Reviewer #3 (Public Review):

The use of mutagenic drugs in combating new viral diseases is increasing, so it is imperative to understand how they might impact the evolutionary trajectory of RNA viruses and weigh their potential benefits versus their harms. The authors examined the impact on treatment outcomes and virus populations of treatment with mutagenic drugs (ribavinin and favipiravir) in a child with severe combined immunodeficiency syndrome and RSV pneumonitis. The authors report that despite a three-fold increase in viral mutation within-host evolution was still slow with only minor gain in viral fitness. The patient's clinical status was stable despite virus non-clearance by the drugs.

Despite looking at only one case, this study illustrates the potential impacts of widespread use of antiviral mutagenic drugs in the event of a viral epidemic. The authors warn in the discussion of the study that the results should be interpreted with caution if the same drugs are given to individuals who are immunocompetent, which I agree.

Reviewer #3 (Public Review):

This is interesting research that uncovers a novel inhibition mechanism for serotonin (SERT) transporters, which is akin to traditional un-competitive inhibitors in enzyme kinetics. These inhibitors are known to preferentially bind to the enzyme-substrate complex, thus stabilizing it, resulting in a decrease of the IC50 with increasing substrate concentrations. In contrast to this classic enzyme inhibition mechanism, the authors show for SERT, through detailed kinetic analysis as well as kinetic modeling, that the inhibitor, ECSI#6, binds preferentially to the inward-facing state of the transporter, which is stabilized by K+. Therefore, inhibition becomes "use-dependent", i.e. increasing substrate concentrations push the transporter to the inward-facing configuration, which then leads to the increased apparent affinity of ECSI#6 binding. Interestingly, this mechanism of action predicts that the inhibitor should be able to rescue SERT misfolding variants. The authors tested this possibility and found that surface expression and function of a misfolding mutant SERT is increased, an important experimental finding. Another strength of the manuscript is the quantitative analysis of the kinetic data, including kinetic modeling, the results of which can reconcile the experimental data very well. Overall, this is important and, in my view, novel work, which may lead to new future approaches in SERT pharmacology.

With that said, some weaknesses of the manuscript should be mentioned. 1) The authors suggest that serotonin and ECSI#6 cannot bind simultaneously to the transporter, however, no direct evidence for this conclusion is provided. 2) How does ECSI#6 access the inward-facing binding site? Does it permeate the membrane and bind from the inward-facing conformation, or is it just a very slowly transported low-affinity substrate that stabilizes the inward-facing state with much higher affinity? Including ECSI#6 in the recording electrode may provide further information on this point. Additionally, it is not clear why displacement experiments were not carried out with cocaine. Since cocaine is a competitive inhibitor but does not induce transport (i.e. doesn't induce the formation of the inward-facing conformation), it should act in a competitive mechanism with ECSI#6. 3) Why are dose-response relationships not shown for electrophysiological experiments? These would be a good double-check for the radiotracer flux data.<br /> Despite these weaknesses, I believe that this is important work, which adds to our understanding of the pharmacology of serotonin transporters, which are of critical nature due to being a target of anti-depressant drugs. The data make a case for the proposed inhibition mechanism and the interpretation of results, as well as conclusions, are generally sound.

Reviewer #3 (Public Review):

Molecular-level interpretations of SAXS data are challenging, especially for oligomeric systems of variable length with intrinsic flexibility and the possibility of multiple association interfaces. In order to make this challenge tractable, a number of assumptions are made here: 1) There is a single pathway by which individual domains associate first into homodimers and then into longer oligomers; 2) the association kinetics is isodesmic, which allows the direct calculation of oligomer distributions based on the given value of a single dissociation constant; 3) the internal dynamics within dimers is restricted essentially to relative domain-domain motions, that are sampled comprehensively via MD simulations. As a result, excellent fits to the SAXS data are obtained and the underlying conformational ensembles are highly plausible. The resulting models are useful to further understand SPOP function, especially in the context of liquid-liquid phase separation.

Reviewer #3 (Public Review):

Wernet et al. show that there are intrinsic protein oscillations at the hyphal tips of A. flagrans, a nematode trapping fungus, that become coordinated when two hyphae become close. They create a mathematical model of this synchronization phenomenon, and then go on to show that calcium is critical to the functioning of these oscillations and hyphal fusion. The concept of inter-hyphal communication through signal synchronization is fascinating, and the visual matching of the output of the model to the data is compelling. However, given that the authors already showed synchronized oscillations in the SofT protein in A. flagrans in Hammadeh et al. 2022 (Figure 4), this diminishes the novelty of the findings in this study. Additionally, as it also has been established that calcium drives other oscillatory communications, the characterization of calcium dependence is not especially novel or bringing new insights into the problem especially since it is unclear if the chelation is having effects due to loss of intracellular supplies and/or because it is the key signal in the dialogue. Right now the mathematical model feels a bit vague with discussion of hypothetical molecules, so the paper would be greatly strengthened if any key regulatory molecules that promote desychronization could be identified or there were some manipulations of the core known proteins that examined consequences of altering the oscillations. As it is, the reader is left intrigued but there are few concrete conceptual advancements.

Reviewer #3 (Public Review):

The issues raised in this review are more conceptual in nature and my suggestions are designed to sharpen the focus of the paper. The paper does a good job of explaining how prices of NIH project have changed over time but leaves the reader wanting a clearer understanding as to why this has happened. The paper raises the issue of price effects compared with compositional effects at the beginning and the very end of the paper. It would have been helpful for the paper to be more explicit about examining price changes and composition changes in the organizing structure of the paper (e.g. the solicited v. unsolicited is a compositional change and should be highlighted as such). The authors conclude that changes in NIH prices are associated with changes in the composition of NIH funding, and the evidence supports that. However, the NIH has inordinate control over prices because of the salary cap imposed in 2012. It would be helpful to see the relative weights of the various components of the BRDPI index in the paper graphed over time. I suspect the personnel salaries receive the highest weight. Figure 1B indicates BRDPI dropped by over 1.5 percentage points once the salary cap was put into place. When the NIH mechanically caps the price increases in salaries, they will hold research inflation (BRDPI) in check.

In addition, many of the notable trends in the data deserve further discussion. For example, in Figure 1A, awards are much higher than awardees, indicating that there are many PIs with multiple awards. This difference narrowed after 2013, but by 2021, there are ~5,000 multiple RPG awardees. This deserves some discussion. Furthermore, in Figures 2 through 4, the real value of NIH funding per project has fallen since the NIH doubling. This is a hugely important point and deserves more discussion. Eyeballing the real drop in value in Figures 3 and 4, it's approximately ~$50,000 (about 10%) close to the cost of one postdoc on an RPG. Clearly, by keeping the real costs of funding per project down, NIH is able to fund more projects. But what are the tradeoffs of this kind of policy? This may be beyond the scope of the paper, but it would be helpful for the authors to discuss the possibility that imposing the salary cap may have had some unintended consequences.

On Page 9 the authors state: "From 2012 through 2021 whisker ranges increased, exceeding levels for the doubling for untransformed costs, and not quite reaching doubling levels for logtransformed costs." Later the authors argue that this is the result of changes in the composition of research grants-that solicited grants are a larger share and cost more. However, it may be possible that the variance of funding costs is a by-product of the salary cap in 2012. When PIs could no longer charge full personnel costs, they may have developed different approaches to maximizing funding from NIH. This should be commented on in the paper. For example, are certain institutions (perhaps those that receive a lot of NIH funding in the first place) better at this kind of budget request than others.

While the authors attribute much of the change in the variance of costs to composition effects (solicited vs. unsolicited projects), the timing of the variance changes is interesting. It's very telling that during the doubling, the variance in grants was higher and then when NIH funding fell in real terms, the variance in funding narrowed (Figure 6). After the salary cap and the 2015 budget increases, the variance in funding increased again. This suggests that when money is tight the variation in funding narrows. I know the authors ran a regression on the time effects of actual funding costs (Figure 13) but not on the variance. Again, the time series of the variance in funding begs for further explanation.

Since much of the change in the composition of NIH grants is between solicited vs. unsolicited projects, it would be helpful to provide more information on the nature of solicited proposals and why NIH has shifted to funding more of them. For example, are these one-time solicitations? Are these U-mechanisms? Some combination of both? How would COVID-related funding appear in the NIH portfolio? A paragraph describing this change in emphasis and the types of projects being solicited would be very helpful.

In the conclusion, it would be helpful to mention the NIH salary cap during the discussion of the Baumol cost disease. While it is true that services will cost more overtime relative to goods (since robots can replace production workers in manufacturing but not postdocs in laboratories), the NIH effectively has its thumb on the price level with the salary cap. Cost disease is not going to be as problematic as long as the salary cap remains in place. However, there is growing evidence that the effective price cap that NIH has in place on NRSA stipend levels is generating shortages of postdocs (see https://www.science.org/doi/pdf/10.1126/science.add6184 and https://www.statnews.com/2022/11/10/tipping-point-is-coming-unprecedented-exodus-of-young-life-scientists-shaking-up-academia/). The authors should comment on the growing reports of labor shortages and consider how NIH may have to respond to this in the coming years.

Reviewer #3 (Public Review):

The authors perform a thorough investigation of the role of Islet2 in the specification of lumbar motor pools. They use a number of approaches, including RNA-seq, behavioral testing, and imaging to establish a role for this transcription factor (TF) in the organization and axonal and dendritic morphology primarily of the Gl motor pool. The experiments are clear, well-presented, and convincing. Concerns about this work stem from the fact that the authors use a null mouse instead of a conditional. While this is not so problematic when examining MN properties such as organization, it makes data on connectivity and behavior hard to interpret. Since the authors perform one experiment with the conditional mouse (showing Pea3 downregulation), it is a bit puzzling that they did not use these mice for the rest of the experiments.

Reviewer #3 (Public Review):

In this study, the authors studied the underlying mechanism of obesity-related inflammation in OA synovitis. They found more pronounced synovitis and enhanced macrophage infiltration accompanied by dominant M1 macrophage polarization in obese OA patients and ApoE-/- mice synovial tissues. Enhanced M1-polarized macrophages in obese synovium decreased growth arrest-specific 6 (GAS6) secretion, which resulted in impaired macrophage efferocytosis in synovial apoptotic cells. Intra-articular injection of GAS6 restored the phagocytic capacity of macrophages, reduced the accumulation of local apoptotic cells, and decreased the levels of TUNEL- and caspase-3-positive cells, preserving cartilage thickness and preventing the progression of obesity-associated OA. The main strengths of the paper are the discovery of the underlying mechanism of obesity-associated osteoarthritis. However, some claims and conclusions were not well supported by their data.

Reviewer #3 (Public Review):

In the human disease multiple sclerosis (MS) and in inflammatory demyelinating mouse models of MS, a subset of oligodendroglia express MHC genes. The role of MHC-expressing oligodendroglia in disease is unknown but thought to relate to a novel antigen-presenting function in these cells.

This study represents a fundamental advancement in approaches to detect and quantify the spatial and temporal expression of MHC I and MHC II genes in vivo through the generation of two reporter mice encoding CD74- or B2m-TdTomato fusion genes. This affords a highly quantitative method to isolate cells expressing the relevant fusion proteins and study their differential gene expression. The study advances the recent concept of oligodendroglia heterogeneity and in particular the presence of MHC expressing immune oligodendroglia.

Prior work has shown oligodendrocyte heterogeneity, induction of MHC I and/or MHC II genes in "stressed" oligodendrocytes, and immunologic OPCs in MS at the transcriptional level (Schirmer 2019, Jakel 2019, Absinta 2021). Authors of the current work have shown that OPC differentiation is impaired by effector T cells, that IFNγ induces the MHC class I in these cells and that class I expressing OPC can present antigen, in vitro, to CD8 T cells (Harrington 2020, Kirby 2019). However, a deeper understanding of 1) how common is this process under different pathologic conditions, 2) where and when does MHC I and MHC II expression in oligodendroglia occur during a multistep pathophysiologic process, and 3) what is the full transcriptional characterization of immune oligodendroglia and how do they differ from other oligodendroglia, is lacking. The work presented in this manuscript address this gap and provides a tool for investigation into these questions for the community.

The investigators created two reporter mice - a CD74-TdTomato (class II) and a B2m-TdTomato (class I) strain. Figure 1 shows the targeting strategy, genotypes, and transgene expression in CD45, CD19, and CD3 cells from blood and secondary lymphoid tissue, demonstrating anticipated expression. Fig 1F and G show expression of CD74-TdT and B2m-TdT, respectively, in transverse histologic sections through the spinal cord of EAE mice with clinical scores of 0, 1.5, and 3.0 (baseline expression in naïve mice is shown in Fig 1 supp 3 and 4). Finally, supplement 5 shows higher power images, and quantitation of TdT as a function of other immunologic markers. The data nicely shows the fidelity of expression in relevant cell types and induction in vivo in EAE. In addition, the data shows that the transgene does not obviously impact expression quantitatively (Fig 1 sup 2).

The data in Figure 2 are central to the overall concept. The authors nicely demonstrate the induction of CD74-TdT and B2m-TdT in interferon gamma-treated oligodendrocytes as well as other cell types. Oligodendrocytes identified by olig2 are present in the spinal cord of mice with EAE and their frequency increases as the EAE severity increases. A strong correlation is seen between the severity of EAE and the percent of olig2 cells expressing the class I or class II gene.

In figure 3, scRNA seq performed on cells isolated from CD74-TdT or B2m-TdT mice with EAE reveals multiple subclusters of oligodendrocytes, one of which is high in MHC l as well as in other genes involved with antigen processing. The experiments are carefully conducted and contaminating cell populations were eliminated from the analysis.

An outstanding accomplishment, providing a resource to study multiple aspects of MHC I and MHC II cell-specific expression, transcriptional profiles in relevant cell types, and temporal course of activation. Most importantly, this resource will allow for a deeper quantitative analysis of the immune oligodendroglia phenotype and explore potential function in disease models.

Reviewer #3 (Public Review):

The manuscript is very well written and the graphics are quite iconic. Moreover, the hypothesis is clear and the rationale is very convincing. Overall, the paper has the potential of being of paramount importance for the TMS-EEG community because it provides a valuable tool for a proper interpretation of several previously published TMS-EEG results.

Unfortunately, in my opinion, the dataset used to train and validate the method does not support the implication and interpretation of the results. Indeed, as clearly visible from most of the figures and mentioned by the authors of the database, the data contains residual sensory artefacts (auditory or somatosensory) that can completely bias the authors' interpretation of the re-entrant activity.

Reviewer #3 (Public Review):

This work by Ishikawa et. al is focused on testing the hypothesis first proposed by Rosenbaum that Ca2+ levels in the primary cilia act as an internal regulator of cilia length by negatively regulating intraflagellar transport (IFT) injection and/or microtubule assembly. The authors first built a mathematical model for Ca2+ based regulation of cilia length through the activity of a Ca2+ dependent kinase. They then tested this model in the growing cilia of Chlamydomonas cells expressing an axonemal localized GCaMP. Ca2+ levels were manipulated genetically with a calcium channel deficient mutant line and with the addition of EGTA. While increases in Ca2+ levels do correlate with cilia length as expected by the model they found that IFT injection was positively correlated with IFT injection and increased axonemal stability which contradicts its potential as a mechanism for the cell to internally regulate cilia length.

Overall the conclusions of the paper are supported by their data. They greatly benefit from first establishing their model in a clear form and then experimentally interrogating the model from multiple angles in order to test its viability. The importance of cilia length to our understanding of human health has only become greater in recent history and the authors are making a significant contribution to our understanding of ciliary length regulation.

Reviewer #3 (Public Review):

The mitochondrial NADH dehydrogenase complex (complex I) is of prime importance for cellular respiration. It has been biochemically and structurally characterized for several groups of organisms, including mammals, fungi, algae, seed plants and protozoa. Furthermore, different complex I conformation have been reported, which are considered to possibly represent distinct physiological states of the enzyme complex. E.g. in mammalian mitochondria, two resting states can be distinguished, designated 'ready-to-go' resting state, and 'deactive' resting state. To better understand the physiological relevance of these states, complex I is here investigated from the fruit fly Drosophila melanogaster, which represents a model for insects but beyond for metazoan in general and which can be easily genetically modified.

Complex I from Drosophila is presented at up to 3.3 Angstrom resolution. It includes 43 of the 45 complex I subunits defined for mammalian complex I. Subunit NDUFA3 has been found in Drosophila complex I for the first time. Overall, Drosophila complex I is remarkably similar in its composition and structure to the mammalian enzyme. Only minor topological differences were found in some subunits. Furthermore, three different complex I states are described, termed Dm1, Dm2 and Dm3. The three states are extensively discussed and compared to the states found in mammalian complex I. Dm1, which is the dominating class, likely represents the active resting state. In Dm2, the two complex I arms are slightly twisted with respect to Dm1. In Dm3, the membrane arm appears to be 'cracked' at the interface between ND2 and ND4. It possibly represents an artefact resulting from detergent-induced loss of stability in the distal membrane domain of the Dm2 state. Both, Dm2 and Dm3 most closely correspond to the mammalian active state. A state resembling the mammalian deactive state could not be found. This result is further supported by biochemical experiments. It is concluded that Drosophila complex I, despite its remarkable similarity to the mammalian enzyme, does not undergo the mammalian-type active/deactive transition.

In conclusion, complex I structure from Drosophila is of limited value for the better understanding of the states of mammalian complex I (which could be stated more clearly). However, insights into complex I structure and function of an insect is highly interesting. The conclusions are justified by the presented data. The manuscript is well written and the figures are thoroughly prepared. The discussion very much focusses on the interpretation of the three complex I states. The deactivate state, which is interpreted to protect mammalian mitochondria from ROS production during reverse electron transfer, might be dispensable in species characterized by a comparatively short life cycle like Drosophila, which is in the range of weeks.

Reviewer #3 (Public Review):

This paper uses single-cell transcriptome sequencing to identify and characterize some of the neuronal populations responsible for sex-specific behaviour and physiology. This question is of interest to many biologists, and the approach taken by the authors is productive and will lead to new insights into the molecular programs that underpin sexually dimorphic development in the CNS. The dataset produced by the authors is of high quality, the analyses are detailed and well described, and the authors have made substantial progress toward the identification and characterization of some of the neuron populations. At the same time, many other cell types whose existence is suggested by this dataset remain to be identified and matched to specific neuron populations or circuits. We expect the value of this dataset to increase as other groups begin to follow up on the data and analyses reported in this paper. In general, the value of this paper to the field of Drosophila neurobiology will be high even if it is published in close to its present form. On the other hand, the current manuscript does not succeed in presenting the key take-home messages to a broader audience. A modest effort in this direction, especially re-writing the Conclusions section, will greatly enhance the accessibility and broader impact of this paper.

While the biological conclusions reached by the authors are generally robust and of high interest, we believe that some conclusions are not sufficiently supported by the analyses that have been performed so far and need to be reexamined and confirmed. A major question concerns the authors' ability to distinguish a shared cell type with sex-biased gene expression from a pair of closely related, sex-limited cell types. There appear to be many cases that fall into this grey area, and the current analysis does not provide an objective criterion for distinguishing between sex-specific and sexually dimorphic clusters. Below we suggest some technical approaches that could be used to examine this issue. A second problem, which we do not believe to be fatal but that needs to be discussed, concerns potential differences in developmental timing and cell cycle phase between males and females, and how these differences might impact the inferences of sexual dimorphism in cell numbers and gene expression. Finally, we identify several areas, including the expression of transcription factors in different neuronal populations, that we believe could be described in more biologically insightful ways.

For our review, we focus on three levels of evaluation:

1). Is the dataset of high quality, useful to a large number of people, well annotated, and clearly described?

The data appear to be high quality. The authors use reasonable neuronal markers to infer that 99% of their cells are neuronal in origin, suggesting extremely low levels of contamination from non-neuronal cells. Moreover, the gene/UMIs detected per cell are high relative to what has been reported in previous Drosophila scRNA-seq neuron papers (e.g. Allen et al., 2020). The cluster annotations are incomplete - which is not surprising, given the complexity of the cell population the authors are working with. 46 of the 113 clusters in the full dataset are named based on published expression data, gene ontology enrichments of cluster marker genes, and overlap with other CNS single cell datasets. This leaves rather a lot outstanding. It is probably unrealistic to aim for a 100% complete annotation of this dataset. But if we're thinking about how this dataset might be used by other researchers, in most cases the validation that a given cluster corresponds to a real, distinct neuron subpopulation will be left to the user.

A major comment we have about the quality of the dataset relates to how doublets are identified and dealt with. The presence of doublets, an unavoidable byproduct of droplet-based scRNAseq protocols (like the 10x protocol used by the authors), could affect the clustering or at least bias the detection of marker genes. In large clusters, one might expect the influence of doublets on marker gene detection to be diluted, but in smaller clusters it could cause more significant problems. In extreme cases, a high proportion of doublets can produce artifactual clusters. The potential for problems is particularly high in cases where the authors identify cells with hybrid properties, such as clusters 86 and 92, which the authors describe as being serotonergic, glutamatergic, and peptidergic. Currently, the authors filter out cells with high UMI/gene counts, but it's unclear how many are removed based on these criteria, and cells can naturally vary in these values so it is not clear to us whether this approach will reliably remove doublets. That said, we acknowledge that by limiting their 'FindMarkers' analysis to genes detected in >25% of cells in a cluster the authors are likely excluding genes derived from doublets that contaminate clusters in low (but not high) numbers. We think it would be useful for the authors to report the number of cells that are filtered out because they met their doublet criteria and compare this value to the number of expected doublets for the number of cells they recovered (10x provides these figures). We would also recommend that the authors trial a doublet detection algorithm (e.g. DoubletFinder) on the unfiltered datasets (that is, unfiltered at the top end of the UMI/gene distribution). Does this identify the same cells as doublets as those the authors were filtering out?

2). What is the value of this study to its immediate field, Drosophila neurobiology? Are the annotation and analysis of specific cell clusters as precise and insightful as they could be? Has all the most important and novel information been extracted from this dataset?

This is the part that we are least qualified to assess, since we, unlike the authors, are not neurobiologists. We hope some of the other referees will have sufficient expertise to evaluate the paper at this level.

One thing we noticed (more on that in Part 3) is that the authors rely on JackStraw plots and clustree plots to identify the optimal combination of PCs and resolution to guide their clustering. This represents a relatively objective way of settling on clustering parameters. However, in a number of the UMAPs it looks like there are sub clusters that go undiscussed. E.g. in Fig. 2E clusters 1 and 3 are associated with smaller, distinct clusters and the same is true of clusters 2 and 6 in Fig 4b. Given that the authors are attempting to assemble a comprehensive atlas of fru+ neurons, it seems important for them to assess (at least transcriptomically) whether these are likely to represent distinct subpopulations.

3). How interesting, and how accessible is this paper to people outside of the authors' immediate field? What does it contribute to the "big picture" science?

Here, we think the authors missed an important opportunity by under-utilizing the Conclusions section. The manuscript has a combined "Results and Discussion" section, where the authors talk about their identification and analysis of specific cell clusters / cell types. Frankly, to a non-specialist this often reads like a laundry list, and the key conclusions are swamped by a flood of details. This is not to criticize that section - given the complexity and potential value of this dataset, we think it is entirely appropriate to describe all these details in the Results and Discussion. However, the Conclusions section does not, in its present form, pull it all back together. We recommend using that section to summarize the 5-8 most important high-level conclusions that the authors see emerging from their work. What are the most important take-home messages they want to convey to a developmental biologist who does not work on brains, or to a neurobiologist who does not work on Drosophila? The authors can enhance the value of this paper by making it more interesting and more accessible to a broader audience.

