Reviewer #2 (Public Review):

This study contains a huge amount of data and the images are of high quality. However, the conclusions are not really well supported. The authors may have reached too far from their results. The roles of SHR, SCR and SCL23 in the shoot apex are not really clarified.

The manuscript by Bahafid et al., reports a study of the functions of SHORTROOT (SHR), a well-established root development regulator in the shoot apical meristem (SAM) development with focus on lateral organ initiation. A large amount of data is included in this paper. This study highly depends on imaging, and the images are in general of very good quality. The authors show reciprocal interactions between SHR and SCR with auxin/MP. There are also a large amount of genetic interactions among several genes, including WUS and CLV3. Although the study provides a vast amount of data, the conclusions are not so well supported. There seem to be many interactions, at the protein level, and at the transcriptional regulation level, but the conclusion is nevertheless ambiguous.

Reviewer #2 (Public Review):

Sørensen and colleagues performed a comprehensive analysis aiming to find how DNA repair genes shape mutational patterns. They take advantage of the Hartwig Medical Foundation (HMF) and TCGA/ICGC databases which have germline and somatic molecular data. These molecular data layers are used as input features for the predictive models of DNA damage response (DDR) gene deficiency.

Of note, the project is of interest to oncology in the sense of unveiling new genes to be further investigated as a therapeutic candidate target in cancer.

This paper brings statistical modelling based on LASSO regression coupled with appropriate metrics for unbalanced data sets. Their finds recapitulate known DDR-associate genes but novel genes that can be explored in animal models or functional assays with cell lines.

Reviewer #2 (Public Review):

The paper describes the participation in CRC screening in Denmark and compliance to colonoscopy in FIT positive screened people during pandemic.

There are interesting data, particularly in the breakdown by age socioeconomic status and immigrant status. Nevertheless, the study remains very descriptive. When a pandemic occurs and different strategies are put in place in different countries to afford this emergency, probably we also need simple descriptions of what happened, considering anything as a natural experiment to be reported. Furthermore, Denmark is one of the few (or the only) European countries that did not stop CRC screening even during the lockdown. Thus it is worth documenting what happened, with a scientific paper. The consequence is that the paper is not very gripping.

The paper is very well written and the report is rigorous, the methods well documented, tables and figure clear.

Reviewer #2 (Public Review):

Previous studies have shown that lhx1 progenitors proliferate upon AKI in adult zebrafish mesonephros. However, these studies have focused primarily on renal progenitor cells (RPCs). This study uses single-cell mRNA sequencing to identify a novel cell type (RICs) in the zebrafish mesonephros that is marked by fabp10a expression within a previously generated GFP transgenic zebrafish line. The authors show that RICs express cox2 to synthesize PGE2 upon gentamicin-mediated AKI, which correlates with PRC proliferation. They demonstrate that PGE2 stimulates RPC proliferation through EP4b receptor activation of PKA, which in turn stabilizes beta-catenin through phosphorylation of both beta-catenin and GSK3beta. They also indicate that beta-catenin stabilization and RPC proliferation is dependent upon wnt4 expression by the RPC itself. The topic of the paper is significant in that it identifies an interstitial in the zebrafish kidney and suggests several mechanisms by which it supports nephron regeneration.

Reviewer #2 (Public Review):

Tan et al. have used state-of-the-art methodology (mouse genetics, superresolution microscopy and synaptic electrophysiology) to further delineate the role of Munc13 proteins by investigating their function within a scenario in which the presynaptic active zone is deprived of major protein scaffolds. The authors have transduced Cre-expressing lentiviruses into hippocampal neuronal cultures from mice with floxed alleles to remove six fundamental components of the presynaptic active zone: RIM1, RIM2, ELKS1, ELKS2, Munc13-1, Munc13-2 and Munc13-3. The first part of the study comprises a comparison between neurons lacking RIM1, RIM2, ELKS1, ELKS2, on one side, and neurons lacking Munc13-1 and Munc13-2 on the other. Within the first group of neurons the levels of Munc13-1 at the nerve terminals are already reduced (assessed by confocal and STED microscopy and western blots) and the residual amount left is located far away from the active zone. Remarkably synapse formation occurs normally upon the hextuple knock-out of active zone proteins, however, vesicle docking is disrupted and single-action potential evoked and spontaneous release is reduced at glutamatergic and GABAergic synapses. A key finding is that the single-action potential evoked release still detected in the RIM/ELS cuadruple knock-out is almost completely abolished upon the additional knock-out of Munc13-1 and Munc13-2. This is a major observation of the study that support, as the authors concluded, that Munc13 promotes the fusogenicity of synaptic vesicles even when Munc13 is not properly located at the active zone. Careful electrophysiological measurements show that in the absence of Munc13 the size of the readily releasable pool (RRP) of synaptic vesicles is further reduced without specific changes in the vesicular release probability. Overall, the electrophysiological data support well the notion that Munc13 is specifically responsible for the remaining RRP and therefore reinforce the notion that Munc13 acts at the priming stage and can do it in part independently of RIMs and ELSs. Importantly, the results further support the notion that synapse formation is a remarkably resilient process that occurs even under strong perturbation of presynaptic function.

