yuan et al., set out to examine the role of functional and structural interaction between Slac and NaVs on the Slack sensitivity to quinidine. Through pharmacological and genetic means they identify NaV1.6 as the privileged NaV isoform in sensitizing Slac to quinidine. Through biochemical assays, they then determine that the C-terminus of Slack physically interacts with the N- and C-termini of NaV1.6. Using the information gleaned from the in vitro experiments the authors then show that virally-mediated transduction of Slack's C-terminus lessens the extent of SlackG269S-induced seizures. These data uncover a previously unrecognized interaction between a sodium and a potassium channel, which contributes to the latter's sensitivity to quinidine.

The conclusions of this paper are mostly well supported by data, but some aspects of functional and structural studies in vivo as well as physically interaction need to be clarified and extended.

1) Immunolabeling of the hippocampus CA1 suggests sodium channels as well as Slac colocalization with AnkG (Fig 3A). Proximity ligation assay for NaV1.6 and Slac or a super-resolution microscopy approach would be needed to increase confidence in the presented colocalization results. Furthermore, coimmunoprecipitation studies on the membrane fraction would bolster the functional relevance of NaV1.6-Slac interaction on the cell surface.

2) Although hippocampal slices from Scn8a+/- were used for studies in Fig. S8, it is not clear whether Scn8a-/- or Scn8a+/- tissue was used in other studies (Fig 1J & 1K). It will be important to clarify whether genetic manipulation of NaV1.6 expression (Fig. 1K) has an impact on sodium-activated potassium current, level of surface Slac expression, or that of NaV1.6 near Slac.

3) Did the epilepsy-related Slac mutations have an impact on NaV1.6-mediated sodium current?

4) Showing the impact of quinidine on persistent sodium current in neurons and on NaV1.6-expressing cells would further increase confidence in the role of persistent sodium current on sensitivity of Slac to quinidine.

Reviewer #3 (Public Review):

This study from the Flores group aims at understanding neuronal circuit changes during adolescence which is an ill-defined, transitional period involving dramatic changes in behavior and anatomy. They focus on DA innervation of the prefrontal cortex, and their interaction with the guidance cue Netrin-1. They propose DA axons in the PFC increase in the postnatal period, and their density is reduced in a Netrin 1 knockdown, suggesting that Netrin abets the development of this mesocortical pathway. In such mice impulsivity gauged by a go-no go task is reduced. They then provide some evidence that Unc5c is developmentally regulated in DA axons. Finally they use an interesting hamster model, to study the effect of light hours on mesocortical innervation, and make some interesting observations about the timing of innervation and Unc5c expression, and the fact that females housed in winter day length conditions display an accelerated innervation of the prefrontal cortex. While this work is novel and on an interesting, understudied topic, several aspects need to be further consolidated, to make it more persuasive.

Main comments<br /> 1. Fig 1 A and B don't appear to be the same section level.<br /> 2. Fig 1C. It is not clear that these axons are crossing from the shell of the NAC.<br /> 3. Fig 1. Measuring width of the bundle is an odd way to measure DA axon numbers. First the width could be changing during adult for various reasons including change in brain size. Second, I wouldn't consider these axons in a traditional bundle. Third, could DA axon counts be provided, rather than these proxy measures.<br /> 4. TH in the cortex could also be of noradrenergic origin. This needs to be ruled out to score DA axons<br /> 5. Netrin staining should be provided with NeuN + DAPI; its not clear these are all cell bodies. An in situ of Netrin would help as well.<br /> 6. The Netrin knockdown needs validation. How strong was the knockdown etc?<br /> 7. If the conclusion that knocking down Netrin in cortex decreases DA innervation of the IL, how can that be reconciled with Netrin-Unc repulsion.<br /> 8. The behavioral phenotype in Fig 1 is interesting, but its not clear if its related to DA axons/signaling. IN general, no evidence in this paper is provided for the role of DA in the adolescent behaviors described.<br /> 9. Fig2 - boxes should be drawn on the NAc diagram to indicate sampled regions. Some quantification of Unc5c would be useful. Also, some validation of the Unc5c antibody would be nice.<br /> 10. "In adolescence, dopamine neurons begin to express the repulsive Netrin-1 receptor UNC5C, and reduction in UNC5C expression appears to cause growth of mesolimbic dopamine axons to the prefrontal cortex".....This is confusing. Figure 2 shows a developmental increase in UNc5c not a decrease. So when is the "reduction in Unc5c expression" occurring?<br /> 11. In Fig 3, a statistical comparison should be made between summer male and winter male, to justify the conclusions that the winter males have delayed DA innervation.<br /> 12. Should axon length also be measured here (Fig 3)? It is not clear why the authors have switched to varicosity density. Also, a box should be drawn in the NAC cartoon to indicate the region that was sampled.<br /> 13. In Fig 3, Unc5c should be quantified to bolster the interesting finding that Unc5c expression dynamics are different between summer and winter hamsters. Unc5c mRNA experiments would also be important to see if similar changes are observed at the transcript level.<br /> 14. Fig 4. The peak in exploratory behavior in winter females is counterintuitive and needs to be better discussed. IN general, the light dark behavior seems quite variable.

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. This effect of anxiety was mediated by the difference in modelled sensitivity to reward versus punishment (relative sensitivity). 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.

However, interpretation of these findings are severely undermined by the fact that the aversiveness of the auditory punisher was largely determined by participants, with the far-reaching impacts of this not being accounted for in any of the analyses. The manipulation check to confirm participants did not mute their sound is highly commendable, but the thresholding of punisher volume to "loud but comfortable" at the outset of the task leaves substantial scope for variability in the punisher delivered to participants. Indeed, participants' ratings of the unpleasantness of the punishment was moderate and highly variable (M = 31.7 out of 50, SD = 12.8 [distribution unreported]). Despite having this rating, it is not incorporated into analyses. It is possible that the key finding of relationships between task-induced anxiety, reward-punishment sensitivity and avoidance are driven by differences in the punisher experienced; a louder punisher is more unpleasant, driving greater task-induced anxiety, model-derived punishment sensitivity, and avoidance (and vice versa). This issue can also explain the counterintuitive findings from re-tested participants; lower/negatively correlated task-induced anxiety and punishment-related cognitive parameters may have been due to participants adjusting their sound settings to make the task less aversive (retest punisher rating not reported). It can therefore be argued that the task may not actually capture meaningful cognitive/motivational traits and their effects on decision-making, but instead spurious differences in punisher intensity.

This undercuts the proposed significance of this task as a translational tool for understanding anxiety and avoidance. More information about ratings of punisher unpleasantness and its relationship to task behavior, anxiety and cognitive parameters would be valuable for interpreting findings. It would also be of interest whether the same results were observed if the aversiveness of the punisher was titrated prior to the task.

Although the procedure and findings reported here remain valuable to the field, claims of novelty including its translational potential are perhaps overstated. This study complements and sits within a much broader literature that investigates roles for aversion and cognitive traits in approach-avoidance decisions. This includes numerous studies that apply reinforcement learning models to behavior in two-choice tasks with latent probabilities of reward and punishment (e.g., see doi: 10.1001/jamapsychiatry.2022.0051), as well as other translationally-relevant paradigms (e.g., doi: 10.3389/fpsyg.2014.00203, 10.7554/eLife.69594, etc).

Reviewer #3 (Public Review):

Eichler et al. set out to map the locations of the mechanosensory bristles on the fly head, examine the axonal morphology of the bristle mechanosensory neurons (BMNs) that innervate them, and match these to electron microscopy reconstructions of the same BMNs in a previously published EM volume of the female adult fly brain. They used BMN synaptic connectivity information to create clusters of BMNs that they show occupy different regions of the subesophageal zone brain region and use optogenetic activation of subsets of BMNs to support the claim that the morphological projections and connectivity of defined groups of BMNs are consistent with the parallel model for behavioral sequence generation.

The authors have beautifully cataloged the mechanosensory bristles and the projection paths and patterns of the corresponding BMN axons in the brain using detailed and painstaking methods. The result is a neuroanatomy resource that will be an important community resource. To match BMNs reconstructed in an electron microscopy volume of the adult fly brain, the authors matched clustered reconstructed BMNs with light-level BMN classes using a variety of methods, but evidence for matching is only summarized and not demonstrated in a way that allows the reader to evaluate the strength of the evidence. The authors then switch from morphology-based categorization to non-BMN connectivity as a clustering method, which they claim demonstrates that BMNs form a somatotopic map in the brain. This map is not easily appreciated, and although contralateral projections in some populations are clear, the distinct projection zones that are mentioned by the authors are not readily apparent. Because of the extensive morphological overlap between connectivity-based clusters, it is not clear that small projection differences at the projection level are what determines the post-synaptic connectivity of a given BMN cluster or their functional role during behavior. The claim the somatotopic organization of BMN projections is preserved among their postsynaptic partners to form parallel sensory pathways is not supported by the result that different connectivity clusters still have high cosine similarity in a number of cases (i.e. Clusters 1 and 3, or Clusters 1 and 2). Finally, the authors use tools that were generated during the light-level characterization of BMN projections to show that specifically activating BMNs that innervate different areas of the head triggers different grooming behaviors. In one case, activation of a single population of sensory bristles (lnOm) triggers two different behaviors, both eye and dorsal head grooming. This result does not seem consistent with the parallel model, which suggests that these behaviors should be mutually exclusive and rely on parallel downstream circuitry.

This work will have a positive impact on the field by contributing a complete accounting of the mechanosensory bristles of the fruit fly head, describing the brain projection patterns of the BMNs that innervate them, and linking them to BMN sensory projections in an electron microscopy volume of the adult fly brain. It will also have a positive impact on the field by providing genetic tools to help functionally subdivide the contributions of different BMN populations to circuit computations and behavior. This contribution will pave the way for further mechanistic study of central circuits that subserve grooming circuits.

Reviewer #3 (Public Review):

The six-transmembrane epithelial antigen of the prostate (STEAP) family comprises four members in metazoans. STEAP1 was identified as integral membrane protein highly upregulated on the plasma membrane of prostate cancer cells (PMID: 10588738), and it later became evident that other STEAP proteins are also over expressed in cancers, making STEAPs potential therapeutic targets (PMID: 22804687). Functionally, STEAP2-4 are ferric and cupric reductases that are important for maintaining cellular metal uptake (PMIDs: 16227996, 16609065). The cellular function of STEAP1 remains unknown, as it cannot function as an independent metalloreductase. In the last years, structural and functional data have revealed that STEAPs form trimeric assemblies and that they transport electrons from intracellular NADPH, through membrane bound FAD and heme cofactors, to extracellular metal ions (PMIDs: 23733181, 26205815, 30337524). In addition, numerous studies (including a previous study from the senior authors) have provided strong implications for a potential metalloreductase function of STEAP1 (PMIDs: 27792302, 32409586).

This new study by Chen et al. aims to further characterize the previously established electron transport chain in STEAPs in high molecular detail through a variety of assays. This is a well-performed, highly specialized study that provides some useful extra insights into the established mechanism of electron transport in STEAP proteins. The authors first perform a detailed spectroscopic analysis of Fe3+-NTA reduction by STEAP2 and STEAP1, confirming that both purified proteins are capable of reducing metal ions. A cryo-EM structure of STEAP2 is also presented. It is then established that STEAP1 can receive electrons from cytochrome b5 reductase, and the authors provide experimental evidence that the flavin in STEAP proteins becomes diffusible.

The specific aims of the study are clear, but it is not always obvious why certain experiments are performed only on STEAP2, on STEAP1, or on both isoforms. A better justification of the performed experiments through connecting paragraphs and proper referencing of the literature would improve readability of the manuscript. Experimentally, the conclusions are appropriate and mostly consistent with the experimental data, although one important finding can benefit from further clarification. Namely, the observation that STEAP1 can form an electron transfer chain with cytochrome b5 reductase in vitro is an exciting finding, but its physiological relevance remains to be validated. The metalloreductase activity of STEAP1 in vitro has been described previously by the authors and by others (PMIDs: 27792302, 32409586). However, when over expressed in HEK cells, STEAP1 by itself does not display metal ion reductase activity (PMID: 16609065), and it was also found that STEAP1 over expression does not impact iron uptake and reduction in Ewing's sarcoma (cancer) cells (PMID: 22080479). Therefore, the physiological relevance of metal ion reduction by STEAP1 remains controversial. The current work establishes an electron transfer chain between STEAP1 and cytochrome b5 reductase in vitro with purified proteins. However, the conformation of this metalloreductase activity of the STEAP1-cytochrome b5 complex will be required in a cell line to prove that the two proteins indeed form a physiological relevant complex and that the results are not just an in vitro artefact from using high concentrations of purified proteins.

The work will be interesting for scientists working within the STEAP field. However, some of the presented data are redundant with previous findings, moderating the study's impact. For instance, the new structural insights into STEAP2 are limited because the structure is virtually identical to the published structures of STEAP4 and STEAP1 (PMIDs: 30337524, 32409586), which is not surprising because of the high sequence similarity between the STEAP isoforms. Moreover, the authors provide experimental evidence to prove the previous hypothesis (PMID: 30337524) that the flavin in STEAP proteins becomes diffusible, but the molecular arrangement of a STEAP protein, in which the flavin interacts with NADPH, remains unknown. Based on the manuscript title, I would also expect the in-depth characterization of STEAP1/STEAP2 hetero trimers (first identified by the authors), but this is only briefly mentioned. When taken together, this study by Chen et al. strengthens and supports previously published biochemical and structural data on STEAP proteins, without revealing many prominent conceptual advances.

Reviewer #3 (Public Review):

The authors present the mechanism, validation, and modular application of LOVtag, a light-responsive protein degradation tag that is processed by the native degradosome of Escherichia coli. Upon exposure to blue light, the c-terminal alpha helix unfolds, essentially marking the protein for degradation. The authors demonstrate the engineered tag is modular across multiple complex regulatory systems, which shows its potential widespread use throughout the synthetic biology field. The step-by-step rational design of identifying the protein that was most dark-stabilized as well as most light-responsive for degradation, was useful in terms of understanding the key components of this system. The most compelling data shows that the engineered LOVTag can be fused to multiple proteins and achieve light-based degradation, without affecting the original function of the fused protein; however, results are not benchmarked against similar degradation tagging and optogenetic control constructs. Creating fusion proteins that do not alter either of the original functions, is often difficult to achieve, and the novelty of this should be expanded upon to drive further impact.

Reviewer #3 (Public Review):

The authors show that ILC2s seem to be important during pregnancy to achieve an optimal fetal growth. This is an important finding to the field and provides ILC2s with new roles distinct from parasite protection and allergy inducers. However, the fetal weight restriction phenotype does not seem very striking. Moreover, the mechanism by which ILC2s promote homeostasis in pregnancy are not well shown. The data shown in Figure 3 is overall a bit confusing and does not lead the reader to the conclusions stated in the text. Figure 4 conclusions are not very informative. The authors also show that ILC2s are protective to fetal loss during LPS infection. Again, the means by which ILC2s could be doing so are not well presented and the supporting data not fully convincing. Throughout the manuscript, the authors present quantitative data as fold change, expressing data in fold change is not as clear as showing actual numbers of cells for each group. Moreover, they should show the flow cytometry plots and gating strategy for all their FACS analysis.

Reviewer #3 (Public Review):

The manuscript by Guss et al. characterizes an extracellular matrix protein, Perlecan (trol), in maintaining axon and synapse stability in motor neurons through its function in maintaining the neural lamella's integrity in Drosophila. Using a combination of immunostaining and protein labeling with fluorescent tags, the authors find that perlecan localizes to the neural lamella. When perlecan is deleted, the authors identify a synapse retraction phenotype as the subsequent result of axon damage. They further suggest that this axon instability is the result of loss of perlecan causing a disruption in the neural lamella, due to the mislocalization of neural lamella protein, Collagen IV (Vkg). Moreover, they find that perlecan acts independently of previously characterized interactions with the wnt signaling and Wallerian degradation pathways, however important controls for these negative results are lacking.

The manuscript offers an interesting and important role for perlecan in motor neuron axon maintenance. However, the experiments attempting to elucidate the mechanism of action of this protein require further validation and clarification.

Reviewer #3 (Public Review):

This study documents the dynamics of Merkel cells and their axonal afferents during the hair growth cycle. Methodologically, the study is impressive-using two transgenic lines and repeated 2-photon imaging allowed the researchers to monitor Merkel cells and afferent axons over the course of weeks. These exciting tools and methods will enable future studies of these cell interactions. The manuscript is well written, the figures are clear and appealing, the statistical analyses are rigorous and appropriate, and potentially confounding issues (e.g., damage caused by 2-photon imaging or hair removal) were thoughtfully considered and controlled for. The clear and rigorously analyzed findings make the conclusions well justified. The impact of this study could be enhanced with further experiments that provide more functional characterization of boutons and kylikes, and that characterize axonal dynamics in Atoh mutants lacking Merkel cells.

Reviewer #3 (Public Review):

In this manuscript, the authors characterized the clonal composition in the medaka pallium and found the dorsal pallium region to be a compartment constituting repeatedly identifiable clonal units. By performing ATAC-seq on the clonal units as well as RNA-seq on different subregions of the pallium, the dorsal pallium was further identified as a unique region with open chromatin regions with regulatory elements enriched for synapse-related genes. Further experimental and bioinformatic evidence supports the region's putative function of synapse generation, with similar TF-binding motifs to homologous brain regions in human. Although the "uniqueness" of the dorsal pallium might be a coincidence of the timing of clonal tracing, the conclusions in the manuscript are largely supported by experimental evidence. The study showcases an elegant model of how anatomical, molecular, and functional diversity arises in a previously under-characterized brain region. The enriched genes in Dd2 are an interesting candidate for future investigation, and the function of the dorsal pallium of teleost fish is of general interest for studies of brain evolution.

Reviewer #3 (Public Review):

This is a highly interesting paper that comprehensively investigates the electrophysiological properties of granule cells in the dentate gyrus at different developmental stages. Using state-of-the-art in vitro electrophysiological techniques, the authors record granule cell responses to fluctuating current injections to study how they encode stimuli. The authors find that while immature granule cells produce less reliable stimulus responses and worse stimulus representations than mature cells (8wks and older), cell populations containing neurons of mixed ages improve overall stimulus reconstruction. These data suggest that the cellular diversity contributed by immature granule cells could be beneficial for transmitting distinct properties of stimuli with rich temporal structure, potentially improving the cellular process of pattern separation.<br /> Major strengths of the paper lie in the precise age determination of immature neurons in Ascl1-CreERT2-Tom mice, recordings of immature neurons, which are rare in in vivo and in vitro studies, precise control over cell-intrinsic properties by blocking excitatory and inhibitory inputs in vitro, and characterization of encoding properties using a spike response model (SRM).

The conclusions drawn are supported by the data, and the results are likely of great interest to a specialist community of hippocampal electrophysiologists.

Reviewer #3 (Public Review):

The manuscript by JY Toshima et al. is an excellent and important study that demonstrates very clearly the existence of an endosomal compartment in yeast, distinct from the trans-Golgi network, to which endocytic vesicles fuse upon internalization. They show that this compartment is enriched in the SNARE protein Tlg2, a yeast homologue of syntaxin, and is segregated from the Golgi-localized Sec7-containing compartment, indicating that the organization of the endocytic system in yeast is similar to that of animal cells. Furthermore, they demonstrate the trafficking machinery required for maturation of this compartment, and that it is also a station on the pathway back to the plasma membrane. Because there have been conflicting reports in the literature as to the existence of an endosomal compartment in yeast distinct from the trans-Golgi network, this paper is of great importance for the cell biology community.

Major strengths of this study are the cutting-edge imaging technology used, and the careful, quantitative analyses carried out. The authors use a super-resolution live cell imaging approach that allows them to discriminate to a high resolution different compartments and membrane domains of highly dynamic yeast organelles, and to follow an internalizing cargo over time. With their manuscript, they have provided a full set of movies, along with quantifications, to support their conclusions.

The authors use fluorescent-protein-labelled endocytic cargo (alpha-factor) and florescent-protein-labelled compartment markers, assaying them in high resolution and super-resolution live cell imaging microscopy systems. In this way, they are able to follow trafficking of cargo through compartments in real time. The authors first demonstrate that the alpha-factor cargo substantially colocalized with the SNARE protein Tlg2, a marker of early endosomes, but very little with Sec7. They also show that Tlg2 marks a sub-compartment distinct from the Sec7 compartment, but adjacent to it. Furthermore, they demonstrate using super-resolution microscopy and triple color 4D imaging that endocytosed alpha-factor cargo structures make contact with the Tlg2 compartment, adjacent to the Sec7 compartment, then disappear, supporting the conclusion that endocytic vesicles first fuse with the Tlg2 compartment. Next the authors show that alpha factor is transported from the Tlg2 compartment to the Vps21 compartment, a process that requires the GGA adaptors Gga1 and Gga2. Finally, the authors show that recycling of the endocytic R-SNARE Snc1 also occurs by passage through the Tlg2 compartment.