Reviewer #3 (Public Review):

This manuscript is well organized, and the author has generally shown good rigor in generating and presenting results. For instance, the author utilized TCRdist and structure-based metrics to remove redundancies and cluster complex structures. Additionally, the consideration of only recent structures (Fig. 2B) and structures that do not overlap with the finetuning dataset (Fig. 2D) is highly warranted.

In some cases, it seems possible that there may be train/test overlap, including the binding specificity prediction section and results, where native complexes being studied in that section may be closely related to or matching with structures that were previously used by the author to fine-tune the AlphaFold model. This could possibly bias the structure prediction accuracy and should be addressed by the author.

Other areas of the results and methods require some clarification, including the generation and composition of the hybrid templates, and the benchmark sets shown in some panels of Figure 2. Overall this is a very good manuscript with interesting results, and the author is encouraged to address the specific comments below related to the above concerns.

1. In the Results section, the statement "visual inspection revealed that many of the predicted models had displaced peptides and/or TCR:pMHC docking modes that were outside the range observed in native proteins" only references Fig. S1. However, with the UMAP representation in that figure, it is difficult for readers to readily see the displaced peptides noted by the author; only two example models are shown in that figure, and neither seems to have displaced peptides. The author should provide more details to support this statement, specifically structures of example models/complexes where the peptide was displaced, and/or summary statistics noting (out of the 130 tested) how many exhibited displaced peptides and aberrant TCR binding modes.

2. The template selection protocol described in Figure 1 and in the Results and Methods should be clarified further. It seems that the use of 12 docking geometries in addition to four individual templates for each TCR alpha, TCR beta, and peptide-MHC would lead to a large combinatorial amount of hybrid templates, yet only 12 hybrid templates are described in the text and depicted in Figure 1. It's not clear whether the individual chain templates are randomly assigned within the 12 docking geometries, as an exhaustive combination of individual chains and docking geometries does not seem possible within the 12 hybrid models.

3. Neither the docking RMSD nor the CDR RMSD metrics used in Figure 2 will show whether the peptide is modeled in the MHC groove and in the correct register. This would be an important element to gauge whether the TCR-pMHC interface is correctly modeled, particularly in light of the author's note regarding peptide displacement out of the groove with AlphaFold-Multimer. The author should provide an assessment of the models for peptide RMSD (after MHC superposition), possibly as a scatterplot along with docking RMSD or CDR RMSD to view both the TCR and peptide modeling fidelity of individual models. Otherwise, or in addition, another metric of interface quality that would account for the peptide, such as interface RMSD or CAPRI docking accuracy, could be included.

4. It is not clear what benchmark set is being considered in Fig. 2E and 2F; that should be noted in the figure legend and the Results text. If needed, the author should discuss possible overlap in training and test sets for those results, particularly if the analysis in Fig. 2E and 2F includes the fine-tuned model noted in Fig. 2D and the test set in Fig. 2E and 2F is not the set of murine TCR-pMHC complexes shown in Fig. 2D. Likewise, the set being considered in Fig. 2C (which may possibly be the same set as Fig. 2E and 2F) is not clear based on the figure legend and text.

5. The docking accuracy results reported in Fig. 2 do not seem to have a comparison with an existing TCR-pMHC modeling method, even though several of them are currently available. At least for the set of new cases shown in Fig. 2B, it would be helpful for readers to see RMSD results with an existing template-based method as a baseline, for instance, either ImmuneScape (https://sysimm.org/immune-scape/) or TCRpMHCmodels (https://services.healthtech.dtu.dk/service.php?TCRpMHCmodels-1.0; this only appears to model Class I complexes, so Class I-only cases could be considered here).

6. As noted in the text, the epitopes noted in Table 1 for the specificity prediction are present in existing structures, and most of those are human epitopes that may have been represented in the AF_TCR finetuning dataset. Were there any controls put in place to prevent the finetuning set from including complexes that are redundant with the TCRs and epitopes being used in the docking-based and specificity predictions if the AF_TCR finetuned model was used in those predictions? For instance, the GILGFVFTL epitope has many known TCR-pMHC structures and the TCRs and TCR-pMHC interfaces are known to have common structural and sequence motifs in those structures. Is it possible that the finetuning dataset included such a complex in its training, which could have influenced the success in Figure 3? The docking RMSD accuracy results in Fig. 5A, where certain epitopes seem to have very accuracy docking RMSDs and may have representative complex structures in the AF_TCR finetuning set, may be impacted by this train/test overlap. If so, the author should consider using an altered finetuned model with no train/test overlap for the binding specificity prediction section and results, or else remove the epitopes and TCRs that would be redundant with the complex structures present in the finetuning set.

7. The alanine scanning results (Figure 6) do not seem to be validated against any experimental data, so it's not possible to gauge their accuracy. For peptide-MHC targets where there is a clear signal of disruption, it seems to correspond to prominently exposed side chains on the peptide which could likely be detected by a more simplistic structural analysis of the peptide-MHC itself. Thus the utility of the described approach in real-world scenarios (e.g. to detect viral escape mutants) is not clear. It would be helpful if the author can show results for a viral epitope variant (e.g. from one of the influenza epitopes, or the HCV epitope, in Table 1) that is known to disrupt binding for single or multiple TCRs, if such an example is available from the literature.

Reviewer #3 (Public Review):

The goal of this study was to probe the transition from the IF to OF conformations inside the cells of a multidrug ABC transporter, ABCG2. In order to do so the authors used an antibody that specifically recognized the IF state (the epitope is 'disorganized' in the OF conformation) and this tool was particularly useful to address the conformational changes of ABCG2 that take place inside the permeabilized cells, depleted or not in ATP, and complemented with different combinations of nucleotides, drugs, and inhibitors. This technique was also used to show that the drugs increase the transition from the IF to the OF state.

By using confocal microscopy, the authors showed that ATP depletion led to a majority of ABCG2 that reside in a mitoxantrone-bound IF conformation.

The fluorescence correlation spectroscopy was another powerful approach used by the authors to convincingly demonstrate that the mitoxantrone drug could bind to ABCG2 in the IF conformation only.

Overall, the experiments are sound and the main conclusions drawn by the authors are very well supported by their data. This study unravels the first steps of the catalytic cycle of ABCG2 inside the cells, from drug-binding to a high-affinity site in the IF conformation to drug release from a low-affinity site in the OF conformation. It helps us to better understand how this transporter works in an environment that is physiologically relevant.

Here’s a lovely blog by Jason Wei that pulls together 137 examples of ’emergent abilities of large language models’. Emergence is a phenomenon seen in contemporary AI research, where a model will be really bad at a task at smaller scales, then go through some discontinuous change which leads to significantly improved performance.

Reviewer #3 (Public Review):

This manuscript presents an analysis of the cellular integration properties of a specific mushroom body output neuron, MBON-α3, using a combination of patch clamp recordings and data from electron microscopy. The study demonstrates that the neuron is electrotonically compact permitting linear integration of synaptic input from Kenyon cells that represent odor identity.

Strengths of the manuscript:

1) The study integrates morphological data about MBON-α3 along with parameters derived from electrophysiological measurements to build a detailed model.<br /> 2) The modeling provides support for existing models of how olfactory memory is related to integration at the MBON.

Weaknesses of the manuscript:

1) The study does not provide experimental validation of the results of the computational model.<br /> 2) The conclusion of the modeling analysis is that the neuron integrates synaptic inputs almost completely linearly. All the subsequent analyses are straightforward consequences of this result.<br /> 3) The manuscript does not provide much explanation or intuition as to why this linear conclusion holds.

In general, there is a clear takeaway here, which is that the dendritic tree of MBON-α3 in the lobes is highly electrotonically compact. The authors did not provide much explanation as to why this is, and the paper would benefit from a clearer conclusion. Furthermore, I found the results of Figures 4 and 5 rather straightforward given this previous observation. I am sceptical about whether the tiny variations in, e.g. Figs. 3I and 5F-H, are meaningful biologically.

Reviewer #3 (Public Review):

In this retrospective study, the authors intend to demonstrate the utility of serum procalcitonin in reducing the use of antibacteral agents in cancer patients with COVID-19, by identifying the subset of their highly immunocompromised population where early discontinuation of antibacterial therapy would not be harmful.

This study has a large population size > 500 patients over the span of 16 months. The groups with low procalcitonin and high procalcitonin have similar baseline characteristics, which makes the subsequent comparisons valid and relevant. The authors have considered all the relevant variables that could affect the outcomes being studied, and used sound statistical methods.

This study has some limitations. It is retrospective by nature, with possibility for confounders. In addition to the limitations mentioned by the authors, the study spans the period from March 2020 to June 2021 through which our knowledge of COVID has evolved, multiple variants have emerged, immunization has become available in the later part of the study period, more therapies (antivirals, monoclonal antibodies) became available, all of which have definitely affected COVID-related mortality, and could be an important confounder here. While the authors report the level of severity of the infection, using proxies such as supplemental oxygen and ICU admission, the use of COVID-directed therapies, including immunosuppressants such as steroids and tocilizumab (which in turn can increase the risk of bacterial infections and decrease the risk of mortality) is not reported. It also seems that the management of antibacterial therapy was left at the discretion of the treating physician, which can lead to a wide variety of practices, the nature of antibacterials administered is not reported here.

The results presented here support the conclusions made by the authors, and one has to appreciate the difficulty of antimicrobial stewardship efforts in an immunocompromised population such as the one being studied here. Many of these patients have been immunosuppressed for prolonged periods of time, could have profound defects in their immune systems, and could have had multiple previous infections, sometimes with atypical presentations. These patients are typically excluded from most large clinical trials, thus retrospective studies such as this one are usually the most informative pieces of literature available to support evidence-based medicine in this special patient population. I think this study should encourage clinicians to consider the use of serum procalcitonin as one additional clue to support their pursuit of antibacterial de-escalation or discontinuation in cancer patients with COVID-19.

Reviewer #3 (Public Review):

This is an interesting study in which the authors record simultaneously from neurons along the medial bank of the rodent PFC as rats perform the restaurant row task, an economic decision-making task in which subjects are offered different reward types with a specified delay, and they need to decide whether to accept or reject the offer. The authors find functional correlates of anatomical subdivisions of the mPFC; interestingly, they find that PL perhaps should be subdivided into dorsal and ventral subregions, a finding that is consistent with some known anatomical features. They characterize the task-related responses of neurons in these different subdivisions and find that in general, the dorsal regions (ACC, dPL) encode decision-related variables, whereas the ventral regions (vPL, IL) encode more motivational variables, such as the trial number in the session and the amount of lingering time.

Strengths:<br /> - The observed dichotomy between decisional and motivational factors mapping onto dorsal and ventral aspects of mPFC is interesting and, as far as I am aware, novel.<br /> - There are a number of rich, interesting observations, such as a lack of encoding of the reward delay in the offer zone, but then encoding of that variable in the wait zone (in all areas except ACC). This is intriguing given that their previous work has suggested that the decisions made in the offer and wait zones are in some ways dissociable, implying that they might rely on distinct neural circuits.<br /> - Overall, the data and analyses are of high quality, and the results are interesting.<br /> - The finding that PL should be subdivided into two distinct subregions will be of broad interest to researchers studying the mPFC. The approach and finding will also be of interest to the growing number of groups using linear silicon (including Neuropixels) probes to record from multiple brain areas simultaneously.

Weaknesses:<br /> - The authors find that dorsal regions of mPFC, particularly ACC, encode the upcoming decision of the animal. However, the upcoming choice will be correlated with animal movements (as is often the case). Given that ACC is adjacent to the motor cortex, and more posterior parts of the cingulate have been documented to reflect particular types of movements, it would be helpful to know if these signals would be observed for movements outside of the task, or if they really reflect the upcoming decision in this behavioral context.<br /> - I think some of the statistical analyses can be strengthened. For instance, the authors correlate neural activity against a large number of behavioral variables, some of which are correlated with each other. I would encourage a regression-based approach, which takes into account the correlations between variables for error bars/significance tests for each regressor.

In general, I think the authors' claims about their data are justified.

Reviewer #3 (Public Review):

KV7 channels play an important role in setting the resting membrane potential of neurons. As such, modulation by reactive oxygen species is an important and physiologically relevant form of channel regulation. Here, the authors propose a mechanism for this modulation in which ROS disrupts the interaction between the S2S3 loop of the channel and CaM, resulting in an overall enhancement of channel activity. The authors propose that this S2S3/CaM interaction is selectively mediated through CaM EF3, and is dependent on Ca2+. The results are supported by patch-clamp data, as well as NMR measurements and a FRET-based binding assay. The paper contains a considerable amount of data that point towards the conclusion.

The authors conclude that the EF3 of CaM is 'by itself sufficient and necessary for the oxidative response of KV7 channel complex and for gating the calcium responsive domain of KV7 channels." This is a very strong conclusion, and while much of the data points towards an important role for EF3, it is difficult to conclude that it is sufficient and necessary. The sparse description of the experiments makes the interpretation of the results a bit challenging. Based on the description provided, some of the results appear contradictory, limiting the conclusions drawn by the authors.

Reviewer #3 (Public Review):

This study by Kato et. al used a combination of computational modeling, in vitro experimentation, and confirmatory in vivo mouse work to define what influences collective cell invasion in squamous cell carcinoma (SCC). Looking at a multitude of parameters, the authors found that cancer cell-cancer cell contacts and matrix degradation work cooperatively in SCC invasion.

The authors provide a rigorous and systematic approach to querying the importance of the parameters tested; first setting their hypothesis computationally followed by in vitro experimentation in two different cancer cell culturing methods (organotypic and spheroid). Importantly, the experimental data convincingly confirmed the computational predictions, lending credence to their methodology. This is a major strength of the manuscript and will be beneficial to the field with regard to investigating invasion in other cancer types.

Additionally, the varied parameters tested (cancer cell-cell adhesion, cancer cell-matrix adhesion, cancer cell-fibroblast adhesion, fibroblast-matrix adhesion, cell-intrinsic motility, matrix displacement, and matrix proteolysis) were thoughtful and rooted in the literature. However, though considerate of the role the extracellular matrix (ECM) may play (via interrogating cancer cell-matrix adhesions as parameters), the characteristics of the matrix itself (e.g. stiffness, alignment) were not investigated. These attributes have been previously shown to affect collective cell invasion. Indeed, while investigating the contributions of matrix proteolysis on invasion, the authors found a parabolic relationship where both too much and too little matrix negatively impacted the ability of SCC cells to invade. Moreover, it is unclear what the role of fibroblast-matrix adhesions was to this system, though it was originally stated as a tested parameter.

Reviewer #3 (Public Review):

This manuscript provides a helpful and transparent guide on the application of granger-causality (GC) to calcium datasets. This is a useful entry point toward understanding the suitability and limitations of GC to neural data. However, it is not entirely convincing that the variations of GC analysis provided in this manuscript can be effectively applied to large-scale calcium datasets without prior knowledge of the underlying circuit, especially when such networks are likely to contain redundancy and recurrent links.

I would like to acknowledge that, at the outset, I held an unfavorable prior belief toward GC, for reasons that are well addressed in this manuscript, including the dangers of applying spectral GC to nonlinear networks, as well as a variety of pathologies that can undermine naive GC.

The manuscript has been helpful, both for its effective presentation of both bivariate GC and its multivariate extension, as well as the practical considerations that are essential to applying it to real-life data. It was particularly helpful to see a treatment of the challenges and their possible resolutions. I commend the authors for their transparency - they should certainly be rewarded rather than punished for their transparency.

Major<br /> 1. Redundant signals: throughout the brain, it's expected that a population of neurons can encode the same information. It's unclear how GC (both the original and the modified versions) can handle this redundancy. Given how pervasive redundant signals are in the brain, this should be addressed in both simulation and experimental data. For example, in one of the manuscript's simulated networks, replace one neuron with 10 copies of it, each with identical inputs and outputs but with the weights scaled by 1/10. Such a network is functionally equivalent to the original but may pose some challenges for the various versions of GC. I believe this issue also accounts for the MVGC results in the hindbrain dataset. It might be more appropriate to apply GC to groups of neurons (as indeed the authors cited), instead of applying it at the single-cell level with redundant signals.<br /> 2. Similarly, there is recurrent connectivity throughout the brain. The current manuscript appears to assume feedforward networks. Is the idea that GC cannot be applied to recurrent networks? If so, this needs to be clearly stated. If the authors believe that GC can recover casual links even in the presence of recurrent connectivity, this needs to be demonstrated.<br /> 3. Both BVGC and MVGC appear to be extremely sensitive to any outlier signals. The most worrying aspect is that the authors developed their corrections and pipelines with the benefit of knowing the structure of the underlying system, whereas in the case where GC would be most useful, the user would be unable to rely on prior knowledge of the underlying structure. For instance, the motion artifact in Fig 3a-c was a helpful example of a vulnerability of naive GC, but one could easily imagine scenarios involving an unmeasured disturbance (e.g. the table is bumped) causing a similar artifact, but if the experimenter is unaware of such unmeasured disturbances then they will not be included in Z, and hence can result in the detection of widespread spurious links.<br /> There is a circularity here that's concerning. It seems that one already needs to have the answer (e.g. circuit connectivity) in order to clean up the data sufficiently for BVGC or MVGC to work effectively. Perhaps the authors would be interested in incorporating ideas from the systems identification literature, which can include the estimation of unmeasured disturbances, perhaps in conjunction with L1 regularization on the GC links. This is certainly out of scope for the present work, but it would be worth acknowledging the difficulties of unmeasured disturbances and deferring a general solution to future work. Similar considerations apply to a common unmeasured neuronal input (e.g. from a brain region not included in the field of view of the imaging).<br /> 4. Interpretation - would it be correct to state that BVGC identifies plausible causal links, while MVGC identifies a plausible system-level model? I think these interpretations, carefully stated, might provide a helpful way of thinking about the two GC approaches. Taking the results of the paper together, neither BVGC nor MVGC is definitive - BVGC may overestimate the true number of causal links but MVGC is prone to a winner-take-all phenomenon that may represent just one of many plausible system-level models that can account for the observed data. This should be more clearly stated in the manuscript.<br /> 5. "correlation completely misses the structure" - links are signed, so they should be shown with "bwr" colormap, with zero mapped to white (i.e. v_min is blue, 0 is white, v_max is red, |v_min| = |v_max|, this is natively supported in PyPlot and can be trivially implemented or downloaded in MATLAB). It is misleading that correlation appears to miss certain links marked in black, until one realizes that these links are inhibitory. It would substantially aid clarity and consistency if all panels followed this signed "bwr" convention. I think the emphasis for the GC panels is on whether links are detected, rather than the weight of the link, so I would suggest indicating detected inhibitory links as -1 (blue) and detected excitatory links as +1 (red), and link not detected as 0 (white).

Reviewer #3 (Public Review):

In this work, Zhou et al. employed the polarization microscope (PM) method to track the orientations of helix 6a in the bacterial amino-acid transporter AdiC. It is very impressive that the authors were able to optimize the technique to achieve an overall resolution of 5{degree sign} for detecting changes in the inclination and rotation angles (𝜃 and 𝜓). However, I am deeply concerned about how the authors linked PM-detected conformational states to the structural states obtained using crystallography. Overall, I think it was an overstatement that the work resolved the equilibrium conditions for the major states in AdiC's transport cycle, and I urge the others to be more transparent with the readers about the limitations of their technique and be more thorough in considering alternative interpretations.

Reviewer #3 (Public Review):

This paper combines experimental structures with careful molecular dynamics to address a crucially important topic in cellular biology - how are mechanosensitive ion channels gated by the membrane? There are many flavors of mechanosensitive proteins, and here the authors study MscS from e. coli and the eukaryotic homolog MSL1 from Arabidopsis. The key finding is that the closed states of both channels induce high curvature in the inner leaflet due to the membrane protruding into the cytoplasm to lipidate exposed hydrophobic patches on the protein. The open state structures exhibit far less membrane deformation. Moreover, comparing the open and closed state structures reveals that the membrane-protein surface area is not significantly different in the two states - hence all of the mathematical models to date (and many experimental models too) that posit that tension-induced gating is driven by expansion of the in-plane area of the protein must be revised. Instead, the authors convincingly argue that the role of tension is to increase the energy of the protein-membrane system in the closed state (with its large membrane deformations) compared to the flat-membrane open state. Forgive me for not going on more about the structures that have been solved here, and how they are likely more representative of the native open state than previously solved structures - I agree with the authors' assertions, and they represent a major step forward in elucidating the full gating transition in both bacterial and eukaryotic systems. This is an important discovery, and it would have been impossible without the structure and simulation coming together. Future work attempting to quantify the energy of the membrane deformations, protein free energy difference between the channels in open and closed states, and the role of tension will be essential but outside the scope of what the authors were trying to do here.

Reviewer #3 (Public Review):

In this manuscript, the authors leverage new single-cell sequencing data to unravel cell type diversity in the head of Loligo vulgaris hatchlings. This analysis recovers 33 clusters and the authors describe the cell type populations with HCR in situ hybridization. This work provides an important next step in describing neural and sensory cells in an understudied class of invertebrates that goes beyond traditional morphological characterization.

Reviewer #3 (Public Review):

In this manuscript the authors ask whether finger movements in non-human primates can be predicted from neural activity recorded from the primary motor cortex. This question is driven by an ultimate goal of using neural decoding to create brain-computer interfaces that can restore upper limb function using prosthetics or functional electrical stimulation systems. More specifically, since functional use of the hand (real or prosthetic) will ultimately require generating very different grasp forces for different objects, these experiments use a constant set of finger kinematics, but introduce different force requirements for the finger muscles using several different techniques. Under these different conditions (contexts), the study examines how population neural activity changed and uses decoder analyses to look at how these different contexts affect offline predictions of muscle forces and finger kinematics, as well as the animals' ability to use different decoders to control 1 or 2-DOF online. In general, the study found that when linear models were trained on one context from offline data, they did not generalize well to the other context. However, when performance was tested online (monkeys controlling a virtual hand in real time using neural activity related to movement of their own hands) with a ReFIT Kalman filter, the animals were able to complete the task effectively, even with a decoder trained without the springs or wrist perturbation. The authors show data to support the idea that neural activity was constrained to the same manifold in the different contexts, which enabled the animals to rapidly change their behavior to achieve the task goals, compared to the more complex requirement of having to learn entirely new patterns of neural activity. This work takes studies that have been conducted for upper-limb movements and extends them to include hand grasp, which is important for creating decoders for brain-computer interfaces. Finally, the authors show using dPCA can extract features during changes in context that may be related to the activity of specific muscles that would allow for improved decoders.

Strengths:

The issue of hand control, and how it compares to arm control, is an important question to tackle in sensorimotor control and in the development of brain-computer interfaces. Interestingly, the experiments use two very different ways of changing the muscle force requirements for achieving the same finger movements; springs attached to a manipulandum and changes in wrist posture. Using both paradigms the decoder analysis clearly shows that linear models trained without any manipulation do not predict muscle forces or finger kinematics well, clearly illustrating the limitations of common linear decoders to generalize to scenarios that might encompass real grasping activities that require forceful interactions. Using a well-described real-time decoder (ReFIT Kalman Filter), the authors show that this performance decrease observed offline is easily overcome in online testing. The metrics used to make these claims are well-described, and the likely explanations for these findings are described well. A particular strength of this manuscript is that, at least for these relatively simple movements and contexts, a component of neural activity (identified using dPCA) is identified that is significantly modulated by the task context in a way that sensibly represents the changes in muscle activity that would be required to complete the task in the new contexts.

Weaknesses:

The differences between exemplar data sets and comprehensively tested contexts was difficult to follow. There are many references to how many datasets or trials were used for a particular experiment, but overall, this is fragmented across the manuscript. As a result, it is difficult to assess how generalizable the results of the manuscript were across time or animal, or whether day-to-day variations, or the different data collection schedules had an effect.

