Intervals exercises 3

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer 3 (Public Review):

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

Specific comments follow:

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

Strengths:

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

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

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

Weaknesses:

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

Reviewer #3 (Public Review):

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

My major concerns as follows:

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

Reviewer #3 (Public Review):

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

Now for my concerns and suggestions:

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

Major concerns:

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

Strengths:

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

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

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

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

Weaknesses:

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

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

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

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

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

Reviewer #3 (Public Review):

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

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

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

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

Strengths:

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

Weakness:

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

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

Reviewer #3 (Public Review):

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

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

In this study, Nasser et al. aim to understand how early-life experience affects 1) developmental behavior trajectory and 2) individuality. They use early life starvation and longitudinal recording of C. elegans locomotion across development as a model to address these questions. They focus on one specific behavioral response (roaming vs. dwelling) and demonstrate that early life (right after embryo hatching) starvation reduces roaming in the first larval (L1) and adult stages. However, roaming/dwelling behavior during mid-larval stages (L2 through L4) is buffered from early life starvation. Using dopamine and serotonin biosynthesis null mutant animals, they demonstrated that dopamine is important for the buffering/protection of behavioral responses to starvation in mid-larval stages, while in contrast, serotonin contributes to early-life starvation's effects on reduced roaming in the L1 and adult stages. While the technique and analysis approaches used are mostly solid and support many of the conclusions made in the manuscript for part 1), there are some technical limitations (e.g., whether the method has sufficient resolution to analyze the behaviors of younger animals) and confounding factors (e.g., size of the animal) that the authors do not yet sufficient address, and can affect interpretation of the results. Additionally, much of the study is descriptive and lacks deep mechanistic insight. Furthermore, the focus on a single behavioral parameter (dwelling vs. roaming) limits the broad applicability of the study's conclusions. Lastly, the manuscript does not provide clear presentation or analysis to address part 2), the question of how early life experience affect individuality.

Reviewer #3 (Public Review):

The work by Ho et al describes the identification of Mec1/Tel1 independent activation of Rad53 after MMS treatment, which could lead to changes of GFP fusion signals for several dozens of proteins and this was partly dependent on Rtg3. Starting from an unbiased, targeted screen, the authors identified proteins whose GFP fusion signals changed intensity in rad53∆ but not in mec1∆ cells using live cell imaging, including Rad54. Using Rad54 as a readout for the subsequent experiments, a second screen amongst kinases/phosphatases and their regulators found that rtg2-3 mutants reduced Rad54-GFP intensity. Mass spectrometry data identified Rad53 phosphorylate sites in mec1∆ tel1∆ cells, consistent with the cell biological data described above. Overall, the work was well done and supported the main conclusions. The concept of Mec1/Tel1-independent and Rtg3-dependent Rad53 activation connects checkpoint signaling with the retrograde pathway.

Reviewer #3 (Public Review):

ZMYM2 is a transcriptional repressor known to bind to the post-translational modification SUMO2/3. It has been implicated in the silencing of genes and transposons in a variety of contexts, but lacking sequence-specific DNA binding, little is known about how it is targeted to specific regions. At least two reports indicate association with TRIM28 targets (Tsusaka 2020 Epigenetics & Chromatin, Graham-Paquin 2022 bioRxiv) but no physical association with TRIM28 targets had been observed. Tsusaka 2020 theorizes an indirect, potentially SUMO-independent, interaction via ATF7IP and SETDB1.

Here, Owen and colleagues show that a subset of ZMYM2-binding sites in U2OS cells are clearly TRIM28 sites, and further find that hundreds of genes are silenced by both ZMYM2 and TRIM28. They next demonstrate that ZMYM2 homes to chromatin, and interacts with TRIM28, in a SUMOylation-dependent manner, suggesting that ZMYM2 is recognizing SUMOylation on TRIM28 itself. ZMYM2 separately homes to SINE elements bound by the ChAHP complex, in an apparently SUMOylation independent manner. Although this is not the first report to show physical interaction between ZMYM2 and ChAHP, it is the first to show that ZMYM2 homes to ChAHP-binding sites and functions as a corepressor at these sites.

The mode by which ZMYM2 and TRIM28 coregulate genic targets remains somewhat unclear. TRIM28/ZMYM2 bind to LTR elements, loss of these proteins results in upregulation of genes distal to (but in the same TAD as) these binding sites.

Overall, the manuscript is well-written, convincing, and fills a significant hole in our understanding of ZMYM2's mechanistic function.

Reviewer #3 (Public Review):

A detailed understanding of how membrane receptor guanylyl cyclases (mGC) are regulated has been hampered by the absence of structural information on the cytoplasmic regions of these signaling proteins. The study by Caveney et al. reports the 3.9Å cryo-EM structure of the human mGC cyclase, GC-C, bound to the Hsp90-Cdc37 chaperone complex. This structure represents a first view of the intracellular functional domains of any mGC and answers without doubt that Hsp90-Cdc37 recognizes mGCs via their pseudokinase (PK) domain. This is the primary breakthrough of this study. Additionally, the new structural data reveals that the manner in which Hsp90-Cdc37 recognizes the GC-C PK domain C-lobe is akin to how kinase domains of soluble kinases docks to the chaperone complex. This is the second major finding of this study, which provides a concrete framework to understand, more broadly, how Hsp90-Cdc37 recruits a large number of other diverse client proteins containing kinase or pseudokinase domains. Finally, the Hsp90-Cdc37-GC-C structure offer clues as to how GC-C may be regulated by phosphorylation and/or ubiquitinylation by serving as a platform for recruitment of PP5 and/or E3 ligases.

Comments:

1. The authors used an interesting approach to obtain the GC-C-Hsp90-Cdc37 complex. Flag-tagged human GC-C was overexpressed in CHO cells with the expectation of co-purifying endogenous hamster homologs of Hsp90 and Cdc37. There are several points worth noting:<br /> a. It is not clear from the data presented (Figure 1C, Suppl Fig 1A) or the Methods the percentage of particles in the cryo-EM specimen that represent the GC-C-Hsp90-Cdc37 complex. Presumably, some fraction of GC-C isolated will not be associated with Hsp90-Cdc37. If a very large portion of GC-C is associated with Hsp90-Cdc37, it would be good to explain why this is to be expected. Are 2D/3D classes corresponding to the activated GC-C dimer found? If not, why?<br /> b. Figure 1A suggests that GC-C is phosphorylated before recruitment of Hsp90-Cdc37. What is the phosphorylation status of the GC-C specimen that was imaged by cryo-EM?<br /> c. The resolution of the cryo-EM map (3.9 Å) is too low for unambiguous identification of proteins. Please provide more precise justification for the claim that the densities observed do in fact correspond to hamster Hsp90 and Cdc37.<br /> d. The authors state that human GC-C pulls down hamster Hsp90-cdc37 but soluble kinases cannot, despite the high sequence identity between human and hamster Hsp90-cdc37. Is this because GC-C recognition is more promiscuous? Can this difference be understood in light of the new structural information presented?

2. A large portion of the enforced GC-C dimer was not visible in the cryo-EM maps. It is not easy to learn from Figure 1 exactly which parts of the GC-C construct was sufficiently ordered and observed structurally. Please improve Figure 1.

3. On page 4, the authors claim that they are able to orient the GC-C-Hsp90-Cdc37 complex "as it would sit on a membrane" and referred to Figure 1B. It is not clear what is implied here. Does Hsp90-Cdc37 binding constrain the complex to face the inner leaflet of the membrane in a specific orientation as shown in Figure 1B? If true, this could potentially have important functional implications. Please illustrate how this was deduced based on the information available.

4. Also on page 4, it is stated that it is sterically unlikely an additional Hsp90-Cdc37 complex is associated with the other copy of GC-C in the leucine zippered dimer. It is not obvious to the reader how this may be the case. An additional figure could help make this more clear. Additional biochemical evidence will also help. The absence of GC-C-Hsp90-Cdc37 dimers in cryo-EM micrographs can also support the argument.

5. Some comments on Figure 2:<br /> a. NTD and CTD are mislabeled in Figure 2A.<br /> b. The authors should show cryo-EM density to support their modeling of GC-C in Figures 2B and C.

6. The authors claim that Hsp90-Cdc37 clients are more similar structurally near the cdc37 interface. Please illustrate this with additional figures. Suppl. Figure 2 is inadequate for this purpose. The authors can also consider adding a more detailed discussion comparing the interactions between the pseudokinase/kinase C-lobe and Cdc37 in known structures. Is shape/charge complementarity a universal feature of cdc37-dependent kinase/pseudokinase recruitment? It would be interesting to also consider if it would be possible to predict which of the ~60 human pseudokinases are possible Hsp90-Cdc37 clients. New structural findings from this study and publicly available AI-predicted protein structures could help.

Reviewer #3 (Public Review):

Summary:

This manuscript sheds light on the cell cycle-dependent post-transcriptional regulation of the oncogenic kinase AURKA. AURKA mRNA is subjected to alternative polyadenylation (APA), resulting in a short and a long 3'UTR isoform. While the ratio long/short isoform is important for AURKA expression and might impact cancer development, it is not unclear how this is regulated throughout the cell cycle. Translation and decay rate of the long isoform only are targeted by let-7a miRNA and in a cell-cycle dependent manner. In contrast, the short isoform is translated highly and constantly throughout interphase. Finally, depletion of the long isoform led to an increase in proliferation and migration rates of cells. In Triple Negative Breast Cancer, where AURKA is typically overexpressed, the short isoform is predominant and its expression correlates with faster relapse times of patients, suggesting that this mechanism might play an important role in this cancer.

Originality and novelty:

The originality of this work is to show the cooperation between APA and miRNA-targeting in controlling gene expression dynamics of AURKA during cell cycle. To investigate this mechanism, the authors have developed an interesting transient single-cell and biochemical assay to rapidly study mRNA-specific gene expression in a way that measures post-transcriptional events. This manuscript puts an emphasis on the cell cycle dependent expression control of AURKA at the translation level. However, the magnitude of the changes in mRNA levels throughout the cell cycle is even greater than that of the changes in translation. Therefore, it remains unclear whether translation really is that important in controlling AURKA expression during the cell cycle. Moreover, (i) AURKA regulation by miRNA is already known (Fadaka et al., Oncotarget 2020, Zhang et al., Arch Med Sci 2020, Yuan et al., Technol Cancer Res Treat 2019, Ma et al., Oncotarget 2015), (ii) the concept of cooperation between APA and translation already is not new (Sandberg et al., Science 2008, Mayr and Bartel, Cell 2009, Masamha et al. Nature 2015), and (iii) previous transcriptome-wide studies already suggested a cell-cycle dependent control of AURKA at the translation level (Tanenbaun et al., eLife 2015, translation efficiency ratio G2/G1 = 1.59) as well as the mRNA level (Krenning et al., eLife 2022). The impact of this manuscript could be increased by investigating (i) the mechanism of cell cycle-dependent regulation by let7a expression (i.e is there changes in let7a expression or activity during the cell cycle in this model) and (ii) the origin of AURKA APA dysregulation in cancer (could it be modulated by CFIm25? (Masamha et al. Nature 2015, Tamaddon et al. Sci Rep 2020)).

Reviewer #3 (Public Review):

Moutard, Laura, et al. investigated the gene expression and functional aspects of Leydig cells in a cryopreservation/long-term culture system. The authors found that critical genetic markers for Leydig cells were diminished when compared to the in-vivo testis. The testis also showed less androgen production and androgen responsiveness. Although they did not produce normal testosterone concentrations in basal media conditions, the cultured testis still remained highly responsive to gonadotrophin exposure, exhibiting a large increase in androgen production. Even after the hCG-dependent increase in testosterone, genetic markers of Leydig cells remained low, which means there is still a missing factor in the culture media that facilitates proper Leydig cell differentiation. Optimizing this testis culture protocol to help maintain proper Leydig cell differentiation could be useful for future human testis biopsy cultures, which will help preserve fertility and child cancer patients.

Methods: In line 226, there is mention that the central necrotic area was carefully removed before RNA extraction. This is particularly problematic for the inference of these results, especially for the RT-qPCR data. Was the central necrotic area consistent between all samples and variables (16 and 30FT)? How big was the area? This makes the in-vivo testis not a proper control for all comparisons. Leydig cells are not evenly distributed throughout the testis. A lot of Leydig cells can be found toward the center of the gonad, so the results might be driven by the loss of this region of the testis.

What did the morphology of the testis look like after culturing for 16 and 30 days? These images will help confirm that the culturing method is like the Nature paper Sato et al. 2011 and also give a sense of how big the necrotic region was and how it varied with culturing time.

There are multiple comparisons being made. Bonferroni corrections on p-value should be done.

Results: In the discussion, it is mentioned that IGF1 may be a missing factor in the media that could help Leydig cell differentiation. Have the authors tried this experiment? Improving this existing culturing method will be highly valuable.

Add p-values and SEM for qPCR data. This was done for hormones, should be the same way for other results.

Regarding all RT-qPCR data-There is a switch between 3bHSD and Actb/Gapdh as housekeeping genes. There does not seem to be as some have 3bHSD and others do not. Why do Igf1 and Dhh not use 3bHSD for housekeeping? If this is the method to be used, then 3bHSD should be used as housekeeping for the protein data, instead of ACTB. Also, based on Figure 1B and Figure 2A (Hsd3b1) there does not seem to be a strong correlation between Leydig cell # and the gene expression of Hsd3b1. If Hsd3b1 is to be used as a housekeeper and a proxy for Leydig cell number a correlation between these two measurements is necessary. If there is no correlation a housekeeping gene that is stable among all samples should be used. Sorting Leydig cells and then conducting qPCR would be optimal for these experiments.

Figure 2A (CYP17a1): It is surprising that the CYP17a1 gene and protein expression is very different between D30FT and 36.5dpp, however, the immunostaining looks identical between all groups. Why is this? A lower magnification image of the testis might make it easier to see the differences in Cyp17a1 expression. Leydig cells commonly have autofluorescence and need a background quencher (TrueBlack) to visualize the true signal in Leydig cells. This might reveal the true differences in Cyp17a1.

Figure 3D: there are large differences in estradiol concentration in the testis. Could it be that the testis is becoming more female-like? Leydig and Sertoli cells with more granulosa and theca cell features? Were any female markers investigated?

Figure 3D and Figure 5A: It is hard to imagine that intratesticular estradiol is maintained for 16-30 days without sufficient CYP19 activity or substrate (testosterone). 6.5 dpp was the last day with abundant CYP19 expression, so is most of the estrogen synthesized on this first day and it sticks around? Are there differences in estradiol metabolizing enzymes? Is there an alternative mechanism for E production?

Reviewer #3 (Public Review):

Signore et al. the synthesized and functionally characterized the recombinant adult hemoglobin (Hb) proteins of extant, extinct, and ancestral sirenians to explore the putative role of Hb in helping Steller's sea cows adapt to life in extremely cold waters. The functional comparisons show that the Hb of the subarctic Steller's sea cows differs in multiple biochemical properties relative to the Hbs of the two extant sirenians in the study, the Florida manatee, and the dugong and also from the Hb inferred for the common ancestor of Steller's sea cow and dugong. Specifically, the Steller's sea cow shows reduced oxygen binding affinity, reduced sensitivity to the allosteric co-factors DPG, Cl-, and H+, increased solubility, and reduced thermal sensitivity. DPG plays an important role in regulating Hb oxygen affinity in mammals, and the lack of sensitivity to it is unique to the Hb of Steller's sea cow. Sequence comparisons show that the Hb of the Steller's sea cow differs at 11 amino acids from that of its sister group, the dugong, one of which is intriguing because it occurs in a position that is invariable among mammals at a site that is critical for DPG binding, a change from Lys to Ans in position 82 of the mature β/δ globin chain. To test the significance of this change, the authors use site directed mutagenesis to insert back a Lys in the Steller's sea cow Hb background (β/δ82Asn→Lys) and test its biochemical properties. The functional assays with the β/δ82Asn→Lys mutant indicate that reverting this position to its ancestral state drastically altered the biochemical properties of the Steller's sea cow Hb, making it functionally similar to the Hbs of manatee, dugong, and the Hb inferred for the common ancestor of Steller's sea cow and dugong.

The study's strength lies in comparing the different recombinant Hbs in an explicit evolutionary framework. The conclusions are supported by the analyses, and the results are relevant in the fields of evolutionary biology, physiology, and biochemistry because they suggest that a single amino acid substitution in a protein can have profound biochemical consequences that impact whole organism physiology.

Reviewer #3 (Public Review):

In this article, Briggs et al. used scRNAseq to get high-resolution cell cycle-regulated transcriptomes of both replicative forms of Trypanosoma brucei (PCF and BSF) without prior synchronization. Briggs et al. also demonstrated that performing the scRNAseq library immediately after thawing cryopreserved samples did not show significant differences. The authors used computational reconstruction of the cell cycle to get the dynamic expression patterns of cycling genes in both life cycle forms. They identified a core cycling transcriptome highly conserved between forms. However, some slight differences were found between them, e.g.: a switch in gene expression associated with the S-G2 transition is much more discrete in PCFs than BSFs. Moreover, as proteomics data across the cell cycle is not available for BSFs, the authors tagged the top most significant genes with transcripts peaking in G1, S, and G2/M phases with a fluorescent epitope. After comparing the transcript expression patterns with protein abundance, the authors found that the majority of genes with periodic cycling transcript and protein levels exhibited a relative delay between peak transcript and protein expression, which was expected. In summary, this work provides a valuable public tool for further investigation into gene expression dynamics throughout the cell cycle in T. brucei.

Reviewer #3 (Public Review):

Nurr1 is an orphan nuclear receptor that may be a significant target for the treatment of neurodegenerative disorders. Targeting Nurr1 with small molecule ligands has been challenging, but there has been some progress in the identification of synthetic ligands that appear to increase Nurr1 activity. Nurr1 functions as a monomer, but may also heterodimerize with RXR. Heterodimerization appears to repress Nurr1 transcriptional activation via NBRE-driven reporters. Importantly, small molecule ligands that appear to selectively activate the Nurr1/RXR heterodimer complex (and not the Nurr1 or RXR homodimers, individually) have been identified. Exactly how these ligands function in this manner is unclear.

