Reviewer #2 (Public Review):

Embryonic development requires differential gene expression, which is regulated by enhancer elements. Regulatory proteins bind to these DNA elements to regulate close-by promoters. Key insights into the molecular mechanisms of enhancer function have been gained by studying fly segmentation, where a hierarchical cascade of gene regulation subdivides the embryo into ever smaller units. However, segmentation in other insects and arthropods as well as in vertebrates relies on a much more dynamic process where repetitive gene expression patterns appear to migrate across tissues similar to waves. Only recently, models have been proposed that make predictions on the underlying gene regulatory networks (GRN) and the properties of the respective enhancers. Specifically, the previously suggested model of the authors, the enhancer switching model, predicted that each gene expression wave should actually be regulated by two GRNS - one based on a "dynamic enhancer", which directs the early wave-like pattern and the other involving a "static enhancer", which directs the more stable expression defining the segment anlagen at the end of each cycle. However, these predicted enhancer types have not been described so far. In flies, where the respective methodology would be available, the segmentation does not show prominent wave-like patterns. In beetles, where pronounced wave-like patterns have been described, the respective methodology has been missing.

With this work, the authors establish a genomic resource and a transgenic line in the red flour beetle in order to establish it as a model system to tackle questions on enhancers driving dynamic expressions during development. First, they determine the open chromatin at early embryonic stages thereby generating a valuable resource for enhancer detection. They did so by dissecting the embryos of two temporal stages into three parts (head, middle part, and growth zone) and then determined open chromatin via ATAC-seq. This setup allowed for a comparison across tissues and stages to identify dynamically regulated chromatin. Indeed, Mau et al. find that dynamic chromatin regulation is a good criterion to enrich for active enhancers.

Second, they established an enhancer reporter system, which allows for visualization of de novo transcription by both in situ hybridization and in vivo. This MS2 system has for the first time been implemented in this beetle and the authors convincingly show its functionality. Indeed, the expression dynamics can be very nicely visualized in vivo at blastoderm stages.

Combing these two resources, they predicted enhancers based on the criterion of dynamic chromatin regulation (from their ATAC-seq resource) and tested them using their novel MS2 system. Out of 9 tested enhancers located close to the gap genes hunchback and Krüppel and the pair-rule gene runt, 4 drove expression. Combining these data with previously published beetle enhancers, they show that DNA regions with differential accessibility were indeed enriched in active enhancers (appr. 60%), providing a good selection criterion.

Finally, they characterize two of the newly identified enhancers that reflect wave-like expression patterns in fixed embryos and in vivo by using the MS2 system to test predictions of the enhancer switching model. The results are compared with an elaboration of their previously suggested enhancer-switching model, which predicts different patterns for static vs. dynamic enhancers. Indeed, they think that the runB enhancer fits the predictions of a static enhancer.

The authors have generated a genomic resource that will be of very high value to the community in the future. The fact that they dissected the embryos for that purpose makes it even more precise and valuable. Likewise, the transgenic system that allows for testing enhancer activity in vivo will be very valuable for the highly active research field dealing with the prediction and analyses of enhancers.

The analysis of the identified enhancers provides partial confirmation for their model. As the authors state in the discussion, finding at least one pair of such enhancers for one gene would be a great test of their hypothesis - finding pairs of static and dynamic enhancers in several genes would be strong support. Unfortunately, they found only one of the two enhancer types in runt and one in hunchback, respectively. Hence, the prediction of the model remains to be tested in the future.

The authors provide a lot of high-quality data visualized nicely in the figures. The text would profit from some re-formulation, re-structuring, and shortening.

Open questions:<br /> What happens with the runB enhancer at later stages of embryogenesis? With what kind of dynamics do the anterior-most stripes fade and does that agree with the model? Do they show the same dynamics throughout segmentation? I think later stages need to be shown because the prediction from the model would be that the dynamics are repeated with each wave. I am not so sure about the prediction for ageing stripes - yet it would have been interesting to see the model prediction and the activity of the static enhancer.<br /> I understand that the mRNA of the reporter gene yellow is more stable than the runt mRNA. This might interfere with the possibility to test your prediction for static enhancers: The criterion is that the stripes should increase in strength as the wave migrates towards the anterior. You show this for runB - but given that yellow has a more stable transcript - could this lead to a "false positive" increase in intensity with the slower migration and accumulation of transcripts? I would feel more comfortable with the statement that this is a static enhancer if you could exclude that the signal is blurred by an artifact based on different mRNA stability. What about re-running the simulation (with the parameters that have shown to well reflect endogenous runt mRNA levels) but increasing the parameter for the stability of the mRNA? Are static and dynamic enhancers still distinguishable? The claim of having found a static enhancer rests on this increase in signal, hence, other explanations need to be excluded carefully.<br /> What about the head domain of the runB enhancer (e.g. Fig. 6A lowest row): This seems to be different from endogenous expression in your work and in Choe et al. Is that aspect different from endogenous expression and can this be reconciled with your model?<br /> The claim of similar dynamics of expression visualized by in situ and MS2 in vivo relies on comparing Fig. 6C with 6A. To compare these two panels, I would need to know to what stage in A the embryo in C should be compared. Actually, the stripe in 6C appears more crisp than the stripes in 6A.<br /> Were the enhancer dynamics tested in vivo at later stages as well? I would appreciate a clear statement on what stages can be visualized and where the technical boundaries are because this will influence any considerations by others using this system.<br /> How do the reported accessibility dynamics of runA enhancer correlate with the activity of the reporter: E.g. is the enhancer open in the middle body region but closed at the posterior part of the embryo? Or is it closed at the anterior - and if so: why is there a signal of the reporter in the head?<br /> You show that chromatin accessibility dynamics help in identifying active enhancers. Is this idea new or is it based on previous experience with Drosophila (e.g. PMID: 29539636 or works cited in https://doi.org/10.1002/bies.201900188)? Or in what respect is this novel?

Reviewer #2 (Public Review):

Gfeller et al. performed an experiment to test the mechanism underlying plant-soil feedback-induced effects on crop yield using two common rotation partners, corn and wheat, that are grown in sequence with one another in agricultural fields across years. The authors use a benzoxazinoid-deficient corn genotype to show that, compared to soil conditioned in year one by a wild-type (normal) corn variety, wheat growth, and yield decreased in year two. As part of this experiment, the authors also showed that benzoxazinoids exuded from corn roots are persistent over time (i.e., they can be detected in the soil long after corn was harvested), resulting in changes to the structure of bacterial and fungal communities, and reduce insect feeding damage to wheat. These effects were replicated across three different wheat cultivars. Weed pressure (benzoxazinoids have previously been shown to be allelopathic towards other plants) and wheat quality were unaffected in the experiment.

Strengths:

The authors use a large-scale field experiment to test their hypothesis. This is a very important aspect of the study. Most plant-soil feedback studies are conducted using potted plants or, at best, small-plot trials. This experiment was performed using large field plots, which is essential for making reliable inferences about crop rotations and yields in agriculture.

The study does a nice job of testing the underlying chemical mechanisms of how plant-soil feedbacks operate. Many studies have shown that conditioning the soil with one plant species affects the performance of a second plant species sharing that soil, but in virtually all cases we don't know, and can only speculate, what mechanisms are causing this effect.

The data reported are impressive for a few reasons. First, the authors make it a point to measure a wide variety of variables, making the findings particularly robust. I was impressed with the breadth of phenotyping considered by the authors. For example, their plant growth measurements were highly detailed, going from early-season crop performance (e.g., seedling emergence, chlorophyll, height, biomass, water content) to late-season yield effects (e.g., tiller density per plant and unit area, kernel weight) and even considering crop quality (e.g., protein, dough stability), which is usually ignored and assumed to not differ. This was clearly a ton of work! As part of this, they comprehensively measured variables related to plants, insects, weeds, soil microbes, etc., making this highly interdisciplinary work. And a second factor related to the data - the treatment effects were very consistent and impressive in magnitude. While not all variables were significantly affected, the ones that showed effects were consistent and not trivial (i.e., they were biologically significant).

Weaknesses:

While corn and wheat are common rotation partners around the world, it still seems like wheat was an odd choice for this experiment. The main reason I say this is that, as the authors point out, both plants produce benzoxazinoids. This makes it difficult to ascertain the effects of corn-derived benzoxazinoids since wheat is also exuding these compounds from its roots. A non-benzoxazinoid crop like soybean seems like it would've been a better choice since you wouldn't have the confounding effect of both the conditioning and feedback plants producing the same secondary metabolites. On the other hand, the fact that wheat produces benzoxazinoid could be a factor driving its yield response (i.e., crops that don't produce benzoxazinoids may show allelopathic-negative reactions).

The authors show that experimentally eliminating benzoxazinoids has a negative effect on the subsequent crop. While this is interesting from a mechanistic standpoint, it's less compelling than if the reverse was true. In other words, the authors simply show why corn is a good rotation crop for wheat, which has been known for a very long time, even if the mechanism was unclear. The authors argue that this could open the door for breeding that targets benzoxazinoids, which may very well be true; however, the outcomes would be more interesting if the study was showing that existing practices result in low yields and they were paving a path for how to ameliorate this.

In the end, it remains unclear whether the effect is driven by a direct effect from benzoxazinoids on wheat or an indirect effect caused by changes in soil microbes. The authors do a good job of speculating on the likelihood of these two mechanisms in the Discussion; however, they can't say with certainty. They would have to use sterilized soil as a separate treatment to differentiate these mechanisms.

Reviewer #2 (Public Review):

This is an interesting manuscript with a worthwhile approach to receptor mechanisms. The paper contains an impressive amount of new data. These single molecule concentration response curves have been compiled with care and the authors deserve great credit for obtaining these data. I judge the main result to be that there are different values of the recently-proposed agonist-related quantity "efficiency". These values are clustered into 5 quite closely spaced groups. The authors propose that these groups are the same whether considering mutations in the binding site or different agonists.

It was unclear to me in several places, what new data and what old data are included in each figure. Therefore readers may have difficulty judging the claimed advance. This difficulty is not helped by the discussion, which includes some previous findings as "results".

A further weakness is that it is unclear how general or how specific these concepts are. The authors assert that they are, by definition, completely universal. However, we do not have reference to previous work or current data on any other receptor than the muscle nicotinic. I could not square the concept that "every receptor works like this" with the evident lack of desire to demonstrate this for any other receptor.

On one hand, if the framework can be extended, this can be a very important concept, and in some sense, could be the missing link to understanding concentration response curves. On the other, if it proves not to be general, or not to be generally applicable because of circumstances.

Reviewer #2 (Public Review):

In this study, Song and colleagues applied a Hidden Markov Model to whole-brain fMRI data from the unique SONG dataset and a grad-CPT task, and in doing so observed robust transitions between low-dimensional states that they then attributed to specific psychological features extracted from the different tasks.

The methods used appeared to be sound and robust to parameter choices. Whenever choices were made regarding specific parameters, the authors demonstrated that their approach was robust to different values, and also replicated their main findings on a separate dataset.

I was mildly concerned that similarities in some of the algorithms used may have rendered some of the inter-measure results as somewhat inevitable (a hypothesis that could be tested using appropriate null models).

This work is quite integrative, linking together a number of previous studies into a framework that allows for interesting follow-up questions.

Overall, I found the work to be robust, interesting, and integrative, with a wide-ranging citation list and exciting implications for future work.

Reviewer #2 (Public Review):

This study addresses the molecular mechanism by which the FruC transcription factor regulates neurogenesis in Drosophila. The authors combine genetics and genomics to profile FruC genomic binding along with that of trithorax-like (Trl) and Su(H) and several histone modifications including H3K27me3. They propose that Fru acts to fine-tune the expression of Notch effector genes and they show that this regulation does not involve changes in H3K27ac, nor H3K4me3, but rather that FruC-regulated gene expression is correlated with changes in H3K27me3 levels at Fru target genes. While the study is well conducted and combines state-of-the-art techniques, there are several aspects that could be improved. The authors propose that Fru fine-tunes the expression of Notch effector genes, but they do not directly measure gene expression in any of the genetic backgrounds. It is important to do this to have some type of precise measure of transcriptional changes (what does 'fine tune' really mean), as the authors' model is based on subtle changes in H3K27me3. It would be important to quantify and correlate both processes more precisely. Similarly, the authors claim that Fru promotes 'low levels' of H3K27me3 at its bound loci throughout the genome, but they do not describe the criteria that define 'low levels' versus high levels of HK27me3.

In the authors' model, FruC likely functions together with PRC2 to regulate gene expression, and local low-level enrichment of repressive histone marks act to fine-tune gene expression. However, in the absence of experiments directly addressing the molecular mechanisms by which Fru regulates transcription, it would be more accurate to claim that changes in H3K27me3 correlate with altered gene expression.

Reviewer #2 (Public Review):

Where this study is interesting is that the authors do a meta-analysis of studies in which metabolic rate was experimentally manipulated and both this rate and glucocorticoid levels were simultaneously measured. Unsurprisingly, there are relatively few such studies and many are from the lab of Michael Romero. While the results of the analysis are compelling, they are not surprising. That said, this work is important.

It is worth noting that in this analysis, the majority of the studies, if not all, are dealing with variation in baseline levels of glucocorticoids. That means the hormone is mostly acting metabolically at these lower levels and not as a stress response hormone as it does when levels are much higher. This difference is probably due to differences in receptors being activated. This could be discussed.

Reviewer #2 (Public Review):

Lalun and co-authors investigate the signalling outputs triggered by the perception of IDA, a plant peptide regulating organs abscission. The authors observed that IDA perception leads to a transient influx of Ca2+, to the production of reactive oxygen species in the apoplast, and to an increase accumulation of transcripts which are also responsive to an immunogenic epitope of bacterial flagellin, flg22. The authors show that IDA is transcriptionally upregulated in response to several biotic and abiotic stimuli. Finally, based on the similarities in the molecular responses triggered by IDA and elicitors (such as flg22) the authors proposed that IDA has a dual function in modulating abscission and immunity. The manuscript is rather descriptive and provide little information regarding IDA signalling per se. A potential functional link between IDA signalling and immune signalling remains speculative.

Reviewer #2 (Public Review):

The central theme of the manuscript is to report on the structure of SBPase - an enzyme central to the photosynthetic Calvin-Benson-Bassham cycle. The authors claim that the structure is first of its kind from a chlorophyte Chlamydomonas reinhardtii, a model unicellular green microalga. The authors use a number of methods like protein expression, purification, enzymatic assays, SAXS, molecular dynamics simulations and xray crystallography to resolve a 3.09 A crystal structure of the oxidized and partially reduced state. The results are supported by the claims made in the manuscript. One of the main weakness of the work is the lack of wider discussion presented in the manuscript. While the structure is the first from a chlorophyte, it is not unique. Several structures of SBPase are available. As the manuscript currently reads, the wider context of SBPase structures available and comparisons between them is missing from the manuscript. Another important point is that the reported structure of crSBPase is 0.453A away from the alphafold model. Though fleetingly mentioned in the methods section, it should be discussed to place it in the wider context.

Reviewer #2 (Public Review):

Microsporidia has a special invasion mechanism, which the polar tube (PT) ejects from mature spores at ultra-fast speeds, to penetrate the host and transfer the cargo to host. This work generated models for the physical basis of polar tube firing and cargo transport through the polar tube. They also use a combination of experiments and theory to elucidate possible biophysical mechanisms of microsporidia. Moreover, their approach also provided the potential applications of such biophysical approaches to other cellular architecture.

The conclusions of this paper are mostly well supported by data, but some analyses need to be clarified.

According to the model 5 (E-OE-PTPV-ExP) in P42 Fig. 6, is the posterior vacuole connected with the polar tube? If yes, how does the nucleus unconnected with the posterior vacuole enter the polar tube? In Fig. 6, would the posterior vacuole become two parts after spore germination? One part is transported via the polar tube, and the other is still in the spore. I recommend this process requires more experiments to prove.

Reviewer #2 (Public Review):

The manuscript "Novel axonemal protein ZMYND12 interacts with TTC29 and DNAH1, and is required for male fertility and flagellum function" by Dacheux et al. interestingly reported homozygous deleterious variants of ZMYND12 in four unrelated men with asthenoteratozoospermia. Based on the immunofluorescence assays in human sperm cells, it was shown that ZMYND12 deficiency altered the localization of DNAH1, DNALI1, WDR66 and TTC29 (four of the known key proteins involved in sperm flagellar formation). Trypanosoma brucei and mouse models were further employed for mechanistic studies, which revealed that ZMYND12 is part of the same axonemal complex as TTC29 and DNAH1. Their findings are solid, and this manuscript will be very informative for clinicians and basic researchers in the field of human infertility.

Reviewer #2 (Public Review):

This study presents valuable findings including the use of an improved method of Raman spectroscopy to measure accumulation of microplastics in ovarian follicular fluid obtained from cows and women and demonstration that experimental direct exposure of bovine eggs to biologically relevant levels of polystyrene, a microplastic found in both cows and women's follicular fluid, negatively influenced ova maturation status and the abundance of proteins involved in oxidative stress, DNA damage, apoptosis, and oocyte maturation. The evidence supporting the claims of the authors is solid but inclusion of human population from which the follicular fluid was obtained (e.g., demographics, reason for assisted reproduction), and details about quality control for proteome profiling experiments (i.e., peptide count cut-off for significant proteins) would have strengthened the study. The work will be of interest to exposure scientists, reproductive toxicologists, regulatory scientists, and reproductive health clinicians.

Reviewer #2 (Public Review):

Yanagihara and colleagues investigated the immune cell composition of bronchoalveolar lavage fluid (BALF) samples in a cohort of patients with malignancy undergoing chemotherapy and with with lung adverse reactions including Pneumocystis jirovecii pneumonia (PCP) and immune-checkpoint inhibitors (ICIs) or cytotoxic drug induced interstitial lung diseases (ILDs). Using mass cytometry, their aim was to characterize the cellular and molecular changes in BAL to improve our understanding of their pathogenesis and identify potential biomarkers and therapeutic targets. In this regard, the authors identify a correlation between CD16 expression in T cells and the severity of PCP and an increased infiltration of CD57+ CD8+ T cells expressing immune checkpoints and FCLR5+ B cells in ICI-ILD patients.

The conclusions of this paper are mostly well supported by data, but some aspects of the data analysis need to be clarified and extended.

1) The authors should elaborate on why different set of markers were selected for each analysis step. E.g., Different set of markers were used for UMAP, CITRUS and viSNE in the T cell and myeloid analysis.

2) The authors should state if a normality test for the distribution of the data was performed. If not, non-parametric tests should be used.

3) The authors should explore the correlation between CD16 intensity and the CTCAE grade in T cell subsets such as EMRA CD8 T cells, effector memory CD4, etc as identified in Figure 1B.

4) The authors could use CITRUS to better assess the B cell compartment.

Reviewer #2 (Public Review):

The authors developed 11 key measures of clinical activity in primary care and measured changes in the frequency of these measures throughout the first 1.5 years of the COVID-19 pandemic. The biggest strength of the study is the data source, which comprises records from 99% of general practices in England. The biggest limitation lies in the analysis of the data: The authors used only descriptive statistics for the investigation of time trends and have not accounted for long-term time trends (only one "control year" was considered). Still, owing to the large study size, the time trends observed are convincing. The work is of high significance to the field because the OpenSAFELY platform will enable the continuous and real-time monitoring of primary care activity.

Reviewer #2 (Public Review):

In this computational study, Delamare et al identify slow neuronal excitability as one mechanism underlying representational drift in recurrent neuronal networks and that the drift is informative about the temporal structure of the memory and when it has been formed. The manuscript is very well written and addresses a timely as well as important topic in current neuroscience namely the mechanisms that may underlie representational drift.

The study is based on an all-to-all recurrent neuronal network with synapses following Hebbian plasticity rules. On the first day, a cue-related representation is formed in that network and on the next 3 days it is recalled spontaneously or due to a memory-related cue. One major observation is that representational drift emerges day-by-day based on intrinsic excitability with the most excitable cells showing highest probability to replace previously active members of the assembly. By using a day-decoder, the authors state that they can infer the order at which the reactivation of cell assemblies happened but only if the excitability state was not too high. By applying a read-out neuron, the authors observed that this cell can track the drifting ensemble which is based on changes of the synaptic weights across time. The only few questions which emerged and could be addressed either theoretically or in the discussion are as follows:

1. Would the similar results be obtained if not all-to-all recurrent connections would have been molded but more realistic connectivity profiles such as estimated for CA1 and CA3?<br /> 2. How does the number of excited cells that could potentially contribute to an engram influence the representational drift and the decoding quality?<br /> 3. How does the rate of the drift influence the quality of readout from the readout-out neuron?

Reviewer #2 (Public Review):

In this manuscript, the authors describe the role of cibarial mechanosensory neurons in fly ingestion. They demonstrate that pumping of the cibarium is subtly disrupted in mutants for piezo, TMC, and nomp-C. Evidence is presented that these three genes are co-expressed in a set of cibarial mechanosensory neurons named md-C. Silencing of md-C neurons results in disrupted cibarial emptying, while activation promotes faster pumping and/or difficulty filling. GRASP and chemogenetic activation of the md-C neurons is used to argue that they may be directly connected to motor neurons that control cibarial emptying.

The manuscript makes several convincing and useful contributions. First, identifying the md-C neurons and demonstrating their essential role for cibarium emptying provides reagents for further studying this circuit and also demonstrates the important of mechanosensation in driving pumping rhythms in the pharynx. Second, the suggestion that these mechanosensory neurons are directly connected to motor neurons controlling pumping stands in contrast to other sensory circuits identified in fly feeding and is an interesting idea that can be more rigorously tested in the future.

At the same time, there are several shortcomings that limit the scope of the paper and the confidence in some claims. These include:

a) the MN-LexA lines used for GRASP experiments are not characterized in any other way to demonstrate specificity. These were generated for this study using Phack methods, and their expression should be shown to be specific for MN11 and MN12 in order to interpret the GRASP experiments.

b) There is also insufficient detail for the P2X2 experiment to evaluate its results. Is this an in vivo or ex vivo prep? Is ATP added to the brain, or ingested? If it is ingested, how is ATP coming into contact with md-C neuron if it is not a chemosensory neuron and therefore not exposed to the contents of the cibarium?

c) In Figure 3C, the authors claim that ablating the labellum will remove the optogenetic stimulation of the md-L neuron (mechanosensory neuron of the labellum), but this manipulation would presumably leave an intact md-L axon that would still be capable of being optogenetically activated by Chrimson.

d) Average GCaMP traces are not shown for md-C during ingestion, and therefore it is impossible to gauge the dynamics of md-C neuron activation during swallowing. Seeing activation with a similar frequency to pumping would support the suggested role for these neurons, although GCaMP6s may be too slow for these purposes.

e) The negative result in Figure 4K that is meant to rule out taste stimulation of md-C is not useful without a positive control for pharyngeal taste neuron activation in this same preparation.

In addition to the experimental limitations described above, the manuscript could be organized in a way that is easier to read (for example, not jumping back and forth in figure order).

Reviewer #2 (Public Review):

Although the trans-synaptic tracing method mediated by the rabies virus (RV) has been widely utilized to infer input connectivity across the brain to a genetically defined population in mice, the analysis of labeled pre-synaptic neurons in terms of cell-type has been primarily reliant on classical low-throughput histochemical techniques. In this study, the authors made a significant advance toward high-throughput transcriptomic (TC) cell typing by both dissociated single-cell RNAseq and the spatial TC method known as BARseq to decode a vast array of molecularly-labeled ("barcoded") RV vector library. First, they demonstrated that a barcoded-RV vector can be employed as a simple retrograde tracer akin to AAVretro. Second, they provided a theoretical classification of neural networks at the single-cell resolution that can be attained through barcoded-RV and concluded that the identification of the vast majority (ideally 100%) of starter cells (the origin of RV-based trans-synaptic tracing) is essential for the inference of single-cell resolution neural connectivity. Taking this into consideration, the authors opted for the BARseq-based spatial TC that could, in principle, capture all the starter cells. Finally, they demonstrated the proof-of-concept in the somatosensory cortex, including infrared connectivity from 381 putative pre-synaptic partners to 31 uniquely barcoded-starter cells, as well as many insightful estimations of input convergence at the cell-type resolution in vivo. While the manuscript encompasses significant technical and theoretical advances, it may be challenging for the general readers of eLife to comprehend. The following comments are offered to enhance the manuscript's clarity and readability.