The introduction allocates a lot of space to discussing the concepts of generating (computing) movements as opposed to representing movements and relates this to ideas of neural dynamics. The distinction between these as described in the introduction is not very clear, nor is it clear what specific hypothesis this leads to for these experiments. Further, this line of thinking is not returned to in the discussion, so the contribution of these experiments to ideas raised in the introduction are unclear.

The complexity of the control that was possible in this task (1 or 2 DOF finger flexion/extension) was low. Further, the manipulations that were used to control context were simple and static. Both these factors likely contribute to the finding that there was little change in the principal angles of the high-variance principal components. While this is not a criticism of the specific results presented here, the simplicity of the task and contexts, contrasted with the complexity of hand control more generally, especially for even moderately dexterous movements, makes it unclear how well the finding of stable manifolds will scale. On a related point, it is unclear whether the feature, identified using dPCA, that could account for changes in muscle activity, could be robustly captured in more realistic behaviors. It is stated that future work is needed, but at this point, the value of identifying this feature is highly speculative.

The maintained control in online BMI trials could also be explained by another factor, which I don't think was explicitly described by either of the two suggestions. Prism goggle experiments introduce a visual shift can be learned quickly, and some BCI experiments have introduced simple rotations in the decoder output (e.g. Chase et. al. 2012, J Neurophys). This latter case is likely similar in concept to in-manifold perturbations. Regardless, the performance can be rapidly rescued by simply re-aiming, which is a simple behavioral adaptation. In a 1DOF or 2DOF control case like used in these experiments, with constant visual feedback on performance, the change in context could likely be rapidly learned by the animals, maybe even within a single trial. In other words, the high performance in the online case may be a consequence of the relatively simple task demands, and the simple biomechanical solution to this problem (push harder). What is the expectation that the results seen in these experiments would be relevant to more realistic situations that require grasp and interaction?

Some of the figures were difficult to read and the captions contained some minor incorrect information. The primary purpose of some of the figures was not immediately clear from the caption. For example, the bar plots in Figures 5 and 6 were very small and difficult to read. This also made distinguishing the data from the two different animals challenging.

There is no specific quantification of the data in Figures 4D and 5D. In Figure 4D it seems apparent that the vast majority of the points are below the unity line. But, it remains unclear, particularly in Figure 5D whether the correlations between the two contexts truly are different or not in a way that would allow conclusive statements.

Reviewer #3 (Public Review):

Precise methods were developed to validate the expression of channelrhodopsin in inner hair cells of the Organ of Corti, to quantify the relationship between blue light irradiance and auditory nerve fiber depolarization, to control light stimulation within the chamber of a high-pressure freezing device, and to measure with good precision the delay between stimulation and freezing of the specimen. These methods represent a clear advance over previous experimental designs used to study this synaptic system and are an initial application of rapid high-pressure freezing with freeze substitution, followed by high-resolution electron tomography (ET), to sensory cells that operate via graded potentials.

Short-duration stimuli were used to assess the redistribution of vesicles among pools at hair cell ribbon synapses. The number of vesicles linked to the synaptic ribbon did not change, but vesicles redistributed within the membrane-proximal pool to docked locations. No evidence was found for vesicle-to-vesicle fusion prior to vesicle fusion to the membrane, which is an important, ongoing question for this synapse type. The data for quantifying numbers of vesicles in membrane-tethered, non-tethered, and docked vesicle pools are compelling and important. These quantifications would benefit from additional presentation of raw images so that the reader can better assess their generality and variability across synaptic sites.

The images shown for each of the two control and two experimental (stimulated) preparation classes should be more representative. Variation in synaptic cleft dimensions and numbers of ribbon-associated and membrane-proximal vesicles do not track the averaged data. Since the preparation has novel stimulus features, additional images (as the authors employed in previous publications) exhibiting tethered vesicles, non-tethered vesicles, docked vesicles, several sections through individual ribbons, and the segmentation of these structures, will provide greater confidence that the data reflect the images.

The introduction raises questions about the length of membrane tethers in relation to vesicle movement toward the active zone, but this topic was not addressed in the manuscript. Seemingly quantification of this metric, and the number of tethers especially for vesicles near the membrane, is straightforward. The topic of EPSC amplitude as representing unitary events due to variation in vesicle volume, size of the fusion pore, or vesicle-vesicle fusion was partially addressed. Membrane fusion events were not evident in the few images shown, but these presumably occurred and could be quantified. Likewise, sites of membrane retrieval could also be marked. These analyses will broaden the scope of the presentation, but also contribute to a more complete story.

Overall, the methodology forms the basis for future studies by this group and others to investigate rapid changes in synaptic vesicle distribution at this synapse.

Reviewer #3 (Public Review):

In this study, Hu et al. aimed to identify the neuronal basis of ultrafast network oscillations in S1 layer 4 and 5 evoked by the optogenetic activation of thalamocortical afferents in vitro. Although earlier in vivo demonstration of this short-lived (~25 ms) oscillation is sparse and its significance in detecting salient stimuli is not known the available publications clearly show that the phenomenon is consistently present in the sensory systems of several species including humans.

In this study using optogenetic activation of thalamocortical (TC) fibers as a proxy for a strong sensory stimulus the in vitro model accurately captures the in vivo phenomenon. The authors measure the features of oscillatory LFP signals together with the intracellular activity of fast-spiking (FS) interneurons in layer 4 and 5 as well as in layer 4 regular spiking (RS) cells. They accurately measure the coherence of intra- and extracellular activity and convincingly demonstrate the synchronous firing of FS cells and antiphase firing of RS and FS cells relative to the field oscillation.

Major points:

1) The authors conclude the FS cell network has a primary role in setting the frequency of the oscillation. While these data are highly plausible and entirely consistent with the literature only correlational not causal results are shown thus direct demonstration of the critical role of GABAergic mechanisms is missing.

2) The authors put a strong emphasis on the role of RS-RS interactions in maintaining the oscillation once it was launched by a TC activity. Its direct demonstration, however, is not presented. The alternative scenario is that TC excitation provides a tonic excitatory background drive (or envelope) for interacting FS cells which then impose ultrafast, synchronized IPSPs on RS cells. Similar to the RS-RS drive in this scenario RS cells can also only fire in the "windows of opportunity" which explains their antiphase activity relative to FS cells, but RS cells themselves do not participate in the maintenance of oscillation. Distinguishing between these two scenarios is critical to assess the potential impact of ultrafast oscillation in sensory transmission. If TC inputs are critical the magnitude of thalamic activity will set the threshold for the oscillation if RS-RS interactions are important intracortical operation will build up the activity in a graded manner.

Earlier theoretical studies (e.g Brunel and Wang, 2003; Geisler et al., 2005) strongly suggested that even in the case of the much slower hippocampal ripples (below 200 Hz) phasic activation of local excitatory cells cannot operate at these frequencies. Indeed, rise time, propagation, and integration of EPSPs can likely not take place in the millisecond (or submillisecond) range required for efficient RS-RS interactions. The alternative scenario (tonic excitatory background coupled with FS-FS interactions) on the other hand has been clearly demonstrated in the case of the CA3 ripples in the hippocampus (Schlingloff et al., 2014. J.Nsci).

When the properties of the ultrafast oscillation were tested as various stimulation strengths (Figure 2) weaker stimulation resulted in less precise timing. If TC input is indeed required only to launch the oscillation not to maintain it, this is not expected since once a critical number of RS cells were involved to start the activity their rhythmicity should no longer depend on the magnitude of the initial input. On the other hand, if the entire transient oscillation depends on TC excitation weaker input would result in less precise firing.

3) The experiments indicating the spread of phasic activity from L4 RS to L5 FS cells can not be accepted as fully conclusive. The horizontal cut not only severed the L4 RS to L5 FS connections but also many TC inputs to the L5 FS apical dendrites as well as the axons of L4 FS cells to L5 FS cells both of which can be pivotal in the translaminar spread.

Reviewer #3 (Public Review):

Tyrosine kinases (TKs) belong to a relatively small family of protein kinases that are a product of later evolution and play a critical role in the regulation of cell behavior in multicellular organisms. Major differences between TKs and Serine/threonine kinases (STKs) are very well known, however, it is still unclear if there are specific sequence signatures that favor a specific inactive conformation of TKs that can be exploited for efficient drug design. The authors used Potts Hamiltonian models (PHMs) along with other computational methods to tackle this problem. The are two main weaknesses of this approach. First, it relies on multiple sequence alignment that requires a large set of related sequences and can't be applied to smaller families. Second, it requires a relatively large number of structures that have similar inactive structures. Although all active kinases have very similar structures, their inactive structures are very diverse. However, there are several groups of inactive conformations that share a high level of similarity. The authors study one of them, the so-called "DFG-out" conformation, and present a set of convincing results that define several key residues that favor this conformation. They demonstrated the strength of the PHMs approach that allows the detection of critical contacts that are specific for certain conformations. These results can be used to predict the "DFG-out" conformation of a TK even if its structure is not known or predict the effects of mutations in a TK if they involve some of the critical residues. In general, the paper presents a set of solid results that will facilitate the development of highly specific inhibitors for TKs.

Reviewer #3 (Public Review):

While full-scale and minimal models are available for CA1 hippocampus and both exhibiting theta and gamma rhythms, it is not fully clear how inhibitory cells contribute to rhythm generation in the hippocampus. This paper aims to address this question by proposing a middle ground - a reduced model of the full-scale model. The reduced model is derived by selecting neural types for which ablations show that these are essential for theta and gamma rhythms. A study of the reduced model proposes particular inhibitory cell types (CCK+BC cells) that play a key role in inhibitory control mechanisms of theta rhythms and theta-gamma coupling rhythms.

Strengths:<br /> The paper identifies neural types contributing to theta-gamma rhythms, models them, and provides analysis that derives control diagrams and identifies CCK+BC cells as key inhibitory cells in rhythm generation. The paper is clearly written and approaches are well described. Simulation data is well depicted to support the methodology.

Weaknesses:<br /> The derivation methodology of the reduced model is hypotheses based, i.e. it is based on the selection of cell types and showing that these need to be included by ablation simulations. Then the reduced model is fitted. While this approach has merit, it could "miss" cell types or not capture the particular balance between all types. In particular, it is not known what is the "error" by considering the reduced model. As a result, the control plots (Fig. 5 and 6) might be deformed or very different. An additional weakness is that while the study predicts control diagrams and identifies CCK+BC cell types as key controllers, experimental data to validate these predictions is not provided. This weakness is admissible, in my opinion, since these recordings are not easy to obtain and the paper focuses on computational investigation rather than computationally guided experiments.

Reviewer #3 (Public Review):

This is a very well written manuscript which addresses the role of the mitotic kinase Polo like kinase 1 in meiosis using the C. elegans fertilized oocyite as a model system. The authors show that PLK-1 localizes at different locations on meiotic spindles and chromosomes and identify the mechanisms required for the different localization patterns. Finally, the authors show which pool of PLK-1 is required for the different functions of PLK-1 in meiosis, using the power of genetics via CRISPR.

The strengths of the manuscript are the temporal inhibition of PLK-1 to study the meiotic roles of this kinase, the identification of the mechanisms that control PLK-1 localization and how this is regulated (phosphorylation) and the combination of cell biology and biochemstry.

This work will be of high interest to both the Polo like kinase and the meiotic communities.

Reviewer #3 (Public Review):

Halling et. al. probe the mechanism whereby calmodulin (CaM) mediates SK channel activity in response to calcium. CaM regulation of SK channels is a critical modulator of membrane excitability yet despite numerous structural and functional studies significant gaps in our understanding of how each lobe participates in this regulation remain. In particular, while Ca2+ binding to the N-lobe of CaM has a clear functional effect on the channel, the C-lobe of CaM does not appear to participate beyond a tethering role, and structural studies have indicated that the C-lobe of CaM may not bind Ca2+ in the context of the SK channel. This study pairs functional and protein binding data to bridge this gap in mechanistic understanding, demonstrating that both lobes of CaM are likely Ca2+ sensitive in the context of SK channels and that both lobes of CaM are required for channel activation by Ca2+.

Strengths:<br /> The molecular underpinnings of CaM-SK regulation are of significant interest and the paper addresses a major gap in knowledge. The pairing of functional data with protein binding provides a platform to bridge the static structural results with channel function. The data is robust, and the experiments are carefully done and appear to be of high quality.<br /> The use of multiple mutant CaMs and electrophysiological studies using a rescue effect in pulled patches to enable a more quantified evaluation of the functional impact of each lobe of CaM provides a compelling assessment of the contribution of each lobe of CaM to channel activation. The calibration of the patch data by application of WT CaM is innovative and provides precise internal control, making the conclusions drawn from these experiments clear. This data fully supports the conclusion that both lobes of CaM are required for channel activation.

Weaknesses:<br /> The paper focuses heavily on the results of multi-angle light scattering experiments, which demonstrate that a peptide derived from the C-terminus of the SK channel can bind to CaM in multiple stochiometric configurations. However, it is not clear if these complexes are functionally relevant in the full channel, making interpretation challenging.

Reviewer #3 (Public Review):

Vagnozzi et al. analyze the role of cadherins in respiratory circuit development. The authors previously identified a combinatorial cadherin code that defines phrenic motor neurons (Vagnozzi et al., eLife 2020). Here they find that combined loss of type I N-cadherin and type II cadherins 6, 9 and 10 results in respiratory failure and reduction in phrenic motor neuron bursting activity. Furthermore, diaphragm innervation, phrenic motor neuron (MN) number, cell body position as well as dendrite orientation are all impaired in mice lacking N-cadherin and cadherins 6, 9, 10. Analysis of different genotypes indicates that phrenic MN cell body position is regulated by N-cadherin, but that dendrite orientation is regulated by the combinatorial action of N-cadherin and cadherins 6, 9, and 10. They subsequently determine that cadherin signaling in presynaptic interneurons is required for phrenic MN bursting activity. Together, the results indicate that cadherins are essential for respiratory circuit function and suggest that a combinatorial cadherin code regulates wiring specificity in this circuit.

The manuscript is well presented with clear figures and text. My comments below mainly revolve around the interpretation of some of the findings and the correlation between phenotypes in NMNΔ6910-/- mice and βγ-catDbx1Δ mice in light of specific cadherin expression patterns and connectivity between rVRG and prenic MNs.

Major points<br /> 1. Page 8: 'In addition, NMNΔ and NMNΔ6910-/- mice showed a similar decrease in phrenic MN numbers, likely from the loss of trophic support due to the decrease in diaphragm innervation (Figure S3c).' This statement should be corrected: phrenic MN number in NMNΔ mice does not differ from controls, in contrast to NMNΔ6910-/- mice (Fig. S3). Similarly, diaphragm innervation is not significantly different from controls in NMNΔ (Fig. S2). Alternatively, these observations could be strengthened by increasing the number of mice analyzed to determine whether there is a significant reduction in PMN number and diaphragm innervation in NMNΔ mice.<br /> 2. A similar comment relates to the interpretation of the dendritic phenotype in NMNΔ and NMNΔ6910-/- mice (Fig. 3m): the authors conclude 'When directly comparing NMNΔ and NMNΔ6910-/- mice, NMNΔ6910-/- mice had a more severe loss of dorsolateral dendrites and a more significant increase in ventral dendrites (Figure 3l-m).' (page 9). The loss of dorsolateral dendrites in NMNΔ6910-/- mice indeed differs significantly from control mice, and is more severe than in NMNΔ mice, which do not differ significantly from controls. For ventral dendrites however, the increase compared to controls is significant for both NMNΔ and NMNΔ6910-/- mice, and the two genotypes do not appear to differ from each other. This suggests cooperative action of N-cadherin and cadherin 6,9,10 for dorsolateral dendrites, but suggests that N-cad is more important for ventral dendrites. This should be phrased more clearly.<br /> 3. Related comment, page 10: 'Furthermore, the fact that phrenic MNs maintain their normal activity pattern in NMNΔ mice suggests that neither cell body position nor phrenic MN numbers significantly contribute to phrenic MN output.' This should be rephrased, phrenic MN number does not differ from control in NMNΔ mice (Fig. S2c).<br /> 4. The authors conclude that spinal network activity in control and NMNΔ6910-/- mice does not differ (page 10, Fig. 4f). It is difficult to judge this from the example trace in 4f. How is this concluded from the figure and can this be quantified?<br /> 5. RphiGT mice: please explain the genetic strategy better in Results section or Methods, do these mice also express the TVA receptor in a Cre-dependent manner? Crossing with the Cdh9:iCre line will then result in expression of TVA and G protein in phrenic motor neurons and presynaptic rVRG neurons in the brainstem, as well as additional Cdh9-expressing neuronal populations. How can the authors be sure that they are looking at monosynaptically connected neurons?<br /> 6. The authors use a Dbx1-cre strategy to inactivate cadherin signaling in multiple brainstem neuronal populations and perform analysis of burst activity in phrenic nerves. Based on the similarity in phenotype with NMNΔ6910-/- mice it is concluded that cadherin function is required in both phrenic MNs and Dbx1-derived interneurons. However, this manipulation can affect many populations including the preBötC, and the impact of this manipulation on rVRG and phrenic motor neurons (neuron number, cell body position, dendrite orientation, diaphragm innervation etc) is not described, although a model is presented in Fig. 7. These parameters should be analyzed to interpret the functional phenotype.<br /> 7. Additional evidence is needed to support the model that a selective loss of excitatory rVRG to phrenic motor neuron connectivity underlies the reduced bursting activity phenotype in NMNΔ6910-/- mice, for instance by labeling the connections from rVRG to phrenic MNs and quantifying connectivity.

Reviewer #3 (Public Review):

The authors compare the ability of several models of musical predictions in their accuracy and in their ability to explain neural data from MEG and EEG experiments. The results allow both methodological advancements by introducing models that represent advancements over the current state of the art and theoretical advancements to infer the effects of long and short-term exposure on prediction. The results are clear and the interpretation is for the most part well reasoned.

At the same time, there are important aspects to consider. First, the authors may overstate the advancement of the Music Transformer with the present stimuli, as its increase in performance requires a considerably longer context than the other models. Secondly, the Baseline model, to which the other models are compared, does not contain any pitch information on which these models operate. As such, it's unclear if the advancements of these models come from being based on new information or the operations it performs on this information as claimed. Lastly, the source analysis yields some surprising results that don't fit with previous literature. For example, the authors show that onsets to notes are encoded in Broca's area, whereas it should be expected more likely in the primary auditory cortex. While this issue is not discussed by the authors, it may put the rest of the source analysis into question.

While these issues are serious ones, the work still makes important advancements for the field and I commend the authors on a remarkably clear and straightforward text advancing the modeling of predictions in continuous sequences.

Reviewer #3 (Public Review):

Hughes et al. report a role for the transcription factor NPAS4 in mediating chronic stress-induced reward-related behavioral changes, but not other depression-like behaviors. The authors find that NPAS4 is transiently upregulated in Camk2a+ PFC neurons following a single bout or repeated social defeat stress, and that knocking down PFC Npas4 prevents anhedonia. Presentation of linked individual data for social interaction/avoidance measures with/without interaction partners (Fig2C, E) is commended - all CSDS papers should show data this way. Npas4 also appears to mediate the known effect of stress on spines in PFC, providing novel mechanistic insight into this phenomenon. Npas4 knockdown altered baseline transcription in PFC, which overlapped with other stress and MDD-associated transcriptional changes and modules. However, stress-induced changes in transcription with knockdown remain unknown. A major drawback is that only male mice were used, although this is discussed to some extent. Results are presented with appropriate context and references to the literature. Conclusions are appropriate.

Additional context: Given NPAS4's role as an immediate early gene, it will be important for future work to elucidate whether IEG knockdown generally dampens transcriptional response to stress/other salient experiences. Nevertheless, the authors do show several pieces of evidence that Npas4 knockdown does not simply make mice less sensitive to stress and/or produce deficits in threat/fear-related learning and memory which is an important piece of this puzzle.

Reviewer #3 (Public Review):

The manuscript by the Qiu and Lu labs investigates the mechanism of desensitization of the acid-activated Cl- channel, PAC. These trimeric channels reside in the plasma membrane of cells as well as in organelles and play important roles in human physiology. PAC channels, like many other ion channels, undergo a process known as desensitization, where the channel adopts a non-conductive conformation in the presence of a prolonged physiological stimulus. For PAC the molecular mechanisms regulating this process are not well understood. Here the authors use a combination of electrophysiological recordings and MD simulations to identify several acidic residues and a conserved histidine side chain as important players in PAC desensitization. The results are overall interesting and clearly indicate a role for these residues in this process. However, there are several weaknesses in the experimental design, inconsistencies between the mutagenesis data and the MD results, as well as in the interpretation of the data. For these reasons I do not think the authors have made a convincing mechanistic case.

Major weaknesses:<br /> The underlying assumption in the interpretation of all the data is that the mutations stabilize or destabilize the desensitized conformation of the channel. However, none of the functional measurements provide direct evidence supporting this key assumption. Without direct evidence supporting the notion that the mutations specifically impact the rate of recovery from desensitization, I do not think the authors have made a convincing mechanistic case.

Overall, the agreement between the MD simulations, functional data, and interpretation are often weak and some issues should be acknowledged and addressed.<br /> For example:<br /> 1) The experimental data suggests that H98, E107, and D109 play analogous roles in PAC desensitization. However, the MD simulations suggest that the H98-D109 interaction energy is ~4 times larger than that of H98-E107. This should lead to a much greater effect of the D109 mutation. How is this rationalized?<br /> 2) The experimental data shows that E94 plays a key role in desensitization and the authors argue that this is due to the interactions of this residue with the β10-11 linker. However, the MD simulations show that these interactions happen for a small fraction, ~10%, of the time and with interaction energies comparable to those of the H98-E107-D109 cluster. It is not clear how these sparse and transient interactions can play such a critical role in desensitization. Also, if the interaction energies are of the same sign, how come one set of mutants favors desensitization and one does not?

The authors' MD analysis critically depends on assumptions on the protonation states of multiple residues, that are often located in close proximity to each other. In the methods, the authors state they use PropKa to estimate the pKa of residues and assigned the protonation states based on this. I have several questions about this procedure:<br /> - What pH was considered in the simulations? I imagine pH 4.0 to match that of the electrophysiological experiments.<br /> - Was the propKa analysis run considering how choices in the protonation state of neighboring residues affect the pKa of the other residues? This is critical because the interaction energies will greatly depend on the protonation state chosen.<br /> - Was the pKa for the mutant constructs re-evaluated? For example, does having a Gln or Arg in place of a His affect the pKa of nearby acidic residues?<br /> - H98R and Q have the same functional effect. The MD partially rationalizes the effect of H98R, however, it is not clear how Q would have the same effect as R on the interaction energies.<br /> - Are 600 ns sufficient to evaluate sampling of the different conformations?

Reviewer #3 (Public Review):

In free flight, flies largely change their course direction through rapid body turns termed saccades. Given how important these turns are in determining their overall behavior and navigation, it is important to understand the neural circuits that drive the timing of triggering these saccades, as well as their amplitude. In this paper the authors leverage the powerful genetic tools available in the fruit fly, Drosophila, to address this question by performing physiology experiments as well as behavioral experiments with inactivation and activation perturbations.

The authors make three primary conclusions based on their experiments: (1) the feature detecting visual pathway (T3) is responsible for triggering saccades in response to moving objects, but not widefield motion, (2) the pathway primarily responsible for wide field motion encoding (T4/T5) is responsible for triggering saccades in response to widefield motion, and (3) the T4/T5 pathways is responsible for controlling the amplitude of both object and widefield motion triggered saccades.

The authors go on to show that using calcium imaging data of T3 activity it is possible to predict under what conditions flies will initiate a saccade when presented with objects moving at different speeds, resulting in a parsimonious model for how saccades are triggered.