Here, the authors demonstrate that Nurr-1/RXR agonists actually function by perturbing the heterodimer formation providing Nurr1 monomers that are much more active in driving transcription. The authors demonstrate this with a range of biochemical, biophysical, and cell-based methodologies. Cotransfection assays examining the activity of Nurr1 on an NBRE reporter illustrate that RXRalpha is a repressor of Nurr1 transcriptional activity and that this is mediated by the RXR LBD. Using this experimental model as well as RXR coactivator interaction assays (biochemical) and RXR/DR1 reporter cotransfection assays, the authors examined multiple classes of RXR ligands (RXR agonists, modulators, antagonists, and Nurr1/RXRa selective agonists) to compare their activities. The Nurr1/RXR heterodimer agonists were quite effective at inducing transcription in the Nurr1/RXR assay but relatively ineffective in the RXR - coactivator binding assay or the RXR cotransfection assay. Using the array of ligands, the authors show that the Nurr1/RXR activity does not correlate to the ability of compounds to induce RXR to recruit a coactivator or activate RXR-mediated transcription. This suggests that Nurr1/RXR heterodimer agonists may not be mediating transcriptional activation via the "standard" mechanism. One weakness here is that some compounds used in the Nurr1/RXR transcription assay are not included in the other assays and may not have been included in the correlation studies. Assessment of RXR/Nurr1 dimerization in the presence of the ligands was assessed by ITC and demonstrated a correlation between the weakening of heterodimer formation and Nurr1/transcriptional activity suggesting that modulation of dimerization may be a mechanism by which the Nurr1/RXR heterodimer specific ligands function. NMR and analytical SEC data support this hypothesis as well. With regard to the physiological significance of these observations, no studies were completed on actual Nurr1 target genes addressing this type of mechanism offering limitations on the applicability of the hypothesis. However, the mechanism proposed is strongly supported by the data and offers a novel paradigm for the development of drugs targeting this receptor and possibly other nuclear receptors as well.

Reviewer #3 (Public Review):

The authors described the one family showing autoinflammatory phenotypes with L236P variant of TNFAIP3 gene. The variant has not been reported on and they evaluated the function of this variant using in vitro and in silico methods. I think this is well-written manuscript and I agree with their interpretation about the pathogenicity of this variant, but the new finding is poor. The variant information was only a new finding.

I recommend the revision of the following points.

In Table 2, T647P seemed to be pathogenic which was evaluated with in vitro assay by Kadowaki.

Two other missense variants, V377I (Niwano, Rheumatology 2022) and T602S (Jiang W, Cellular Immunol 2022) were recently reported. These should be included in the discussion.

Reviewer #3 (Public Review):

Genome-wide association studies on asthma have been challenging, due to the accuracy of phenotyping and potential gene-environment interactions. Thus, the authors aimed to identify genetic loci associated with subtypes of childhood wheezing. One main strength of this investigation is that the data availability of wheeze from birth to adolescence among multiple birth cohorts allowed the sub-phenotyping on a large scale and high statistical power. The study is properly designed and the conclusions are well-supported. Understanding the heterogeneity in subtypes of childhood wheezing is of great clinical interest and may help inform future directions in disease prediction and prevention.

Reviewer #3 (Public Review):

Numerous experimental models are phenotyped in this manuscript including mouse neurons, iPSC-derived human neurons, knock-in mice, and knock-in iPSCs. Expression of acetylation-mimic or acetylation-null TDP-43 protein is achieved either with overexpression or CRISPR-Cas9-based knock-in. A complex phenotype is observed including loss of TDP-43 function (reduced autoregulation, increased cryptic splicing) and a gain of TDP-43 (increased insoluble TDP-43 protein). These correlate with downstream neurobehavioral changes which are most consistent with a cortical/hippocampal phenotype without a motor phenotype. Post-translational modifications of disease-associated proteins are thought to contribute to neurodegenerative disease pathogenesis, and this study succeeds in demonstrating that TDP-43 acetylation results in downstream molecular and behavioral phenotypes.

There are numerous additional strengths. TDP-43 acetylation is a post-translational modification that is known to be associated with TDP-43 inclusions that are characteristic of human diseases. An important strength is the rigorous use of multiple different experimental models (rodent cells, iPSC-derived neurons, mice, overexpression, knock-in) with overall consistent results. Moreover, multiple orthogonal endpoints are presented including histology/cytology/immunostaining, biochemistry, molecular biology, and neurobehavioral assays. As TDP-43 acetylation is known to block RNA binding, these novel cellular and mouse models represent interesting albeit complex tools to study the functional consequences of a partial loss of function. As TDP-43 regulates its own expression (i.e. autoregulation), the complexity lies at least in part due to the loss of RNA binding leading to a functional loss of TDP-43 function which includes the increased expression of the TARDBP transcript and TDP-43 protein.

Conceptually, there is a disconnect in that the mouse model exhibits primarily a cortical/hippocampal phenotype more akin to frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP), while TDP-43 acetylation is only seen in ALS tissues and not in FTLD-TDP tissues because most of the pathologic protein in the latter is N-terminally truncated (i.e. the acetylation site is not present). That being said, there is no mouse model which completely and faithfully recapitulates the human disease, and this mouse model avoids overt overexpression (increased TDP-43 protein expression stemming from altered autoregulation) and avoids the use of synthetic/artificial mutation (such as mutation of the TDP-43 nuclear localization signal).

In terms of the CNS phenotype, it is difficult to interpret the reduced density of NeuN-positive neurons in the mouse model as a neurodegenerative phenotype. The reduction in NeuN density but not overall cellular density is only suggestive of neurodegeneration (as opposed to, for example, a developmental phenotype) without more rigorous stereological approaches that take into account potential volumetric changes. Indeed, the absence of astrocytosis and microgliosis argues against neurodegeneration.

Some of the loss of function measures (CFTR construct splicing, shift in SORT1 protein) are subtle, although RNA sequencing clearly shows many splicing aberrations including cryptic splicing events which overall supports that a loss of TDP-43 function is observed.

Finally, there are multiple instances where multiple measurements are made on a few biological replicates. ANOVA or t-tests are not appropriate in these instances (lack of independence).

Reviewer #3 (Public Review):

The current manuscript describes the expression of multiple Natriuretic peptides and their receptor during the early embryo development in the amphibian Xenopus laevis. This signaling pathway is well known to control a broad range of physiological processes but its role in embryogenesis has not been studied before. Thus, the study presents some important novel findings. After defining the combination of ligands and receptors expressed during embryogenesis, they used loss of function experiment to test the requirement of this signaling pathway to the development of neural crest cells.

The loss of function experiments are well controlled as they use both chemical inhibitors and Morpholino that block the translation of the protein. They also rescue the phenotypes by using either mRNA from the human protein (receptor), or purified peptides (Ligands).

The results clearly show that loss of either Npr1 or Npr3 affects the development of both neural crest and placodes, while Npr2 had no visible effect. Similarly, they found that the loss of the ligands Nppa and Nppc affected neural crest and placode development while Nppb had no effect. Again, the loss of function was achieved with both Morpholino KD and inhibitors. In general, the loss of neural crest and placodal marker are associated with an expansion of neural marker (Sox2) and a corresponding decrease of epidermal marker (Keratin).

For Npr3 the author show that the loss of the protein is associated with an increase in cell death and decrease in cell proliferation which match some previous work on the role of the receptor in other cell type. It is unclear how much this can account for the striking difference in patterning observed and experiment to test this have not been performed.<br /> Overall, this work is important for the field as it shows novel genes that are critical for craniofacial and sensory development. It is likely that mutations in any genes involved in this pathway could result in birth defect which could be corrected pharmacologically.

Reviewer #3 (Public Review):

The function of a brain area is defined by its interaction with other regions. Accordingly, two areas communicate via axons and dendrites, but the language is plurimodal along the neurotransmitter-receptor dimension. Consequently, reading its neurochemical constituents is the most advanced way to characterize the brain into functional territories.

Along this theoretical line of research, Rapan et al. produced an exceptional report on the structure of the macaque frontal lobes based on cytoarchitectural division complemented with functional connectivity and neurochemical data. Results are lavishly illustrated. They report 35 cytoarchitectural areas in the prefrontal lobe with precise, different connectivity and neurotransmitter profiles together with practical anatomical landmarks. All data is openly available to the community and will constitute a cornerstone for future neuroscientific research in the macaque frontal lobes.

I congratulate the authors for this already extraordinary work.

Reviewer #3 (Public Review):

The manuscript concerns the cleavage of the Gag polyprotein lattice from the HIV virion membrane, a key stage in HIV lifecycle, and one that is required for HIV to become infectious. Since cleavage requires homodimerization between the small fraction (5%) of such Gag polyproteins that carry a protease domain, referred to as Gag-Pol, this raises questions regarding how such homodimerization can take place, and whether it can happen on the required timescales, given that Gag-Pol is typically embedded in a lattice that is observed to form one large connected component.

The authors address these questions in silico, using particle-based reaction diffusion simulations. Such simulations are rigid-body and "structure-resolved" meaning that they rigidly incorporate the geometry of the polyproteins, and their various binding interfaces, based on existing structural data. Other aspects of the simulations are also in-line with available data, including copy numbers, lattice curvature, and dissociation rates. This focused approach is a strength of the work and allows the authors to make credible claims that their simulations have relevance to HIV (as does their commitment to comparison with HALO-SNAP-based measurements of dimerization kinetics as well as iPALM experiments that characterize lattice dynamics).

A central part of the model is that it allows for the "possibility of imperfect alignment of molecules in the lattice", presumably due to the incompatibility of regular hexagonal tiling and surfaces with non-zero Gaussian curvature, such as a sphere. This is implemented via the ad-hoc imposition of a free-energy penalty when complete hexamers are formed, implying that hexamers are less stable than six ideal bonds. By varying this strain penalty, the authors can change the stability of the lattice independently of individual binding affinities, allowing its use as an effective fitting parameter when comparing to HALO-SNAP data. In the latter case, agreement between simulation and experiment can only be found at moderate levels of lattice stability.

However, such energetic penalties are present whenever the polyprotein structure must undergo deformations which, on surfaces with nonzero Gaussian curvature, should be the case for partial tilings as well as complete ones (where all six interfaces form bonds). This, therefore, appears to be a weakness of the work. An elastic implementation of polyprotein structure, for example, would permit strain to accumulate (and therefore stresses to propagate) throughout the lattice naturally, irrespective of whether complete hexamers were formed, and might reasonably be expected to impact the likelihood of different lattice structures. Whilst it is not clear how or whether this would lead to qualitatively or quantitatively different results, it is nevertheless worth remarking upon since the authors high-level claim is that lattice structure is an important determinant of the mean-first-passage times to dimerization.

Overall, I find this to be a valuable study, carried out in a solid and comprehensive manner. The primary impact of the work appears to be twofold: the unification of different experimental measurements under a single model, and the further identification of the salient parts of that model that most impact biological function. The results advance the understanding of one of the steps of the HIV lifecycle, via a better description of the mechanisms underpinning Gag-Pol dimerization. Notably, the authors stop short of drawing parallels to many related concepts and models in statistical physics, such as those concerning percolation and diffusion limited aggregation as well as the notions of dislocations and defects in crystalline matter on curved surfaces. These might reasonably have provided a basis for better understanding and quantification of the authors' simulations, as well as improving the scope for extensions and conceptual clarity.

Reviewer #3 (Public Review):

This report by Mohebi et al. provides new answers to old questions by showing that the activity of striatal cholinergic interneurons (CINs) escalates progressively during specific reward-related behaviors and that this correlates with previously observed ramps in dopamine (DA) release in the nucleus accumbens core. The report is strong and provides evidence for the authors' hypothesis that DA ramps are independent of DA neuron activity, but are instead the result of CIN activity and corresponding acetylcholine (ACh) release. The authors further demonstrate that the fidelity of CIN activation and consequent driving of DA release is even more robust in vivo than observed ex vivo slice preparations, which is fundamental for understanding the role of ACh-DA interactions in behavior. The findings complement the authors' previous evidence ventral tegmental area (VTA) DA neuron firing patterns do not show a ramping pattern; the previously reported VTA data are appropriately included here (in Fig. 3) to illustrate the absence of VTA firing during the time-locked increases in CIN activity and DA release. The present studies stop short of showing a direct link between CIN activity and DA release, however, which would require examining DA release during behavior in the presence of an antagonist of nicotinic ACh receptors. The authors also extend the understanding of the regulation of DA release by acetylcholine (ACh) by showing that optical activation of CINs in vivo promotes DA release responses that do not attenuate with repetitive stimulation. This contrasts with previous results in ex vivo striatal slices in which ACh-evoked DA release has been found to decline progressively from rundown and/or receptor desensitization. The authors propose that in vivo, AChE may be more effective in curtailing local ACh levels than in slices because of the slightly lower temperature typically used for slice studies, as well as the use of superfusion that might facilitate some AChE washout (AChE inhibitors are still effective in slices, of course). Overall, the report not only provides evidence for the cellular substrate for DA ramps but also shows the robustness of ACh-driven DA release in vivo. A few points to strengthen the report are listed below.

1) The authors give a few details about how CINs were activated at the beginning of the results, but say only that DA dynamics were monitored using fiber photometry. Given that the methods are at the end, a brief summary should be given here to indicate whether this means direct monitoring of DA or indirect via GCaMP, for example. It would be helpful to note the sensor used in the abstract, as well. In this light, as it were, RdLight1 should be described upon the first mention.

2) The authors show that infusion of DHbE in the NAc likelihood of decisions to approach the center port, as did antagonism of DA receptors. This supports the authors' argument that ramping of CIN activity and consequent ACh release underlies observed ramps in DA release. However, to show a causal interaction requires testing whether the observed DA ramps are absent after DHbE infusion in the NAc, under the same conditions that attenuated behavior.

3) In Fig. 3, the y-axis title for the upper panels should specify VTA, not simply "rate". This is stated in the legend, but should also be specified in the figure panel.

4) A recent preprint in BioRxiv by AC Krok, NX Tritsch et al. shows a related correlation between ACh and DA release in vivo in a reward task, as well as differences in other conditions. This report shows also that cortical input to CINs indeed plays a role, as suggested in the concluding sections of the present report. Consideration of the data in the preprint in the context of the present results could be valuable for the field.

Reviewer #3 (Public Review):

In this manuscript, the authors explore the mechanism of ATRIP recruitment to single-stranded DNA (ssDNA), which is important for ATR activation and the subsequent control of DNA repair and cell cycle progression. Using Xenopus egg extracts, in vitro interaction assays, and ssDNA constructs, the authors found that AP endonuclease 1 (APE1) plays a role in the recruitment of ATRIP to ssDNA independently of RPA. Moreover, APE1 domains are characterized for ssDNA, ATRIP, and RPA interaction, determining that the nuclease activities of APE1 are not required for this new mode of ATRIP recruitment. Overall, the work presented makes a compelling case for a novel role for APE1 in ATRIP recruitment that seems crucial for ATR activation (at least in the Xenopus system). The results are likely to have an important impact on our understanding of the determinants for activation of ATR signaling and cellular responses to DNA damage and replication stress. It remains unclear whether the findings will be extended to other organisms and be relevant for different types of DNA lesions. Also, there are several points of concern in the manuscript that require further clarification, especially regarding some of the quantitative analyses presented and the claimed importance of the RPA-independent mode of ATRIP recruitment for ATR activation.

Reviewer #3 (Public Review):

This work describes transcriptome profiling of dissected skin of zebrafish at post-embryonic stages, at a time when adult structures and patterns are forming. The authors have used the state-of-the-art combinatorial indexing RNA-seq approach to generate single cell (nucleus) resolution. The data appears robust and is coherent across the four different genotypes used by the authors.

The authors present the data in a logical and accessible manner, with appropriate reference to the anatomy. They include helpful images of the biology and schematics to illustrate their interpretations.

The datasets are then interrogated to define cell and signalling relationships between skin compartments in six diverse contexts. The hypotheses generated from the datasets are then tested experimentally. Overall, the experiments are appropriate and rigorously performed. They ask very interesting questions of interactions in the skin and identify novel and specific mechanisms. They validate these well.

The authors use their datasets to define lineage relationships in the dermal scales and also in the epidermis. They show that circumferential pre-scale forming cells are precursors of focal scale forming cells while there appeared a more discontinuous relationship between lineages in the epidermis.

The authors present transcriptome evidence for enamel deposition function in epidermal subdomains. This is convincingly confirmed with an ameloblastin in situ. They further demonstrate distinct expression of SCPP and collagen genes in the SFC regions.

The authors then demonstrate that Eda and TH signalling to the basal epidermal cells generates FGF and PDGF ligands to signal to surrounding mesenchyme, regulating SFC differentiation and dermal stratification respectively.

Finally they exploit RNA-seq data performed in parallel in the bnc2 mutants to identify the hypodermal cells as critical regulators of pigment patterning and define the signalling systems used.

Whilst these six interactions in the skin are disparate, the stories are unified by use of the sci-RNA-seq data to define interactions. Overall, it's an assembly of work which identifies novel and interesting cell interactions and cross-talk mechanisms. There are some aspects that require clarification:

With respect to the discontinuous relationship noted in Figure 2I in the epidermis, the authors did not make mention of the fact that there are in fact two independent sources of periderm in the zebrafish. The first periderm derives from the EVL, is segregated a gastrulation, and gradually replaced from the basal epidermis during post-embryonic stages. Could this residual EVL-derived periderm have reduced sensitivity of the trajectory mapping from basal to periderm? The authors should comment whether their transcriptome dataset likely had residual EVL-derived periderm and if this might have impacted their trajectory continuity interpretation.