The use of mutants that affect different stages of endosomal trafficking is a strength of the manuscript, as it allows elucidation of the mechanism of transport through successive compartments. Importantly, using a gga1-delta gga2-delta mutant, the authors demonstrate convincingly that the GGA adaptors Gga1 and Gga2 are required for alpha factor transport from the Tlg2 compartment to the Vps21 compartment.

Throughout this study, the authors use fluorescent protein-labelled cargo and compartment markers (EGFP, mCherry, iRFP), but don't explicitly state to what extent these fusion proteins are functional compared to the endogenous proteins. They could cite previous publications or their results describing the functionality of the fusion proteins used.

Reviewer #3 (Public Review):

One key finding of this work is the identification of Xanthomonas oryzae pv. oryzae (Xoo) strains in Africa, based on their genomes sequence and their TALE repertoires, have high similarity with Asian strains. Asian Xoo strains typically overcome NLR-mediated recognition of TALEs in rice by so-called iTALEs. Moreover, some Asian strains contain a TALE resembling PthXo1, a TALE protein that was shown to overcome xa5 resistance.

The authors now found that some of the newly identified African strains have iTALEs and PthXo1-like TALEs. Such newly evolved African strains were found to be fully virulent on the African rice elite variety Komboka, which is resistant to a broad panel of African Xoo strains.

Previous studies have shown that TALEs bind to effector binding elements (EBEs) present in promoters of rice SWEET genes to promote disease. Work from the lab of the authors and other labs has shown that TALEs can no longer promote the disease if matching EBEs are changed or deleted by CRISPR or TALEN-mediated mutagenesis. In fact, pioneering work by Bing Yang, one of the authors of this article published about ten years ago a Nature Biotechnology article where he showed that rice plants with mutated EBEs are resistant to Xoo. Recently, a combined effort of the Yang and Frommer labs resulted in two further Nature Biotechnology publications (2019), in which they described along with other useful tools rice lines where multiple EBEs were mutagenized in parallel and that provide broad spectrum resistance.

The work under review describes now CRISPR mutagenesis of an African elite cultivar resulting in a line that mediates resistance to Asian and newly evolved African strains.

Overall, the work is technically sound. Yet, the approach that has been described - mutagenesis of multiple EBEs - has been used before and is a routine procedure for labs that are focused on such undertakings. While such approaches do not provide new insights for fundamental research, they nevertheless are certainly important and useful in translational research, as demonstrated here.

Reviewer #3 (Public Review):

Huff et.al further characterise the anatomy and function of a population of excitatory medullary neurons, the Post-inspiratory Complex (PiCo), which they first described in 2016 as the origin of the laryngeal adduction that occurs in the post-inspiratory phase of quiet breathing. They propose an additional role for the glutamatergic and cholinergic PiCo interneurons in coordinating swallowing and protective airway reflexes with breathing, a critical function of the central respiratory apparatus, the neural mechanics of which have remained enigmatic. Using single allelic and intersectional allelic recombinase transgenic approaches, Huff et al. selectively excited choline acetyltransferase (ChAT) and vesicular glutamate transporter-2 (VGluT2) expressing neurons in the intermediate reticular nucleus of anesthetised mice using an optogenetic approach, evoking a stereotyped swallowing motor pattern (indistinguishable from a water-induced swallow) during the early phase of the breathing cycle (within the first 10% of the cycle) or tonic laryngeal adduction (which tracked tetanically with stimulus length) during the later phase of the breathing cycle (after 70% of the cycle).

They further refine the anatomical demarcation of the PiCo using a combination of ChAT immunohistochemistry and an intersectional transgenic strategy by which fluorescent reporter expression (tdTomato) is regulated by a combinatorial flippase and cre recombinase-dependent cassette in triple allelic mice (Vglut2-ires2-FLPO; ChAT-ires-cre; Ai65).

Lastly, they demonstrate that the PiCo is anatomically positioned to influence the induction of swallowing through a series of neuroanatomical experiments in which the retrograde tracer Cholera Toxin B (CTB) was transported from the proposed location of the putative swallowing pattern generator within the caudal nucleus of the solitary tract (NTS) to glutamatergic ChAT neurons located within the PiCo.

Methods and Results<br /> The experimental approach is appropriate and at the cutting edge for the field: advanced neuroscience techniques for neuronal stimulation (virally driven opsin expression within a genetically intersecting subset of neurons) applied within a sophisticated in vivo preparation in the anaesthetized mouse with electrophysiological recordings from functionally discrete respiratory and swallowing muscles. This approach permits selective stimulation of target cell types and simultaneous assessment of gain-of-function on multiple respiratory and swallowing outputs. This intersectional method ensures PiCo activation occurs in isolation from surrounding glutamatergic IRt interneurons, which serve a diverse range of homeostatic and locomotor functions, and immediately adjacent cholinergic laryngeal motor neurons within the nucleus ambiguous (seen by some as a limitation of the original study that first described the PiCo and its roll in post-I rhythm generation Anderson et al., 2016 Nature 536, 76-80). These experiments are technically demanding and have been expertly performed.

The supplemental tracing experiments are of a lower standard. CTB is a robust retrograde tracer with some inherent limitations, paramount of which is the inadvertent labelling of neurons whose axons pass through the site of tracer deposition, commonly leading to false positives. In the context of labelling promiscuity by CTB, the small number of PiCo neurons labelled from the NTS (maybe 5 or 6 at most in an optical plane that features 20 or more PiCo neurons) is a concern. Even assuming that only a small subset of PiCo neurons makes this connection with the presumed swallowing CPG within the cNTS, interpretation is not helped by the low contrast of the tracer labelling (relative to the background) and the poor quality of the image itself. The connection the authors are trying to demonstrate between PiCo and the cNTS could be solidified using anterograde tracing data the authors should already have at hand (i.e. EYFP labelling driven by the con-fon AAV vectors from PiCo neurons (shown in Fig5), which should robustly label any projections to the cNTS).

The retrograde labelling from laryngeal muscles seems unnecessary: the laryngeal motor pool is well established (within the nAmb and ventral medulla), and it would be unprecedented for a population of glutamatergic neurons to form direct connections with muscles (beyond the sensory pool).

The authors support their claim that PiCo neurons gate laryngeal activity with breathing through the demonstration that selective activation of glutamatergic and cholinergic PiCo neurons is sufficient to drive oral/pharyngeal/laryngeal motor responses under anaesthesia and that such responses are qualitatively shaped by the phase of the respiratory cycle within which stimulation occurs. Optical stimulation within the first 10% of the respiratory cycle was sufficient to evoke a complete, stereotyped swallow that reset the breathing cycle, while stimuli within the later 70% of the cycle, evoked discharge of the laryngeal muscles in a stimulus length-dependent manner. Induced swallows were qualitatively indistinguishable from naturalistic swallow induced by the introduction of water into the oral cavity. The authors note that a detailed interpretation of induced laryngeal activity is probably beyond the technical limits of their recordings, but they speculate that this activity may represent the laryngeal adductor reflex. This seems like a reasonable conclusion.

The authors propose a model whereby the PiCo impinges upon the swallowing CPG (itself a poorly resolved structure) to explain their physiological data. The anatomical data presented in this study (retrograde transport of CTB from cNTS to PiCo) are insufficient to support this claim. As suggested above, complementary, high-quality, anterograde tracing data demonstrating connectivity between these structures as well as other brain regions would help to support this claim and broaden the impact of the study.

This study proposes that the PiCo in addition to serving as the site of generation of the post-I rhythm also gates swallowing and respiration. The scope of the study is small, and limited to the subfields of swallowing and respiratory neuroscience, however, this is an important basic biological question within these fields. The basic biological mechanisms that link these two behaviors, breathing and swallowing, are elusive and are critical in understanding how the brain achieves robust regulation of motor patterning of the aerodigestive tract, a mechanism that prevents aspiration of food and drink during ingestion. This study pushes the PiCo as a key candidate and supports this claim with solid functional data. A more comprehensive study demonstrating the necessity of the PiCo for swallow/breathing coordination through loss of function experiments (inhibitory optogenetics applied in the same transgenic context) along with robust connectivity data would solidify this claim.

Reviewer #3 (Public Review):

The relevance of Y90 phosphorylation as a regulatory mechanism is shown by the comparison of Src kinase activity, transforming potential, cell invasiveness, and lateral diffusion in membranes. Mechanistically, Y90E mutation affects the opening of the structure, estimated from FRET experiments, and the phosphorylation status of the three main downstream signaling pathways.

The effect of the Y90E mutation is very clear, although its description as "phosphomimicking" is, in my opinion, not accurate. Glutamic acid has a negative charge but is significantly different from phosphotyrosine. Maybe other polar mutants (lysine, glutamine...) would have a similar destabilizing effect on hydrophobic interactions. Erpel 1995 showed some effects of the Y90A mutants.

The effect of tyrosine phosphorylation on the SH3 domain of proteins having the conserved ALYDY motif supports the proposed role, although the evidence for in vivo Y90 phosphorylation in c-Src is scarce. The possible autophosphorylation of Y90 is suggested but the evidence is not very strong and does not rule out other kinases, especially some downstream of Src itself -as already suggested by the authors.

The authors suggest that the perturbation of Y90 reduces the interaction with the connector domain. This is a reasonable explanation, supported by the opening of the structure, but additional effects may exist: The SH3 hydrophobic region including Y90 is also the binding site for the myristoyl group (Le Roux et al. iScience, 12, 194-203) and mutations in the SH3 RT loop significantly affected lipid binding. This could contribute to the observed reduced diffusion in the lipid bilayer.

Reviewer #3 (Public Review):

By using chemogenetic manipulations of direct pathway neurons in the dorsomedial part of the striatum (DMS) of anesthetized mice combined with fMRI, Markicevic et al explore changes in BOLD dynamics at local (striatum) and macro-scale (brain-wide) levels. The article is appropriately written, and the main findings are well organized and presented in 7 figures. Figures 1 and 2 schematize the techniques and document the motor effects of chemogenetic manipulations. Figures 3-7 describe neural changes induced by these manipulations. The main strength of this work is the level of specificity of the chemogenetic manipulations, which combined with brain-wide functional exploration, provide a very useful map of the consequences of activating a specific striatal subpopulation. In my opinion, the main weakness of this work is that the results are under-discussed and not appropriately contextualized in the current views of the functions of the basal ganglia. My main concerns are exposed in the following lines:

1. In the first finding the authors show that D1 activation/inactivation produces reliable changes in the infected region (DMS), but most importantly, also produced changes in adjacent areas, suggesting intra-striatal communication. The way the data is presented and discussed appears to be confirmatory of what has been previously described with electrophysiological recordings. In my opinion, the most important part of this section would be to fully describe the differences between activation and inactivation groups. Is interesting that opposite manipulations of D1 receptors produced very similar maps of discrimination (Fig. 3). Therefore, it would be necessary to discuss the meaning of obtaining similar classification accuracy indices with opposite manipulations. Perhaps, the use of SVM classifiers can be complemented with other analytical techniques to further disentangle the consequences of manipulating intrastriatal D1 receptors.

2. The second finding (Fig. 4) indicates that thalamic regions forming "closed loops" with the striatum were more affected by chemogenetic manipulations. We knew from anatomical studies that the BG are part of anatomically segregated cortico-BG-thalamic loops. Therefore, it would be expected that these anatomical boundaries would somehow limit functional connectivity maps. Here again, I consider that the manuscript would be improved with further analysis or discussion. For example, it would be interesting to perform further analysis relating the previous section (local striatal connectivity) with this one. In this section, several thalamic nuclei presented higher levels of classification accuracy, but in the previous section, the authors showed that DMS manipulation also produced the same effects in different intrastriatal regions. Therefore, it is not possible to know if the thalamic effects are related to the manipulation of D1 in the DMS or its adjacent regions.

3. In the third finding (Fig. 5) the authors show that the most "sensitive" cortical regions to the manipulations were classified as "unimodal". This is an interesting result; however, it would be necessary to at least provide further discussion on its potential meaning. It is important to consider that the cortical regions with significant changes, for example, primary sensorimotor cortices, mainly target the dorsolateral, not the dorsomedial striatum. In this context, would it be possible to establish a new analysis to characterize potential correlations between cortical regions and striatal subregions?

4. The fourth finding (Figure 6) is that thalamic but not cortical regions presented low-frequency fluctuations. What is the meaning of an increase in slow fluctuations? Why did D1 activation (and not inactivation) induced this effect? Are striatal sub-regions also presenting these slow fluctuations?

5. In the last finding (Figure 7), the authors explored potential changes in functional connectivity (FC) between the striatum and cortical and subcortical regions. Contrary to the results obtained with the SVM-based analytical tool, FC analysis revealed that D1 activation and inactivation produced opposite results, while D1 activation decreased FC in several cortical and subcortical regions, D1 inactivation increased it. While this set of data is clearly described, the implications of these relationships could be further discussed. For example, how do the authors explain that FC with SSp was not significantly changed with this analytical method, but was one of the most affected regions with the Balanced Classification Accuracy method?

6. Finally, there is no section in the discussion where the behavioral effects observed in figure 2 are contextualized in the massive set of BOLD results presented in the following sections.

Reviewer #3 (Public Review):

The manuscript by Bravo-Plaza et al. identifies and characterizes new mutations (E6K and G540S) in the Uso1 globular head domain that suppress the loss of function mutations in Rab1. Further experiments show that the combined E6K/G540S mutant restores apparent Golgi-localization of Uso1 in Rab1 deficient cells, that this mutant preferentially co-purifies with ER/Golgi SNARE proteins, that monomeric E6K/G540S globular head-domain binds more avidly to purified Bos1 SNARE protein than wild type head-domain, and that overexpression of E6K/G540S or wild type head-domain alone is sufficient for viability. Based on these findings the authors propose that long-distance tethering by Uso1 is dispensable and that the head domain provides an essential function to directly regulate ER/Golgi SNARE-dependent membrane fusion.

Strengths of the study are that an unbiased screen was used to identify new Rab1 suppresser mutations that land in the Uso1 globular head domain. Characterization of these suppressor mutants reveals that SNARE binding activity of Uso1 resides in the head domain and that elevated expression of the Uso1 head domain is sufficient for viability. Imaging experiments document the localization and dynamics of Uso1 on Golgi compartments and biochemical studies show the properties and binding activity of Uso1 domain mutants. These are new findings and the conclusion that monomeric globular head-domain interacts with specific SNAREs to maintain viability is justified.

Weaknesses are that it is well documented that both Rab1 and Uso1 activity can be bypassed by activation of ER/Golgi SNARE machinery either by overexpression of SNARE proteins or by the single copy SLY1-20 allele. Therefore, it was not surprising that tethering by the Uso1 coiled-coil domain is dispensable. The proposal that the E6K mutation in the head domain of Uso1 promotes membrane targeting was not well supported by experimental evidence. And while the AlphaFold modeling of Uso1 with the ER/Golgi fusion machinery was intriguing, the proposed molecular models remain speculative until further tested.

Reviewer #3 (Public Review):

The strongest aspects of this study are the structural analysis of the 90 residue KER domain. This is an important advance, discovering a founding member of a novel class of DNA binding motifs, termed a SAH-DBD (single alpha helix-DNA binding domain). Interestingly, they define a subregion of KER (termed "middle-A", residues 155-204 of Cac1) that has nearly the same DNA binding affinity and confers similar in vivo phenotypes as the full KER domain.

This study also shows that the biological role of KER partially overlaps compensatory factors in vivo, both within the same Cac1 protein subunit (e.g. the WHD domain) and also with other proteins acting in parallel (e.g. Rtt106). That is, the presence of either WHD or Rtt106 renders the drug-resistance and silencing assays employed here insensitive to loss of the KER domain.

However, the drug resistance and gene silencing phenotypes are inherently indirect measures of the most important claim of this work, that KER is a molecular ruler for DNA for the purpose of ensuring sufficiently large templates deposition of histone H3/H4 cargoes. Therefore, this study would be of greater impact if the authors more directly tested this measurement idea in assays that directly assess histone deposition. There are multiple options. Since the authors have in hand recombinant wild-type and mutant CAF-1 complexes, one could examine the number and/or spacing of nucleosomes formed during in vitro deposition reactions. Complementary in vivo experiments using the authors' existing mutant strains could be based on the finding that CAF-1 is particularly important for histone deposition onto nascent Okazaki fragments during DNA replication (Smith and Whitehouse, 2012; pmid: 22419157), and that the spacing pattern of nucleosomes on this DNA is greatly perturbed in cac1-delete cells.

Reviewer #3 (Public Review):

This study describes a descending circuit that can modulate pain perception in the drosophila larvae. While descending inhibition is a major component of mammalian pain perception, it is not known if a similar circuit design exists in fruit flies. Overall the authors use clean logic to establish a role for DSK and its receptor in regulating nociception. The following concerns still stand:

1) It's not completely clear why the authors are staining animals with an FLRFa antibody. Can the authors stain WT and DSK KO animals with a DSK antibody? Also, can the authors show in supplemental what antigen the FLRFa antibody was raised against, and what part of that peptide sequence is retained in the DSK sequence? This overall seems like a weakness in the study that could be improved on in some way by using DSK-specific tools.

2) What is the phenotype of DSK-Gal4 x UAS-TET animals? They should be hyper-reactive. If it's lethal maybe try an inducible approach.

3) Figure 9. This was not totally clear, but I think the authors were evaluating spontaneous (i.e. TRPA1-driven) rolling at 35C. The critical question is "Does activating DSK-expressing neurons suppress acute heat nociception?" and this hasn't really been addressed. The inclusion of PPK Gal4 + DSK Gal4 in the same animal clouds the overall conclusions the reader can draw. The essential experiment is to express UAS-dTRPA1 in DSK-Gal4 or GORO-Gal4 cells, heat the animals to ~29C, and then test latency to a thermal heat probe (over a range of sub and noxious temperatures). Basically, prove the model in Figure 10 showing ectopic activation or inhibition for each major step, then test heat probe responses.

4) It would also then be interesting to see how strong the descending inhibition circuit is in the context of UV burn. If this is a real descending circuit, it should presumably be able to override sensitization after injury.

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

Overall, the manuscript provides new insights into the beneficial effects of the type I PRMT inhibitor MS023 for skeletal muscle regeneration. The description of the MS023-induced transcriptional and metabolic changes in MuSC is interesting and the effects on MuSC transplantation and muscle strength are stunning. However, I have the following comments and concerns:

* Control experiments with the TP-064 inhibitor (previously shown to be specific for CARM1/PRMT4) were not done for the transplantation and muscle strength experiments, which is a clear shortcoming in my view. Since MS023 is a non-selective inhibitor of type I PRMTs with comparable IC50 values for PRMT1 and PRMT4 (CARM1), and lower IC50 values for PRMT6 and PRMT8, it is still not clear whether the enhanced transplantation efficiency and the increased muscle strength is indeed only caused by inhibition of PRMT1. The authors justify their statements by pointing out that gene expression of Prmt1 is highest among the type I PRMTs in MuSCs, which is a rather poor argument, as seen by the strong effects caused by the inactivation of PRMT4.

* Clustering of the M1-M5 subpopulations. I expressed my concern about the separation of the subclusters, which appear more or less in the same cloud. The authors answered that each cluster has some genes, which are only expressed in the respective cluster. I do not doubt this observation but apparently, the transcriptional differences are minor, otherwise one would have seen a much better separation of the subpopulations.

* The authors have not done additional experiments but simply toned-down the statements about the relevance of the proposed "metabolic reprogramming" of MuSC by the type I PRMT inhibitor MS023, which was a major conclusion in the original submission. Again, the changes in the expression of metabolically relevant genes upon MS023 treatment are interesting and should be analyzed in respect to causality. It is not a solution to more or less disabandon the original hypothesis by changing the wording.

* I specifically asked the authors to check whether the dramatic six-fold increase of MuSC engraftment after MS023 treatment really goes along with the incorporation of transplanted MuSC into the MuSC niche, raising concerns that a huge share of the transplanted cells may linger around in the interstitium. It should be very easy to identify and quantify transplanted MuSC outside and inside the basal lamina. Instead of doing the requested experiment, the authors argue about suppression of endogenous MuSC competition by irradiation, at the same time admitting that several GFP-negative fibers have formed.

* I expressed my doubts that a 3-day treatment with MS023 is sufficient to dramatically enhance muscle function in mdx mice via "improvement" of the MuSC population, as reported by the authors, even 30 days after administration of MS023. It seems much more likely that MS023 exerts additional effects that are responsible for the dramatic improvement of muscle function in mdx mice. I maintain my view that this needs to be interrogated more carefully since the improvement of muscle function of dystrophic mice is a central point of the study. It has to be made clear whether this is really due to "improved" functions of MuSC. Many other processes might be involved or responsible for the effect (e.g. impact on inflammation?).