Together, the imaging, behavior, and modeling provide compelling evidence for claims 1 and 2, however, the evidence and modeling for point 3 - the amplitude of the saccades - is lacking. The statistical analysis does not go into sufficient detail in comparing across different cases, and in particular, there is little mention of the effect sizes, which appear to be quite small (this is primarily in reference to 3F and 4E). The data suggest that both the T3 and T4/T5 pathways contribute to saccade amplitude, instead of T4/T5 being the only or primary drivers.

Reviewer #3 (Public Review):

Haenelt et al. used sub-mm resolution fMRI and quantitative R1 and R2*imaging in humans to investigate the relationship between putative myelin densities and functional responses confined to different mesoscale sub-compartments of area V2. Specifically, they presented color and disparity-varying stimuli, which are known to preferentially activate thick and thin V2 stripes in human and nonhuman primates. Based on these color and disparity signals, they created ROIs corresponding to the color-biased thin stripes, disparity-biased thick stripes, and the third non-thick non-thin compartment, putatively corresponding to the pale (or inter) stripes. Comparison of the R1 values across these functionally defined V2 sub-compartments revealed lower R1 values in both the color-biased thin and disparity-biased thick stripes relative to the putative pale stripes. The interpretation is that myelin densities in pale stripes is higher than in the two other V2 compartments, which corroborates previous studies using post-mortem Gallyas staining (myelin) in primates (yet not other histological studies using other markers for myelin density). The authors conclude that the combination of high-resolution high-sensitive quantitative and functional MRI enables studies whereby the relationship between anatomical and functional properties can be investigated in-vivo.

This study builds upon previous studies of the authors who now combined forces to merge their respective skills in mesoscale functional imaging on the one hand and quantitative MRI on the other hand. The distinction between color- and disparity-biased thin and thick stripes has been previously shown by Nasr, Polimeni and Tootell, yet it is the combination with R1 and R2* imaging that is unique in this study. Dumoulin et al. previously used T1/T2 ratios instead of R1 and R2*values to investigate exactly the same question. Surprisingly, that previous study led to the opposite conclusion, as they showed that pale stripes contain lower myelin densities compared to thick and thin stripes -possibly due to the use of other functional markers in their attempt to differentiate between thin and thick stripes, as also discussed in the present manuscript. The only other study, to the best of my knowledge, that used MRI techniques to separate the three stripe compartments in are V2, was a macaque study, also using the T1/T2 ratio as a surrogate for myelin densities. That monkey study yielded basically the same results as the current study by Haenelt and colleagues: pale stripes are more myelinated than the thick and inter stripes.

Hence the present study aids to resolve existing and important controversies in both the histology and (f)MRI literature. It needs to be kept in mind, however, that all the MRI measures used so far are a 'proxy' for determining myelin densities, hence the final ground-truth will have to come from a combination of functional studies with (novel?) histological methods to determine exactly myelin densities, which can then be used to compare with functional properties segregating the three V2 compartments.<br /> Given the prior discrepancies between histological studies and between different MRI studies, and given the intrinsic importance to link function to fine-grained structural properties, the present study is potentially of great importance for the neuroimaging field -despite the relative small number of participating subjects. The experiments are well performed using state-of-the-art equipment, the analyses are well-done and the writing is excellent showing the scholastic skills of the authors. In addition, the authors discuss and exclude a number of alternative explanations for their results, which is highly informative for the reader.

Reviewer #3 (Public Review):

The manuscript by Kirtani et al. describes intracellular recordings from barrel cortex neurons identified under 2p microscopy in vivo during whisking. The major strengths of this work are that it is a technical feat and represents a unique dataset. It is a building block for future studies. The major weakness however is that it is a purely descriptive and observational study. There are no experimental manipulations, nor are there attempts to integrate the observations into a larger framework. As a result, there are no mechanistic or functional insights from this study. There is some speculation and discussion about how these results might fit into other studies of circuit connectivity or computational modeling, however, but this is relatively limited.

Reviewer #3 (Public Review):

This compelling manuscript by Mihaljević et al. describes an unusual regulatory mechanism for the proton-activated channel (PAC) where phosphatidylinositol (4,5)-biphosphate (PI(4,5)P2) inhibits the channel by direct interaction with a binding pocket in its extracellular/lumenal domain. This conclusion is supported by electrophysiology data collected on endogenously expressed channels in a human cell line. The authors support their finding with a structural model of acyl groups determined by cryo-electron microscopy. The core experimental design is sound and the data support the narrow conclusions of the paper.

This manuscript must consider the biological context of PI(4,5)P2 and the relevance of this interaction. Previous studies have documented that PI(4,5)P2 exists on the outer leaflet of the plasma membrane, but as a minor component relative to the overall levels of membrane PI(4,5)P2. The same applies for endosomes, where PIPs are enriched on the cytosolic membrane. The inositol headgroup is unresolved in the structural model of PI(4,5)P2-bound PAC, indicating that this interaction is nonspecific for PI(4,5)P2. This brings up the question as to whether PI(4,5)P2 is the relevant endogenous antagonist for PAC or whether it is a proxy for another ligand that has yet to be determined.

Reviewer #3 (Public Review):

The link between gut microbiota and maintenance of skeletal muscle mass was demonstrated in previous publications (including Lahiri et al., 2019), which also revealed that supplementing germ-free mice with a cocktail of short-chain fatty acids (SCFAs) could rescue the decreased skeletal muscle mass of germ-free mice. Increased MSTN expression in skeletal muscle causes sarcopenia (Cho et al., 2022). Moreover, the idea that Myostatin (MSTN) changes the composition of intestinal microorganisms is not novel (Pei et al., 2021 and Wen et al., 2022). In this manuscript, Quan et al. showed that knockout of MSTN in pigs affected the composition of gut microbes and that fecal microbiota transplantation (FMT) from MSTN KO pigs into mice caused hypertrophy of the GP muscle via activation of the Akt/mTOR pathway and increased presence of fast type IIb fibers. This effect was attributed to MSTN KO FMT-derived valeric acid, a SCFA, which when administered alone could recapitulate the phenotype of mice that were subjected to MSTN KO FMT. While the phenotypic results of this study are convincing, it lacks novelty in that the mechanisms that are studied were previously known. Instead, it would be interesting to explore how exactly does MSTN affect the composition of gut microbiota. This question was only briefly addressed (the authors showed that MSTN KO leads to changes in intestinal structure), however, a causal relationship was not established. Also, it is unclear how the mechanism of action of valeric acid is any different from the cocktail of acetic acid, butyric acid, or propanoic acid that was previously used. Therefore, overall, this study scores lowly in uniqueness. Nevertheless, the link of gut microbiota to MSTN is interesting and should be pursued by the authors in greater detail.

Reviewer #3 (Public Review):

This work studied age-related alterations in the ovarian immune cells in mice using single-cell RNA sequencing and flow cytometry. Based on gene expression profiles, the authors identified cell clusters corresponding to immune cell populations in mouse ovaries and compared their abundance in aged compared to adult animals. The authors identified two parallel immune processes in aging ovaries: a decrease in proportions of myeloid cells such as macrophages and neutrophils accompanied by an increase in proportions of CD3+ T cells. The latter cell population was increased in abundance due to an expansion of CD3+ cells that do not express CD4 and CD8, referred to as "double-negative T cells." These immune alterations were identified by single-cell RNA sequencing using small numbers of mice, and the authors partially validated the data using flow cytometry analysis in larger groups of animals. In addition, based on the gene expression data, they predicted which signaling pathways were altered in the aged immune cells and analyzed putative changes in the chemokine and cytokine networks, pointing at potential crosstalk of immune cell populations with senescent cells in aging ovaries.

The combination of single-cell RNA sequencing and flow cytometry used by the authors is a robust and unbiased approach to characterize immune cell alterations in aging ovaries. Overall, the data and analyses presented in this study reveal profound modifications of the immune system in the aging reproductive system in mice. Additional computational approaches predicting cell-cell communications affected by aging in the ovaries presented in this study can extend our understanding of the aging immune system. However, most of the conclusions from single-cell RNA sequencing results are not confirmed using additional approaches, including a more detailed flow cytometry analysis of ovarian immune cell subsets and functional validations of the predicted biological processes affected by aging.

The presented data do not specify whether the identified changes in the ovarian immune system are specific to aging ovaries or reflect a common alteration of the aging immune system in mice. Recently, several papers unbiasedly identified immune alterations associated with aging in different tissues using single-cell RNA sequencing and flow cytometry techniques (e.g., Almanzar et al., Nature 2019; Kimmel et al., Genome Res 2019; Mogilenko et al., Immunity 2021). This study does not compare the findings with previous single-cell-based results from different tissues and does not clearly state if the immune aging in the ovaries is paralleled by similar alterations in immune cell subsets in other tissues in mice.

The authors show that the CD4- CD8- double-negative T cell subset is profoundly increased in abundance in aging ovaries. However, the population of double-negative T cells is not sufficiently characterized in the study. For example, it is unclear if similar cells can be found in aged tissues other than the ovaries. Moreover, using single-cell RNA sequencing, the authors show that the double-negative T cells co-express Trbc2 (TCRb) and Tcrgc2 (TCRg) genes, but the flow cytometry analysis of TCRg/d expression on these cells is not presented. The authors speculate that the double-negative T cells might have a regulatory function. However, a recent paper identified a population of pro-inflammatory T cells that co-express TCRab and TCRgd in mice and humans (including CD4- CD8- double-negative cells) (Edwards et al., J Ex Med 2020), suggesting that the double-negative T cells might be pro-inflammatory. It remains unclear if the double-negative T cell subset is unique to aging ovaries or phenotypically similar to the previously characterized double-negative TCRab+ and TCRgd+ cells.

The authors identified multiple transcriptional changes in genes encoding cytokines and chemokines, reflecting their decreased expression in aged ovarian immune cells. This observation is interesting because it contradicts the basic assumption of enhanced inflammation in old tissues. However, the presented findings are limited by the single-cell RNA sequencing level of evidence and are not supported or exemplified by an orthogonal analysis showing similar changes at the protein levels.

The authors claim that aging affects the recognition of senescent cells by ovarian immune cells. This exciting statement is based only on the single-cell RNA sequencing data in immune cells. The interaction between the immune cells and senescent cells in the ovaries involving the discussed pathways is not validated at protein levels in this study.

Reviewer #3 (Public Review):

Using whole-cell patch-clamp measurements, the authors nicely elaborate the competitive inhibition mechanism of UCPH-101 on EAAT1, concluding that it blocks conformational changes during transmembrane translocation, without inhibiting Na+/glutamate binding. The authors demonstrate that UCPH-101 binds to ASCT2 with strongly reduced affinity. Informed by sequence comparison between EAAT1 and ASCT2, the authors identify a pair of mutations, which makes the putative allosteric-binding pocket (which has been identified by crystallography earlier) in ASCT2 more similar to EAAT1 and restores the inhibitory effect of UCPH-101 in ASCT2. Overall, the electrophysiological experiments appear sound and convincing.

Furthermore, using virtual screening against the UCPH-101 binding pocket in ASCT2, the authors identified a novel (non-UCPH-101-like) compound #302 that they experimentally demonstrate to also inhibit ASCT-2. However, the study lacks a detailed investigation of the inhibition mechanism of this compound and it remains unclear if #302 also mediates allosteric inhibition as the authors propose. Furthermore, the study lacks any experimental verification of the assumed binding site of #302.

In addition, the study includes molecular-dynamics (MD) simulations on interactions of UCPH-101 with EAAT1 and ASCT2. These simulations intend to support the interpretations of the electrophysiological experiments, i.e., relatively tight interactions of UCPH-101 with EAAT1 and weaker binding to ASCT2, which can be restored using two point-mutations in ASCT-2. Unfortunately, this is a relatively weak part of the study. Due to the lack of any convergence analysis, the statistical significance of the drawn conclusions remains unclear. Furthermore, since it is not reported how UCPH-101 has been parameterized, the chemical accuracy of these models is unclear.

Reviewer #3 (Public Review):

This significant EEG-fMRI study highlights the functionality of the neurovascular coupling in response to somatosensory stimuli in the somatosensory cortices of premature neonates. The methods here developed are highly compelling and go beyond the current state of the art. This neurovascular adaptation is described together with an analysis of the relationship between changes in microstate cortical activity and the hemodynamic activities that suppose an already well-organized hierarchical processing of sensory information.

Strengths:

Analyzing simultaneously the changes in microstates (EEG) and BOLD signal (fMRI) in relation to somatosensory stimuli in preterm neonates allowed to demonstrate a correlation between the duration of the microstates and the amplitude of the BOLD response in premature neonates.<br /> The procedure for recording simultaneously EEG and fMRI in preterm neonates is a real challenge that has been very well conducted in terms of methodology.

Weaknesses:

Although the paper does have strengths in principle, the weaknesses of the paper are that the authors did not discuss the changes in neurovascular coupling in response to spontaneous bursts of activities or external stimuli in preterm neonates using other modalities such as fetal MEG or simultaneous EEG-fNIRS. While it can be easily understandable that the number of preterm neonates is small, the age range is wide and as discussed by the authors changes in EEG activities are important during the last trimester of gestation.<br /> The sleep stage is not reported but authors might present raw data of the microstates (around 30 secs). In addition, the lack of discussion about the effect of discontinuity which is a characteristic of EEG in premature neonates

Reviewer #3 (Public Review):

This study aims at determining the contribution of propriospinal neurons projecting from cervical to lumbar segments to the coordination of inter-limb coordination. In addition, the impact of silencing these neurons on motor parameters affected by spinal cord injury was assessed. While the study contains many important data describing the contribution of these propriospinal neurons, there is little information about the underlying circuit mechanisms.

Reviewer #3 (Public Review):

This study documents an empirical investigation of a fundamental brain process: adaptation to systematic cross-sensory discrepancies. The question is important, the experiment is carefully designed, and the results are striking. Following an unsupervised recalibration block, perceptual judgments of self-motion on the basis of visual and vestibular cues are systematically altered. These behavioral effects are mirrored by changes in the response properties of single neurons in areas MSTd and PIVC (provided that neurons in these areas exhibited selectivity for the sensory cue). Remarkably, neurons in downstream area VIP adjust their response properties in a very different manner, seemingly exclusively reflecting vestibular recalibration (which is opposite in direction to visual perceptual shifts). In the former two areas, the neural-behavior association follows the stimulus dynamics. In VIP, this association remains high beyond the life span of the stimulus. VIP typically exhibits strong choice signals. These decreased in strength after recalibration (an effect unique to area VIP). Together, these findings further dissociate VIP's functional role from that of MSTd and PIVC, without however, fully revealing what that role may be. These results offer a novel perspective on the neural basis of cross-sensory recalibration and will inspire future modeling studies of the neural basis of perception of self-motion.

Reviewer #3 (Public Review):

Results of this manuscript provide a new link between oxygen sensing and cholesterol synthesis. In previous studies, this group showed that the cholesterol synthetic enzyme squalene monooxygenase (SM) is subjected to partial proteasomal degradation, which leads to the production of a truncated, constitutively active enzyme. In this study, the authors provide evidence for the physiological significance of SM truncation. In a series of experiments, the authors show that subjecting cells to hypoxia (oxygen deprivation) induces truncation of SM. The synthesis of cholesterol requires 11 molecules of oxygen and SM is the first oxygen-dependent enzyme in the cholesterol-committed branch of the pathway. Evidence is presented that hypoxia causes squalene, the substrate of SM, to accumulate, which results in the enzyme's truncation. In addition, hypoxia stabilizes MARCHF6, the E3 ligase required for sterol-dependent ubiquitination and degradation of SM. Finally, the authors provide an experiment showing that truncation of SM correlates with hypoxia in endometrial cancer tissues.

Overall, the data presented in this manuscript are compelling for the most part. Hypoxia-induced truncation of SM and MARCHF6 is very clear according to the presented results. The specificity of SM-induced truncation is strong; both direct addition and inhibitor studies are presented. The major strength of this manuscript is that it provides the physiological relevance for the authors' previous finding that squalene accumulation leads to truncation of SM. However, there are a few issues that should be addressed to improve the interpretation of the data presented. The manner in which quantified immunoblots are presented is very confusing and difficult to interpret. This is evident in experiments in several figures. For example, it is difficult to determine the role of ubiquitination (Figure 2D) and MARCHF6 (Figure 2E) in the generation of truncated SM. The authors should present quantified data of all lanes of the immunoblots to reduce confusion.

The other important finding of this manuscript is that hypoxia stabilizes MARCHF6. This is supported by the results of Fig. 3A; however, the result of Figure 3B is not clear. A new band appears upon inhibition of VCP and MG-132 seems to reduce protein expression. These results could be removed from the manuscript without impacting the conclusions drawn. Finally, the results shown in Figure 5 showing that truncation of SM correlates with hypoxia in endometrial cancer tissues are a little preliminary. Multiple bands are detected in SM immunoblots, which interferes with interpretation. This experiment could be removed and speculated upon in the discussion.

Acho que podemos utilizar alguma foto de Hero, talvez da Kakau, para deixar um ar mais familiarizado

Reviewer #3 (Public Review):

The goal of this study was to determine the conditions in which adaptive copy-number mutations interfere with point mutations. One of the strengths of this study is its experimental design. The authors engineered a genetic reporter system to 'easily' distinguish between the two types of mutations: copy-number and point mutations. Thus, this system allows capturing mutations that appear 'de novo' during the evolution experiment and could be broadly used to study early duplication events. This system is also powerful given that gene expression demand can be tuned, allowing determining the conditions in which the Amplification Hindrance hypothesis holds. Finally, by combining measures of single-cell fluorescence and sequencing of the promoter region, the authors give more support to their conclusions (e.g., confirming the presence/absence of mutations).

An additional strength of this study is the use of three additional random promoter sequences. Even if the evolutionary dynamics for one of the promoters differed from the original promoter, the authors propose that this is due to the promoter mimicking a low expression demand. Thus, the use of three additional random promoter sequences strengthens their conclusion that negative epistasis between copy-number and point mutations occurs in low gene expression demand environments.

Overall, the methods and analyses are sound, and the conclusion that gene amplification hinders the fixation of adaptive mutations is correctly supported by the data. These findings have the potential to have broad implications for our understanding of the adaptive process in bacteria given that it provides a new mechanism for rapid adaptation that does not require de novo point mutations.

Reviewer #3 (Public Review):

This work adds to our understanding of the many diverse ways that different species of social insects organize the regulation of foraging behavior. This work compares model results with data previously collected on Camponotus sanctus, an ant species that collects nectar. Unlike other species in which foragers collect prey, seeds or other items that they do not ingest, in nectar-feeding species such as this one, the foragers drink nectar and then must unload it by regurgitating to other workers at the nest. This work presents a model that suggests that, like honey bees who also collect nectar, a C. sanctus forager's decision to exit the nest on its next trip depends on when it can unload the nectar, which is linked to the amount of nectar currently held by other workers.

Reviewer #3 (Public Review):

This paper investigates the emergence of color categories as a result of acquiring object recognition. The authors find that color categorization is an emergent property of a Convolutional Neural Network (CNN) trained with ImageNet for object recognition. In short, they find CNN, precisely a ResNET, can represent color in a categorical manner. They also show the categories obtained through the model are meaningful for more complex images and tasks. Analyzing how deep neural networks represent color categories is an under-studied but important problem in cognition and the authors did an excellent job presenting their analysis and results. The finding reveals features of deep neural networks in color processing and can also guide future theoretical and empirical work in high-level color vision. The method can be used to investigate other questions in high-level vision.

Strength:

The current modeling results support the immediate conclusion that color categories can emerge from learning object recognition. The method is novel and the result is intriguing. Most of the analysis is clear and the paper is easy to follow. Extensive experiments are done with the model and convincing results are presented.

Weakness:

The main weakness of the paper is the scope. In many places in the paper, the authors write that the results support several unsolved issues in biological color processing and color categorization. I am not convinced how the results, purely obtained from modeling CNN, connect to the biological color processing as the authors speculated in many places in the article including Introduction and Discussion. To support these claims, psychophysical data or experimenting with published psychophysical data are needed.

Specifically, I find the following speculations not immediately supported by the results from this paper.

First, I am not sure about the connection the author draws between the emergence of color categories from CNN (findings in this paper) with the debate of Universalists and Relativists, and support that "categories can emerge independent of language development". The fact that output layers of CNN trained on object recognition can cluster color into categories does not mean the color categories used in humans are formed before they have language. Even though the network isn't explicitly trained with color names, the CNN has been trained with object labels. Aren't the object labels part of language acquisition?

Second, the authors wrote "The current findings can explain why the general development of categories is so similar across languages: If color categorization is a side effect of acquiring basic visual skills (given relatively similar circumstances across the globe) color categories are expected to shape in a similar fashion throughout many cultures". There are no explicit measurements of how different cultures would agree on these color categories. The current results only support that CNN trained on object recognition can discover limited color categories. It doesn't say anything about human color categorization across cultures.

Third, in the Discussion, the authors wrote "they can explain why the emergence of color categories over cultures broadly follows a universal pattern". How can a CNN trained with ImageNet explain broad cultures? Even though ImageNet contains common objects labeled mostly by people from western countries, they do not represent a diversity of cultures. The current results suggest a relationship between object recognition and color categorization. But this relationship may vary from culture to culture.

Finally, it would be great if the authors can experiment with network architectures other than ResNET. An alternative model trained on different image datasets can answer the question of under what circumstance color categories emerge from pre-trained models.

Reviewer #3 (Public Review):

Identifying the critical tissues and cell types in which genetic variants exert their effects on complex traits is an important question that has attracted increasing attention. Feng et al propose a new method, SpecVar, to first construct context-specific regulatory networks by integrating tissue-specific chromatin states and gene expression data, and then run stratified LD score regression (LDSC) to test if the constructed regulatory network in tissue is significantly associated with the trait, measured by a statistic called trait relevance score in this study. They apply their method to 6 traits for which there exists prior evidence on the most relevant tissues in the literature, and then further apply to 206 traits in the UK Biobank. They find that compared to LDSC using other sources of information to define context-specific annotations, their method can "improve heritability enrichment", "accurately detect relevant tissues", helps to "interpret SNPs" identified from GWAS, and "better reveals shared heritability and regulations of phenotypes" between traits. However, I think it requires more work to understand where exactly the benefits come from and the statistical properties of their proposed test statistic (e.g., how to perform hypothesis tests with their relevance score and whether the false positive rate is under control). In addition, it's not clear to me what they can conclude about the shared heritability (which means genetic correlation) by comparing their relevance score correlation across tissues to the phenotypic correlation between traits.

They show that SpecVar gives much higher heritability enrichment than the other methods in the trait-relevant tissues (Fig. 2). The fold enrichment from SpecVar is extremely high, e.g., more than 600x in the right lobe of the liver for LDL. First, I think a standard error should be given so that the significance of the differences can be assessed. Second, it is very rare (hence suspicious) to observe such a huge enrichment. Since SpecVar is based on LDSC, the same methodology that other methods in comparison depend on, the differences to the other methods must come from the set of SNPs annotated for each tissue. I think it is important to understand the difference between the SpecVar annotated SNPs and those from other methods. For example, is the extra heritability enrichment mainly from the SpecVar-specific annotation or from the intersection narrowed down by SpecVar?

They propose to use the relevance score (R score) to prioritise trait-relevant tissues. In Fig. 3, they show tissue-trait pairs with the highest R scores, and from there they prioritise several tissues for each trait (Table 1). I can see that some tissue has an outstanding R score, however, it is not clear to me where they draw the line to declare a positive result. The threshold doesn't seem to be even consistent across traits. For example, for LDL, only the right lobe of the liver is identified although other tissues have R scores greater than 100, whereas, for EA, Ammor's horn and adrenal gland are identified although their R scores are apparently smaller than 100. It seems to me they use some subjective criteria to pick the results. It leads to a serious question on how to apply their R score in a hypothesis test: how to measure the uncertainty of their R score? What significance threshold should be used? Whether the false positive rate is under control? Without knowing these statistical properties, readers won't be able to use this method with confidence in their own research.