Major points:<br /> 1. I find it difficult to comprehend the rationale behind labeling inhibitory neurons in the VISp through long-distance retrograde labeling from the VISal or Thalamus (Fig. 2F, I and Fig. S3) since long-distance projectors in the cortex are nearly 100% excitatory neurons. It is also unclear why such a large number of inhibitory neurons was labeled at a long distance through RV vector injections into the RSP/SC or VISal (Fig. 3K). Furthermore, a significant number of inhibitory starter cells in the somatosensory cortex was generated based on their projection to the striatum (Fig. 5H), which is unexpected given our current understanding of the cortico-striatum projections.

2. It is unclear as to why the authors did not perform an analysis of the barcodes in Fig. 2. Given that the primary objective of this manuscript is to evaluate the effectiveness of multiplexing barcoded technology in RV vectors, I would strongly recommend that the authors provide a detailed description of the barcode data here, including any technical difficulties or limitations encountered, which will be of great value in the future design of RV-barcode technologies. In case the barcode data are not included in Fig. 2, I would suggest that the authors consider excluding Fig. 2 and Fig. S1-S3 in their entirety from the manuscript to enhance its readability for general readers.

3. Regarding the trans-synaptic tracing utilizing a barcoded RV vector in conjunction with BARseq decoding (Fig. 5), which is the core of this manuscript, I have a few specific questions/comments. First, the rationale behind defining cells with only two rolonies counts of rabies glycoprotein (RG) as starter cells is unclear. Why did the authors not analyze the sample based on the colocalization of GFP (from the AAV) and mCherry (from the RV) proteins, which is a conventional method to define starter cells? If this approach is technically difficult, the authors could provide an independent histochemical assessment of the detection stringency of GFP positive cells based on two or more colonies of RG. Second, it is difficult to interpret the proportion of the 2,914 barcoded cells that were linked to barcoded starter cells (single-source, double-labeled, or connected-source) and those that remained orphan (no-source or lost-source). A simple table or bar graph representation would be helpful. The abundance of the no-source network (resulting from Cre-independent initial infection of the RV vector) can be estimated in independent negative control experiments that omit either Cre injection or AAV-RG injection. The latter, if combined with BARseq decoding, can provide an experimental prediction of the frequency of double-labeled events since connected-source networks are not labeled in the absence of RG. Third, I would appreciate more quantitative data on the putative single-source network (Fig. 5I and S6) in terms of the distribution of pre- and post-synaptic TC cell types. The majority of labeling appeared to occur locally, with only two thalamic neurons observed in sample 25311842 (Fig. S6). How many instances of long-distance labeling (for example, > 500 microns away from the injection site) were observed in total? Is this low efficiency of long-distance labeling expected based on the utilized combinations of AAVs and RV vectors? A simple independent RV tracing solely detecting mCherry would be useful for evaluating the labeling efficiency of the method. I have experienced similar "less jump" RV tracing when RV particles were prepared in a single step, as this study did, rather than multiple rounds of amplification in traditional protocols, such as Osakada F et al Nat Protocol 2013.

Reviewer #2 (Public Review):

In this valuable manuscript Li & Jin record from the substantial nigra and dorsal striatum to identify subpopulations of neurons with activity that reflects different dynamics during action selection, and then use optogenetics in transgenic mice to selectively inhibit or excite D1- and D2- expressing spiny projection neurons in the striatum, demonstrating a causal role for each in action selection in an opposing manner. They argue that their findings cannot be explained by current models and propose a new 'triple control' model instead, with one direct and two indirect pathways. These findings will be of broad interest to neuroscientists, but lacks some direct evidence for the proposal of the new model.

Overall there are many strengths to this manuscript including the fact that the empirical data in this manuscript is thorough and the experiments are well-designed. The model is well thought through, but I do have some remaining questions and issues with it.

Weaknesses:<br /> 1. The nature of 'action selection' as described in this manuscript is a bit ambiguous and implies a level of cognition or choice which I'm not sure is there. It's not integral to the understanding of the paper really, but I would have liked to know whether the actions are under goal-directed/habitual or even Pavlovian control. This is not really possible to differentiate with this task as there are a number of Pavlovian cues (e.g. lever retraction interval, house light offset) that could be used to guide behavior.<br /> 2. In a similar manner, the part of the striatum that is being targeted (e.g. Figures 4E,I, and N) is dorsal, but is central with regards to the mediolateral extent. We know that the function of different striatal compartments is highly heterogeneous with regards to action selection (e.g. PMID: 16045504, 16153716, 11312310) so it would have been nice to have some data showing how specific these findings are to this particular part of dorsal striatum.<br /> 3. I'm not sure how I feel about the diagrams in Figure 4S. In particular, the co-activation model is shown with D2-SPNs represented as a + sign (which is described as "having a facilitatory effect to selection" in the caption), but the co-activation model still suggests that D2-SPNs are largely inhibitory - just of competing actions rather than directly inhibiting actions. Moreover, I am not sure about these diagrams because they appear to show that D2-SPNs far outnumbers D1-SPNs and we know that this isn't the case. I realize the diagrams are not proportionate, but it still looks a bit misrepresented to me.<br /> 4. There are a number of grammatical and syntax errors that made the manuscript difficult to understand in places.<br /> 5. I wondered if the authors had read PMID: 32001651 and 33215609 which propose a quite different interpretation of direct/indirect pathway neurons in striatum in action selection. I wonder if the authors considered how their findings might fit within this framework.<br /> 6. There is no direct evidence of two indirect pathways, although perhaps this is beyond the scope of the current manuscript and is a prediction for future studies to test.

Reviewer #2 (Public Review):

In this study, the authors design a study to examine how place cell representations in the hippocampus change when the rules of a navigational task change. In one group of animals (group 1), the rules change in the same environment as the initial task was performed, and in the second group of animals (group 2), the task with the new rules is presented in a different environment, and then the animals are returned to the first environment with the original rule. (Briefly, on a cross maze, animals first learned to turn right, then the task rule changed to require turning east, and then the rule changed back to turning right). Broadly, using one photon calcium imaging with head mounted mini microscopes, the authors show that, at both the single cell and population level, more remapping occurs in group 1 animals in the initial environment than in group 2 animals.

This work is bolstered by the unique and rigorous way in which the authors track cells across days, in which they compare the rotation angles of crossed-registered groups of cells-I will definitely be using this in the future! The work also benefits from the extensive analysis of both temporal and spatial correlations of cellular activity. However, there are several shortcomings of the behavioral setup and learning conditions that need to be addressed in order to fully support the conclusions of the authors:

First, group 1 animals spend significantly more time in maze 1 than group 2 animals, since group two animals were switched to a different maze when the rule was changed. It is thus difficult to make direct comparison between the two groups, particularly in the last phase of experimentation when although both groups are in the first environment with the task rule, group 1 has experienced maze one for 6 days while group 2 has only experienced in for 3 days. It is therefore potentially difficult to disentangle differences caused by task changes versus length of environmental exposure.

Secondly, and similarly, during the task period, group 1 animals only have exposure to one environment while group 2 animals have exposure to 2 environments. Ideally, group 1 animals would also be exposed to environment 2, to rule out any potential effects of experiencing a novel environment may have on place cell representations, otherwise this cannot be disentangled from the effect of a task rule change.

Third, two concerns about how the animals are trained: First, if I am interpreting the methods correctly, both Group 1 and Group 2 animals are trained so turn-right is on one maze and turn east is on another way. As such, both groups thus have an "original understanding" that different rules are associated with different mazes. This seems potentially confounding given that it is consistent with the future training of Group 2 but not Group 1 mice. Additionally confounding is the fact that, because of the pretraining, group 1 mice have actually experienced the task in 3 different environments; I am unclear if and how this might be expected to affect results. Additionally, it is methodically unclear why pre-training occurs in a different environment than testing does, and what the criterion is for switching the animals from pre-training to training.

It would additionally be useful to discuss the results of this study in the context of spatial and non-spatial tasks. The authors, usefully, spend a significant portion of the paper comparing their results to results seen during fear extinction. It might be worth contextualizing the differences in how fear conditioning has a contextual "background" (i.e., the animals are conditioned to the context) while in their experiment the entire task is based entirely on navigation.

Overall, this is an interesting manuscript that attempts to address how contextual representations change as task parameters change. While the paper contains thorough statistical analysis but could benefit from more discussion of behavior in the context of learning as well as more rigorous behavioral controls. This work will be of interest to researchers studying hippocampus, navigation, and learning.

Reviewer #2 (Public Review):

Respiratory chain complexes assemble in higher-ordered structures termed supercomplexes or respirasomes. The functional significance of these assemblies is currently investigated, there are two main hypothesis tested, namely that supercomplexes provide kinetic advantages or structural stability. Here, the authors use the fruitfly to reveal that, while the respiratoy chain in the organism normally does not form higher-order assemblies, it does so under conditions when their assembly is impaired. Because the rather moderate increase in supercomplex formation does not change oxygen consumption stimulated by CI or CII substrate, the authors conclude that supercomplex formation has more a structural than a functional role. The main strength of this work is that the technical quality of the experiments is high and that the authors induced defects in respiratory chain assembly through sets of well-controlled genetic models. The obtained data are mostly descriptive using standard approaches and are very well executed. The authors claim that their experiments allow to conclude that the role of supercomplex formation is restricted to a structural role and, hence, exclude a function directly related to electron transport efficiency. However, while the authors can show convincingly that supercomplexes form in the mutants, but not in the wild type, their main claim is not well supported by data and both the structural mechanism of supercompelx formation and their significance remain unknown. While the supercomplex formation observed only in mitochondrial mutants per se is interesting, it would be good to great to define structural aspects of supercomplex formation and their potential impact on the stability of the respiratory chain complexes in these mutants.

Reviewer #2 (Public Review):

There is increasing evidence that viruses manipulate vectors and hosts to facilitate transmission. For arthropods, saliva plays an essential role for successful feeding on a host and consequently for arthropod-borne viruses that are transmitted during arthropod feeding on new hosts. This is so because saliva constitutes the interaction interface between arthropod and host and contains many enzymes and effectors that allow feeding on a compatible host by neutralizing host defenses. Therefore, it is not surprising that viruses change saliva composition or use saliva proteins to provoke altered vector-host interactions that are favorable for virus transmission. However, detailed mechanistic analyses are scarce. Here, Zhao and coworkers study transmission of rice stripe virus (RSV) by the planthopper Laodelphax striatellus. RSV infects plants as well as the vector, accumulates in salivary glands and is injected together with saliva into a new host during vector feeding.

The authors present evidence that a saliva-contained enzyme - carbonic anhydrase (CA) - might facilitate virus infection of rice by interfering with callose deposition, a plant defense response. In vitro pull-down experiments, yeast two hybrid assay and binding affinity assays show convincingly interaction between CA and a plant thaumatin-like protein (TLP) that degrades callose. Similar experiments show that CA and TLP interact with the RSV nuclear capsid protein NT to form a complex. Formation of the CA-TLP complex increases TLP activity by roughly 30% and integration of NT increases TLP activity further. This correlates with lower callose content in RSV-infected plants and higher virus titer. Further, silencing CA in vectors decreases virus titers in infected plants. Interestingly, aphid CA was found to play a role in plant infection with two non-persistent non-circulative viruses, turnip mosaic virus and cucumber mosaic virus (Guo et al. 2023 doi.org/10.1073/pnas.2222040120), but the proposed mode of action is entirely different.

While this is an interesting work, there are, in my opinion, some weak points. The microinjection experiments result in much lower virus accumulation in rice than infection by vector inoculation, so their interpretation is difficult. Also, the effect of injected recombinant CA protein might fade over time because of degradation or dilution. The authors claim that enzymatic activity of CA is not required for its proviral activity. However, this is difficult to assess because all CA mutants used for the corresponding experiments possess residual activity. It remains also unclear whether viral infection deregulates CA expression in planthoppers and TLP expression in plants. However, increased CA and TLP levels could alone contribute to reduced callose deposition.

Reviewer #2 (Public Review):

This manuscript discusses the posttranscriptional regulation of flagella synthesis in Escherichia coli. The bacterial flagellum is a complex structure that consists of three major domains, and its synthesis is an energy-intensive process that requires extensive use of ribosomes. The flagellar regulon encompasses more than 50 genes, and the genes are activated in a sequential manner to ensure that flagellar components are made in the order in which they are needed. Transcription of the genes is regulated by various factors in response to environmental signals. However, little is known about the posttranscriptional regulation of flagella synthesis. The manuscript describes four UTR-derived sRNAs (UhpU, MotR, FliX, and FlgO) that are controlled by the flagella sigma factor σ28 (fliA) in Escherichia coli. The sRNAs have varied effects on flagellin protein levels, flagella number, and cell motility, and they regulate different aspects of flagella synthesis.<br /> UhpU corresponds to the 3´ UTR of uhpT.

UhpU is transcribed from its own promoter inside the coding sequence of uhpT.

MotR originates from the 5´ UTR of motA. The promoter for motR is within the flhC CDS and is also the promoter of the downstream motAB-cheAW operon.

FliX originates from the 3´ UTR of fliC. Probably processed from parental mRNA.

FlgO originates from the 3´ UTR of flgL. Probably processed from parental mRNA.

This is a very interesting study that shows how sRNA-mediated regulation can create a complex network regulating flagella synthesis. The information is new and gives a fresh outlook at cellular mechanisms of flagellar synthesis. The presented work could benefit from additional experiments to confirm the effect of endogenous sRNAs expressed at natural level.

Reviewer #2 (Public Review):

In this study, the authors describe a pipeline to sequence expressed var genes from RNA sequencing that improves on a previous one that they had developed. Importantly, they use this approach to determine how var gene expression changes with short-term culture. Their finding of shifts in the expression of particular var genes is compelling and casts some doubt on the comparability of gene expression in short-term culture versus var expression at the time of participant sampling. The authors appear to overstate the novelty of their pipeline, which should be better situated within the context of existing pipelines described in the literature.

Other studies have relied on short-term culture to understand var gene expression in clinical malaria studies. This study indicates the need for caution in over-interpreting findings from these studies.

The novel method of var gene assembly described by the authors needs to be appropriately situated within the context of previous studies. They neglect to mention several recent studies that present transcript-level novel assembly of var genes from clinical samples. It is important for them to situate their work within this context and compare and contrast it accordingly. A table comparing all existing methods in terms of pros and cons would be helpful to evaluate their method.

Reviewer #2 (Public Review):

In this work Ushio et al. combine environmental DNA metabarcoding with novel statistical approaches to demonstrate how fish communities respond to changing sea temperatures over a seasonal cycle. These findings are important due to the need for new techniques that can better measure community stability under climate change. The eDNA metabarcoding dataset of 550 water samples over two years is, I feel, of sufficient scale to provide power to detect fine-scale ecological interactions, the experiments are well controlled, and the statistical analysis is thorough.

The major strengths of the manuscript are: (1) the magnitude of the dataset, which provides densely replicated sampling that can overcome some of the noise associated with eDNA metabarcoding data and scale up the number of data points to make unique inferences; (2) the novel method of transforming the metabarcode reads using endogenous qPCR "spike-in" data from a common reference species to obtain estimates of DNA concentration across other species; and (3) the statistical analysis of time-series and network data and translating it into interaction strengths between species provides a cross-disciplinary dimension to the work.

I feel like this kind of study showcases the power of eDNA metabarcoding to answer some really interesting questions that were previously unobtainable due to the complexities and cost of such an exercise. Notwithstanding the problems associated with PCR primer bias and PCR stochasticity, the qPCR "spike-in" method is easy to implement and will likely become a standardised technique in the field. Further studies will examine and improve on it.

Overall I found the manuscript to be clear and easy to follow for the most part. I did not identify any serious weaknesses or concerns with the study, although I am not able to comment on the more complex statistical procedures such as the "unified information-theoretic causality" method devised by the authors. The section on limitations of the study is important and acknowledges some issues with interpretation that need to be explained. The methods, while brief in parts, are clear. The code used to generate the results has been made available via a GitHub repository. The figures are clear and attractive.

Reviewer #2 (Public Review):

This paper explores the possibility of integrating diverse and multiple DNA fragments in the genome taking advantage of plasmids in arrays, and CRISPR. Since the efficiency of integration in the genome is low, they, as others in the field, use selection markers to identify successful events of integration. The use of these selection markers is common and diverse, but they use a couple of distinct strategies of selection to:

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

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

The strengths of the study are the clever design of the selection markers, which enrich the collection of this type of markers. While the work is not methodologically novel - it adds to other recent studies, e.g. from Nonet, Mouridi et al., and Malaiwong et al, that use the integration of single and multiple/diverse DNA sequences in the C. elegans genome - it provides a protocol for doing so and tool to make it practical. A limited number of experiments using the method are presented here, and the real test of this method will be its use to address biological questions.

Reviewer #2 (Public Review):

This study reports a novel role of thalamic activity in the late components of a cortical event related potential (ERP). To show this association, the authors used high-density EEG together with multiple deep electrophysiological recordings combined with electrical stimulation of superficial and deep cortical layers. Stimulation of deep layers elicits a late ERP component that is closely related to bursts of thalamic activity during quiet wakefulness. This relationship is quite noticeable when deep layers of the cortex are stimulated, and it does depend on arousal state, being maximal during quiet wakefulness, diminished during active wakefulness, and absent during anesthesia.

The study is very well performed, with a high number of subjects and appropriate methodology. Performing simultaneous recording of EEG and several neuropixels probes together with cortical microstimulation is no small feat considering the size of the mouse head and the fact that mice are freely behaving in many of the experiments. It is also noticeable how the authors use a seemingly outdated technique (electrical microstimulation) to produce compelling and significant research. The conclusions regarding the thalamic contributions to the ERP components are strongly supported by the data.

The spatiotemporal complexity is almost a side point compared to what seems to me the most important point of the paper: showing the contribution of thalamic activity to some components of the cortical ERP. Scalp ERP's have long been regarded as purely cortical phenomena, just like most of EEG, and this study shows convincing evidence to the contrary.

The data presented seemingly contradicts the results presented in Histed et al. (2009), who asserts that cortical microstimulation only affects passing fibers near the tip of the electrodes, and results in distant, sparse, and somewhat random neural activation. In this study, it is clear that the maximum effect happens near the electrodes, decays with distance, and it is not sparse at all, suggesting that not only passing fibers are activated but that also neuronal elements might be activated by antidromic propagation from the axonal hillock. This appears to offer proof that microstimulation might be much more effective than it was thought after the publication of Histed 2009, as the uber-successful use of DBS to treat Parkinson disease has also shown.

Reviewer #2 (Public Review):

The authors should be commended for developing a high throughput platform for the formation and study of human cardiac tissues, and for discussing its potential, advantages and limitations. The study is addressing some of the key needs in the use of engineered cardiac tissues for pharmacological studies: ease of use, reproducible preparation of tissues, and high throughput.

There are also some areas where the manuscript should be improved. The design of the platform and the experimental design should be described in more detail.

It would be of interest to comprehensively document the progression of tissue formation. To this end, it would be helpful to show the changes in tissue structure through a series of images that would correspond to the progression of contractile properties shown in Figure 3.

The very interesting tissue morphology (separation into the two regions) that was observed in this study is inviting more discussion.

Finally, the reader would benefit from more specific comparisons of the contractile function of cardiac tissues measured in this study with data reported for other cardiac tissue models.

Reviewer #2 (Public Review):

The authors provide compelling data to demonstrate that the Notch-related transcription factor RBP-J can influence the number of circulating and recruited monocytes. The authors first delete the Rbpj gene in the myeloid lineage (Lyz2) and show that, as a proportion, only Ly6Clo monocytes are increased in the blood. The authors then attempted to identify why these cells were increased but ruled out proliferation or reduced apoptosis. Next, they investigated the gene signature of Rbpj null monocytes using RNA-sequencing and identified elevated Ccr2 as a defining feature. Crossing the Rbpj null mice to Ccr2 null mice showed reduced numbers of Ly6Clo monocytes compared with Rbpj null alone. Finally, the authors identify that an increased burden of blood Ly6Clo monocytes is correlated with increased lung recruitment and expansion of lung interstitial macrophages.

The main conclusion of the authors, that there is a 'cell intrinsic requirement of RBP-J for controlling blood Ly6CloCCR2hi monocytes' is strongly supported by the data. However, other claims and aspects of the study require clarification and further analysis of the data generated.

Strengths<br /> The paper is well written and structured logically. The major strength of this study is the multiple technically challenging methods used to reinforce the main finding (e.g. parabiosis, adoptive transfer). The finding reinforces the fact that we still know little about how immune cell subsets are maintained in situ, and this study opens the way for novel future work. Importantly, the authors have generated an RNA-sequencing dataset that will prove invaluable for identifying the mechanism - they have promised public access to this data via GEO.

Weaknesses - The main weakness of the study, is that although the main result is solidly supported, as written it is mostly descriptive in nature. For instance, there is no given mechanism by which RBP-J increases Ly6Clo monocytes. The authors conclude this is dependent on CCR2, however CCR2 deletion has a global effect on monocyte numbers and importantly in this study, it does not remove the Ly6Clo bias of cell proportions, if anything it seems to enhance the difference between the ly6C low and high populations in Rbpj null mice (figure 5C). This oversight in data interpretation likely occurred because this experiment is missing a potentially important control (Lyz2cre/cre Ccr2RFP/RFP or RBP-J variations). In general, there seemed to be a focus on the Ly6C low cells, where the mechanism may be more identifiable in their precursors - likely the Ly6C high monocytes.

Other specific weaknesses were identified:<br /> 1) The confirmation of knockout in supplemental figure 1A shows only a two third knockdown when this should be almost totally gone. Perhaps poor primer design, cell sorting error or low Cre penetrance is to blame, but this is below the standard one would expect from a knockout.<br /> 2) Many figures (e.g. 1A) only show proportional data (%) when the addition of cell numbers would also be informative<br /> 3) Many figures only have an n of 1 or 2 (e.g. 2B, 2C)<br /> 4) Sometimes strong statements were based on the lack of statistical significance, when more n number could have changed the interpretation (e.g. 2G, 3E)<br /> 5) There is incomplete analysis (e.g. Network analysis) and interpretation of RNA-sequencing results (figure 4), the difference between the genotypes in both monocyte subsets would provide a more complete picture and potentially reveal mechanisms<br /> 6) The experiments in Figures 5 and 7 are missing a control (Lyz2cre/cre Ccr2RFP/RFP or the Rbpj+/+ versions) and may have been misinterpreted. For example if the control (RBP-J WT, CCR2 KO) was used then it would almost certainly show falling Ly6C low numbers compared to RBP-J WT CCR2 WT, but RBP-J KO CCR2 KO would still have more Ly6c low monocytes than RBP-J WT, CCR2 KO - meaning that the RBP-J function is independent of CCR2. I.e. Ly6c low numbers are mostly dependent on CCR2 but this is irrespective of RBP-J.<br /> 7) Figure 6 was difficult to interpret because of the lack of shown gating strategy. This reviewer assumes that alveolar macrophages were gated out of analysis<br /> 8) The statements around Figure 7 are not completely supported by the evidence, i) a significant proportion of CD16.2+ cells were CCR2 independent and therefore potentially not all recently derived from monocytes, and ii) there is nothing to suggest that the source was not Ly6C high monocytes that differentiated - the manuscript in general seems to miss the point that the source of the Ly6C low cells is almost certainly the Ly6C high monocytes - which further emphasises the importance of both cells in the sequencing analysis<br /> 9) The authors did not refer to or cite a similar 2020 study that also investigated myeloid deletion of Rbpj (Qin et al. 2020 - https://doi.org/10.1096/fj.201903086RR). Qin et al identified that Ly6Clo alveolar macrophages were decreased in this model - it is intriguing to synthesise these two studies and hypothesise that the ly6c low monocytes steal the lung niche, but this was not discussed

Reviewer #2 (Public Review):

Zheng et al. have investigated the effects of PTPMT1 Knock-out on cellular metabolic flexibility. Using several types of appropriate tissue-specific mouse models, the authors have generated data that are both reasonable and broadly significant. While the central mechanism driving the metabolic fuel preference and flexibility remains elusive as the author mentioned in the main text, the finding that the absence of PTPMT1 inhibits glucose (pyruvate) utilization and promotes FAO, resulting in cellular stress and damage, particularly in skeletal and cardiac muscle cells, is intriguing and has practical implications for further research. However, some quantitative data are lacking and certain explanations may be misleading, warranting revisions.