Reviewer #3 (Public Review):

The authors report that the secretion of endosome-derived exosomes is enhanced by a calcium-dependent response to damage to the plasma membrane of cells. The authors present convincing evidence that in response to the influx of calcium that follows damage to the plasma membrane annexin A6 is recruited to multivesicular bodies (MVBs) and likely serves to tether the MVBs to the plasma membrane causing a concomitant release of exosomes. Although it is not directly addressed in the Discussion, I am left with the impression that the authors are hinting that exosome secretion is more a byproduct of plasma membrane repair rather than a means of intercellular communication. In other words, the cell needs the membrane material from the MVB to patch and repair holes in the plasma membrane and exosome ejection from the cell is a secondary (perhaps even irrelevant) consequence. Obviously, these two possibilities are not mutually exclusive. The authors are encouraged to speculate about which possibility they favor and how their findings might change our understanding of the cell biology of exosome secretion.

Reviewer #3 (Public Review):

In this manuscript, Li et al. examine how the expression of the chemokine receptor CCR4 impacts the movement of thymocytes within the thymus. It is currently known that the chemokine receptor CCR7 is important for developing thymocytes to migrate from the cortical region into the medullary region and CCR7 expression is therefore often used to define medullary localization. This is important because key developmental outcomes, like enforcing tolerance to self-antigens amongst others, occur in the medullary environment. The authors demonstrate that the chemokine receptor CCR4 is induced on thymocytes prior to expression of CCR7 and thymocytes exhibit responsiveness to CCR4 ligands earlier in development. Using elegant live confocal microscopy experiments, the authors demonstrate that CCR4 expression is important for the entry and accumulation of specific thymocyte subsets while CCR7 expression is needed for the accumulation of more mature thymocyte subsets. The use of cells deficient in both CCR4 and CCR7 and competitive migration/accumulation experiments provide strong support for this conclusion. The elimination of CCR4 expression results in decreases in apoptosis of thymocyte subsets that have been signalled through their antigen receptor and are responsive to CCR4 ligands. As expected, more mature thymocyte subsets show decreased apoptosis when CCR7 is absent. Distinct antigen-presenting cells in the thymus express CCR4 ligands supporting a model where CCR4 expressing thymocytes can interact with thymic antigen-presenting cells for induction of apoptosis. The absence of CCR4 results in an increase in peripheral T cells that can respond to self-antigens presented by LPS-activated antigen-presenting cells providing further support for the model. Collectively, the manuscript convincingly demonstrates a previously unappreciated role for CCR4 in directing a subset of thymocytes to the medulla.

Strengths:

Relevant model systems and elegant experimental techniques are used throughout the manuscript. The experiments are extensively replicated resulting in robust and convincing data sets. These findings represent an important conceptual advance in our understanding of the processes and cellular regulation of T cell development in the thymus.

Weaknesses:

Evidence demonstrating a direct interaction between CCR4 expressing thymocytes and CCR4-ligand expressing antigen-presenting cells is lacking. Furthermore, increased self-reactivity in the absence of CCR4 is measured using mature peripheral CD4 T cells, but altered self-reactivity of thymocytes is not evaluated similarly.

Reviewer #3 (Public Review):

The authors put together a rigorous study to model the impact of HPV vaccine programme disruptions on cervical cancer incidence and meeting WHO elimination goals in a low-income country - using India as an example. The study explores possible scenarios by varying HPV vaccination strategies for 10-year-old children between a) increasing vaccine coverage in a girls-only vaccination programme and b) vaccinating boys in addition to girls (i.e a gender-neutral vaccination programme).

The main strength of this study is the strength of the modelling methodology in helping to make predictions and in contingency planning. The study methodology is rigorous and uses models that have been validated in other settings. The study employs a high level of detail in calibrating and adapting the model to the Indian context despite poor data availability. The detailed methodology allows future studies to employ the model and techniques with locally-contextualised parameters to study the potential impact of HPV vaccine programme disruptions in other countries.

The work in this field can begin to help lower-income countries explore varying HPV vaccination strategies to reduce cervical cancer incidence, keeping in mind the potential for future supply chains or other related disruptions. However, the scenarios could be better sculpted to model potentially realistic scenarios to guide policymakers to make decisions in situations with limited vaccine supplies - in other words comparing scenario alternatives based on a fixed number of vaccines being available. Using comparative alternatives will help policymakers grapple with the decisions that need to be made regarding planning national HPV vaccination programmes. The results could afford to provide readers with a clearer measure of vaccine strategy 'resilience'.

In all, the authors are able to successfully explore the potential impact of varying HPV vaccination strategies on cervical cancer cases prevented in the context of vaccine disruptions, and make valid conclusions. The results produced are rich in information and are worthy of deeper discussion.

Reviewer #3 (Public Review):

Using the zebrafish model system, this manuscript assessed the roles of Rif1 protein in replication timing control and transcription during early development, and successfully demonstrated the differential impact of Rif1 protein in replication timing control and transcription. Moreover, the comprehensive assessments of the impacts of mutating Rif1 on animal development (including animal survival and sexual development) were assessed. Although there are works that examined Rif1's implications in replication timing and transcription separately, this work is unique in assessing all these points at once.

The strength of this manuscript is the genomic analyses of replication timing and transcription being combined in a single model system. Consequently, this manuscript clearly demonstrates the differential impact of Rif1 in these processes during zebrafish development.

The weakness of this manuscript is, as the authors comment in the Discussion, analyses of replication timing and transcription were performed using bulk embryos. There is a possibility that tissue-specific changes could have been masked. Tissue-specific or single-cell analysis in the future will fill the gap in the knowledge.

Some of the findings presented in this manuscript are consistent with previous findings using different models such as Drosophila and mice, whereas other findings do not necessarily agree. I hope further studies will reveal more clearly what is common in these systems, and what is different.

Also, the suggestion that the Rif1 protein may be implicated in a function similar to Fanconi-Anemia genes/proteins is very intriguing.

Overall, the data presented in this manuscript sufficiently justify the authors' claims. Moreover, this manuscript provides interesting insights into Rif1's function, as well as how development could be controlled.

Reviewer #3 (Public Review):

Mullen et al present an important study describing how DHODH inhibition enhances efficacy of immune checkpoint blockade by increasing cell surface expression of MHC I in cancer cells. DHODH inhibitors have been used in the clinic for many years to treat patients with rheumatoid arthritis and there has been a growing interest in repurposing these inhibitors as anti-cancer drugs. In this manuscript, the Singh group build on their previous work defining combinatorial strategies with DHODH inhibitors to improve efficacy. The authors identify an increase in expression of genes involved in the antigen presentation pathway and MHC I after BQ treatment and they narrow the mechanism to be strictly pyrimidine and CDK9/P-TEFb dependent. The authors rationalize that increased MHC I expression induced by DHODH inhibition might favor efficacy of dual immune checkpoint blockade. This combinatorial treatment prolonged survival in an immunocompetent B16F10 melanoma model.

Previous studies have shown that DHODH inhibitors can increase expression of innate immunity-related genes but the role of DHODH and pyrimidine nucleotides in antigen presentation has not been previously reported. A strength of the manuscript is the use of multiple controls across a panel of cell lines to exclude off-target effects and to confirm that effects are exclusively dependent on pyrimidine depletion. Overall, the authors do a thorough characterization of the mechanism that mediates MHC I upregulation using multiple strategies. Furthermore, the in vivo studies provide solid evidence for combining DHODH inhibitors with immune checkpoint blockade.

However, despite the use of multiple cell lines, most experiments are only performed in one cell line, and it is hard to understand why particular gene sets, cell lines or time points are selected for each experiment. It would be beneficial to standardize experimental conditions and confirm the most relevant findings in multiple cell lines. The differential in vivo survival depending on dosing schedule is interesting. However, this section could be strengthened with a more thorough evaluation of the tumors at endpoint.

Overall, this is an interesting manuscript proposing a mechanistic link between pyrimidine depletion and MHC I expression and a novel therapeutic strategy combining DHODH inhibitors with dual checkpoint blockade. These results might be relevant for the clinical development of DHODH inhibitors in the treatment of solid tumors, a setting where these inhibitors have not shown optimal efficacy yet.

Reviewer #3 (Public Review):

Summary:

In their study the authors analyze the localization of multiple organelles and subcellular structure of blood stage malaria parasites with unprecedented detail. They use a 3D super-resolution imaging technique that has gained popularity in the protozoan field, ultrastructure expansion microscopy. Building on markers and labels established in the field they generate an appealing collection of images for all stages of the intraerythrocytic developmental stages of asexual blood stage parasites with some focus on nuclear division and cell segmentation stages.

Strengths:

The authors generated an impressive amount of imaging data that presents the most comprehensive analysis of ultrastructural organization of the parasite cell so far. This atlas can serve as a reference for researchers studying the cell biology of the intraerythrocytic development cycle. The authors achieve a nice catalogue of the reorganization of well-established markers, which together with the improved resolution allows them to highlight some novel observations and consolidate previous findings. They e.g. improve our understanding of organization, duplication and constitutive tethering of the malaria parasite centrosome to the plasma membrane. Further they provide some interesting observations on rhoptry biogenesis, cytostome morphology, and organelle fission during segmentation.

Weaknesses:

While the comprehensiveness of the study is its strength the authors do not present any novel markers, stainings, or imaging protocols. There is no fundamentally new mechanistic insight derived from this study although some earlier findings are consolidated by the higher spatial resolution.

In the following I want to comment on some major points.

Most importantly, in order to justify the authors claim to provide an "Atlas", I want to strongly suggest they share their raw 3D-imaging data (at least of the main figures) in a data repository. This would allow the readers to browse their structure of interest in 3D and significantly improve the impact of their study in the malaria cell biology field.

The organization of the manuscript can be improved. Aside some obvious modifications as citing the figures in the correct order (see also further comments and recommendations), I would maybe suggest one subsection and one figure per analyzed cellular structure/organelle (i.e. 13 sections). This would in my opinion improve readability and facilitate "browsing the atlas".

Considering the importance of reliability of the U-ExM protocol for this study the authors should provide some validation for the isotropic expansion of the sample e.g. by measuring one well defined cellular structure.

In the absence of time-resolved data and more in-depth mechanistic analysis the authors must down tone some of their conclusions specifically around mitochondrial membrane potential, supellicular microtubule depolymerization, and kinetics of the basal complex. More detailed suggestions for improvement are provided as further comments.

In conclusion the authors provide an exciting cell biological reference framework and new working hypotheses about the function of some subcellular structures, which are still largely enigmatic in the malaria parasite, and can be investigated in the future.

Reviewer #3 (Public Review):

The manuscript by Yang et al. investigated in mice how hypobaric hypoxia can modify the RBC clearance function of the spleen, a concept that is of interest. Via interpretation of their data, the authors proposed a model that hypoxia causes an increase in cellular iron levels, possibly in RPMs, leading to ferroptosis, and downregulates their erythrophagocytic capacity. However, most of the data is generated on total splenocytes/total spleen, and the conclusions are not always supported by the presented data. The model of the authors could be questioned by the paper by Youssef et al. (which the authors cite, but in an unclear context) that the ferroptosis in RPMs could be mediated by augmented erythrophagocytosis. As such, the loss of RPMs in vivo which is indeed clear in the histological section shown (and is a strong and interesting finding) can be not directly caused by hypoxia, but by enhanced RBC clearance. Such a possibility should be taken into account.

Major points:

1) The authors present data from total splenocytes and then relate the obtained data to RPMs, which are quantitatively a minor population in the spleen. Eg, labile iron is increased in the splenocytes upon HH, but the manuscript does not show that this occurs in the red pulp or RPMs. They also measure gene/protein expression changes in the total spleen and connect them to changes in macrophages, as indicated in the model Figure (Fig. 7). HO-1 and levels of Ferritin (L and H) can be attributed to the drop in RPMs in the spleen. Are any of these changes preserved cell-intrinsically in cultured macrophages? This should be shown to support the model (relates also to lines 487-88, where the authors again speculate that hypoxia decreases HO-1 which was not demonstrated). In the current stage, for example, we do not know if the labile iron increase in cultured cells and in the spleen in vivo upon hypoxia is the same phenomenon, and why labile iron is increased. To improve the manuscript, the authors should study specifically RPMs.

2) The paper uses flow cytometry, but how this method was applied is suboptimal: there are no gating strategies, no indication if single events were determined, and how cell viability was assessed, which are the parent populations when % of cells is shown on the graphs. How RBCs in the spleen could be analyzed without dedicated cell surface markers? A drop in splenic RPMs is presented as the key finding of the manuscript but Fig. 3M shows gating (suboptimal) for monocytes, not RPMs. RPMs are typically F4/80-high, CD11-low (again no gating strategy is shown for RPMs). Also, the authors used single-cell RNAseq to detect a drop in splenic macrophages upon HH, but they do not indicate in Fig. A-C which cluster of cells relates to macrophages. Cell clusters are not identified in these panels, hence the data is not interpretable).

3) The authors draw conclusions that are not supported by the data, some examples:

a) they cannot exclude eg the compensatory involvement of the liver in the RBCs clearance (the differences between HH sham and HH splenectomy is mild in Fig. 2 E, F and G)

b) splenomegaly is typically caused by increased extramedullary erythropoiesis, not RBC retention. Why do the authors support the second possibility? Related to this, why do the authors conclude that data in Fig. 4 G,H support the model of RBC retention? A significant drop in splenic RBCs (poorly gated) was observed at 7 days, between NN and HH groups, which could actually indicate increased RBC clearance capacity = less retention.

c) lines 452-54: there is no data for decreased phagocytosis in vivo, especially in the context of erythrophagocytosis. This should be done with stressed RBCs transfusion assays, very good examples, like from Youssef et al. or Threul et al. are available in the literature.

d) Line 475 - ferritinophagy was not shown in response to hypoxia by the manuscript, especially that NCOA4 is decreased, at least in the total spleen.

4) In a few cases, the authors show only representative dot plots or histograms, without quantification for n>1. In Fig. 4B the authors write about a significant decrease (although with n=1 no statistics could be applied here; of note, it is not clear what kind of samples were analyzed here). Another example is Fig. 6I. In this case, it is even more important as the data are conflicting the cited article and the new one: PMCID: PMC9908853 which shows that hypoxia stimulates efferocytosis. Sometimes the manuscript claim that some changes are observed, although they are not visible in representative figures (eg for M1 and M2 macrophages in Fig. 3M)

5) There are several unclear issues in methodology:

- what is the purity of primary RPMs in the culture? RPMs are quantitatively poorly represented in splenocyte single-cell suspensions. This reviewer is quite skeptical that the processing of splenocytes from approx 1 mm3 of tissue was sufficient to establish primary RPM cultures. The authors should prove that the cultured cells were indeed RPMs, not monocyte-derived macrophages or other splenic macrophage subtypes.<br /> - (around line 183) In the description of flow cytometry, there are several missing issues. In 1) it is unclear which type of samples were analyzed. In 2) it is not clear how splenocyte cell suspension was prepared.<br /> - In line 192: what does it mean: 'This step can be omitted from cell samples'?<br /> - 'TO method' is not commonly used anymore and hence it was unclear to this Reviewer. Reticulocytes should be analyzed with proper gating, using cell surface markers.<br /> - The description of 'phagocytosis of E. coli and RBCs' in the Methods section is unclear and incomplete. The Results section suggests that for the biotinylated RBCs, phagocytosis? or retention? Of RBCs was quantified in vivo, upon transfusion. However, the Methods section suggests either in vitro/ex vivo approach. It is vague what was indeed performed and how in detail. If RBC transfusion was done, this should be properly described. Of note, biotinylation of RBCs is typically done in vivo only, being a first step in RBC lifespan assay. The such assay is missing in the manuscript. Also, it is not clear if the detection of biotinylated RBCs was performed in permeablized cells (this would be required).

Reviewer #3 (Public Review):

The mechanistically diverse SLC26 transporters play a variety of physiological roles. The current manuscript establishes the SLC26A6 subtype as electroneutral chloride/bicarbonate exchanges and reports its high-resolution structure with chloride bound.

The claims in this manuscript are all well-supported by the data. Strengths include the comprehensive functional analysis of SLC26A6 in reconstituted liposome vesicles. The authors employ an array of assays, including chloride sensors, a newly developed fluorescent probe for bicarbonate, and assays to detect the electrogenicity of anion exchange. With this assortment of assays, the authors are able to establish the anion selectivity and stoichiometry of SLC26A6. Another strength of the manuscript is the functional comparison with SLC26A9, which permits fast, passive chloride transport, in order to benchmark the SLC26A6 activity. The structural analysis, including the assignment of the chloride binding site, is also convincing. The structural details and the chloride binding site are well-conserved among SLC26s. Finally, the authors present an interesting discussion comparing the structures of SLC26A5, SLC26A6, and SLC26A9, and how the details of the chloride binding site might influence the mechanistic distinctions between these similar transporters.

Reviewer #3 (Public Review):

Dux (or DUX4 in human) is a master transcription factor regulating early embryonic gene activation and has garnered much attention also for its involvement in reprogramming pluripotent embryonic stem cells to totipotent "2C-like" cells. The presented work starts with the recognition that DUX contains five conserved c. 100-amino acid carboxy-terminal repeats (called C1-C5) in the murine protein but not in that of other mammals (e.g. human DUX4). Using state-of-the-art techniques and cell models (BioID, Cut&Tag; rescue experiments and functional reporter assays in ESCs), the authors dissect the activity of each repeat, concluding that repeats C3 and C5 possess the strongest transactivation potential in synergy with a short C-terminal 14 AA acidic motif. In agreement with these findings, the authors find that full-length and active (C3) repeat containing Dux leads to increased chromatin accessibility and active histone mark (H3K9Ac) signals at genomic Dux binding sites. A further significant conclusion of this mutational analysis is the proposal that the weakly activating repeats C2 and C4 may function as attenuators of C3+C5-driven activity.

By next pulling down and identifying proteins bound to Dux (or its repeat-deleted derivatives) using BioID-LC/MS/MS, the authors find a significant number of interactors, notably chromatin remodellers (SMARCC1), a histone chaperone (CHAF1A/p150) and transcription factors previously (ZSCAN4D) implicated in embryonic gene activation.

The experiments are of high quality, with appropriate controls, thus providing a rich compendium of Dux interactors for future study. Indeed, a number of these (SMARCC1, SMCHD1, ZSCAN4) make biological sense, both for embryonic genome activation and for FSHD (SMCHD1).

A critical question raised by this study, however, concerns the function of the Dux repeats, apparently unique to mice. While it is possible, as the authors propose, that the weak activating C1, C2 C4 repeats may exert an attenuating function on activation (and thus may have been selected for under an "adaptationist" paradigm), it is also possible that they are simply the result of Jacobian evolutionary bricolage (tinkering) that happens to work in mice. The finding that Dux itself is not essential, in fact appears to be redundant (or cooperates with) the OBOX4 factor, in addition to the absence of these repeats in the DUX protein of all other mammals (as pointed out by the authors), might indeed argue for the second, perhaps less attractive possibility.

In summary, while the present work provides a valuable resource for future study of Dux and its interactors, it fails, however, to tell a compelling story that could link the obtained data together.

Reviewer #3 (Public Review):

Summary. This study sought to clarify the connection between inositol pyrophosphates (PP-IPs) and their regulation of phosphate homeostasis in the yeast Saccharomyces cerevisiae to answer the question of whether any of the PP-IPs (1-IP7, 5-IP7, and IP8) or only particular PP-IPs are involved in regulation. PP-IPs 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 PP-IPs throughout the study was critical to the interpretation of the data and the strength of the manuscript.

There were a few places in which a deeper discussion of the data was warranted: not discussed was an explanation for the decrease in the levels of all of the PP-IPs upon phosphate starvation, nor of the phosphate regulation of two target genes of Pho4 when Pho4 is constitutively nuclear.

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 a 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):

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:

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:

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):

The authors investigated the mechanism of transport of the GLUT5 sugar porter using enhanced sampling molecular dynamics simulations and biochemical analysis.<br /> The results suggest a possible general mechanism by which binding to a transported substrate stabilizes an occluded intermediate conformation between outward and inward-facing states of the alternating access conformational change of the protein, thereby enabling transport.

The authors also identified key elements of this transition, associated with residues involved in sugar binding, and through elegant biochemical experiments demonstrated how mutations of the latter affect the protein function, including mutations of gating residues that can recover the function of inactive mutants.<br /> The general computational methodology used by authors is appropriate for addressing these questions and compared to other techniques has the advantage of bringing forth an unbiased molecular description of the transport process. The results are overall qualitatively in line with the proposed conclusions.

A major weakness of this work is that, in contrast to previous studies with the same type of methodology, the authors do not report error analysis or careful statistical assessment of the computational results. Therefore, it is not clear whether the latter is solid or if they support the proposed conclusions. The computational data might generally benefit from an improved methodological design, such as including more degrees of freedom (or collective variables) in the description of the minimum free energy pathway, e.g. the salt-bridges.

Another weakness is that some of the details of the computational analysis are not reported, therefore other investigators would not know how to reproduce the results.

Once these issues are addressed, this work could potentially provide important insights into the mechanism of transport of sugar porters, which as suggested by other recent studies might also apply to other types of membrane transporters.