As a secondary conclusion, the authors point out that postsynaptic response is intact, this specific point should be further discussed and analyzed.

The study is very clearly written and presents very relevant findings of interest for readers in the field of the molecular mechanisms of synaptic operation.

Reviewer #2 (Public Review):

In this article, Iyer et al discuss the mechanics of reprogramming challenges encountered by supporting cells towards their potential pathway of dedifferentiating into hair cell types. Previous literature has shown the ability of ATOH-1, GFl-1, and POU4F3 to transform supporting cells into hair-like cell types. Here authors suggest that the combinatorial expression of these TFs can enhance the efficiency of the transcriptional remodeling of supporting cells to initiate the reprogramming toward hair-like cell lineage. It is a well-conducted study. Please see my comments/concerns below.

1. In the representative images, the effect of GFl-1 seems to be less efficient or has no effect on reprogramming the lineage of supporting cells to hair cell-like cells in comparison to two other groups ATOH-1 alone or ATOH-1, GFl-1, and Pou4F3 combined (Figure 1, 1- S2, 2B, 4A) and even the single-cell RNA seq can be interpreted similarly (Figure 3C, 6C. According to authors and previous literature, GFl1 is supposed to be acting in concert to enhance the efficiency of this lineage conversion at least in older animals. The representative images and single-cell UMAPs show that either GFl-1 is not efficient or less efficient than ATOH-1 alone or ATOH-1, GFl-1, and Pou4F3 combined. Hence, why authors chose not to explore ATOH-1 and Pou4F3 without GFl-1.

2. In Figure 3C, the authors find the most reduction in cell numbers in lateral GER during transcriptional reprogramming. Can authors comment on why the cells in this region are more susceptible to lineage reprogramming into hair cell-like cells?

3. In figure 5A, how can the existing hair cells be distinguished from newly formed hair cell-like cells.

4. Authors cited previous literature showing that existing hair cells can affect lineage reprogramming of supporting cells through Notch signaling. So would it not be a better experimental design when the hair cells were depleted prior to transcriptional reprogramming.

5. Genetic mutations that lead to functional disruptions in supporting cells are also linked to hearing loss. Can authors predict how feasible would be the idea of in vivo conversion of one important cell type to another important cell type?

6. Are reprogrammed hair cell-like cells transcriptionally similar to outer hair cells, inner hair cells, or none?

Reviewer #2 (Public Review):

The manuscript entitled "Fixation Can Change the Appearance of Phase Separation in Living Cells" discussed the different fixation artefacts that can change the appearance of LLPS. The manuscript points out a fundamental question in the field of phase separation which is rarely discussed. The authors found that PFA fixation can both enhance and diminish putative LLPS behaviors; in some cases, it can also create condensates that did not exist in living cells. Using a simple but elegant model, they found that protein localization in fixed cells depends on an intricate balance of protein-protein interaction dynamics, the overall rate of fixation, and notably, the difference between fixation rates of different proteins. They conclude that less dynamic interactions are better captured by PFA fixation. The text is clearly written, the experiments are well designed and the simulations give an interesting explanation of the different artefacts observed after fixation.

To describe LLPS or to distinguish between polymer-polymer phase separation and LLPS, recent studies have used single particle tracking, a technique allowing to follow the dynamics of individual proteins in living cells (https://doi.org/10.7554/eLife.60577; https://doi.org/10.7554/eLife.69181; https://doi.org/10.7554/eLife.47098). The authors should mention that such an approach can be a good alternative to avoid the artefact of fixation.<br /> Using techniques such as single particle tracking or FCS, it is possible to estimate the effective diffusion coefficient of protein-living cells. When a liquid phase separation is formed, it is also possible to estimate the diffusion coefficient of the protein of interest (POI) inside versus outside of the LLPS. The authors say that less dynamic interactions are better captured by PFA fixation. In the simulation part, would it be possible to predict from the diffusion coefficients of the POI inside a condensate the effect of the PAF fixation?

Finally, the authors propose that in the future, it will be important to design novel fixatives with significantly faster cross-linking rates than biomolecular interactions to eliminate fixation artifacts in the cell. It would be even more interesting if the authors could propose some ideas of potential novel fixatives. Did they test several concentrations of PFA, for example? Did they test different times of PFA incubation? Did they test cryofixation and do they know what would be their effect on LLPS? Do they have novel fixatives in mind?