Reviewer #2 (Public Review):

In this manuscript, Mizukami et al. investigate the differences in coronary vasculature morphology across several diverse species to investigate the transition of extrinsic coronary arteries existing on the outflow track in non-amniotes to arteries presenting on the ventricle surface itself in amniotes. They use various visualization techniques, including resin-filling, tissue staining, and fluorescence microscopy to compare the gross morphology and orifice locations of the aortic subepicardial vessels (ASVs) between several amniotes and non-amniotes. Intriguingly, the authors show that the embryonic amniotes rely on a similar ASV structure to adult non-amniotes, but this primitive structure is lost during development in favor of the formation of true coronary arteries on the ventricle surface. While these data intend to show that the difference in coronary artery structure exists between amniotes and non-amniotes, the authors only investigated mice and quail as amniote representatives. Without the inclusion of an ectothermic reptile species as an additional amniote representative, it is entirely possible that the difference in coronary artery structure may instead exist across the endotherm-ectotherm axis as opposed to amniotes and non-amniotes. Despite these concerns, Mizukami et al. show intriguing evolutionary differences between coronary artery structure that draw parallels to changes observed during amniote development.

Reviewer #2 (Public Review):

In this work, the authors extend a mathematical model that they previously developed. Their original paper (Niehaus..Momeni, Nature Comm., 2019) models species interactions using mediators (i.e. metabolites) that species produce and that can affect other species' growth rates. Here, they extend the original model, which was well-mixed, to study communities in space. To do this, here they assume that species grow on a 1D grid, that species can possibly overlap in the same grid spot, and that species and mediators can diffuse in space. They find that spatial structure promotes the coexistence of species when interactions are more facilitating than inhibiting, and when species dispersal is low. Both of these features separately allow for species to self-organize in a way that allows them to be closer in space to partners that facilitate their growth. Properties of the metabolic interactions, such as the amount of metabolites produced and consumed, consumption and production rates, and metabolite diffusion also have effects on species coexistence.

Strengths: The authors extend their previously published model (Niehaus..Momeni, Nature Comm., 2019) to study the role of space in maintaining species diversity. The authors have the goal of modeling realistic bacterial communities; they in fact claim that the model's motivation is to "capture situations in which microbes can disperse inside a matrix", such as the mucosal layer of the digestive or intestinal tract, yogurt or cheese. To do this, the authors add relevant spatial aspects to their previous well-mixed model: species grow on a grid (even though 1D), where they can possibly overlap in the same grid spot, and species and mediators can diffuse in space. The advantage of the model they develop here is that it is simple enough for it to be used to explore general features of systems for which the assumptions of the model are justified. The authors perform a thorough investigation of the effect of spatial structure on the diversity that is maintained in the system. Their investigation includes the role of different types of interactions (facilitation and inhibition), species dispersal, and a range of properties of the metabolic interactions (number of mediators consumed and produced, consumption and production rates, mediator diffusion). Every scenario is compared to the well-mixed scenario to highlight the role of space.

Weaknesses: We are not convinced about some assumptions the authors make when extending their model from well-mixed (Niehaus..Momeni, Nature Comm., 2019) to spatial (this manuscript). The authors want to model a spatially structured system, with a framework that resembles the metacommunity framework, to which they add specific biophysical processes, such as the diffusion of metabolites. However, when adding these specific biophysical processes, the authors use parameters that seem to be unrealistic. One example is the packing of cells: 10^9, which implies a ratio between cells and the environment of 1:1000 volume-wise. Another example is the diffusion of molecules, which is 10 times slower than stated in the literature. With these parameters, the authors aim at describing physical processes in their model, but overall the parameters seem to be far from real values. Thus we suggest either changing these parameters to realistic values, discussing why the chosen parameters are meaningful or reframing the model as an heuristic model.

Overall, we think that the contribution of the paper is to extend a previously published work (Niehaus..Momeni, Nature Comm., 2019) to model spatial communities. It is thus fundamental that the assumptions made by the authors to model the spatial dynamics are well justified. Several physical parameters are chosen to values that do not represent realistic values for spatially structured communities. The authors should discuss if the results hold also for more realistic values.

Reviewer #2 (Public Review):

This paper uses single-cell RNA sequencing to assess the B cell response in a mouse model of autoimmunity. The authors find that the B cell response is transcriptionally similar to the response induced by protein immunization. They further determine that the memory B cell response is composed of transcriptionally distinct subsets that may have distinct spatial distributions.

A major strength of this manuscript is the author's use of an elegant model of autoimmunity in which self-reactive B cells can escape negative selection to become activated and participate in the germinal center response. This system allows the author's a system to study the development of B cells in an autoimmune setting without restricting the repertoire of those cells though the use of BCR transgenes. This single-cell data generated in this study is also likely to be useful to individuals interested in understanding the differences in the B cell response between autoimmune and protein immunization settings.

One weakness of this study is that its main findings do not seem to represent a major conceptual advancement. There are already many published single-cell RNA-seq data sets that show that heterogeneity exists within B cell subsets. Therefore, the author's data primarily extends these findings to indicate that heterogeneity also exists in their model of autoimmunity.

Another major weakness of this study is that the authors only analyze about 13K cells in their single cell RNA-seq experiment with only 3.3K coming from the immunized mice. This low number of cells likely prevents the authors from identifying differences between specific B cell subsets between the two disease settings because there are likely very few cells in many of the clusters in the immunized group.

Finally, the author's data in which they seek to validate their use of Fcrl5 and CD23 to identify memory B cell subsets is not convincing. The flow cytometry gating used to distinguish the memory B cell subsets seem somewhat arbitrary with there not being a clear separation between the four populations shown using the author's gating strategy. This strategy also causes many CD23+ cells to not be analyzed in Fig. 6G.

The imaging data is also not clear as it is not apparent whether the S1pr2-expressing cells indicated by the authors express Fcrl5 since Fcrl5 does not encircle the indicated cell. The authors also do not quantify their images. While the authors do see a difference between the populations following in vivo labeling, it is not clear why the CD45+ population among the Fcrl5+ cells have a higher staining intensity than the Cd23+ cells. It is expected that cells that are exposed to circulation would have a similar staining intensity. Therefore, it is possible that there may be a technical issue with this data. Finally, it is not clear whether the results in figure 6 were repeated with several of the plots only having three mice per group limiting the conclusions that can be drawn from this data.

Reviewer #2 (Public Review):

In this paper, Bond et al. build on previous behavioral modelling of a reversal-learning task. They replicate some features of human behavior with a spiking neural network model of cortical basal ganglia thalamic circuits, and they link some of these same behavioral patterns to corresponding areas with BOLD fMRI. I applaud the authors for sharing this work as a preprint, and for publicly sharing the data and code.

While the spiking neural network model offers a helpful tool to complement behavior and neuroimaging, it is not very clear which predictions are specific to this model (and thus dissociate it from, or go beyond, previous work). Thus, the main strength of this work (combining behavior, brain, and in silico experiments) is not fully fleshed out and could be stronger in the conclusions we can draw from them.

It would be helpful to know more about which features of the spiking NN model are crucial in precisely replicating the behavioral patterns of interest (and to be more precise in which behaviors are replicated from previous work with the same task, vs. which ones are newly acquired because the task has changed - or the spiking CBGT model has afforded new predictions for behavior). Throughout, I am wondering if the authors can compare their results to a reasonable 'null model' which can then be falsified (e.g. Palminteri et al. 2017, TICS); this would give more intuition about what it is about this new CBGT model that helps us predict behavior.

The same question about model comparison holds for the behavior: beyond relying on DIC score differences, what features of behavior can and cannot be explained by the family of DDMs?

Reviewer #2 (Public Review):

Modi and colleagues describe a multivariate framework to analyze local field potentials, which is specifically applied to CA1 data in this work. Multivariate approaches are welcome in the field and the effort of the authors should be appreciated. However, I found the analyses presented here are too superficial and do not seem to bring new insights into hippocampal dynamics. Further, some surrogate methods used are not necessarily controlling for confounding variables. These concerns are further detailed below.

1. The authors in reality do not analyze oscillations themselves in this manuscript but only the power of signals filtered at determined frequency bands. This is particularly misleading when the authors talk about "spindles". Spindles are classically defined as a thalamico-cortical phenomenon, not recorded from hippocampus LFPs. Thus, the fact that you filter the signal in the same frequency range matching cortical spindles does not mean you are analyzing spindles. The terminology, therefore, is misleading. I would recommend the authors to change spindles to "beta", which at least has been reported in the hippocampus, although in very particular behavioral circumstances. However, one must note that the presence of power in such bands does not guarantee one is recording from these oscillations. For example, the "fast gamma" band might be related to what is defined as fast gamma nested in theta, but it might also be related to ripples in sleep recordings. The increase of "spindle" power in sleep here is probably related to 1/f components arising from the large irregular activity of slow wave sleep local field potentials. The authors should avoid these conceptual confusions in the manuscript, or show that these band power time courses are in fact matching the oscillations they refer to (for example, their spindle band is in fact reflecting increased spindle occurrence).

2. The shuffling procedure to control for the occupancy difference between awake and sleep does not seem to be sufficient. From what I understand, this shuffling is not controlling for the autocorrelation of each band which would be the main source of bias to be accounted for in this instance. Thus, time shifts for each band would be more appropriate. Further, the controls for trial durations should be created using consecutive windows. If you randomly sample sleep bins from distant time points you are not effectively controlling for the difference in duration between trial types. Finally, it is not clear from the text if the UMAP is recomputed for each duration-matched control. This would be a rigorous control as it would remove the potential bias arising from the unbalance between awake and sleep data points, which could bias the subspace to be more detailed for the LFP sleep features. It is very likely the results will hold after these controls, given it is not surprising that sleep is a more diverse state than awake, but it would be good practice to have more rigorous controls to formalize these conclusions.

3. Lots of the observations made from the state space approach presented in this manuscript lack any physiological interpretation. For example, Figure 4F suggests a shift in the state space from Sleep1 to Sleep2. The authors comment there is a change in density but they do not make an effort to explain what the change means in terms of brain dynamics. It seems that the spectral patterns are shifting away from the Delta X Spindle region (concluding this by looking at Fig4B) which could be potentially interesting if analyzed in depth. What is the state space revealing about the brain here? It would be important to interpret the changes revealed by this method otherwise what are we learning about the brain from these analyses? This is similar to the results presented in Figure 5, which are merely descriptions of what is seen in the correlation matrix space. It seems potentially interesting that non-REM seems to be split into two clusters in the UMAP space. What does it mean for REM that delta band power in pyramidal and lm layers is anti-correlated to the power within the mid to fast gamma range? What do the transition probabilities shown in Figures 6B and C suggest about hippocampal functioning? The authors just state there are "changes" but they don't characterize these systematically in terms of biology. Overall, the abstract multivariate representation of the neural data shown here could potentially reveal novel dynamics across the awake-sleep cycle, but in the current form of this manuscript, the observations never leave the abstract level.

Reviewer #2 (Public Review):

In the present study, Briana M. Bohannon et al. expand on the study of the effect of Polyunsaturated fatty acids (PUFAS) on Iks (KV7.1 + KCNE1), a delayed rectifier potassium channel of critical relevance in cardiac physiology. PUFAs are amphipathic molecules that activate IKs channels by interacting with positively charged residues on the voltage sensor domain and in the channel's pore. The authors aim to characterize the molecular mechanisms behind the Iks activation by PUFA analogs that contains a tyrosine head group instead of the carboxyl or sulfonyl group present in other PUFAs.

The authors present a well-written manuscript with clear data and well-presented figures. The authors describe the effects of various tyrosine-PUFA analogs and unveil the mechanistic nature of their interactions with the channel. The focus is the N -(alpha-linolenoyl) Tyrosine (NALT), a potent activator by shifting the channel G-V by more than 50mV facilitating the opening of the channel, although the authors tested other tyrosine-PUFA analogs. Remarkably, the hydroxyl group in the tyrosine head is essential to shift the voltage-dependence of activation due to an H-bond with a threonine from the S3-S4 linker that helps coordinate the PUFA together with an electrostatic interaction with arginine in the S4. Furthermore, to test whether the aromatic ring from the tyrosine had a role in the interaction, the authors took a fascinating and exciting approach by modifying it and making the ring more electronegative by adding negatively charged atoms. Interestingly, they discovered that an electronegative-modified aromatic PUFA could increase the channel's conductance, an effect mediated by a specific interaction with a Lysine at the top of the S6 helix.

Although the question addressed in the manuscript is fascinating due to the possible use of these tyrosine-PUFA analogs as IKs modulators, the presented work is very mechanistic and specialized. While the effect of tyrosine-PUFA analogs is robust, the authors could improve the story by highlighting their interest in them and discussing whether they have potential therapeutic uses.

Due to the relevance of IKs currents in cardiac physiology and Long QT syndrome, the discovery and characterization of activators are highly relevant. The present manuscript presents a group of potent IKs channel activators that have the potential to impact the cardiac physiology field dramatically if they can perform under pathophysiological conditions or in the presence of disease-causing mutations.

Reviewer #2 (Public Review):

In this manuscript, the authors used an original empirical design to test if somatic mutation rates are different depending on the plant growth rates. They detected somatic mutations along the growth axes of four trees - two individuals per species for two dipterocarp tree species growing at different rates. They found here that plant somatic mutations are accumulated are a relatively constant rate per year in the two species, suggesting that somatic mutation rates correlate with time rather than with growth, i.e. the number of cell divisions. The authors then suggest that this result is consistent with a low relative contribution of DNA replication errors (referred to as α in the manuscript) to the somatic mutation rates as compared to the other sources of mutations (β). Given that plants - in particular, trees - are generally assumed to deviate from the August Weismann's theory (a part of the somatic variation is expected to be transmitted to the next generation), this work could be of interest for a large readership interested by mutation rates as a whole, since it has implications also for heritable mutation rates too. In addition, even if this is not discussed, the putatively low contribution of DNA replication errors could help to understand the apparent paradox associated to trees. Indeed, trees exhibit clear signatures of lower molecular evolution (Lanfear et al. 2013), therefore suggesting lower mutation rates per unit of time. Trees could partly keep somatic mutations under control thanks to a long-term evolution towards low α values, resulting in low α/β ratios as compared to short-lived species. I therefore consider that the paper tackles a fundamental albeit complex question in the field.

Overall, I consider that the authors should clearly indicate the weakness of the studies. For instance, because of the bioinformatic tools used, they have reasonably detected a small part of the somatic mutations, those that have reached a high allele frequency in tissues. Mutation counts are known to be highly dependent on the experimental design and the methods used. Consequently, (i) this should be explicit and (ii) a particular effort should be made to demonstrate that the observed differences in mutation counts are robust to the potential experimental biases. This is important since, empirically, we know how mutation counts can vary depending on the experimental designs. For instance, a difference of an order of magnitude has been observed between the two papers focusing on oaks (Schmid-Siegert et al. 2017 and Plomion et al. 2018) and this difference is now known to be due to the differences in the experimental designs, in particular the sequencing effort (Schmitt et al. 2022).

Having said that, my overall opinion is that (i) the authors have worked on an interesting design and generated unique data, (ii) the results are probably robust to some biases and therefore strong enough (but see my comments regarding possible improvements), (iii) the interpretations are reasonable and (iv) the discussion regarding the source of somatic mutations is valuable.

Reviewer #2 (Public Review):

Harnessing macrophages to attack cancer is an immunotherapy strategy that has been steadily gaining interest. Whether macrophages alone can be powerful enough to permanently eliminate a tumor is a high-priority question. In addition, the factors making different tumors more vulnerable to macrophage attack have not been completely defined. In this paper, the authors find that chromosomal instability (CIN) in cancer cells improves the effect of macrophage targeted immunotherapies. They demonstrate that CIN tumors secrete factors that polarize macrophages to a more tumoricidal fate through several methods. The most compelling experiment is transferring conditioned media from MSP1 inhibited and control cancer cells, then using RNAseq to demonstrate that the MSP1-inhibited conditioned media causes a shift towards a more tumoricidal macrophage phenotype. In mice with MSP1 inhibited (CIN) B16 melanoma tumors, a combination of CD47 knockdown and anti-Tyrp1 IgG is sufficient for long term survival in nearly all mice. This combination is a striking improvement from conditions without CIN.

Like any interesting paper, this study leaves several unanswered questions. First, how do CIN tumors repolarize macrophages? The authors demonstrate that conditioned media is sufficient for this repolarization, implicating secreted factors, but the specific mechanism is unclear. In addition, the connection between the broad, vaccination-like IgG response and CIN is not completely delineated. The authors demonstrate that mice who successfully clear CIN tumors have a broad anti-tumor IgG response. This broad IgG response has previously been demonstrated for tumors that do not have CIN. It is not clear if CIN specifically enhances the anti-tumor IgG response or if the broad IgG response is similar to other tumors. Finally, CIN is always induced with MSP1 inhibition. To specifically attribute this phenotype to CIN it would be most compelling to demonstrate that tumors with CIN unrelated to MSP1 inhibition are also able to repolarize macrophages.<br /> Overall, this is a thought-provoking study that will be of broad interest to many different fields including cancer biology, immunology and cell biology.

Reviewer #2 (Public Review):

This article examines the ability of dietary supplementation with indole-3-actetate (I3A) to attenuate western diet-induced fatty liver disease. The experiments are appropriately described, and convincing data are provided that I3A can attenuates fat accumulation in the liver. Several possible mechanisms of action were explored and one likely mechanism, an alteration in AMPK signaling pathway was observed, and is likely involved in the observed phenotype. However, I3A has already been shown to yield similar data in a high fat diet mouse model system (PMID: 31484323), although the I3A was administered through IP injection, not in the drinking water. In both studies the effects seen may well be due to activation of PPAR-alpha. Another study (PMID: 19469536) gave acetic acid in the drinking water and obtained data similar to this manuscript, supporting that the effect seen in this study may not be specific to I3A. These references should be included and discussed. Overall, the data and experimental approach taken support the stated conclusions.

Reviewer #2 (Public Review):

Pinos et al present five atherosclerosis studies in mice to investigate the impact of dietary supplementation with b-carotene on plaque remodeling during resolution. The authors use either LDLR-ko mice or WT mice injected with ASO-LDLR to establish diet-induced hyperlipidemia and promote atherogenesis during 16 weeks, and then they promote resolution by switching the mice for 3 weeks to a regular chow, either deficient or supplemented with b-carotene. Supplementation was successful, as measured by hepatic accumulation of retinyl esters. As expected, chow diet led to reduced hyperlipidemia, and plaque remodeling (both reduced CD68+ macs and increased collagen contents) without actual changes in plaque size. But, b-carotene supplementation resulted in further increased collagen contents and, importantly, a large increase in plaque regulatory T-cells (TREG). This accumulation of TREG is specific to the plaque, as it was not observed in blood or spleen. The authors propose that the anti-inflammatory properties of these TREG explain the atheroprotective effect of b-carotene, and found that treatment with anti-CD25 antibodies (to induce systemic depletion of TREG) prevents b-carotene-stimulated increase in plaque collagen and TREG.

An obvious strength is the use of two different mouse models of atherogenesis, as well as genetic and interventional approaches. The analyses of aortic root plaque size and contents are rigorous and included both male and female mice (although the data was not segregated by sex). Unfortunately, the authors did not provide data on lesions in en face preparations of the whole aorta.

Overall, the conclusion that dietary supplementation with b-carotene may be atheroprotective via induction of TREG is reasonably supported by the evidence presented. Other conclusions put forth by the authors (e.g., that vitamin A production favors TREG production or that BCO1 deficiency reduces plasma cholesterol), however, will need further experimental evidence to be substantiated.

The authors claim that b-carotene reduces blood cholesterol, but data shown herein show no differences in plasma lipids between mice fed b-carotene-deficient and -supplemented diets (Figs. 1B, 2A, and S3A). Also, the authors present no experimental data to support the idea that BCO1 activity favors plaque TREG expansion (e.g., no TREG data in Fig 3 using Bco1-ko mice).

As the authors show, the treatment with anti-CD25 resulted in only partial suppression of TREG levels. Because CD25 is also expressed in some subpopulation of effector T-cells, this could potentially cloud the interpretation of the results. Data in Fig 4H showing loss of b-carotene-stimulated increase in numbers of FoxP3+GFP+ cells in the plaque should be taken cautiously, as they come from a small number of mice. Perhaps an orthogonal approach using FoxP3-DTR mice could have produced a more robust loss of TREG and further confirmation that the loss of plaque remodeling is indeed due to loss of TREG.

Reviewer #2 (Public Review):

Manuscript entitled "Uremic toxin indoxyl sulfate (IS) induces trained immunity via the AhR-dependent arachidonic acid pathway in ESRD" presented some interesting findings. The manuscript strengths included use of H3K4me3-CHIP-Seq, AhR antagonist, IS treated cell RNA-Seq, ALOX5 inhibitor, MTA inhibitor to determine the roles of IS-AhR in trained immunity related to ESRD inflammation and trained immunity.

Reviewer #2 (Public Review):

The paper describes the various types of immune cells interacting with SARS-CoV-2 spike protein and undergoing pathological changes upon different routes of administration into mice mainly in the absence of human ACE-2. Multiple murine cell types in the lungs, the cremaster muscle and surrounding tissues, and the liver were studied. The spike interactions with various cells from the human peripheral blood ex vivo and in cultures were also examined. This study focused on hACE-2-independent effects of the spike protein in vivo in mice and in vitro on human leukocytes and touched upon the potential involvement of sialic-acid-binding lectins (Siglec) as non-hACE-2 receptors for spike. Hence, a multitude of aspects about spike-cell interactions was studied, although each was covered without significant depths and the key findings are difficult to parse through. Many inconsistencies are not explained and the critical experimental parameters and controls are missing. Ultimately, the main message of the study is buried among supporting vs confounding data.

Reviewer #2 (Public Review):

The manuscript by Sebastian-Perez describes determinants of heterochromatin domain formation (chromocenters) at the 2-cell stage of mouse embryonic development. They implement an inducible system for transition from ESC to 2C-like cells (referred to as 2C+) together with proteomic approaches to identify temporal changes in associated proteins. The conversion of ESCs to 2C+ is accompanied by dissolution of chromocenter domains marked by HP1b and H3K9me3, which reform upon transition back to the 2C-like state. The innovation in this study is the incorporation of proteomic analysis to identify chromatin-associated proteins, which revealed SMARCAD1 and TOPBP1 as key regulators of chromocenter formation.

In the model system used, doxycycline induction of DUX leads to activation of EGFP reporter regulated by the MERVL-LTR in 2C+ cells that can be sorted for further analysis. A doxycycline-inducible luciferase cell line is used as a control and does not activate the MERVL-LTR GFP reporter. The authors do see groups of proteins anticipated for each developmental stage that suggest the overall strategy is effective.

The major strengths of the paper involve the proteomic screen and initial validation. From there, however, the focus on TOPBP1 and SMARCAD1 is not well justified. In addition, how data is presented in the results section does not follow a logical flow. Overall, my suggestion is that these structural issues need to be resolved before engaging in comprehensive review of the submission. This may be best achieved by separating the proteomic/morphological analyses from the characterization of TOPBP1 and SMARCAD1.

Reviewer #2 (Public Review):

The authors introduce "HAMA", a new automated pipeline for architectural analysis of the bacterial cell wall. Using MS/MS fragmentation and a computational pipeline, they validate the approach using well-characterized model organisms and then apply the platform to elucidate the PG architecture of several members of the human gut microbiota. They discover differences in the length of peptide crossbridges between two species of the genus Bifidobacterium and then show that these species also differ in cell envelope stiffness, resulting in the conclusion that crossbridge length determines stiffness.

The pipeline is solid and revealing the poorly characterized PG architecture of the human gut microbiota is worthwhile and significant. However, it is unclear if or how their pipeline is superior to other existing techniques - PG architecture analysis is routinely done by many other labs; the only difference here seems to be that the authors chose gut microbes to interrogate.

I do not agree with their conclusions about the correlation between crossbridge length and cell envelope stiffness. These experiments are done on two different species of bacteria and their experimental setup therefore does not allow them to isolate crossbridge length as the only differential property that can influence stiffness. These two species likely also differ in other ways that could modulate stiffness, e.g. turgor pressure, overall PG architecture (not just crossbridge length), membrane properties, teichoic acid composition etc.

Reviewer #2 (Public Review):

In this paper, the authors utilize optogenetic stimulation and imaging techniques with fluorescent reporters for pH and membrane voltage to examine the extent of intracellular acidification produced by different ion-conducting opsins. The commonly used opsin CheRiff is found to conduct enough protons to alter intracellular pH in soma and dendrites of targeted neurons and in monolayers of HEK293T cells, whereas opsins ChR2-3M and PsCatCh2.0 are shown to produce negligible changes in intracellular pH as their photocurrents are mostly carried by metal cations. The conclusion that ChR2-3M and PsCatCh2.0 are more suited than proton conducting opsins for optogenetic applications is well supported by the data.