Another related comment to the above is to investigate false positive associations, they should show the results for all tissues tested to see if SpecVar tends to give higher R scores even in tissues that are not relevant to the trait. It would also be useful to include some negative control traits, such as height for brain tissues.

Fig. 3 shows that tissues prioritised by LDSC-SAP and LDSC-SEG seem to make less sense than those from SpecVar. However, some of the results are not consistent with the LDSC-SEG paper (Finucane et al 2018). For example, LDL was significantly associated with the liver in Finucane et al (Fig. 2), but not in this study. How to explain the difference?

The authors highlight an example where SpecVar facilitates the interpretation of GWAS signals near FOXC2. They find GWAS-significant SNPs located in a CNCC-specific RE downstream of FOXC2 and reason these SNPs affect brain shape by regulating the expression of FOXC2. I think more work can be done to consolidate the conclusion. For example, if the GWAS signals are colocalised with the eQTL for FOXC2 in the brain. Also, note that the top GWAS signal is actually on the left of the CNCC-specific RE (Fig. 4b). A deeper investigation should be warranted.

They show that SpecVar's relevance score correlation across tissues can better approximate phenotypic correlation between traits. However, the estimation of the phenotypic correlation between traits is neither very interesting nor a thing difficult to do (it can be directly estimated from GWAS summary statistics). A more interesting question is to which extent the observed phenotypic correlation is due to common genetic factors acting in the shared tissues/cell types/pathways/regulatory networks between traits. Note that in their Abstract, they use words "depict shared heritability and regulations" but I don't seem to see results supporting that.

Line 396-402: "For example, ... heritability could select most relevant tissues ... but failed to get correct tissues for other phenotypes ... P-value could obtain correct tissues for CP ... but failed to get correct tissues for ... SpecVar could prioritize correct relevant tissues for all the six phenotypes." Honestly, I find hard to judge which tissues are "correct" or "incorrect" for a trait in real life. It would be more straightforward to compare methods using simulation where we know which tissues are causal.

Reviewer #3 (Public Review):

In this manuscript, Yuan et al. examined the relationship between a magnesium transporter and sleep behavior. They find that the knockdown of a magnesium efflux transporter (uex) in neurons increases bout length of inactivity and recovery activity of the flies with neuronal knockdown of uex with a human homolog CNNM1. The authors suggest a model in which Mg2+ promotes sleep through the inhibition of Ca2+ levels that are wake-promoting in the mushroom body and PDF+ neurons. Overall, the idea explored here that ion homeostasis in the neurons contributes to behavior is an area that is timely and interesting to the neuroscience community. The transgenic lines of human CNNMs could be a useful tool for scientists studying metal transport and ion homeostasis in flies. Unfortunately, the results of the experiments do not entirely support the authors' conclusions.

The authors fall short of showing that the increased inactivity is sleep behavior as Mg2+ changes in neurons could be affecting the mobility of the fly. To validate that the increased inactivity is sleep, the authors should have used a combination of negative geotaxis, arousal threshold, or multibeam/video monitoring. Another characteristic of sleep is the presence of compensatory rebound following sleep deprivation. Here, when the authors sleep deprive the flies with uex knockdown, the flies do not have increased rebound sleep over control flies. Together the current data suggest that the increased inactivity may not be sleep and more evidence to the contrary should be shown.

In Fig 1, the authors show that there is a huge developmental effect on rest:activity rhythms when using the elav-gal4>uex RNAi compared to the inducible elav-geneswitch > uex RNAi, but in Fig 2, the authors use gal4 drivers rather than an inducible system. Use of an inducible system such as geneswitch, AGES, or TARGET is important to rule out developmental effects. Again, in Fig 4, the authors use the gal4 rather than the geneswitch for knocking down the other magnesium channels/transporters so it is unclear whether any sleep increase may be due to the role magnesium plays in development. In Fig 6 elav-gal4 was also used instead of GS. According to previously published work on UEX in fly neurons (Wu et al. eLife 2020 PMID: 33242000), UEX is primarily in the mushroom body and much lower expression in the PI or PDF+ neurons of the adult brains, further suggesting that sleep increases in the PDF+ and PI gal4s driving uex RNAi may be developmental.

From this work, the authors suggest that Mg2+ is sleep-promoting, and in the absence of uex efflux transporter to remove the Mg2+, Mg2+ increases to the point of inhibiting Ca2+, a wake-promoting signal; however, not all the Mg2+ transporters assayed efflux out Mg2+, but rather regulate the influx of Mg2+ into the cells. If a channel regulating Mg2+ influx is inhibited, the prediction would be that Mg2+ would be decreased and thus the flies should sleep less. But in Fig 4H that was not the case. All the Mg2+ transports/channel RNAi lines increased sleep. The authors do not reconcile this data with their proposed model. It is possible the Mg2+ transporter RNAi lines result in increased Mg2+ in the relevant neuronal subgroup in which case Mg2+ levels should be measured in the RNAi lines.

Reviewer #3 (Public Review):

The authors re-analyze published datasets of value-based decision making with and without unavailable distractors, i.e., with ternary and binary choices. By setting the accuracy of binary choices as baseline, they show that a phantom distractor effect appears even without the presence of distractor. This result suggests that distractor effects could be partially explained by target-related covariation. They test how reward and probability are integrated under their datasets. The additive model wins over the multiplicative model in predicting both true and phantom distractor effects in binary choices. Then they test how multiple alternatives interfere with each other in ternary choices. They find that the model with the assumption of rank dominance wins over normalization models. They also replicate the correlation between individual-level decision noise and distractor-related parameters, which implies distractor effects can be emergent properties from a normative decision policy.

I see three strengths of this work.

First, the highlight of this work is that they explore the integration of the multi-attribute and multi-alternative information by bridging distinct distractor effects and providing a unified explanation. The result has a potential impact on a neuroscience topic that attracts a lot of attention in recent years-how the brain represents multiple features and items (e.g. Rigotti, Nature, 2013; Flesch et al., Neuron, 2022; Fusi et al., Curr. Opin. Neurobiol., 2016).

Second, the results of the trial-by-trial baseline approach warn that, due to the complexity of multi-attribute and multi-alternative problem, the studies of the effect should be designed and analyzed with care to prevent possible confounding factors from high dimensionality.

Third, besides static models that can only account for accuracy, the authors implement a dynamic accumulator frame to test all hypotheses. The dynamic accumulator models take into account both accuracy and reaction time. This approach strengthens their model comparison.

Overall, I think this paper is an impressive piece of work that clarifies the true effect of distractors by well-designed analysis and provides a model that bridges distinct distractor effects. Their analysis supports their claims.

Reviewer #3 (Public Review):

This study used the ex vivo optic nerve preparation from adult mice to examine the organization of blood vessels and the mechanisms or neurovascular coupling (NVC). Strengths of the study include the benefits of the isolated preparation, which allows visualization of vessels and pericytes with high resolution and control over axonal activity and the extracellular environment, and the elegant analyses performed. Imaging at high resolution is critical, because vessel diameter changes can be small and slow to develop. The authors leverage this preparation to define the organization of blood vessels and pericytes in the nerve. They then examine the extent of NVC, showing that some aspects appear to be distinct. In particular, dilation does not present rapidly (over minutes) during axon stimulation, but rather emerges after the stimulation, increasing progressively over tens of minutes. It is similarly dependent on oligodendrocyte NMDARs and prostaglandin E4 receptors, but the latter only appears to be engaged during low oxygen conditions. There are several notable limitations of these studies. Less is known about NVC in the intact optic nerve, so it is unclear how well this preparation mimics the in vivo environment. All studies of NVC were performed in the presence of U46619 (an agonist of prostaglandin H2 receptors) to pre-constrict the vessels, which may interfere with NVC. The degree of vessel change was small and slow to develop, and the magnitude and timecourse of the dilation were not closely linked to the stimulation frequency, raising concerns about tissue stability and cell viability. Finally, the studies examined the role of oligodendrocyte NMDARs in NVC using a conditional gene knockout strategy to inactive the NR1 subunit in these cells. To control for possible developmental effects, additional studies could be performed using acute application of NMDAR antagonists, as this preparation contains only neuronal axons, and a further analysis of vessel structure and pericyte organization should be performed using the methodologies developed to characterize their properties in control nerves. Importantly, extracellular stimulation of the nerve, which triggers near simultaneous activation of axons may not mimic activity patterns in these nerves that occur during vision.

Reviewer #3 (Public Review):

The authors describe the use of single-cell RNA sequencing (scRNA-seq) of the zebrafish inner ear at various stages ranging from embryos to adults and they characterize 3 major cell types: supporting cells, progenitor cells, and hair cells. While scRNA-seq experiments have been performed on adult inner ear tissues and the lateral line previously, a detailed characterization of the cellular subtypes in the inner ear at the embryonic through adult stages has not been accomplished before at the transcriptomic and spatial levels and is an important contribution to the field.

In the manuscript, the authors describe the transcriptomic profiles of the inner ear at single-cell resolution followed by spatial validation. In agreement with previously published research, they identify 3 major cell types in the inner ear and use advanced bioinformatic analysis to identify distinct support and hair cell subtypes that reside in the hearing vs balance organs. They elucidate the transcriptomic differences between support and hair cell types that reside in the larval lateral line vs inner ear and demonstrate that these systems are different. Finally, they provide the groundwork for comparisons between zebrafish and mouse transcriptomic profiles and show conservation in the hair cell population. Most importantly, the authors validate their transcriptomic sequencing findings at the single-cell level with spatial information in the inner ear tissues using in situ hybridization assays.

The work performed takes several stages of inner ear development as well as sub-organ dissections coupled to scRNA-seq to carefully identify key cell types and map them to their matching mouse counterparts (when they exist). This work represents the groundwork for many comparative studies across species at the molecular level.

Reviewer #3 (Public Review):

This manuscript describes a Vegfc-independent mechanism of lymphatic vessel formation that is controlled by Svep1 and an orphan endothelial receptor tyrosine kinase Tie1. Based on similarities in the phenotype of svep1 and tie1 mutant zebrafish in the head and trunk vasculature, as well as genetic interaction between the two during parachordal lymphangioblast migration, the authors propose that svep1 is a component of the tie1 signaling pathway. Specifically, svep1 and tie1 mutants show a unique phenotype with the absence of facial collecting lymphatic vessel (that forms in Vegfc mutants) while other facial vessels (that are dependent on Vegfc/Vegfr3 signaling) were only partially affected. Svep1 and tie1 mutants also show similar defects in the formation of brain LECs, the number and migration of parachordal lymphangioblasts from the horizontal myoseptum, and DLAV formation. In contrast, tie2, which is the major angiopoietin receptor in mammals, was found to be dispensable for vascular development in zebrafish.

The presented experiments are performed well and the data are conclusive. The novel findings are the identification of a role of tie1 in zebrafish lymphatic development, and svep1 as a component of the tie1 signaling pathway. The latter raises the possibility that svep1 regulates the activity of Angiopoietin or even acts as a ligand for tie1. However, the conclusion on Svep1 and Tie1 being in the same pathway is based solely on the comparison of mutant phenotypes and genetic interaction studies. Any biochemical data on how svep1 regulates tie1 signaling would greatly strengthen this conclusion.

Reviewer #3 (Public Review):

The authors provide a centralized annotation of miRNA and miRNA-like hairpins in fungi. They aim to develop a standardized pipeline and criteria for miRNA annotation in fungi focusing only on sRNAs derived from hairpin structures, seeking to identify essential characteristics of fungal miRNA and miRNA-like.

Overall this paper will be of interest to readers trying to understand the characteristics and functions of miRNA and miRNA-like hairpins in fungi. The conclusions of this paper are mostly well supported by data, but some aspects of the methodology need to be clarified and extended. The absence of follow-up experiments somewhat limits the impact of this paper. Subsequent work should focus on searching and validating targets of miRNA in fungi. In particular, the strong mi/milRNAs candidates detected in their work.

Reviewer #3 (Public Review):

Protofilament number changes have been observed in in vitro assembled microtubules. This study by Guyomar and colleagues uses cryo-ET and subtomogram averaging to investigate the structural plasticity of microtubules assembled in vitro from purified porcine brain tubulin at high concentrations and from Xenopus egg extracts in which polymerization was initiated either by addition of DMSO or by adding a constitutively active Ran. They show that the microtubule lattice is plastic with frequent protofilament changes and contains multiple seams. A model is proposed for microtubule polymerization whereby these lattice discontinuities/defects are introduced due to the addition of tubulin dimers through lateral contacts between alpha and beta tubulin, thus creating gaps in the lattice and shifting the seam. The study clearly shows quantitatively the lattice changes in two separate conditions of assembling microtubules. The high frequency of defects they observe under their microtubule assembly conditions is much higher than what has been observed in vivo in intact cells. Their observations are clear and supported by the data, but it is not at all clear how generalizable they are and whether the defect frequencies they see are not a result of the assembly conditions, dilutions used and presence of kinesin with which the lattice is decorated. The study definitely has implications for mechanistic studies of microtubules in vitro and raises the question of how these defects vary for protocols from different labs and between different tubulin preparations.

DOI: 10.7554/eLife.82094

Resource: (ZFIN Cat# ZDB-ALT-120117-3,RRID:ZFIN_ZDB-ALT-120117-3)

Curator: @MufeiL

SciCrunch record: RRID:ZFIN_ZDB-ALT-120117-3

What is this?

Reviewer #3 (Public Review):

Garratt et al. investigated that transient exposure of young mice during their first two months of life with olfactory cues from con-specific adults would have long-lasting effects on their late-life health and lifespan. They find that the olfactory cues have sex-specific effects on lifespan, which only the lifespan of young females can be extended by odours from adult females but no other combinations, neither young females with adult males nor young males with either sex. Interestingly, their data also suggested that depletion of G protein Gαo in the olfactory system played no role in the lifespan extension, indicating it might be another unknown factor(s) mediating this sex-specific effect on longevity in mice. While the conclusions of this study are well supported by the data, there are some issues with parts of the data analysis and presentation that would need to be clarified and extended.

1) The authors suggested that the G protein Gαo played no role in lifespan extension in the case that transient exposure of young females with olfactory cues from female adults, as they showed in Figure 1. However, it is not clear if the depletion of G Gαo (Gαo mutant) itself has effects on lifespan, compared to its wild type. It would be important to show the lifespan curves from wild type and Gαo mutant individually alongside the pooled lifespan curves, as well as regarding data in a table, followed with a proper discussion.

2) Regarding the functional tests, the authors showed that there was only a small fraction of experiments showed differences between treatments, which were all in figure 2. However, it is necessary to also show the data with no differences, particularly since the conclusion of the study suggested the underlying mechanisms are not clear yet. In my opinion, body weight, plasma glucose, and body temperature all deserve to have their figures individually with all data points.

3) As the authors mentioned in the Introduction, the age at sexual maturity correlates positively with the median lifespan across mice strains (Yuan et al. 2012, Wang et al. 2018). Also, young female mice that were exposed to male odours during their developmental stage accelerated sexual maturity (Drickamer 1983), and the same happened to young males that were exposed to the odours from the opposite sex (Vandenbergh 1971). It is, therefore, surprising to see in this study, the exposure of young females or young males to the olfactory information from their opposite sex had no effects on lifespan. One of the solutions to solve this disparity is to measure the sexual maturity of the mice in this study. The authors should seek the possibility to check the record of when the first litter of pups was born between treatments (Shindyapina et al. 2022) or examine preputial separation and vaginal opening (Hoffmann 2018), for instance.

In sum, this is a great piece of work suggesting the importance of sex differences on olfactory cues mediated lifespan and pointing out some directions for future works.

Reviewer #3 (Public Review):

Using cultured human podocytes the expression of SGLT2 is established using immunostaining and western blotting. An analysis of podocyte RNA wasn't performed, but the expression in cultured podocytes was comparable to that seen in human cultured proximal tubular cells. This work then paved the way for treatment of immortalized cells obtained from an Alport syndrome mouse model (Col4A3-/-), representing an autosomal recessive form of Alport syndrome. Podocytes from Alport syndrome mice showed a lipid droplet accumulation which was reduced to some extent by SGLT2 inhibition. In a series of metabolic experiments, it was shown that SGLT2 inhibition reduced the formation of pyruvate as a metabolic substrate in Alport podocytes. In vivo experiments showed an improvement in survival of Col4a3-/- mice treated with SGLT2 inhibition. When compared to ace inhibitor, SGLT2 inhibition has a similar effect on renal function and no additive effect was seen with SGLT2 inhibitor plus ace inhibitor. Like the cell assays, the in vivo treatment seemed to prevent the podocyte lipid accumulation in Alport syndrome mice.

This data in cells and animals generally supports the findings in SGLT2 inhibitor human studies, where Alport syndrome patients with proteinuria and progressive CKD seem to benefit. The work paves the way for a dedicated trial of SGLT2i in Alport patients and a reassessment of the human podocyte disease phenotype in this condition, before and after treatment. There are patients with mutations in SGLT2 with familial renal glycosuria - it would be interesting to test via urine derived podocytes whether a similar metabolic switch was occurring and its consequences to pave the way for long term treatment regimes.

Reviewer #3 (Public Review):

Authors were aiming to bring a deeper understanding of CEP78 function in the development of cone-rod dystrophy as well as to demonstrate previously not reported phenotype of CEP78 role in male infertility.

It is important to note, that the authors 're-examined' already earlier published human mutation, 10 bp deletion in CEP78 gene (Qing Fu et al., 10.1136/jmedgenet-2016-104166). This should be seen as an advantage since re-visiting an older study has allowed noting the phenotypes that were not reported in the first place, namely impairment of photoreceptor and flagellar structure and function. Authors have generated a new knockout mouse model with deleted Cep78 gene and allowed to convey the in-depth studies of Cep78 function and unleash interacting partners.

The authors master classical histology techniques for tissue analysis, immunostaining, light, confocal microscopy. They also employed high-end technologies such as spectral domain optical coherence tomography system, electron and scanning electron microscopy. They performed functional studies such as electroretinogram (ERG) to detect visual functions of Cep78-/- mice and quantitative mass spectrometry (MS) on elongating spermatids.

The authors used elegant co-immunoprecipitation techniques to demonstrate trimer complex formation.

Through the manuscript, images are clear and support the intended information and claims. Additionally, where possible, quantifications were provided. Sample number was sufficient and in most cases was n=6 (for mouse specimens).

The authors could provide more details in the materials and methods section on how some experiments were conducted. Here are a few examples. (i) Authors have performed quantitative mass spectrometry (MS) on elongating spermatids lysates however, did not present specifically how elongating spermatids were extracted. (ii) In the case of co-IPs authors should provide information on what number of cells (6 well-plate, 10 cm dish etc) were transfected and used for co-IPs. Furthermore, authors could more clearly articulate what were the novel discoveries and what confirmed earlier findings.

The authors clearly demonstrate and present sufficient evidence to show CEP78/Cep78 importance for proper photoreceptor and flagellar function. Furthermore, they succeed in identifying trimer complex proteins which help to explain the mechanism of Cep78 function.

The given study provides a rather detailed characterization of human and mouse phenotype in response to the CEP78/Cep78 deletion and possible mechanism causing it. CEP78 was already earlier associated with Cone-rod dystrophy and, this study provides a greater in-depth understanding of the mechanism underlying it. Importantly, scientists have generated a new knock-out mouse model that can be used for further studies or putative treatment testing.

CEP78/Cep78 deletion association with male infertility is not previously reported and brings additional value to this study. We know, from numerous studies, that testes express multiple genes, some are unique to testes some are co-expressed in multiple tissues. However, very few genes are well studied and have clinical significance. Studies like this, combining patient and animal model research, allow to identify and assign function to poorly characterized or yet unstudied genes. This enables data to use in basic research, patient diagnostics and treatment choices.

Reviewer #3 (Public Review):

This study asks a simple question and provides a clear and convincing answer. The question is whether sister neurons derived from the same progenitor, using Notch receptor signaling as the underlying cell fate determinant, share pre- and post-synaptic partners. The answer, not in the case of V2a and V2b neurons in the fish spinal cord. Authors show that V2a and V2b neurons derived from the same progenitor are recruited to distinct spinal neural networks.

Reviewer #3 (Public Review):

3' UTRs of mRNAs in bacteria have emerged as a reservoir for trans-acting small RNAs (sRNAs) processed from the full-length transcript by endonucleolytic cleavage. Most sRNAs exert their activity through the formation of imperfect base-pairing interactions with cognate target transcripts, and typically either repress or stimulate translation of bound mRNAs. Best studied in enterobacterial species like E. coli and Salmonella, sRNAs oftentimes rely on the presence of an RNA chaperone, Hfq, which facilitates annealing of complementary RNAs.

In the manuscript, Miyakoshi and co-workers report on the enterobacterial sRNA GlnZ which is released from the 3'UTR of glnA mRNA through RNase E cleavage. Miyakoshi and co-workers demonstrate how GlnZ is induced under nitrogen-limiting conditions. Employing a conserved seed sequence, GlnZ post-transcriptionally regulates target mRNAs, including sucA and aceE mRNAs. When inhibiting RNase E-mediated processing (through mutation of recognition sites), SucA regulation is abrogated, suggesting that full-length glnA mRNA is inactive as a post-transcriptional regulator.

A characterization of GlnZ, mainly focusing on the E. coli K12 variant, has recently been published elsewhere (Walling et al., 2022, NAR), and it is important to highlight additional findings of this manuscript.

One strength of the manuscript is the comparison of GlnZ-mediated regulation between two different enterobacterial species, Salmonella and E. coli, however this aspect should be assessed more thoroughly. The authors have identified additional targets through a pulse-expression experiment of GlnZ in Salmonella, but the Salmonella-specific targets await validation.

The mechanism by which GlnZ represses its targets sucA and aceE mRNAs through binding far upstream of the ribosome binding site is interesting but not discussed.

The authors speculate on the role of translation regarding the question why GlnZ but not glnA mRNA are able to engage in target regulation. Given the variation in sequence among different enterobacteria it is an open question whether the distance between the translation stop and the sRNA seed influences the regulatory activity.

Reviewer #3 (Public Review):

The authors used smartphone-based mobility data to assess indoor and outdoor activities. By doing so, they were able to show seasonality in the ratio between indoor and outdoor activities and to relate it to a certain extent to seasonality in infectious diseases. They were also able to show that data at the county level is necessary to achieve proper assessment of behavior and that the COVID pandemic considerably impacted behavior patterns.

The major strength of the paper is the simplicity of the concept (proportion of indoor activities compared to outdoor activities), which makes it very straightforward to understand. Another strength is the considerable amount of data (5 million locations) that have been taken into account, and the comparison between the 3 years.

There is nonetheless a limitation in the interpretation of the results, as the definition of indoor and outdoor is not always easy, and most importantly that home is not part of the considered locations. This is a limitation clearly exposed by the authors and their discussion reflects it.

Authors have been able to demonstrate how human behavior could influence seasonality, among others factors, and is not strictly related to climate or weather conditions. Moreover, they used the results to show how COVID impacted behavior (whether because of the disease or non-pharmaceutical interventions), and how precise data are necessary to perform appropriate modelling.

This is an important article, as it shows the potential influence of human behavior on infectious diseases seasonality, but also a very straightforward method that could be reproduced easily.

Finally, it also confirms the necessity to take into account the seasonality of human behavior in future modelling, in order to provide relevant information to public health deciders.

Reviewer #3 (Public Review):

The authors used a previously established optical tweezers-based assay to measure the regulation of the working stroke of curled protofilaments of bovine microtubules by magnesium. To do so, the authors improved the assay by attaching bovine microtubules to trapping beads through an incorporated tagged yeast tubulin.