Reviewer #3 (Public Review):

In this work, Elbahnsi and colleagues use enhanced sampling MD simulation, to recapitulate step by step, the electromechanical coupling between VSD and the pore in HCN1 channels. Building on the available cryoEM structures of HCN1 with the VSD in resting and active state, the authors characterize by MD a subset of interactions that seemingly stabilize the open channel. This subset is, in turn, used in enhanced-sampling simulations to guide channel opening.<br /> The main findings are that S4 movement induces a rearrangement of the hydrophobic interaction at the level of S1- S4- and S5 interfaces. Occupancy of lipids seems therefore state-dependent and highlights their regulatory role in HCN gating.

The approach is rather innovative, and it apparently allows the reconstruction of the whole mechanism of gating, pushing the predictive power of MD simulation well beyond its actual temporal limitations. At the same time, the initial choice of interactions is crucial for this approach, because the result cannot differ from the inputs. And reading the paper it does not emerge clearly how the correctness of the reconstructed gating pathway can be verified, if not by functional validation.

Here are my comments on the main interactions that were used to feed the final MD simulation:

1. W281-N300: this interaction, previously identified and studied in SpH channels (Ramentol et al, 2020; Wu et al, 2021), has been elegantly confirmed in this paper. Its inclusion in the initial subset seems appropriate.<br /> In the other two cases, the choice of interactions requires further explanations and experimental validation.

2. D290 and K412: the validation of this interaction shown in Figure 3 and suppl Figure 1 is missing a control, i.e., the effect of the addition of Cd++ on the wt channel. Please add.

3. Modelling the open state of HCN1 pore (page 18), is done on the structure of the distantly related hERG rather than on the available open pore structure of HCN4. This choice is justified as follows by the authors:

a) "Available structures in the CNBD channel family for which representative structures have been solved in closed and open states".<br /> b) "The structural mechanism of pore gating (i.e. the ⍺ to 𝜋 helix occurring at the glycine657 hinge in hERG) observed in rEAG/hERG may be a conserved gating transition in the CNBD family of channels"<br /> I encourage the authors to consider the following:

a) The structure of hERG channel is not available in the closed/open configuration, indeed the comparison must be done with the closed configuration of the related channel rEAG. On the contrary, HCN4 is available in the closed/open configurations. Moreover, one of the open pore structures shows S4-S5-S6 in a very similar conformation to the lock open mutant (F186C/S264C) of HCN1 (Saponaro et al, 2021). With an available HCN4 open structure, forcing HCN1 to the open pore structure of hERG channel (which opens in depolarization and is not regulated by cAMP) seems not necessary.

To my knowledge, hERG is the only channel of the CNBD family for which the transition ⍺ to 𝜋 helix reported by the Authors, occurs in S6. It is not reported for other CNBD family members, in particular for the CNG channels mentioned by the Authors (Zheng et al., 2020; Xue et al., 2021, 2022). Task 4 (Zheng et al) does not show it. Its pore opens by a right-handed twist of S6 at glycine 399, a conserved glycine in all CNG. Human CNGA1 too, opens the pore by a rotational movement of S6 hinged at the equivalent glycine (glycine 385) (Xue et al, 2021). And the same occurs in the non-symmetrical channel CNGA1/B1 (Xue te al, 2022). So, it seems that CNG channels do not show the ⍺ to 𝜋 helix transition in the open pore. Moreover, hERG excluded, all other members of the CNBD family, CNG, EAG, and HCN4 included, do not bend at the hinge glycine 657 of hERG, but at another glycine (gly 648 in hERG numbering) located upstream. Further, their opening is due to a rotation of S6 associated with an outward movement, rather than to the lifting of the lower part of S6, as in hERG.

4- V390-I302: this interaction is predicted to stabilize the open pore configuration and was included in the subset. The contact between V390 on S6 and I302 on S5 is observed in the homology model discussed above when the S6 is twisted at the glycine hinge, rotating the preceding residue (V390) out of its pore-lining position and is.<br /> Again, I can only disagree with this hypothesis because it has been experimentally demonstrated (Cheng et al, J Pharmacol Exp Ther. 2007 Sep;322(3):931-9) that the side chain of Valine390 is inside the cavity of the open pore of HCN1 channels as it controls the affinity for the pore blocker ZD7288.

In conclusion, modelling the open state pore of HCN1 on hERG rather than on that of HCN4 seems not justified based on accumulated evidence in the published literature. Therefore, the choice of the authors to use it as the open pore model of HCN1 channels needs to be experimentally validated. One possibility is to mutate the glycine hinge, gly391 in HCN1, into an Alanine in order to remove the flexible hinge. If this mutation alters pore gating, it will support the choice of the Authors.

Reviewer #3 (Public Review):

Parab et al. investigate the requirement of specific Vegf ligands during the embryonic development of new blood vessels in different brain regions. The authors implement their previously published experimental paradigm (Parab et al 2021 eLife) combined with new transgenic and mutant zebrafish lines to show that vegf ligands (vegfaa, vegfab, vegfc, and vegfd) are required in various combinations to drive angiogenesis in distinct brain regions. Specifically, they show that individual loss of different vegf ligands causes either undetectable or partial effects in angiogenesis, while combined loss of vegf ligands results in severe defects in brain region-specific angiogenesis. As different blood vessel types (i.e. arteries, veins, lymphatics) require specific angiogenic cues, this study provides interesting new data on how the combination of these signals drives brain region-specific vascular development.

While the conclusions of the paper are generally well supported by the data, the authors overstate some of their findings, particularly with respect to the development of fenestrated capillaries. In this study, the authors use the zebrafish transgenic reporter line, plvap:EGFP, as an indicator of fenestrations. However, the authors do not provide any evidence of fenestrations of the blood vessels of the choroid plexuses or the cranial vessels used for quantification (Figures 1, 3, and 4). While expression of Plvap protein is often used as a marker for non-blood brain barrier endothelial cells, as Plvap is the major component of the diaphragms of fenestrated capillaries, plvap:EGFP expression alone does not indicate fenestrations. This is an important point because previous work has demonstrated that targeted deletion of Plvap does not cause a loss of fenestrations, but instead a loss of the diaphragms associated with fenestrations (Stan et al 2012 Dev Cell; Gordon et al 2019 Development). Similarly, Plvap expression alone does not necessarily indicate fenestrations as an expression of Plvap is not sufficient for fenestration formation. In fact, Plvap has initially been expressed in brain endothelial cells during initial angiogenesis to the brain without evidence of fenestrations, and subsequently, Plvap expression disappears during the maturation of the BBB. Thus, to conclude that specific vegf ligands are required for the development of fenestrated capillaries, transmission electron microscopy (TEM) should be used on the capillaries examined in this study or the language describing the results should be modified accordingly. Conversely, the authors did show TEM for the choriocapillaris (Figure 5A-C) but did not show plvap:EGFP expression in these vessels.

Additionally, the authors' usage of the phrase "development of fenestrated vessels" suggests that the study was examining signals that regulate the formation of fenestrations and not angiogenesis of vessels that may become fenestrated as demonstrated here. Therefore, as Plvap expression does not necessarily equate fenestrations (and vice-versa), the title and some of the major claims of the study are somewhat overstated.

Reviewer #3 (Public Review):

The study presents a systematic analysis of how a range of dystroglycan mutations alter CCK/CB1 axonal targeting and inhibition in hippocampal CA1 and impact seizure susceptibility. The study follows up on prior literature identifying a role for dystroglycan in CCK/CB1 synapse formation. The careful assay includes comparison of 5 distinct dystroglycan mutation types known to be associated with varying degrees of muscular dystrophy phenotypes: a forebrain specific Dag1 knockout in excitatory neurons at 10.5, a forebrain specific knockout of the glycosyltransferase enzyme in excitatory neurons, mice with deletion of the intracellular domain of beta-Dag1 and 2 lines with missense mutations with milder phenotypes. They show that forebrain glutamatergic deletion of Dag1 or glycosyltransferase alters cortical lamination while lamination is preserved in mice with deletion of the intracellular domain or missense mutation. The study extends prior works by identifying that forebrain deletion of Dag1 or glycosyltransferase in excitatory neurons impairs CCK/CB1 and not PV axonal targeting and CB1 basket formation around CA1 pyramidal cells. Mice with deletion of the intracellular domain or missense mutation show limited reductions in CCK/CB1 fibers in CA1. Carbachol enhancement of CA1 IPSCs was reduced both in forebrain knockouts. Interestingly, carbachol enhancement of CA1 IPSCs was reduced when the intracellular domain of beta-Dag1was deleted, but not I the missense mutations, suggesting a role of the intracellular domain in synapse maintenance. All lines except the missense mutations , showed increased susceptibility to chemically induced behavioral seizures. Together, the study, is carefully designed, well controlled and systematic. The results advance prior findings of the role for dystroglycans in CCK/CB1 innervations of PCs by demonstrating effects of more selective cellular deletions and site specific mutations in extracellular and intracellular domains. The interesting finding that deletion of intracellular domain reduces both CB1 terminals in CA1 and carbachol modulation of IPSCs warrants further analysis. Lack of EEG evaluation of seizure latency is a limitation.

Specific comments<br /> 1. Whether CCK/CB1 cell numbers in the CA1 are differentially affected in the transgenic mice is not clarified.<br /> 2. Whether basal synaptic inhibition is altered by the changes in CCK innervation is not examined.

Reviewer #3 (Public Review):

With a soft-spoken, matter-of-fact attitude and almost unwittingly, this brilliant study chisels away one of the pillars of hippocampal neuroscience: the special role(s) ascribed to theta oscillations. These oscillations are salient during specific behaviors in rodents but are often taken to be part of the intimate endowment of the hippocampus across all mammalian species, and to be a fundamental ingredient of its computations. The gradual anticipation or precession of the spikes of a cell as it traverses its place field, relative to the theta phase, is seen as enabling the prediction of the future - the short-term future position of the animal at least, possibly the future in a wider cognitive sense as well, in particular with humans. The present study shows that, under suitable conditions, place cell population activity "sweeps" to encode future positions, and sometimes past ones as well, even in the absence of theta, as a result of the interplay between firing rate adaptation and precise place coding in the afferent inputs, which tracks the real position of the animal. The core strength of the paper is the clarity afforded by the simple, elegant model. It allows the derivation (in a certain limit) of an analytical formula for the frequency of the sweeps, as a function of the various model parameters, such as the time constants for neuronal integration and for firing rate adaptation. The sweep frequency turns out to be inversely proportional to their geometric average. The authors note that, if theta oscillations are added to the model, they can entrain the sweeps, which thus may superficially appear to have been generated by the oscillations.

The main weakness of the study is the other side of the simplicity coin. In its simple and neat formulation, the model envisages stereotyped single unit behavior regulated by a few parameters, like the two time constants above, or the "adaptation strength", the "width of the field" or the "input strength", which are all assumed to be constant across cells. In reality, not only assigning homogeneous values to those parameters seems implausible, but also describing e.g. adaptation with the simple equation included in the model may be an oversimplification. Therefore, it remains important to understand to what extent the mechanism envisaged in the model is robust to variability in the parameters or to eg less carefully tuned afferent inputs.

The weak adaptation regime, when firing rate adaptation effectively moves the position encoded by population activity slightly ahead of the animal, is not novel - I discussed it, among others, in trying to understand the significance of the CA3-CA1 differentiation (2004). What is novel here, as far as I know, is the strong adaptation regime, when the adaptation strength m is at least larger than the ratio of time constants. Then population activity literally runs away, ahead of the animal, and oscillations set in, independent of any oscillatory inputs. Can this really occur in physiological conditions? A careful comparison with available experimental measures would greatly strengthen the significance of this study.

Reviewer #3 (Public Review):

In this manuscript, D'Ambra and colleagues report the effects of stimulating the deep cerebellar nuclei (DCN) on neurons in the core and the medial shell of the nucleus accumbens (NAc). Electrical stimulation results in both excitation and inhibition, with excitation preceding the inhibition. In general, neurons that underwent excitation had lower baseline activity than neurons that underwent inhibition. They observed no relationship between the location of the stimulation site within the DCN, and the type of response observed in the NAc. In order to identify disynaptic connections between the two areas, the authors combined the injection of a retrograde tracer in the NAc with an anterograde tracer in the DCN. These experiments led them to describe co-localization of the anterograde and retrograde signals within two regions, the intralaminar thalamus (IL), and the ventral tegmental area (VTA). In order to confirm these results, they then used an anterograde transsynaptic viral tracing strategy to mark neurons in the IL and the VTA that project to the NAc. In addition, by injecting an excitatory opsin into the DCN, and stimulating these axons within the VTA and the IL, the authors were able to demonstrate increased activity in the NAc and describe the latency of these responses. Thus, using a series of rigorous and complementary experiments, the authors provide evidence for a disynaptic connection between the DCN and the NAc, via the VTA and the IL.

Novelty and relationship to previous studies: The presence of a disynaptic connection between the DCN and the NAc has previously been shown, as has the projection from the DCN to the parafascicular nucleus of the intralaminar thalamus (Fujita et al. 2020); however, the intermediary nodes of the disynaptic connection between the DCN and NAc have not previously been mapped. Some other pieces of these results have also been shown previously: DCN to VTA: Watabe-Uchida et al. 2012, DCN-VTA-NAc Beier et al. 2015, Xiao and Schieffele 2018) Interestingly, the Beier et al. paper suggests that the connection from DCN-VTA-NAc is an extremely small proportion of the total inputs to the NAc. In contrast to the Fujita et al. paper, here the authors also stimulate or trace projections from the two other deep cerebellar nuclei, the lateral and the interposed (this is relevant for a comment below). In addition, previous studies have shown a projection from the DCN to the IL and, separately, from the IL to the NAc. Thus, the existence of the pathways described here is in line with previous work. Moreover, this study expands on previous ones through its electrophysiological measurement and description of neural responses to stimulation of DCN and DCN projections.

Strengths: The strengths of this paper include the authors' use of multiple techniques to confirm the presence of the connections that they describe. Any one of the experiments using electrical stimulation, combining anterograde and retrograde tracing, transsynaptic tracing, or optical stimulation of DCN axons in the IL and VTA has its own caveats. However, the combination of these techniques nullifies many of these caveats.

Weaknesses: While this is an interesting and exciting paper, there are a few weaknesses, listed below:

- The novelty of this paper lies in the mapping of projections from the interposed and the lateral nuclei of the cerebellum, as the authors themselves mention. However, in some of the experiments the medial nucleus is also clearly injected (Fig. 4B and 6B). In those experiments, it is impossible to distinguish which nucleus these projections come from, and they could be the ones from the medial nucleus that were previously described (see above).<br /> - A strength of the paper is the use of both electrical and optogenetic stimulation. However, the responses to the two in the NAc are very different - electrical stimulation results in both excitation and inhibition, whereas opto stimulation mostly results in only excitation.<br /> - The stimulation frequency at which the electrical stimulation in Fig 1 is done to identify responses in the NAc is 200 Hz for 25 ms. Is this physiological? In addition, responses in the NAc are measured for 500 ms after, which is a very long response time.<br /> - Previous studies have described how different cell types within the DCN have different downstream projections (Fujita et al. 2020). However, the experiments here bundle together all this known heterogeneity.<br /> - Previous studies have also highlighted the importance of different cell types within the NAc and how input streams are differentially targeted to them. Here, that heterogeneity is also obscured.<br /> - In Fig. 4C, E and F, the experiments on overlap between anterograde and retrograde tracers are not particularly convincing - it's hard to see the overlap.

Reviewer #3 (Public Review):

This work proposes a novel computational methodology that, using available structures of homologous proteins in different structural states, evolutionary couplings and machine-learning protocols, allows to predict structural states of a membrane transporter during the transport cycle. The core of the methodology is to use convolutional neural networks to distinguish state specific evolutionary contacts and drive alphafold2 models into a specific state based on the predicted contacts (using rosettaMP and short MD relaxation). The authors then derived the free energy landscape of the alternating access transition of GLUT5 (in absence of substrate) from enhanced sampling simulations biased along variables based on the previously mentioned contacts. The variables are constructed using a machine learning approach that allows distinguishing different structural states.

The advantage of this approach is that it uses a combination of advanced modeling and innovative computational techniques that might help the structural characterization of the alternating access cycle of membrane transporters. An important innovation is the use of machine learning methods that, based on previous structural information, allow to construct collective variables for free energy calculations in an objective, data-driven manner.

The results of the modellng part of the work are encouraging but could benefit from using more specific descriptors that better distinguish structural differences between states.

An important weakness of this work is that there are critical flaws in the simulation analysis. Another weakness is that the different free energy landscapes calculated do not appear strongly consistent to each other, which suggests the presence of significant errors in the calculations that are not discussed. An additional important point is that a quantitative assessment of the quality of the models used in the simulations is currently lacking and this could affect the reliability of the simulation results. In this regard, previous systematic studies (Proteins 2012; 80:2071-2079) have shown that small imperfections in the predicted models (such as in backbone and side chains conformations) could lead to simulations that drift away from the initial structure in the multi microseconds time domain.

Reviewer #3 (Public Review):

There is a lack of consensus about the best way to isolate EVs from biofluids, mainly due to EVs being present at low levels in clinically relevant samples and difficult to quantify. As a following study of one previous eLife paper (https://elifesciences.org/articles/70725) from the same group, the authors have extended their Simoa assay to ApoB-100, the major protein component of several lipoproteins. Combining with previously developed Simoa assays, the authors developed a quick framework to quantify EVs, albumin, and lipoproteins on the same platform. Additionally, the authors developed a new EV isolation method that combines two additional resins (i.e., cation-exchange resin and Capto Core 700) as a bottom layer below the SEC layer. Although not greater than the density gradient centrifugation, EVs isolated using the newly developed method showed better purity than with SEC alone or dual-mode chromatography. A device automatically running columns in parallel for EV isolation was further developed to increase the throughput and reproducibility of column-based EV isolation. The development of Simoa assay to ApoB-100 and the Tri-Mode Chromatography would be of great relevance to EV studies.

Reviewer #3 (Public Review):

In this manuscript, Chao et al seek to understand the role of brummer, a triglyceride lipase, in the Drosophila testis. They show that Brummer regulates lipid droplet degradation during differentiation of germ and somatic cells, and that this process is essential for normal development to progress. These findings are interesting and novel, and contribute to a growing realisation that lipid biology is important for differentiation.

Major comments:

1) The data in Figs 1 and 2, while helpful in setting the scene, do not add much to what was previously shown by the same group, namely that lipid droplets are present in both early germ cells and early somatic cells in the testis, and that Bmm regulates their degradation (PMID: 31961851). Measuring the distance of lipid droplets from the hub, while helpful in quantifying what is apparent, that only stem and early differentiated stages have lipid droplets, is not as informative as the way data are presented later (Fig. 2I), where droplets in specific stages are measured. Much of this could be condensed without much overall loss to the manuscript.

2) It would be important to show images of the clones from which the data in Fig. 2I are generated. The main argument is that Bmm regulates lipid droplets in a cell autonomous manner; these data are the strongest argument in support of this and should be emphasised at the expense of full animal mutants (which could be moved to supplementary data). Similarly, the title of Fig. S2 ("brummer regulates lipid droplets in a cell autonomous manner") should be changed as the figure has no experiments with cell (or cell-type)-specific knockdowns/mutants. This figure does show changes in lipid droplets in both lineages in bmm mutants, so an appropriate title could be "brummer regulates lipid droplets in both germ and soma".

3) Interestingly, the clonal data show that bmm is dispensable in germ cells until spermatocyte stages, as no increase in lipid droplet number is seen until then. This should be more clearly stated, as it indicates that the important function of Bmm is to degrade lipid droplets at the transition from spermatogonial to spermatocyte stages. This is consistent with the phenotypes observed in which late stage germ cells are reduced or missing. However, the effect on niche retention of the mutant GSCs at the expense of neighbouring wildtype GSCs is hard to explain. Are lipid droplets in mutant GSCs larger than in control? Is there any discernible effect of bmm mutation on lipids in GSCs? Additionally, bam expression is delayed, suggesting that bmm may have roles on cell fate in earlier stages than its roles that can be detected on lipid droplets.

4) The bmm loss-of-function phenotype could be better described. Some of the data is glossed over with little description in the text (see for example the reference to Fig. 3A-C). For instance, in the discussion, the text states "loss of bmm delays germline differentiation leading to an accumulation of early-stage germ cells" (p13, l.259-60). However, this accumulation has not been clearly shown, or at least described in the manuscript. Most of the data show a reduction (or almost complete absence) of differentiated cell types. This could indeed be due to delayed differentiation, or alternatively to a block in differentiation or to death of the differentiated cells. The clonal data presented show a decrease in the number of cells recovered, but do not allow inferences as to the timing of differentiation, making it hard to distinguish between the various possibilities for the lack of differentiated spermatids. Apart from data showing that GSCs are more likely to remain at the niche, no further data are shown to support the fact that mutant germ cells accumulate in early stages. While additional experiments could help resolve some of these issues, much of this could also be resolved by tempering the conclusions drawn in the text.