Reviewer #2 (Public Review):

Sadanandan et al describe their studies in mice of HDAC and Polycomb function in the context of vascular endothelial cell (EC) gene expression relevant to the blood-brain barrier, (BBB). This topic is of interest because the BBB gene expression program represents an interesting and important vascular diversification mechanism. From an applied point of view, modifying this program could have therapeutic benefits in situations where BBB function is compromised.

The study involves comparing the transcriptomes of cultured CNS ECs at E13 and adult stages and then perturbing EC gene expression pharmacologically in cell culture (with HDAC and Polycomb inhibitors) and genetically in vivo by EC-specific conditional KO of HDAC2 and Polycomb component EZH2.

This reviewer has several critiques of the study.

First, based on published data, the effect of culturing CNS ECs is likely to have profound effects on their differentiation, especially as related to their CNS-specific phenotypes. Related to this, the authors do not state how long the cells were cultured.

Second, the use of qPCR assays for quantifying ChIP and transcript levels is inferior to ChIPseq and RNAseq. Whole genome methods, such as ChIPseq, permit a level of quality assessment that is not possible with qPCR methods. The authors should use whole genome NextGen sequencing approaches, show the alignment of reads to the genome from replicate experiments, and quantitatively analyze the technical quality of the data.

Third, the observation that pharmacologic inhibitor experiments and conditional KO experiments targeting HDAC2 and the Polycomb complex perturb EC gene expression or BBB integrity, respectively, is not particularly surprising as these proteins have broad roles in epigenetic regulation in a wide variety of cell types.

Reviewer #2 (Public Review):

In the study by Hreich et al, the potency of P2RX7 positive modulator HEI3090, developed by the authors, for the treatment of Idiopathic pulmonary fibrosis (IPF) was investigated. Recently, the authors have shown that HEI3090 can protect against lung cancer by stimulating dendritic cell P2RX7, resulting in IL-18 production that stimulates IFN-γ production by T and NK cells (DOI: 10.1038/s41467-021-20912-2). Interestingly, HEI3090 increases IL-18 levels only in the presence of high eATP. Since the treatment options for IPF are limited, new therapeutic strategies and targets are needed. The authors first show that P2RX7/IL-18/IFNG axis is downregulated in patients with IPF. Next, they used a bleomycin-induced lung fibrosis mouse model to show that the use of a positive modulator of P2RX7 leads to the activation of the P2RX7/IL-18 axis in immune cells that limits lung fibrosis onset or progression. Mechanistically, treatment with HEI3090 enhanced IL-18-dependent IFN-γ production by lung T cells leading to a decreased production of IL-17 and TGFβ, major drivers of IPF. The major novelty is the use of the small molecule HEI3090 to stimulate the immune system to limit lung fibrosis progression by targeting the P2RX7, which could be potentially combined with current therapies available. However, there is the lack of information on the reproducibility of data, especially for the data presented in Figures 3 and 4, and related supplementary figures, as well as the lack of support data for experiments that emphasize the role of P2RX7 expressed on immune cells (e.g. frequency of transferred cells compared to endogenous cells).

intervallically more diverse. The most common method of generating an open voicing is to drop certain notes from a close-position chord down an octave. In a “drop 2” voicing, the second note, counting from the top note, is dropped down an octave. “Drop 2” refers to voicings above the bass in which the bass note is not counted as one of the voices being “dropped.” Each chord in Figure 4.15 includes three “drop 2” voicings because the three notes above the bass can be rotated three times.

see figure 4.15 on p 47

https://docdrop.org/pdf/Miller---Unknown---Modal-Jazz-Composition-and-Harmony.-Vol.2-ggtxc_ocr.pdf/?src=ocr

Modal Jazz Composition and Harmony Vol. 2 Miller, R 1996

Reviewer #2 (Public Review):

This study describes the development of a robotic system that allows investigators to track the movements of Drosophila larvae for extremely long time durations. Prior studies were limited by the fact that tracking of larval movements needed to be stopped whenever the animal reached the edge of a behavioral arena. This new study overcomes this limitation with a robot arm that gently picks up the larvae when they reach the edge of the arena and then gently releases them again so that tracking can be resumed. The very long periods of data acquisition are performed with a video camera that provides a low-resolution 64x64 pixel representation of the larvae. Nevertheless, the authors are able to extract postural information from the animals using a sophisticated machine vision based neural network. The authors use this system to continuously track the behaviors of individual larvae for six hours in the presence or absence of a thermal gradient. They argue that high inter-animal variability in a navigation index occurs in the presence of a thermal gradient but not in its absence. The intra-animal mean navigation also appears to be bimodal, apparently switching between "non-navigating" and "strongly navigating" states (not the authors' words). Interestingly, when only the population means are investigated a single mode is indicated with an overall weak navigation index. This comparison very nicely illustrates the power of this method to reveal richness in the data that leads to insights that cannot be observed with short-term measurements. Another impressive feature of the robotic system design is that it is capable of delivering small droplets of food to individual larvae. This allowed the authors to track a single larva for a remarkable 30 hours in which it is seen to crawl for more than 48 meters. Overall, the robotic system presented here will allow the researchers to investigate behaviors of larvae in long-term experiments in ways that were previously unimaginable.

Reviewer #2 (Public Review):

This is an interesting manuscript in which the authors demonstrate the power of serial section reconstruction at the EM level of a volume within the anterior ventral cochlear nucleus (aVCN) containing bushy cells and their large afferent synapses - the endbulbs of Held. Integration of this information with compartmental modelling of the neuronal excitability is then used to make observations about the form and function of these neurons and their synaptic inputs. While this is technically impressive (in regards to both the structure and modelling) there are significant weaknesses because this integration makes massive assumptions and lacks a means of validation; for example, by checking that the results of the structural modelling recapitulate the single-cell physiology of the neuron(s) under study. This would require the integration of in vivo recorded data, which would not be possible (unless combined with a third high throughput method such as calcium imaging) and is well beyond the present study. The authors need to be more open about the limitations of their observations and their interpretations and focus on the key conclusions that they can glean from this impressive data set. The manuscript would be considerably improved by re-writing to focus the science on the most important results and provide clear declarations of limitations in interpretation.

Reviewer #2 (Public Review):

Maturation of inhibitory synapses requires multiple vital biological steps including, i) translocation of cargos containing GABAARs and scaffolds (e.g. gephyrin) through microtubules (MTs), ii) exocytosis of inhibitory synapse proteins from cargo followed by the incorporation to the plasma membrane for lateral diffusion, and iii) incorporation of proteins to inhibitory synaptic sites where gephyrin and GABAARs are associated with actin. A number of studies have elucidated the molecular mechanisms for GABAARs and gephyrin translocation in each step. However, the molecular mechanisms underlying the transition between steps, particularly from exocytosis to lateral diffusion of inhibitory proteins, still need to be elucidated. This manuscript successfully characterizes three stages of inhibitory synapses during maturation, cluster1: an initial stage that receptors are being brought in and out by the MT system; cluster2: lateral diffusion stage; cluster 3: matured postsynapses anchored by gephyrin and actin, by quantifying the abundance of MAP2 or Actin in inhibitory synapse labeled by gephyrin. Importantly, the authors' findings suggest that TEN2, a trans-synaptic adhesion molecule that has two EB1 binding motifs, plays an important role in the transition from clusters 1 to 2, and inhibitory synapse maturation. The imaging results are impressive and compelling, these data will provide new insights into the mechanisms of protein transport during synapse development. However, the present study contains several loose ends preventing convincing conclusions. Most importantly, (1) it remains more TEN2 domain characterization on inhibitory synapse maturation, (2) further validation of the HA knock-in TEN2 mouse model is required, and (3) it requires additional physiology data that complement the authors' findings.

Reviewer #2 (Public Review):

In this manuscript, the authors have proposed that the suppression of hepatic GPR110, known as a tumorigenic gene, could improve non-alcoholic fatty liver disease (NALFD). With AAV-mediated GPR110 overexpression or a GalNAc-siGPR110 experiment, they have suggested that GPR110 could increase hepatic lipids through SCD1.

Major comments<br /> 1. Although the authors claimed that GPR110 could enhance SCD1-mediated hepatic de novo lipogenesis, the level of GPR110 expression was decreased in obese mice (Figure 1E-F). However, it has been reported that the levels of de novo lipogenic genes, including SCD1, are upregulated in HFD-fed mice (PMID: 18249166, PMID: 31676768). Thus, they should show the levels of hepatic lipids and lipogenic gene expression, including SCD-1, in liver tissues from NCD vs. HFD-fed mice, which will provide insights between GPR110 level and hepatic lipogenic activity.

2. In Figure 2, the authors have characterized metabolic phenotypes of hepatic GPR110 overexpression upon HFD, exhibiting significant phenotypes (including GTT, ITT, HOMA-IR, serum lipids, and hepatic lipid level). However, it is likely that these phenotypes could stem from increased body weight gain. Since they cannot explain how hepatic GPR110 overexpression could increase body weight, it is hard to conclude that the increased hepatic lipid level would be a direct consequence of GPR110 overexpression. Also, given the increased fat mass in GPR110 overexpressed mice, they should test whether GPR110 overexpression would affect adipose tissue. Along the same line, they have to carefully investigate the reason of increased body weight gain in GPR110 overexpressed mice (ex., food intake, and energy expenditure).

3. GPR110 enhances hepatic lipogenesis via SCD1 expression (Figures 5 and 6). To verify whether GPR110 would specifically regulates SCD1 transcript, they have to provide the expression levels of other lipogenic genes, including Srebf1, Chrebp, Acaca, and Fasn. Also, measurement of de novo lipogenic activity using primary hepatocyte with GPR110 overexpression or knockdown would be valuable to affirm the authors' proposed model.

4. In Figure 6, the author should provide the molecular mechanisms how GPR110 signaling could enhance SCD-1 transcription.

5. Figure 9C shows the increased level of GPR110 with NAFLD severity. They should test whether the levels of hepatic GPR110 and SCD-1 might be elevated in a severe NAFLD mouse model. If it is the case, it would be better to show the beneficial effects of GPR110 suppression against NAFLD progression using a severe NAFLD (ex., NASH) mouse model.

Reviewer #2 (Public Review):

This manuscript describes a study of a novel role of FAM76B in regulation of NF-kB-mediated inflammation, specially in neuroinflammation both in animal model and human brain disease. This study was logically designed and laid out and data from gene knockdown and knockout cell line and animals strongly support the note that FAM76B is involved in the neuroinflammatory diseases. This notion was further confirmed in patients with brain inflammatory diseases. Importantly, the authors further dissected the cellular molecular action of FAM76B in regulation of NF-kB pathway through binding to the hnRNPA2B1. However, it is still unclear how the FAM76B regulates/or affects the cytoplasmic translocation of hnRNPA2B1 in brain cells after a variety of inflammatory stimuli or injuries. Nonetheless, this study greatly enhances our understanding of the mechanisms of the brain inflammation and inflammation related brain degeneration.

Reviewer #2 (Public Review):

While aging is known to cause cerebral blood flow deficits, some studies suggested that exercise could reverse - at least partially - these deficits. In this study, the authors used technically-challenging techniques and approaches to test the hypothesis that 5 months of voluntary exercise reverses impairments in cerebrovascular function and cognition. Overall, I find the evidence for a favorable impact of exercise on microvascular perfusion and oxygenation convincing. The impact of exercise was most evident in the white matter and deep cortical tissues, which I believe to be a major finding of the study. The methods are very well-detailed and easy to follow. It is not clear, however, why the authors chose to study only one sex (female mice). This is an important consideration given that age-dependent hormonal changes could play a role in the findings. There are a few instances where it is unclear whether the number of vessels or animals were used for statistical analyses. It'd be very useful for the reader to understand why whisker stimulation led to a reduction in detected light intensity that reflects hyperemia as previously published by the authors (Sencan et al., 2022 JCBFM).

Reviewer #2 (Public Review):

In this manuscript by Huang et al. the authors explore the genetic underpinnings that may cause human oocyte meiotic arrest. The meiotic arrest of oocytes can cause female infertility leading patients to seek treatment at IVF clinics to assist in having genetically related babies. However, because oocytes fail to develop to MII, oocytes from these patients cannot be fertilized, leaving no current options for genetically related babies for patients with this pathology. Huang et al identified 50 IVF patients with this phenotype, and after the whole exome sequence, 3 patients had mutations in a spindle assembly checkpoint regulator, Mad1bp1. This study describes these mutations in detail, shows how these mutations affect Mad1bp1 expression, evaluates gross function in mouse oocytes, and explores therapeutic treatment in human oocytes. Overall, this is an important translational study that adds to the growing body of literature that genetic mutations impact oocyte quality and fertility.

In its current form, I find that the strengths exist in the analysis of the patients' genomes and pedigree information. This is unique data and is important for the field. The expression in oocytes, structure modeling, and conservation in evolution, while not essential for this study, add interesting information for the reader to consider. I sometimes find these distracting in manuscripts, but appreciate them here in this context. The conclusion using human oocytes to propose possible treatment takes the study to completion and is not an easy approach to carry out.

I do find some weaknesses that weaken the conclusions. The conclusion described is that the SAC is not satisfied in oocytes from these patients. The authors attempt to show this by analysis of mouse oocytes using polar body extrusion and its timing as an assay. There could be many reasons contributing to arrest, therefore a singular assay is not ideal to justify the conclusions. While I do suspect the authors are correct, an intact SAC should be shown at the molecular level to fully justify this conclusion. There are many assays routinely performed in mouse oocytes that the authors can consider (check papers by authors from Wassmann, FitzHarris, and Schindler labs for example).

Reviewer #2 (Public Review):

In this work the authors use a simple biophysical model to predict evolutionary trajectories of resistance to pyrimethamine - inhibitor of PfDHFR from P. falciparum and PvDHFR from P. vivax - pathogens causing malaria which presents a worldwide health concern. The authors use a simple fitness model that posits that selection coefficient -relative change in fitness between WT and mutant strains is determined by the fraction of unbound (to antibiotic inhibitor) DHFR. The population genetics simulations use the Kimura formula which is applicable to low mutation high selection regime where populations are monoclonal. The authors use computational tool Rosetta Flex ddG to assess binding of the antibiotic ligand to WT and mutant protein and compare their predicted evolutionary trajectories with lab evolution and data on naturally evolved variants worldwide and find semi-quantitative agreement, albeit sith significant variation in detail.

The paper is of potential interest as it presents one of the first (but not the first) attempts to compare evolutionary dynamics based on biophysics inspired fitness model with laboratory evolution and natural data for very important problem of emergence and fixation of antibiotic resistant alleles. As such it can be a useful starting point for more detailed and biophysical realistic models of evolution of resistance against anti-DHFR drugs.

Reviewer #2 (Public Review):

Microfluidics-assisted live-cell imaging is often the method of choice to gain insight into the growth behavior of single cells, in particular unicellular organisms with simple shapes. While growth rate measurements of symmetrically dividing and rod-shape organisms such as E.coli or fission yeast are simplified by their geometry, measurements of the common model organism budding yeast are more complicated due to growth in three dimensions and asymmetric 'budding'. As a consequence, analysis of live-cell imaging experiments typically still requires time-consuming manual work, in particular, to correct automated segmentation and tracking, assign mother-bud pairs, and determine the time point of cell division. In the present manuscript, Pietsch et al. aim to address this important issue by developing deep-learning-based analysis software named BABY for the automated extraction of growth rate measurements performed with microfluidic traps that are designed to keep mother cells, but quickly lose newborn daughters.

To achieve this, Pietsch et al. introduce several innovative approaches. 1.) In contrast to previous deep-learning segmentation tools they allow 3D data (z-stacks) as inputs and allow for overlapping segmentation masks. 2.) By introducing 3 different object categories based on their size, they can take more specified approaches for each category and for the segmentation of overlapping objects 3.) By using cell edges and bud necks as additional predicted channels, they facilitate downstream post-processing of segmentation masks and mother-bud pairing, respectively. 4.) By using machine learning to predict tracking and mother-bud pairs from multiple features, they develop a novel approach to automate these steps. Using their automated analysis pipeline, the authors then study the growth behavior in different mutants and propose a novel mechanism in which growing buds are regulated by a combination of a 'sizer' and a 'timer' mechanism.

This manuscript introduces exciting steps towards a fully automated analysis of bright-field microscopy data of growing yeast cells, which makes this manuscript an important contribution to the field. However, in part the quantitative reporting on the actual performance is not sufficient. For example, what is the actual overall success-rate in predicting mother-bud pairs? How accurately can cell cycle durations be predicted? This lack of information makes it hard to evaluate how appropriate using fully automated BABY actual is. In addition, the experiments supporting the major biological insight, i.e. the sizer-timer transition for bud growth are rather limited, and further experiments would be needed to strengthen this conclusion.

Reviewer #2 (Public Review):

The manuscript addresses the important question of how EVs are targeted to their recipient cells once they are produced and released.

The present manuscript contains 4 messages:<br /> First, it shows that the transmembrane protein Sas gets incorporated into EVs and that this protein binds to its receptor Ptp10D on target cells, thus targeting the EVs. Second, the manuscript shows that the Sas cytoplasmic domain ICD binds to dARC1 protein (and perhaps darc1 RNAs), which are incorporated into EVs where they form capsids, before being targeted to recipient cells. dARC1 is important for neuron development in flies! Interestingly the motif in the Sas ICD is conserved in mammalian APP that also binds ARC1, suggesting a conserved mechanism of targeting EVs in mammalian neural development. Third, exposure of target cells (ex vivo wing discs) to EVs positive to FL Sas leads to its increased targetting when the target cells also expressed Ptp10D and Numb, which are acting as Sas receptors in a synergetic manner. Fourth, dARC1 ORF expression in the EV-producing cells (SG) leads to the increased expression of dARC1 protein and mRNAs in the recipient cells in vivo (Trachea). Many techniques are used, including IEM, fly genetics, S2 cells, and Ips. It is broad, and well executed, and the questions are interesting.

However, the manuscript should be strengthened. It is a lot of data and techniques but because there are so many messages in the paper, each needs more substances and controls.

1: Use of more extensive fly genetics using specific Ptp10D LOF in wing discs and trachea (to show the converse of the GOF).<br /> Does Ptp10D acts as the MAIN receptor to FL Sas? Numb LOF, a combination of LOF and GOF?<br /> does Ptp10D GOF compensate for Numb and vice versa?

2: What is the specificity for FL Sas? The expression of short Sas should not lead to its incorporation in EVs and their overnight addition should not lead to the same effect (Figure 3). This should be better investigated as short Sas is a good control for FL Sas.

3: A better quantitative analysis should be provided. For instance, there is no quantitative data for Figure 5.

4: All experiments are done with flies. There is no data on mammalian neurons in culture. This is missing. Exposure of neurons with SAS-positive EVs (or APP)

5: Are the capsid reconstitution with purified dARC1 and 2 performed in the presence of darc1 rRNA? Any RNA (figure 2).

6: The dAC1 increased expression in the target cells upon dARC1 increased production in SG(Figure 5) becomes an important part of the paper (and the model) but is not investigated!<br /> How does it work? Does the delivery of darc1 mRNAs packaged in capsids simply lead to more dARC1 translation? Is it proportional?<br /> OR is there also stimulation of darc1 transcription? Is there also an increase in the mRNA level (I cannot see the SG control of 5o (sage>+) supporting the authors' claim on line 562!).

7: Most (all) experiments are performed with overexpression of FL Sas or ICD. Does endogenous Sas bind endogenous Ptp10D and dARC1? ICDs? Also full-length APP?

Reviewer #2 (Public Review):

The manuscript by Tang et al investigates the potential difference between the enteric nervous system derived from different axial regions of chicken embryos. By applying single cell RNA-sequencing (scRNA-seq) analysis of virally traced enteric cell populations, the authors conclude that vagal and sacral neural crest may contribute to different neural subtypes and non-neural cells in the sub-umbilical ENS. Confirming previous studies, their method also demonstrates the exact axial levels of the GI-tract populated by sacral neural crest. The analysis suggests that NPY/VIP+ neurons mainly arise from vagal neural crest in both the pre- and postumbilical ENS, while sacral neural crest mainly contribute with Th/Dbh/Ddc+ neurons. Sacral neural crest also appears to generate a greater proportion of schwann cell-like cells and melanocytes to the gut.

While early studies in the chicken model (combined with quail) founded many of the key principles underlying the emergence of the ENS from different neural crest sources, the chicken model currently lags behind in the implementation of modern transcriptomic and neurophysiological approaches. This paper provides a long-saught comprehensive scRNA-seq datasets of the chicken ENS which is clearly lacking in the ENS field. The elegant viral delivery allows targeting of both vagal and sacral neural crest in the same embryo offering clear advantages to other commonly used model systems (including the mouse). However, analytical approaches are in the current form preliminary and not enough to draw firm biological conclusions. While the datasets are large (which is highly appreciated), they represent a relatively early stage of ENS development and possible differences between vagal and sacral-derived populations could partially be attributed to difference in maturity. Maturity will surely not explain the whole difference observed but needs to be factored into the interpretation. As scRNA-seq datasets from the mature chicken ENS are lacking (as well as detailed IHC-based neural classification system) the inference made in the paper between molecular classes and functional types are premature.

Specific concerns:<br /> 1) Analysis of scRNA-sequenced sacral- versus vagal-derived ENS reveals clusters consistent with a non-ENS identity (endothelial, muscle, vascular and more). Previous studies in mouse using the neural crest tracing line Wnt1-Cre has not demonstrated such diverse progenies of neural crest from any region. An exception being a small population of mesenchymal-like cells (Ling and Sauka-Spengler, Nat Cell Biol. 2019; Zeisel et al., Cell 2018; Morarach et al., 2021; Soldatov et al., Science 2019). Therefore, the claimed broad potential of neural crest giving rise to diverse gut cell populations warrants more validating experiments.

2) Several earlier studies have revealed that parts of the ENS is derived from neural crest that attach to nerve bundles, obtain a schwann cell precursor-like identity and thereafter migrate into the gut (Uesaka et al. J Neurosci 2015 and Espinosa-Medina et al, PNAS 2017). The current work in chicken needs to be interpretated in the light of these findings and the publications should be discussed in relevant sections of the introduction and discussion.<br /> 3) The analysis indicates the presence of melanocytes. It is not clear why they are part of the GI-tract preparations. Could they correspond to another cell type, with partially overlapping gene expression profile as melanocytes?

4) As evident, the sacral- and vagal-derived ENS are not clonally related. To decipher differentiation paths and relations between clusters, individual analysis of the different datasets are needed. With only one UMAP representing the merged datasets combined with little information on markers, it is hard to evaluate the soundness of the conclusions regarding cell-identities of clusters and lineage differentiation.

5) E10 is a relatively early stage in chicken ENS development. Around E7, the intestines do not contain differentiated neurons even. The relative high expression of Hes5 (marking mature enteric glia in the mouse; Morarach et al., 2021) in the vagal neural crest population might be explained by the more mature state of vagal versus sacral ENS. As also outlined below, Th/Dbh are known to be transiently expressed in the developing ENS why they could indicate the relative immaturity of sacral neural crest rather than differential neural identities. These issues need to be taken into account when interpreting biology from scRNA-seq data.

6) Unlike the guineapig, and to some extent pig and murine ENS, the physiology of chicken enteric neurons has not been well characterized yet. Therefore, it is highly advisable to refrain from a nomenclature of clusters designating functions. Several key molecular markers are known to differ between murine, guineapig, rat and human systems. IPANs are a good example where differential expression is seen (SST in human but not mice; CGRP labels some IPANS in mouse, but not in guineapig, where Tac1 instead is expressed). IPANs are not defined in the chicken very well, and molecular markers found in other species may not be valid. Adrenergic and noradrenergic neurons have not been validated in the ENS (although, TH and Dbh have been observed in the especially in the submucosal ENS). Cholinergic neurons are also mentioned in the text, but do not appear in the figures as a defined group. Another reason to refrain from functional nomenclature is that a rather early stage is analysed in the present study, without possibilities to compare with scRNA-seq data from the mature chicken ENS (which was performed in Morarach et al, 2021 for the mouse). Recent data suggest that considerable differentiation may occur even in postmitotic neurons, and several markers are known to display a transient expression pattern (TH, DBH and NOS1; Baetge and Gershon 1990; Bergner et al., 2014; Morarach et al., 2021) why caution should be taken to infer neuronal identities to clusters.