The assay is state-of-the-art and provides a direct measurement of the stroke size of protofilaments and its dependence on magnesium.

The authors have achieved all their goals and the manuscript is well written.

The reported findings will be of high interest for the cell biology community.

Reviewer #3 (Public Review):

How parallel retinal outputs are processed in recipient visual areas is largely unknown. The present paper tackles this issue in the mouse superior colliculus - a key target of retinal outputs. Calcium signals of SC neurons were measured in response to a set of stimuli known to differentiate retinal ganglion cells. The resulting responses were then clustered to identify distinct cell types. These measurements were repeated in several transgenic mice with specific subsets of SC neurons labeled. The experiments and analysis generally support the conclusions well. There are several places, however, where the work could be presented more clearly.

Reviewer #3 (Public Review):

Loreau et al. have presented a well-written manuscript reporting clever, original work taking advantage of fairly new biotechnology - the generation and use of single chain antibodies called nanobodies. The authors demonstrate the production of multiple nanobodies to two titin homologs in Drosophila and use these nanobodies to localize these proteins in several fly muscle types and discover interesting aspects of the localization and span of these elongated proteins in the muscle sarcomere. They also demonstrate that one of these single chain antibodies can be expressed in muscle fused to a fluorescent protein to image the localization of a segment of one of these giant proteins called Sallimus in muscle in a live fly. Their project is well-justified given the limitations of the usual approaches for localizing and studying the dynamics of proteins in the muscle of model organisms such as the possibility that GFP tagging of a protein will interfere with its localization or function, and poor penetration of large IgG or IgM antibodies into densly packed structures like the sarcomere after fixation as compared to smaller nanbodies.

They achieved their goals consistent with the known/expected properties of nanobodies: (1) They demonstrate that at least one of their nanobodies binds with very high affinity. (2) They bind with high specificity. (3) The nanobodies show much better penetration of fixed stage 17 embryos than do conventional antibodies.

They use their nanobodies mostly generated to the N- and C-terminal ends of Sallimus and Projectin to learn new information about how these elongated proteins span and are oriented in the sarcomere. For example, in examining larval muscles which have long sarcomeres (8.5 microns), using nanobodies to domains located near the N- and C-termini, they show definitively that the predicted 2.1 MDa protein Sallimus spans the entire I-band and extends a bit into the A-band with its N-terminus embedded in the Z-disk and C-terminus in the outer edge of the A-band. Using a similar approach they also show that the 800 kDa Projectin decorates the entire myosin thick filament except for the H-zone and M-line in a polar orientation. Their final experiment is most exciting! They were able to express in fly larval muscles a nanobody directed to near the N-terminus of Sallimus fused to NeonGreen and show that it localizes to Z-disks in living larvae, and by FRAP experiments demonstrate that the binding of this nanobody to Sallimus in vivo is very stable. This opens the door to using a similar approach to study the assembly, dynamics, and even conformational changes of a protein in a complex in a live animal in real time.

There are only a few minor weaknesses about their conclusions: (1) They should note that in fact their estimate of the span of Sallimus could be an underestimate since their Nano2 nanobody is directed to Ig13/14 so if all of these 12 Ig domains N-terminal of their epitope were unwound it would add 12 X 30 nm = 360 nm of length, and even if unwound would add about 50 nm of length. (2) They discuss how Sallimus and Projectin are the two Drosophila homologs of mammalian titin, however, they ignore the fact that there is more similarity between Sallimus and Projectin to muscle proteins in invertebrates. For example, in C. elegans, TTN-1 is the counterpart of Sallimus, and twitchin is the counterpart of Projectin, both in size and domain organization. The authors present definitive data to support Figure 9, their nice model for a fly larval sarcomere but fail to point out that this model likely pertains to C. elegans and other invertebrates. In Forbes et al. (2010) it was shown that TTN-1, which can be detected by western blot as ~2 MDa protein and using two polyclonal antibodies spans the entire I-band and extends into the outer edge of the A-band, very similar to what the authors here have shown, more elegantly for Sallimus. In addition, several studies have shown that twitchin (Projectin) does not extend into the M-line; the M-line is exclusively occupied by UNC-89, the homolog of Obscurin.

Reviewer #3 (Public Review):

The purpose of this work is to test the hypothesis that uncertainty modulates the relative contributions of episodic and incremental learning to decisions. The authors test this using a "deck learning and card memory task" featuring a 2-alternative forced choice between two cards, each showing a color and an object. The cards are drawn from different colored decks with different average values that stochastically reverse with fixed volatility, and also feature objects that can be unfamiliar or familiar. Objects are not shown more than twice, and familiar objects have the same value as they did when shown previously. This allows the authors to construct an index of episodic contributions to decision-making: in cases where the previous value of the object is incongruous with the incrementally observed value, the subject's choice reveals which strategy they are relying on.

The key manipulation is to introduce high- and low- volatility conditions, as high volatility has been shown to induce uncertainty in incremental learning by causing subjects to adopt an optimal low learning rate. The authors find that the subjects show a higher episodic choice index in the high-volatility condition, and in particular immediately after reversals when the model predicts uncertainty is at a maximum. The authors also construct a trial-wise index of uncertainty and show that episodic index correlates with this measure. The authors also find that at the subject level, the overall episodic choice index correlates with the ability to accurately identify familiar objects, and the reason that this indicates higher certainty in episodic memory is predicting the usage of episodic strategies. The authors replicate all of their findings in a second subject population.

This is a very interesting study with compelling results on an important topic. The task design was a clever way to disentangle and measure different learning strategies, which could be adopted by others seeking to further understand the contributions of different strategies to decision-making and its neural underpinnings. The article is also very clearly written and the results clearly communicated.

A number of questions remain regarding the interpretation of the results that I think would be addressed with further analysis and modeling.

At a conceptual level, I was unsure about the equivalence drawn between volatility and uncertainty: the main experiments and analyses all regard reversals and comparisons of volatility conditions, but the conclusions are more broadly about uncertainty. Volatility, as the authors note, is only one way to induce uncertainty. It also doesn't seem like the most obvious way to intervene on uncertainty (eg manipulated trial-wise variance seems more obvious). The trial-wise relative uncertainty measurements in Fig 4 speak a bit more to the question of uncertainty more generally, but these were not the main focus and also do not disambiguate between trial-wise uncertainty derived from reversals versus within block variation.

Another key question I had about design choice was the decision to use binary rather than drifting values. Because of this, the subjects could be inferring context rather than continuously incrementing value estimates (eg Gershman et al 2012, Akam et al 2015): the subjects could be inferring which context they are in rather than tracking the instantaneous value + uncertainty. I am not sure this would qualitatively affect the results, as volatility would also affect context confidence, but it is a rather different interpretation and could invoke different quantitative predictions. And it might also have some qualitative bearing on results: the subjects have expectations about how long they will stay in a particular environment, and they might start anticipating a context change after a certain amount of time which would lead to an increase in uncertainty not just immediately after switches, but also after having stayed in the environment for a long period of time. Moreover, depending on the variance within context, there may be little uncertainty following context shifts.

Reviewer #3 (Public Review):

In this study, the authors fuse a promiscuous biotin ligase (TurboID) to mitofusin1 to identify new players involved in mitochondrial fission and fusion. They identify an ER membrane protein, ABHD16A, that has been previously established as a phospholipid hydrolase. They rename this protein as Aphyd and go on to study its role in mitochondrial fission and fusion. Using elegant cell biology techniques, their striking images and rigorous analysis convincingly show a key role for Aphyd in recruiting both fission and fusion machineries to ER-associated mitochondrial nodes. Rates of fission and fusion are markedly decreased in the absence of Aphyd. They also show that Aphyd is required for constrictions. The identification of a new player that may regulate mitochondrial fusion and fission is an exciting advance for the field. Going forward, further biochemical analysis of Aphyd's lipid-modifying activities will be needed to shed light on the mechanisms used by Aphyd to deform membranes. In this initial study, the authors provide some tantalizing clues as to how this may occur by showing that versions of Aphyd that have mutations in their lipid-modifying (acyltransferase and hydrolase) domains are impaired in their abilities to generate ER-associated mitochondrial nodes. I look forward to the next chapters of this story to learn more about how Aphyd works.

Reviewer #3 (Public Review):

The major strength of this work is that the authors take a complementary approach to understand Ca2+ binding to ferroportin. Importantly, the following lines of evidence are used to establish Ca2+ binding - transport assays, cryo-EM structure, mutagenesis studies (using both transport of Ca2+ and ITC to measure direct binding). These all convincingly indicate that Ca2+ can indeed bind to ferroportin. The authors go on to show that Co2+ can inhibit binding of Ca2+ but not the converse. The authors need to take into account some prior in interpreting their data.

I suggest the following considerations to improve the manuscript:

1) Line 38-39 - the authors state that the S2 site has a more prominent role in iron transport than the S1 site. Billesboelle et al 2020 argues the converse based on the fact that mutations in the S2 site lead to iron overload diseases, suggesting that the S2 site cannot be the key site for iron transport. This is also seen in mutagenesis studies by Bonaccorsi etal FEBS J 2014, which reported that mutation of the S1 site completely abolished iron transport. The authors should consider these alternative models in addition to citing their prior work.

2) It is unclear from the introduction/study why it is important to understand Ca2+ transport by ferroportin. Deshpande et al 2018 established that Ca2+ can regulate Fe2+ transport by ferroportin. While it is clear that Ca2+ binds ferroportin, I am not clear on why this is important from a biological perspective. The authors state that Ca2+ binding may integrate signaling with ferroportin activity, but this is not clearly explored, either with prior studies or in this study. If ferroportin acts as a uniporter, how would it be regulated to prevent inappropriate Ca2+ influx? Is there a clear reason why Ca2+ influx would integrate with iron biology? Overall, the premise of the study seems confusing to me despite the well done biochemistry/structural biology.

3) Can the authors reliably exclude the ability of this new site to bind and/or transport other metals? The CryoEM structure at this resolution cannot reliably distinguish other possibilities (e.g. Zn2+), and it is possible that the observed effects are not specific to Ca2+.

4) The maximal concentration of Ca2+ tested in Figure 4 is 500 micromolar - based on this, the authors indicate that Ca2+ has no effect on Fe2+ transport. This stands in contrast to work by Deshpande et al 2018 and Billesboelle et al 2020 which show that there is a Ca2+ effect on Fe2+ and Co2+ transport (though at higher concentrations). Have the authors tested higher Ca2+ concentrations? Given the extracellular concentration of Ca2+ (2 mM), this seems important.

Reviewer #3 (Public Review):

The manuscript focuses on three central questions (line 64), and having those spelt out explicitly and early on is very helpful. I organize my evaluation around these questions:

"(1) whether phoneme-level features contribute to neural encoding even when acoustic contributions are carefully controlled, as a function of language comprehension":

The manuscript finds that phoneme-level features based on language statistics have a much stronger effect in the native language than the foreign language. The result adds important convergent evidence to a body of work suggesting that such features can isolate brain responses associated with higher-order representations which relate to comprehension.

(2) whether sentence- and discourse-level constraints on lexical information (operationalized as word entropy) impacted the encoding of acoustic and phoneme-level features":

This is a really interesting question, but I have some potential concerns about the method used to analyze it. The Methods section could definitely benefit from a more explicit description (perhaps analogous to Table 8, which is very helpful), so I apologize if I misinterpreted the analysis. The manuscript says "TRFs including all phoneme features were estimated for each condition and language" (260), implying that separate TRFs were estimated for the high and low entropy conditions: One for only high entropy words, and one for only low entropy words. I don't understand how this was implemented, since the continuous speech/TRF paradigm does not allow neatly sorting words into bins (as could be done in trial-based designs). Instead, the response during each word is a mix of early responses to the current word and late responses to the previous word.

My interpretation of the available description (260 ff.) is that two versions of each predictor were created, one for high entropy words setting the predictor to zero during low entropy words, and vice versa. Separate TRFs were then estimated for the low- and high-entropy predictor sets. If this is indeed the case, then I am hesitant to interpret the results, because such a high entropy set of predictors is not just predicting a response in high entropy words, it is equally predicting the absence of a response in low entropy words (and vice versa). This might lead to side effects in the estimated TRFs. Furthermore, such models would estimate responses without controlling for ongoing/overlapping responses to preceding words, which may be substantial (Figure 4 implies that condition changes approximately every 2 words).

"(3) whether tracking of acoustic landmarks (viz., acoustic edges) was enhanced or suppressed as a function of comprehension."

The analysis suggests that in French (foreign language), acoustic neural responses are enhanced compared to Dutch (native language). This is an interesting data-point, and linked to a theoretically interesting claim (that lower-order representations are suppressed when higher-order categories are activated). There is a potential qualification though. Dutch and French are different languages which are probably associated with different acoustic statistics. Furthermore, the audiobooks were most likely read by different speakers (I did not find this information in the Methods section - apologies if I missed it), which, again, might be associated with different acoustic properties. Differences in acoustic responses may thus also be due to confounded differences in the acoustic structure of the stimuli.

Reviewer #3 (Public Review):

This is a comprehensive and extensive investigation of the auxin-dependent role of four GRAS family proteins (SHR, SCR, JKD, and SCL23) in regulating organ initiation and shoot apical meristem (SAM) maintenance. The authors present a detailed phenotypical analysis of the shr and scr mutants, which have fewer cell layers, reduced auxin maximum, and halted proliferation of cells in the G1 phase, indicating SHR and SCR both influence SAM maintenance and organ initiation. Auxin distribution, mediated through MONOPTEROS, was also found to regulate SHR activity in organ initiation. Furthermore, the authors hypothesize and show evidence for a coordinated regulation of CYCD6;1, a known marker for asymmetric cell divisions in the root, by SHR, SCR, JKD, and SCL23 in the SAM. Finally, the roles of SCL23 and WUSCHEL were investigated with respect to SHR-SCR activity to investigate their roles in stem cell maintenance. Overall, the authors presented a thorough and sound analysis of SAM organ initiation and this reviewer applauds the authors for this extensive systematic and comprehensive study, which has produced as well as leveraged new and established material to demonstrate similar mechanisms for organ initiation found in the shoot and root apical meristems.

Reviewer #3 (Public Review):

In the present study, Tan and colleagues studied synaptic transmission, presynaptic protein levels, and synaptic ultra-structure in hippocampal cultures of mice lacking the key active-zone proteins RIM (1, 2), ELKS (1, 2), and Munc13 (1, 2). Compared to cultures lacking only RIM and ELKS, additional loss of Munc13 results in a further decrease of synaptic Munc13-1 levels, a similar reduction of the number of docked synaptic vesicles, and a more pronounced decrease of total synaptic vesicle number. At the physiological level, these RIM-ELKS-Munc13 hextuple KO cultures display a further decrease in the pool of release-ready synaptic vesicles with largely unchanged release probability compared with RIM-ELKS quadruple KO cultures.

The data presented in the study are of high quality, and the generation of RIM-ELKS-Munc13 hextuple KO mouse cultures further demonstrates the feasibility of complex KO mouse models. A major question that remains to be addressed is if the release that remains in the absence of RIM and ELKS indeed mostly depends on Munc13.

Reviewer #3 (Public Review):

The authors compare the detection of biomolecular condensates in living cells overexpressing fluorescently tagged IDR proteins and upon fixation with paraformaldehyde (PFA). Given that they observe differences in the number and size of the condensates in the fixed versus living cells the authors conclude that the fixation method can introduce an artifact in the visualization of these condensates. Next, through kinetic modeling simulations, the authors propose a model in which the extent of the artifact introduced by PFA fixation correlates with the strength of the protein-protein interaction: artifacts are lower when the protein‐protein interactions are stable and less dynamic compared with the overall fixation rate. Based on their comparative analysis of PFA fixation and the kinetic modeling the authors strongly recommend caution in the interpretation of data obtained in PFA-fixed cells and suggest that parallel studies with living cells should be performed.

Understanding whether/how fixation methods affect the detection of biomolecular condensates is of broad interest given the importance of LLPS in regulating different aspects of cell biology. However, in this manuscript, the authors use only paraformaldehyde as a fixation method and study only fluorescently-labelled IDR proteins. The work would benefit from a comparison between living cells and cells fixed with other fixation methods; in addition, it would be useful to test the impact of these fixation methods on the detection of endogenous proteins or IDR proteins without fluorescent tag.

Reviewer #3 (Public Review):

This is a potentially fundamental study in which the authors used intrinsic signal optical imaging to characterize orientation, spatial frequency (SFs), and color maps in area V2 and V4 of macaques. They show that foveal regions have higher SF preferences and V1 has a preference for higher SFs compared to V2 and V4. They also show that color regions prefer lower SFs. Strikingly, they show that orientation and SFs are mapped orthogonally in V2 and V4. Finally, they show evidence of periodicity in SF preference in V2.

The data look convincing, but I would like the authors to clarify/discuss certain aspects of the analyses, as detailed below. Overall, I think this study is well done and adds to our understanding of the architecture of the primate visual cortex.

Major:

1. I found the proposed hypercolumn architecture in Figure 1B very difficult to understand. SFs vary in a continuum, so why are only two levels (low SF and high SF) shown in two different colors? Iso-orientation and Iso-SF lines could have been shown in different colors also (say HSV colors for orientation to show the circular mapping and gray colormap for SF going from low to high). Similar to what has been done for iso-hue and iso-brightness lines in the color region. Perhaps it may be worthwhile to show the same proposed architecture in V1 as well, in which orientation maps form pinwheels and colors are in separate blobs. It was unclear to me how this architecture could have pinwheels/blobs as well.

2. It is not clear to me how the details of the functional maps depend on the choice of stimuli. In single unit studies, typically a large number of orientations and SFs are used to independently map the SF and orientation tuning preferences. In contrast, here only 2 orientations are used in one case to map the color space. Even for mapping the orientation space, only 4 orientations are used. For mapping the color space also, only the hues along the red-green axis are varied (L-M pathway). I understand that some of these choices could be due to the recording modality (imaging), but it would be very useful if the authors could discuss how/if these stimulus choices can affect their results. More details of the stimuli, such as the drift rate of the gratings, and the cie (x,y, Y) coordinates of red & green hues would be useful.

3. Can you show the iso-contour lines for orientation on the orientation maps also as a supplementary figure to see how well the algorithm works? Figure 5A shows iso-orientation lines on the SF map. The iso-SF contours shown in Figure 5B easily correspond to the colors in the SF map shown in 5A, but I had difficulty mapping the orientation. Also, I was wondering whether the way the comparisons are done to get the maps (for example, in Figure 4, the same 4 stimuli are compared in two different ways to get orientation and color maps) can potentially impose some constraints on those maps. I say this because it is striking to me that almost every red and blue line shown in 5C and 5G appears to intersect orthogonally (as also shown in 5D and 5H).

4. To me the orthogonality of SF and Orientation contours in Figure 5 was the most striking result. Can you show how this analysis looks for V1? The supplementary figure also shows only V2 and V4.

5. The claim about periodicity is not well quantified. If the authors wish to make this claim, they need to show the Fourier transform of the activation pattern as a function of space and show clear peaks in the spectrum. Also, the authors can perhaps clarify what is the spatial resolution of the imaging technique itself.

Reviewer #3 (Public Review):

By assembling the vast majority of global tafenoquine pharmacology data from clinical treatment studies that led to the 8-aminoquinoline's registration in 2018, the authors of this manuscript have convincingly made their argument that the currently recommended treatment dosage of 300mg (in combination with chloroquine) is too low and needs to be increased by at least 50%. Access to the multiple data sets is thorough, the modelling reasonable and the conclusion reached is sound.

How did we get here (again) under-dosing malaria patients with a class of drugs we have been working on for a century? Speaking as someone who was associated with tafenoquine development over two decades, it seems that worry about adverse events, specifically hemolysis in G6PD deficient persons, overcame the operational requirement to give enough drugs in a single dose regimen. However, tafenoquine is very safe in G6PD normal persons who by definition were the ones entered into the clinical treatment trials. Risk-benefit judgments cannot always be weighted towards "safety" especially when the real concern was that a single severe adverse event would derail the entire development program. Real-world effectiveness matters and should now result in the studies the authors state are needed to certify the higher dose regimen.

The schizophrenic nature of tafenoquine development needs to be mentioned. This manuscript discusses malaria treatment and includes nearly all the relevant data, but extensive work was also done to support the chemoprophylaxis indication largely sponsored by the US Army. These prophylaxis efforts were often separate from the parallel efforts on treatment indication to the loss of both groups who were ostensibly working on the same drug. 450mg tafenoquine is not a large dose; 600mg (over 3 days) is routinely given at the beginning of malaria chemoprophylaxis. Up to twice that amount was given in phase 2 studies done in Kenya in 1998 which resulted in the only described severe hemolytic reaction when one G6PD deficient heterozygote woman was given 1200mg over 3 days due to incorrect recording of her G6PD status. It is not easy to hemolyze even G6PD-deficient erythrocytes due to the slow metabolism of tafenoquine. Nearly all clinical trials of both primaquine and tafenoquine have experienced similar hemolytic events when there were errors in the determination of G6PD status. This does not mean that all 8-aminoquinolines are dangerous drugs, only that a known genetic polymorphism needs to be accounted for when treating vivax malaria.

The authors point out the utility of 7-day methemoglobin concentrations in predicted drug success/failure in the prevention of subsequent relapses. This is important and stresses the requirement of drug metabolism to a redox-active intermediate as being a common property of all 8-aminoquinolines. Tafenoquine and primaquine are similar but not identical and the slow metabolism of tafenoquine to its redox-active intermediates explains its main advantage of being capable of supporting a single-dose cure. The main reason this was not appreciated much earlier is we were looking in the wrong place. Metabolic end-products (5,6 orthoquinones) are in very low concentrations after single-dose tafenoquine in the blood, but being water-soluble they are easily located in the urine. Such urine metabolites indicative of redox action are very likely to be complementary to methemoglobin measurements which mark the redox effect on the erythrocyte. Despite earlier simplifying assumptions made during tafenoquine development (no significant metabolites exist), metabolism to redox-active intermediates must be embraced as the explanation of drug efficacy and not a cause of undesirable adverse events.

Another dark cloud over tafenoquine mentioned by the authors was the very disappointing results of the INSPECTOR trial in Indonesia whose full results are yet to be published. The failure of P vivax relapse prevention using 300mg tafenoquine with dihydroartemisinin-piperaquine in Indonesian soldiers was largely ascribed to under-dosing. Although this may have been partially true, another aspect indicated in figure 15 of the appendix is the nature of the partner drug. Artemisinin combinations with tafenoquine do not produce the same amount of methemoglobin (indicative of redox metabolism) as when combined with the registered partner drug chloroquine. We do not understand tafenoquine metabolism, but it is increasingly clear that what drug is combined with tafenoquine makes a very substantial difference. Despite the great operational desire to use artemisinin combination therapy for all malaria treatment regimens, this may not be possible with tafenoquine. Chloroquine likely is driving tafenoquine metabolism as it has no real effect on latent hypnozoites in the liver by itself. Increased dose studies with tafenoquine need to be done with chloroquine, not artemisinin.

Treatment of P vivax malaria to prevent relapse by tafenoquine is the first but not the only indication of this long-acting 8-aminoquinoline. Besides chemoprophylaxis, tafenoquine has also been recently shown in controlled human challenges and field studies in Africa to block transmission at very low dosage regimens. If we are to realize tafenoquine's potential to block transmission in a population to eliminate malaria, we first have to get the treatment regimen and its combination partner right. This paper is another good step along the road to really understanding how to use this new antimalarial drug.

Reviewer #3 (Public Review):

In this manuscript, Emily Heckman and Chris Doe outline their investigation of how two specific partner neurons interact during the development of the Drosophila larval nerve cord, a specific proprioceptive sensory neuron (called 'dbd') and one of its postsynaptic partners (called 'A08a').