The authors ask if dermal SFCs express proteins associated with cartilage formation and use Col10a1 orthologues as markers (Fig 3B, I). I wonder if these are the best transcripts to answer this question as this has also been described to label osteoblasts in certain contexts in the fish and the authors might want to refer to Li et al 2009 or Avaron et al 2005. Were other markers of cartilage formation present such as collagen2 genes? These may be more definitive. The authors might want to reinterrogate their datasets for true cartilage markers or reframe their question.

Finally, of interest, were there any clear clusters on the UMAP plots (Fig 1 Supp3A) of unassigned identity? Even comment on these and how they were dealt with would be of significant interest to the field, as it is highly unlikely all cell types in the skin have been defined. This dataset promises to be a critical reference for defining these in the future.

Minor clarification:

Fig 2E top. The authors interpret that fate-mapped SFCs at the posterior margin are progressively displaced towards the scale focus. This is confusing as the margin SFC in Fig 2E seems to show them staying largely at the margin. Please clarify if this is what you meant.

Reviewer #3 (Public Review):

This paper addresses an apparent contradiction related to the interaction between spontaneous neural activity and neural plasticity during circuit development. It is well established that spontaneous activity contains instructive information for developmental downstream circuit refinement, for instance for the formation of retinotopic projections in the visual system. These developmental cues are contained in activity correlations, and thus can be picked up by Hebbian mechanisms. However, previous work has shown that informative correlations in spontaneous activity can be found on slow time scales (100s of milliseconds). Here it is shown that in this case correlations on fast time scales arise simultaneously from the integration of unrefined inputs and that these likely interfere with developmental plasticity. The paper shows that such fast, "parasitic" correlations can appear during retinal wave activity, and provides evidence that NMDA receptors can suppress them to avoid their influence during developmental plasticity.

This work is based on detailed biophysical models of thalamic relay neurons, which were fit to data from in-vitro whole-cell recordings. A genetic algorithm was used to fit these models, which provides a family of suitable models instead of a point estimate of good parameters. This approach is a real strength as it shows that the effects generalise well to many plausible models, and do not depend on specific parameter choices. The model neurons are then placed in simulated networks (without or with anatomically informed recurrent connections) and driven by retinal wave activity recorded from the mouse. Together the simulation and analysis show under which conditions fast, undesirable correlations do and do not appear. Specifically, the key model ingredient this work identifies for the suppression of fast correlations is the presence of NMDA receptors on the recipient synapses of the relay neurons.

An open question is how the parasitic correlations are actually suppressed by NMDA receptors. Is it correct that a stronger NMDAR contribution to the transmitted activity simply low-pass filters the incoming spike trains, and that fast correlations are smoothed out as a result? So are developing circuits tuned to slower activity? This could also explain why AMPA receptors subunits with slower kinetics are expressed during development in many circuits.

Taken together, I think this paper presents a very interesting set of results. The issue with parasitic correlations is quite obvious in retrospect, and clearly, a problem developing circuits will generally face. Additionally, the presence of NMDA receptors is often linked to plasticity and is seen as less important in shaping postsynaptic integration. Although no developmental plasticity has been modelled, would it be possible to predict the possible effects of experimental manipulations?

Dual of the dual is just the original space.

It's an example of theorem 2.7-9. A linear operator has boundedness and continuity being an equivalent conditions.

Reviewer #3 (Public Review):

In their study: Comprehensive characterization of tumor microenvironment in colorectal cancer via histopathology-molecular analysis, Wu et al., aim to examine the contribution of the tumour microenvironment (TME) on biological and clinical heterogeneity in colorectal cancer (CRC).

To achieve this the authors use a vast array of publicly available datasets across a variety of biological modalities (transcriptomic, epigenetic, mutational). Using thoughtfully curated genesets the authors classify CRC into 4 holistic comprehensive characterised CRC (CCCRC) subtypes which comprise immune, stromal, and tumour features of CRC biology.

The authors investigate the association of their novel CCCRC subtypes with current "gold standard" classification schemes.

The authors' integration of deep learning methods for HE classification and subsequent association with "Tumor level" CCCRC subtypes is a refreshing addition to the study. Comment on the degree of heterogeneity observed in HE samples and correlation to the heterogeneity of CCCRC subtypes would be a welcomed addition. It is likely publicly available datasets from such platforms as 10X Genomic Visium would be available for this type of analysis.

Whilst one of the main outcomes of the study is the addition of another classification scheme to the field of colorectal cancer, the CCCRC scheme represents a holistic perspective on CRC classification.

The authors provide a welcomed graphical overview of the complex narrative of the study in Figure 7.

The authors focus on the classification of inter-patient heterogeneity and its associated predictive and prognostic utility. There appears to be a significant degree of overlap between immunosuppressive and immune excluded, and proliferative and immuno-modulatory signatures in Figure 1A. One of the major limitations of patient response to treatment is intra-patient heterogeneity, it would be nice for the authors to comment briefly on the degree of intra-patient heterogeneity of the CCCRC subtypes.

Overall the authors succeed in providing a holistic deep characterisation of CRC from the perspective of a variety of biological modalities. The authors provide a novel classification scheme for the field of CRC which demonstrates prognostic and predictive utility, which would benefit from further validation from external datasets. The authors demonstrate a pathway for integration and interpretation of complex high-dimensional data into clinically translatable currency such as the H&E.

Reviewer #3 (Public Review):

The authors report a study where, using multiple datasets with [18F]FDG PET bolus + continuous infusion ("functional PET") and BOLD fMRI data, they re-evaluate the metabolic and hemodynamic properties of the default mode network (DMN) in a task-evoked context, with a focus on posteromedial DMN due to its relevance for across-network integration.<br /> They show how posterior DMN is differently engaged depending on the chosen task: while visual and motor tasks lead to BOLD deactivations and glucose metabolic decrease, specifically in the dorsal posterior cingulate cortex (PCC) area, working memory tasks produce BOLD deactivations but metabolic increases, specifically in ventral PCC, as shown in their previous paper (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118). This aims to solve the controversies elicited by findings of both increased and decreased glucose consumption in the presence of BOLD deactivation in the DMN.

Additionally, they show how task-evoked glucose metabolism in posterior DMN seems to be shaped by that of the corresponding task-positive networks, with a positive link with dorsal attention and a negative link with frontoparietal network metabolism. This is explored using a type of directional connectivity analysis called "metabolic connectivity mapping", drawn from their previous work (Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113; Hahn et al. 2020, https://doi.org/10.7554/eLife.52443). They go on to speculate that concomitant BOLD deactivation and reductions in glucose expense might relate to decreased glutamatergic signaling, while BOLD deactivations accompanied by increased glucose consumption might depend on increased GABAergic neuronal activity.

This is a relevant topic because it not only shows how the DMN is flexibly engaged in different tasks but also allows us to better understand the complex relationships between BOLD fMRI and [18F]FDG PET signals, which are still not fully characterized to this day. Of course, while in resting state the situation is further complicated by the more uncertain physiological meaning of the resting BOLD signal, task-evoked states are expected to provide a more interpretable intermodal link between metabolism and hemodynamics, due to the known major changes in blood flow, blood volume, and glucose metabolism - which underlie BOLD and [18F]FDG signal changes - in response to neural activation. However, even in task states, there is not always a strong association between the two responses, as previously shown by the authors themselves (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079). This is something I think the authors should stress out a little more, as they have previously done (Rischka et al. 2018, https://doi.org/10.1016/j.neuroimage.2018.06.079), both in the introduction and in reference to Figure 1, which shows clear differences between BOLD and [18F]FDG activations/deactivations (e.g., widespread negative responses in the cerebellum for [18F]FDG).

Overall, the analyses reported in the manuscript are simple and seem mostly sound, drawing from well-established methods in PET and fMRI activation studies, with additional approaches previously developed by some of the authors themselves (e.g., "metabolic connectivity mapping", Riedl et al. 2016, https://doi.org/10.1073/pnas.1513752113). Moreover, a clear strength of the paper is the high number of subjects, at least from a PET perspective, i.e., n = 50 for the Tetris task, plus group averages of previously published data for working memory (Stiernman et al. 2021, https://doi.org/10.1073/pnas.2021913118) and motor tasks (Hahn et al. 2018, https://doi.org/10.1007/s00429-017-1558-0).

The conclusions are in line with the results, and, though a little speculative, are potentially relevant for further exploration aimed at characterizing the neurotransmitter pathways underlying positive and negative BOLD and [18F]FDG responses. Moreover, the language is sufficiently clear to allow a proper understanding of the aims and the results, as well as the details of the analyses. As a side note, the title should probably be adjusted to "Task-evoked metabolic demands of the posteromedial default mode network are shaped by dorsal attention and frontoparietal control networks", to emphasize that the findings do not necessarily generalize to the resting state.

In conclusion, I am overall quite positive about this manuscript, which seems to nicely position itself within the existing literature, making some additional contributions.

Reviewer #3 (Public Review):

In this paper, the authors explore the effects of the environment, specifically temperature, on male harm to females. Male harm is the phenomenon where males reduce female fitness in polyandrous systems, where a single female may mate with multiple males. The selection of males to increase their reproductive success in male-male competition can lead to genetic conflict that increases male fitness at the expense of female fitness. Typically, male harm has been studied in single environments under optimal conditions. However, there is an increasing focus on the effect of the environment on fitness costs of male harm to females, as a way to better understand the effect of male harm on population fitness in more realistic ecological contexts. In this paper, the authors add to these studies by exploring the effect of temperature on male harm and female fitness, using the fruit fly Drosophila melanogaster, as a model system. They find that temperature affects the impact of male harm on female fitness, with male harm having the greatest effect at 24˚C relative to 20˚C and 28˚C. The authors then go on to disentangle how temperature affects the various components of male harm that impact female fitness (e.g. harassment, ejaculate toxicity). The paper demonstrates that male harm depends on ecological context, which has implications for understanding its impact on population fitness under realistic ecological scenarios, particularly with respect to climate change.

The strength of the paper is that it demonstrates that male harm (presented as differences in female life reproductive success between monogamous and polyandrous matings) changes with temperature. The authors dissect this general observation by showing that different aspects of pre-copulatory reproductive behavior, for example, male-male aggression, copulation rate, and female rejection rate, also change with temperature. Further, they demonstrate that correlates for male ejaculate quality also change with temperature, suggesting that temperature also affects post-copulatory mechanisms of male harm.

The weakness of the paper is that the method and results section are difficult to follow, which negatively impacts the interpretation of the data. The experiments are complex and need to be for what the authors are studying. Nevertheless, the paper is written in a way that makes it challenging for the reader to fully understand how precisely the experiments were conducted. Further, the authors do not explain clearly how some of the experiments relate to the phenomenon ostensibly being assayed. For example, a more detailed explanation of why mating duration and remating latency are assays for ejaculate quality in the context of sperm competition would be very helpful in interpreting the data. Further, a clearer explanation of the statistical analyses conducted

Reviewer #3 (Public Review):

Henthorn and coworkers obtain a single cell atlas of the parasite nematode Brugia malayi to search for excretory secretory products. These are involved in therapeutic responses but it is unknown what are the cell types that express them. In fact this seems as an ideal question to be addressed with single cell transcriptomics. The authors analyse their dataset, coming to the conclusion that many of these ES products are expressed broadly throughout the parasite, including secretory and non secretory types. This would be a nice conclusion if supported by the data. Then they go on to compare responses to exposure of ivermectin at the single cell level.

I must praise the attempt of using single cell transcriptomics to examine this question. These relatively novel methods have been so far used to collect information of cell types, but have immense potential for the investigation of important questions in neglected diseases like this. Fundamental knowledge about the biology of Brugia malayi and the tissue and cell types present are key to understanding their pathogenesis and advancing new therapeutic options. The authors start this research project with the right model and right technique.

My major concern is the quality of their single cell data. The authors perform no FACS or other methods to clear their suspension from cellular debris. This arises from all cell types, and then gets encapsulated with single cells in the droplet-based single cell transcriptomic process. Then, all cell barcodes receive genes from a single cell but also from a collection of cellular debris particles (arising from all other cell types). This, and only this, can in principle explain one of the major findings in the abstract: secreted antigens are expressed broadly in many cell types. This same caveat might explain their finding of pan neuronal markers broadly expressed - conceptually very similar to what the authors hold for secreted antigens, but that the authors only mention briefly and do not explain.

Reviewer #3 (Public Review):

In this manuscript, the authors (Salas et al.) have investigated the communication strategies among various strains or species of Trichomonas vaginalis. T. vaginalis is a parasite that is responsible for non-viral sexually transmitted infections, worldwide. The authors have demonstrated that highly adherent parasite uses cytoneme-like membrane structures and extracellular vesicles (EVs) to communicate with poorly adherent isolates and mount a stronger response to hosts.

The major strength of this work is the use of state-of-the microscopic techniques to analyze cytonemes and EVs. However, the weakness is the experiments shown in the manuscript are more descriptive than mechanistic. The significance of this work is high because it demonstrates the presence of a unique communication strategy in Trichomonas vaginalis. Trichomonas uses cytoneme-like elongated membrane structures and extracellular vesicles to interact with each other and induce a robust pathogenic response in host cells.

The authors have used state-of-the-art cell biology techniques to conduct the study and the data analysis is solid.

Overall, the experiments are solid and the authors were able to accomplish their objectives of demonstrating parasite communication in T. vaginalis.

Reviewer #3 (Public Review):

A current topic in the translational control field revolves around the idea that "the ribosome" is not a singular monolith machine, but rather that there are a variety of ribosomes, some with specialized functions. The presence of evolutionarily conserved ribosomal protein gene paralogs provides a platform for testing this idea. Presumably, if a paralog is required to translate a specific mRNA or class of mRNAs in a cell or organ type specific manner, it's loss should generate an observable phenotype. In this study, Xu and colleagues exploit the evolutionarily conserved eS27 and eS27L proteins to probe this hypothesis. Technically, the work is on the cutting edge of the field. Advanced genetic engineering techniques were used to generate mice lacking either paralogous gene, to create reciprocal swaps of each coding sequence into the other locus, and even to create genetically homogenous mice. The authors also use state of the art molecular biology methods, e.g. paralog-specific ribosome profiling, to search for differences in the mRNAs translated by ribosomes containing either of the two homologs.

Some phylogenetic evidence was presented suggesting that the paralogs first appeared during a gene duplication event in vertebrates: however, only and bird and one amphibian are represented. It is recommended that this analysis go deeper, parsing the amphibians and fish more finely. Although not identifying evidence for specialized ribosomes, they did find that it is essential that at least two copies of eS27 or eS27L are retained. Interestingly, the embryonic lethality of truncation alleles of either of the two paralogs result manifested at different stages of development, pointing to some kind of functional differences during development. The finding that eS27L containing ribosomes are more prevalent in lactating mammary gland and liver is an interesting observation, and that such ribosomes are preferentially associated with mRNAs involved in the cell cycle. From this the authors conclude that the data support subfunctionalization model of eukaryotic ribosomal protein S27 evolution rather than a specialized ribosome model. I also note that this is the most comprehensive and technologically advanced study of its kind in the translational control field and that it represents a significant contribution to the field of evolutionary biology.

Reviewer #3 (Public Review):

Mating changes an animal's behavior. In Drosophila, mated females have higher energy needs, suggesting that their consumption of caloric foods may be altered. While previous studies have examined post-mating changes in the consumption of specific nutrients such as salt and protein, it was not known whether the intake of sugar, their primary energy source, is also changed. This study describes a post-mating increase in sugar intake and identifies the neural circuit that mediates this change. By using precise genetic manipulations, behavioral assays, and new connectome datasets, the authors provide high-quality data to support their claims.

This study reveals several new insights into the regulation of behavior after mating: 1) Female flies increase sugar intake after mating, and this is an "anticipatory" change rather than a homeostatic change resulting from energy depletion. 2) The post-mating change in sugar intake is mediated by the sex peptide circuit, SPSNs-SAG-pC1, which is known to regulate other post-mating changes. 3) The authors identify a new downstream target of pC1, the pCd-2 neurons, which regulate feeding. pCd-2 neurons do not affect egg-laying, and neurons downstream of pC1 that regulate egg-laying or receptivity after mating do not affect sugar intake. Thus, the SPSN-SAG-pC1 circuit that regulates post-mating behaviors diverges downstream of pC1 into multiple branches regulating different behaviors. 4) The authors identify cells downstream of pCd-2, median bundle cells expressing Lgr3, the receptor for Dilp8. These cells are inhibited by pCd-2, suggesting that they are active in mated females, and promote sugar consumption. Because previous studies showed that Dilp8 and Lgr3 are expressed more highly in fed flies and suppress feeding, the present study suggests that Lgr3+ cells integrate hunger and mating signals to regulate feeding. This is an interesting circuit motif that could extend to mammals. In future studies, it will be interesting to test how hunger and mating signals are integrated within these cells (e.g. do they function redundantly, additively, etc).

Reviewer #3 (Public Review):

Seelbinder et al present local heating of the cell nucleus in live cells as a perturbation of the nucleus, which they use to interrogate mechanical properties of the nucleus. The authors use their recently developed technique of generating local heat gradients (Mittasch et al, 2018) and apply it to the cell nucleus, where they then measure the displacements/strains of chromatin as a function of distance from the heat source. They show that during the heat perturbation the nuclear area and shape remain unchanged. They measure spatially resolved strains across the nucleus and find that different parts of the nucleus exhibit different mechanical behavior. Their analysis reveals that chromatin shows both elastic and viscous properties at the timescales of seconds, with heterochromatin showing solid-like properties. In addition, they find that the nucleolus shows high resistance to the heat-induced deformation at the seconds' timescale.

Conceptually, this is an interesting and thought-provoking work allowing for new ways to perturb the cell nucleus and study its internal mechanics.