7) The immunohistochemical analysis (Figure 5,6) is an essential complementary addition and validation of scRNA-seq. However, it is very difficult to discern staining when magenda and red are combined to display co-expression.

8) To give more information to the field and body of evidence for claims made, quantifications relating to the analysis in Figures 5 and 6 are warranted as well as an expanded set of marker genes that align with the scRNA-seq results.

9) Correlations between genes and functions/neuron class are in many cases wrong (including Grm3, Gad1, Nts, Gfra3, Myo9d, Cck and more).

10) Attempts to subcluster neuronal populations are needed (Figure 7). However, to understand the biology, it is important to address which cells are sacral versus vagal-derived. Additionally, related to previous comment, as the vagal and sacral neurons are not clonally related, it would be important to make separate analysis of neurons relating to each region.

Reviewer #2 (Public Review):

In this study, Yang et al. used single-cell technology to construct the cell profiles of normal and pathological ligaments and identified the critical cell subpopulations and signaling pathways involved in ligament degeneration. The authors identified four major cell types: fibroblasts, endothelial cells, pericytes, and immune cells from four normal and four pathological human ligament samples. They further revealed the increased number of fibroblast subpopulations associated with ECM remodelling and inflammation in pathological ligaments. In addition, the authors further resolved the heterogeneity of endothelial and immune cells and identified an increase in pericyte subpopulations with muscle cell characteristics and macrophages in pathological ACL. Ligand-receptor interaction analysis revealed the involvement of FGF7 and TGFB signaling in interactions between pathological tendon subpopulations. Spatial transcriptome data analysis also validated the spatial proximity of disease-specific fibroblast subpopulations to endothelial and macrophages, suggesting their interactions in pathological ligaments. This study offers a comprehensive atlas of normal and pathological cells in human ligaments, providing valuable data for understanding the cellular composition of ligaments and screening for critical pathological targets. However, more in-depth analyses and experimental validation are needed to enhance the study.

1) In this study, the authors performed deconvolution analysis between bulk RNA sequencing results and scRNA-seq results (L204-L208). However, the analysis of this section is not sufficiently in-depth and the authors failed to present the proportion of different cell subpopulations of the bulk sequencing samples to further increase the reliability of the results of the single cell data analysis.<br /> 2) In results 5, the authors should clearly describe whether the analysis is based only on pathological subpopulations of ligament cells or includes a mixture of normal and pathological subpopulations; the corresponding description should also be indicated in Figure 5. Besides, Although the authors claimed that "the TGF-β pathway was involved in many cell-cell interactions among fibroblasts subpopulations and macrophages", Figure 5C displayed that the CD8+NKT-like cells displayed the most TGFB signaling interactions with fibroblasts subpopulations.<br /> 3) In result 6, the authors performed spatial transcriptome sequencing, however, the sample numbers were relatively limited, with only one sample from each group; in addition, the results of this part failed to correlate and correspond well with the single-cell results. The subgroups labelled in L382 and L384 should be carefully checked. Besides, expression data of FGF7 and TGFB ligand and receptor molecules based on the spatial transcriptomes should be added to further confirm the critical signalling pathway in regulating the cellular interactions in pathological ACL.

Reviewer #2 (Public Review):

Agrawal et al. propose an interesting model in which the autophagy pathway in adult mouse skeletal muscle fibers is orchestrated by two independent mechanisms: a) the activity of the NADPH oxidase (Nox) 2 enzyme necessary for autophagosome biogenesis and maturation and b) the level of acetylation of the microtubule (MT) network more selectively responsible for the fusion of the autophagosomes to the lysosomes. Using the well-known mdx mouse, a model for Duchenne muscular dystrophy, the authors perform a quite impressive (but rather traditional) biochemical characterization of the autophagy pathway and found that biogenesis and maturation of the autophagosomes are impaired in mdx mice muscle fibers by means of altered expression of components of the class III phosphatidylinositol 3-kinase complex (PI3K) such as Beclin, VPS15 (both upregulated in mdx mice), ATG14L and VPS34 (both downregulated), and by the reduced expression of JNK and JIP-1, required for the formation of the heterodimer between Beclin and ATG14L-VPS34. In mdx mice, defective nucleation of the phagophore appears to be coupled to altered elongation and expansion as confirmed by decreased expression of WIPI-1, an early marker of autophagosome formation, required for the assembly of the ATG5-12 complex. Clearance of sequestered cytosolic components necessitates the fusion of the autophagosome with the lysosome, a process that the authors found impaired in mdx mice due to altered formation of the SNARE tertiary complex (STX17-SNAP29-VAMP8), as a result of the marked reduction of STX17 expression.

In a previous work (Pal et al., Nat Commun 2014), the same group described the generation of an mdx-based mouse model where Nox2 activity was abolished by genetic ablation of the p47phox component. These mice presented with a better outcome in terms of dystrophic pathophysiology by means of reduced oxidative stress and improved autophagy. Further characterization of these mice in the present study reveals that in p47-/-/mdx mice abolishment of Nox2 activity restores autophagosome nucleation and maturation thanks to the increased expression of p-JNK, JIP-1 and improved stability of the Beclin-ATG14L complex, but no amelioration is observed on the formation of the SNARE tertiary complex indicating that the biogenesis of autophagosomes is dependent on Nox2 activity but not the fusion between autophagosomes and lysosomes. Given the existing body of evidence in non-muscle cells pointing at alpha-tubulin acetylation as a regulator of MT activity facilitating the fusion of autophagosomes to lysosomes, the authors thought to investigate the level of MT acetylation in mdx mice muscle fibers and found that acetylation is reduced but can be restored by inhibiting the HDAC6 enzyme via the FDA-approved, highly selective pharmacological inhibitor Tubastatin A (Tub A). Treatment of mdx mice at 3 weeks of age (before the onset of pathological manifestations) with Tub A not only restored the normal level of alpha-tubulin acetylation (without altering the organization and density of the MT network) but also curbed the intracellular redox status and improved the autophagic flux by stabilizing the SNARE tertiary complex. Interestingly, treatment of dystrophic mice with Tub A results in substantial improvement of the dystrophic phenotype as confirmed by a reduced level of apoptosis, diminished tissue inflammation, improved sarcolemma integrity, and superior force generation capacity in ex vivo experiments using the diaphragm and Extensor Digitorum Longus (EDL) muscle fibers of Tub A-treated mdx mice compared to untreated mdx and healthy counterparts.

The in-depth characterization of the steps orchestrating the autophagy pathway in the mdx mouse model on the one hand, and the comprehensive evaluation of the phenotype of the mdx mice treated with the HDAC6 inhibitor Tubastatin A on the other, support the conclusions proposed by the authors. Nonetheless, some aspects deserve consideration.

1) The effect of increased alpha-tubulin acetylation by means of genetic and pharmacological strategies (i.e., in vivo overexpression of alpha-tubulin acetyltransferase-aTAT1 and treatment with Tubacin or Tubastatin A, respectively) has been previously explored in isolated cardiomyocytes and skeletal muscle fibers and revealed that augmented MT acetylation, due to selective inhibition of HDAC6, increases cytoskeletal stiffness and favors Nox2 activation (Coleman et al., J Gen Physiol 2021).

2) Altered organization and density of the MT network in mdx FDB muscle fibers with loss of vertical directionality is not a novelty as well and it has been reported by others (see Randazzo et al., Hum Mol Genet 2019), who also observed that overexpression of a single beta-tubulin (tubb6) in normal Flexor Digitorum Brevis (FDB) muscle fibers mimic the disruption to the MT network of mdx FDB fibers, increases the level of detyrosinated tubulin and increases Nox2 activity (through elevated expression of gp91phox). Conversely, downregulation of the same beta-tubulin restores normal MT organization in mdx FDB. Previous work from the authors (Loehr et al., eLife 2018) reported that in p47-/-/mdx mice MT organization in diaphragm muscle fibers is normalized and autophagy improved. Accordingly, it is puzzling that increased alpha-tubulin acetylation determines such a wide range of ameliorations in terms of physiological and morphological aspects in dystrophic skeletal muscle fibers treated with Tubastatin A whereas no improvement in the overall MT organization is observed, as reported by Agrawal and colleagues.

3) Given that p47-/-/mdx mice present with levels of acetylated alpha-tubulin and HDAC6 expression comparable to mdx while showing significant improvement of the dystrophic phenotype despite partial rescue of the autophagic flux (as reported in Loehr et al., eLife 2018), it would have been of great interest to investigate the effect of HDAC6 inhibition in p47-/-/mdx mice as well.

Reviewer #2 (Public Review):

The study had an especially relevant aim for aging research and utilized various data types in an especially interesting human population. Multi-omics perspective adds great value to the work. The researchers aimed to evaluate how different indicators of biological age (BA) behave in children during their developmental stage. In the analysis, relationships between indicators of BA, health risk factors, and developmental factors were assessed in cross-sectional data comprising children aged 5-12 years. The manuscript is well-written and easy to follow. The methodology is good. The authors succeeded to reach the aim in most parts.

In the study, previously known and unknown biological age indicators were used. Known indicators included telomere length and Horvath's epigenetic age. Unknown (novel) indicators, transcriptomic and immunometabolic clocks, were developed in the present study and they showed a strong correlation with calendar age in this population, also in the validation data set. Although the transcriptomic and immunometabolic clocks have the potential of being true indicators of biological age, they are still lacking scientific evidence of being such indicators in adults. That is, their associations with age-related diseases and mortality are yet to be shown. Thus, the major remark of the study relates to the phrasing: these novel transcriptomic and immunometabolic clocks should be presented as BA indicator candidates waiting for the needed evidence.

Reviewer #2 (Public Review):

Wei et al. analysed the composition of immune cells, mostly macrophages, and neutrophils, in the context of zebrafish cardiac injury while utilizing clodronate liposomes (CL) to inhibit regeneration via alteration of the immune response. This work is a direct continuation of Shih-Lei et al. which compared the regenerative outcomes of zebrafish vs the non-cardiac regenerative medaka. In that work, the authors used CL to pre-deplete macrophages and showed significant effects on neutrophil clearance, revascularization, and cardiomyocyte proliferation. In this work, the authors used the same pre-depletion method to study the dynamics, composition, and transcriptomic state of macrophages and neutrophils, to overall assess the effect on cardiac regeneration. Using bulk RNA-seq at CL vs PBS treated hearts 7 and 21 days post cryo injury (dpci) a delayed\altered immune response was evident. Single-cell analysis at 1,3 and 7 dpci showed a wide range of immune populations in which most diverse are the macrophage populations. Pre-depletion using CL, altered the composition of immune cells resulting in the complete removal of a single resident macrophage population (M2) or dramatically reducing the overall numbers of other resident populations, while other populations were retained. Looking at the injury time course and distribution of macrophage populations, the authors identified several macrophage populations and neutrophil population 1 as pro-regenerative as their presence compared to CL-treated hearts correlates with regeneration. CL-treated hearts also show a marked sustained neutrophil retention suggesting that interaction with depleted macrophage populations is required for neutrophil clearance. As the marked reduction in populations 2 and 3 occurs after CL treatment, the authors tested whether early CL treatment (8 days or 1 month prior to injury) could reduce the non-recoverable populations and affect regenerative outcomes and indeed they observed a reduction in key genes characterizing M2 and M3 which caused marked reduction in revascularization, CM proliferation, neutrophil retention, and overall higher scaring of the heart.

The findings of this paper could be broadly separated into the characterization of myeloid cells after injury and in non-regenerating animals and assessing the effects of early pre-depletion of macrophages on various cardiac functions involved in regeneration. Both parts draw conclusions that are supported by the facts however several questions remain to be clarified.

1. In figures 2 and 3 the main claim is that the main resident macrophage populations, M2 and M3 are depleted and are largely unable to replenish after injury, similar to resident macrophages in mice 1. However, as the identification of this population is made solely using scRNA-seq, an alternative explanation would be that these cell populations do replenish but are sufficiently changed due to CL treatment (directly or indirectly) and thus would be a part of another cluster. To address this, we suggest:<br /> A. Run trajectory analysis to ascertain whether the different cell clusters are due to differentiating states of the cells<br /> B. Create a reporter line for M2 and M3 macrophages and assess whether they are indeed depleted or changing.

2. One of the major findings of this paper is that some macrophage populations can persist throughout injury and promote the regenerative response. Considering that macrophages have a half-life of less than a day in tissue 2 (although could be different in zebrafish and in this population), we estimate that the resident populations should be proliferative. As there is only a single proliferating macrophage population (M5) we speculate that it is a combination of several populations which are clustered together due to the high expression of cell cycle genes. To verify whether the resident populations are proliferating we suggest:<br /> A. Perform cell-cycle scoring and regression (found in Seurat package) and assess whether after regressing out cell cycle genes there are contributions of M5 to other clusters.<br /> B. Perform EDU labelling experiments with cell cycle identifiers (staining for hbaa1, Timp4.3) and assess their proliferative dynamics.

3. In connection to the previous point if indeed these resident macrophage populations are proliferative, even a smaller portion of remaining cells should be sufficient to partly replenish given sufficient time after CL 1. However as seen in Fig. 3B, the M2 population has a similar proportion of cells on days 1 and 3 after CL treatment and by day 7 it declines in numbers. Given that CL should not be present anymore, we expect this population to increase in numbers over time.

4. In Figure 6 the authors show a reduction in mpeg+ population however a persistent, large population ({plus minus}70% of the original mpeg+) is retained. The authors suggest that this population is comprised of other, non-macrophage, cell types however as this method is the very core of the paper and the persistence of macrophages could alter our understanding of the results, it must be verified.

Dick, S. A. et al. Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction. Nature Immunology 20, 29-39, doi:10.1038/s41590-018-0272-2 (2019).<br /> 2 Leuschner, F. et al. Rapid monocyte kinetics in acute myocardial infarction are sustained by extramedullary monocytopoiesis. J Exp Med 209, 123-137, doi:10.1084/jem.20111009 (2012).

Reviewer #2 (Public Review):

Here the effect of overall transcription blockade, and then specifically depletion of YAP/TAZ transcription factors was tested on cytoskeletal responses, starting from a previous paper showing YAP/TAZ-mediated effects on the cytoskeleton and cell behaviors. Here, primary endothelial cells were assessed on substrates of different stiffness and parameters such as migration, cell spreading, and focal adhesion number/length were tested upon transcriptional manipulation. Zebrafish subjected to similar manipulations were also assessed during the phase of intersegmental vessel elongation. The conclusion was that there is a feedback loop of 4 hours that is important for the effects of mechanical changes to be translated into transcriptional changes that then permanently affect the cytoskeleton.

The idea is intriguing, but it is not clear how the feedback actually works, so it is difficult to determine if the events needed could occur within 4 hrs. Specifically, it is not clear what gene changes initiated by YAP/TAZ translocation eventually lead to changes in Rho signaling and contractility. Much of the evidence to support the model is preliminary. Some of the data is consistent with the model, but alternative explanations of the data are not excluded. The fish washout data is quite interesting and does support the model. It is unclear how some of the in vitro data supports the model and excludes alternatives.

Major strengths: The combination of in vitro and in vivo assessment provides evidence for timing in physiologically relevant contexts, and rigorous quantification of outputs is provided. The idea of defining temporal aspects of the system is quite interesting.

Major weaknesses: The evidence for a "loop" is not strong; rather, most of the data can also be interpreted as a linear increase in effect with time once a threshold is reached. Washout experiments are key to setting up a time window, yet these experiments are presented only for the fish model. A major technical challenge is that siRNA experiments take time to achieve depletion status, making precise timing of events on short time scales problematic. Also, Actinomycin D blocks most transcription so exposure for hours likely leads to secondary and tertiary effects and perhaps effects on viability. No RNA profiling is presented to validate proposed transcriptional changes.

Reviewer #2 (Public Review):

The study by Liu et al. reports on the establishment and characterization of telencephalon eye structures that spontaneously form from human pluripotent stem cells. The reported structures are generated from embryonic cysts that self-form concentric zones (centroids) of telencephalic-like cells surrounded by ocular cell types. Interestingly, the cells in the outer zone of these concentric structures give rise to retinal ganglion cells (RGCs) based on the expression of several markers, and their neuronal morphology and electrophysiological activity. Single-cell analysis of these brain-eye centroids provides detailed transcriptomic information on the different cell types within them. The single-cell analysis led to the identification of a unique cell-surface marker (CNTN2) for the human ganglion cells. Use of this marker allowed the team to isolate the stem cell-derived RGCs.

Overall, the manuscript describes a method for generating self-forming structures of brain-eye lineages that mimic some of the early patterning events, possibly including the guidance cues that direct axonal growth of the RGCs. There are previous reports on brain-eye organoids with optic nerve-like connectivity; thus, the novel aspect of this study is the self-formation capacity of the centroids, including neurons with some RGC features. Notably, the manuscript further reports on cell-surface markers and an approach to generating and isolating human RGCs.

Reviewer #2 (Public Review):

This study proposed the AG fibroblast-neutrophil-ILC3 axis as a mechanism contributing to pathological inflammation in periodontitis. However, the immune response in the vivo is very complex. It is difficult to determine which is the cause and which is the result. This study explores the relevant issue from one dimension, which is of great significance for a deeper understanding of the pathogenesis of periodontitis. It should be fully discussed.

1) Many host cells participate in immune responses, such as gingival epithelial cells. AG fibroblast is not the only cell involved in the immune response, and the weight of its role needs to be clarified. So the expression in the conclusion should be appropriate.

2) This study cannot directly answer the issue of the relationship between periodontitis and systemic diseases.

Reviewer #2 (Public Review):

In this manuscript, Funabiki and colleagues investigated the co-evolution of DNA methylation and nucleosome remolding in eukaryotes. This study is motivated by several observations: (1) despite being ancestrally derived, many eukaryotes lost DNA methylation and/or DNA methyltransferases; (2) over many genomic loci, the establishment and maintenance of DNA methylation relies on a conserved nucleosome remodeling complex composed of CDCA7 and HELLS; (3) it remains unknown if/how this functional link influenced the evolution of DNA methylation. The authors hypothesize that if CDCA7-HELLS function was required for DNA methylation in the last eukaryote common ancestor, this should be accompanied by signatures of co-evolution during eukaryote radiation.

To test this hypothesis, they first set out to investigate the presence/absence of putative functional orthologs of CDCA7, HELLS and DNMTs across major eukaryotic clades. They succeed in identifying homologs of these genes in all clades spanning 180 species. To annotate putative functional orthologs, they use similarity over key functional domains and residues such as ICF related mutations for CDCA7 and SNF2 domains for HELLS. Using established eukaryote phylogenies, the authors conclude that the CDCA7-HELLS-DNMT axis arose in the last common ancestor to all eukaryotes. Importantly, they found recurrent loss events of CDCA7-HELLS-DNMT in at least 40 eukaryotic species, most of them lacking DNA methylation.

Having identified these factors, they successfully identify signatures of co-evolution between DNMTs, CDCA7 and HELLS using CoPAP analysis - a probabilistic model inferring the likelihood of interactions between genes given a set of presence/absence patterns. As a control, such interactions are not detected with other remodelers or chromatin modifying pathways also found across eukaryotes. Expanding on this analysis, the authors found that CDCA7 was more likely to be lost in species without DNA methylation.

In conclusion, the authors suggest that the CDCA7-HELLS-DNMT axis is ancestral in eukaryotes and raise the hypothesis that CDCA7 becomes quickly dispensable upon the loss of DNA methylation and/or that CDCA7 might be the first step toward the switch from DNA methylation-based genome regulation to other modes.

The data and analyses reported are significant and solid. However, using more refined phylogenetic approaches could have strengthened the orthologous relationships presented. Overall, this work is a conceptual advance in our understanding of the evolutionary coupling between nucleosome remolding and DNA methylation. It also provides a useful resource to study the early origins of DNA methylation related molecular process. Finally, it brings forward the interesting hypothesis that since eukaryotes are faced with the challenge of performing DNA methylation in the context of nucleosome packed DNA, loosing factors such as CDCA7-HELLS likely led to recurrent innovations in chromatin-based genome regulation.

Strengths:

- The hypothesis linking nucleosome remodeling and the evolution of DNA methylation.<br /> - Deep mapping of DNA methylation related process in eukaryotes.<br /> - Identification and evolutionary trajectories of novel homologs/orthologs of CDCA7.<br /> - Identification of CDCA7-HELLS-DNMT co-evolution across eukaryotes.

Weaknesses:

- Orthology assignment based on protein similarity.<br /> - No statistical support for the topologies of gene/proteins trees (figure S1, S3, S4, S6) which could have strengthened the hypothesis of shared ancestry.

Reviewer #2 (Public Review):

This manuscript by Walker et al describes an elegant study that synergizes our knowledge of virulence gene regulation of Vibrio cholerae. The work brings a new element of regulation for CRP, notably that CRP and the high density regulator HapR co-occupy the same site on the DNA but modeling predicts they occupy different faces of the DNA. The DNA binding and structural modeling work is nicely conducted and data of co-occupation are convincing. The work seeks to integrate the findings into our current state of knowledge of HapR and CRP regulated genes at the transition from the environment and infection. The strength of the paper is the nice ChIP-seq analysis and the structural modeling and the integration of their work with other studies. The weakness is that it is not clear how representative these data are of multiple hapR/CRP binding sites or how the work integrates as a whole with the entire transcriptome that would include genes discovered by others. Overall this is a solid work that provides an understanding of integrated gene regulation in response to multiple environmental cues.

Reviewer #2 (Public Review):

The paper by Maiti et al. reporting a highly interesting, previously un-noticed, phenomenon of cell size increase as part of the response to chronic proteotoxic stresses, such as heat shock, which the authors term "rewiring stress response". Furthermore, they establish that it is mediated via HSF1, and, strikingly, necessitates a certain threshold levels of HSP90. Dwelling deeper into the underlying mechanisms, they find that HSP90 help scale protein synthesis with the increased cell sizes, and when diminished, this scaling is impaired, and also cell viability in chronic stress is also compromised. These findings correspond with a previous study by this group on the lethality of HSP90 deficient mice, and moreover, have implications to our understanding of cellular adaptation to stress, and generate interesting hypotheses about the possible links of this mechanism to the impairments of the ability to cope with stress during aging and senescence.

This is an excellent study, with highly novel and important findings, which illuminate a new phenomenon related to cellular adaptation to chronic stress. I have only one major concern, about some technical aspects, specifically over-crowding effects, which could confound the results, which should be answered by the authors. Other than that, further details which I think are pertinent to the study most likely already exist in the experiments performed, and most could be answered with additional simple experiments and by further analyses of the proteomics data which has already been performed, but which results are not sufficiently shown in detail.

Reviewer #2 (Public Review):

Work of Rong Li´s lab, published in Nature 2017 (Ruan et al, 2017), led the authors to suggest that the mitochondrial protein import machinery removes misfolded/aggregated proteins from the cytosol and transports them to the mitochondrial matrix, where they are degraded by Pim1, the yeast Lon protease. The process was named mitochondria as guardian in cytosol (MAGIC).

The mechanism by which MAGIC selects proteins lacking mitochondrial targeting information, and the mechanism which allows misfolded proteins to cross the mitochondrial membranes remained, however, enigmatic. Up to my knowledge, additional support of MAGIC has not been published. Due to that, MAGIC is briefly mentioned in relevant reviews (it is a very interesting possibility!), however, the process is mentioned as a "proposal" (Andreasson et al, 2019) or is referred to require "further investigation to define its relevance for cellular protein homeostasis (proteostasis)" (Pfanner et al, 2019).

Rong Li´s lab now presents a follow-up story. As in the original Nature paper, the major findings are based on in vivo localization studies in yeast. The authors employ an aggregation prone, artificial luciferase construct (FlucSM), in a classical split-GFP assay: GFP1-10 is targeted to the matrix of mitochondria by fusion with the mitochondrial protein Grx5, while GFP11 is fused to FlucSM, lacking mitochondrial targeting information. In addition the authors perform a genetic screen, based on a similar assay, however, using the cytosolic misfolding-prone protein Lsg1 as a read-out.

My major concern about the manuscript is that it does not provide additional information which helps to understand how specifically aggregated cytosolic proteins, lacking a mitochondrial targeting signal could be imported into mitochondria. As it stands, I am not convinced that the observed FlucSM-/Lsg1-GFP signals presented in this study originate from FlucSM-/Lsg1-GFP localized inside of the mitochondrial matrix. The conclusions drawn by the authors in the current manuscript, however, rely on this single approach.