Experiments were executed that ask three questions:<br /> 1. How might dendrites of the A08a neuron postsynaptic to dbd change when this sensory neuron is silenced or over-activated?<br /> 2. How might those A08a dendrites change when the dbd presynaptic partner is experimentally removed?<br /> 3. Is there are critical period when dbd killing has maximal effect on changes to A08a dendrites?

The aim was to reveal some of the cellular mechanisms that principally shape the development of postsynaptic dendrites during nervous system development.

Overall, the paper is well written and the figures beautifully presented. However, I have reservations about the manuscript as it stands.<br /> Foremost is the question as to what new insights this work reveals that have not already been clearly demonstrated by a number of other studies across a range of model systems, including those cited in the discussion? This work might distinguish itself at the level of detail achieved by precision made possible through the genetic tools available, yet it does not make use of other aspects, such as the connectome also available to probe more deeply into changes that the above manipulations provoke.

The final experiment of inducing dbd cell killing at different stages of embryonic and larval development reveals what might be a critical period for cell contact-based regulation of postsynaptic dendritic growth regulation. This is a nice touch and could be the basis for interesting future work. However, much stronger would have been to have had a more robust sample size, clear demonstration of the dynamics of dbd cell killing itself (as potentially relevant to the A08a response) and, ideally, an independent verification via a separate method or on a different cell pair.

Water is a business product

Water is not considered to be a human right like food, shelter, and dignity are. Evidence: no explicit right to water is expressed in the international treaty

Reviewer #3 (Public Review):

The authors attempted to elucidate mechanisms underlying adrenal dysfunction in severe inflammation.

Utilizing transcriptomic, proteomic and metabolomic analyses of adrenocortical cells in male mice after lipopolysaccharid induced systemic inflammation is a major strength of this study.

The use of sophisticated methods and the results support the conclusion of the authors that the Interleukin 1beta - DNA methyltransferase 1 - succinate dehydrogenase b axis with increased succinate and reduced ATP levels disrupts steroid production in lipopolysaccharid induced systemic inflammation.

Various inflammatory conditions in humans are treated with steroids and this animal based study may help identify future therapeutic targets besides the administration of glucocorticoids.

Reviewer #3 (Public Review):

Neverov et al. conduct an analysis of the SARS-CoV-2 phylogeny to identify pairs of sites in the rapidly evolving spike protein that fix concordant mutations more or less frequently than expected, reflective of epistasis between spike mutations. The authors modify an existing method to this end, making some updates to their algorithm that I find logically intuitive. I find this to be an interesting question that is important for understanding the molecular forces that influence future SARS-CoV-2 evolution. I find the study uncovers some valuable examples of epistasis, but have some key questions about the Methods that make it unclear to me how efficiently the method is performing.

Reviewer #3 (Public Review):

The authors provide interesting data showing that ventral hippocampal (vH) cells show rapid remapping when an open area appears in the environment, displaying a concentration of place field center in the new open area. Additionally, distinct direction-dependent neural activity is lower in the open areas and activity in the closed area can be used to predict the extent of exploration in the open area.

Though the authors provide some interesting new findings, several key classic place cell-related metrics were not evaluated, decreasing the potential impact of the work. For example, What percent of vH cells are place cells? What is are the place field size, information content, and peak and mean firing rate of open and closed preferring cells? Is there any characteristic in common among cells that show a shift in their place field towards the open space before the open space is shown? What is the stability of spatial representation of the same cell across days and across the same session?

There are not many hippocampal remapping papers related to threat exposure, but the authors fail to cite the few relevant papers that exist. The authors should include in their discussion the results from Wang et al., 2012 and Wang et al., 2015 (PMID: 26085635 and PMID: 23136419). The authors also should discuss Kong et al., 2021 (PMID: 34533133) and Schuette et al., 2021 (PMID: 32958567). These papers have related results on hippocampal remapping during exposure to threatening environments. The absence of these papers being cited provides a misleading view that the results are more novel than they actually are when considering the relevant literature.

Reviewer #3 (Public Review):

This manuscript by Jean-Pierre et al. describes the creation and experimentation with a model CF lung community in an artificial sputum medium. The group uses data from 16S rRNA sequencing studies to select organisms for creating the model and then performs experiments to determine outcomes of growth competition and antibiotic tolerance in a community context. The main finding of the manuscript is that P. aeruginosa, notorious for its antimicrobial resistance phenotypes, is more susceptible to tobramycin in the community context than when grown alone. The manuscript is well prepared and follow-up experiments with mutant strains and phenazines greatly strengthen the project overall. The initial results paragraph where the authors go through the rationale for selecting the different organisms is perhaps a bit overkill, the organisms selected make sense based on their prevalence in CF airways, which in and of itself is a strong enough rationale. This aspect of the manuscript could be minimized to focus more on the exciting culture experiments in the latter parts of the results. Overall, this is a strong and well-crafted manuscript that will have a broad interest in the CF and microbial ecology fields.

Major Critiques:

I have two major critiques of this study.

1. Prevotella growth in monoculture. After reading the methods section it appears that the cultures were extensively washed and prepped prior to the inoculation into ASM. Prevotella did not grow alone, is this due to oxygen penetration of the cells during preparation? Perhaps oxygen is present in ASM prior to placement in an anaerobic bag? It is interesting, and perhaps worth exploring, whether the mixed community draws down oxygen from the media explaining the ability of Prevotella to grow. I suspect this is the case, but more detail is needed in the methods and this experiment would help us understand this interesting result.

2. Dilution of the community reproducing toby tolerance of P. aeruginosa. In supplemental figures, the replication of the 1:1000 dilution of the mixed community with P. aeruginosa shows poor replication and very large error bars. This experiment should be repeated to ensure it is reproducible.

Reviewer #3 (Public Review):

In this manuscript, the authors aim to study the relationship between the Brain Age Gap (BAG) measure - based on functional connectivity and structural features - and different AD biomarkers such as amyloid, tau, cognition, and neurodegeneration in cognitively healthy and demented individuals. The main results showed increased BAG in cognitively impaired individuals. In this subgroup of individuals BAG models based on structural data were associated with more advanced AD pathology and lower cognitive performance. The BAG models based on fMRI data seem to show a U-curve in the health-disease continuum. The authors discuss the results in terms of a biphasic response of fMRI - while structural-based BAG would capture progression as well as highlight the advantages of multimodal data to understand health and disease in healthy aging.

While the study has its merits such as the use of novel metrics, a decent sample with biomarkers and fMRI, etc., I believe some of the main conclusions of this paper are not fully substantiated by the results. The results based on the structural BAG model are solid (i.e., CI participants have older BA compared to healthy controls). However, I find the conclusions regarding the fMRI-BAG and the multimodal-BAG models are not fully supported by the results. The biphasic response of fMRI-BAG results - and the subsequent advantage of multimodal BAG - is based on p-values between .05 and .10 which have very low evidential value (e.g., Benjamin et al, 2018). I strongly discourage reporting these results as "marginal" and drawing assertive interpretations on this basis. Further, the poor performance of fMRI seems to add little information (in the stacked model) to the structural-only BA model.

The aim of the authors is to be commended, that is to take advantage of powerful machine learning methods and multimodal imaging to better understand the health-disease continuum in aging. This path is promising and can lead to both, better predictive tools and a better understanding of the aging brain. Further, the sample is good, from a single cohort, with multiple MRI modalities and biomarker information and the manuscript is easy to read and includes a very informative introduction. Also, it has some interesting findings such as in Fig. 2C and 2E where the graphs seem to show how BAG seems to be most useful at younger ages if used to predict dementia. Having said that, the "marginal" effects are central to the conclusions of this paper and are a critical caveat. Other methodological limitations of this paper are the parcellation used for structural BAG, which is relatively gross, a possible effect of motion on preprocessed functional connectivity, and the lack of multiple comparisons correction. Finally, the lack of a detailed description of the higher-level statistical analysis is detrimental to the clarity of the manuscript and leads to some confusion regarding the carried analyses.

Reviewer #3 (Public Review):

The authors use two-photon imaging to visualize various axonal organelle populations that they have virally labeled with fluorescent proteins, including DCVs and late endosomes/ lysosomes. The latter topic is a bit contentious, as the authors use two labels that tag potentially overlapping and not highly specific markers so that the nature of the tagged organelle populations remains unclear. Notably, the authors also have previously published a detailed account of how DCVs traffic in vivo, so the novelty is mostly in comparing the behavior of different organelles and the potential influence of activity.

Overall, the reported results mostly corroborate the expectations from previous in vitro and in vivo work on these organelles and other cargoes, performed by the authors and their collaborators, as well as in many other laboratories:<br /> (i) Different organelles have different transport behaviors regarding speed, the ratio of anterograde to retrograde moving organelles, etc.<br /> (ii) Organelles move in different ways when they pass specific anatomical landmarks in the axons, such as presynaptic terminals.<br /> (iii) Activity of a neuron (here measured by calcium imaging) can impact the measured transport parameters, albeit in a subtle and mechanistically not well-defined manner. The chosen experimental design precludes a more detailed analysis, for example of the precise movement behavior (such as defining the exact pausing/movement behavior of organelles, which would require higher imaging speeds) or of a correlation of different organellar behavior at synaptic sites or during activity (which would require three-channel simultaneous imaging of two organelle classes plus a synaptic or activity marker).

In summary, this publication uses sophisticated in vivo labeling and imaging methods to corroborate and complement previous observations on how different axonal organelles move, and what influences their trafficking.

Reviewer #3 (Public Review):

This manuscript presents a highly valuable dataset with multimodal functional human brain imaging data (fMRI and MEG) as well as behavioural annotations of the stimuli used (thousands of images from the THINGS collection, systematically covering multiple types of concrete nameable objects).

The manuscript presents details about the dataset, quality control measures, and a careful description of preprocessing choices. The tools and approaches that were used follow the state of the art of the field in human functional brain imaging and I praise the authors for being transparent in their methodological approaches by also sharing their code along with the data. The manuscript also presents a few analyses with the data: 1) multi-dimensional embedding of perceived similarity judgments 2) decoding of neural representations of objects both with fMRI and MEG 3) A replication of findings related to visual size and animacy of objects 4) representation similarity analysis between functional brain data and behavioural ratings 5) MEG-fMRI fusion.

Reviewer #3 (Public Review):

The PCNT gene is found on human chromosome 21, and the same group previously showed that its increased expression is associated with reduced trafficking to the centrosome and reduced cilia frequency, which suggests a possible connection between cilia and ciliary trafficking, SHH signaling, and Down syndrome phenotypes. Jewett et al build upon this prior work by closely examining the trafficking phenotypes in cellular models with different HSA21 ploidy, or its mouse equivalent, thereby increasing the copy number of PCNT (3 or 4 copies of HSA21). They show that most of the trafficking defects can be reversed through the knockdown of PCNT in the context of HSA21 polyploidy. They also begin to examine the in vivo consequences of these trafficking disruptions, using a mouse model (Dp10) that partially recapitulates trisomy 21, including an increased copy number of PCNT. While I think this work advances our understanding of the trafficking defects caused by increased PCNT and has significant implications for our understanding of the cellular basis of a major hereditary human disorder, some improvements can be made to strengthen the conclusions and improve readability.

Major points:

I'm a little confused by the authors' conclusion that the increased PCNT levels in T21 and Q21 result in delayed but not attenuated ciliogenesis. The data show lower percentages of ciliated cells at all time points analyzed (Fig 1E) by quite a large margin in both T21 and Q21. Do the frequencies of cilia in the T21 or Q21 cells ever reach the same level as D21, say after 48-72 hours? If not it seems like not simply a delay. A bit more clarity about this point is needed.

The in vivo analysis of the cerebellum was interesting and important but it felt a bit incomplete given that it was a tie between the cell biology and a specific DS-associated phenotype. For example, it is interesting that the EGL of the P4 Dp10 pups is thinner. Does this translate into noticeable defects in cerebellar morphology later? Is there a reduction in proliferation that follows the reduced cilia frequency? I think it would be possible to look at the proliferation and cerebellar morphology at some additional stages without becoming an overly burdensome set of experiments. At a minimum, are there defects in cerebellar morphology at P21 or in the adult mice? The authors allude to developmental delays in these animals - maybe that complicates the analysis? But additional exploration and/or discussion on this point would help the paper.

It was a bit unclear to me why specific cell lines were used to model trisomy 21 and why this changed part way through the paper. I understand the justification for making the Dp10 mice- to enable the in vivo analysis of the cerebellum, but some additional rationale for why the RPE cell line is initially used and then the switch back to mouse cells would improve readability.

Reviewer #3 (Public Review):

The work presented by McKay et al. details the development of a new wireless network-enabled automated feeder system with which diet amount and schedule can be controlled across individually housed killifish. The manuscript describes the characterization of the system and demonstrates the robustness, precision, and high fidelity in feeding control achieved due to modular design.

The technique in principle can be applied to hundreds of tanks and to other species that are reared in similar tank system racks.

Strengths:

- The authors provide a convincing account of the use of automated feeder systems for implementing experiments where diet is controlled precisely. The experimental design allows the authors to clearly demonstrate feeding schedules optimal for killifish growth, reproduction, and longevity. Their characterization and results will be highly valuable for a growing community of researchers who are beginning to use killifish in laboratory settings and can choose the regimen most suited to their research goals. The system presented in this study may also allow for better husbandry practices with the potential to mimic the ephemeral natural habitats of this species more closely in the laboratory.<br /> - The authors also conducted additional experiments comparing restricted food delivery schedules. The conclusion they reach that a time and quantity restricted feeding regimen increases the lifespan of males based on this experiment is well justified from the data presented. The differences between the sexes are interesting to note as the authors observed similar results with two different cohorts, though cohorts can differ in the median and maximum lifespan.

Weaknesses:

- The authors imply the value of automated feeders is in scaling to hundreds of individual animals/tanks. I agree with the author's assessment of this need in research labs, however, it is not easy to infer exactly how many automated feeders were operating simultaneously in this study. Estimates of the costs of building, and operating (maintenance, server use, and cloud computing costs) for conducting 1 experiment (2 conditions, 24 animals per condition) running over 100 days will be valuable for other researchers interested in adapting this resource. A clearer supplementary video 1 that demonstrates the entire feeder properly, in the home tank will also be valuable for the researchers interested in adapting the system.<br /> - The proof of concept experiment showing associative learning is extremely interesting but is quite difficult to assess, based on the detail provided in the results and the method. The rationale behind key considerations for behavioral measures, whether based on previous studies or, due to technical constraints are difficult to judge. This needs a better description. In particular, results mention a "pipeline", but this is obscure, in the methods section. Clearer definitions would also be needed to evaluate if an objective scoring system for was used in measures such as the"startle" response. In principle, as all trajectories are recorded, it should be possible to describe a range of acceleration/velocity changes that quantify most parameters such as startle, unless it was manually scored. As this will be a first, clarity on how "early" and "late" sessions were categorized; exact experimental design on the number of trails that made up a session; whether all animals went through same number of trials in Figure 5, etc. will improve the description and future adaptations of the experimental design.<br /> - One more cautionary note is in the interpretation that young individuals had significantly higher learning index scores than old individuals, as the size of the effects can't be estimated from the type of data provided and the analysis used. Given the fairly small sample size for animals used in learning index calculations (< 15), and as the authors demonstrate in diet restriction experiments there can be cohort-dependent differences as well, I would caution against such an interpretation. The p values reported in Suppl. Figure 4E especially brings home the need to move away from dichotomous thinking of yes/no based on a threshold, without taking into account effect sizes. Please refer to this recent post in eNeuro on the inherent issues with such interpretations, and methods to overcome them (https://doi.org/10.1523/ENEURO.0091-21.2021). The deficiency in "old" may not be as large, and it would be important to interpret this appropriately. Other normalization issues, rather than learning could account for small differences between the young and the old. For instance, a small latency in the average velocity and/or other locomotion kinematics differences between fish categorized as old vs. young could result in the criterion of "3 seconds before the food drops" to meet the "threshold of learning" being unmet. The data available in the paper at present can't be used to evaluate such a point.

Reviewer #3 (Public Review):

In this manuscript, the authors characterize the localization and function of two proteins, BdPOLAR and BdPAN1 in the asymmetric cell divisions required for stomatal patterning in Brachypodium distachyon (Bd). The authors clearly demonstrate these proteins are required for normal stomatal complex formation. Most excitingly, the authors reveal that these proteins occupy two opposing polar domains during stomatal formation, particularly the localization of BdPOLAR defines a novel polar domain that is dependent on BdPAN1 for its unique accumulation. The authors clearly link the functions of these proteins to cell division orientation and division potential and show an impact on stomatal function. The data presented here are clearly described and well documented and the figures are clear and well constructed. Their results support a broadly interesting hypothesis whereby polarization of cell fate-dependent and -independent factors pattern stomata in this grass. It will be very interesting to see how/if similar or other new polarity domains function in other developmental contexts in plants where control of cell division orientation is critical for cell fate and tissue function.

The authors' careful and elegant experiments clearly demonstrate a fascinating new avenue for exploration into plant cell polarity and cell division control. Their results will be of interest to scientists interested in development and cell biology across species, as well as those broadly interested in plant biology topics. Developmental patterning of the stomata in grasses is an elegant system to address important basic biological questions about the regulation of cellular asymmetries, cell division, and cell morphology. Additionally, the function of stomata is critical to the productivity and survival of plants, including in carbon intake (for photosynthesis). Understanding the developmental framework underlying pore formation provides insights into plant patterning processes and, importantly, provides a toolbox from which plant biologists can work to engineer improved crop plant performance in a rapidly changing climate.

Reviewer #3 (Public Review):

In this work the authors identify genes and pathways important for CO2 and thermotolerance in Cryptococcus neoformans. They additionally rule out the contribution of the bicarbonate or cAMP-dependent activation of adenylyl cyclase to this pathway, which is important for CO2 sensing in other fungi, further solidifying the need to characterize CO2 sensing in basidiomycetes. The authors establish the importance of focusing on CO2 tolerance by testing the impact of CO2 on fluconazole susceptibility with varied pH, suggesting the ability of CO2 to sensitize cryptococcal cells to fluconazole. Furthermore, the authors compared the CO2 tolerance of clinical reference strains to environmental isolates. The characterization of the RAM pathway Cbk1 kinase illustrated the integration of multiple stress signaling pathways. By using a series of CBK1OE insertions in strains with deletions in other pathways, the ability of Cbk1 over-expression to rescue several strains from CO2 sensitivity was apparent. Additionally, NanoString expression analysis comparing cbk1∆ to H99 validated the author's screen of CO2-sensitive mutants as 16/57 downregulated genes were found in their screen, further confirming the interconnected nature of these pathways. The importance of the RAM pathway in maintaining CO2 and thermotolerance was also incredibly clear.

Perhaps most interestingly, the authors identify suppressor colonies with distinctive phenotypes that allowed for the characterization of downstream effectors of the RAM pathway. These suppressor colonies were found to have mutations in SSD1 and PSC1 which somewhat restore growth at 37oC with CO2 exposure. Further confirming the importance of the RAM pathway, the cbk1∆ strain had markedly attenuated virulence during infection. Interestingly, the generated suppressor strains had varying impacts on fungal infection in vivo. While the sup1 suppressor was completely cleared from the lungs during both intranasal and IV infection, the sup2 strain, containing mutations in SSD1, maintained a high fungal load in the lungs and was able to disseminate into host tissues during IV infection but not intranasal infection.

The authors make a strong case for the exploration of thermotolerance and CO2 tolerance as contributors to virulence. Through screening and characterization of RAM pathway kinase CBK1's ability to rescue other mutants from CO2 sensitivity, the overlapping contributions of several signaling pathways and the importance of this kinase were revealed. This work is important and will be valuable to the field. However, the cbk1∆ strain does show reduced melanization, urease secretion, and higher sensitivity to cell wall stressor Congo Red in SI Appendix, Figure S4. While the authors make a strong argument that these well-established virulence factors are not perfect predictors of virulence in vivo, the cbk1∆ strain is not an example of such a case as it does have defects in these important factors in addition to thermotolerance and CO2 tolerance. Not acknowledging the changes in these virulence factors in the cbk1∆ and their potential contribution to phenotypes observed is a weakness of the manuscript. Interestingly, the sup1 and sup2 strains also rescue these virulence factors compared to cbk1∆. Additionally, the assertion that "the observation that only sup2 can survive, amplify, and persist in animals stresses the importance of CO2 tolerance in cryptococcal pathogens" due to the sup2's slightly higher CO2 tolerance compared to sup1, could be better supported by the data. These suppressors did not restore transcript abundances of the differentially expressed genes to WT levels, suggesting post-transcriptional regulation. However, there may be differences in the ability of sup2 to resist stress better than sup1 especially given the known Ssd1 repression of transcript translation in S. cerevisiae. Finally, pH appears to impact the sup1 and sup2 strain's sensitivity to CO2 in SI Appendix Figure 4. This could be better explained and interrogated in the manuscript. Finally, this work includes a variety of genes in several signaling pathways. The paper would be greatly clarified by a graphical abstract indicating how CBK1 may be integrating these pathways or by indicating which genes belong to which pathways in the Figure 1 legend to make this figure easier to follow.

Reviewer #3 (Public Review):

This work provides a series of tests of hypothesis, which are not mutually exclusive, on how genomic diversity is structured within human microbiomes and how community diversity may influence the evolution of a focal species.

Strengths:<br /> The paper leverages on existing metagenomic data to look at many focal species at the same time to test for the importance of broad eco-evolutionary hypothesis, which is a novelty in the field.

Weaknesses:<br /> It is not very clear if the existing metagenomic data has sufficient power to test these models.<br /> It is not clear, neither in the introduction nor in the analysis what precise mechanisms are expected to lead to DBD.<br /> The conclusion that data support DBD appears to depend on which statistics to measure of community diversity are used. Also, performing a test to reject a null neutral model would have been welcome either in the results or in the discussion.

Reviewer #3 (Public Review):

To motivate the proposal, Karageorgiou et al. first identify a problem in applying current multivariable MR (MVMR) methods with many correlated exposures. I believe this problem can really be broken into two pieces. The first is that MVMR suffers from weak instrument bias. The second is that some traits may have nearly co-linear genetic associations, making it hard to disentangle which trait is causal. These problems connect in that inclusion of co-linear traits amplifies the problem of weak instrument bias - traits that are nearly co-linear with another trait in the study will have no or very few conditionally strong instruments.<br /> The authors then propose a solution: Apply a dimension reduction technique (PCA or sparse PCA) to the matrix of GWAS effect estimates for the exposures. The identified new components can then be used in MVMR in place of the directly measured exposures.

I think that the identified problem is timely and important. I also like the idea of applying dimension reduction techniques to GWAS effect estimates. However, I don't think that the manuscript in its current form achieves the goals that it has set out. Specifically, I will outline the weaknesses of the work in three categories:<br /> 1. The causal effects measured using this method are poorly defined.<br /> 2. The description of the method lacks important details.<br /> 3. Applied and simulation results are unconvincing.<br /> I will describe each of these in more detail below.

1. To me, the largest weakness of this paper is that it is not clear how to interpret the putatively causal effects being measured. The authors describe the method as measuring "the causal effect of the PC on outcome" but it is not obvious what this means.

One possible implication of this statement is that the PC is a real biological variable (say some hidden regulator) that can be directly intervened on. If this is the intention it should be discussed. However, this situation would imply that there is one correct factorization and there is no guarantee that PCs (or sparse PCs) come close to capturing that.