Adding some precisions about these points in the simulation and in the fixation protocol would increase the impact of the manuscript. Otherwise, the study is interesting and thought-provoking.

Reviewer #2 (Public Review):

This paper reports the results of optical imaging experiments on areas V4, V2, and V1 in anesthetised macaque monkeys. The experiments were designed to reveal details of the representation of spatial frequency (SF), orientation (OR), and color in these areas. Evidence for gradients of SF selectivity across the areas is presented. It is also shown that SF and OR maps in V2 and V4 have iso-parameter contours that intersect at right angles, in agreement with, and extending, observations made in V1 and visual areas in cats and other species. Color domains tend to be located in low-SF domains and avoid the higher SF regions. Relationships between V2 stripes and SF preference are also established.

These findings are a potentially valuable contribution to understanding the maps that exist in V4, which have received less attention than those in areas V1 and V2. However, I have some serious concerns about the validity of the results.

Reviewer #2 (Public Review):

Auwerx et al. present a framework for the integration of results from expression quantitative trait loci (eQTL), metabolite QTL (mQTL) and genome-wide association (GWA) studies based on the use of summary statistics and Mendelian Randomization (MR). The aim of their study is to provide the field with a method that allows for the detection of causal relationships between transcript levels and phenotypes by integrating information about the effect of transcripts on metabolites and the downstream effect of these metabolites on phenotypes reported by GWA studies. The method requires the mapping of identical SNPs in disconnected mQTL and eQTL studies, which allows MR-based inference of a causal effect from a transcript to a metabolite. The effect of both transcripts and metabolites on phenotypes is evaluated in the same MR-based manner by overlaying eQTL and mQTL SNPs with SNPs present in phenotypic GWA studies.

The aim of the presented approach is two-fold: (1) to allow identification of additional causal relationships between transcript levels and phenotypes as compared to an approach limited to the evaluation of transcript-to-phenotype associations (transcriptome-wide MR, TWMR) and (2) to provide information about the mechanism of effects originating from causally linked transcripts via the metabolite layer to a phenotype.

The study is presented in a very clear and concise way. In the part based on empirical study results, the approach leads to the identification of a set of potential causal triplets between transcripts, metabolites and phenotypes. Several examples of such causal links are presented, which are in agreement with literature but also contain testable hypotheses about novel functional relationships. The simulation study is well documented and addresses an important question pertaining to the approach taken: Does the integration of mQTL data at the level of a mediator allow for higher power to detect causal transcript to phenotype associations?

Major Concerns<br /> 1. Our most salient concern regarding the presented approach is the presence of multiple testing problems. In the analysis of empirical datasets (p. 4), the rational for setting FDR thresholds is not clearly stated. While this appears to be a Bonferroni-type correction (p-value threshold divided by number of transcripts or metabolites tested), the thresholds do not reflect the actual number of tests performed (7883 transcripts times 453 metabolites for transcript-metabolite associations, 87 metabolites or 10435 transcripts times 28 complex phenotypes). The correct and more stringent thresholds certainly decrease the overlap between causal relationships and thus reduce the identifiable number of causal triplets. Furthermore, we believe that multiple testing has to be considered for correct interpretation of the power analysis. The study compares the power of a TWMR-only approach to the power of mediation-based MR by comparing "power(TP)" against "power(TM) * power(MP)" (p. 12). This comparison is useful in a hypothetical situation given data on a single transcript affecting a single phenotype, and with potential mediation via a single metabolite. However, in an actual empirical situation, the number of non-causal transcript-metabolite-phenotype triplets will exceed the number of non-causal transcript-phenotype associations due to the multiplication with the number of metabolites that have to be evaluated. This creates a tremendous burden of multiple testing, which will very likely outweigh the increase in power afforded by the mediation-based approach in the hypothetical "single transcript-metabolite-phenotype" situation described here. Thus, for explorative detection of causal transcript-phenotype relationships, the TWMR-only method might even outperform the mediation-based method described by the authors, simply because the former requires a smaller number of hypotheses to be tested compared to the latter. The presented simulation would only hold in cases where a single path of causality with a known potential mediator is to be tested.