Reviewer #3 (Public Review):

In this manuscript, Li et al. describe the contribution of the ATM-E6AP-MASTL pathway in recovery from DNA damage. Different types of DNA damage trigger an increase in protein levels of mitotic kinase MASTL, also called Greatwall, caused by increased protein stability. The authors identify E3 ligase E6AP to regulate MASTL protein levels. Depletion or knockout of E6AP increases MASTL protein levels, whereas overexpression of E6AP leads to lower MASTL levels. E6AP and MASTL were suggested to interact in conditions without damage and this interaction is abrogated after DNA damage. E6AP was shown to be phosphorylated upon DNA damage on Ser218 and a phosphomimicking mutant does not interact with MASTL. Stabilization of MASTL was hypothesized to be important for recovery of the cell cycle/mitosis after DNA damage.

The identification of this novel pathway involving ATM and E6AP in MASTL regulation in the DNA damage response is interesting. However, is surprising that authors state that not a lot is known about DNA damage recovery while Greatwall and MASTL have been described to be involved in DNA damage (checkpoint) recovery. In addition, PP2A, a phosphatase downstream of MASTL is a known mediator of checkpoint recovery, in addition to other proteins like Plk1 and Claspin. Although some of the publications regarding these known mediators of DNA damage recovery are mentioned, the discussion regarding the relationship to the data in this manuscript are very limited.

The regulation of MASTL stability by E6AP is novel, although the data regarding this regulation and the interaction are not entirely convincing. In addition, several experiments presented in this paper suggest that E6AP is (additionally) involved in checkpoint signalling/activation, whereas the activation of the G2 DNA damage checkpoint was described to be independent of MASTL. Has E6AP multiple functions in the DNA damage response or is ATM-E6AP-MASTL regulation not as straightforward as presented here?

Altogether, in my opinion, not all conclusions of the manuscript are fully supported by the data.

Reviewer #3 (Public Review):

Martino et al. demonstrated that BRCA2-deficient cells (but not BRCA1-deficient cells) bear additional vulnerabilities (i.e., cytokinesis failure) outside S phase that could represent new synthetic lethality targets. Strengths of the study include the ability of the authors to recapitulate the cell death by ROCK inhibition by inhibiting another key cytokinesis enzyme, CITK. The claims are well supported by the data, and because the study indicates HR failure/replication stress is not the only possible way to achieve synthetic lethality in BRACA mutant cancers the study will be of broad interest to potential readers.

Reviewer #3 (Public Review):

In the work by Zhou, et.al., the authors pursue a mechanistic understanding of chromosome size scaling in development, a problem first noted in 1912 by Conklin and largely unstudied until very recently. Using the tools available in the Xenopus genus developmental biology system (cell extracts, related species of differing size, etc.), they nicely show that condensin I levels directly correlates with chromosome size. Further, importin levels decrease leading to axial shortening of chromosomes during development. The combined physical outcome is that Mitotic Chromatin looping changes, resulting in axial compression of chromosomes. This work represents a major step in the molecular understanding of how the genome is regulated through development and changing cell size, which also occurs in many other adult tissues and cancers. Further work to understand other contributing factors and understanding how loop structure changes the polymer dynamics of mitotic chromatin will be exciting in the future.

Reviewer #3 (Public Review):

International researchers from the International Agency for Research on Cancer and cancer screening program experts from six countries in Latina America (Argentina, Colombia), Asia (Sri Lanka, Bangladesh, Thailand), and Northern Africa (Morocco), provide detailed information on the impact of the COVID Pandemic on cancer screening and diagnostic services.

The authors examine countries that have had screening programs and a surveillance system/registry to see how the volume of screening, diagnostic procedures, and detection of precancers or cancers are impacted. The data are presented as case studies with an explanation of the program, the technologies, and then the impact. They describe no matter how low or high income the country, there was a considerable impact on the volume of screening.

Usually, the impact of the pandemic on cancer screening has been limited to Europe or North America and is usually not quantified. This information will be helpful for these countries to examine the impact on stage distribution and eventually mortality impact through modeling studies. The authors also comment on some interesting hypotheses such that the impact on recovery based on if one is detecting precancers (e.g. colon cancer/cervical cancer) vs. invasive cancers (breast cancer). Strategies that require less frequent screening,self-collection, where screening and treatment can be combined in fewer visits, or where some visits can occur via telehealth are valuable strategies or lessons learned that will allow for quicker recovery time after a pandemic.

The authors acknowledge the limitations and strengths of these case-based studies well.

It is beautiful storytelling with both qualitative and quantitative data.

Reviewer #3 (Public Review):

Ketamine has been shown to be effective at producing a rapid-antidepressant effect at low doses, but the underlying molecular mechanism of this effect is still not clear. Previous studies have suggested that the effect of low-dose ketamine may occur by promoting neuronal plasticity in the hippocampus. However, this goes against the findings that ketamine acts as a noncompetitive NMDA receptor antagonist, which should prevent NMDAR-dependent plasticity. Furthermore, a therapeutic dose of ketamine has been shown to increase neuronal Ca2+ signaling, which again does not conform to its antagonistic action on NMDA receptors. In this paper, the authors provide evidence that therapeutic low-dose ketamine increases the expression of Ca2+-permeable AMPA receptors (CP-AMPARs) by increasing phosphorylation of GluA1 subunit of AMPARs and surface expression of GluA1-containing CP-AMPARs. They further provide evidence that this is likely mediated by a decrease in calcineurin activity and that blocking CP-AMPARs prevent the antidepressant effect of ketamine in mice. One interesting finding of this study is that the authors see heightened sensitivity of ketamine in female mice, both at the level of behavioral readout and for molecular correlates. This finding is interesting in light of the different pharmacokinetics of ketamine reported in females and that ketamine metabolites can bind estrogen receptors.

Based on their data and previous findings, the authors outline a plausible molecular signaling mechanism for the antidepressant effect of ketamine. Specifically, the authors propose that reduced neuronal activity, which could be triggered by ketamine-induced NMDAR antagonism, causes homeostatic plasticity to upregulate GluA1-containing CP-AMPARs. Their data would support this idea, as phosphorylation of GluA1 as well as increased surface expression and functional incorporation of CP-AMPARs at synapses have been shown before in models of homeostatic plasticity.

Overall, the study is well-done and the data presented support the main conclusions. One main question is whether the current finding provides a conceptual advancement in our understanding of the molecular signaling involved in ketamine's antidepressant effects. There are previous studies that showed an increase in CP-AMPARs in the nucleus accumbens and an increase in the expression of GluA1 in the hippocampus with low-dose ketamine. In addition, ketamine's antidepressant effect has been shown to require GluA1 phosphorylation. The main contribution of this paper might be that it provides the potential molecular signaling within the same preparation (i.e. hippocampal neurons) and provides a causal link of CP-AMPARs in mediating the behaviorally measured antidepressant effect of ketamine.

Another question is whether the behavioral effect of ketamine is due to molecular changes in the hippocampus as outlined in this paper. A more targeted inhibition of CP-AMPAR function could resolve this issue. With the systemic application of CP-AMPAR antagonist as done in this study, it would be hard to know the role of CP-AMPAR upregulation in the hippocampus in mediating ketamine's effect. Especially, considering that low-dose ketamine has been shown to upregulate CP-AMPARs in the nucleus accumbens. While it would have been nice to know the site of action, this does not alter the conclusion that CP-AMPARs are involved in mediating the antidepressant effect of ketamine on behavioral readouts.

Reviewer #3 (Public Review):

This paper describes fundamental work which attempts to understand how universal stress proteins Rv1636/Msmeg3811 function as a sink allowing mycobacteria to use intra-bacterial cAMP. Because cAMP is a major second messenger, Rv1636 remains essential to mycobacteria. A compound that inhibits cAMP binding of Rv1636 also can effectively inhibit mycobacterial growth. The major strength of the manuscript is that the authors probed their hypothesis by different approaches. In general, the conclusions from the results are largely justified. However, I find the manuscript quite difficult to follow. Also, the results and functional analyses are inadequate as they rely on a limited set of experiments, thereby making the evidences less than compelling.

Reviewer #3 (Public Review):

In this manuscript, Villalobos-Cantor et al. have implemented the method for monitoring cellular proteome that their lab has established in cell culture models of Drosophila brains. The method uses a puromycin analog (O-propargyl-puromycin, OPP) that is locked by the addition of phenylacetyl group (PhAc-OPP) that can be unlocked by expression of Penicillin G acetylase (PGA) to tag the proteins translated in a specific cell type. When unlocked, OPP can get incorporated into the newly translating nascent peptide, and abort translation while allowing click chemistry addition of various tags, such as fluorophore-azide to visualize or biotin-azide to immunopurify polypeptides. The authors demonstrate the use of the method in adult drosophila brains expressing PGA in neurons or glia, showing that the addition of OPP is indeed PGA dependent and the proteins are only tagged in the cells that express PGA. The authors also show that when fluorophore azide is used to visualize the proteome and the samples are run on a gel, bands of various sizes can be observed to have incorporated OPP, arguing the method labels the proteome indiscriminately. The authors also optimized the protocol by titrating the amount of PhAc-OPP to use to minimize cellular stress. Also, they show that driving the expression of PGA with elav-Gal4 or repo-Gal4 is not toxic and does not cause phenotypes although Actin-Gal4 driven expression causes phenotypes. Finally the authors demonstrate the use of the technique to show that there is an age-induced decrease in total protein synthesis in the fly brain. This is a nice technique to implement in fly but the characterization of the technique is not complete in its current state. It is not clear what percentage of the nascent peptides are tagged, and whether the cells in the tissue are equally represented in the lysates for immunopurification.

Reviewer #3 (Public Review):

The myeloid bias in hematopoietic stem and progenitor cells upon genetic inactivation of Pcgf1, a component of the PRC1 complex is convincingly demonstrated by a Pcgf1 conditional allele crossed with a tamoxifen-inducible Are, combined with transplantation. The overproduction of myeloid cells may be contributed to by the derepression of two PRC target genes Cebpa and Hoxa9 at the multipotent HSPC and lineage-committed GMP levels. The involvement of these two genes is demonstrated by decreased H2AK119ub1, elevated CEBPa expression, and increased CEBPa binding motifs in KO HSCs. Functional rescue by manipulating CEBPa levels on the background of Pcgf1 KO is attempted in vitro. The derepression of Hoxa9 is shown in Pcgf1 KO GMPs, which expanded in the KO mice. Finally, constitutive inactivation of Pcgf1 results in lethal myeloproliferation. Together, this paper demonstrates another HSPC regulator, whose loss of function leads to myeloid-biased hematopoiesis, which in extreme cases could end in myeloid transformation.

Reviewer #3 (Public Review):

In this study, Cunha et al. examined the role of different oxygen tensions (21%, 5%, and 1% O2) and HIF-1α stabilisation in regulating murine and human CD8+ T cell proliferation and function. The authors find that hypoxia (1% O2) and pharmacological PHD inhibition with FG-4592, enhance murine T cell activation but impair proliferation. Furthermore, adoptive cell transfer (ACT) therapy of CD8+ T cells from both conditions reduced tumour burden in a B16-OVA melanoma model. Short hypoxic conditioning (1% O2) of human CD8+ T cells for 1 day increased HIF-1α stabilisation, with increased activation, glycolysis, and mitochondrial function still observed following 6 days of normoxic cell culture. Short hypoxic conditioning of HER2 and CD19 CAR-T cells improved their activation and cytotoxicity in vitro, while HER2 CAR-T cell counts were increased in vivo, reducing tumour burden, and increasing survival when compared to 21% O2.

Strengths:<br /> The paper convincingly demonstrates that short hypoxic conditioning in a defined window improves CAR-T cell function through in vitro cytotoxicity assays and following adoptive transfer in a preclinical HER2+-SKOV3+ positive tumour model. Thus, the major conclusion of the paper is mostly well supported by the data and could represent a novel strategy to improve CAR-T cell immunotherapy for solid tumours in the future.

Weaknesses:<br /> The extent to which hypoxic conditioning-mediated improvement in CAR-T cell function is dependent on HIF-1a-driven metabolic reprogramming is unclear and other potential mechanisms are not explored. 5FG-4592 and VHL silencing in HER2 CAR-T cells did not phenocopy each other faithfully. In addition, neither approach was as effective as short hypoxic conditioning with 1% O2 in improving CAR-T cell function in vitro or in vivo. Although the authors suggest the temporal dynamics of HIF-1α stabilisation is the key point, this is not convincingly proven, and no metabolic characterisation of these CAR-T cells was performed. It is unclear how changes elicited during short hypoxic conditioning are maintained following continued normoxic cell culture. Hypoxia is known to rapidly regulate histone methylation and chromatin structure in a HIF-independent manner (PMID: 30872525; PMID: 30872526). Are similar epigenetic changes observed in T cells, and if so, could these epigenetic changes underlie improved T cell activation?<br /> Complications may also arise when comparing different oxygen tensions given recent data that suggests standard cell culture conditions can lead to local hypoxia through a combination (https://www.biorxiv.org/content/10.1101/2022.11.29.516437v1) of cellular respiration and poor O2 diffusion. Although it is unclear how this will impact suspension T cells it does beg the question as to whether HIF-1α stability following T cell activation is (at least in part) mediated by pericellular O2 limitations in cell culture over time, even in presumed hyperoxic (21% O2) conditions? Or if T cells subsequently cultured at 21% O2 following short hypoxic conditioning (1% O2) still experience local hypoxia during the 6-day culturing protocol? It would be important to assess this in future work and at least discuss these potential weaknesses.

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Reviewer #3 (Public Review):

This is an elegant article that shows the reciprocal actions of Alkbh5 gene modulation and of a putative target, Lpin2. The work focuses on the DRG; it is important to note that the group also examines a retinal ganglion model where changes in Alkbh5 are not as prominent, and perhaps because of that the resulting effects of Alkbh5 modulation are not as pronounced. Thus, the effects of a broadly acting agent such as Alkbh5 might differ depending on the exact biology of the model. This doesn't diminish the finding at all, but the group nicely shows that the efficacy of Alkbh5 gene modulation might vary among different models.

A strength of the manuscript is the paper's use of reciprocal experimental designs to demonstrate effects. For instance, in Fig. 3 the authors show that Alkbh5 knockdown (KD) improves axonal regeneration and in Fig. 4 they show that Alkbh5 KD increases levels of methylated LPIN2, a transcript implicated in promoting axonal regeneration. Conversely, Figs. 5 and 6 also show that WT Alkbh5 over-expression inhibits axonal regeneration while an inactive Alkbh5 mutant has no effect. Alkbh5 KD is expected to increase levels of its target transcripts, and indeed, the group focuses on LPIN2 and shows that Alkbh5 KD increases LPIN2 adenosine methylation which is associated with a reciprocal decrease in total LPIN2 transcript (because m6A promotes degradation of the labeled transcripts). In Fig. 7 they go on to look at a specific target site of m6A on LPIN2. The hypothesis is that methylation of the target m6A site on LPIN2 will lead to a reduction in transcript levels; they show that mutating this target adenosine prevents the resulting effects of Alkbh5, implicating adenosine methylation in the modulation of LPIN2 levels. Finally, they show that over-expressing LPIN2 inhibits axonal regeneration while inhibiting LPIN2 increases regeneration. Thus, the experimental design includes many levels of reciprocal actions that are observed examining regeneration, adenosine methylation, and LPIN2 transcript levels. Taken together this approach is very convincing. The last figure examines the extent to which the KD approach extends to other models; the authors show that Alkbh5 is less active in a retinal ganglion model. The limited efficacy in the retinal ganglion is disappointing but serves to highlight the strength of the actions in the DRG model and provide a warning that the actions of Alkbh5 might vary significantly depending on the particular pathophysiology to which its gene modulation is being applied.

The manuscript does have some weaknesses, but the weaknesses are modest and do not change the overall interpretations of the manuscript. For instance, interpreting the quantitative efficacy in Fig. 1 and 2 depends on knowing the efficiency of uptake of the RNAi for Alkbh5 and subsequent virally transduce shAlkbh5, however, the authors do not show this efficiency. But such weaknesses are quite minor and do not change any of the conclusions.

Reviewer #3 (Public Review):

Araya-Secchi and coauthors present a very interesting study on the role of PIP2 lipids in the potential modulation of prolactin receptor signaling. The study is well-conducted and employs an integrated approach that combines NMR spectroscopy, modeling (primarily coarse-grain MD simulations), and cell biology. This combination of methods is crucial for gaining a deeper understanding of cell receptors, from their biophysical properties to their cellular functions.

The modelling work is mainly based on both coarse grain forcefield versions Martini2.2 and Martini3. These two versions of the forcefield may produce different results. Therefore, depending on the system being modeled, the results presented here should be considered in light of the limitations inherent to each version of the forcefield.

Reviewer #3 (Public Review):

In this study, the authors probe the molecular changes that occur in a neural circuit for learned behavior that depends on sensory input to maintain stereotypy. Songbirds, as the Bengalese finches used here are, are premier systems in which to ask these questions because they produce a highly stereotyped song that emerges after sensory learning becomes integrated into the function of a sensorimotor neural circuit responsible for singing. By deafening a group of birds (who show a shift in their song structure) and comparing them to hearing birds, clues as to how plasticity in motor output may emerge from genomic changes that alter the function of cells within the various components of the neural circuit.

There are multiple strengths of the paper:<br /> 1. The results may have broad implications because the type of sensorimotor neural circuit (cortico-basal ganglia-thalamic-cortical loop) used for singing is generally necessary for learned behaviors.

2. The methods and analyses are generally rigorous, including the parsing of song elements, and the type of detailed RNA sequencing and analysis that demonstrates the power of a genomic view of neural plasticity as it relates to behavioral plasticity.