5) In the discussion (p.14, l-273 onwards), the authors suggest that products of triglyceride breakdown are important for spermatogenesis. However, an alternative interpretation of the results presented here (especially those using the midway mutant) could be that triglycerides impede normal differentiation directly. Indeed, preventing the cells' ability to produce triglycerides in the first place can rescue many of the defects observed. A better discussion of these results with a model for the function of triglycerides and their by-products would be a great improvement to this manuscript.

Reviewer #3 (Public Review):

In this manuscript, Man et al. describe a new signaling pathway for regulation of the voltage-gated calcium channel Cav1.2 and show that it can modulate synaptic plasticity in the hippocampus. Studies with specific inhibitors, phosphopecific antibodies, and gene knockdown show that activation of alpha-1 adrenergic receptors induces downstream activation of the serine/threonine protein kinase PKC and the tyrosine protein kinases Pyk2 and Src, which bind to the Cav1.2 channel through its large intracellular segment connecting domains II and III. This signaling complex leads to tyrosine phosphorylation of Cav1.2 and increased channel activity. Block of this novel signaling pathway in hippocampal slices with specific inhibitors of Pyk2 and Src reduced a specific component of long-term potentiation whose induction requires Cav1.2 channel activity.

This work is an important advance, as it presents a novel signaling pathway through which the ubiquitous neurotransmitter norepinephine and the neurohormone epinephrine can regulate synaptic plasticity, attention, learning, and memory. The experiments are comprehensive, carefully done, and clearly presented. The authors should consider revisions and responses to the points below.

1. Figure 2B, D. Inhibitors reduce Ica below control. Is there endogenous stimulation of this regulatory pathway under control conditions?

2. As noted by the authors, it would be interesting to know if peptides from the linker between domains II and III block this signaling pathway. This would be an important result because, without this information, it is not clear if this is the correct functional site of interaction for this regulatory complex.

3. Figure 4B. The Brain IP for Src has a weak signal. The authors should replace this panel with a more convincing immunoblot.

4. Scatter plots are provided for the electrophysiological results but not immunoblots. For immunoblots that are quanitified, it would be valuable to add a scatter plot of the replicates.

Reviewer #3 (Public Review):

This manuscript by Modi et al represents a novel and significant advance in the neurobiology of memory retrieval. The authors employ a novel behavioral paradigm in order to investigate memory generalization and discrimination. They investigate the role of two different populations of dopamine neurons (DANs) targeting different compartments involved in aversion learning, i.e. α3 (MB630B) and γ2α'1 (MB296B).

The behavioral platform is clear and convincing but lacks natural reinforcement comparisons. The entire paper uses optogenetic reinforcement of said DAN populations.

The authors identify that the gamma DANs can enable both easy and hard odour discrimination, while the alphas DANs can only do easy.

The odours can be separated by calcium imaging analysis of Kenyon cells. Subsequent calcium imaging of the gamma DANs themselves showed that a single training event was insufficient to enable easy odor discrimination at the gamma DAN level, but strangely not for the hard discrimination that gamma DANs can mediate. Seemingly, this is due to the lack of the temporal contiguity of odors (present in behavioral experiments but not in the initial imaging experiments.

However, in gamma DANs, Odour transitions enabled discrimination of odours in hard discrimination, based on the depression of calcium activity in DANs after training that was odour-specific. The same was not true for alpha DANs, though the authors used natural electric shock pairings instead of optogenetic stimulation of DANs for the alpha experiment. However, statistical comparisons are done within group and need also be provided for between the groups for both pre and post-training. The authors persuasively show that hard discrimination can only happen in transitions. They also argue that the same engram can be read in two different ways. This is convincing overall, but they claim it is happening downstream of the Kenyon cells just because they do not see it in the Kenyon cells, and I cannot comment on the modelling in Figure 5 (expertise).

Experimental methods used are appropriate, as are data analysis strategies.

The manuscript itself is well written in parts, though at times paragraphs are quite patchy, especially in the discussion. There are also a visible number of typos. The figures are well constructed, and generally well organized. The overall document is concise and has sufficient detail.

Reviewer #3 (Public Review):

Clay and colleagues investigate the proteostasis and longevity benefits derived from translation inhibition in C. elegans by examining the impacts of chemical translation initiation inhibitors (IIs) and translation elongation inhibitors (EIs) on thermotolerance, protein folding stress, aggregation and longevity. They observe somewhat distinct impacts by the two chemical groups. IIs increased longevity in wild-type animals in an hsf-1 dependent manner, whereas, EIs only extended hsf-1 mutants' lifespan. Only EIs protected against proteasome dysfunction. Both protected against heat stress but with differing hsf-1 dependence. The authors utilize these observations to derive conclusions regarding two dominant points of view on the mechanism by which translation inhibition improves lifespan and proteostasis.<br /> The study is based on interesting observations and several promising avenues of further investigation can be identified. However, the manuscript appears somewhat preliminary in nature, with many of the observations, while interesting, only explored superficially for mechanistic insights. The rationale behind some of the interpretations was also difficult to interpret. For example, the authors make conclusions about 'selective translation' being adopted upon IIs treatment without directly testing this. Protein aggregation, while possibly predictive, is not a reliable readout for selective translation of some mRNAs. Similarly, the evidence for a reduction in 'newly-synthesized protein load' by EIs is thin based on one reporter. Previous studies from the Blackwell lab have identified differential impacts of SKN-1 on select cytoprotective genes' expression and proteasomal gene expression based on inhibition of translation initiation or elongation. So there is precedence for both the differential impact of initiation vs. elongation inhibition as well as genetic background. There are several other such studies that reduce the impact of the observations presented here. With limited novelty and mechanistic insight, the impact of the study on the field is likely to be moderate.

Reviewer #3 (Public Review):

How chromatin state is defined is an important question in the epigenetics field. Here, Jamge et al. proposed that the dynamics of histone variant exchange control the organization of histone modifications into chromatin states. They found 1) there is a tight association between H2A variants and histone modifications; 2) H2A variants are major factors that differentiate euchromatin, facultative heterochromatin, and constitutive heterochromatin; 3) the mutation in DDM1, a remodeler of H2A variants, causes the mis-assembly of chromatin states in TE region. The topic of this paper is of general interest and results are novel.

Overall, the paper is well-written and results are clearly presented. The biochemical analysis part is solid.

Reviewer #3 (Public Review):

Neininger-Castro and colleagues developed software tools for the quantification of sarcomeres and sarcomere-precursor features in immunostained human induced pluripotent stem cell-derived cardiac myocytes (hiCMs). In the first part they used a deep-learning- based model called a U-Net to construct and train a network for binarization of immunostained cardiomyocyte images. They also wrote graphical user interface (GUI) software that will assist other labs in using this approach and made it publicly available. They did not compare their approach to existing ones, but an example from one image suggests their binarization tool outperforms Otsu thresholding binarization.

In the second part they developed a software tool called sarcApp that classifies sarcomere structures in the binarized image as a Z-Line or Z-Body and assigns each to either a myofibril or to stress fibers. The tools can then automatically count and measure multiple features (33 per cell and 24 per myofibril) and report them on a per-cell, per-myofibril, and per- stress fiber basis.

To test the tools they used Blebbistatin to inhibit sarcomere assembly and showed that the sarcApp tool could capture changes in multiple features such as fewer myofibrils, fewer Z-Lines, decreased myofibril persistence, decreased Z-Line length and altered myofibril orientation in the Blebbistatin treated cells. With some changes the tool was also shown to quantify sarcomeres in titin and myomesin stained cardiomyocytes.

Finally they used sarcApp to quantify the changes in sarcomere assembly after siRNA mediated knockout of MYH7, MYH7, or MYOM. The analysis indicates that neither MYH6 nor MYH7 knockdown perturbed the assembly of Z- or M-lines, and that knockdown of MYOM perturbed the A-band/M-Line but not the Z-Line assembly according to features captured by the sarcApp tool.

Overall the authors developed and made publicly available an excellent software tool that will be very useful for labs that are interested in studying sarcomere assembly. Multiple features that are difficult to measure or count manually can be automatically measured by the software quickly and accurately.

There are however some remaining questions about these tools:<br /> 1. The binarization tool which is tailored to sarcomere image binarization appears promising but was not systematically compared with existing approaches.<br /> 2. How robust is the tool? The tool was tested on images from one type of cardiomyocytes (hiCMs) taken from one lab using Nikon Spinning Disk confocal microscope equipped with Apo TIRF Oil 100X 1.49 NA objective or instant Structured Illumination Microscopy (iSIM), using deconvolution (Microvolution software) and in a specific magnification. It remains to be seen whether the tool would be equally effective with images taken with other microscopy systems, with other cardiomyocytes (chick or neonatal rat), with different magnifications, live imaging, etc.<br /> 3. The tool was developed for evaluation of sarcomere assembly. The authors show that for this application it can detect the perturbation by Blebbistatin, or knockdown of sarcomeric genes. It remains to be seen if this tool is also useful for assessment of sarcomere structure for other questions beside sarcomere assembly and in other sarcomere pathologies.

Reviewer 3 (Public Review):

The main question of this article is as follows: "To what extent does having information on brain-age improve our ability to capture declines in fluid cognition beyond knowing a person's chronological age?" While this question is worthwhile, considering that there is considerable confusion in the field about the nature of brain-age, the authors are currently missing an opportunity to convey the inevitability of their results, given how brain-age and the brain-age gap are calculated. They also argue that brain-cognition is somehow superior to brain-age, but insufficient evidence is provided in support of this claim.

Specific comments follow:

- "There are many adjustments proposed to correct for this estimation bias" (p3). Regression to the mean is not a sign of bias. Any decent loss function will result in over-predicting the age of younger individuals and under-predicting the age of older individuals. This is a direct result of minimizing an error term (e.g., mean squared error). Therefore, it is inappropriate to refer to regression to the mean as a sign of bias. This misconception has led to a great deal of inappropriate analyses, including "correcting" the brain age gap by regressing out age.

- "Corrected Brain Age Gap in particular is viewed as being able to control for both age dependency and estimation biases (Butler et al., 2021)" (p3). This summary is not accurate as Butler and colleagues did not use the words "corrected" and "biases" in this context. All that authors say in that paper is that regressing out age from the brain age gap - which is referred to as the modified brain age gap (MBAG) - makes it so that the modified brain age gap is not dependent on age, which is true. This metric is meaningless, though, because it is the variance left over after regressing out age from residuals from a model that was predicting age. If it were not for the fact that regression on residuals is not equivalent to multiple regression (and out of sample estimates), MBAG would be a vector of zeros. Upon reading the Methods, I noticed that the authors use a metric from Le et al. (2018) for the "Corrected Brain Age Gap". If they cite the Butler et al. (2021) paper, I highly recommend sticking with the same notation, metrics and terminology throughout. That would greatly help with the interpretability of the present manuscript, and cross-comparisons between the two.

- "However, the improvement in predicting chronological age may not necessarily make Brain Age to be better at capturing Cognitionfluid. If, for instance, the age-prediction model had the perfect performance, Brian Age Gap would be exactly zero and would have no utility in capturing Cognitionfluid beyond chronological age" (p3). I largely agree with this statement. I would be really careful to distinguish between brain-age and the brain-age gap here, as the former is a predicted value, and the latter is the residual times -1 (i.e., predicted age - age). Therefore, together they explain all of the variance in age. Changing the first sentence to refer to the brain-age gap would be more accurate in this context. The brain-age gap will never be exactly zero, though, even with perfect prediction on the training set, because subjects in the testing set are different from the subjects in the training set.

- "Can we further improve our ability to capture the decline in cognitionfluid by using, not only Brain Age and chronological age, but also another biomarker, Brain Cognition?". This question is fundamentally getting at whether a predicted value of cognition can predict cognition. Assuming the brain parameters can predict cognition decently, and the original cognitive measure that you were predicting is related to your measure of fluid cognition, the answer should be yes. Upon reading the Methods, it became clear that the cognitive variable in the model predicting cognition using brain features (to get predicted cognition, or as the authors refer to it, brain-cognition) is the same as the measure of fluid cognition that you are trying to assess how well brain-cognition can predict. Assuming the brain parameters can predict fluid cognition at all, it is then inevitable that brain-cognition will predict fluid cognition. Therefore, it is inappropriate to use predicted values of a variable to predict the same variable.

- "However, Brain Age Gap created from the lower-performing age-prediction models explained a higher amount of variation in Cognitionfluid. For instance, the top performing age-prediction model, "Stacked: All excluding Task Contrast", generated Brain Age and Corrected Brain Age that explained the highest amount of variation in Cognitionfluid, but, at the same time, produced Brian Age Gap that explained the least amount of variation in Cognitionfluid" (p7). This is an inevitable consequence of the following relationship between predicted values and residuals (or residuals times -1): y=(y-y ̂ )+y ̂. Let's say that age explains 60% of the variance in fluid cognition, and predicted age (y ̂) explains 40% of the variance in fluid cognition. Then the brain age gap (-(y-y ̂)) should explain 20% of the variance in fluid cognition. If by "Corrected Brain Age" you mean the modified predicted age from Butler et al (2021), the "Corrected Brain Age" result is inevitable because the modified predicted age is essentially just age with a tiny bit of noise added to it. From Figure 4, though, this does not seem to be the case, because the lower left quadrant in panel (a) should be flat and high (about as high as the predictive value of age for fluid cognition). So it is unclear how "Corrected Brain Age" is calculated. It looks like you might be regressing age out of brain-age, though from your description in the Methods section, it is not totally clear. Again, I highly recommend using the terminology and metrics of Butler et al (2021) throughout to reduce confusion. Please also clarify how you used the slope and intercept. In general, given how brain-age metrics tend to be calculated, the following conclusion is inevitable: "As before, the unique effects of Brain Age indices were all relatively small across the four Brain Age indices and across different prediction models" (p10).

"On the contrary, the unique effects of Brain Cognition appeared much larger" (p10). This is not a fair comparison if you do not look at the unique effects above and beyond the cognitive variable you predicted in your brain-cognition model. If your outcome measure had been another metric of cognition other than fluid cognition, you would see that brain-cognition does not explain any additional variance in this outcome when you include fluid cognition in the model, just as brain-age would not when including age in the model (minus small amounts due to penalization and out-of-sample estimates). This highlights the fact that using a predicted value to predict anything is worse than using the value itself.

"First, how much does Brain Age add to what is already captured by chronological age? The short answer is very little" (p12). This is a really important point, but the paper requires an in-depth discussion of the inevitability of this result, as discussed above.

"Third, do we have a solution that can improve our ability to capture Cognitionfluid from brain MRI? The answer is, fortunately, yes. Using Brain Cognition as a biomarker, along with chronological age, seemed to capture a higher amount of variation in Cognitionfluid than only using Brain Age" (p12). I suggest controlling for the cognitive measure you predicted in your brain-cognition model. This will show that brain-cognition is not useful above and beyond cognition, highlighting the fact that it is not a useful endeavor to be using predicted values.

"Accordingly, a race to improve the performance of age-prediction models (Baecker et al., 2021) does not necessarily enhance the utility of Brain Age indices as a biomarker for Cognitionfluid. This calls for a new paradigm. Future research should aim to build prediction models for Brian Age indices that are not necessarily good at predicting age, but at capturing phenotypes of interest, such as Cognitionfluid and beyond" (p13). I whole-heartedly agree with the first two sentences, but strongly disagree with the last. Certainly your results, and the underlying reason as to why you found these results, calls for a new paradigm (or, one might argue, a pre-brain-age paradigm). As of now, your results do not suggest that researchers should keep going down the brain-age path. While it is difficult to prove that there is no transformation of brain-age or the brain-age gap that will be useful, I am nearly sure this is true from the research I have done. If you would like to suggest that the field should continue down this path, I suggest presenting a very good case to support this view.

Reviewer #3 (Public Review):

The current study examined 13 monosomic yeast strains that lost different individual chromosomes. By comparing the fitness of monosomic strains and several heterozygous deletion strains, the authors observed strong positive epistasis for fitness. The transcriptomes of monosomic strains indicated that general gene-dose compensation is not the reason for fitness gains. On the other hand, gene expression of ribosomal proteins was up-regulated and proteasome subunit expression was down-regulated in all tested monosomic strains. The authors speculated that overexpression in combination with decreased degradation of the insufficient proteins might explain the positive epistasis observed in monosomic strains. This study investigates an important biological question and has some interesting results. However, I have some reservations about the data interpretations listed below.

1) In Figure 3b (and line 179), the authors stated that those haploinsufficient genes were not transcribed at elevated rates, but almost half of them are in reddish colors (indicating that the expression is higher than 1-fold). Obviously, many haploinsufficient genes are up-regulated in monosomic strains. What the data really show is that the level of overexpression is not correlated with the fitness effect of the deletion (since all the p values are not significant). The authors need to correct their conclusions.

2) Why are some monosomic strains removed from the transcriptomics analysis, especially when the chromosome IV and XV strains show very strong positive epistasis? The authors need to provide an explanation here.

3) The authors stated that diploidy observed in chromosome VII and XIII strains were due to endoreplication after losing the marked chromosomes (lines 97 and 117). Isn't chromosome missegregation an equally possible explanation? Since monosomic cells are generated by chromosome missegregation during mitosis, another chromosome missegregation event may occur to rescue the fitness (or viability) of monosomic cells in these strains.

Reviewer #3 (Public Review):

Understanding the changes in the brain during the progression of neurodegenerative diseases may provide a critical entry point towards medical treatments. Many genes have been directly or indirectly implemented in an array of neurodegenerative diseases, including the microtubule associated protein tau (MAPT). Various studies have shown that misexpression of tau can cause behavioral, genetic as well as molecular phenotypes that display properties of human neurodegenerative diseases connected to tauopathies. Here the authors use the fruit fly as model to assess phenotypic defects at single-cell resolution. Pan-neuronal misexpression of a mutant form of tau (R406W) and single-cell RNAseq at different time points provides the basis for the investigation.

The authors assess which cell-types are affected (by comparing it with previously described brain cell atlas identities) and find that certain cell types are missing (or less abundant) while other appear unaffected. They do this comparison in relative abundance; both neurons and glia cells are affected.

As next step they compare this with the cell-cluster changes during aging and compare both types of analysis; the investigation here includes the analysis of differentially expressed genes in defined cell clusters. One particularly affected pathway in response to tau is the NFκB signaling pathway. The authors investigate the gene expression changes of the NFκB signaling pathway in the current dataset in more detail. In the last section the authors compare single-cell transcriptomic analyses between fly and human postmortem tissue, showing that the NFκB signaling pathway might be a conserved aspect of neurodegeneration.

The manuscript is overall an elegant example of how single-cell RNAseq can be employed as tool to study the impact of genetic modulators of neurodegeneration (in this case tau) and that it allows direct comparison with human tissues. The results are clean, logically presented and accordingly discussed. It shows that such approaches are indeed powerful for genetic dissection of mechanisms at a descriptive level and opening doors for functional studies.

Reviewer #3 (Public Review):

Buruli ulcer is a severe skin infection in humans that is caused by a bacterium, Mycobacterium ulcerans. The main clinical sign is a massive tissue necrosis subsequent to an edema stage. The main virulence factor called mycolactone is a polyketide with a lactone core and a long alkyl chain that is released within vesicles by the bacterium. Mycolactone was already shown to account for several disease phenotypes characteristic of Buruli ulcer, for instance tissue necrosis, host immune response modulation and local analgesia. A large number of cellular pathways in various cell types was reported to be impacted by mycolactone. Among those, the Sec61 translocon involved in the transport of certain proteins to the endoplasmic reticulum was first identified by the authors of the study and is currently the most consensual target. Mycolactone disruption of Sec61 function was then shown to directly impact on cell apoptosis in macrophages, limited immune responses by T-cells and increased autophagy in dermal endothelial cells and fibroblasts. In their manuscript, Tzung-Harn Hsieh and their collaborators investigated the Sec61- dependent role of mycolactone on morphology, adhesion and migration of primary human dermal microvascular endothelial cells (HDMEC). They used a combination of sugar and proteomic studies on a live image-based phenotypic assay on HDMEC to characterize the effect of mycolactone. First, they showed that upon incubation of monolayer of HDMEC with mycolactone at low dose (10 ng/mL) for 24h, the cells become elongated before rounding and eventually detached from the culture dish at 48h. Next, mycolactone was probed on a scratch assay and migration of the cells ceased upon a 24h incubation. The same effect as mycolactone on these two assays was observed for two other Sec61 inhibitors Ipomoeassin F and ZIF-80. Then, the authors resorted to the widely established mouse footpad model of M. ulcerans infection to evidence fibrinogen accumulation outside the blood vessel within the endothelium at 28 days post-infection, correlating with severe endothelial cell morphology changes.