2. A second concern regards the interpretation of the results based on the empirical datasets. For the identified 206 transcript-metabolite-phenotype causal triplets, the authors show a comparison between TWMR-based total effect of transcripts on phenotypes and the calculated direct effect based on a multivariable MR (MVMR) test (Figure 2B), which corrects for the indirect effect mediated by the metabolite in the causal triplet. The comparison shows a strong correlation between direct and total effect. A thorough discussion of the potential reasons for deviation (in both negative and positive directions) from the identity line is missing. Furthermore, no test of significance for potential cases of mediation is presented. Due to the issues of multiple testing discussed above, the significance of the inferred cases of mediation is drawn into question. The examples presented for causal triplets (involving the ANKH and SLC6A12 transcripts) feature transcripts with low total effects and a small ratio between direct and total effect, in line with the power analysis. However, in these examples, the total effects are also quite low. Its significance has to be tested with an appropriate statistical test, incorporating multiple testing correction. Furthermore, the analysis of the empirical data indicates that the ratio between direct and indirect effect of a transcript on a phenotype is in most cases close to identity, except for triplets with low total effects. This fact should be considered in the power analysis, which assigned the highest gain in power by the mediation analysis to cases of low direct to total effect ratio. The empirical data indicate that these cases might be rare or of minor relevance for the tested phenotypes.

3. Related to the interpretation of causal links: horizontal pleiotropy needs to be considered. The authors report the identification of causal links between TMEM258, FADS1 and FADS2, arachidonic acid-derived lipids and complex phenotypes. However, they also mention the high degree of pleiotropy due to linkage disequilibrium at the underlying eQTL and mQTL region as well as the network of over 50 complex lipids known to be associated with the expression of the above transcripts. Thus, it seems possible that the levels of undetected lipid species may be more important for the phenotypic effect of variation in these transcripts and that the reported "mediators" are rather covariates. Such horizontal pleiotropy would violate a basic assumption of the MR approach. While we think that this does not invalidate the approach altogether, it does affect the interpretation of specific metabolites as mediators. This is aggravated by the fact that metabolic networks are more tightly interconnected than macromolecular interaction networks (assortative nature of metabolic networks) and that single point-measurements of metabolites may not be generally informative about the flux through a specific metabolic pathway.

Reviewer #2 (Public Review):

In the manuscript, Mijnheer et al mainly exploited CyTOF Helios mass cytometer and TCRβ repertoire sequencing to investigate the T cell composition and distribution in peripheral blood and synovial fluid, and further explored the temporal and spatial dynamics of regulatory T cells (Tregs) and non-Tregs in the inflamed joints of Juvenile Idiopathic Arthritis (JIA) patients. Their results indicate that the activated effector T cells and hyper-expanded Treg TCRβ clones found at the inflamed joints are highly persistent in the circulation, and the dominant of high degree of sequence similarity of Treg clones could serve as the novel therapeutic targets for the JIA treatment. Overall, the research design is appropriate, and the methods are adequately described in the study. However, several issues are required to be addressed.

(1) The criteria for the JIA patient's recruitment should be clearly presented in the method section. For example, what is the specific included criteria and excluded criteria? Or did the patients take medicines for the treatment during the study?<br /> (2) As for the correlation analysis of the entire spectrum of node frequencies, the SFMCs and PBMCs isolated from 3 patients were conducted in the study. The sample size is too limited to obtain robust results and to make a convincing conclusion from the correlation analysis. And it is shown that a total of 9 JIA patients have been involved in the study. Could the author clarify it?<br /> (3) The results of the study indicate that the hyper-expanded T cell clones are shared between left and right knee joints. Since JIA may affect one or more joints, did the author check other joints to see if the same expanded T cell clones infiltrate multiple joints, such as hand or wrist?<br /> (4) For Fig.2B, the Treg CD25+FOXP3+ population was significantly enriched in synovial fluid (SF). Is it from the left knee joints or the right knee joints?<br /> And in the context of Line 144-148, it indicated the SF, however, the title of axis in Fig.2B indicated Synovial Fluid Mononuclear Cells (SFMCs). Please keep consistent.<br /> (5) For the longitudinal sampling timelines of JIA patients shown in Supplementary Fig.3, the interval of PB and SF sample collection is not consistent. And only 1 patient completed 4 visits and the sample collection. It is hard to make any conclusion from 1 patient.

Reviewer #2 (Public Review):

The current treatment for the radical cure of Plasmodium vivax malaria is primaquine, it was first made available in the 1950s and there is a need for better treatments. Recently a new drug was licensed, tafenoquine. Tafenoquine is a single-dose treatment due to the drug's long half-life. The expected increase in treatment adherence is an important advantage, however, the drug's slow elimination has also a drawback. Patients must be tested for a ubiquitous enzyme (glucose-6-phosphate dehydrogenase) deficiency prior to treatment, as the use of this drug in the G6DP deficient population could lead to life-threatening haemolysis. Implementing accurate quantitative testing in remote malaria-endemic areas is challenging. Providing point-of-care test equipment, supplies and training may not be cost-effective as the efficacy of tafenoquine has not been proven non-inferior to primaquine.