3. Because the authors assayed the pallial (cortical) areas, as well as the basal ganglia component, of the sensorimotor circuit they were able to creatively compare how different facets of the network contributed to a) unmodified brain properties, b) properties perturbed after the loss of the auditory input that is required to stabilize song structure. As a result, they have added to the known molecular profiles for each of these brain areas, the accounting of how they may be specialized in comparison to the surrounding non-song brain, and what changes occur after deafening. Utilizing some existing single-cell sequencing data, the authors present for the first time some insight into what cell types may be showing the most robust changes, and therefore which may be driving the shift in song structure. The analysis further pushes in new ways to suggest how the molecular properties of a given brain area may relate to those of directly-connected areas. Together, these findings provide valuable clues as to the specific cell types and signaling properties that may be central to the production of stabilized, learned behavior.

4. One of the cortical brain areas, LMAN, was lesioned in a subset of the hearing subjects because it projects to the area that showed the greatest molecular difference between deafened and hearing birds (RA). The idea was to compare how this affected molecular properties with properties after the loss of auditory input; because RA is the output motor area for the song, its properties may be most directly tied to song structure. Using unilateral lesions was a strong choice of experimental design that allowed for rigorous analysis of this idea, and was interpretable because birds do not have a direct inter-hemispheric callosum.

The foundation of the paper is solid, though the results shown raise several questions that are not fully addressed, and limit some of the power of the implications.

The biggest questions arise from the finding that RA shows the largest number of molecular changes after deafening. The analysis and interpretations do not fully incorporate what we know of this circuit, at least from another well-studied songbird, the zebra finch, from which the authors derive other types of information. For example, it is not yet clear if RA is most changed because it is most directly involved in song output or because it receives projections from two areas within the sensorimotor circuit (LMAN and HVC). How do we consider the fact that by adulthood, LMAN and HVC cells project onto the same RA neurons? Are those the cell types being identified here? Would HVC lesions be expected to have the same effect as LMAN lesions? Are the cell types showing the greatest change those that are most involved in song output (e.g. are they projecting to nXIIts)? How do these results relate to the findings of changes in RA after HVC and LMAN lesions reported decades ago? How do these findings compare to an earlier study that also performed sequencing on areas from the sensorimotor circuit in deafened juveniles? Further, RA also receives information from the auditory processing regions of the brain, via the immediate structure RA-cup. It is not yet explicitly addressed how some effects may be from the loss of this more direct access to auditory information, rather than from information and projections originating within the sensorimotor circuit, and reinforces the question of whether or not the number of inputs to a particular brain area is a driving factor in the general pattern of changed RNAs after perturbation.

Importantly, since the LMAN lesions did not create significant changes in the song structure, it is difficult to know how to interpret the meaning of these molecular changes in RA, alone and in combination with the comparison to the RA profiles from deafened birds. Of importance is the question of whether or which molecular profiles are thus signatures of behavioral plasticity or not.

Reviewer #3 (Public Review):

This is an interesting paper but not entirely surprising. Given the known voltage dependency of intrinsic oscillations of IO neurons, the fact that a hyperpolarizing input from a GABAergic synapse or a depolarization from an excitatory input can phase shift an oscillation depending on the timing is not surprising. It could be predicted from what is already known about the underlying conductances of the oscillations in these cells. The authors, however, do provide some quantification for both the inputs and the effects they have on the oscillations. Whether or not this quantification can be extrapolated to in vivo conditions, however, remains to be seen. There are multiple technical issues that the authors need to address.

Reviewer #3 (Public Review):

In this paper the authors report a new pathway by which PTH, by activating PTH1R, increases bone mass. Specifically, they describe that PTH1R and the transcription factor Zfp467 are part of a feedback loop that promotes PTH-induced osteogenesis. By using in vivo mouse models, they indicate that deletion of Zfp467 in osteoblast progenitors increases bone mass and osteogenic differentiation whereas deletion of Zfp467 in adipocytes does not cause any bone phenotype, suggesting that deletion of Zfp467 in mesenchymal progenitors is important for bone mass. With a series of in vivo studies, they demonstrate that PTH suppresses Zfp467 expression via the cyclic AMP-PKA pathway and genetic deletion of Zfp467 causes increased PTH1R transcription by increasing nuclear translocation of p50 and activation of the P2 promoter, thus increasing the expression of PTH1R and the cellular responsiveness to PTH.

The strength of the paper are the use of genetically modified animal models, the analysis of both female and male mice and the logic flow of the in vitro data. The weaknesses of the paper are the wrong conclusion that the phenotype of the PrrxCre Zfp467 mice perfectly recapitulates one of the global Zfp467 KO mice, the lack of histomorphometric data showing increased osteoblastogenesis and the missing evidence that the forward feedback loop is relevant to the response to PTH in vivo. The analysis is therefore incomplete. Once those points are addressed, this paper would be of great interest in the bone field.

Reviewer #3 (Public Review):

The goal of this study is to identify mitochondrial pathways that would have an impact on the process of pathological cardiac hypertrophy. The paper presents a state-of-the-art analysis of the SNP variant and regulatory hot spots associated with quantitative traits, here mitochondrial protein levels and cardiac hypertrophy, using the Hybrid Mouse Diversity Panel. They identify 3 hotspots of trans-acting genetic loci that correlate with the level of proteins involved in complex I assembly, mitochondrial mRNA stability, and CoQ synthesis.

The study is overall very interesting and brings valuable information to the field. However, the impact of each of these loci on cardiac hypertrophy level, even if statistically significant, seems to be rather limited raising questions on the clinical relevance of the findings. It is an interesting study anyway and points to pathways that will deserve to be further explored in the future in clinical studies on human patients.

Reviewer #3 (Public Review):

The authors provide a timely description of new mermithid nematodes from Cretaceous amber and use it to argue an important shift in insect host exploitation. The descriptions are state-of-the-art and will become valid once the appropriate zoobank numbers are used after publication. The authors also compiled crucial and detailed new information on the host exploitation in amber nematodes in the supplementary material. This data is also depicted in pie diagrams and seems at first glance to support their interpretations of a shifts in host exploitation in fossil amber deposits when analysed appropriately and statistically but such an true analysis and depiction should be part of the main manuscript to do the compilation and interpretation justice. For the sake of reproducibility and the field, such fundamental statistical analysis as well as a statistical comparison with modern hosts would make this broad-sweeping claim of a major host shift and importance of amber deposits containing such nematode-insect interactions since the Cretaceous (even) more robust and fundamental.

Reviewer #3 (Public Review):

Li, Ruggiero-Ruff et al. examine the role of RELMα, an anti-inflammatory macrophage signature gene, in mediating sex differences in high-fat diet (HFD)-induced obesity in young mice. Specifically, the authors hypothesize that RELMα protects females against HFD-induced obesity. Comparisons between RELMα-knockout (KO) and wildtype (WT) mice of both sexes revealed sex- and RELMα-specific differences in weight gain, immune cell populations, and inflammatory signaling in response to HFD. RELMα-deficiency in females led to increased weight gain, expansion of pro-inflammatory macrophage populations, and eosinophil loss in response to HFD. Female RELMα-deficiency could be rescued by RELMα treatment or eosinophil transfer. Single-cell RNA-sequencing (scRNA-seq) of adipose stromal vascular fraction (SVF) revealed sex- and RELMα-dependent differences under HFD conditions and identified potential "pro-obesity" and "anti-obesity" genes in a cell-type-specific manner. Using trajectory analysis, the authors suggest dysregulation of macrophage-to-monocyte transition in RELMα-deficient mice.

The conclusions of this paper are mostly well supported by the data, but some aspects of the statistical and single-cell analyses will need to be corrected, clarified, and extended to enhance the report.

Strengths:<br /> The authors use several orthogonal approaches (i.e., flow cytometry, immunohistochemistry, scRNA-Seq) and models to support their hypotheses.

The authors demonstrate that phenotypes observed in HFD-fed females with RELMα-deficiency (i.e., weight gain, loss of eosinophils, a gain of M1 macrophages) can be rescued by RELMα treatment or eosinophil transfer.

The authors recognized the complexity of macrophage activation that is beyond the 'M1/M2' paradigm and informed readers in the introduction as to why this paradigm was used in this study. During the scRNA-seq analyses, the authors further sub-cluster macrophages to include more granularity.

Weaknesses:<br /> There are several instances in the text where the authors claim that there is a significant difference between the two groups, but the statistics for these comparisons are not shown in the figure.

It is unfortunate that eosinophils could not be identified in the single-cell analysis since this population of cells was shown to be important in rescuing the RELMα-deficiency in HFD-fed females. The authors should note in the discussion how future scRNA-Seq experiments could overcome this limitation (i.e., enriching immune cells prior to scRNA-Seq).

There are several issues with the scRNA-Seq analysis and interpretation. More details on the steps taken in the single-cell analyses should be included in the methods section. With regards to the 'pseudobulk' analyses presented in Figs. 5-6, several of the differentially expressed genes identified in Fig. 6 are hemoglobin genes (i.e., Hba, Hbb genes). It is not uncommon to filter these genes out of single-cell analysis since their presence usually indicates red blood cell (RBC) contamination (PMID: 31942070, PMID: 35672358). We would recommend assessing RBC contamination as well as removing Fig. 6 from the manuscript and focusing on cell-type-specific analyses. Re-analysis will likely have an impact on the overall conclusions of the study.

Within the text, there are several instances where the authors claim that a pathway is upregulated based on their Gene Ontology (GO) over-representation analysis (ORA). To come to this conclusion, the authors identify genes that are upregulated in one condition and then perform GO-ORA on these genes. However, the authors do not consider negative regulators, whose upregulation would actually decrease the pathway. Authors should either replace their GO-ORA analysis with one that considers the magnitude and direction of differentially expressed genes and provides an activation z-score (i.e., Ingenuity Pathway Analysis) or replace instances of 'upregulated' or 'downregulated' pathways with 'over-represented' pathways.

For Fig.7A, a representative tSNE plot for each group (WT Female, KO Female, WT Male, KO Male) should be shown to ensure there is proper integration of the clusters across groups. There are some instances where the scRNA-Seq data do not appear to be integrated properly (i.e., Supplemental Figure 2C). The authors should explore integration techniques (i.e., Seurat; PMID: 29608179) to correct for potential batch effects within the analysis.

LncRNA Gm47283 is identified as a gene that is differentially expressed by genotype in HFD females (Fig. 7G); however, according to Ensembl this gene is encoded on the Y-chromosome (https://uswest.ensembl.org/Mus_musculus/Gene/Summary?g=ENSMUSG00000096768;r=Y:90796007-90827734). The authors should use the RELMα genotype and sex chromosomally-encoded genes to confirm that their multiplexing was appropriate.

For Fig. 8, samples should be co-clustered and integrated across groups before performing trajectory analysis to allow for direct comparisons between groups.

Since the experiments presented in this report were from young mice using a single diet intervention, the authors should comment on how age and other obesogenic diets may impact the results found here. Also, the authors should expand their discussion as to what upstream regulators (i.e., hormones or genetics) may be driving the sex differences in RELMα expression in response to HFD.

Reviewer #3 (Public Review):

ODA-DC anchors ODA, the main force generator of ciliary beating, onto the doublet microtubules. Vertebrate ODA-DC contains 5 proteins, including Calaxin and Armc4, whose mutations are associated with defective ciliary motility in animals and human. By generating calaxin-/- and armc4-/- knockout zebrafish lines, this manuscript examined the Kupffer's vesicle cilia and spermatozoa. They showed that calaxin-/- and armc4-/- knockouts both affect ciliary motility but to different degrees. The authors conducted careful structural analyses using cryo-ET and subtomo averaging on both mutants, revealing a partial loss of ODA in calaxin-/- and a complete loss of ODA in armc4-/-. I really like the distribution analysis of calaxin-/- OADs (Figure 5), which emphasizes the strength of cryo-ET in uncovering the molecule distribution of distinct conformational states in situ. Fitting of the atomic models of ODA and ODA-DC into the cryo-ET density maps and Calaxin rescue experiments showed how Calaxin stabilizes ODA at a molecular detail. By using olfactory epithelium, the authors also presented the possible assembly mechanism of ODA-DC proteins, which is also a beautiful experiment. Finally, the authors also investigated how Ca2+ regulate the ODA-DC using cryo-ET.

The thorough structural and functional analyses of Calaxin and Armc4 in WT and gene KO animals could serve as a reference for future study of the detailed function of other ciliary proteins. The experiments are overall well designed and conducted, but some aspects need to be clarified and improved.

The authors interpret the vertebrate ODC-DC to include four linkers (line 193). However, the authors also said that loss of one linker (Calaxin) makes ODA to attach on the DMT through two linkers (line 199 and 246). These descriptions are confusing. It would make more sense to interpret the vertebrate ODC-DC as containing three linkers (CCDC151/114, Armc4/TTC25, Calaxin).

To confirm whether Calaxin directly interacts with β-tubulin (line 213), a control experiment could be needed by incubating WT axoneme with mEGFP-Calaxin followed by IF imaging.

The Immunoblotting experiment should be improved in Figure 5E. Could the authors get the same results in repeating experiments? Why is the Dnah8 signal higher in 50 mM NaCl of the (+)Calaxin group compared to that in 0 NaCl? This makes me doubt if the difference between (-)Calaxin and (+)Calaxin groups are significant.

The authors have covered several important points in the Discussion section. Now that the function of Calaxin in both mouse and zebrafish have been reported, the authors could discuss the similarity and difference of Calaxin function in different species and tissues.

Because of the limited resolution, the authors should be more careful when interpreting the small densities in the difference map, for example, in Figure 4F-G black arrows. Considering that the CCDC151/114 coiled coil is overall poorly resolved both in the WT and mutant cryo-ET maps, the different densities could be due to different map quality or data processing. This makes the following statement suspicious "This structure corresponds to the N-terminus region of CCDC151/114, suggesting that Calaxin affects the conformation of neighboring DC components".

Reviewer #3 (Public Review):

Aging impacts many cognitive functions, and how these changes affect performance in different tasks is an important question. By testing 42 older and 36 younger healthy adults with a novel learning task and MR spectroscopy, Rmus et al addressed the important question whether age-related declines in learning are driven by WM, or by deficiencies of the RL system. The task varied the role of working memory in learning by asking participants to learn about either 3 or 6 stimulus response associations from feedback (set sizes 3 and 6). The paper combines a detailed computational account of participants behaviour and striatal and prefrontal/parietal MR spectroscopy in order to assess individual glutamate and GABA levels.

The authors report an effect of set-size on learning in both are groups, and show that participant age is associated with (1) worse accuracy, (2) a larger set size performance difference, and (3) a heightened sensitivity to reward. Computational modelling showed that working memory decay differed between age groups, but that reliance on WM to perform the task at hand was similar in both age groups (similarly differing between conditions in both groups). Turning to the MRS results, the paper shows that an aggregate measure of glutamate relates to aggregate task performance, that prefrontal glutamate specifically relates to WM decay observed in the task, and that age was negatively associated with glutamate levels.

While the paper is well worth reading and offers many interesting data points, the title's suggestion that "Age-related decline in prefrontal glutamate predicts failure to efficiently deploy working memory in working memory" is, in my opinion, not fully supported by the evidence. First, the authors don't report clear evidence for any age-related differences in WM reliance in the task overall. Second, the authors find that MFG glutamate relates significantly only to WM decay, not the parameter that captures WM deployment. Third, correlations don't imply predictive relations.

Another important open question relates to the relatively large age difference in the effect of set-size on performance. The authors write that working memory will contribute less to performance in higher set size conditions. Yet, age differences are largest in the set size 6 condition, suggesting that RL-dependent learning is most severely impaired in learning (set size 6 performance), rather than WM dependent learning (set size 3 performance). Finally, a statistically significant age difference in reward sensitivity seems to be hardly integrated into the authors' overall interpretation.

The issues laid out above set aside, the paper has the potential to make an important contribution to the literature on cognitive aging.

Reviewer #3 (Public Review):

This theoretical study provides a theoretical explanation for a puzzling question arising from recent experiments: How can chromosomes behave like polymers collapsed in a poor solvent but also contain "open" active chromatin sections? The authors propose that the binding of proteins (e.g. RNAP's) to the active sections can effectively change the solvent quality for these sections and thus open them. They suggest further that chromosomes show micellar structures with inactive blocks forming the cores of the micelles. Protein binding causes swelling of the micellar shells which affects the whole chromosome structure by changing the total number of micelles. This theory fits well to live imaging data of chromatin in Drosophila larvae, like the one shown in the striking Figure 1.

The manuscript is written very clearly.

My only suggestion is that the authors, in both the theory and simulation parts, are more explicit about how the interactions between the various components are modeled. From what I could see, in the theory part, one needs to look closely at Eq. 5 to understand how the influence of the binding of proteins affects the interaction between active monomers, and in the simulation part, one needs to go to the appendix to learn that interaction strengths between monomers within the active blocks and monomers within the inactive blocks have different values. The latter is crucial to understand the micellar structure shown at the top of Fig. 5A.

Reviewer #3 (Public Review):

Although the response to stress has been extensively studied in pulmonary epithelium and mesenchyme, the post-injury proliferation and subsequent regeneration of pulmonary capillary endothelial cells remain poorly understood. Following their previous study on identifying mouse lung endothelial cell heterogeneity, Niethamer et al. reported a lung capillary subpopulation, CAP1_B with highly enriched Atf3. This capillary subpopulation expanded and increased the expression of genes involved in vascular regeneration in response to influenza-induced lung injury. Loss of Atf3 in lung endothelial cells led to abnormal alveoli structure and loss of endothelial cells through inhibiting cell proliferation and inducing apoptosis. This manuscript provided strong evidence to demonstrate the importance of Atf3 in mediating endothelial response to lung injury, which is novel to the field.