To dissect the molecular pathways involved in these phenotypes, the authors performed an HDMEC membrane protein analysis and showed a decrease in the numbers of proteins involved in glycosylation and adhesion. As protein glycosylation mainly occurs in the Golgi apparatus, a deeper analysis revealed that enzymes involved in glycosaminoglycan (GAG) synthesis were lost in mycolactone treated HDMEC. A combination of immunofluorescence and flow cytometry approaches confirmed the impact of mycolactone on the ability of endothelial cells to synthesize GAG chains. The mycolactone effect on cell elongation was phenocopied by knock-down of galactosyltransferase II (B3Galt6) involved in GAG biosynthesis. A second extensive analysis of the endothelial basement membrane component and their ligands identified multiple laminins affected by mycolactone. Using similar functional studies as for GAG, the impact of mycolactone on cell rounding and migration could be reversed by the addition of laminin α5.

The major strengths of the study relies on a combination of cleverly designed phenotypic assays and in-depth cleverly designed membrane proteomic studies and follow-up analysis.<br /> The results really support the conclusions. Congratulations!<br /> The discussion takes into account the current state of the art, which has mostly been established by the authors of the present manuscript.

Reviewer #3 (Public Review):

I found this paper fascinating. It is a study that needed to be done in the field of behavioral endocrinology, as it addresses our understanding of exactly how steroid hormone action might regulate behavioral output like few other published studies. For decades, researchers have been implanting animals with steroids and observing corresponding changes in behavior, noting that some behavioral traits are immediately expressed, while others take time to be expressed. Why would this be? The answer lies in the temporal dynamics of steroid action, but few have ever addressed this. Having said this, I do have several issues with the manuscript that I think need to be addressed.

1) My biggest concern is the sample size. Most of the time points only have 5 or 6 individuals represented, and I question whether these numbers provide sufficient statistical power to uncover the effects the authors are trying to explore. This is a particular problem when it comes to evaluating the supposed "transient" of testosterone on gene expression. There is currently little basis for distinguishing such effects from noise that accrues because of low power. This can be a major problem with studies of gene expression in non-model species, like canaries, where among-individual variability in transcript abundance is quite high. Thus, it is possible that one or two outliers at a given time point cause the effect testosterone at this time point to become indistinguishable from the controls; if so, then a gene may get put into the transient category, when in fact its regulation was not likely transient.

2) More on the transient categorization. Would a gene whose expression is not immediately upregulated (within 1 hour), but is upregulated later on (say in the 14d group) be considered transient? If so, this seems problematic. Aren't the authors setting the null expectation of "non-transient" as a gene that does not increase immediately after 1 hour of treatment? The authors even recognize that it is quite surprising that gene expression changes after an hour. It may be that some genes whose regulation is classified as transient are simply slower to upregulate; but, really, would we say their expression in transient per se? Maybe I'm misunderstanding the categorizations?

3) The authors don't fully explain the logic for using females in this study to measure a "male-typical" behavior (singing). My understanding is that females have underlying circuitry to sign, and T administration triggers it; thus, this situation that creates a natural experiment in which we can explore T's on brain and behavior, unlike in males which have fluctuating T. First, it might be good to clarify this logic for readers, unless perhaps I'm misunderstanding something. Second, I found myself questioning this logic a little. Our understanding of basic sex differences and the role that steroid hormones play in generating them has changed over the last few decades. There are, for example, a variety of genetic factors that underlie the development of sex differences in the brain (I'm especially thinking about the incredible work from Art Arnold and many others that harness the experimental power of the four core genotype mice). Might some of these factors influence female development, such that T's effects on the female brain and subsequent ability to increase HVC size and sing is not the same as males.

4) I was surprised by the authors assertion that testosterone would only influence several tens or hundreds of genes. My read of the literature says that this is low, and I would have expected 100s, if not 1,000s, of genes to be influenced. I think that the total number of genes influenced by T is therefore quite consistent with the literature.

5) I found the GO analyses presented herein uncompelling. As the authors likely know, not all GO terms are created equally. Some GO terms are enriched by hundreds of genes and thus reflect broad functional categories, whereas other GO terms are much more specific and thus are enriched by only a few genes. The authors report broad GO terms that don't tell us much about what is happening in the HVC functionally. This is particularly the case when a good 50% of the genome is being differentially regulated.

6) The Genomatix analyses are similarly uncompelling. This approach to finding putative response elements can uncover many false positives, and these should always be validated thoroughly. Don't get me wrong-I appreciate that these validations are not trivial, and I value the authors response element analysis.

7) I'm sceptical about the section of the paper that speculates about modification of steroid sensitivity in the HVC. These conclusions are based on analyses of mRNA expression of AKR1D1, SRD5A2, and the like. However, this does not reflect a different in the capacity to metabolize steroids, or at least there is little evidence to suggest this. Note that many of these transcripts have different isoforms, which could also influence steroidal metabolism.

Reviewer #3 (Public Review):

Overall, the data quality and analyses are solid. The authors have extracted a lot of detailed information about gene expression in specific cell types of the sea star embryo, and this descriptive narrative forms much of the Results section. However, the most interesting analyses will be the between-species comparisons. The authors identify several striking differences in the apparent presence or absence of specific cell types between seastar and sea urchins. Some confirm well-known differences, such as the absence of pigmented and skeletogenic mesenchyme cells in seastar embryos based on morphological comparisons. Other findings are novel, such as transcriptionally distinct left and right coelomic pouches as early as late gastrula and the apparent absence of germ cells in seastar embryos. These findings are based on solid evidence, highly informative regarding molecular details, and will no doubt inspire many future studies, both into developmental mechanisms per se and into the evolution of development. While the descriptive part of this study is solid and highly informative, the evolutionary interpretations are more problematic. The Abstract and Introduction emphasize the promise of sc/snRNAseq to shed light on the evolution of cell types and novelty, but the data themselves tell a less clear-cut story. Indeed, for me, the biggest takeaway from reading this manuscript is that it is quite difficult to identify when a novel cell type has evolved based solely on analysis of embryonic stages. The last stage examined is late gastrula, which means that some cell types may appear to be missing simply because they have not yet begun to differentiate transcriptionally. An example would be germ cells since adults make gametes. Another limitation is that just two species are compared. This means that for any given difference in cell type composition, it is not possible to distinguish whether this represents a novel cell type in one species or the loss (or delay in differentiation) of a cell type in the other species. The authors are generally careful to identify these limitations when presenting results, but it does lead me to wonder why they did not choose to examine later stages of development when more cells are clearly differentiated.

Reviewer #3 (Public Review):

The goal of this work was to better understand how cell fate decisions at the neural plate border (NPB) occur. There are two prevailing models in the field for how neural, neural crest and placode fates emerge: (i) binary competence which suggests initial segregation of ectoderm into neural/neural crest versus placode/epidermis; (ii) neural plate border, where cells have mixed identity and retain the ability to generate all the ectodermal derivatives until after neurulation begins.

The authors use single-cell sequencing to define the development of the NPB at a transcriptional level and suggest that their cell classification identified increased ectodermal cell diversity over time and that as cells age their fate probabilities become transcriptionally similar to their terminal state. The observation of a placode module emerging before the neural and neural crest modules is somewhat consistent with the binary competence model but the observation of cells with potentially mixed identity at earlier stages is consistent with the neural plate border model.

Differences in the timing of analyses and techniques used can account for the generation of these two original models, and in essence, the authors have found some evidence for both models, possibly due to the period over which they performed their studies. However, the authors propose recognizing the neural plate border as an anatomical structure, containing transcriptionally unstable progenitors and that a gradient border model defines cell fate choice in concert with spatiotemporal positioning.

The idea that the neural plate border is an anatomical structure is not new to most embryologists as this has been well-recognized in lineage tracing and transplantation assays in many different species over many decades. The authors don't provide molecular evidence for transcriptional instability in any cells. It's a molecular term and phenomenon inaccurately applied to these cells that are simply bipotential progenitors. Lastly, there's no evidence of a gradient that fits the proper biochemical or molecular definition. Graded or sequential are more appropriate terms that reflect the lineage determination or segregation events the authors characterize, but there's no data provided to support a true role for a gradient such as that achieved by a concentration or time-dependent morphogen.

A limitation of the study is that much of it reads like a proof-of-principle because validation comes primarily from known genes, their expression patterns in vivo, and their subsequent in vivo functions. Thus, the authors need to qualify their interpretations and conclusions and provide caveats throughout the manuscript to reflect the fact that no functional testing was performed on any novel genes in the emerging modules classified as placode versus neural or neural crest.

Lastly, a limitation of gene expression studies is that it provides snapshots of cells in time, and while implying they have broad potential or are lineage fated, do not actually test and confirm their ultimate fate. Therefore, in parallel with their studies, the authors really need to consider, the wealth of lineage tracing data, especially single-cell lineage tracing, which has been performed using the embryos of the same stage as that sequenced in this study, and which has revealed critical data about the potential cells through when and where lineage segregation and cell fate determination occurs.

Reviewer #3 (Public Review):

This manuscript investigates how a seemingly random choice of odourant receptor (OR) gene expression is organised into sterotypic zones of OR expression along the olfactory epithelium. Using a varietty of functional genomics methods, the authors find that along the differentiation axis (progenitor to mature olfactory sensory neuron, OSN) multiple ORs are initally transcribed and from among these, only one OR is selected for expression. The rest are suppressed through chromatin silencing. In addition to this, the authors report a dorso-ventral gradient in OR expression at the immature stage - dorsally expressed ORs are also expressed ventrally and these then get silenced. The expression of the ventrally expressed ORs, on the other hand, are restricted to the ventral region. They suggest a role for the transcription factor NF1 in this dorsoventral process.

This is a valuable study. The data are compelling and generally well presented.

Reviewer #3 (Public Review):

In this article by Bastidas et al. the authors examine the functions of the Chlamydia deubiquitinating enzyme 1 (Cdu1) during infections of human cells. First, a mutant lacking Cdu1 but not Cdu2 was constructed using targetron and quantitative proteomics was used to identify differences in ubiquitinated proteins (both host and bacterial) during infection. While they found minimal changes in host protein ubiquitination, they identified three Chlamydia effector proteins, IpaM, InaC and CTL0480 were all ubiquitinated in the absence of Cdu1. Microscopy and immunoprecipitations found Cdu1 directly interacts with these Chlamydia effectors and confirmed that Cdu1 mediates the stabilization of these effectors at the inclusion membrane during late infection time points. Surprisingly rather than deubiquitination driving this stabilization, the acetylation function of Cdu1 was required, and acetylation on lysine residues prevented degradative ubiquitination of Cdu1, IpaM, InaC and CTL0480. In line with this observation the authors show that loss of Cdu1 phenocopies the loss of single effector mutants of InaC, IpaM and CTL0480, including golgi stack formation and the recruitment of MYPT1 to the inclusion. The aggregation of changes to the Chlamydia inclusion does not alter growth but controls extrusion of chlamydia from cells with reduced extrusion in Cdu1 mutant Chlamydia infections. The strengths of the manuscript are the range of assays used to convincingly examine the biochemical and cellular biology underlying Cdu1 functions. The finding that acetylation of lysine residues is a mechanisms for bacterial effectors to block degradative ubiqutination is impactful and will open new investigations into this mechanism for many intracellular pathogens. There are a few weaknesses that temper enthusiasm for the manuscript in its current form. These include caveats related to the timing of proteomics, the lack of an effect of Cdu1 directly on bacterial growth, and discussion of previous studies. Altogether this is an important series of findings that help to understand the mechanisms underpinning Chlamydia pathogenesis using orthologous methods with a few caveats that lower the overall impact.

Reviewer #3 (Public Review):

Lu et al. describe Vangl2 as a negative regulator of inflammation in myeloid cells. The primary mechanism appears to be through binding p65 and promoting its degradation, albeit in an unusual autolysosome/autophagy dependent manner. Overall, the findings are novel and the crosstalk of PCP pathway protein Vangl2 with NF-kappaB is of interest. Whether PCP is anyway relevant or if this is a PCP-independent function of Vangl2 is not directly explored (the later appears more likely from the manuscript/discussion). PCP pathways intersect often with developmentally important pathways such as WNT, HH/GLI, Fat-Dachsous and even mechanical tension. It might be of importance to investigate whether Vangl2-dependent NF-kappaB is influenced by developmental pathways. Are Vangl2 phosphorylations (S5, S82 and S84) in anyway necessary for the observed effects on NF-kappaB or would a phospho-mutant (alanine substitution mutant) Vangl2 phenocopy WT Vangl2 for regulation of NF-kappaB? Another area to strengthen might be with regards to specificity of cell types where this phenomenon may be observed. LPS treatment in mice resulted in Vangl2 upregulation in spleen and lymph nodes, but not in lung and liver. What explains the specificity of organ/cell-type Vangl2 upregulation and its consequences observed here? Why is NF-kappaB signaling not more broadly or even ubiquitously affected in all cell types in a Vangl2-dependent manner, rather than being restricted to macrophages, neutrophils and peritoneal macrophages, or, for that matter, in spleen and LN and not liver and lung? After all, one may think that the PCP proteins, as well as NF-kappaB, are ubiquitous. Regardless, Vangl2 as a negative regulator of NF-kappaB is an important finding. There are, however, some concerns about methodology and statistics that need to be addressed.

Reviewer #3 (Public Review):

The authors describe a method to tether proteins via DNA linkers in magnetic tweezers and apply it to a model membrane protein. The main novelty appears to be the use of DBCO click chemistry to covalently couple to the magnetic bead, which creates stable tethers for which the authors report up to >1000 force-extension cycles. Novel and stable attachment strategies are indeed important for force spectroscopy measurements, in particular for membrane proteins that are harder and therefore less studied in this regard than soluble proteins, and recording >1000 stretch and release cycles is an impressive achievement. Unfortunately, I feel that the current work falls short in some regards to exploring the full potential of the method, or at least does not provide sufficient information to fully assess the performance of the new method. Specific questions and points of attention are included below.

- The main improvement appears to be the more stable and robust tethering approach, compared to previous methods. However, the stability is hard to evaluate from the data provided. The much more common way to test stability in the tweezers is to report lifetimes at constant force(s). Also, there are actually previous methods that report on covalent attachment, even working using DBCO. These papers should be compared.

- The authors use the attachment to the surface via two biotin-traptavidin linkages. How does the stability of this (double) bond compare to using a single biotin? Engineered streptavidin versions have been studied previously in the magnetic tweezers, again reporting lifetimes under constant force, which appears to be a relevant point of comparison.

- Very long measurements of protein unfolding and refolding have been reported previously. Here, too, a comparison would be relevant. In light of this previous work, the statement in the abstract "However, the weak molecular tethers used in the tweezers limit a long time, repetitive mechanical manipulation because of their force-induced bond breakage" seems a little dubious. I do not doubt that there is a need for new and better attachment chemistries, but I think it is important to be clear about what has been done already.

- Page 5, line 99: If the PEG layer prevents any sticking of beads, how do the authors attach reference beads, which are typically used in magnetic tweezers to subtract drift?

- Figure 3 left me somewhat puzzled. It appears to suggest that the "no detergent/lipid" condition actually works best, since it provides functional "single-molecule conjugation" for two different DBCO concentrations and two different DNA handles, unlike any other condition. But how can you have a membrane protein without any detergent or lipid? This seems hard to believe.<br /> Figure 3 also seems to imply that the bicelle conditions never work. The schematic in Figure 1 is then fairly misleading since it implies that bicelles also work.

- When it comes to investigating the unfolding and refolding of scTMHC2, it would be nice to see some traces also at a constant force. As the authors state themselves: magnetic tweezers have the advantage that they "enable constant low-force measurements" (page 8, line 189). Why not use this advantage?<br /> In particular, I would be curious to see constant force traces in the "helix coil transition zone". Can steps in the unfolding landscape be identified? Are there intermediates?

- Speaking of loading rates and forces: How were the forces calibrated? This seems to not be discussed. And how were constant loading rates achieved? In Figure 4 it is stated that experiments are performed at "different pulling speeds". How is this possible? In AFM (and OT) one controls position and measures force. In MT, however, you set the force and the bead position is not directly controlled, so how is a given pulling speed ensured?<br /> It appears to me that the numbers indicated in Figures 4A and B are actually the speeds at which the magnets are moved. This is not "pulling speed" as it is usually defined in the AFM and OT literature. Even more confusing, moving the magnets at a constant speed, would NOT correspond to a constant loading rate (which seems to be suggested in Figure 4A), given that the relationship between magnet positions and force is non-linear (in fact, it is approximately exponential in the configuration shown schematically in Figure 1).

- Finally, when it comes to the analysis of errors, I am again puzzled. For the M270 beads used in this work, the bead-to-bead variation in force is about 10%. However, it will be constant for a given bead throughout the experiment. I would expect the apparent unfolding force to exhibit fluctuations from cycle to cycle for a given bead (due to its intrinsically stochastic nature), but also some systematic trends in a bead-to-bead comparison since the actual force will be different (by 10% standard deviation) for different beads. Unfortunately, the authors average this effect away, by averaging over beads for each cycle (Figure 4). To me, it makes much more sense to average over the 1000 cycles for each bead and then compare. Not surprisingly, they find a larger error "with bead size error" than without it (Figure 5A). However, this information could likely be used (and the error corrected), if they would only first analyze the beads separately.<br /> What is the physical explanation of the first fast and then slow decay of the error (Figure 5B)? I would have expected the error for a given bead after N pulling cycles to decrease as 1/sqrt(N) since each cycle gives an independent measurement. Has this been tested?

Reviewer #3 (Public Review):

Fulton et al. look to apply approaches for tackling the readout of gene regulatory networks (GRNs) to a system where cell position itself is continually changing. The objective is highly laudable. GRN analysis has proven to be a powerful approach for understanding how cell fates are determined by morphogenetic inputs, but it has thus far been applied in a limited number of systems. Here, the authors look to substantially extend the application of GRNs to more dynamic systems. The theoretical and experimental approaches are integrated to achieve the analysis of the GRN. In principle, this has wide potential impact and applicability to other systems.

Unfortunately, in its current form, the manuscript does not do justice to the central aims of the authors. The manuscript is unclear in nearly all sections, and figures and analysis can be substantially improved. The quantifications are not shown in a fitting manner. The modelling itself stands as the strongest part of the manuscript, but improvements are needed. Currently, the main claims of the authors cannot be evaluated based on the quality of the presented data.

Reviewer #3 (Public Review):

This study by Wang et al. investigates the role of the focal adhesion protein vinculin in osteocytes and its effect on bone mass. First, they showed decreased levels of vinculin in osteocytes in trabecular bone from aged individuals compared to young, suggesting a potential role for vinculin in regulating bone mass with aging. Next, they deleted vinculin in late osteoblasts and osteocytes in young and older mice and found decreased bone mineral density and trabecular bone mass. This was due to impaired bone formation, which the authors attributed to increased sclerostin levels. Further in vitro experiments showed that vinculin regulates sclerostin via the transcription factor Mefc2. Conditional knockout of vinculin in late osteoblasts and osteocytes had no effect on the bone of mice lacking Sost, further implicating an essential role for sclerostin in mediating the effects of vinculin in osteocytes. Interestingly, the vinculin conditional knockout mice had an impaired response to mechanical loading, suggesting an important role for vinculin in the osteocyte mechanoresponse. Finally, the authors showed that while ovariectomy increased osteoclast formation and bone resorption in control mice, it had no effect on the bone of the vinculin conditional knockout mice.

Overall, the authors show convincing data for the important role of vinculin in osteocytes in regulating the anabolic effects of bone formation under physiological conditions. They also show that osteocyte vinculin may be a regulator of bone resorption under conditions mimicking postmenopausal osteoporosis. However, not all of the conclusions are fully supported by the data.

Strengths:

The use of both in vivo and in vitro approaches to determine the role of vinculin in osteocytes provides compelling evidence for its importance under basal conditions and in regulating the anabolic effects of mechanical loading. The in vitro assays nicely demonstrate a potential mechanism through Mef2c/ECR5.

The creation of the vinculin and Sost double conditional knockout mouse model provides further convincing evidence for the causative role of sclerostin in the effects of vinculin knockout in osteocytes.

The use of both young and older male mice links nicely with the human samples where vinculin expression appears to be reduced in osteocytes with aging. The authors need to be careful in describing 14-month-old mice as aged though, as these mice would not be typically thought of as old.

Weaknesses:

The methods section is lacking in basic details (e.g., there is no information on the CRISPR deletion of Vcl in the MLO-Y4 cells). While referencing their previous papers is fine, a brief description of the methods should be included in this paper.

While much of the data linking vinculin to sclerostin is convincing, it is surprising that the authors show decreased trabecular bone volume in the vinculin cKO mice, yet show increased sclerostin levels in the cortical bone. If increased sclerostin is responsible for impaired bone formation in the vinculin cKO mice, why is there no cortical bone phenotype? It would be important for the authors to also show the sclerostin immunostaining in the trabecular bone of these animals.

The authors do not provide any potential explanation for the effects of vinculin cKO in the ovariectomized mice. Under physiological conditions, osteocyte vinculin has no effect on osteoclast number or bone resorption. How is osteocyte vinculin affecting osteoclasts after ovariectomy? Are there differences in the osteocyte expression of Rankl or Opg in response to the loss of estrogen in the vinculin cKO and control mice?