Thus strategies to increase tafenoquine efficacy are of paramount importance to raise the public health relevance of the first new drug developed for the radical cure of vivax malaria in the last 70 years. This paper polled together and analysed the clinical information of participants of the tafenoquine clinical trials, and using models they evaluated the influence of dose per weight on the recurrence rate of the disease. They predict that the tafenoquine efficacy would surpass the primaquine one if the tafenoquine dose was increased. They also correlated the levels of methaemoglobin production and the reduction of relapses, implying that methaemoglobin can be a surrogate marker of efficacy.

As with any model prediction, these results should be confirmed in clinical trials, especially the safety profile of the suggested regimen. The surrogate marker of oxidative drug activity is a very interesting indirect efficacy measurement, although it is limited to any indirect outcome. Even the vivax recurrence rate itself has limitations as vivax relapse and reinfection cannot be differentiated. Still, these results provide a solid support for future clinical trials that might reinforce the public health relevance of tafenoquine.

Reviewer #2 (Public Review):

In this manuscript, the authors use tandem mass spectrometry to identify peptides from the eggshell protein struthiocalcin-1 preserved in a fossilized eggshell ~6.5 Ma years old. They report multiple peptide spectral matches (peptide identifications) to a small section of the struthiocalcin-1 protein. The high-resolution, well-annotated spectral matches provided by the authors show that that region of struthiocalcin-1 may be preferentially preserved in ancient ostrich shell tissue.

These findings are important because Cenozoic tissues greater than 1 Ma in age are drastically unexplored in terms of protein preservation. Thus, these data represent a big step forward in the investigation of proteomic preservation throughout the Cenozoic.

Reviewer #2 (Public Review):

The authors report a series of genetic experiments that allow for the rigorous evaluation of the role of pre-synaptic contact and activity on dendrite morphogenesis and/or stabilization between a model pair of connected sensory and interneurons in the Drosophila larval CNS. Experiments to mis-position the presynaptic arbors of the DBD neuron reveal contact-dependent effects on post-synaptic dendrite growth. Ablation, silencing and activation experiments support the interesting model that neuronal activity in the sensory afferents act to globally constrain post-synaptic dendrite growth. Thus, coordination of presynaptic contact and activity act in opposition to sculpt post-synaptic dendrites. One weakness/limitation of the study is the inability of the authors to evaluate whether the observed effects are due to changes in the initiation of dendrite growth as opposed to maintenance or stabilization effects. The authors adequately acknowledge and discuss this limitation. Overall, this is a beautifully conducted study that adds new insights into synaptic partner matching.

Reviewer #2 (Public Review):

The study included all women aged 23-64 years invited for cervical cancer screening in Denmark in 2015-2021 (n=2,220,00). The Danish registries provide an ideal setting for the study. Classification of explanatory covariates followed Danish and international standards. The authors estimated the prevalence ratios using a generalised linear model with a log link for the Poisson family. Material and statistical methods are appropriate for the study's aims.

As the authors write, several studies have demonstrated lower participation among immigrants and women with lower socioeconomic status. However, the authors wanted to evaluate whether divergence may have been exacerbated during the pandemic. Unfortunately, they do not provide any justification for why they would hypothesize that to happen.

The authors write that women who unregistered from the screening programme within 1 year since invitation (n=56,920) were excluded. If those who are at higher risk of cancer and with lower participation rates unregister themselves, the compliance to screening could be overestimated.

The authors find that some age groups i.e. women aged 40-49 and those aged 60-64 years had a lower participation rate and conclude that it could indicate that the restrictions within a society affect different age groups disproportionally. The authors do not try to explain the finding and it should be scrutinized to rule out a chance. Comorbidity is strongly associated with age so if this is attributed to self-isolation, there should be a gradient. Why 50-59 years old would be different from 60-64 years?

In general, study results support the conclusions. The authors consider the inconsistent health messages as a reason for women not to participate. What about fear? In several countries, there was a clear decrease in emergency admissions to the hospital which suggest that people were avoiding hospital because they were nervous about catching COVID-19.

Reviewer #2 (Public Review):

The present manuscript provides a mechanistic explanation for an event in adrenal endocrinology: the resistance which develops during excessive inflammation relative to acute inflammation. The authors identify disturbances in adrenal mitochondria function that differentiate excessive inflammation. During severe inflammation the TCA in the adrenal is disrupted at the level of succinate production producing an accumulation of succinate in the adrenal cortex. The authors also provide a mechanistic explanation for the accumulation of succinate, they demonstrate that IL1b decreases expression of SDH the enzyme that degrades succinate through a methylation event in the SDH promoter. This work presents a solid explanation for an important phenomenon. Below are a few questions that should be resolved experimentally.