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

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

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

Reviewer #3 (Public Review):

This contribution focuses on the zinc(II) transporter YiiP, a widely used model system of the Cation Diffusion Facilitator (CDF) superfamily. CDF proteins function as dimers and are typically involved in the maintenance of homeostasis of transition metal ions in organisms from all kingdoms of life. The system investigated here, YiiP, is a prokaryotic zinc(II)/H+ antiporter that exports zinc(II) ions from the cytosol. The authors addressed multiple crucial questions related to the functioning of YiiP, namely the specific role of the three zinc(II) binding sites present in each protomer, the zinc(II):H+ stoichiometry of antiport, and the impact of protonation on the transport process. Clarity on all these aspects is required to reach a thorough understanding of the transport cycle.

The experimental approach implemented in this work consisted of a combination of site-directed mutagenesis, high-quality 3D structural determination by cryoEM, microscale electrophoresis, thermodynamic modeling and molecular dynamics. The mutants generated in this work removed one (for the structural characterization) or two (for microscale electrophoresis) of the three zinc(II) binding sites of YiiP, allowing the authors to unravel respectively the structural role of metal binding at each site and the metal affinity of every site individually. pH-dependent measurements and constant pH molecular dynamics simulations, together with the metal affinity data, provided a detailed per-site overview of dissociation constants and Ka values of the metal-binding residues, casting light on the interplay between protonation and metal binding along the transport cycle. This thermodynamic modeling constitutes an important contribution, whose impact is however limited by the lack of an evaluation of whether the measured affinities in the various mutants differ significantly vs the affinities in the WT protein. In particular, this is true for the mutations disrupting site C, which cause a large-scale change in the quaternary structure of the protein.

Overall the authors were successful in providing a model of the transport cycle (Figure 5) that is convincing and well supported by the experimental data. The demonstration that two protomers act asymmetrically during the cycle is another nice achievement of this work, confirming previous suggestions. This novel overview of the cycle can constitute a basis for future work on other systems such as human ZnT transporters, also exploiting a methodological approach for the thermodynamic of these proteins similar to the one deployed here. The latter approach may be applicable also to other superfamilies of metal transporters.

Reviewer #3 (Public Review):

This study sought to identify relations between parameters of the diffusion decision model (DDM) and concentration of the neurotransmitters glutamate and GABA, as measured by magnetic resonance spectroscopy, and to evaluate the possibility that age moderates these relations in a developmental sample spanning middle childhood through young adulthood. The authors find a set of age-by-neurotransmitter concentration interaction effects indicating that lower levels of glutamate and greater levels of GABA in the intraparietal sulcus are related to faster non-decision times (lower values of the Ter parameter of the DDM) for "younger" participants but have the opposite relations in "older" participants (although given the way that the results are reported, the reader has little indication of what age group the terms "younger" and "older" refer to). The authors find similar interaction effects regarding relations between neurotransmitter concentration and connectivity in a visuomotor network and between neurotransmitter concentration and a fluid intelligence test. They then test moderated mediation models to determine whether functional connectivity in the visual-motor network mediates relations between neurotransmitter concentration and Ter, and whether Ter mediates the relation between neurotransmitter concentration and intelligence.

Strengths of the study include the relatively large sample size and the unique combination of brain and behavioral measures. The reported bivariate associations indicate an intriguingly consistent pattern of age-related moderation effects on the relation between neurotransmitter concentrations and several variables relevant to cognition (Ter, visuomotor connectivity, intelligence test scores) that could provide valuable insights to the field about the interplay between neurotransmitters and cognitive processes across development. However, the inferences that can be drawn from this work are seriously limited by an array of conceptual and methodological concerns.

A major conceptual issue is that the study is motivated by the premise that the nondecision time parameter (Ter) of the DDM is a major mechanistic underpinning of intelligence during child and adolescent development. There are several reasons why this premise is not well-supported. Although Ter is sometimes found to have weak correlations with scores on intelligence tests, the clearest pattern of findings across multiple studies is instead that individual differences in intelligence are primarily related to the DDM's drift rate (v) parameter (e.g., Schmiedek et al., 2007; Schulz-Zhecheva et al., 2016; Schubert & Frischkorn, 2020). The authors highlight the earlier finding that Ter mediates the effect of age on intelligence in the Krause et al. (2020) paper, but this paper is of questionable relevance to the current study. Krause et al. (2020) investigated cognitive changes in aging (ages 18-62) which are quite different from the current study's focus on development from middle childhood through young adulthood (ages ~ 7-24). Aging in older adults is known to have limited and task-specific effects on drift rate but strong effects on boundary and Ter whereas development from childhood through young adulthood coincides with the rapid maturation of drift rate (as shown in both prior research and in the current study's supplemental plots). Beyond the relatively weak evidence that Ter is a major contributor to intelligence during development, it is important to note that Ter is a nonspecific "residual" parameter (Schubert & Frischkorn, 2020) that is, theoretically, the summation of a wide array of different processes that are difficult to dissociate (perceptual encoding, visual search, motor responding). Therefore, in contrast to the drift rate and boundary parameters, it is difficult to interpret Ter as indexing a unitary mechanistic process, which is consistent with earlier findings that Ter shows limited evidence of psychometric validity as a task-general trait (Schubert et al., 2016). Finally, it is notable that bivariate tests of DDM parameters' relations with intelligence in the current study's sample (Supplemental File 9) suggest that Ter does not show a robust relation with intelligence, whereas drift rate shows relatively strong relations with intelligence in every group except for "older" individuals, who have likely fully matured and may therefore have less variance in both v and intelligence.

There are several opaque and potentially problematic features of the EZ DDM analysis. The tasks have relatively few trials spread across multiple different conditions within each task and it is unclear whether the DDM parameters were estimated separately in each condition or were estimated from trial-level data that were collapsed across conditions. This is especially concerning for the ANT, which has 96 trials distributed across 12 conditions, or apparently only 8 trials per design cell. Given the relatively low number of trials (both per design cell and overall) it is also concerning that parameter recovery studies do not appear to have been completed to ensure that this number of trials is sufficient to reliably estimate DDM parameters. In addition, accuracy rates and other behavioral summary statistics are not reported for any of the tasks. As ceiling levels of accuracy (i.e., few error RTs) can also cause prevent accurate estimation of parameters, this is another indication that assessing parameter recovery could be critical for inferences in this study.

A broader concern related to the measurement of DDM parameters is that they are each assumed to reflect the same mechanistic process across the three different tasks, but this assumption is not explicitly modeled (e.g., as a latent factor). Although the fact that Ter parameters across all three tasks have similar patterns of results is consistent with this assumption, constructing a latent factor using EFA or CFA would provide an explicit, and critical, test of the assumption. Latent factors formed from DDM parameters across the three tasks would also have several key methodological advantages over single-task measures, including separating variance in the cross-task mechanism of interest from task-specific "method variance", increasing statistical power by improving measurement of the latent mechanism, and reducing the number of multiple comparisons that need to be corrected for. The last two points are particularly critical for this study because it is possible that poor (i.e., single task) measurement and the large number of comparisons that were corrected for may have resulted in a consequential Type II error, such as a failure to detect effects involving DDM parameters other than Ter (drift rate and boundary). Related to these points, it is generally difficult for readers to judge the study's claims about the cross-task relevance of each DDM parameter or about dissociations between the different parameters because no intercorrelations between the parameters (within and between tasks) are reported or discussed.

Although the setup of the bivariate and moderation tests of relations between neurotransmitter concentrations and other variables is generally rigorous, it is concerning that only linear effects of age appear to have been considered. There appears to be clear evidence of nonlinear age-related trends in the scatterplots of parameter values displayed in Supplementary File 2.

The mediation analyses are central to the study's claims, but their results are particularly difficult to draw conclusions from due to several problematic methodological details. First, the confidence interval (CI) used to evaluate the significance of effects in these analyses was a 90% CI, essentially changing the alpha level for these tests from the conventional p<.05 to p<.10. Although this is claimed, in the Methods section, to be justified because these were "follow-up analyses based on the significant results obtained in advance", it is a highly unusual change that appears likely to have been made post hoc. Another apparent post hoc change is also mentioned in Methods: "we additionally removed cases that fell beyond three standard deviations after running the multiple regression models". It is not clear what variable or residual score this statement refers to and it is also not clear why this outlier trimming step was carried out only at the mediation stage, and not prior to the earlier bivariate analyses. The combination of an unusually high effective alpha level and potential post hoc adjustments to researcher degrees of freedom seriously undermines confidence in the mediation results. Even if the statistical hypothesis tasks are taken at face value, though, the evidence for mediation still appears very weak because the standardized effects are small and only significant in one age group (either "older" or "younger" but not both).

A broader challenge for readers is that neither the absolute ages nor the general developmental period of participants is mentioned anywhere in the main text or main plots of the paper. "Younger" and "older" mean very different things when referring to an aging sample (e.g., in the cited Klaus et al. study) versus a developing sample that spans middle childhood through young adulthood. Even if the range of the current study's sample is known, plots that split groups up by plus or minus one SD of age obscure age-related trends because the ages of the subgroups are not known. It may be easier to interpret findings if groups are instead split by neurotransmitter levels and plotted by absolute age.

Reviewer #3 (Public Review):

This work describes intracellular recordings from motor neurons of the zebrafinch. The authors use isolated brain slices allowing careful analysis of both voltage- and current-clamp recordings to document differences in action potentials in two motor cortical areas. RAPN neurons are associated with vocal commands that generate bird song, while Ald neurons are also motor neurons, but are not involved in song.

RAPN neurons are found to have much faster action potentials than Ald neurons, and pharmacological experiments provide evidence for the involvement of a particular class of voltage-gated potassium channels, Kv3, in RAPNs that presumably contributes to a faster rate of action potential repolarization and a concomitant narrowing of the action potential width. A set of experiments is included to verify that the findings obtained under normal ex vivo recording conditions (23 deg C) are retained under more physiological conditions (40 deg C). Consistent with the role of Kv3, the action potentials, and underlying potassium currents, are modified by imperfect pharmacological tools TEA, 4AP, and AUT5. Even though imperfect, together they provide support for the role of Kv3. Examination of transcripts for Kv3 family members documents that Kv3.1 is more highly expressed in RAPN than Ald neurons.

The experiments are adequately replicated and the paper is written very clearly. The authors claim that these cells are similar to Betz cells, highly specialized pyramidal neurons mainly found in the primate motor cortex and that they may play a similar role in primates and birds in generating fine motor behavior.

Some weaknesses include missing controls such as reversibility of pharmacological effects and improved statistical analysis. In addition, the linkage of RAPN neurons to Betz cells is not very strong.

Reviewer #3 (Public Review):

Here, the authors identify and characterize the role of C. elegans putative hydroxysteroid dehydrogenase gene let-767 to be essential for both lipid and endoplasmic reticulum (ER) homeostasis. They demonstrated that plays a role in lipid storage, maintaining ER morphology and that the lack of let-767 inhibits the unfolded protein response (UPR) upon proteotoxic stress, presumably by the accumulation of the predicted metabolite directly upstream of LET-767, 3-oxoacyl.

Strengths of the manuscript<br /> The complementary data in human cell line huh-7 that support the authors findings in C. elegans. The ablation of let-767 in C. elegans render the animal incapable of mounting a UPR response upon proteotoxic stress (tunicamycin). Similarly, supplementing the media of huh-7 cells with LET-767 precursor, 3-oxoacyl, attenuates the UPR activation by tunicamycin.

Overall, the experiments are well designed and in logical order throughout the manuscript.

Weakness of the manuscript<br /> The biggest weakness of this manuscript is the difficulty to appreciate the differences reported by the authors from the images provided. Providing images of higher quality or highlighting the differences to note within the figure panels will make the interpretation of data easier.

Additionally, many of the reported data are from biological duplicates. The lack of additional biological replicate might undermine the authors' findings.

Reviewer #3 (Public Review):

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

Important points to be considered:

- The presence of tRNA-based modifications is well characterised across life forms including genus Mycobacterium (Mycobacterium tuberculosis: Varshney et al, NAR, 2004; Mycobacterium bovis: Chionh et al, Nat Commun, 2016; Mycobacterium abscessus: Thomas et al, NAR, 2020). These modifications are shown to be essential for pathogenesis of multiple organisms. A comparison of tRNA modification and their respective enzymes with host organism as well as other mycobacterium strains is required. This can be discussed in detail to understand the role of common as well as specific tRNA modifications implicated in pathogenesis.

- Authors state in line 293 "Several strong signatures were detected in Mtb tRNAs but not in E. coli". Authors can elaborate more on the unique features identified and their relevance in Mtb infection in the discussion or result section.

- Deletion of MnmA is shown to be essential for E. coli growth under oxidative stress (Zhao et al, NAR, 2021). In similar lines, MnmA deleted Mtb suffers to grow in macrophage. Is oxidative stress in macrophage responsible for slow Mtb growth?

- Authors state in line 311-312 "Mtb does not contain apparent homologs of the tRNA modifying enzymes that introduce the additional modifications to s2U". This can be characterised further to rule out the possibility of other enzyme specifically employed by Mtb to introduce additional modification.

Reviewer #3 (Public Review):

In this study, Ciampa and colleagues demonstrate that HIF-1α activity is increased with gestation in humans and mice placentas and use several in vitro models to indicate that HIF activation in trophoblasts may release factors (yet to be identified) which promote myometrial contraction. Previous studies have linked placental factors to the preparation of the myometrium for labour (e.g. prostaglandins), but HIF-1α has not been implicated. Due to several issues regarding the experimental design, the results do not currently support the conclusions.

Major concerns:

1) The hypothesis states that placental aging promotes parturition via HIF-1a activation, the study does not provide any evidence of an aged placenta. Aging is considered a progressive and irreversible loss of functional capacity, inability to maintain homeostasis, and decreased ability to repair the damage. The placenta retains all these abilities throughout pregnancy [PMID: 9462184], and there's no evidence that the placenta functionally declines between 35-39 weeks, otherwise, it wouldn't be able to support fetal development. However, there is evidence of a functional decline in post-term placentas (i.e. >40 weeks in humans) but the authors compare preterm placentas with E17.5 mice placentas or 39-week human placentas, both these gestational periods are prior to the onset of parturition in most pregnancies (human = 40wkGA, mice=E18.5).

2) While the authors provide evidence that HIF-1α activity increases in both the human and mice placenta as gestation progresses, the mechanistic link between placental HIF-1α and parturition is not strongly supported. For example, most of the evidence is based on in vitro studies showing that conditioned media from trophoblasts treated with CoCl2 increased the contraction of myometrial cells. The specific factor responsible was not identified but the authors allude to pro-inflammatory factors such as cytokines. It was therefore interesting to note that the conditioned media had undergone a filtration step that removes all substances >10kDa, which includes the majority of cytokines and hormones.

3) An alternative explanation is that CoCl2 treatment-induced trophoblast differentiation and the effects on myometrial contraction may be related to differences in secreted factors produced by cytotrophoblasts versus syncytiotrophoblast. Although JAR cells do not spontaneously differentiate, they can be induced to syncytialise upon cAMP stimulation. Ref 39 the authors cite shows this. Indeed, the morphology of the cells in Fig5F that were exposed to CoCl2 indicates that they may be syncytialised. Syncytialised trophoblasts also express markers of senescence including increased SA-β-gal activity and reductions in mitochondrial activity.

4) The in vivo experiment showing reduced gestation length in pregnant mice receiving DMOG injection is interesting. However, we cannot exclude the effects of DMOG on non-placental tissues (both maternal and fetal) or the non-specific effects of DMOG (i.e. HIF-1α independent). Furthermore, previous studies using a more direct approach to alter HIF-1α activity in the placenta using trophoblast-specific overexpression of HIF-1α in mice do not lead to changes in gestation length [PMID: 30808910].

5) Lack of appropriate experimental models. E.g. JAR choriocarcinomas are not an ideal model of the human trophoblast as they are malignant. Much better models are available e.g. primary human trophoblasts from term placentas or human trophoblast stem cells from first-trimester placentas. Similarly, the mouse model is also not specific as discussed above.

6) Lack of cohesion between the different experimental models. E.g. CoCl2 was used to induce hypoxia/HIF1α in mouse TBs, but DMOG was used in vivo in mice. SA-β Gal staining was carried out in cells but not in mouse or human tissues.

7) Evidence of senescence and mitochondrial abundance could be strengthened by providing additional markers. E.g. only GLB1 mRNA expression is provided as evidence of senescence, and COX IV protein for mitochondrial abundance in mouse and human placentas.

8) Given that the main goal of this study was to investigate the role of hypoxia, hypoxia (i.e. low oxygen) was never directly induced and the results were based on chemical inducers of HIF-1α which have multiple off-target effects.

Reviewer #3 (Public Review):

In this study, Kierdorf and colleagues investigated the function of hemocytes in oxidative stress response and found that non-canonical DNA damage response (DDR) is critical for controlling JNK activity and the expression of cytokine unpaired3. Hemocyte-mediated expression of upd3 and JNK determines the susceptibility to oxidative stress and systemic energy metabolism required for animal survival, suggesting a new role for hemocytes in the direct mediation of stress response and animal survival.

Strength of the study:<br /> 1. This study demonstrates the role of hemocytes in oxidative stress response in adults and provides novel insights into hemocytes in systemic stress response and animal homeostasis.<br /> 2. The single-cell transcriptome profiling of adult hemocytes during paraquat treatment, compared to controls, would be of broad interest to scientists in the field.

Weakness of the study:<br /> 1. The authors claim that the non-canonical DNA damage response mechanism in hemocytes controls the susceptibility of animals through JNK and upd3 expression. However, the link between DDR-JNK/upd3 in oxidative stress response is incomplete and some of the descriptions do not match their data.<br /> 2. The schematic diagram does not accurately represent the authors' findings and requires further modifications.

Reviewer #3 (Public Review):

Male infertility is an important health problem. Among pathologies with multiple morphological abnormalities of the flagellum (MMAF), only 50% of the patients have no identified genetic causes. It is thus primordial to find novel genes that cause the MMAF syndrome. In the current work, the authors follow up the identification of two patients with MMAF carrying a mutation in the CCDC146 gene. To understand how mutations in CCDC146 lead to male infertility, the authors generated two mouse models: a CCDC146-knockout mouse, and a knockin mouse in which the CCDC146 locus is tagged with an HA tag. Male CCDC146-knockout mice are infertile, which proves the causative role of this gene in the observed MMAF cases. Strikingly, animals develop no other obvious pathologies, thus underpinning the specific role of CCDC146 in male fertility.