In the 2017 paper the authors state: "... we speculate that protein aggregates engaged with mitochondria via interaction with import receptors such as Tom70, leading to import of aggregate proteins followed by degradation by mitochondrial proteases such as Pim1." Based on the new data shown in this manuscript the authors now conclude "that MP (misfolded protein) import does not use Tom70/Tom71 as obligatory receptors." The new data presented do not provide a conclusive alternative. More experiments are required to draw a conclusion.<br /> In my view: to confirm that MAGIC does indeed result in import of aggregated cytosolic proteins into the mitochondrial matrix, a second, independent approach is needed. My suggestion is to isolate mitochondria from a strain expressing FlucSM-GFP and perform protease protection assays, which are well established to demonstrate matrix localization of mitochondrial proteins. In case the authors are not equipped to do these experiments I feel that a collaboration with one of the excellent mitochondrial labs in the US might help the MAGIC pathway to become established.

Reviewer #2 (Public Review):

This study follows up on a previous study by the group (Sibille et al Nature Communications 2022) in which high density Neuropixel probes were inserted tangentially through the superficial layers of the superior colliculus (SC) to record the activity of retinocollicular axons and postsynaptic collicular neurons in anesthetized mice. By correlating spike patterns, connected pairs could be identified which allowed the authors to demonstrate that functionally similar retinal axon-SC neuron pairs were strongly connected.

In the current study, the authors use similar techniques in vGAT-ChR2 mice and add a fiber optic to identify light-activated GABAergic and non-light-activated nonGABAergic neurons. Using their previously verified techniques to identify connected pairs, within regions of optogenetic activation they identified 214 connected pairs of retinal axons and nonGABAergic neurons and 91 pairs of connected retinal axons and GABAergic neurons. The main conclusion is that retinal activity contributed more to the activity of postsynaptic nonGABAergic SC neurons than to the activity of postsynaptic GABAergic SC neurons.

The study is very well done. The figures are well laid out and clearly establish the conclusions. My main comments are related to the comparison to other circuits and further questions that might be addressed in the SC.

It is stated several times that the superior colliculus and the visual cortex are the two major brain areas for visual processing and these areas are compared throughout the manuscript. However, since both the dorsal lateral geniculate nucleus (dLGN) and SC include similar synaptic motifs, including triadic arrangements of retinal boutons with GABAergic and nonGABAergic neurons, it might be more relevant to compare and contrast retinal convergence and other features in these structures.

The GABAergic and nonGABAergic neurons showed a wide range of firing rates. It might be interesting to sort the cells by firing rates to see if they exhibit different properties. For example, since the SC contains both GABAergic interneurons and projection neurons it would be interesting to examine whether GABAergic neurons with higher firing rates exhibit narrower spikes, similar to cortical fast spiking interneurons. Similarly, it might be of interest to sort the neurons by their receptive field sizes since this is associated with different SC neuron types.

The recording techniques allowed for the identification of the distance between connected retinocollicular fibers and postsynaptic neurons. It might also be interesting to compare the properties of connected pairs recorded at dorsal versus ventral locations since neurons with different genetic identities and response properties are located in different dorsal/ventral locations (e.g. Liu et al. Neuron 2023). Also, regarding the strength of connections, previous electron microscopy studies have shown that the retinocollicular terminals differ in density and size in the dorsal/ventral dimension (e.g Carter et al JCN 1991).

Was optogenetic activation of GABAergic neurons ever paired with visual activation? It would be interesting to examine the receptive fields of the nonGABAergic neurons before and after activation of the GABAergic neurons (as in Gale and Murphy J Neurosci 2016).

Reviewer #2 (Public Review):

Segas et al motivate their work by indicating that none of the existing myoelectric solution for people with trans-humeral limb difference offer four active degrees of freedom, namely forearm flexion/extension, forearm supination/pronation, wrist flexion/extension, and wrist radial/ulnar deviation. These degrees of freedom are essential for positioning the prosthesis in the correct plan in the space before a grasp can be selected. They offer a controller based on the movement of the stump.

The proposed solution is elegant for what it is trying to achieve in a laboratory setting. Using a simple neural network to estimate the arm position is an interesting approach, despite the limitations/challenges that the approach suffers from, namely, the availability of prosthetic hardware that offers such functionality, information about the target and the noise in estimation if computer vision methods are used. Segas et al indicate these challenges in the manuscript, although they could also briefly discuss how they foresee the method could be expanded to enable a grasp command beyond the proximity between the end-point and the target. Indeed, it would be interesting to see how these methods can be generalise to more than one grasp.

One bit of the results that is missing in the paper is the results during the familiarisation block. If the methods in "intuitive" I would have thought no familiarisation would be needed. Do participants show any sign of motor adaptation during the familiarisation block?

In Supplementary Videos 3 and 4, how would the authors explain the jerky movement of the virtual arm while the stump is stationary? How would be possible to distinguish the relative importance of the target information versus body posture in the estimation of the arm position? This does not seem to be easy/clear to address beyond looking at the weights in the neural network.

I am intrigued by how the Generic ANN model has been trained, i.e. with the use of the forward kinematics to remap the measurement. I would have taught an easier approach would have been to create an Own model with the native arm of the person with the limb loss, as all your participants are unilateral (as per Table 1). Alternatively, one would have assumed that your common model from all participants would just need to be 'recalibrated' to a few examples of the data from people with limb difference, i.e. few shot calibration methods.

Reviewer #2 (Public Review):

The study "A rapid microglial metabolic response controls metabolism and improves memory" by Drougard et al. provides evidence that short-term HFD has a beneficial effect on spatial and learning memory through microglial metabolic reprogramming. The manuscript is well-written and the statistics were properly performed with all the data. However, there are concerns regarding the interpretation of the data, particularly the gap between the in vivo observations and the in vitro mechanistic studies.

In the PLX-5622 microglial depletion study, it is unclear what happened to the body weight, food intake, and day-night behavior of these mice compared to the vehicle control mice. It is important to address the innate immunity-dependent physiology affected by a long period of microglial depletion in the brain (also macrophages in the periphery). Furthermore, it would be beneficial to validate the images presented in Fig.1F by providing iba1 staining in chow diet-fed mice with or without PLX-5622 for 7-10 days. Additionally, high-quality images, with equal DAPI staining and comparable anatomical level, should be provided in both chow diet-fed mice and HFD-fed mice with or without PLX-5622 in the same region of hypothalamus or hippocampus. These are critical evidences for this project, and it is suggested that the authors provide more data on the general physiology of these mice, at least regarding body weight and food intake.

It is also unclear whether the microglia shown in Fig.3A were isolated from mice 4 weeks after Tamoxifen injection. It is suggested that the authors provide more evidence, such as additional images or primary microglia culture, to demonstrate that the mitochondria had more fusion upon drp1 KO. It is recommended to use mito-tracker green/red to stain live microglia and provide good resolution images.

Regarding the data presented in Fig.5A, it is suggested that the authors profile the metabolomics of the microglial conditioned media (and provide the methods on how this conditioned media was collected) to determine whether there was already abundant lactate in the media. Any glucose-derived metabolites, e.g. lactate, are probably more preferred by neurons as energy substrates than glucose, especially in embryonic neurons (which are ready to use lactate in newborn brain).<br /> Finally, it is important to address whether PLX-5622 affects learning and spatial memory in chow diet-fed animals. Following the findings shown in Fig 5J and 5K, the authors should confirm these by any morphological studies on synapse, e.g. by synaptophysin staining or ultrastructure EM study in the area shown in Fig 5I.

Reviewer #2 (Public Review):

Breast cancer is the most common malignant tumor in women. One of subtypes in breast cancer is so called triple-negative breast cancer (TNBC), which represents the most difficult subtype to treat and cure in the clinic. Chemotherapy drugs including epirubicin and cisplatin are widely used for TNBC treatment. However, drug resistance remains as a challenge in the clinic. The authors uncovered a molecular pathway involved in chemotherapy drug resistance, and molecular players in this pathway represent as potential drug targets to overcome drug resistance. The experiments are well designed and the conclusions drawn mostly were supported by the data. The findings have potential to be translated into the clinic.

Reviewer #2 (Public Review):

In this study the authors sought to investigate how the metabolic state of iNKT cells impacts their potential pathological role in allergic asthma. The authors used two mouse models, OVA and HDM-induced asthma, and assessed genes in glycolysis, TCA, B-oxidation and FAS. They found that acetyl-coA-carboxylase 1 (ACC1) was highly expressed by lung iNKT cells and that ACC1 deficient mice failed to develop OVA-induced and HDM-induced asthma. Importantly, when they performed bone marrow chimera studies, when mice that lacked iNKT cells were given ACC1 deficient iNKT cells, the mice did not develop asthma, in contrast to mice given wildtype NKT cells. In addition, these observed effects were specific to NKT cells, not classic CD4 T cells. Mechanistically, iNKT cell that lack AAC1 had decreased expression of fatty acid-binding proteins (FABPs) and peroxisome proliferator-activated receptor (PPAR)γ, but increased glycolytic capacity and increased cell death. Moreover, the authors were able to reverse the phenotype with the addition of a PPARg agonist. When the authors examined iNKT cells in patient samples, they observed higher levels of ACC1 and PPARG levels, compared to healthy donors and non-allergic-asthma patients.

Reviewer #2 (Public Review):

The authors embarked on a study to identify SNPs in clinical isolates of S. aureus that influence sensitivity to serum killing. Through a phenotypic screen of 300 previously sequenced S. aureus bacteremia (SAB) isolates, they identified ~40 SNPs causing altered serum survival. The remainder of the study focuses of tcaA, a gene with unknown function. They show that when tcaA is disrupted, it results in increased resistance to glycopeptides and antimicrobial components of human serum.

They perform an elegant series of experiments demonstrating how a tcaA knockout is more resistant to killing by whole serum. arachadonic acid, LL-37 and HNP-1. They provide compelling evidence that in the absence of tcaA resistance to arachidonic acid is mediated through release of wall teichoic acids from the cell wall, which acts as a decoy and sequesters the fatty acid.

Similarly, they suggest that resistance to cationic antimicrobial peptides is through alteration of the net charge of the cell wall due to loss of negatively charged WTAs based on reduced cytochrome C binding.

They continue to show that tcaA is induced in the presence of human serum, which causes increased resistance to the glycopeptide teichplanin.

They propose that tcaA disruption causes altered cell wall structure based on morphologic changes on TEM and increased sensitivity to lysostaphin and increased autolysis via triton x-100 assay.

Finally, they propose that tcaA influences mortality in SAB based on raw differences in 30-day morality. Interestingly they do decreased fitness during murine bacteremia model compared to wild-type.

The strengths of this manuscript are that it is well written and the identification of SNPs leading to altered serum killing is convincing and valuable data. The mechanism for tcaA-mediated resistance to arachadonic acid and AMPs is compelling and novel. The murine infection data demonstrating that tcaA mutants exhibit reduced virulence is important data.

The weakness of this manuscript mainly concerns the proposed mechanism that tcaA mutants show reduced peptidoglycan crosslinking. This conclusion is based on qualitative TEM images and increased sensitivity to lysostaphyin/autolysis. While these data are suggestive. it is difficult to draw such a conclusion without analysis of the cell wall by LC-MS.

Overall, I think this is a good submission and the majority of their conclusions are supported by the data. The mechanism behind the clinically relevant tcaA mutation is important, given its known role in glycopeptide resistance and therefore likely clinical outcomes. This manuscript would benefit from the inclusion of some additional experiments to help support their finding.

Reviewer #2 (Public Review):

Inorganic carbon (Ci) uptake by autotrophic organisms is often the rate-limiting process in overall photosynthetic productivity. Aquatic autotrophs including the cyanobacteria have evolved elaborate and metabolically expensive, yet very efficient CO2 concentrating mechanisms (CCMs) to over-come this limitation. The work examines the regulation of SbtA, which is a high affinity sodium dependent symporter. Current evidence suggests that this SbtA is highly regulated both at the transcriptional and post-transcriptional levels. For example, the sbtA gene is transcriptionally upregulated under conditions of inorganic carbon limitation and the transport activity of the expressed SbtA protein is apparently regulated allosterically by multiple factors, including those exerted by the binding of the small trimeric protein, SbtB. SbtB is a PII-type regulator that conditionally binds to the cytoplasmic face of the trimeric SbtA to form a hetero-complex apparently inactivating SbtA to which it is bound. The factors affecting this interaction remains to be clarified, but it is already clear that there is considerable complexity that needs to be unraveled since as with other PII proteins, multiple effector molecules act as ligands.

Using a novel protein-protein interaction assay combined with physiological analysis of various mutants, the authors present new information on the regulation of SbtA from Cyanobium sp. PCC7001 and Synechococcus elongatus PCC7942. Because of their novelty, additional validation may be important to establish their validity, yet they do appear to be robust overall..The work builds on earlier studies indicating negative regulation of SbtA and helps clarify other work, including detailed analysis of the orthologous, albeit somewhat more complex protein from Synechocystis PCC6803. The key significance of the present findings is that the energy charge of the adenylate system, a ubiquitous metabolic control mechanism in the biological world, is the prime and perhaps overriding regulatory parameter governing of SbtA activity. Based on this a model for the diurnal control transporter activity was proposed based on energy charge.

Reviewer #2 (Public Review):

N6-methyladenosine (m6A), the most abundant mRNA modification, is deposited by the m6A methyltransferase complexes (MTC). While MTC in mammals/flies/plants consists of at least six subunits, yeast MTC was known to contain only three proteins. Ensinck, Maman, et al. revisited this question using a proteomic approach and uncovered three new yeast MTC components, Kar4/Ygl036w/Dyn2. By applying sequence and structure comparisons, they identified Kar4, Ygl036w, and Slz1 as homologs of the mammalian METTL14, VIRMA. ZC3H13, respectively. While these proteins are essential for m6A deposition, the dynein light chain protein, Dyn2, is not involved in mRNA methylation. Interestingly, while mammalian and fly MTCs are configured as MAC (METTL3 and METTL14), and MACOM (other subunits) complexes, yeast MTC subunits appear to have different configurations. Finally, Kar4 has a different role as a transcription regulator in mating, which is not mediated by other MTC members. These data establish an important framework for the yeast MTC and also provide novel insights for those studying m6A deposition.

Reviewer #2 (Public Review):

The manuscript by Mastwal and colleagues explores how transient adolescent stimulation of ventral midbrain neurons that project to the frontal cortex may help to improve performance on certain memory tasks. The manuscript provides an interesting set of observations that DREADD-based activation over only 3 days during adolescence provides a fast-acting and long-lasting improvement in performance on Y-maze spontaneous alternation as well as aspects of neuronal function as assessed using in vivo imaging methods. While interesting, there are several weaknesses. First and foremost, it is not clear that the effects the authors are observing are mediated by dopamine. It has been clearly documented that the DAT-Cre line provides a better representation of midbrain dopamine cells in the mouse, particularly near the midline of the ventral midbrain (Lammel et al., Neuron 2015). This is precisely where the cells that project to the frontal cortex are located. Therefore, the selection of TH-Cre is problematic. It is very likely that the authors are labeling a substantial number of non-dopaminergic cells.

Reviewer #2 (Public Review):

The authors attempt to show distinct contributions of selective attention and neuromodulators (both cholinergic and catecholaminergic) during a spatial attention task. To do this, they had participants perform a Posner cueing task using random dot motion stimuli, with a typical 80/20 split of valid to invalidly cued trials. In addition, they designed a within-subjects paradigm wherein participants took placebo (PLA), Donepezil (DNP), or Atomoxetine (ATX). Both behaviour and EEG measures were taken in order to investigate the interaction or lack thereof of Drug and Cue factors with respect to these measures, and relative timing of EEG differences to derive potential neuromechanistic similarities/differences. In this context, an interaction of Drug and Cue factors (e.g. faster valid vs invalid RTs in ATX vs PLA) might indicate a role of that neuromodulator in the mechanisms of spatial attention. This is in fact not what they found, rather most findings pointed towards a lack of interaction of Drug and Cue, hence the central thesis of the paper of distinct contributions of neuromodulator and selective attention.

Strengths:<br /> - The experimental design is well done, especially the blinding of the drug taken in each session. However, it is an important caveat to any results that participants were obviously aware they had taken an active drug in ATX condition (Supp Info).<br /> - The analyses are in general quite solidly performed, with most analysis choices relating to behaviour and EEG making sense, albeit with exceptions below.<br /> - The research question and how it relates to the experiment is very interesting, and the question worthy of consideration.

Weaknesses:<br /> - The main weakness of the paper lies in the strength of evidence provided, and how the results tally with each other. To begin with, there are a lot of significance tests performed here, increasing the chances of false positives. Multiple comparison testing is only performed across time in the EEG results, and not across post-hoc comparisons throughout the paper. In and of itself, it does not invalidate any result per se, but it does colour the interpretation of any results of weak significance, of which there are quite a few. For example, the effect of Drug on d' and subsequent post-hoc comparisons, also effect of ATX on CPP amplitude and others.<br /> - The lack of an overall RT effect of Drug leaves any DDM result a little underwhelming. How do these results tally? One potential avenue for lack of RT effect in ATX condition is increased drift rate but also increased non-decision time, working against each other. However, it may be difficult to validate these results theoretically.<br /> - There is an interaction between ATX and Cue in terms of drift rate, this goes against the main thesis of the paper of distinct and non-interacting contributions of neuromodulators and attention. This finding is then ignored. There is also a greater EDAN later for ATX compared to PLA later in the results, which would also indicate interaction of neuromodulators and attention but this is also somewhat ignored.<br /> - The CPP results are somewhat unclear. Although there is an effect of ATX on drift rate algorithmically, there is no effect of ATX on CPP slope. On the other hand, even though there is no effect of DNP on drift rate, there is an effect of DNP on CPP slope. Perhaps one may say that the effect of DNP on drift rate trended towards significance, but overall the combination of effects here is a little unconvincing. In addition, there is an effect of ATX on CPP amplitude, but how does this tally with behaviour? Would you expect greater CPP amplitude to lead to faster or slower RTs? The authors do recognise this discrepancy in the Discussion, but discount it by saying the relationship between algorithmic and CPP parameters in terms of DDM is unclear, which undermines the reasoning behind the CPP analyses (and especially the one correlating CPP slope with DDM drift rate).<br /> - The posterior component effects are problematic. The main issue is the lack of clarification of and justification for the choice of posterior component. The analysis is introduced in the context of the target selection signal the N2pc/N2c, but the component which follows is defined relative to Cue, albeit post-target. Thus this analysis tells us the effect of Cue on early posterior (possibly) visual ERP components, but it is not related to target selection as it is pooled across target/distractor. Even if we ignore this, the results themselves wrt Drug lack context. There is a trending lower amplitude for ATX at later latencies at temporo-parietal electrodes, and more positive for DNP, relative to PLA. Is this what one would expect given behaviour? This is where the issue of correct component identification becomes critical in order to inform any priors on expected ERP results given behaviour.

Given the issues above; mainly a) weak statistical evidence, b) contradictory behavioural and EEG evidence, and c) lack of theoretical background to inform priors on what to expect from the EEG results in order to develop a coherent narrative, I would say that what remains is moderate/incomplete evidence towards the thesis of the paper. This work is however a very fruitful effort at approaching the research question as to whether there is an interaction of neuromodulators and spatial attention. I commend the authors on a transparent and rigorous analysis of the current data.

Reviewer #2 (Public Review):

In this study, the authors explore the structure/function of the DCLK kinases, most specifically DCLK1 as it is the most studied to date. Recently, the C-terminal domain has garnered attention as it was found to regulate the kinase domain, however, the different isoforms retain additional amino acid sequences with as-yet-undefined functions. The authors provide an evolutionary and biochemical characterization of these regions and provide evidence for some functionality for these additional C-terminal sequences. While these experiments are informative they do require that the protein is soluble and not membrane-bound as has been suggested to be important for functionality in other studies. Still, this is a major contribution to understanding the structure/function of these proteins that will be important in future experimental designs.

Reviewer #2 (Public Review):

Ruby et al. investigated whether demographic aging was absent in the naked-mole rat (Heterocephalus glaber); an exceptionally long-lived small mammal that appears to challenge Gompertzian patterns of increased mortality hazard with age. In particular, this study replicates a previous one in which the authors show that the mortality hazard does not increase with age as it is expected for mammals, especially small ones. The main motivation of this replication is to address the current controversy surrounding the "perpetual neoteny" reported by the authors. The study also extends to the exploration of the role of social factors on the observed patterns in mortality hazard across age and to a meta-analysis comparing mortality hazards across species of mole-rats which highlights the unique pattern of demographic aging (or the absence of) in naked mole-rats. This study is of broad interest to readers in the field of demography, aging, and life history evolution. The key claims of the manuscript state that naked-mole rats avoid an increase in mortality hazard as they age. Although this work raises new evolutionary questions concerning the unexpected gradual (or fully absent) increase versus Gompertzian increase in hazard among mammals, I also identified weaknesses that I discuss below.

Strengths:<br /> Sample sizes - The sample sizes across analyses are vast and the data curation described demonstrates careful thought during the data analysis processes.

Social factors - The analysis testing associations between body mass (as proxy for dominance) and colony size (as proxy for social competition) are novel and provide insights into potential evolutionary drivers for the observed lack of increase in mortality hazard.

Across species comparison - The analysis using Fukomys mole-rats offered a novel phylogenetic comparison of the mortality hazard across age and raises new evolutionary questions concerning the unexpected gradual versus Gompertzian increase in hazard. This study encourages new ones exploring alternative life histories among mammals.

Weaknesses:<br /> Censored data - A significant number of individuals remained alive (~50%) at the end of the study, and thus I wonder how much can the authors say about increased hazard if the individuals have not reach old ages. Maybe the individuals do live long and show increased hazard are very old ages.

Independence between studies - The study provides the replication of a prior study using the same captive population, but I understand that many observations are not independent across studies given repeated measurements. Although this provides reliability, I wonder how independent the conclusions are. This represents a weakness to me because we still do not know whether this is a unique evolutionary trait of this particular captive population. If this is the case, I agree this makes the population a great model for aging studies but do the authors findings have further implications across populations or species? I wonder if populations raised under different conditions would present similar patterns of mortality hazard across age.

Analysis - Another weakness concerns the analysis used. Authors make the claims that social hierarchy may affect mortality hazards and decide to explore associations between body mass and hazard. I wonder if a Cox regression model is more appropriate for the available continuous data, relative to a Kaplan-Meir method. A Cox regression will allow the authors to control for several continuous variables simultaneously, without the limitation of categorical assumptions. A Cox model could also be extended to time-varying covariates allowing for the hazard to change over time (if that is the case). If the authors understand that their approach is equivalent, I suggest a discussion on it. This also applies to the analysis on colony size.

In summary, I see value in this study. There is vast evidence for the penalty of becoming old among mammals. Thus, studies like this one reporting novel patterns are of high impact. I agree that such findings must be replicated and validated. I also see a lot of potential for the use of the available data for more extensive meta-analyses comparing life histories across social mammals or across species with similar use of habitat (underground). Such analyses may allow the authors to move beyond descriptions and discuss why such life history traits may have evolved. Yet, I am not sure how much novelty this study brings, relative to prior studies. It seems the authors may need more than 5 years to allow their individuals to reach older ages.

Reviewer #2 (Public Review):

This paper is an attempt to extend or augment muscle synergy and motor primitive ideas with task measures. The authors idea is to use information metrics (mutual information, co-information) in 'synergy' creation including task information directly. My reading of the paper is that the framework proposed radically moves from attempts to be analytic in terms of physiology and compositionality with physiological bases, instead into more descriptive ML frameworks that may not support physiological work easily.

This approach is very different from the notions of physiological compositional elements as muscle synergies and motor primitives, and to me seems to really be striving to identify task relevant coordinative couplings. This is a meta problem for more classical analyses. Classical analyses seek compositional elements stable across tasks. These elements may then be explored in causal experiments and generative simulations of coupling and control strategies. The present work does not convince me that the joint 'meta' analysis proposed with task information added is not unmoored from physiology and causal modeling in some important ways. It also neglects publications and methods that might be inconvenient to the new framework.

Information based separation has been used in muscle synergy analyses using infomax ICA, which is information not variance based at core. Though linear mixing of sources is assumed, minimized mutual information is the basis.

Physiological causal testing of synergy ideas is neglected in the literature reviews in the paper. Although these are in animal work, the clear connection of muscle synergy choices and analyses to physiology is important, and needs to be managed in the new methods proposed. Is any correspondence assumed? Possible?