From their in vitro experiments, the authors deduce that loss of vinculin affects osteocyte attachment. However, their images would suggest that it is the formation of dendrites that is strongly inhibited in the cells lacking vinculin. It is surprising that no investigation of osteocyte dendrite number or connectivity was performed in the vinculin cKO mice. This is particularly important as a decrease in osteocyte dendrites and connectivity has been observed in the bones of aged mice (see Tiede-Lewis et al., Aging. 2017) and osteocyte dendrites are important for mechanosensation.

Reviewer #3 (Public Review):

The authors propose a mechanistic model of how the interplay between activity-independent growth and an activity-dependent synaptic strengthening/weaken model influences the dendrite shape, complexity and distribution of synapses. The authors focus on a model for stellate cells, which have multiple dendrites emerging from a soma. The activity independent component is provided by a random pool of presynaptic sites that represent potential synapses and that release a diffusible signal that promotes dendritic growth. Then a spontaneous activity pattern with some correlation structure is imposed at those presynaptic sites. The strength of these synapses follow a learning rule previously proposed by the lab: synapses strengthen when there is correlated firing across multiple sites, and synapses weaken if there is uncorrelated firing with the relative strength of these processes controlled by available levels of BDNF/proBDNF. Once a synapse is weakened below a threshold, the dendrite branch at that site retracts and loses its sensitivity to the growth signal

The authors run the simulation and map out how dendrites and synapses evolve and stabilize. They show that dendritic trees growing rapidly and then stabilize by balancing growth and retraction (Figure 2). They also that there is an initial bout of synaptogenesis followed by loss of synapses, reflecting the longer amount of time it takes to weaken a synapse (Figure 3). They analyze how this evolution of dendrites and synapses depends on the correlated firing of synapses (i.e. defined as being in the same "activity group"). They show that in the stabilized phase, synapses that remain connected to a given dendritic branch are likely to be from same activity group (Figure 4). The authors systemically alter the learning rule by changing the available concentration of BDNF, which alters the relative amount of synaptic strengthening, which in turn affects stabilization, density of synapses and interestingly how selective for an activity group one dendrite is (Figure 5). In addition the authors look at how altering the activity-independent factors influences outgrowth (Figure 6). Finally, one of the interesting outcomes is that the resulting dendritic trees represent "optimal wiring" solutions in the sense that dendrites use the shortest distance given the distribution of synapses. They compare this distribute to one published data to see how the model compared to what has been observed experimentally.

There are many strengths to this study. The consequence of adding the activity-dependent contribution to models of synapto- and dendritogenesis is novel. There is some exploration of parameters space with the motivation of keeping the parameters as well as the generated outcomes close to anatomical data of real dendrites. The paper is also scholarly in its comparison of this approach to previous generative models. This work represented an important advance to our understanding of how learning rules can contribute to dendrite morphogenesis

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.

The conclusions of this paper are mostly well-supported, but some aspects should be clarified and extended.

1. The authors claim the beiging of WAT of ADGAT DKO mice is partially through the SNS; however, housing these mice at thermoneutrality did not block the beiging, which seems to negate that claim. Is there evidence of increased cAMP/PKA activation in the adipose tissues of ADGAT DKO to support the premise that the beiging is activated by the SNS, even at thermoneutrality? Alternatively, if the authors block beta-adrenergic receptors with antagonists, such as propranolol, does this block the beiging?

2. It's been shown that autocrine FGF21 signaling is sufficient to promote beiging of iWAT (PMID 34192547). The authors show Fgf21 mRNA is increased in iWAT of chow-fed ADGAT DKO mice. Is Fgf21 also increased in iWAT of HFD-fed mice? This and measurement of local FGF21 secretion by adipocytes would strengthen this study.

3. The primary adipocytes in Figure S6A do not appear to have any depletion in TG stores, suggesting this may not be an appropriate model to study the cell autonomous effects of ADGAT DKO on beiging. The authors should use DGAT inhibitors instead to corroborate or investigate this question.

4. Multiple studies have shown the importance of lipolysis for the activation of brown and beige thermogenic programs (PMID 35803907, 34048700) and can be potentiated by HFD feeding (PMID 34048700). In the absence of DGAT activity in ADGAT DKO mice, it seems plausible that free fatty acids could be elevated, especially in the context of HFD. Are free fatty acids elevated in the adipose tissues, which could promote thermogenic gene expression?

5. The lack of ectopic lipid deposition in the ADGAT DKO mice is striking, especially under HFD conditions. Can the increased energy expenditure fully account for the difference in whole body fat accumulation between Control and DKO mice or have the mice activated other energy disposal mechanisms? Please discuss or include measurement of fat excretion in the feces to strengthen this study.

Reviewer #3 (Public Review):

This study examines how the correlation structure of a perceptual decision making task influences history biases in responding. By manipulating whether stimuli were more likely to be repetitive or alternating, they found evidence from both behavior and a neural signal of decision formation that history biases are flexibly adapted to the environment. On the whole, these findings are supported across an impressive range of detailed behavioral and neural analyses. The methods and data from this study will likely be of interest to cognitive neuroscience and psychology researchers. The results provide new insights into the mechanisms of perceptual decision making.

The behavioral analyses are thorough and convincing, supported by a large number of experimental trials (~600 in each of 3 environmental contexts) in 38 participants. The psychometric curves provide clear evidence of adaptive history biases. The paper then goes on to model the effect of history biases at the single trial level, using an elegant cross-validation approach to perform model selection and fitting. The results support the idea that, with trial-by-trial accuracy feedback, the participants adjusted their history biases due to the previous stimulus category, depending on the task structure in a way that contributed to performance.

The paper then examines MEG signatures of decision formation, to try to identify neural signatures of these adaptive biases. Looking specifically at motor beta lateralization, they found no evidence that starting-level bias due to the previous trial differed depending on the task context. This suggests that the adaptive bias unfolds in the dynamic part of the decision process, rather than reflecting a starting level bias. The paper goes on to look at lateralization relative to the chosen hand as a proxy for a decision variable (DV), whose slope is shown to be influenced by these adaptive biases.

This analysis of the buildup of action-selective motor cortical activity would be easier to interpret if its connection with the DV was more explicitly stated. The motor beta is lateralized relative to the chosen hand, as opposed to the correct response which might often be the case. It is therefore not obvious how the DV behaves in correct and error trials, which are combined together here for many of the analyses.

Reviewer #3 (Public Review):

In this manuscript, Yang et al. showed that two nuclear receptor genes, COUP-TFI and -TFII, displayed distinct expression patterns and functions during the development of the dorsal and ventral hippocampus. The phenotypes in the presented single and double conditional knockout mice are striking and intriguing, which expands our knowledge of hippocampus development, especially the ventral part. Nevertheless, the manuscript is a bit descriptive without in-depth molecular mechanisms.

My major concerns as follows:

1. Quantification and statistical analysis to support their conclusions are almost absent throughout the whole manuscript, especially in relation to the numbers of DG, CA1, and CA3 neurons.<br /> 2. Only TFI conditional knockout mice, not TFII knockout mice, were used to test for behavioral abnormalities. It is important to determine whether the abnormal ventral hippocampus in TFII loss leads to any psychiatric illness.<br /> 3. Behavior defects were only tested on TFI conditional knockout mice but not on TFII knockout mice. TFII loss predominantly affects the ventral hippocampus which is involved in psychiatric disorders, and this should be tested.

Reviewer #3 (Public Review):

This manuscript describes interesting experiments on how information from the two eyes is combined in cortical areas, sub-cortical areas, and perception. The experimental techniques are strong and the results are potentially quite interesting. But the manuscript is poorly written and tries to do too much in too little space. I had a lot of difficulty understanding the various experimental conditions, the complicated results, and the interpretations of those results. I think this is an interesting and useful project so I hope the authors will put in the time to revise the manuscript so that regular readers like myself can better understand what it all means.

Now for my concerns and suggestions:

The experimental conditions are novel and complicated, so readers will not readily grasp what the various conditions are and why they were chosen. For example, in one condition different flicker frequencies were presented to the two eyes (2Hz to one and 1.6Hz to the other) with the flicker amplitude fixed in the eye presented to the lower frequency and the flicker amplitude varied in the eye presented to the higher frequency. This is just one of several conditions that the reader has to understand in order to follow the experimental design. I have a few suggestions to make it easier to follow. First, create a figure showing graphically the various conditions. Second, come up with better names for the various conditions and use those names in clear labels in the data figures and in the appropriate captions. Third, combine the specific methods and results sections for each experiment so that one will have just gone through the relevant methods before moving forward into the results. The authors can keep a general methods section separate, but only for the methods that are general to the whole set of experiments.

I wondered why the authors chose the temporal frequencies they did. Barrionuevo et al (2014) showed that the human pupil response is greatest at 1Hz and is nearly a log unit lower at 2Hz (i.e., the change in diameter is nearly a log unit lower; the change in area is nearly 2 log units lower). So why did the authors choose 2Hz for their primary frequency? And why did the authors choose 1.6Hz which is quite close to 2Hz for their off frequency? The rationale behind these important decisions should be made explicit.

By the way, I wondered if we know what happens when you present the same flicker frequencies to the two eyes but in counter-phase. The average luminance seen binocularly would always be the same, so if the pupil system is linear, there should be no pupil response to this stimulus. An experiment like this has been done by Flitcroft et al (1992) on accommodation where the two eyes are presented stimuli moving oppositely in optical distance and indeed there was no accommodative response, which strongly suggests linearity.

Figures 1 and 2 are important figures because they show the pupil and EEG results, respectively. But it's really hard to get your head around what's being shown in the lower row of each figure. The labeling for the conditions is one problem. You have to remember how "binocular" in panel c differs from "binocular cross" in panel d. And how "monocular" in panel d is different than "monocular 1.6Hz" in panel e. Additionally, the colors of the data symbols are not very distinct so it makes it hard to determine which one is which condition. These results are interesting. But they are difficult to digest.

The authors make a strong claim that they have found substantial differences in binocular interaction between cortical and sub-cortical circuits. But when I look at Figures 1 and 2, which are meant to convey this conclusion, I'm struck by how similar the results are. If the authors want to continue to make their claim, they need to spend more time making the case.

Figure 5 is thankfully easy to understand and shows a very clear result. These perceptual results deviate dramatically from the essentially winner-take-all results for spatial sinewaves shown by Legge & Rubin (1981); whom they should cite by the way. Thus, very interestingly the binocular combination of temporal variation is quite different than the binocular combination of spatial variation. Can the pupil and EEG results also be plotted in the fashion of Figure 5? You'd pick a criterion pupil (or EEG) change and use it to make such plots.

My main suggestion is that the authors need to devote more space to explaining what they've done, what they've found, and how they interpret the data. I suggest therefore that they drop the computational model altogether so that they can concentrate on the experiments. The model could be presented in a future paper.

Reviewer #3 (Public Review):

Yang and colleagues investigated whether information on two task-irrelevant features that induce response conflict is represented in a common cognitive space. To test this, the authors used a task that combines the spatial Stroop conflict and the Simon effect. This task reliably produces a beautiful graded congruency sequence effect (CSE), where the cost of congruency is reduced after incongruent trials. The authors measured fMRI to identify brain regions that represent the graded similarity of conflict types, the congruency of responses, and the visual features that induce conflicts.

Using several theory-driven exclusion criteria, the authors identified the right dlPFC (right 8C), which shows 1) stronger encoding of graded similarity of conflicts in incongruent trials and 2) a positive correlation between the strength of conflict similarity type and the CSE on behavior. The dlPFC has been shown to be important for cognitive control tasks. As the dlPFC did not show a univariate parametric modulation based on the higher or lower component of one type of conflict (e.g., having more spatial Stroop conflict or less Simon conflict), it implies that dissimilarity of conflicts is represented by a linear increase or decrease of neural responses. Therefore, the similarity of conflict is represented in multivariate neural responses that combine two sources of conflict.

The strength of the current approach lies in the clear effect of parametric modulation of conflict similarity across different conflict types. The authors employed a clever cross-subject RSA that counterbalanced and isolated the targeted effect of conflict similarity, decorrelating orientation similarity of stimulus positions that would otherwise be correlated with conflict similarity. A pattern of neural response seems to exist that maps different types of conflict, where each type is defined by the parametric gradation of the yoked spatial Stroop conflict and the Simon conflict on a similarity scale. The similarity of patterns increases in incongruent trials and is correlated with CSE modulation of behavior. However, several potential caveats need to be considered.

One caveat to consider is that the main claim of recruitment of an organized "cognitive space" for conflict representation is solely supported by the exclusion criteria mentioned earlier. To further support the involvement of organized space in conflict representation, other pieces of evidence need to be considered. One approach could be to test the accuracy of out-of-sample predictions to examine the continuity of the space, as commonly done in studies on representational spaces of sensory information. Another possible approach could involve rigorously testing the geometric properties of space, rather than fitting RSM to all conflict types. For instance, in Fig 6, both the organized and domain-specific cognitive maps would similarly represent the similarity of conflict types expressed in Fig1c (as evident from the preserved order of conflict types). The RSM suggests a low-dimensional embedding of conflict similarity, but the underlying dimension remains unclear.

Another important factor to consider is how learning within the confined task space, which always negatively correlates the two types of conflicts within each subject, may have influenced the current results. Is statistical dependence of conflict information necessary to use the organized cognitive space to represent conflicts from multiple sources? Answering this question would require a paradigm that can adjust multiple sources of conflicts parametrically and independently. Investigating such dependencies is crucial in order to better understand the adaptive utility of the observed cognitive space of conflict similarity.

Taken together, this study presents an exciting possibility that information requiring high levels of cognitive control could be flexibly mapped into cognitive map-like representations that both benefit and bias our behavior. Further characterization of the representational geometry and generalization of the current results look promising ways to understand representations for cognitive control.

Reviewer #3 (Public Review):

The manuscript by Lin et al. reveals a novel positive regulatory loop between ZEB2 and ACSL4, which promotes lipid droplets storage to meet the energy needs of breast cancer metastasis. It is of interest, however, some concerns should be addressed to strengthen the finding.

Major concerns:

1. The effect of ZEB2 overexpression is not fully demonstrated in the whole study. This point should be addressed.

2. Does the addition of oleate restore the ability of migration or invasion in ACSL4 knockdown cells?

3. Which cellular compartment does ACSL4 localize in and interact with ZEB2 to stabilize ZEB2?

4. The ubiquitination assay and Co-IP assay are just performed in HEK293T cells. This result should be confirmed in MDA-MB-231 cells or Taxol-resistant MCF-7 cells.

5. How does ACSL4 regulate ZEB2 at the mRNA level？Please verify.

6. In Fig. 2F, the silencing efficiency for ACSL4 and ZEB2 should be shown. In addition, the protein level of ZEB2 or ACSL4 in shZEB2 and shZEB2+ACSL4 groups should also be addressed.

7. What is the survival status of patients with both high expression of ACSL4 and ZEB2 in TCGA. In addition, more survival data from databases especially patients with both high expression of ACSL4 and ZEB2 are needed to analyze to support the finding.

Reviewer #3 (Public Review):

The study examines how different cell types in various regions of the mouse dorsal cortex respond to visuomotor integration and how antipsychotic drugs impacts these responses. Specifically, in contrast to most cell types, the authors found that activity in Layer 5 intratelencephalic neurons (Tlx3+) and Layer 6 neurons (Ntsr1+) differentiated between open loop and closed loop visuomotor conditions. Focussing on Layer 5 neurons, they found that the activity of these neurons also differentiated between negative and positive prediction errors during visuomotor integration. The authors further demonstrated that the antipsychotic drugs reduced the correlation of Layer 5 neuronal activity across regions of the cortex, and impaired the propagation of visuomotor mismatch responses (specifically, negative prediction errors) across Layer 5 neurons of the cortex, suggesting a decoupling of long-range cortical interactions.

The data when taken as a whole demonstrate that visuomotor integration in deeper cortical layers is different than in superficial layers and is more susceptible to disruption by antipsychotics. Whilst it is already known that deep layers integrate information differently from superficial layers, this study provides more specific insight into these differences. Moreover, this study provides a first step into understanding the potential mechanism by which antipsychotics may exert their effect.

Whilst the paper has several strengths, the robustness of its conclusions is limited by its questionable statistical analyses. A summary of the paper's strengths and weaknesses follow.

Strengths:

The authors perform an extensive investigation of how different cortical cell types (including Layer 2/3, 4 , 5, and 6 excitatory neurons, as well as PV, VIP, and SST inhibitory interneurons) in different cortical areas (including primary and secondary visual areas as well as motor and premotor areas), respond to visuomotor integration. This investigation provides strong support to the idea that deep layer neurons are indeed unique in their computational properties. This large data set will be of considerable interest to neuroscientists interested in cortical processing.

The authors also provide several lines of evidence that visuomotor information is differentially integrated in deep vs. superficial layers. They show that this is true across experimental paradigms of visuomotor processing (open loop, closed loop, mismatch, drifting grating conditions) and experimental manipulations, with the demonstration that Layer 5 visuomotor integration is more sensitive to disruption by the antipsychotic drug clozapine, compared with cortex as a whole.

The study further uses multiple drugs (clozapine, aripiprazole and haloperidol) to bolster its conclusion that antipsychotic drugs disrupt correlated cortical activity in Layer 5 neurons, and further demonstrates that this disruption is specific to antipsychotics, as the psychostimulant amphetamine shows no such effect.

In widefield calcium imaging experiments, the authors effectively control for the impact of hemodynamic occlusions in their results, and try to minimize this impact using a crystal skull preparation, which performs better than traditional glass windows. Moreover, they examine key findings in widefield calcium imaging experiments with two-photon imaging.

Weaknesses:

A critical weakness of the paper is its statistical analysis. The study does not use mice as its independent unit for statistical comparisons but rather relies on other definitions, without appropriate justification, which results in an inflation of sample sizes. For example, in Figure 1, independent samples are defined as locomotion onsets, leading to sample sizes of approx. 400-2000 despite only using 6 mice for the experiment. This is only justified if the data from locomotion onsets within a mouse is actually statistically independent, which the authors do not test for, and which seems unlikely. With such inflated sample sizes, it becomes more likely to find spurious differences between groups as significant. It also remains unclear how many locomotion onsets come from each mouse; the results could be dominated by a small subset of mice with the most locomotion onsets. The more disciplined approach to statistical analysis of the dataset is to average the data associated with locomotion onsets within a mouse, and then use the mouse as an independent unit for statistical comparison. A second example, for instance, is in Figure 2L, where the independent statistical unit is defined as cortical regions instead of mice, with the left and right hemispheres counting as independent samples; again this is not justified. Is the activity of cortical regions within a mouse and across cortical hemispheres really statistically independent? The problem is apparent throughout the manuscript and for each data set collected.

An additional statistical issue is that it is unclear if the authors are correcting for the use of multiple statistical tests (as in for example Figure 1L and Figure 2B,D). In general, the use of statistics by the authors is not justified in the text.

Finally, it is important to note that whilst the study demonstrates that antipsychotics may selectively impact visuomotor integration in L5 neurons, it does not show that this effect is necessary or sufficient for the action of antipsychotics; though this is likely beyond the scope of the study it is something for readers to keep in mind.

Reviewer #3 (Public Review):

This paper reveals interesting physical connections between Elg1 and CST proteins that suggest a model where Elg1-mediated PCNA unloading is linked to regulation of telomere length extension via Stn1, Cdc13, and presumably Ten1 proteins. Some of these interactions appear to be modulated by sumolyation and connected with Elg1's PCNA unloading activity. The strength of the paper is in the observations of new interactions between CST, Elg1, and PCNA. These interactions should be of interest to a broad audience interested in telomeres and DNA replication.

What is not well demonstrated from the paper is the functional significance of the interactions described. The model presented by the authors is one interpretation of the data shown, and proposes that the role of sumolyation is temporally regulate the Elg1, PCNA and CST interactions at telomeres. This model makes some assumptions that are not demonstrated by this work (such as Stn1 sumolyation, as noted) and are left for future testing. Alternative models that envision sumolyation as a key in promoting spatial localization could also be proposed based on the data here (as mentioned in the discussion), in addition to or instead of a role for sumolyation in enforcing a series of switches governing a tightly sequenced series of interactions and events at telomeres. Critically, the telomere length data from the paper indicates that the proposed model depicts interactions that are not necessary for telomerase activation or inhibition, as telomeres in pol30-RR strains are normal length and telomeres in elg1∆ strains are not nearly as elongated as in stn1 strains. One possibility mentioned in the paper is the PCNAS and Elg1 interactions are contributing to the negative regulation of telomerase under certain conditions that are not defined in this work. Could it also be possible that the role of these interactions is not primarily directed toward modulating telomerase activity? It will be of interest to learn more about how these interactions and regulation by Sumo function intersect with regulation of telomere extension.