The authors have carefully characterised the subcellular roles of CCDC146 by using a combination of expansion and electron microscopy. They demonstrate that all microtubule-based organelles, such as the sperm manchette, the centrioles, as well as the sperm axonemes are defective when CCDC146 is absent. Their data show that CCDC146 is a microtubule-associated protein, and indicate, but do not prove beyond any doubt, that it could be a microtubule-inner protein (MIP).<br /> This is a solid work that defines CCDC146 as a novel cause of male infertility. The authors have performed comprehensive phenotypic analysis to define the defects in CCDC146 knockout mice. Surprisingly, the authors provide virtually no information on the penetrance of those defects - in most cases they simply show descriptive micrographs. The message of this manuscript would have been more convincing if the key phenotypes of the CCDC146 knockout mice were quantified, in particular those shown in Fig. 2E, 7A, 11B, 13.

The manuscript text is well written and easy to follow also for non-specialists. The introduction and discussion chapters contain important background information that allow putting the current work into the greater context of fertility research. The figures could have been designed more carefully, with additional information on the genotype and other details such as the antibodies used etc. directly added to the figure panels, which would improve their readability. The author might also consider pooling small figures with complementary content into one bigger figure in order to group related information together, and again facilitate the reading of the manuscript.

Overall, this manuscript provides convincing evidence for CCDC146 being essential for male fertility, and illustrates this with a large panel of phenotypic observations, which however mostly lack quantification in order to judge their penetrance. Together, the work provides important first insights into the role of a so-far unexplored proteins, CCDC146, in spermatogenesis, thereby broadening the spectrum of genes involved in male infertility.

Reviewer #3 (Public Review):

The manuscript entitled "Calcium transients trigger switch-like discharge of prostaglandin E2 (PGE2) in an ERK-dependent manner" by Watabe et al. investigates the interaction between PGE2, PKA, calcium and ERK signaling in MCDK cells and in mouse epidermis. By expressing PKA, calcium and ERK activity reporters, the authors conclude that calcium transients trigger release of PGE2 that signals through GPCR receptors EP2 and EP4 to recruit PKA in neighboring cells. Determining the dynamics of signaling molecules and their interrelationships is important to fully identify the spatiotemporal aspects of signaling mechanisms. This study addresses some aspects of the calcium-PGE2-GPCR-Erk-PKA signaling pathway in a cell line and in mouse skin ex vivo.

However, the sequential recruitment of the different signaling molecules has been described in previous studies. Hence, the novelty of the findings is limited.<br /> Additionally, the interpretation of some of the data is too speculative, more likely explanations are not considered, or not well supported by the data presented. The main conclusions the authors present are potentially artifactual (ie, cell density-dependent phenomena) and the authors need to either do further experiments to better support their conclusions or re-interpret the physiological significance of their findings.

Reviewer #3 (Public Review):

In this manuscript, Huang et al. use a variety of experimental approaches to investigate division of labor and cheater "policing" during biofilm formation in Bacillus velezensis SQR9. The authors show that SQR9 cells differentiate into two populations during biofilm development - one cell type produces extracellular matrix (ECM) and the other does not (referred to as "cheaters"). The authors go on to demonstrate that the ECM producing cells utilize a bacillunoic acid toxin system to selectively kill cheaters, keeping the cheater population in check which maintains the stability of the community. Further, the authors demonstrate that coordination of ECM production and synthesis of bacillunoic acid/immunity via acetyl-CoA carboxylase is mediated in part by Spo0A. I find the work as a whole to be compelling and thorough, and I expect it to be of broad interest to various fields of research.

Reviewer #3 (Public Review):

In this manuscript, the authors detail an exciting protocol to knock-in c-terminal tags at the endogenous locus of a gene of interest in the short-lived vertebrate, the African Turquoise Killifish. The technique is clearly explained and will be a significant advancement for the field. The method relies on the injection of a cocktail containing cas9 protein, proprietary-modified gRNA and dsDNA ordered from IDT, and a chemical enhancer of HDR.

I believe the authors demonstrate that killifish is a tractable emerging system to integrate stable fluorescent tags at desired loci. The method should be easy to reproduce since the components are all commercially available, but the proprietary nature of the modifications could make it a pricey technique for smaller labs. A protospacer adjacent motif (PAM) sequence near the desired insertion site is still a restriction using this method. That being said, I think this represents a significant advancement in knock-in methods that could be adopted in other systems. This manuscript is rather simple and straightforward, and I do not have additional criticisms or critiques.

As a side note, I think the brain sections look very professional, but since I am not a neurobiologist I will defer to the other reviewers about the accuracy and claims about the regions labeled.

For additional context, I suggest reading Wierson et al. 2020 and Seleit et al. 2022, which can both be found in the reference section.

Reviewer #3 (Public Review):

I found the results very interesting but wondered why the ERP results for the global vs. local effects are not reported. This analysis is mentioned in the methods section, but I do not find it in the results. Is this what is shown in the mid row in panel D? If yes, it should be made clearer. Is there a significant local and global deviant response in each patient group?

Additionally, eyeballing Figure 1, there are a few potential issues that may be affecting the conclusion re HER:

(1) Panel D top: it seems that the orange trace (MCS) is largely the same in both the "Local" and "global" condition. But the blue trace (UWS) shows a larger negative going deflection in the "global" case. Put differently, the UWS, but not MCS patients appear to generate a different response to the Global effect relative to the local effect. Is this the case?<br /> (2) There are some MCS subjects that appear to show a global effect that is larger than that observed in EMCS and healthy controls. How do you interpret these data?<br /> (3) How do you interpret the negative average HER data shown by many UWS patients?

Reviewer #3 (Public Review):

The authors sought to define a role for the Kazrin protein in the endosomal pathway. This effort is built on past observations of the impact of Kazrin over-expression on clathrin-mediated endocytosis. However, new Kazrin depletion experiments revealed no impact on endocytosis but a defect in the movement of early endosomes towards the nucleus. This observation that Kazrin depletion results in the dispersion of early endosomes is supported by shRNA knockdowns, CRISPR KO experiments, and the rescue of the phenotype by restoring Kazrin expression. The generalizability of the findings is supported by experiments in 2 different cell types (COS7 and MEFs). A direct role for Kazrin in linking early endosomes to dynein-dynactin is supported by observations that Kazrin is early present on endosomes and interacts with proteins of endosomes as well as with dynein-dynactin. A possible interaction with PI3P (a lipid enriched on early endosomes) is supported by a lipid binding assay. However, definitive results on this interaction would require validation by additional methods. With respect to the dynein-dynactin interactions, the authors strengthen confidence in this interaction and its putative functional relevance by identifying sequence homology between Kazrin and BICDR1 and hook3, 2 proteins with well-characterized functionally relevant roles in linking dynein-dynactin to cargos. The methods that were used to establish these functions for Kazrin were well aligned with the goals of this research and with the conclusions that were drawn. Efforts were made to quantify key observations and to provide statistical tests to establish the significance of differences that were observed. While these quantitative efforts are generally sufficient to support the major claims of the study, the data presentation would be stronger if the authors could better define the experimental sample size and the number of replicates that were performed for each experiment. Furthermore, the idea that the C-terminal region of Kazrin helps to promote the formation of "condensates" was not thoroughly supported by experimental data even if the presence of an intrinsically disordered region is supportive of this interpretation of the formation of Kazrin puncta on or near endosomes.

Reviewer #3 (Public Review):

The manuscript by Mondoloni et al characterizes two-bottle choice oral nicotine consumption and associated neurobiological phenotypes in the antiparticle nucleus (IPN) using mice. The paper shows that mice exhibit differential oral nicotine consumption and correlate this difference with nicotine-evoked inward currents in neurons of the IPN. The beta4 nAChR subunit is likely involved in these responses. The paper suggests that prolonged exposure to nicotine results in reduced nAChR functional responses in IPN neurons. Many of these results or phenotypes are reversed or reduced in mice that are null for the beta4 subunit. These results are interesting and will add a contribution to the literature. However, there are several major concerns with the nicotine exposure model and a few other items that should be addressed.

Strengths:<br /> Technical approaches are well-done. Oral nicotine, electrophysiology, and viral re-expression methods were strong and executed well.<br /> The scholarship is strong and the paper is generally well-written. The figures are high-quality.

Weaknesses:<br /> Two bottle choice (2BC) model. 2BC does not examine nicotine reinforcement, which is best shown as a volitional preference for the drug over the vehicle. Mice in this 2BC assay (and all such assays) only ever show indifference to nicotine at best - not preference. This is seen in the maximal 50% preference for the nicotine-containing bottle. 2BC assays using tastants such as saccharin are confounded. Taste responses can very likely differ from primary reinforcement and can be related to peripheral biology in the mouth/tongue rather than in the brain reward pathway. Moreover, this assay does not test free choice, as nicotine is mixed with water which the mice require to survive. Since most concentrations of nicotine are aversive, this may create a generalized conditioned aversion to drinking water - detrimental to overall health and a confounding factor. What plasma concentrations of nicotine are achieved by 2BC? When nicotine is truly reinforcing, rodents and humans titrate their plasma concentrations up to 30-50 ng/mL. The Discussion states that oral self-administration in mice mimics administration in human smokers (lines 388-389). This is unjustified and should be removed. Similarly, the paragraph in lines 409-423 is quite speculative and difficult or impossible to test. This paragraph should be removed or substantially changed to avoid speculation. Overall, the 2BC model has substantial weaknesses, and/or it is limited in the conclusions it will support.

Statistical testing on subgroups. Mice are run through an assay and assigned to subgroups based on being classified as avoiders or non-avoiders. The authors then perform statistical testing to show differences between the avoiders and non-avoiders. It is circular to do so. When the authors divided the mice into avoiders and non-avoiders, this implies that the mice are different or from different distributions in terms of nicotine intake. Conducting a statistical test within the null hypothesis framework, however, implies that the null hypothesis is being tested. The null hypothesis, by definition, is that the groups do NOT differ. Obviously, the authors will find a difference between the groups in a statistical test when they pre-sorted the mice into two groups, to begin with. Comparing effect sizes or some other comparison that does not invoke the null hypothesis would be appropriate.

Decreased nicotine-evoked currents following passive exposure to nicotine in minipumps are inconsistent with published results showing that similar nicotine exposure enhances nAChR function via several measures (Arvin et al, J Neurosci, 2019). The paper does acknowledge this previous paper and suggests that the discrepancy is explained by the fact that they used a higher concentration of nicotine (30 uM) that was able to recruit the beta4-containing receptor (whereas Arvin et al used a caged nicotine that was unable to do so). This may be true, but the citation of 30 uM nicotine undercuts the argument a bit because 30 uM nicotine is unlikely to be achieved in the brain of a person using tobacco products; nicotine levels in smokers are 100-500 nM. It should be noted in the paper that it is unclear whether the down-regulated receptors would be active at concentrations of nicotine found in the brain of a smoker. The statement in lines 440-41 ("we show that concentrations of nicotine as low as 7.5 ug/kg can engage the IPN circuitry") is misleading, as the concentration in the water is not the same as the concentration in the CSF since the latter would be expected to build up over time. The paper did not provide measurements of nicotine in plasma or CSF, so concluding that the water concentration of nicotine is related to plasma concentrations of nicotine is only speculative.

The results in Figure 2E do not appear to be from a normal distribution. For example, results cluster at low (~100 pA) responses, and a fraction of larger responses drive the similarities or differences.

10 mg/kg/day in mice or rats is likely a non-physiological exposure to nicotine. Most rats take in 1.0 to 1.5 mg/kg over a 23-hour self-administration period (O'Dell, 2007). Mice achieve similar levels during SA (Fowler, Neuropharmacology 2011). Forced exposure to 10 mg/kg/day is therefore 5 to 10-fold higher than rodents would ever expose themselves to if given the choice. This should be acknowledged in a limitations section of the Discussion.

Are the in vivo recordings in IPN enriched or specific for cells that have a spontaneous firing at rest? If so, this may or may not be the same set/type of cells that are recorded in patch experiments. The results could be biased toward a subset of neurons with spontaneous firing. There are MANY different types of neurons in IPN that are largely intermingled (see Ables et al, 2017 PNAS), so this is a potential problem.

Related to the above issue, which of the many different IPN neuron types did the group re-express beta4? Could that be controlled or did beta4 get re-expressed in an unknown set of neurons in IPN? There is insufficient information given in the methods for verification of stereotaxic injections.

Data showing that alpha3 or beta4 disruption alters MHb/IPN nAChR function and nicotine 2BC intake is not novel. In fact, some of the same authors were involved in a paper in 2011 (Frahm et al., Neuron) showing that enhanced alpha3beta4 nAChR function was associated with reduced nicotine consumption. The present paper would therefore seem to somewhat contradict prior findings from members of the research group.

Sex differences. All studies were conducted in male mice, therefore nothing was reported regarding female nicotine intake or physiology responses. Nicotine-related biology often shows sex differences, and there should be a justification provided regarding the lack of data in females. A limitations section in the Discussion section is a good place for this.

Reviewer #3 (Public Review):

The manuscript by Wang et al. investigates the mechanisms and physiological consequences of presynaptic plasticity at parallel fiber synapses of the cerebellum. Using a wide range of molecular, cellular, and genetic approaches, they show that a signaling pathway involving cAMP, EPAC, and PKCε leads to phosphorylation of RIM1α in parallel fiber terminals. Using EM and electrophysiology, they show that RIM1α (by forming a protein complex with Rab3A and Munc13) promotes docking of synaptic vesicles and increased vesicle release probability. The authors demonstrated that EPAC/PKCε are necessary for the induction of presynaptic LTP at parallel fiber synapses. The authors then extend this work to the behavioral level by showing the mice lacking EPAC or PKCε expression in cerebellar granule cells lack presynaptic LTP at parallel fiber synapses and display motor learning deficits during adaptation of the vestibular ocular reflex, a common test of cerebellum-dependent learning. The mechanisms of synaptic plasticity at parallel fiber synapses have been long investigated, but still remain unclear. This work makes a significant and convincing contribution to understanding presynaptic plasticity mechanisms. Likewise, the relative contribution of various pre- and postsynaptic forms of plasticity to cerebellar learning has long been debated but remains unsettled. This work provides novel evidence that presynaptic plasticity contributes to motor learning, possibly complimenting postsynaptic forms of plasticity. However, given the experimental conditions, it is difficult to extrapolate the slice electrophysiology findings to mechanisms of motor learning in vivo (see detailed comments below).

This manuscript provides compelling evidence for the role of EPAC and PKCε in regulating RIM1α and vesicle release. The authors use an impressive range of cellular, molecular, and genetic approaches to establish each link in the chain of the cAMP/EPAC/PKC signaling. In general, the conclusions are well supported by the data, often with multiple approaches used to address each question. In a few cases, the conclusions are overstated or not well supported by the data.

Specific comments:

1. While the data are generally very convincing, the authors overstated the conclusions in several instances. For example, the authors state that EPAC and PKCε are "required" or "essential" for vesicle docking and release. However, the author's own data show that both vesicle docking and release are clearly present (though reduced) in the absence of EPAC and PKCε, demonstrating they are not absolutely required. The language could be toned down without diminishing the impact of the excellent work.

2. The authors used analysis of cumulative EPSCs to estimate release probability (Pr) and the readily releasable pool (RRP) size. Unfortunately, this approach is likely not suited for low release probability synapses such as parallel fibers (the authors estimate Pr to be 0.04-0.06). Thanawala and Regehr (2016) extensively investigated the validity of cumulative EPSC analysis under a variety of conditions. They found that this analysis produces large errors in Pr and RRP at synapses with a Pr below ~0.2. In addition, 20 Hz EPSC stimulation (as was used here) produces much larger errors compared to the more commonly used 100 Hz stimulation. Between the low Pr at parallel fiber synapses and the low stimulus frequency used, it is likely that the cumulative EPSC analysis provides a poor estimate of Pr and RRP in this case.

3. Using a combination of genetic knockouts and pharmacology, this paper convincingly shows that presynaptic EPAC/PCKε are necessary for presynaptic LTP, but do not alter postsynaptic LTP/ LTD. However, given the experimental conditions in the slice experiments, it is difficult to extrapolate from the slice data to in vivo plasticity during motor learning. Synaptic plasticity in the cerebellar cortex is quite complex and can depend significantly on age, temperature, location, and ionic conditions. Unfortunately, these were not well matched between slice and in vivo experiments. Slice experiments used P21 mice, while in vivo experiments were performed at P60. Slice experiments were performed in the vermis, while VOR expression/adaptation generally requires the vestibulo-cerebellum/flocculus. Slice experiments were performed at room temperature, not physiological temperature. Lastly, slice experiments used 2 mM Ca2+ in the ACSF, somewhat high compared to the physiological extracellular fluid. Each of these factors can significantly affect the induction and expression of plasticity. These differences leave one wondering how well the slice data translate into understanding plasticity in the in vivo context.

4. Many experiments use synaptosomal preparation. The authors identify excitatory synapses by VGLUT labelling, but it is unclear how, or if, the authors distinguish between parallel fiber, climbing fiber, and mossy fiber synaptosomes. These synapses likely have very different properties and molecular composition, some quantification or estimation of how many synaptosomes are derived from each type of synapse would be helpful.

5. The math1-cre mouse line is used to selectively knockout EPAC or PKCε expression in cerebellar granule cells. This line also expresses Cre in unipolar brush cells (UBCs) of the cerebellum (Wang et al., 2021). This is likely not a factor in the molecular/slice studies of EPAC/PKC signaling, but UBC dysfunction could play a role in motor/learning deficits observed in vivo. This possibility is not considered in the text.