The authors should confirm through direct biochemical assays of enzymatic activity that steroidogenesis enzyme activity is not impaired. Many of these enzymes are located in the mitochondria and their activity may be diminished due to the disturbed, high succinate environment of the cortical cell as opposed to the low ATP production.

What is the effect of high ROS production. Is steroidogenesis resolved if ROS is pharmacologically decreased even if the reduction of ATP is not resolved?

Does increased intracellular succinate (through cell permeable succinate treatment) inhibit steroidogenesis even if there is not a blockage of OXPHOS?

It should be demonstrated the genetic loss of IL1 signaling in adrenal cortical cells results in a loss of the effect of LPS on reduced steroidogenesis and increased succinate accumulation.

It should be demonstrated the genetic loss of IL1 signaling in adrenal cortical cells results in a loss of the effect of LPS on SDH activity and ATP production and SDH promoter methylation

It should be shown that the silencing of DNMT eliminates or diminishes the effect of LPS on reduced steroidogenesis and increased succinate accumulation.

Does silencing of DNMT reduce OXPHOS in adrenal cortical cells?

The effects of LPS on reduced adrenal steroidogenesis are not elaborated at the physiological level. The manuscript should demonstrate the ramifications of the adrenal function decreasing after LPS. Does CORT release become less pronounced after subsequent challenges? Does baseline CORT decrease at some point? No physiological consequences are shown. Similarly, these physiological consequences of decreased adrenal function should be dependent on decreased SDH activity and OXPHOS in adrenal cells and this should be demonstrated experimentally.

Reviewer #2 (Public Review):

The authors use the phylogeny of SARS-CoV-2 to find signals of functional interactions among the evolving amino acids of the spike protein. They do this by looking for pairs of substitutions that either tend to appear consecutively on branches, indicating positive interactions, or to appear on separate branches, indicating negative interactions. Although a massive number of SARS-CoV-2 sequences have been collected, many of these sequences have errors in them or are similar to each other. This affects the accuracy of the reconstructed phylogeny and the placement of mutations on it, creating difficulties for this approach. Still, the authors are able to identify several sets of sites with clear signals of interaction, and where the interaction makes sense given the structure of the protein. Some of these sites are carried by the Omicron variant, indicating that positive epistasis likely played a role in its evolution.

Reviewer #2 (Public Review):

The authors examined the neural activity of the ventral hippocampus (vH) during exploration of anxiogenic environments. They first recorded vH neuronal activity when animals explored the elevated plus maze (EPM). Although they observed that peak firing activity increased when rats explored anxiogenic locations, this effect was difficult to quantify since rats did not often explore these locations. In order to resolve this issue, they developed a novel type of elevated linear maze (ELM). In the anxiogenic location of the ELM, they observed anxiety-related neuronal activity and demonstrated that the direction-dependent activity of vH neurons became homogenized. Additionally, the authors demonstrated that the activity of the vH neurons reflected and predicted, using a support vector machine (SVM), the exploration of an anxiogenic location, suggesting that vH neurons do not only code for anxiogenic environments, but also may reflect the intention to explore anxiogenic locations.

Strength:<br /> S1. In their study, the authors introduced a modified ELM task that can instantly reconfigure side walls in the anxiogenic environment while rats are being recorded on the maze. This method was intended to overcome the low-sampling issue observed in the anxiogenic environments where animals usually avoid entering. In fact, this modification allowed them to study between non-anxiogenic and anxiogenic conditions within the same maze and in a single recording session.

S2. Also, it is known that recording large number of cells from vH has been quite challenging in the field. The authors successfully examined more than 130 neurons from the vH area across six rats and determined remapping effect when animals were exposed to the anxiogenic environment.

S3. The authors tried to examine the neural population carefully to exclude any other factors to focus solely on the effect of anxiety, although it has been shown that abrupt changes in the environment can cause the hippocampus to remap.

Weakness:<br /> Despite the fact that the authors are trying to answer potentially important and intriguing questions in the anxiety field, some important details are missing from their description of the data.

W1. It is remarkable and impactful that the authors found that the vH neurons overrepresent, remap, and lose directionality under anxiogenic conditions. Conceptually, such dramatic changes as well as prospective biased memory 'replays' have been reported in the dorsal hippocampus under anxiogenic task settings, such as using electrical foot shocks, for example, Wu et al, Nat.Neuro, 2017. Also, another paper (Girardeau et al.., 2017, Nat Neuro) reported that an aversive trajectory is more reactivated in the dorsal hippocampus.

W2. Technically, they used tetrodes in vH and were able to collect more than 130 units, with histological data indicating that recording sites ranged from CA1 to CA3 of vH (Figure 1B). They used a semi-automated clustering method to isolate individual units but did not subdivide them into CA1, CA3 and/or pyramidal cells or interneurons. It appears that the representative examples in Figure 1C contain both pyramidal cells and interneurons, which are well characterized in terms of remapping in the dorsal area.