The counterfactual implied by estimating the effects of PCs in MVMR is that it is possible to intervene on and alter one PC while holding all other PCs constant.<br /> In the introduction, the authors note (and I agree) that one weakness of MR applied to correlated traits is that "MVMR models investigate causal effects for each individual exposure, under the assumption that it is possible to intervene and change each one whilst holding the others fixed." However, it is not obvious that altering one PC while holding the others constant is more reasonable.

2. This section combines a few items that I found unclear in the methods section. The most critical one is the lack of specification on how to select instruments.<br /> For the lipids application, the authors state that instruments were selected from the GLGC results, however, these only include instruments for LDL, HDL, and TG, so 1) it would not be possible to include variants that were independently instruments for one of the component traits alone and 2) there would be no instruments for the amino acids. There is no discussion of how instruments should be selected in general.<br /> This choice could also have a dramatic impact on the PCs estimated. The first PC is optimized to explain the largest amount of variance o of the input data which, in this case, is GWAS effect estimates. This means that the number of instruments for each trait included will drive the resulting PCs. It also means that differences in scaling across traits could influence the resulting PCs.

The other detail that is either missing or which I missed is what is used as the variant-PC association in the MVMR analysis. Specifically, is it the PC loadings or is it a different value? Based on the computation of the F-statistic I suspect the former but it is not clear. If this is the case, what is the effect of using loadings that have been shrunk via one of the sparse methods? It would be nice to see a demonstration of the bias and variance of the resulting method, though it is not clear to me what the "truth" would be.

3. In the lipids application, the fact that M.LDL.PL changes sign in MVMR analysis are offered as evidence of multicollinearity. I would generally associate multicollinearity with large variance and not bias. Perhaps the authors could offer some more insight on how multicollinearity would cause the observation.<br /> A minor point of confusion: I was unable to interpret this pair of sentences "Although the method did not identify any of the exposures as significant at Bonferroni-adjusted significance level, the estimate for M.LDL.PL is still negative but closer to zero and not statistically significant. The only trait that retains statistical significance is ApoB." The first sentence says that none of the exposures were significant while the second sentence says that Apo B is significant. The GRAPPLE results don't seem clearly bad, indeed if only Apo B is significant, wouldn't we conclude that of the 118 exposures, only Apo B is causal for heart disease? It would help to discuss more how the conclusions from the PC-based MVMR analysis compare to the conclusions from GRAPPLE.

It is a bit hard to interpret Table 4. I wasn't able to fully determine what "VLD, LDL significance in MR" means here. From the text, it seems that it means that any PC with a non-zero lodaing on VLDL or LDL traits was significant, however, this seems like a trivial criterion for the PCA method, since all PCs will be dense. This would mean this indicator only tells us whether and PCs were found to "cause" heart disease.

In simulations, I may be missing something about the definition of a true and false positive here. I think this is similar to my confusion in the previous paragraph. Wouldn't the true and false positive rates as computed using these metrics depend strongly on the sparsity of the components? It is not clear to me what ideal behavior would be here. However, it seems from the description that if the truth was as in Fig 7 and two methods each yielded one dense component that was found to be causal for Y, these two methods would get the same "score" for true positive and false positive rate regardless of the distribution of factor loadings. One method could produce a factor that loaded equally on all exposures while the other produced a factor that loaded mostly on X1 and X2 but this difference would not be captured in the results.

Reviewer #3 (Public Review):

Riquelme et al. develop a spiking neural network model based on experimental measurements from ex vivo turtle visual cortex (neuronal parameters, connectivity profiles, synaptic strength distributions). Within the constraints given, the connectivity is random. The analyses in the manuscript are based on multiple instantiations (300) of the network and multiple simulations of each. The principle finding is that, if a randomly selected excitatory neuron is induced to emit an action potential, a reliable sequence of spikes follows (in more than 90% of cases). They then examine the role of connectivity in this phenomenon, including the frequency of specific motifs in the spike cascade and the comparative role of strong and weak connections. In particular, the authors show that rare strong connections are vital for producing (long) reliable sequences. The authors then examine how the sequences can be broken down into sub-sequences that may or may not occur for a given trigger. They show that the sub-sequences are characterized by strong internal connections (compared to those between sub-sequences). Moreover, they show that the spike sequence can be routed by exciting or depressing the 'gate' neurons (i.e. those at the beginning of a particular sub-sequence) raising the intriguing possibility of context-driven routing of activity. Finally, the authors demonstrate that their model has interesting combinatorial properties, as the results of triggering two sequences at once cannot be accounted for in a linear fashion. All in all, this is a solid piece of work with well-thought-through analyses which is an interesting contribution to the fundamental question of how the brain manages reliable computation in a noisy world.

Strengths

"Ensemble approach" I appreciated the approach to generate many networks from the same distributions rather than (as is often the case) basing all their conclusions on one instantiation. In general, the statistical rigour is high.

Well-chosen analyses to tease apart the relationships between structure and dynamics.

Figures (for the most part) clearly support the conclusions of the paper.

Weaknesses

The spontaneous activity of the network is extremely low, with [0.02 0.09] spks/s considered as a high activity range. Granted, this is based on ex vivo measurements. However, if this phenomenon is to be considered computationally relevant, as the authors claim, the paper should have examined the reliability of propagation and routing with in vivo activity levels.

The above weakness is a special case of the issue that the limits of applicability/robustness of results to model assumptions have not been well established. In particular, it is not clear how strong the strongest weights must be whilst still enabling long sequences, and what is the dependence of the results on the parameters of the distance-dependent connectivity.

The figures are too densely packed and many of the elements are too small or too fine to be distinguished, especially if your eyesight is not the greatest. Although many people read online, where zooming is possible, the aim should still be that all elements of the figure can be perceived by a person over 45 who has printed the paper on regular A4 paper.

Reviewer #3 (Public Review):

This manuscript presents a new method to estimate the selective effect of heterozygous loss of function mutations. The authors offer a sequential Monte Carlo algorithm coupled with ABC estimates based on forward population genetics simulations. The method is of obvious interest to the field. The result confirms that DFE distribution for PTVs is broad with the mean and median exceeding 1% and ~20% of genes associated with more than 10% loss in fitness. The new quantitative estimates are likely an improvement over the state-of-the-art. Importantly, the authors include estimates for PTVs on the X chromosome, which are expectedly higher. The authors demonstrate that de novo PTVs leading to a substantial fitness loss are highly enriched in individuals affected by severe complex disorders including neuropsychiatric disorders. They also provide estimates of allelic ages for variants with specific selection coefficients. This work is of interest to both population and medical geneticists.

Reviewer #3 (Public Review):

This is a very interesting study examining for the first time the influence of lateral tilt of the whole body on orientation tuning in macaque IT. They employed two types of displays: one in which the object was embedded in a scene that had a horizon and textured ground surface, and a second one with only the object. For the first type, they examined the orientation tuning with and without tilting the subject. However, the effect of tilt for the scene stimuli is difficult to interpret in terms of gravitational reference frame since varying the orientation of the object relative to the horizon leads to changes in visual features between the horizon and object. If neurons show tolerance for the global orientation of the scene (within the 50{degree sign} manipulation range) then the consistent orientation tuning across tilts may just reflect tuning for the object-horizon features (like the angle between the object and the horizon line/surface) that is tolerant for the orientation of the whole scene. Thus, the effects of tilt can be purely visually-driven in this case and may reflect feature selectivity unrelated to gravitation. The difference between retinal and gravitational effects can just reflect neurons that do not care about the scene/horizon background but only about the object and neurons that respond to the features of the object relative to the background. Thus, I feel that the data using scenes cannot be used unambiguously as evidence for a gravitational reference frame. The authors also tested neurons with an object without a scene, and these data provide evidence for a gravitational reference frame. The authors should concentrate on these data and downplay the difficult-to-interpret results using scenes. Furthermore, the analysis of the single object data should be improved and clarified.

Reviewer #3 (Public Review):

In this paper, Van Eyndhoven et al. use a quantitative and system immunology approach to dissect the factors contributing to the fate of early IFN-I responders. Overall, this manuscript is quite elegant and technically very strong. My questions/comments are limited to (1) the fraction of cells that respond in the absence of Poly(I:C), (2) the source of stimulation for the second responders in this system.

1. For the small fraction of cells that respond in the absence of Poly(I:C), are these cells just showing IRF7 translocation or are they fully responding with IFNB production? Has this been observed in other experimental systems or contexts? Do you also observe secondary responders in the unstimulated samples (as shown in the stimulated in Fig. 2G-I)?

2. Do the second responders only arise through direct IFN-I production by first responders? Is it possible that this response has any relationship with the initial transfection with Poly(I:C)?

Reviewer #3 (Public Review):

To determine how the clinical-stage inhibitor vamifeport interacts with ferroportin, the authors used cryogenic electron microscopy (cryo-EM) to determine several structures of ferroportin in complex with newly isolated sybodies. They found that the highest resolution structure shows an occluded state of the transporter bound to sybody 3 and vamifeport. The inhibitor occupies a small portion of a large occluded cavity, interacts with both the N and the C lobe of the transporter, and overlaps with the binding site for both hepcidin and the iron ion binding site 2. The authors also use binding assays and mutagenesis to confirm that the residues in the vamifeport binding site are important for binding.

As the authors point out, the vamifeport inhibitor can readily be modeled in two orientations. The authors provide a reasonable argument that one orientation provides more specific interactions, but the case would be stronger if the structure had a high enough resolution to distinguish between the two orientations, or if the authors could provide some complementary supporting evidence. Still, the manuscript provides convincing evidence to explain how the compound inhibits ion transport and the similarities and differences between this inhibitor and the endogenous regulatory protein hepcidin.

The authors describe the occluded conformation that they resolve with bound sybody 3 and vamifeport as "on the transport pathway". However, this occluded conformation was captured in the presence of two ligands that are not on-pathway, the inhibitor and the sybody. It seems plausible (maybe even likely?) that the conformation is off-pathway and trapped by these additional ligands. The study would therefore benefit from additional evidence as to whether this conformation is indeed on-pathway.

Reviewer #3 (Public Review):

The manuscript by Vandry et al analyzes the circuitry connecting LEC to MEC, identifying a new connection with potential significance for cortico-hippocampal coding and memory. Using a combination of viral tracing, patch-clamp electrophysiology, and optogenetics, the authors reveal a new excitatory projection from Fan cells of LEC layer 2 to superficial neurons of MEC. Specifically, Fan cells synapse on MEC L2 stellate and pyramidal neurons, as well as layer 1 and layer 2 local interneurons, which provide fast and slow local feedforward inhibition to MEC excitatory neurons. The authors observe substantial cell-to-cell heterogeneity in the excitatory-to-inhibitory ratio, which does not seem to be a result of anatomical location. This heterogeneity is conserved during theta-like stimulation. This new connection allows for a kind of unidirectional "cross-talk", in which LEC can speak to MEC prior to or during communication of both of these regions with the hippocampus.

The results are generally clear and well-contextualized by the text. The authors use multiple complementary anatomical methods to identify the LEC to MEC connection, all of which agree. This is supported by the electrophysiological measurements, which are straightforward and generally convincing. The results provide important data for understanding the previously underappreciated reciprocal circuitry between MEC and LEC, which, as the authors nicely lay out in the introduction, is likely key for understanding the operation of memory networks.

The work described in this manuscript, which is all in vitro, appears nicely conducted and solid and is well presented and analyzed appropriately. However, it is not clear how this information can be used to glean an improved understanding of how LEC and MEC interact in the intact system, which is obviously the big question. In vivo experiments of this kind are quite challenging, but without some observation or perturbation of circuit dynamics in the intact animal, or at the very least a compelling model of how hippocampal/memory information processing is influenced by this new circuit, it may be hard for readers to know what to make of the new data the authors provide.

Reviewer #3 (Public Review):

In this manuscript, Raval et al. investigated the cost and benefit of maintaining seemingly redundant components of the translation machinery in the E. coli genome. They used systematic deletion of different components of the translation machinery including tRNA genes, tRNA modification enzymes, and ribosomal RNA genes to create a collection of mutant strains with reduced redundancy. Then they measured the effect of the reduced redundancy on cellular fitness by measuring the growth rate of each mutant strain in different growth conditions.

This manuscript beautifully shows how maintaining multiple copies of translation machinery genes such as tRNA or ribosomal RNA is beneficial in a nutrient-rich environment, while it is costly in nutrient-poor environments. Similarly, they show how maintaining parallel pathways such as non-target tRNA which directly decodes a codon versus target tRNA plus tRNA modifying enzymes which enable wobble interactions between a tRNA and a codon have a similar effect in terms of cost and benefit.

Further, the authors show the mechanisms that contribute to the increased or reduced fitness following a reduction in gene copy number by measuring tRNA abundance and translation capacity. This enables them to show how on one hand reduced copy numbers of tRNA genes result in lower tRNA abundance in rich growth media, however in nutrient-limiting media higher copy number leads to increased expression cost which does not lead to an increased translation rate.<br /> Overall, this work beautifully demonstrates the cost and benefits of the seemingly redundant translation machinery components in E. coli.

However, in my opinion, this work's conclusion should be that the seeming redundancy of the translation machinery is not redundant after all. As mentioned by the authors, it is known that tRNA gene copy number is associated with tRNA abundance (Dong et al. 1996, doi: 10.1006/jmbi.1996.0428), this effect is also nicely demonstrated by the authors in the section titled "Gene regulation cannot compensate for loss of tRNA gene copies". Moreover, this work demonstrates how the loss of the seeming redundancy is deleterious in a nutrient-rich environment. Therefore, I believe the experiments presented in this work together with previous works should lead to the conclusion that the multiple gene copies and parallel tRNA decoding pathways are not redundant but rather essential for fast growth in rich environments.

Reviewer #3 (Public Review):

In this study, the authors explore an under-studied but widely observed phenomenon that polyA site selection often occurs in clusters leading to the excepted interpretation that cleavage and polyadenylation are imprecise. Here, the authors use 3READS to map polyA sites in yeast and human cells to define trends in intra-cluster polyA site usage as it relates to RNAPII speed. They observe clear trends in cleavage events that correlate with either increased or decreased RNAPII elongation rate and make a further identification that downstream GC content also correlates with these trends. The potential impact of this work is to explain the imprecise behavior of cleavage and Polyadenylation as a component of local elongation rates that are influenced by nucleotide content.

Reviewer #3 (Public Review):

The authors are designing a novel continuous evidence accumulation task to look at neural and behavioral adaptations of continuously changing evidence. They particularly focus on centroparietal EEG potential that has been previously linked with evidence accumulation. This paper provides a novel method and analysis to investigate evidence accumulation in a continuous task set-up.

I am not familiar with either the EEG or evidence accumulation literature, therefore cannot comment on the strength of the findings related to centroparietal EEG in evidence accumulation. I have therefore commented only on the coherence and details of the method and clarity of the argumentation and results.

The main strength is in the task design which is novel and provides an interesting approach to studying continuous evidence accumulation. Because of the continuous nature of the task, the authors design new ways to look at behavioral and neural traces of evidence. The reverse-correlation method looking at the average of past coherence signals enables us to characterize the changes in signal leading to a decision bound and its neural correlate.<br /> By varying the frequency and length of the so-called response period, that the participants have to identify, the method potentially offers rich opportunities to the wider community to look at various aspects of decision-making under sensory uncertainty.

The main weaknesses that I see lie within the description and rigor of the method. The authors refer multiple times to the time constant of the exponential fit to the signal before the decision but do not provide a rigorous method for its calculation and neither a description of the goodness of the fit. The variable names seem to change throughout the text which makes the argumentation confusing to the reader. The figure captions are incomplete and lack clarity.<br /> The authors claim that the method enables continuous analysis of decision-making and evidence accumulation which is true. The analysis of the signals that come prior to the decision provides a rich opportunity to characterize decision bound in this task. The behavioral and neural analyses globally lack clarity and description and thus do not strongly support the claims of the paper. The interpretation of the figures within the figure caption and the lack of a neutral and exhaustive description of what is being shown prevent the claims to be strongly supported.

The continuous nature of the task and the computation of those evidence kernels are valuable methods to look at evidence accumulation that could be of use within the community. However, due to the lack of rigor in the analysis and description of the method, it is hard to know if the current dataset is under-exploited or whether the choice of the parameters for this set of experiment does not enable stronger claims.

Reviewer #3 (Public Review):

The work provides direct evidence for the coherent activity of head-direction (HD) cells in the anterior thalamus and retrosplenial cortex (RSC). RSC is one of two major direct cortical recipients of the subcortical HD signal, the other being the postsubiculum (POS). While it is established that POS inherits its HD tuning from ADN (Peyrache et al, 2015), it is not known whether HD cells in RSC show similar coordination with ADN. The manuscript employs technically challenging dual electrophysiological recordings from ADN and RSC to establish that the local internal representations of HD encoded in ADN and RSC are coherent during free exploration but also show coordinated realignment after cue rotation as well as coordinated drift in darkness. The work thus provides evidence that HD and RSC assemblies represent the same internal heading direction, at least in the behavioural paradigms tested and at the investigated temporal resolution. The manuscript also makes a claim that the RSC is unlikely to mediate the realignment of the HD signal following cue rotation because the HD signal realigns itself synchronously across the two brain regions. This claim is additionally supported by the sparse anatomical projection and the paucity of putative direct synaptic connections from RSC to ADN.

The manuscript convincingly demonstrates overall ADN-RSC coordination in two different paradigms. While such coordination is expected in instances when HD representations in both areas are precisely aligned with the current HD, it may not be the case in instances of sensory conflict or limited sensory information. The fact that internal HD in both ADN and RSC drifts coherently in darkness provides strong evidence of the tight functional coupling between the two areas. Additionally, while the cue rotation paradigm used in the study often failed to elicit the full realignment of the HD signal, this variability was certainly utilized to the manuscript's advantage as it makes the coupling evident even when the HD signal realigns only partially. The overall conclusions of the manuscript are largely supported by the presented data but the strength of the argument, especially with regard to the zero-lag coupling between ADN and RSC, is somewhat affected by the technical limitations.

1) The manuscript relies heavily on supervised decoding of the internal HD from population activity in RSC and ADN and in turn suffers from relatively low numbers of simultaneously recorded neurons, which is especially evident in the representative images in Figure 2C. The reported average decoding errors are much higher than those reported elsewhere (Peyrache et al, 2015; Viejo et al, 2018; Xu et al, 2019), which may occlude the effects of RSC activity on ADN that are more subtle and/or occur at shorter timescales than the bin size used in the decoding algorithm. The manuscript includes no discussion of how much these factors could contribute to the observed variability in the data.

2) RSC-HD cells recorded in the study are relatively poorly tuned to HD, which is contrary to the reports of HD cells recorded in RSC (Lozano et al, 2017; Javob et al, 2017; Keshavarzi et al, 2021). In fact, the median directional information score for RSC-HD cells is the same as that for non-HD cells in ADN (Supplementary Figure 2B). In fact, due to their relatively low HD modulation, it may be more appropriate to refer to them as 'HD-modulated' cells. While the electrode positions indicate that RSC was sampled across layers and sub-regions so missing the HD cell 'hot spots' like granular RSCb is unlikely, the apparent poor directional tuning of RSC cells could possibly be due to the nature of the recording environment (e.g. low light condition with the LED landmark being the only light source). Importantly, the manuscript lacks a control 'baseline' condition in which HD cells are recorded in a standard, well-lit open field, as well as a discussion of the discrepancy between the observed HD tuning and that reported in the literature.

3) Analysis of decoding error, which features prominently in the manuscript, is critically dependent on the quality of behavioural tracking - errors in tracking could lead to the accumulation of decoding errors and this could dominate decoding error analyses. Indeed, Figure 2A shows many gaps in the tracked HD of the mouse, which may point to the sub-optimal quality of the behavioural tracking. This is especially important for analyses like the one in Figure 2D which shows that internal HD representations in ADN and RSC are coordinated at zero lag (+/- 20ms). The observed zero-lag peak could be instead explained by errors in behavioural tracking dominating the analysis, which would affect both representations simultaneously and show spurious zero-lag positive correlations. As such, the analysis that is missing is the difference between internal HD decoded from ADN and RSC at different time lags, without reference to the HD tracked behaviourally.

4) The work often uses a number of trials as their 'n' sample size for statistical analyses and the methods state that tetrodes were regularly advanced, but there is no indication of whether multiple trials at the same tetrode position were included in the same statistical comparison (except for recordings '4 days apart' for the HD tuning and synaptic connectivity analyses). Multiple trials with a high likelihood of recording the same cell population should not be counted as separate samples when calculating statistical significance.

Reviewer #3 (Public Review):

This manuscript by Takahashi et al., reveals the structure of a chimeric VRAC channel composed by the LRRC8C and a short domain corresponding to the intracellular loop of LRRC8A. Homomeric LRRC8C channels are not functional but this chimera has been shown to "rescue" the functional and pharmacological properties of heteromeric VRAC channels. The authors obtained the Cryo-EM structure for this chimera, which provide some interesting insights about these channels. The major finding of this work is that the channel is asymmetrically formed by 7 protomers, with associated lipid-like densities that are proposed to play a role in gating. Unfortunately, critical domains of this structure could not be solved, which limit the interpretations of this new work. These missing domains include the entire LRRD, the N-terminus and the first intracellular loop containing the 25 amino acids incorporated from the LRRC8A. While this work is very interesting, the data presented are not enough to support the author claims. Particularly, the idea that the 'lipid blocked pore' is associated with gating.

Reviewer #3 (Public Review):

This study investigates the recently published findings by Sugisawa et al that microbial ssRNA40, a known agonist for the immune surveillance system activates the mechanically gated ion channel Piezo1. In addition to providing mechanistic insights into the study, this finding also had much broader implications as it suggested a novel role for the channel as a physiological receptor for ssRNA. Although there is nothing that prevents Piezo1 from carrying out such a role in principle, the finding caused a great deal of interest and professional skepticism among Piezo researchers and, more broadly, in the field of mechanobiology. This manuscript set out to reproduce the main findings of Sugisawa et al using the same approaches, and in addition utilized other techniques to address potential differences in experimental conditions. In summary, the authors failed to reproduce the major Piezo1-related findings reported by Sugisawa et al, while all the new experiments pointed to the absence of a functional interaction between ssRNA40 and Piezo1. The study is well-designed, with appropriate controls and statistical analyses.

Reviewer #3 (Public Review):

Schneggenburger and colleagues set out to reveal roles for D1R+ and Adora+ amygdala-striatal transition zone neurons in fear learning. In the first two experiments, the authors expressed fluorescent calcium indicators in D1R+ or Adora+ neurons, measuring change in fluorescence during habituation, training and testing of tone-shock conditioning. In the next experiments, the authors expressed archeorhodopsin (or a control fluorophore) in D1R+ or Adora+ neurons and illuminated with yellow light just before and after foot shock delivery. Freezing was quantified during training and retrieval. Finally, retrograde tracing was performed to reveal direct synaptic inputs on D1R+ and Adora+ neurons.

The paper is potentially interesting. However, some important weaknesses include: the authors use of only male mice, the lack of validation of the Cre lines used in the study, and the data acquisition pipeline.

Reviewer #3 (Public Review):

Yang and colleagues provide a thorough characterization of a transgenic mouse model expressing fluorescently tagged synaptotagmin. In particular, they present key controls validating this mouse model as a tool, including co-localization of the tagged synaptotagmin with other synaptic markers as well as normalcy of synaptic transmission mediated by synaptic terminals expressing the tagged synaptotagmin. Importantly, the authors present data on the potential use of neuronal cultures obtained from these mice in synaptic co-culture assays. In these assays, synaptic cell adhesion molecules expressed on non-neuronal cell lines such as HEK-293 cells or COS cells are used to test the sufficiency of these molecules to trigger synapse assembly. This mouse model will be a useful addition to existing models expressing fluorescently-tagged synaptic vesicle proteins such as synaptophysin, synaptotagmin as well as synaptobrevin.