Questions and concerns with the framework as an overall tool:

First, muscle based motor information sources have influences on different time scales in the task mechanics. Analyses of synergies in the methods proposed will be very much dependent on the number and quality of task variables included and how these are managed. Standardizing and comparing among labs, tasks sets and instrumentation differences is not well enough considered as a problem in this new proposed method toolset, at least in my reading. Will replication, and testing across groups ever be truly feasible in this framework? Muscle based motor information sources have influences on different time scales in the task mechanics. Kinematic analyses, dynamic analyses and force plate analyses of the same task may provide task variables that alter the results in the proposed framework it seems.

Second, there is a sampling problem in all synergy analyses. We cannot record all muscles or all task parameters. Examining synergies across multiple tasks seeks 'stationary' compositionality. Including task specific elements may or may not reinforce or give increased coordinative precision to the stationary compositionality.<br /> To me the new methods proposed seem partly orthogonal to the ideas of stable compositionality. The 'synergies' obtained will likely differ, and are more likely to be coordinative control groupings of recurrent task and muscle motifs (based on instrumentation) which may or may not relate to core compositionality in physiology. Is there any expectation that the framework should relate to core compositionality and physiology. This is not clear in the paper as written.

It would be useful to explore the approach with a range of neuromechanical models and controllers and simulated data to explore the issues I am raising and convince readers that this analysis framework adds clarity rather than dissolving the generalizability and interpretability of analyses in terms of underlying causal mechanisms.

The authors need to better frame their work in relation to causal analyses if they are claiming links to muscle synergies analyses and claim extension/refinement. Alternatively, these may not be linked, and instead parallel approaches exploring different hypotheses and goals using different organizational data descriptors.<br /> To me this appears a data science tool that may not help any reductionist efforts and leads into less interpretable descriptions of motor control. Not invalid, but sufficiently different that common term use muddies the water.

Reviewer #2 (Public Review):

The authors present a computational tool for high-throughput generation of bacterial strain-specific metabolic models. The study seems interesting. However, I have the following concerns.

1. In the results section "description of Bactabolize", the authors present technical details on how to generate a metabolic model. For the input and output, please provide concrete examples to show the functionality of Bactabolize.

2. KpSC pan-metabolic reference model is provided. Are they required as input for Bactabolize? Are the gene, metabolite information open accessible by users?

3. To generate metabolic models, the authors present comparison results with other methods. However, the authors only present the numbers in genes, metabolites and substrates. Since the interactions between gene, metabolite, and substrate are also critical, if possible, please provide the coverage details about these interactions. Venn diagram is recommended to compare these coverage differences.

4. Are quality control and gap-filling needed to be processed when constructing a new metabolic model?

5. Are there any visualization results to check the status of the generated draft model?

Reviewer #2 (Public Review):

This manuscript describes colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential S. pombe genes in 131 conditions. 3492 mutants, including 124 mutants of 'priority unstudied' proteins conserved in humans, providing varied functional clues.

Phenotype-correlation networks provide evidence for the roles of poorly characterized proteins through guilt by association with known proteins. Gene Ontology (GO) terms were predicted using machine learning methods that take advantage of protein-network and protein-homology data.

Integrated analyses produced 1,675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation for genes involved in cellular ageing were obtained.

A method called NET-FF, which combines network embeddings and protein homology data to predict GO annotations, was developed. The authors demonstrate NET-FF predicts GO terms better than random and compare the information content of the predicted terms with the PomBase GO annotations. The phenotypic data was used to filter the GO annotation predictions made by NET-FF and then explore specific biological examples supported by both datasets

This is a very impressive and rich resource of phenotypic data and it will be particularly useful for the S. pombe research community and generally useful for the functional characterization of highly conserved eukaryotic genes. Overall, the analysis is powerful and sound.

Reviewer #2 (Public Review):

The purpose of this study is to develop a tool that serves as a starting point for investigating and uncovering genes and pathways associated with aging. The tool utilizes information from the GTEx public database, which contains post-mortem human data. It focuses on identifying age-related gene expression changes across different age range, biological sexes, and medical histories, with a focus on specific tissues.

Additionally, the authors envision the platform as continuously evolving, with ongoing development and expansion to include new data and features, ensuring it remains a cutting-edge resource for researchers studying aging.

# Strengths<br /> voyAGEr presents a tool for exploring gene expression changes across multiple tissues in the context of aging. One of the main strengths of the tool is its intuitive and user-friendly interface, which allows for easy navigation and exploration of gene expression patterns for biologists. Users can explore changes in gene expression of single genes across multiple tissues, enabling them to identify genes of interest that can be further investigated.

A particularly noteworthy strength of the tool is its ability to show tissue-specific gene expression patterns. This feature is essential for elucidating the paradigm of tissue-specific asynchronous aging and provides a unique and valuable resource for the aging community.

Overall, the tool offers an entry point for further investigation of genes involved in aging, and its ability to show tissue-specific gene expression patterns provides a unique and valuable resource for the scientific community.

Lastly, the tool is accompanied by a clear and thorough tutorial that explains each of its functionalities and provides examples. The authors also acknowledge the limitations of the statistical inference tests used in the tool, which adds to its overall transparency.

# Weaknesses

## Underlying data analysis<br /> In this tool/resource paper, it is crucial that the data used is up-to-date to provide the most comprehensive and relevant information to users. However, the authors utilized GTEx v7, which is an outdated (2016) version of the dataset. It is worth noting that GTEx v8 includes over 940 individuals, representing a 35% increase in individuals, and a 50% increase in the total number of samples. The authors should check the newer versions of GTEx and update the data.

The authors did not address any correction for batch effects or RNA integrity numbers, which are known to affect transcriptome profiles. For instance, our analysis of GTEx v8 Cortex tissue revealed that after filtering out lowly expressed genes, in the same way authors did, PC1 (which accounts for 24% of the variation) had a Spearman's correlation value of 0.48 (p<6.1e-16) with RNA integrity number.

The data analyzed in the GTEx dataset is not filtered or corrected for the cause of death, which can range from violent and sudden deaths to slow deaths or cases requiring a ventilator. As a result, the data may not accurately represent healthy aging profiles but rather reflect changes in the transcriptome specific to certain diseases due to the age-related increase in disease risk. While the authors do acknowledge this limitation in the discussion, stating that it is not a healthy cohort and disease-specific analysis is not feasible due to the limited number of samples, it would be useful for users to have the option to analyze only cases of fast death, excluding ventilator cases and deaths due to disease. This is typically how GTEx data is utilized in aging studies. Alternatively, the authors should consider including the "cause of death" variable in the model.

The age distribution varies across tissues which may impact the results of the study. The authors' claim that age distribution does not affect the outcomes is inconclusive. Since the study aims to provide cross-tissue analysis, it is important to note that differing age distributions across tissues can influence the overall results. To address this, the authors should conduct downsampling to different age distributions across tissues and evaluate the level of tissue-specific or common changes that remain after the distributions are made similar.

The GTEx resource is extremely valuable, however, it comes with challenges. GTEx contains tissue samples from the same individuals across different tissues, resulting in varying degrees of overlap in sample origin across tissues as not all tissues are collected for all individuals. This could affect the similar/different patterns observed across tissues. As this tool is meant for broader use by the community, it is crucial for the authors to either rule out this possibility by conducting a cross-tissue comparison using a non-parametric model that accounts for the dependency between samples from the same individual, or to provide information on the degree of similarity between samples so that the users can keep this possibility in mind when using the tool for hypothesis generation.

## Visualisation and analysis platform<br /> The authors aimed to create an open-source and ever-evolving resource that could be adapted and improved with new functionality. However, this goal was only partially achieved. Although the code for the web app is open source, crucial components such as the statistical tests or the linear model are not included in the repository, limiting the tool's customizability and adaptability.

Furthermore, the authors' choice of visualization platform (R shiny) may not be the best fit for extensibility and open-source collaboration, as it lacks modularity. A more suitable alternative could be production-oriented platforms such as Flask or FastAPI.

To facilitate collaboration and improve the tool's adaptability, data resulting from the pre-processing pipeline should be made publicly available. This would make it easier for others to contribute and extend the tool's functionality, ultimately enhancing its value for the scientific community.

Reviewer #2 (Public Review):

In this manuscript, Birkbak and colleagues use a novel approach to transform multi-omics datasets in images and apply Deep Learning methods for image analysis. Interestingly they find that the spatial representation of genes on chromosomes and the order of chromosomes based on 3D contacts leads to best performance. This supports that both 1D proximity and 3D proximity could be important for predicting different phenotypes. I appreciate that the code is made available as a github repository. The authors use their method to investigate different cancers and identify novel genes potentially involved in these cancers. Overall, I found this study important for the field.

The major points of this manuscript could be grouped in three parts:

1. While the authors have provided validation for their model, it is not always clear that best approaches have been used.<br /> a. In the methods there is no mention of a validation dataset. I would like to see the authors training on a cancer from one cohort and predict on the same cancer from a different cohort. This will convince the reader that their model can generalise. They do something along those lines for the bladder cancer, but no performance is reported. At the very least they should withhold a percentage of the data for validation. Maybe train on 100 and validate on the remaining 300 samples. They might have already done something along these lines, but it was not clear from the methods.<br /> b. It was not clear how they used "randomised cancer types as the negative control". Why not use normal tissue data or matched controls?<br /> c. If Figure 2B, the authors claim they have used cross validation. Maybe I missed it, but what sort of cross validation did they use?<br /> 2. Potential improvement to the method<br /> a. It is very encouraging the use of HiC data, but the authors used a very coarse approach to integrate it (by computing the chromosome order based on interaction score). We know that genes that are located far away on the same chromosome can interact more in 3D space than genes that are relatively close in 1D space. Did the authors consider this aspect? Why not group genes based on them being located in the same TAD?<br /> b. Authors claim that "given that methylation negatively correlates with gene expression, these were considered together". This is clearly not always the case. See for example https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02728-5. What would happen if they were not considered together?<br /> 3. Interesting results that were not explained.<br /> a. In Figure 3A methylation seems to be the most important omics data, but in 3B, mutations and expression are dominating. The authors need to explain why this is the case.

Reviewer #2 (Public Review):

Murata et al have characterized a new transcription activator termed PFG, which regulates gene expression in female gametocytes. The authors show solid evidence that PFG is a partner of the previously described transcription factor AP2-FG and describe three sets of genes: genes activated by PFG or AP2-FG alone and genes activated by the complex. The authors also show differential binding to the target DNA sequences by AP2-FG to either a 10bp, if in a complex with PFG or a 5bp motif if alone. In all, this is a useful study which further elucidates the underlying regulatory network that drives development of sexual stages and ultimately transmission to mosquitoes. The data presented are clear and solid and the conclusions drawn are mostly supported by the results shown. However, in the absence of evidence of physical interaction, it remains unclear if AP2-FG and PFG actually interact directly or as part of the same complex.

Reviewer #2 (Public Review):

This manuscript focused on why aging leads to decreased beiging of white adipose tissue. The authors used an inducible lineage tracing system and provided in vivo evidence that de novo beige adipogenesis from Pdgfra+ adipocyte progenitor cells is blocked during early aging in subcutaneous fat. Single-cell RNA sequencing of adipocyte progenitor cells and in vitro assays showed that these cells have similar beige adipogenic capacities in vitro. Single-cell nucleus RNA sequencing of mature adipocytes indicated that aged mice have more Npr3 high-expressing adipocytes in the subcutaneous fat from aged mice. Meanwhile, adipocytes from aged mice have significantly lower expression of genes involved in de novo lipogenesis, which may contribute to the declined beige adipogenesis.

The mechanism that leads to age-related impairment of white adipose tissue beiging is not very clear. The finding that Pdgfra+ adipocyte progenitor cells contribute to beige adipogenesis is novel and interesting. It is more intriguing that the aging process represses Pdgfra+ adipocyte progenitor cells from differentiating into beige adipocytes during cold stimulation. Mature adipocytes that have high de novo lipogenesis activity may support beige adipogenesis is also novel and worth further pursuing. The study was carried out with a nice experimental design, and the authors provided sufficient data to support the major conclusions. I only have a few comments that could potentially improve the manuscript.

1. It is interesting that after three days of cold exposure, aged mice also have much fewer beige adipocytes. Is de novo adipogenesis involved at this early stage? Or does the previous beige adipocyte that acquired white morphology have a better "reactivation" in young mice? It would be nice if the author could discuss the possibilities.<br /> 2. Is the absolute number of Pdgfra+ cells decreased in aged mice? It would be nice to include quantifications of the percentage of tomato+ beige adipocytes in total tomato+ cells to reflect the adipogenic rate.

Reviewer #2 (Public Review):

This is a very interesting paper about the coupling of Slack and Nav1.6 and the insight this brings to the effects of quinidine to treat some epilepsy syndromes.

Slack is a sodium-activated potassium channel that is important to hyperpolarization of neurons after an action potential. Slack is encoded by KNCT1 which has mutations in some epilepsy syndromes. These types of epilepsy are treated with quinidine but this is an atypical antiseizure drug, not used for other types of epilepsy. For sufficient sodium to activate Slack, Slack needs to be close to a channel that allows robust sodium entry, like Nav channels or AMPA receptors. but more mechanistic information is not available. Of particular interest to the authors is what allows quinidine to be effective in reducing Slack.

In the manuscript, the authors show that Nav, not AMPA receptors, are responsible for Slack's sensitization to quinidine blockade, at least in cultured neurons (HeK293, primary cortical neurons). Most of the paper focuses on the evidence that Nav1.6 promotes Slack sensitivity to quinidine.

The paper is very well written although there are reservations about the use of non-neuronal cells or cultured primary neurons rather than a more intact system. I also have questions about the figures. Finally, riluzole is not a selective drug, so the limitations of this drug should be discussed. On a minor point, the authors use the term in vivo but there are no in vivo experiments.

Reviewer #2 (Public Review):

This paper introduces a new model that aims to explain the generators of temporal decoding matrices (TGMs) in terms of underlying signal properties. This is important because TGMs are regularly used to investigate neural mechanisms underlying cognitive processes, but their interpretation in terms of underlying signals often remains unclear. Furthermore, neural signals are often variant over different instances of stimulation despite behaviour being relatively stable. The author aims to tackle these concerns by developing a generative model of electrophysiological data and then showing how different parameterizations can explain different features of TGMs. The developed technique is able to capture empirical observations in terms of fundamental signal properties. Specifically, the model shows that complexity is necessary in terms of spatial configuration, frequencies and latencies to obtain a TGM that is comparable to empirical data.

The major strength of the paper is that the novel technique has the potential to further our understanding of the generators of electrophysiological signals which are an important way to understand brain function. Furthermore, the used techniques are state-of-the-art and the developed model is publicly shared in open source code.

On the other hand, the results of comparisons between simulations and real data are not always clear for an inexperienced reader. For example, the comparisons are qualitative rather than quantitative, making it hard to draw firm conclusions. Relatedly, it is unclear whether the chosen parameterizations are the only/best ones to generate the observed patterns or whether others are possible. In the case of the latter, it is unclear what we can actually conclude about underlying signal generators. It would have been different if the model was directly fitted to empirical data, maybe of different cognitive conditions. Finally, the neurobiological interpretation of different signal properties is not discussed. Therefore, taken together, in its currently presented form, it is unclear how this method could be used exactly to further our understanding of the brain.

Reviewer #2 (Public Review):

The article presents 'Mesotrode,' a technique that integrates chronic widefield calcium imaging and electrophysiology recordings using tetrodes in head-fixed mice. This approach allows recording the activity of a few single neurons in multiple cortical/subcortical structures, in which the tetrodes are implanted, in combination with widefield imaging of dorsal cortex activity on the mesoscale level, albeit without cellular resolution. The authors claim that Mesotrode can be used to sample different combinations of cortico-subcortical networks over prolonged periods of time, up to 60 days post-implantation. The results demonstrate that the activity of neurons recorded from distinct cortical and subcortical structures are coupled to diverse but segregated cortical functional maps, suggesting that neurons of different origins participate in distinct cortico-subcortical pathways. The study also extends the capability of Mesotrode by conducting electrophysiological recordings from the facial motor nerve. It demonstrates that facial nerve spiking is functionally associated with several cortical areas( PTA, RSP, and M2), and optogenetic inhibition of the PTA area significantly reduced the facial movement of the mice.

Studying the relationship between widefield cortical activity patterns and the activity of individual neurons in cortical and subcortical areas is very important, and Murphy's lab has been a pioneer in the field. However, the choice of low-yield recording methods (tetrode) instead of more high-yield recording techniques, such as silicon probes, makes the approach presented in this study somewhat less appealing. Also, the authors claim that a tetrode-based approach can allow chronic recordings of single neural activity over days - a topic that is very controversial. In terms of results, I was under the impression that most of the conclusions presented in the bulk of the paper ( Figures 1-5) are very similar to what previous work from Murphy's lab and other labs has shown using acute preparation. In this respect, the paper can benefit from a more in-depth analysis of the heterogeneity of single-neuron functional coupling. The last part of the facial nerve recording is interesting (Figure 6), but I think it can be integrated better into the rest of the paper.

Reviewer #2 (Public Review):

In this manuscript, Hoops et al., using two different model systems, identified key developmental changes in Netrin-1 and UNC5C signaling that correspond to behavioral changes and are sensitive to environmental factors that affect the timing of development. They found that Netrin-1 expression is highest in regions of the striatum and cortex where TH+ axons are travelling, and that knocking down Netrin-1 reduces TH+ varicosities in mPFC and reduces impulsive behaviors in a Go-No-Go test. Further, they show that the onset of Unc5 expression is sexually dimorphic in mice, and that in Siberian hamsters, environmental effects on development are also sexually dimorophic. This study addresses an important question using approaches that link molecular, circuit and behavioral changes. Understanding developmental trajectories of adolescence, and how they can be impacted by environmental factors, is an understudied area of neuroscience that is highly relevant to understanding the onset of mental health disorders. I appreciated the inclusion of replication cohorts within the study.

Reviewer #2 (Public Review):

Summary:<br /> The authors develop a computational approach-avoidance-conflict (AAC) task, designed to overcome limitations of existing offer based AAC tasks. The task incorporated likelihoods of receiving rewards/ punishments that would be learned by the participants to ensure computational validity and estimated model parameters related to reward/punishment and task induced anxiety. Two independent samples of online participants were tested. In both samples participants who experienced greater task induced anxiety avoided choices associated with greater probability of punishment. Computational modelling revealed that this effect was explained by greater individual sensitivities to punishment relative to rewards.

Strengths:<br /> Large internet-based samples, with discovery sample (n = 369), pre-registered replication sample (n = 629) and test-retest sub group (n = 57). Extensive compliance measures (e.g. audio checks) seek to improve adherence.

There is a great need for RL tasks that model threatening outcomes rather than simply loss of reward. The main model parameters show strong effects and the additional indices with task based anxiety are a useful extension. Associations were broadly replicated across samples. Fair to excellent reliability of model parameters is encouraging and badly needed for behavioral tasks of threat sensitivity.

The task seems to have lower approach bias than some other AAC tasks in the literature. Although this was inferred by looking at Fig 2 (it doesn't seem to drop below 46%) and Fig 3d seems to show quite a strong approach bias when using a reward/punishment sensitivity index. It would be good to confirm some overall stats on % of trials approached/avoided overall.

Weaknesses:<br /> The negative reliability of punishment learning rate is concerning as this is an important outcome.

The Kendall's tau values underlying task induced anxiety and safety reference/ various indices are very weak (all < 0.1), as are the mediation effects (all beta < 0.01). This should be highlighted as a limitation, although the interaction with P(punishment|conflict) does explain some of this.

The inclusion of only one level of reward (and punishment) limits the ecological validity of the sensitivity indices.

Appraisal and impact:<br /> Overall this is a very strong paper, describing a novel task that could help move the field of RL forward to take account of threat processing more fully. The large sample size with discovery, replication and test-retest gives confidence in the findings. The task has good ecological validity and associations with task-based anxiety and clinical self-report demonstrate clinical relevance. The authors could give further context but test-retest of the punishment learning parameter is the only real concern. Overall this task provides an exciting new probe of reward/threat that could be used in mechanistic disease models.

Reviewer #2 (Public Review):

The authors combine genetic tools, dye fills and connectome analysis techniques to generate a "first-of-its-kind", near complete, synaptic resolution map of the head bristle neurons of Drosophila. While some of the BMN anatomy was already known based on previous work by the authors and other researchers, this is the first time a near complete map has been created for the head BMNs at electron microscopy resolution.

Strengths:<br /> 1. The authors cleverly use techniques that allow moving back and forth between periphery (head bristle location) and brain, as well as moving between light microscopy and electron microscopy data. This allows them to first characterize the pathways taken by different head BMNs to project to the brain and also characterize anatomical differences among individual neurons at the level of morphology and connectivity.<br /> 2. The work is very comprehensive and results in a near complete map of all head BMNs.<br /> 3. Authors also complement this anatomical characterization with a first-level functional analysis using optogenetic activation of BMNs that results in expected directed grooming behavior.

Weaknesses:<br /> 1. The clustering analysis is compelling but cluster numbers seem to be arbitrarily chosen instead of by using some informed metrics.<br /> 2. It could help provide context if authors revealed some of the important downstream pathways that could explain optogenetics behavioral phenotypes and previously shown hierarchical organization of grooming sequences.<br /> 3. In contrast to the rigorous quantitative analysis of the anatomical data, the behavioral data is analyzed using much more subjective methods. While I do not think it is necessary to perform a rigorous analysis of behaviors in this anatomy focused manuscript, the conclusions based on behavioral analysis should be treated as speculative in the current form e.g. calling "nodding + backward walking" as an avoidance response is not justified as it currently stands. Strong optogenetic activation could lead to sudden postural changes that due to purely biomechanical constraints could lead to a couple of backward steps as seen in the example videos. Moreover since the quantification is manual, it is not clear what the analyst interprets as backward walking or nodding. Interpretation is also concerning because controls show backward walking (although in fewer instances based on subjective quantification).

Summary:<br /> The authors end up generating a near-complete map of head BMNs that will serve as a long-standing resource to the Drosophila research community. This will directly shape future experiments aimed at modeling or functionally analyzing the head grooming circuit to understand how somatotopy guides behaviors.

Reviewer #2 (Public Review):

The manuscript provides new insight into a family of human enzymes. It demonstrates that STEAP2 can reduce iron and STEAP1 can be promiscuous regarding the source of electron donors that it can use. The quality of the kinetics experiment and the structural analysis is excellent. I am less enthusiastic about the interpretation of data and the take-home message that the manuscript intends to deliver. Above all, the work combines data on STEAP2 and STEAP1 and it remains unclear which questions are ultimately addressed. A second critical point is about the interpretation of the experiment demonstrating that STEAP1 can be reduced by cytochrome b5 reductase. The abstract states that "We show that STEAP1 can form an electron transfer chain with cytochrome b5 reductase" whereas the main text discusses the data by suggesting that "we speculate that FAD on b5R may partially dissociate to straddle between the two proteins". The two statements seem to be partly contradictory. Cytochrome b5 reductases do not easily release FAD but rather directly donate electrons to heme-protein acceptors (PMID: 36441026). According to the methods section, no FAD was added to the reaction mix used for the cytochrome b5 reductase experiment. Overall, the data seem to indicate that the reductase might reduce the heme of STEAP1 directly. Would it be possible to check whether FAD addition affects the kinetics of the process? And to perform a control experiment to check that NAD(P)H does not directly reduce the heme of STEAP1 (though unlikely)? A final point concerns the "slow Fe3+-NTA reduction by STEAP2". The reaction is not that slow as the initial phase is 2 per second. The reaction does not show dependence on the substrate concentration suggesting "poor substrate binding". I am not convinced by this interpretation. Poor substrate binding would give rise to substrate dependency as saturation would not be achieved. A possible interpretation could be that substrate binding is instead tight and the enzyme is saturated by the substrate. Can it be that the reaction is limited by non-productive substrate-binding and/or by interconversions between active and non-active conformations?

Roosevelt does not seem to have much faith in the words of the charter itself, but seems to call for example and action throughout her defense and explanation of the charter. She believed that only living the character would guide the actions and behavior of others. This hope that Roosevelt have would become real, as the U.N's declaration of human rights has become a point of behavioral guidance for humanity, as can be seen in the 50th anniversary of the U.N's declaration of human rights.