W3. Readers may find Figure 5 difficult to follow. They are not intuitive to understand how to read/interpret the figure panels.

Reviewer #2 (Public Review):

Differences between the infection environment and in vitro model systems likely contribute to disconnects between the antimicrobial susceptibility profile of bacterial isolates and the clinical response of patients. The authors of this paper focus on a specific aspect of the infection environment, the polymicrobial nature of some chronic infections like those in people with Cystic Fibrosis (CF), as a factor that could impact antibiotic tolerance. They first use published genomic datasets and computational techniques to identify a clinically relevant, four-member polymicrobial community composed of Pseudomonas aeruginosa, Staphylococcus aureus, Streptococcus spp., and Prevotella spp. They then develop a high throughput methodology in which this community grows and persists in a CF-like environment and in which antibiotic susceptibility can be tested. The authors determine that living as a member of this community decreases the antibiotic tolerance of some strains of biofilm-associated P. aeruginosa and increases the tolerance of most strains of planktonic and biofilm-associated S. aureus and planktonic and biofilm-associated Streptococcus. They focus on the decreased tolerance of P. aeruginosa and determine that a ΔlasR mutant of P. aeruginosa does not display increased tobramycin susceptibility in the mixed community. One of the phenotypes associated with a ΔlasR mutant is an overproduction of phenazines. The authors find that by deleting the phenazine biosynthesis genes from ΔlasR, they can restore community-acquired susceptibility. They further investigate this phenomenon by showing that a specific type of phenazine, PCA, is significantly increased in mixed communities with the ΔlasR mutant compared to WT. Finally, they demonstrate that adding a specific phenazine, pyocyanin, to mixed communities can restore the tolerance of WT P. aeruginosa.

Strengths:

With this study the authors address a very important problem in infectious disease microbiology - our in vitro drug susceptibility assays do a poor job of mimicking the infection environment and therefore do a poor job of predicting how effective particular drugs will be for a particular patient. By demonstrating how an infection-relevant community modifies tolerance to a clinically relevant drug, tobramycin, the authors identify specific interactions that could be targeted with therapeutics to improve our ability to treat the chronic infections associated with CF. In addition, this study provides a framework for how to effectively model polymicrobial infections in vitro.

The experiments in the paper are very rigorous and well-controlled. Statistical analysis is appropriate. The paper is very well-written and clear.

The authors do an admirable job of using in silico analysis to inform their in vitro studies. Specifically, they provide a comprehensive rationale for why they chose and studied the specific community they did.

The authors provide a very robust dataset which includes determining how strain differences of each of their four community members affect community dynamics and antibiotic tolerance. These types of analyses are laborious but very important for understanding how broadly applicable any given result is.

Weaknesses:

The authors very clearly and convincingly demonstrate that WT P. aeruginosa becomes more susceptible to tobramycin in their mixed community. Our ability to turn these types of observations into therapeutic development depends on mechanistic insight. That said, it is unclear if the authors can make any solid conclusions about what specific aspects of the polymicrobial environment cause WT P. aeruginosa to become more susceptible. The authors make a compelling case that increased phenazine production by the ΔlasR mutant restores tolerance in the mixed community and that exogenous phenazine addition increases the survival of WT P. aeruginosa in the mixed community. However, it remains a plausible explanation that the effects of phenazines on tobramycin susceptibility are independent of the initial observation that WT. P. aeruginosa becomes susceptible to tobramycin in the mixed community.

Some aspects of the methodology are unclear. Specifically, the authors note that they use a specific sealed container system to grow their strains in anoxic conditions, which mimic portions of CF sputum. However, it is unclear how the authors change medium over the course of their experiments, or how they test susceptibility to tobramycin, without exposing the cells to oxygen. It is well understood that oxygen exposure impacts the susceptibility of P. aeruginosa to tobramycin, so it is very important that the methodology involving oxygen deprivation and exposure is described in detail.

Reviewer #2 (Public Review):

Millar et al. have used multimodal brain magnetic resonance imaging (MRI) data from subjects with preclinical AD (cognitively normal with amyloid pathology), cognitive impairment, and matched cognitively normal (CN) subjects to predict the subject's age (i.e. brain age). To do so they have trained a Gaussian Process Regression (GPR) model using data from 3 datasets. The predicted age was then compared to the chronological age to calculate the brain age gap (BAG). Using resting-state functional MRI (rsfMRI) they calculated functional connectivity across 300 brain regions. Similarly, T1w MRI images have been used to calculate volumetric measures across 68 cortical and 33 subcortical regions. The results were then used as features in two separate models resulting in FC-BAG and Vol-BAG measures, respectively. A third model is then devised by "stacking" the previous model's predictions as features in a new model resulting in Vol+FC-BAG. The models were then applied to the test dataset and BAG measures were calculated for subjects in CN, preclinical AD, and cognitively impaired (CI) groups. All models show significantly higher BAGs for subjects with cognitive impairments. Finally, the authors have examined the relationship between BAGs measures and Amyloid Markers, Tau Markers, Neurodegeneration Markers, and Cognition.