Reviewer #3 (Public Review):

Aimon and colleagues investigated brain activity in flies during spontaneous and forced walking. They used light-field microscopy to image calcium activity in the brain at high temporal resolution as the animal walked on a ball and they used the statistical inference methods PCA and ICA to tease out subregions of the brain that had distinct patterns of activity. They then sought to relate those patterns to walking. Most interesting are the experiments they performed comparing forced walking to spontaneous walking because this provides a framework to generate hypotheses about which aspects of neural activity are reporting the animal's movements versus generating those movements. The authors identify subregions and neuron types that may be involved in generating vs reporting walking. Their analysis is reasonable but could be further strengthened with a more powerful statistical framework that explicitly considered the multiple hypotheses being tested. More broadly, the work serves as a starting point to investigate the role of different regions in the brain and should spur follow-up investigations that involve more perturbative approaches in addition to the correlative approaches presented here.

Reviewer #3 (Public Review):

Bacteria sense and respond to multiple signals and cues to regulate gene expression. To define the complex network of signaling that ultimately controls transcription of many genes in cells requires an understanding of how multiple signaling systems can converge to effect gene expression and ensuing bacterial behaviors. The global transcription factor CRP has been studied for decades as a regulator of genes in response to glucose availability. It's direct and indirect effects on gene expression have been documented in E. coli and other bacteria including pathogens including Vibrio cholerae. Likewise, the master regulator of quorum sensing (QS), HapR), is a well-studied transcription factor that directly controls many genes in Vibrio cholerae and other Vibrios in response to autoinducer molecules that accumulate at high cell density. By contrast, low cell density gene expression is governed by another regulator AphA. It has not yet been described how HapR and CRP may together work to directly control transcription and what genes are under such direct dual control.

Using both in vivo methods with gene fusions to lacZ and in vitro transcription assays, the authors proceed to identify the smaller subset of genes whose transcription is directly repressed (7) and activated (2) by HapR. Prior work from this group identified the direct CRP binding sites in the V. cholerae genome as well as promoters with overlapping binding sites for AphA and CRP, thus it appears a logical extension of these prior studies is to explore here promoters for potential integration of HapR and CRP. Inclusion of this rationale was not included in the introduction of CRP protein to the in vitro experiments.

Seven genes are found to be repressed by HapR in vivo, the promoter regions of only six are repressed in vitro with purified HapR protein alone. The authors propose and then present evidence that the seventh promoter, which controls murPQ, requires CRP to be repressed by HapR both using in vivo and vitro methods. This is a critical insight that drives the rest of the manuscripts focus.

The DNase protection assay conducted supports the emerging model that both CRP and HapR bind at the same region of the murPQ promoter, but interpret is difficult due to the poor quality of the blot. There are areas of apparent protection at positions +1 to +15 that are not discussed, and the areas highlighted are difficult to observe with the blot provided.

The model proposed at the end of the manuscript proposes physiological changes in cells that occur at transitions from the low to high cell density. Experiments in the paper that could strengthen this argument are incomplete. For example, in Fig. 4e it is unclear at what cell density the experiment is conducted. The results with the wild type strain are intermediate relative to the other strains tested. Cell density should affect the result here since HapR is produced at high density but not low density. This experiment would provide important additional insights supporting their model, by measuring activity at both cell densities and also in a luxO mutant locked at the high cell density. Conducting this experiment in conditions lacking and containing glucose would also reveal whether high glucose conditions mimicking the crp results.

Throughout the paper it was challenging to account for the number of genes selected, the rationale for their selection, and how they were prioritized. For example, the authors acknowledged toward the end of the Results section that in their prior work, CRP and HapR binding sites were identified (line 321-22). It is unclear whether the loci indicated in Table 1 all from this prior study. It would be useful to denote in this table the seven genes characterized in Figure 2 and to provide the locus tag for murPQ. Of the 32 loci shown in Table 1, five were selected for further study using EMSA (line 322), but no rationale is given for studying these five and not others in the table.

Since prior work identified a consensus CRP binding motif, the authors identify the DNA sequence to which HapR binds overlaps with a sequence also predicted to bind CRP. Genome analysis identified a total of seven sites where the CRP and HapR binding sites were offset by one nucleotide as see with murPQ. Lines 327-8 describe EMSA results with several of these DNA sequences but provides no data to support this statement. Are these loci in Table 1?

Using structural models, the authors predict that HapR repression requires protein-protein interactions with CRP. Electromobility shift assays (EMSA) with purified promoter DNA, CRP and HapR (Fig 5d) and in vitro transcription using purified RNAP with these factors (Figure 5e) support this hypothesis. However, the model proports that HapR "bound tightly" and that it also had a "lower affinity" when CRP protein was used that had mutations in a putative interaction interface. These claims can be bolstered if the authors calculate the dissociation constant (Kd) value of each protein to the DNA. This provides a quantitative assessment of the binding properties of the proteins. The concentrations of each protein are not indicated in panels of the in vitro analysis, but only the geometric shapes denoting increasing protein levels. Panel 5e appears to indicate that an intermediate level of CRP was used in the presence of HapR, which presumably coincides with levels used in lane 4, but rationale is not provided. How well the levels of protein used in vitro compare to levels observed in vivo is not mentioned.

The authors are commended for seeking to connect the in vitro and vivo results obtained under lab conditions with conditions experienced by V. cholerae in niches it may occupy, such as aquatic systems. The authors briefly review the role of MurPQ in recycling of the cell wall of V. cholerae by degrading MurNAc into GlcNAc, although no references are provided (lines 146-50). Based on this physiology and results reported, the authors propose that murPQ gene expression by these two signal transduction pathways has relevance in the environment, where Vibrios, including V. cholerae, forms biofilms on exoskeleton composed of GlcNAc.

The conclusions of that work are supported by the Results presented but additional details in the text regarding the characteristics of the proteins used (Kd, concentrations) would strengthen the conclusions drawn. This work provides a roadmap for the methods and analysis required to develop the regulatory networks that converge to control gene expression in microbes. The study has the potential to inform beyond the sub-filed of Vibrios, QS and CRP regulation.

Reviewer #3 (Public Review):

The authors investigated whether reactivation of wake EEG patterns associated with left- and right-hand motor responses occurs in response to sound cues presented during REM sleep.

The question of whether reactivation occurs during REM is of substantial practical and theoretical importance. While some rodent studies have found reactivation during REM, it has generally been more difficult to observe reactivation during REM than during NREM sleep in humans (with a few notable exceptions, e.g., Schonauer et al., 2017), and the nature and function of memory reactivation in REM sleep is much less well understood than the nature and function of reactivation in NREM sleep. Finding a procedure that yields clear reactivation in REM in response to sound cues would give researchers a new tool to explore these crucial questions.

The main strength of the paper is that the core reactivation finding appears to be sound. This is an important contribution to the literature, for the reasons noted above.

The main weakness of the paper is that the ancillary claims (about the nature of reactivation) may not be supported by the data.

The claim that reactivation was mediated by high theta activity requires a significant difference in reactivation between trials with high theta power and trials with low theta, but this is not what the authors found (rather, they have a "difference of significances", where results were significant for high theta but not low theta). So, at present, the claim that theta activity is relevant is not adequately supported by the data.

The authors claim that sleep replay was sometimes temporally compressed and sometimes dilated compared to wakeful experience, but I am not sure that the data show compression and dilation. Part of the issue is that the methods are not clear. For the compression/dilation analysis, what are the features that are going into the analysis? Are the feature vectors patterns of power coefficients across electrodes (or within single electrodes?) at a single time point? or raw data from multiple electrodes at a single time point? If the feature vectors are patterns of activity at a single time point, then I don't think it's possible to conclude anything about compression/dilation in time (in this case, the observed results could simply reflect autocorrelation in the time-point-specific feature vectors - if you have a pattern that is relatively stationary in time, then compressing or dilating it in the time dimension won't change it much). If the feature vectors are spatiotemporal patterns (i.e., the patterns being fed into the classifier reflect samples from multiple frequencies/electrodes / AND time points) then it might in principle be possible to look at compression, but here I just could not figure out what is going on.

For the analyses relating to classification performance and behavior, the authors presently show that there is a significant correlation for the cued sequence but not for the other sequence. This is a "difference of significances" but not a significant difference. To justify the claim that the correlation is sequence-specific, the authors would have to run an analysis that directly compares the two sequences.

Reviewer #3 (Public Review):

Smithers et al. examine the effects of large differences in target-flanker depth on peripheral visual crowding. To investigate this, they developed a novel real-depth display and measured the perceptual errors caused by the presence of flanker objects that were presented at different distances and at either the same or at different depths from a target object that the participants had to recognize.

Their primary result is that large depth differences between flanking and target objects increase the magnitude of crowding. Interestingly, it appears to be a two-faced finding: when the target is at fixation depth, crowding is more pronounced if the flankers are behind the target as opposed to in front of it. Yet, when the flankers are at fixation depth, crowding is more pronounced if the target is behind the flankers. They explain their finding in terms of increased clutter in areas outside the limits of binocular fusion. This conclusion of the study is well supported by the data and experiments. The work provides compelling evidence that real depth may affect peripheral crowding under the specific circumstances of their experiment. Whether this finding would also apply to more natural viewing conditions, in which there is much more clutter, to begin with, remains to be determined.

Strengths:<br /> By introducing a novel multi-depth plane display authors contribute to future research on the effect of real depth differences on several visual functions and increase the potential ecological validity of their results.<br /> By using perceptual error as their dependent variable and linear mixed models to analyze their data, authors improve their ability to represent the variability in the data.<br /> The authors explain the discrepancies between their results and previous research with sufficient additional experiments and data.<br /> The inclusion of a large number of participants, which is fairly uncommon in this type of experiment.

Weaknesses:<br /> 1. At several points in the paper authors refer to the 'natural three dimensional scenes'. Indeed, the authors increase the ecological validity of their experiment by introducing actual depth differences, therefore allowing for depth cues such as accommodation, vergence and defocus blur. This is indeed a significant improvement over previous studies. However, they still use relatively impoverished visual stimuli in a tightly controlled psychophysical experiment requiring head stabilization by means of a chin rest. So, their experiment is still far removed from deploying actual, ecologically valid, conditions. Consequently, their stimuli mostly lack the complexity and associated clutter of natural stimuli as well as other potential depth cues that an observer might gain from parallax, aerial perspective, lighting, or shading. Therefore, their suggestion "that crowding has a more significant impact on our perception of natural three-dimensional environments than previously estimated with 2D displays." is stretching what can be concluded from their present work.<br /> 2. The inclusion of a large number of participants, in which none of the participants seemed to have performed all the conditions, is both a strength and a potential weakness. Their current approach of including (presumably) naive participants and having each do a portion of the experiments in itself is valid. But it also adds to the complexity of their study and presumably adds variability to their data.

Reviewer #3 (Public Review):

In this manuscript the authors use novel techniques and analytical methods on an up and coming animal model for brain evolution. The manuscript utilizes the cavefish Astyanax mexicanus, which can provide future important insights into the field of neurobiology and in evolution in general.<br /> The authors however, only argue that Astyanax is a powerful system for functionally determining basic principles of brain evolution (which clearly it will be), but fail to actually describe what brain evolution insights Astyanax gives. The data is in the paper, but the interpretation needs refinement. This would be a much more valuable paper with a thorough evolutionary context based on the already existing, extensive literature. I believe this manuscript has the potential to be extremely impactful.

Reviewer #3 (Public Review):

The authors use an impressive array of techniques to determine the role of the NBR1 autophagy receptor protein specifically in the clearing of photodamaged chloroplasts. The authors describe the mechanism(s) by which this receptor operates in this context and demonstrate that this NBR1-mediated process occurs independently of SP1 and PUB4 (whose own roles in other aspects of chloroplast autophagy have previously been shown). The authors further dissect the functional domains of NBR1 to identify which are important in this process.

The major strength of this work is the myriad techniques used to approach the problem. The data are of high quality, and on the whole, well replicated and statistically analysed. In the main, these data substantiate the findings of the authors, although some findings are quite correlative/descriptive. However, the authors show good circumspection in their conclusions and discussion. One potential weakness is that the genetic data (use of mutants) rely on single mutant alleles, therefore whilst genetic linkage to the mutations is assumed, it cannot strictly be guaranteed. The authors performed effective genetic complementation to analyse the domain structure of NBR1 shown in Figure 7. It would have been good if complementation of nbr1 and atg1 mutants and/or alternative mutant alleles had been used for experiments described in Figures 1 to 6. Without this, I think even more circumspection regarding the data obtained from these single-allele mutants would be advised.

Reviewer #3 (Public Review):

This paper examines the mechanisms that control division orientation in the basal layers of the epidermis. Previous work established LGN as a key promoter of divisions where one of the siblings populates the differentiated layers (perpendicular). This work addresses two important, related issues - the mechanisms that determine whether a particular division is planar vs perpendicular, and the function of AGS3, and LGN paralog that has been enigmatic. A central finding is that AGS3 is required for the normal distribution of planar and perpendicular divisions (roughly equal) such that in its absence the distribution is skewed towards the perpendicular. Interestingly, however, the authors find that AGS3 has no detectable effect on orientation if the orientation is measured at anaphase. This timing aspect builds upon previous work from this group demonstrating a phenomenon they term "telophase correction" in which the orientation changes at the latest phases of division (and possibly post division?). Thus AGS3 seems to exert its effect using these later mechanisms and this is supported by further analysis by the authors. Importantly, the authors show that AGS3 acts through LGN, based on localization data and an epistasis analysis. The function of AGS3 has been highly enigmatic so resolving this issue while providing a useful step towards understanding how the division orientation decision is made, makes for exciting progress towards an important problem. I found the overall narrative and presentation to be quite good and especially appreciated the thoughtful discussion section that did an excellent job of putting the results in context and speculating how unknown aspects of the mechanism might work based on current clues. With that said, I think there are some important issues that should be resolved.

Regarding the orientation measurements, the authors should specify how the midbody marker was used to mark sibling cells, especially given the midbody can move following division. For example, how can the authors be confident that the siblings in the middle panel of 1A are correct and not an adjacent cell?

Regarding quantification, it would be useful for the authors to comment on how the following would influence their measurements: 1) movements along the z-axis, and 2) movement of the nucleus within the cell.

A similar question is how much telophase correction really happens in telophase. How confident are the authors that the process actually occurs during division and not subsequent to it? What is drawn in their previous paper and in Figure 7A implies that post-division movements may be important. It would be useful for the authors to comment on whether they can make the distinction and whether or not it might be important.

Does the division angle in the AGS3 OE experiment (Figure 1D) correlate with AGS3 levels within the cell?

I found the localization data to be the weakest part of the paper and feel that some reconsideration and reanalysis are warranted.

First, the quantifications in Figures 2C, 3B, and 3F are unnecessarily vague scoring-based metrics. In 2C, "Localization pattern" should be replaced with membrane/cytoplasm ratio or an equivalent quantification. In 3B "LGN localization" should be replaced with apical/cytoplasmic and apical/basal ratios or equivalents. In 3F, "Polarized LGN frequency" should be replaced with apical/basal ratio or equivalent. It seems to me that non-AI processed data would be most appropriate for these quantifications unless such processing can be justified.

Second, it is important to note that the cytoplasmic localization of AGS3 does not allow one to conclude that AGS3 is not on the membrane. Unfortunately, high cytoplasmic signal can preclude the determination of membrane-bound signal.

Finally, I had difficulty reconciling the images of LGN shown in Figure 3 with the conclusions made by the authors.

The challenge of the localization data is troubling because an important conclusion of the paper is that AGS3 acts via LGS. The localization data provided one leg of support for this conclusion and the other is provided by an epistasis analysis. Unfortunately, this data seems to be right on the edge because it is based on the difference between the solid and dashed blue lines in Figure 5B not being significant. However, we can see how close this is by comparing the solid and dashed red lines in the adjacent 5C, which are significantly different. Between the localization data, which doesn't seem clear cut, and the epistasis experiment, which is on the razor's edge, I'm concerned that the conclusion that AGS3 acts through LGN may be going beyond what the data allows.

Reviewer #3 (Public Review):

This work by Nikolopoulos et al. expands on prior studies demonstrating the ability of a member of the Drosophila melanogaster gut microbiome, Lactiplantibacillus plantarum, to support juvenile development in nutrient-limiting conditions. Previously, the authors identified the pbpX2-dltXABCD operon of L. plantarum that when mutated eliminated the growth-promoting ability of the bacterium to flies experiencing malnutrition (protein starvation). To better understand the bacterial components that support this larval development, the authors used a combination of structural, biochemical, and mutational analysis to describe the physiological role of the DltE, a previously uncharacterized gene within the pbpX2-dltXABCD operon. Although annotated as a serine-type D-Ala-D-Ala-carboxypeptidase, this work supports its role instead as a D-ala esterase that acts upon D-alanylated lipoteichoic acids, which are directly sensed by the host to induce peptidase expression and support juvenile growth in flies.

Overall, the data is compelling, and the conclusions are well-supported. The multiple methods used to examine and support their findings - the combination of structural and biochemical analyses, and the use of both bacterial and fly mutants to substantiate and demonstrate physiological relevance was elegant in execution.

The identification of a role for this bacterial cell component is exciting as it has not previously been appreciated as a bacterial-derived signal in fly immunity and/or metabolism. This work adds to the growing evidence for the breadth and diversity of bacterial metabolites and products that underlie fly-microbiome interactions and may have implications in other animal-microbe interactions, especially L. plantarum-mediated host growth promotion in other models including mammals.

An intriguing aspect of the work is the evidence of a bifurcation of this bacterial signal on immunity and metabolism, with the pathway regulating the latter yet unknown. Likewise, determining how these cell components are sensed by the host will also be of future interest. Another unknown that may limit the implications of this study is the ubiquity of D-ala LTA production among D. melanogaster-associated L. plantarum strains and whether this is a common or rare signal/role.