Roosevelt highlights this point which is very interesting, because the United Nations does not enforce the Declaration of human rights. Despite Roosevelt's assertive comments about human rights and the push for the U.N's declaration of human rights to be completed, the declaration of human rights has only served as moral guidance for the world.

Roosevelt here appeals to pathos to encourage motivation about the attempt of creating effort toward freedom and individual rights for everyone, where everyone has individual freedom and rights that are not controlled but belong to the individual, and are respected. The U.N has accomplished Roosevelt's vision of what the U.N's declaration of human rights should be to people and the world as is seen in the below documentation of the U.N's declaration of human rights' 50th anniversary.

Eleanor agrees to the inclusion of economic rights at the request of Russia. Russia argued that a declaration of human rights should include social and economic rights, not just political rights. The U.N's declaration of human rights originally included political rights, but not economic or social rights. Despite this, Russia still did not assent to the U.N's declaration of human rights, Roosevelts move here was to appease the Russians to draw them towards assenting to the U.N's declaration of human rights through persuasion by being agreeable to Russia's appeal to logos. This however did not work.

Despite what Roosevelt states here, she did not have the same approach to Russia when drafting the United Nations Declaration of human rights. She was often frustrated with their push to redefine human rights, and their push to include economic and social rights into the declaration of human rights. Despite her including economic rights in the declaration of human rights. Russia still did not want to agree with the content in the declaration of human rights.

The focal point of Roosevelt's essay is her frustration with communist countries. The attack on the U.N's declaration of human rights is primarily definitional in substance (though ideological in dispute). Although The U.N's declaration of human rights is presumptive about the terms democracy and human freedom, there is not universal agreement on what those terms mean.

Reviewer #2 (Public Review):

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

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

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

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

Reviewer #2 (Public Review):

Carla de la Fuente et al., utilize a diversity of approaches to understand which plant traits contribute to the stress resilience of pearl millet in the Sahelian desert environment. By comparing data resulting from crop modeling of pearl millet growth and meteorological data from a span of 20 years, the authors clearly determined that early season drought resilience is contributed by accelerated growth of the seedling primary root, which confirms a hypothesis generated in a previous study, Passot et al., 2016. To determine the genetic basis for this trait, they performed a combination of GWAS, QTL analysis, and RNA sequencing and identified a previously unannotated coding sequence of a glutaredoxin C9-like protein, PgGRXC9, as the strongest candidate. Phenotypic analysis using a mutant of the closest Arabidopsis homolog AtROXY19 suggests the broad conservation of this pathway. Comparisons between the transcript of PgGRXC9 by in situ hybridization (this work) and AtROXY19 pattern expression (Belin et al., 2014) support the hypothesis that this pathway acts in the elongation zone of the root. Additional analysis of cell production and elongation rates in root apex in both pearl millet and A. thaliana suggests that PgGRXC9 specifically regulates primary root through the promotion of cell elongation. While several studies have established the connection between redox status of cells and root growth, the current study represents an important contribution to the field because of the agricultural importance of the plant studied, and the connection made between this developmental trait and stress resilience in a specific and stressful environmental context of the Sahelian desert.

While the study presents a compelling narrative that is based on a diverse range of approaches, some aspects require further refinement to be fully convincing.

First, while it is appreciated that working with pearl millet presents certain technical challenges regarding genetic characterization, and the authors have done outstanding work by combing the power of GWAS and QTL mapping to reproducibly identify genetic loci associated with root growth, the related work in Arabidopsis is not fully substantiated. In particular, only one mutant allele was utilized to test the function of this gene in root growth. The lack of a second characterized allele or evidence of genetic complementation makes it difficult to definitively contribute the root developmental defects to the characterized mutation in ROXY19.

The role of redox status in contributing to root growth differences between accessions was not directly tested here. The manuscript is not able to mechanistically link the molecular function of ROXY19 to the change in root growth rate, however, this limitation of the study was not clearly described in the text.

The authors state the use of cell elongation rate (Morris and Silk, 1992) as a parameter to estimate the difference in root growth between contrasted pearl millet lines and A. thaliana roxy19 mutant versus wild type; however, there are inconsistencies in what data are presented. First, in Figure 2E, regarding the comparison between different genotypes of pearl millet lines, they use the parameter of maximum cell length but when authors compare cell elongation between A. thaliana genotypes, in Figure 4D, they use the elongation rate parameter. Second, while the cell elongation rate is based exclusively on the cell length data of the "elongation only" zone (Morris and Silk, 1992), the authors profile the cell length in the whole root apex, from the quiescent center to the beginning of the differentiation zone and it is not clear how they discriminate between each developmental zone and what data was used to estimate elongation rate.

Reviewer #2 (Public Review):

In this manuscript the authors present and characterize LOVtag, a modified version of the blue-light sensitive AsLOV2 protein, which functions as a light-inducible degron in Escherichia coli. Light has been shown to be a powerful inducer in biological systems as it is often orthogonal and can be controlled in both space and time. Many optogenetic systems target regulation of transcription, however in this manuscript the authors target protein degradation to control protein levels in bacteria. This is an important advance in bacteria, as inducible protein degradation systems in bacteria have lagged behind eukaryotic systems due to protein targeting in bacteria being primarily dependent on primary amino acid sequence and thus more difficult to engineer. In this manuscript, the authors exploit the fact that the J-alpha helix of AsLOV2, which unwinds into a disordered domain in response to blue light, contains an E-A-A amino acid sequence which is very similar to the C-terminal L-A-A sequence in the SsrA tag which is targeted by the unfoldases ClpA and ClpX. They truncate AsLOV2 to create AsLOV2(543) and combine this truncation with a mutation that stabilizes the dark state to generate AsLOV2*(543) which, when fused to the C-terminus of mCherry, confers light-induced degradation. The authors do not verify the mechanism of degradation due to LOVtag, but evidence from deletion mutants contained in the supplemental material hints that there is a ClpA dominated mechanism. They demonstrate modularity of this LOVtag by using it to degrade the LacI repressor, CRISPRa activation through degradation of MCP-SoxS, and the AcrB protein which is part of the AcrAB-TolC multidrug efflux pump. In all cases, measurement of the effect of the LOVtag is indirect as the authors measure reduction in LacI repression, reduction in CRISPRa activation, and drug resistance rather than directly measuring protein levels. Nevertheless the evidence is convincing, although seemingly less effective than in the case of mCherry degradation, although it is hard to compare due to the different endpoints being measured. The authors further modify LOVtag to contain a known photocycle mutation that slows its reversion time in the dark, so that LOVtag is more sensitive to short pulses of light which could be useful in low light conditions or for very light sensitive organisms. They also demonstrate that combining LOVtag with a blue-light transcriptional repression system (EL222) can decrease protein levels an additional 269-fold (relative to 15-fold with LOVtag alone). Finally, the authors apply LOVtag to a metabolic engineering task, namely reducing expression of octanoic acid by regulating the enzyme CpFatB1, an acyl-ACP thioesterase. The authors show that tagging CpFatB1 with LOVtag allows light induced reduction in octanoic acid titer over a 24 hour fermentation. In particular, by comparing control of CpFatB1 with EL222 transcriptional repression alone, LOVtag, or both the authors show that light-induced protein degradation is more effective than light-induced transcriptional repression. The authors suggest that this is because transcriptional repression is not effective when cells are at stationary phase (and thus there is no protein dilution due to cell division), however it is not clear from the available data that the cells were in stationary phase during light exposure. Overall, the authors have generated a modular, light-activated degron tag for use in Escherichia coli that is likely to be a useful tool in the synthetic biology and metabolic engineering toolkit.

Reviewer #2 (Public Review):

This is a well-written paper using gene expression in tree sparrow as model traits to distinguish between genetic effects that either reinforce or reverse initial plastic response to environmental changes. Tree sparrow tissues (cardiac and flight muscle) collected in lowland populations subject to hypoxia treatment were profiled for gene expression and compared with previously collected data in 1) highland birds; 2) lowland birds under normal condition to test for differences in directions of changes between initial plastic response and subsequent colonized response.

The question is an important and interesting one but I have several major concerns on experimental design and interpretations.

1) The datasets consist of two sources of data. The hypoxia treated birds collected from the current study and highland and lowland birds in their respective native environment from a previous study. This creates a complete confounding between the hypoxia treatment and experimental batches that it is impossible to draw any conclusions. The sample size is relatively small. Basically correlation among tens of thousands of genes was computed based on merely 12 or 9 samples.

2) Genes are classified into two classes (reversion and reinforcement) based on arbitrarily chosen thresholds. More "reversion" genes are found and this was taken as evidence reversal is more prominent. However, a trivial explanation is that genes must be expressed within a certain range and those plastic changes simply have more space to reverse direction rather than having any biological reason to do so.

3) The correlation between plastic change and evolved divergence is an artifact due to the definitions of adaptive versus maladaptive changes. For example, the definition of adaptive changes requires that plastic change and evolved divergence are in the same direction (Figure 3a), so the positive correlation was a result of this selection (Figure 3d).

Reviewer #2 (Public Review):

Balmas et al., continue the previous work from multiple groups that suggested the implication of uterine ILC2s and signals that activate them, i.e., IL-33/ST2 axis, in healthy and complicated pregnancies and move forward to understand further their role. The authors leverage available and appropriate tools to address more specifically the role of ILC2s during pregnancy and endotoxin-induced abortion, namely mouse models of selective ILC2 deficiency (Roraflox/floxIl7raCre/wt) and transcriptomic analysis of the immune response.

The authors demonstrate, and therewith confirm findings by Bartemes et al. (2018), that ILC2 reside in the mouse uterus, depend on IL-33 and expand during pregnancy. Moreover, they show the Il33 expression by CD45- cells of the uterine stroma. What remains unclear is the kinetics of Il33 expression and ILC2 expansion upon gestation and whether the local ILC2 population expands or arrives from the periphery.

Lack of maternal ILC2, in a mouse genetic model, resulted, as expected, in the absence of uILC2 but also in lighter fetuses at term, similar to the phenotype observed in the absence of maternal IL-33. It would be interesting to understand whether the effect of the IL-33 signaling is a direct ILC2 mediated effect, as for example by using the ST2flox/flox mice. Do the fetuses catch up in weight with their WT controls during weaning time? Do they have any long-term cognitive/behavioral impairment?<br /> The authors showcase the impairment in the remodeling of uterine wall vessels in dams lacking ILC2. It remains to be verified whether this is dependent on IL-33 and whether it is a direct effect of ILC2 or ILC2-dependent infiltration of eosinophils. Further, the absence of ILC2 is accompanied by an increase in Il1b in the uterine tissue suggesting that uILC2 contribute to the uterine microenvironment.

The authors perform RNA sequencing analysis on the bulk samples of uterine ILC2, where uILC2 cluster separately from corresponding lung and LN cells and are featured with higher expression of typical ILC2 markers. Somewhat odd, the authors report on the Foxp3/FoxP3 expression among uILC2, however the staining is not very bright and a Treg control as well as biological negative control should be provided. Moreover, FoxP3 is also not expressed among intestinal ILC2 with regulatory function (Wang et al. 2017). I suggest this data panel to be re-evaluated. A scRNA-Seq analysis would probably be more comprehensive in this case, but might be beyond the scope of this publication.

Absence of uILC2 results in the increased numbers of DCs, macrophages and neutrophils in the uterus, an impact which is not visible in the spleen, which is why the authors argue that this is a uterus-restricted phenomenon, although perturbances in the large intestine and lungs could be expected. Moreover, it remains to be investigated whether these effects are restricted to mid-term pregnancy or preserved until term.

Upon establishing the role of uILC2 in maintaining healthy pregnancy, the authors demonstrate a role for uILC2 in endotoxin-mimicked bacterial infection and abortion. An impressive set of data demonstrate that dams that lack uILC2 have a significantly higher fetus resorption rate than WT dams upon LPS challenge. It remains to be understood whether this phenotype is also dependent on IL-33. Finally, mechanistically, using a somewhat reductionist in vitro model, the authors suggest a protective feedback mechanism between type 2-secreting uILC2 and IL-1b-expressing DCs. This is an interesting concept that still needs a formal confirmation in vivo. Are uILC2 also subjected to plasticity upon IL-1b treatment (Ohne et al. 2016)?

In conclusion, the authors provide a well-conceived study that will be useful for reproductive and tissue immunologists. The data are collected using validated models and methodology and analyzed in a solid manner and can be used as a starting point for further mechanistic studies, assessing the protective potential of uILC2 in pregnancy during infections.

Reviewer #2 (Public Review):

Clary et al. utilized 2-photon intravital imaging techniques to investigate the dynamic behavior of Merkel cells and their innervation during homeostasis and hair regeneration. The authors demonstrated that both Merkel cells (Atoh1-GFP) and the branched axons (TrkC) innervating them undergo significant plasticity and remodeling during homeostasis. Merkel cells were added, removed, and relocated, while axons showed growth and regression. By taking advantage of live imaging, the authors identified two different ways in which Merkel cells interact with axons: creating the stable kylikes and the previously undescribed dynamic Bouton structure. Using live imaging and extensive quantification tools, the authors thoroughly characterized Merkel cell and axon plasticity. They found that Merkel cell plasticity is associated with the hair cycle, while axon plasticity is not. Moreover, newly generated Merkel cells have a short lifespan. By comparing the survival of afferents associated with Merkel cells to empty ones and analyzing Atoh1 cKO, the authors concluded that Merkel cells have a stabilizing effect on axon terminals.

Strengths:

The authors developed an intravital imaging system that enables the simultaneous tracking of both Merkel cells and axon branches. Live imaging, combined with numerous quantification tools, enabled an in-depth characterization of the different behaviors and dynamic nature of Merkel cells, axon branches, and their interaction. The authors' approach has the particular strength of allowing for the comparison of the dynamic behavior of axons associated with Merkel cells to those not innervating Merkel cells within the same touch dome, as well as describing a Bouton structure as a novel morphology mediating Merkel cell and nerve interaction.

Weaknesses:

Although the authors provide an in-depth analysis of Merkel cell dynamics and its association with hair growth, these concepts have been previously reported by the authors and others. Therefore, the extent of novel concepts and scientific advances should be better explained.

The authors suggest that Merkel cell association is a stabilizing factor on innervated axon branches by comparing branch plasticity between branches connected to Merkel cells and empty ones and using Atoh cKO. While the first set of experiments are compelling and provide interesting observations, additional experimental models, such as Merkel cell ablation in adults, may better strengthen the authors' claims. The authors currently use K14-Cre;Atoh1 cKO to support their observations. However, the absence of Merkel cell development in this model, might also lead to developmental defects in nerve patterning (absence of target organ) leading to the phenotype observed by the authors.

Finally, the authors use intravital imaging to describe the Bouton structure and dynamic. Though very interesting there is not enough data to support authors claim for interaction between axon and Merkel cells through the Bouton structure. The paper can benefit from additional functional analysis of this structure.

Reviewer #2 (Public Review):

In this study, Isoe and team produced an atlas of the telencephalon of the adult medaka fish with which they better defined pallial and subpallial regions, characterized the expression of neurotransmitters, and performed clonal analysis to address their organization and maintenance during the continuous neurogenesis. They show that pallial anatomical regions are formed by independent clonal units. Furthermore, the authors demonstrate that pallial compartments exhibit region-specific chromatin landscapes, suggesting that gene expression is differentially regulated. Specifically, synaptic genes have a distinct chromatin landscape and expression in one of the regions of the dorsal pallium, the Dd2. Using the region-specific RNA expression and chromatin accessibility data they have generated; the authors propose several transcription factors as candidate regulators of Dd2 specification. Lastly, the authors use the enrichment of transcription factor binding motifs to establish homology between medaka and human telencephalon, aiming to describe an evolutionary origin for the Dd2 region.

Overall, the study carefully describes diverse aspects of neurogenesis in the telencephalon of the adult medaka fish. As such, the manuscript has the potential to contribute insights to the understanding of circuits and neurogenesis in teleosts and the medaka fish, as well as the evolution of cellular heterogeneity and organization of the telencephalon. Furthermore, the atlas, if easily accessible to the broader community, could be a substantial resource to researchers interested in medaka and teleosts neuroscience. However, there are some conceptual and technical concerns that should be addressed to strengthen this work.

Improving the atlas: The different interpretations of the imaging data generated remain isolated or fragmented and could be better integrated to describe anatomical, connectivity, and ontogeny differences through pallial and subpallial regions.<br /> Molecular differences across regions and species: Differential gene expression and chromatin accessibility throughout regions should be better and more deeply characterized and presented, exhibiting more region-specific features, and leading to a better description of candidate transcription factors that could differentially regulate regional gene expression. The comparison between medaka fish and human telencephalon regions would benefit from a more extensive molecular analysis. Comparison of gene expression and accessible regions could expand the analysis together with TF-binding motif enrichment.<br /> Lineage tracing: The authors claim that the functional compartmentalization of the pallium relies on different cell lineages, which also mostly share connectivity patterns and, at least to some extent, expression patterns. It would be interesting to see how homogenous these lineages are at the molecular level and whether their compartmentalization is retained when neurons reach maturity.

Reviewer #2 (Public Review):

This study provides the proteomic and phosphoproteomics data for our understanding of the molecular alterations in adipose tissue and skeletal muscle from women with PCOS. This work is useful for understanding of the characteristics of PCOS, as it may provide potential targets and strategies for the future treatment of PCOS. While the manuscript presents interesting findings on omics and phenotypic research, the lack of in-depth mechanistic exploration limits its potential impact.

The study primarily presents findings from omics and phenotypic research, but fails to provide a thorough investigation into the underlying mechanisms driving the observed results. Without a thorough elucidation of the mechanistic underpinnings, the significance and novelty of the study are compromised.

Reviewer #2 (Public Review):

Human bactericidal/permeability-increasing protein (huBPI) is known to have in vitro antibacterial activity against Pa, but in vivo, its antibacterial activity is significantly lowered due to binding by autoantibodies called BPI-ANCA. The authors of this study hypothesized that non-human BPIs would escape neutralization by intrinsic BPI-ANCA and retain full antibacterial activity against Pa. Through bioinformatic analysis, the authors anticipated that scorpion BPI (scoBPI) has enough similarity with huBPI to retain antimicrobial activity while escaping recognition by BPI-ANCA. This hypothesis is supported by the following observations: 1) scoBPI fails to capture any BPI-ANCA, 2) scoBPI prevents E. coli- and Pa-LPS dependent inflammatory responses like huBPI and 3) scoBPI exhibits remarkable antimicrobial activity against MDR-Pa in the nanomolar range. Antimicrobial activity of scoBPI was also demonstrated against E. coli suggesting a conserved mechanism of activity against Gram-negative bacteria. The authors use immobilization methods to demonstrate that scoBPI does not bind BPI-ANCA, but a drawback of this method is that some molecular interactions may be disrupted due to immobilization. Moreover, any inhibitory effects of BPI-ANCA on scoBPI activity in the bactericidal assays were not explored. Regardless, the results of this study clearly support their original hypothesis. These findings have broad implications in identifying novel chemotherapies to treat drug-resistant Gram-negative bacterial infections.

Reviewer #2 (Public Review):

The paper by Arribas et al. examines the coding properties of adult-born granule cells in the hippocampus at both single cell and network level. To address this question, the authors combine electrophysiology and modeling. The main findings are:<br /> - Noisy stimulus patterns produce unreliable spiking in adult-born granule cells, but more reliable responses in mature granule cells.<br /> - Analysis of spike patterns with a spike response model (SRM) demonstrates that adult-born and mature GCs show different coding properties.<br /> - Whereas mature GCs are better decoders on the single cell level, heterogeneous networks comprised of both mature and adult-born cells are better encoders at the network level.

Based on these results, the authors conclude that granule cell heterogeneity confers enhanced encoding capabilities to the dentate gyrus network.

Although the manuscript contains interesting ideas and initial data, several major points need to be addressed.

Major points:<br /> 1. The authors use and noisy stimulation paradigm to activate granule cells at a relatively high frequency. However, in the intact network in vivo, granule cells fire much more sparsely. Furthermore, granule cells often fire in bursts. How these properties affect the coding properties of granule cells proposed in the present paper remains unclear. At the very least, this point needs to be better discussed.

2. The authors induce spiking in granule cells by injection of current waveforms. However, in the intact network, neurons are activated by synaptic conductances. As current and conductance have been shown to affect spike output differently, controls with conductance stimuli need to be provided. Dynamic clamp is not a miracle anymore these days.

3. The greedy procedure is a good idea, but there are several issues with its implementation. First, it is unclear how the results depend on the starting value. What we end up with the same mixed network if we would start with adult-born cells? Second, the size of the greedy network is very small. It is unclear whether the main conclusion holds in larger networks, up to the level of biological network size (1 million). Finally, the fraction of adult-born granule cells in the optimal network comes out very large. This is different from the biological network, where clearly four or five-week-old granule cells cannot represent the majority. Much more work is needed to address these issues.

4. Likewise, the idea of dynamic pattern separation seems quite nice. However, the authors focus on the differences between mixed and pure networks, which are extremely small. Furthermore, the correlation coefficients of "low", "medium", and "high" correlation groups are chosen completely arbitrarily. A correlation coefficient of 0.99, considered low here, would seem extremely high in other contexts. Whether dynamic pattern separation is possible over a wider range of input correlation coefficients is unclear (see O'Reilly and McClelland, 1995, Hippocampus, for a possible relationship). Finally, aren't code expansion and lateral inhibition the key mechanisms underlying pattern separation? None of these potential mechanisms are incorporated here.

5. A main conclusion of the paper is that while mature GCs are better decoders on the single cell level, heterogeneity in mixtures improves coding in neuronal networks. However, this seems to be true only for r^2 as a readout criterion (Fig. 4F). For information, the result is less clear (Fig. 4G). The results must be discussed in a more objective way. Furthermore, intuitive explanations for this paradoxical observation are not provided. Saying that "this is an interesting open question for future work" is not enough.

6. The authors ignore possible differences in the output of mature and adult-born granule cells in their thinking. If mature and adult-born granule cells had different outputs, this could affect their contributions to the code (either positively or negatively). At the very least, this possibility should be discussed.

Reviewer #2 (Public Review):

In this manuscript Toshima et al document the use of sophisticated microscopy - with powerful spatial and time resolution - to image markers of the yeast endosomal system.

The initial work documented in this paper does a good job of defining the compartment endocytic cargoes internalise to. This is convincingly shown to be a compartment that is not marked by Sec7 but is instead a distinct (sub)compartment marked by the SNARE protein Tlg2. This agrees with many previous studies, (including biochemical experiments and microscopy of cargoes in a series of membrane trafficking mutants) but has different conclusions to another study (Day et al 2018 - Developmental Cell). Although the microscopy techniques used in the two studies are different, the yeast system and many of the reporters (FP tagged Tlg1, Sec7, Vps21 and fluorescently labelled mating factor) are the same. The Day et al study is suitably referenced throughout the manuscript but as to why the authors have come to fundamentally different answers about endocytic cargoes internalising to a Sec7+ compartment, is not discussed.

The work goes on to show endocytic carriers (marked by Abp1) and endocytic cargoes like fluorescently labelled mating factor internalise to the Tlg2+ compartment. The forward trafficking of these molecules is then observed to transit to a later endosome compartment labelled by Vps21. The super-resolution and time lapse imaging, sometimes even using 3 colours - is of very high quality and fully support the model presented at the end of the paper for this trafficking itinerary. Trafficking mutants are also used (such as a defective allele of arp3 and deletion of VPS21 / YPT52 GTPases) to interrupt trafficking routes and define the pathways followed by endocytosed mating factor.

The endocytic trafficking from Tlg2+ to Vps21 compartments is shown to be defective in mutants lacking GGA adaptors (gga1∆ gga2∆), with cargoes accumulating in the Tlg2+ compartment and other clathrin adaptor mutants not causing this defect. This research avenue also reveals that the GGA proteins are required to maintain the distinct Tlg2 sub compartment.

The final section of the paper uses the same tools to analyse the localisation of the recycling v-SNARE protein Snc1. This is arguably the most important set of experiments in the paper, not only is Snc1 a putative v-SNARE that functionally interacts with Tlg2, but this cargo, unlike pheromone, allows the investigation of recycling back to the PM from TGN/endosomes. However, the authors do not comment on the fact that Snc1 does not localise to the plasma membrane in either experiments using different microscopy techniques (Figure 5A + 5B), calling into question whether the recycling pathway is operating properly or that the FP-tagged machinery has disrupted processing? The steady state localisation of Snc1 in WT cells only looks normal in Supplemental figure, this discrepancy should be discussed or addressed.