Reviewer #3 (Public Review):

Most farming is done by subtracting or adding what people want based in nature. However, in nature, crops interact with various objects, and mostly we are unaware of their effects. In order to increase agricultural productivity, finding useful objects is very important. However, in an uncontrolled environment, it coexists with so many biological objects that it is very inefficient to verify them all experimentally. It is therefore necessary to develop an effective screening method to identify external environmental factors that can increase crop productivity. This study identified factors presumed to be important to crop growth based on metabarcoding analysis, field sampling, and non-linear analysis/information theory, and conducted a mesocosm experiment to verify them experimentally. In conclusion, the object proposed by the author did not increase rice yield, but rather rice growth rate.

Strength<br /> In actual field data, since many variables are involved in a specific phenomenon, it is necessary to effectively eliminate false positives. Based on the metabarcoding technique, various variables that may affect rice growth were quantitatively measured, although not perfectly, and the causal relationship between these variables and rice growth was analyzed by using information transfer analysis. Using this method, two new players capable of manipulating rice growth were verified, despite their unknown functions until now. I found this process to be very logical, and I think it will be valuable in subsequent ecological studies.

Weaknesses<br /> CK treatment's effectiveness remains questionable. Rice's growth was clearly altered by CK treatment. The validation of the CK treatment itself is not clear compared to the GN treatment, and the transcriptome data analysis results do not show that DEG is not present. The possibility of a side effect caused by a variable that the author cannot control remains a possibility in this case. Even though this part is mentioned in Discussion, it is necessary to discuss various possibilities in more detail.

Reviewer #3 (Public Review):

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

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

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

I enjoyed that the authors leveraged the RNNs to model remapping in a 2D navigation task that is harder to understand from data alone, at least with current experimental capabilities. I would recommend clarifying that you're studying a 2D environment that consists of two circular variables. Currently, this is not clear from the text, and it is more natural to interpret the task schematic in Figure 4A as depicting an arena without periodic boundary conditions. Figure 4F depicts neural activity for this task as a standard torus, however, I suspect the neural activity might actually lie along the surface of a Clifford torus as Cueva, Ardalan et al. 2021 found when they trained a RNN to store two circular variables. As a disclaimer, I am one of the authors of that study.

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

Reviewer #3 (Public Review):

The manuscript presents novel findings regarding the metacognitive judgment of difficulty of perceptual decisions. In the main task, subjects accumulated evidence over time about two patches of random dot motion, and were asked to report for which patch it would be easier to make a decision about its dominant color, while not explicitly making such decision(s). Using 4 models of difficulty decisions, the authors demonstrate that the reaction time of these decisions are not solely governed by the difference in difficulties between patches (i.e., difference in stimulus strength), but (also) by the difference in absolute accumulated evidence for color judgment of the two stimuli. In an additional experiment, the authors eliminated part of the uncertainty by informing participants about the dominant color of the two stimuli. In this case, reaction times were faster compared to the original task, and only depended on the difference between stimulus strength.

Overall, the paper is very well written, figures and illustrations clearly and adequately accompanied the text, and the method and modeling are rigor.

The weakness of the paper is that it does not provide sufficient evidence to rule out the possibility that judging the difficulty of a decision may actually be comparing between levels of confidence about the dominant color of each stimulus. One may claim that an observer makes an implicit color decision about each stimulus, and then compares the confidence levels about the correctness of the decisions. This concern is reflected in the paper in several ways:

1. It is not clear what were the actual instructors to the participants, as two different phrasings appear in the methods: one instructs participants to indicate which stimulus is the easier one and the other instructs them to indicate the patch with the stronger color dominance. If both instructions are the same, it can be assumed that knowing the dominant color of each patch is in fact solving the task, and no judgment of difficulty needs to be made (perhaps a confidence estimation). Since this is not a classical perceptual task where subjects need to address a certain feature of the stimuli, but rather to judge their difficulties, it is important to make it clear.

2. Two step model: two issues are a bit puzzling in this model. First, if an observer reaches a decision about the dominant color of each patch, does it mean one has made a color decision about the patches? If so, why should more evidence be accumulated? This may also support the possibility that this is a "post decision" confidence judgment rather than a "pre decision" difficulty judgment. Second, the authors assume the time it takes to reach a decision about the dominant color for both patches are equal, i.e., the boundaries for the "mini decision" are symmetrical. However, it would make sense to assume that patches with lower strength would require a longer time to reach the boundaries.

3. Experiment 2: the modification of the Difference model to fit the known condition (Figure 5b), can also be conceptualized as the two-step model, excluding the "mini" color decision time. These two models (Difference model with known color; two-step model) only differ from each other in a way that in the former the color is known in advance, and in the second, the subject has to infer it. One may wonder if the difference in patterns between the two (Figure 3C vs. Figure 6B) is only due to the inaccuracies of inferring the dominant color in the two-step model.

An additional concern is about the controlled duration task: Why were these specific durations chosen (0.1-1.65 sec; only a single duration was larger than 1sec), given the much longer reaction times in the main task (Experiment 1), which were all larger on average than 1sec? This seems a bit like an odd choice. Additionally, difficulty decision accuracies in this version of the task differ between known and unknown conditions (Figure 7), while in the reaction time version of the same task there were no detectable differences in performance between known and unknown conditions (Figure 6C), just in the reaction times. This discrepancy is not sufficiently explained in the manuscript. Could this be explained by the short trial durations?

Reviewer #3 (Public Review):

Wu and colleagues are characterising the function of Styxl2 during muscle development, a pseudo-phosphatase that was already described to have some function in sarcomere morphogenesis or maintenance (Fero et al. 2014). The authors verify a role for Styxl2 in sarcomere assembly/maintenance using zebrafish embryonic muscles by morpholino knock-down and by a conditional Styxl2 allele in mice (knocked-out in satellite cells with Pax7 Cre).

Experiments using a tamoxifen inducible Cre suggest that Styxl2 is dispensable for sarcomere maintenance and only needed for sarcomere assembly.

BioID experiments with Styxl2 in C2C 12 myoblasts suggest binding of nonmuscle myosins (NMs) to Styxl2. Interestingly, both NMs are downregulated when muscles differentiate after birth or during regeneration in mice. This down-regulation is reduced in the Styxl2 mutant mice, suggesting that Styxl2 is required for the degradation of these NMs.

Impressively, reducing one NM (zMyh10) by double morpholino injection in a Styxl2 morphant zebrafish, does improve zebrafish mobility and sarcomere structure. Degradation of Mhy9 is also stimulated in cell culture if Styxl2 is co-expressed. Surprisingly, the phosphatase domain is not needed for these degradation and sarcomere structure rescue effects. Inhibitor experiments suggest that Styxl2 does promote the degradation of NMs by promoting the selective autophagy pathway.

Strengths:

A major strength of the paper is the combination of various systems, mouse and fish muscles in vivo to test Styxl2 function, and cell culture including a C2C12 muscle cell line to assay protein binding or protein degradation as well as inhibitor studies that can suggest biochemical pathways.

Weakness:

The weakness of this manuscript is that the sarcomere phenotypes and also the western blots are not quantified. Hence, we rely on judging the results from a single image or blot.<br /> Also, Styxl2 role in sarcomere biology was not entirely novel.

Few high resolution sarcomere images are shown, myosins have not been stained for.

Reviewer #3 (Public Review):

The authors claim that this dataset covers a timepoint of embryogenesis that is not well covered in the other published single cell datasets (Tintori et al 2016 and Packer et al 2019). The Tintori data indeed do not cover the 28-102-cell stages sufficiently, but it is unclear how the data presented here compare to the Packer et al data. It is true that the Packer et al data have fewer cells at earlier timepoints than at later ones, but given that they sequenced tens of thousands of cells, they report that they still have ~10,000 cells <210 min of embryogenesis. If the authors want to make any claims about how their data enables exploration of a stage that was previously not accessible, this would require a better comparison to the available data.

The authors provide thorough support for how they assigned cell identities in their data. It is surprising though that at the 102-cell stage they only identify 37 unique cell identities. They suggest that this is because there are many equivalence groups at this stage. However, I would strongly encourage the authors to perform a similar analysis or otherwise compare their obtained identities with the data from Packer et al. 2019. It seems possible that given the low number of cells in this dataset, the authors are missing certain identities and it would be important to know this.

The main analysis the authors perform is to look at expression patterns of various classes of TFs and ask whether they are enriched in particular lineages or at specific timepoints. This analysis is interesting but would be more informative if the authors provided in Figure 3d the numbers of each class of TFs. The authors then focus on the homeodomain class of TFs as they display interesting lineage-specific expression patterns, which when mapped on the embryo form stripes. The stripe pattern however is not that obvious, at least not as shown in Figure 4b (for example all three darker shades of blue looks indistinguishable). Perhaps separate embryo schematics showing the different TF expression patterns would show this more clearly. Moreover, given the relatively small number of cell identities found in this dataset (particularly at the 102-cell stage), a similar analysis using the Packer data would provide further support to these patterns. The localization of cells with shared expression patterns does show a stripe pattern at the 28-cell stage, but also not so clearly beyond this timepoint.

I am also unsure about the validity/value of the comparison of the stripes to Drosophila and the centrality of homeodomain TFs to anterior-posterior positional identity. First, it would be important to map other TFs, very likely there are several other TFs that correlate with positional identity. Also, even if the expression of the homeodomain TFs in C. elegans form stripes, there are still several cells within that stripe that do not express these TFs, it is thus unclear whether these TFs encode positional information or the identity of cells with different positions in the embryo.

Reviewer #3 (Public Review):

Elkind et al. have devised a strategy to detect cells in whole brain samples of the large, publicly accessible Allen Mouse Brain Connectivity database. They put together an analysis pipeline to quantify cell numbers and -density as well as volumes for all annotated brain areas in these samples. This allowed them to make several important discoveries such as (1) strain-, sex- and hemisphere-specific differences in cell densities, (2) a large interindividual variability in cell numbers, and (3) an absence of linear scaling of cell count with volume, among others. The key strength of this work lies in its comprehensive analysis, the large sample size that the authors have drawn from (making their conclusions particularly robust), and the fact that they have made their analysis tools accessible. A weakness of the current manuscript is the dense layout and overplotting of several of the figures, and the lack of necessary information to understand them more easily. Another, conceptual weakness of using the autofluorescence channel for cell detection is that the identity (neuronal vs non-neuronal) of the underlying cells remains unresolved. Overall, however, I believe that this study has the potential to serve as a valuable reference point, and I would expect this work to have a lasting impact on quantitative studies of mouse brain cytoarchitecture.

Reviewer #3 (Public Review):

Trigo & Kawaguchi study how small somatic subthreshold depolarizations that do not trigger full blown APs can propagate to presynaptic endings and modulate transmitter release. To this end they directly recorded from small cerebellar MLI boutons. In paired somatic and presynaptic recordings, they demonstrate that small synaptic potentials can travel within 2 to 3 ms to the bouton and arrive there with an amplitude attenuated by 20 to 70% with respect to the somatically recorded potential. As expected, this amplitude attenuation depends on axon length. In recordings of MLI-Purkinje cell pairs the authors further demonstrate that small somatic subthreshold depolarizations of about 20 mV size can enhance AP-triggered IPSCs recorded in the Purkinje cells and change synaptic plasticity during AP trains. In order to address mechanisms of such presynaptic modulation, the authors measure presynaptic AP waveforms via cell attached recordings and found these very stable. On the other hand, presynaptic ICa(V) directly recorded in voltage-clamped MLI boutons facilitated in response to small pre-depolarizations and such facilitated ICa(V) produced larger IPSCs in paired recordings of MLI boutons and coupled Purkinje cells. The authors propose that an accumulation of partially gated channels during small presynaptic depolarizations is able to produce more rapid gating of VGCCs during the AP waveform on arrival of an invading presynaptic AP.

Electrotonic coupling between soma and presynaptic endings to the extent that small subthreshold depolarizations such as synaptic potentials can travel to the bouton has been demonstrated before. However direct quantification of such coupling is difficult because of the small size of presynaptic compartments. Trigo & Kawaguchi have now pioneered such very challenging direct presynaptic recordings in the form of recordings of MLI soma and bouton pairs or paired pre- and postsynaptic recordings.

The data is convincing and I do not see a need for additional experiments. But the manuscript in its present form falls short with respect to the presentation and discussion of the data. The authors conclusion about the mechanism of presynaptic ICa(V) facilitation should be verified with proper kinetic simulations using a kinetic scheme such as that proposed by Li, Bischofberger & Jonas (2007) J.Neurosci. which should be adapted to the presynaptic ICa(V) in MLI boutons. This would strengthen the manuscript which otherwise, regarding mechanisms, remains somewhat speculative.

Reviewer #3 (Public Review):

The fungus Entomophthora muscae infects flies and in turn manipulates the flies to produce a summiting behavior that is believed to enhance spore dispersal that happens upon the eventual death of the fly. In this study, the authors undertake a Herculean effort to identify the neural pathways that are manipulated by the fungus to cause summiting. In a major advance, the authors develop techniques that allow them to track behaviors of infected flies over the course of several days. This allows them to investigate summiting behaviors that occur just prior to death with unprecedented detail. In their analysis, the authors find that summiting flies show a burst of increased locomotion just prior to death. Importantly, they show that this burst of locomotion is not seen in flies that are dying from other causes (starvation or desiccation). The burst of locomotion is also found to coincide with an increase in elevation that occurs with summiting, but other results indicate that a change in elevation may be an indirect consequence of increased locomotion. With this new knowledge in hand, the authors screen for genes and neuronal pathways that either disrupt or enhance the burst of locomotion that is characteristic of summiting. These experiments clearly indicate that neurons and genes controlling circadian rhythms play a major role in summiting behaviors. The authors focus their attention on a particular subset of clock neurons (DN1p) as potentially mediating summiting behavior. It is worth noting that DN1p neurons have been implicated in a variety (and in some cases contradictory) of circadian processes and that the interpretation of manipulations of these neurons may be an oversimplification. In particular, prior studies have implicated these cells in temperature entrainment/compensation so interpreting thermogenetic manipulations of these cells might be complicated. The authors also zoom in on a specific region of the brain containing neurons of the pars intercebralis, since they find infiltration by the fungus in this region and the effects of drivers targeting the PI. Converging and convincing lines of evidence to suggest that the PI neurons output to the corpora allata and effects of summing may be mediated by the CA. The already impressive series of experiments are further clinched by the development of a machine vision-based classifier that allows the authors to automatically identify summiting flies so that they may be collected for metabolomic analyses. The authors are automatically emailed and seemingly roused themselves in the middle of the night in order to obtain the precious flies they needed. They find a bunch of compounds that appear in summiting flies and even inject hemolymph from the infected animals into naive flies to find that circulating compounds can affect behaviors. Overall, this paper is a tour de force that addresses a system of long-standing interest and brings it into the modern age. Many new questions are now raised for the future by this fascinating study.

Reviewer #3 (Public Review):

In this paper, Gochman et al examine TRPV1-3 channel sensitization by CBD, specifically in the context of 2-APB activation. The authors primarily used classic electrophysiological techniques to address their questions about channel behavior but have also used structural biology in the form of cryo-EM to examine drug binding to TRPV2. The authors have carefully observed and quantified sensitization of the rat TRPV2 channel to 2-APB by CBD. While this sensitization has been reported previously (Pumroy et al, Nat Commun 2022), the authors have gone into much more detail here and carefully examined this process from several angles, including a comparison to some other known methods of sensitizing TRPV2. Additionally, the authors have also revealed that CBD sensitizes rat TRPV1 and mouse TRPV3 to 2-APB, which has not been reported previously. Up to this point, the work is well thought through and cohesive.

The major weakness of this paper is that the authors' efforts to track down the structural and molecular basis for CBD sensitization neither give insight into how sensitization occurs nor provide a solid footing for future work on the topic. The structural work presented in this paper lacks proper controls to interpret the observed states and the authors do nothing to follow up on a potentially interesting second binding site for CBD. Overall, the structural work feels detached from the rest of the paper. The mutations chosen to examine sensitization are based on setting up TRPV1 in opposition to TRPV2 and TRPV3, which makes little sense as all three channels show sensitization by CBD, even if to different extents. The authors chose their mutations based on the assumption that response to CBD is the key difference between the channels for sensitization, yet the overall state of each channel or the different modes of activation by 2-APB seem to be more likely candidates. As a result, it is not particularly surprising that none of the mutations the authors make reduce CBD sensitization in TRPV2 or increase CBD sensitization in TRPV1.

A difficulty in examining TRPV1-3 as a group is that while they are highly conserved in sequence and structure, there are key differences in drug responses. While it does seem likely that CBD would bind to the same location in TRPV1-3, there is extensive evidence that 2-APB binds at different sites in each channel, as the authors discuss in the paper. Without more basic information about where 2-APB binds to each channel and confirmation that CBD does indeed bind TRPV1-3 at the same site, it may not be possible to untangle this particular mode of channel sensitization.

Reviewer #3 (Public Review):

In this paper, the authors used a cohort study to link immune signatures in blood 30 days after COVID-19 infection as possible predictors of prolonged symptomatology. The paper partially achieves its aims. While the selected analyses are comprehensive, the cohort design is appropriate and the mechanistic ex vivo work is clever and convincing, the strength of conclusions is somewhat limited by the selection of imprecise clinical endpoints, and the lack of analyses examining T regulatory signatures.

Strengths of the paper are:<br /> • The paper includes a comprehensive and structured immune analysis.<br /> • The paper is extremely clearly written.<br /> • The use of manual gating and unsupervised analysis in Fig 1 is complementary and helpful.<br /> • Bystander T cell experiments with IL-15 are useful and attempt to explore mechanisms from human samples which are traditionally very challenging.<br /> • The experiments shown in Figure 4 documenting equal Cov2 T cell responses in all 3 cohorts are an extremely important result.

Major concerns are:<br /> • The significance of the study is somewhat limited by the small sample size.<br /> • The symptomatic outcome scale for PASC is blunt and poorly captures severity. More state-of-the-start scales of symptomatic severity and heterogeneity exist for PASC. I suggest this and other papers as an example: https://pubmed.ncbi.nlm.nih.gov/36454631/<br /> • The omission of analyses examining T regulatory functions is a missed opportunity and these may be impaired in this population.<br /> • This is a challenging question that can be applied to many exploratory studies of this nature: how can we rule out the possibility that statistically significant differences in Figs 1, 2 & 3 are statistically significant but biologically meaningless? All cellular and cytokine measures of immune responses shown in these figures are not routinely measured in the clinic. Are there studies that can be cited to show that these differences are sufficient to have a causal impact on prolonged symptoms and tissue damage rather than just correlations with these outcomes?

Reviewer #3 (Public Review):

The co-suppressive molecule CTLA-4 has a critical role in the maintenance of peripheral tolerance, primarily by Treg mediated control of the co-stimulatory molecules CD80 and CD86. As stated by the authors, previous studies have found a variety of effects of anti-CTLA-4 antibody treatment or genetic loss of CTLA-4 on B-cells. These include increased B-cell activation and antibody production, autoantibody production, impairment of B-cell production in the bone marrow and loss of peripheral B-cells. In this article Muthana et al use a CTLA-4 humanized mouse model and examine the effects of drug conjugated CTLA-4 on the immune system. They observe a transient loss of B-cells in the blood of the treated mice. They then use a range of immune interventions such as T-cell depletion and blocking antibodies to demonstrate that this effect is dependent on T-cell activation.

Since anti-CTLA-4 immunotherapy is in active clinical use exploration of its effects are welcome, this is helped by the use of a humanized CTLA-4 system which should be considered a strength of the paper. However, currently, the central premise of this paper, that B-cells are depleted, seems underexplored. Direct evidence of T-cell killing of B-cells is never presented, rather it is inferred from the reduced numbers of B-cells in the blood. The status of B-cells in sites that contain a large proportion of B-cells such as the spleen and lymph nodes is not examined. Additionally, no examination of B-cell antibody production is performed.

Reviewer #3 (Public Review):

This is a well-written and referenced paper from the laboratory of an outstanding senior investigator. Dr. Corn and colleagues demonstrate convincingly that correction of three transcription factor binding sites in the delta-globin gene promoter results in high levels of delta-globin expression in HUDEP-2 clonal cell populations (Fig. 2B and C) and in CD34+ HSPC (hematopoietic stem and progenitor cells) clonal cell expansions (Fig. 3C). Although correction of the mutant KLF1 binding site has previously been shown to upregulate delta-globin gene transgenes, this new data demonstrate that correction of multiple factor binding sites is required to achieve high-level expression of the delta-globin gene in the endogenous beta-globin gene locus. The results are important because high delta-globin protein levels inhibit the formation of sickle hemoglobin (HbS) polymers that cause sickle cell disease.

Unfortunately, high levels of delta-globin gene expression were not observed after editing of pooled (non-clonal) populations of HUDEP-2 cells (Fig. 1D) or CD34+ HSPC pooled cell populations (Fig. 3B). This result suggests that correction of all 3 promoter elements on individual alleles in CD34+ HSPC populations is far below the level required to be clinically relevant. Also, NHEJ is high in CD34+ HSPC (Fig. 3A); therefore, promoter deletions will inactivate many alleles, and total hemoglobin levels in erythrocytes derived from populations of edited CD34+ HSPC will be much less than normal (29 pg/cell). These cells would be extremely beta-thalassemic.