Strengths:

The manuscript is very clearly written. The study is well designed and for the most parts the method section contains all the necessary information to replicate the steps. The sample size is comparable to similar studies investigating brain age in clinical populations and the inclusion and exclusion criteria are clearly stated. In order to avoid bias and overfitting in the predictive models the authors have (1) used a separate training (+validation) set and test set and (2) removed any subjects with potential pathology or impairment from the training set. Using data points on the plots as well as a combination of boxplot+violin plots makes the results clear and data distributions are provided when necessary. Furthermore, chronological age has been used as a covariate to correct the relationship between BAG and age, making the results more interpretable and reliable. Focusing on the stronger section of the results, the study shows a higher Vol-BAG (or Vol+FC-BAG) in subjects with CI, which is significantly related to higher Amyloid PET, higher PET and CSF-related Tau measures, and lower global cognition. In conclusion, the Vol-BAG results are clear and clinically relevant, based on a model with a reasonable prediction performance.

Weaknesses:

The manuscript follows authors' recently published work on FC-BAG in symptomatic and preclinical Alzheimer disease (Millar et al, Neuroimage 2022) by adding T1w volumetric measures from Freesurfer. Based on the results the additional value of rsfMRI connectivity is at best marginal. The FC-BAG model has a weak performance and is outperformed by Vol-BAG. The marginal benefit of adding FC-BAG to the Vol-BAG model is around 10% which comes with the additional cost of a new and more computationally demanding modality as well as making the biological relevance of the model almost untraceable. The preclinical findings reported as "Specifically, FC-BAG may capture a unique biphasic response to preclinical AD pathology" while potentially interesting are based on an unreliable model (FC-BAG) and can be a spurious finding. These results need further validation both robustness analysis within the current sample and in independent datasets. The other findings related to preclinical AD are based on the hippocampal volume which as the authors have mentioned in the discussion limitation is part of the features included in the Vol-BAG model and absent from FC-BAG.

In conclusion, the manuscript has clear findings based on the Vol-BAG model differentiating the cognitively impaired subjects from other groups and these results relate to the clinical severity of the disease as measured by Amyloid Markers, Tau Markers, and Cognition.

Reviewer #2 (Public Review):

Here the authors used viral expression and two-photon imaging, a very demanding approach, to explore the transport dynamics of three membrane markers (Neuropeptide Y-dense core vesicles, LAMP1-endolysosomes and RAB7-late endosomes) in vivo in the mouse brain. This allowed deciphering for the first time anterograde and retrograde velocities in vivo rather than in cultured neurons. The authors showed that the different vesicular compartments have different anterograde and retrograde velocities, pausing at synapses. They further used brain slices to explore the effect of increased calcium levels.

Major strengths reside in the novelty of the approach (in vivo!).

The main weakness relates to the lack of novel mechanisms and the difficulty of using such a sophisticated setup on a routine basis.

This is a technical 'tour-de-force', a clear reference article for future studies addressing vesicular transport in vivo.

Of course, one would be curious to see many more markers studied in this setup. Also, the same study in mouse mutants would be extremely interesting.

Reviewer #2 (Public Review):

Hebart et al., present a large-scale multi-model dataset consisting of fMRI, EEG, and behavioral similarity measures towards the study of object representation in the mind and brain. The effort is immense, the methods are rigorous, and the data are of reasonable quality, the demonstrative analyses are extensive and provocative. (One small note regarding one leg of this multi-modal dataset is that the fMRI design consisted of a single image presentation for 0.5s without repetitions for most of the images; this design choice has particular analysis implications, e.g. the dataset will have more power when leveraging a priori grouping of images. However, unlike other datasets of this kind, here the number of images and how they were selected does support this analysis mode, e.g. multiple exemplars per object concept, and rich accompanying meta-data and behavioral data.)

The manuscript is well-written, and the THINGs website that lets you explore the datasets is easy to navigate, delivering on the promise of making this an integrated, expanding worldwide initiative. Further, the datasets have clear complementary strengths to recent other large-scale datasets, in terms of the ways that the images were sampled (not to mention being multi-modal)-thus I suspect that the THINGs dataset will be heavily used by the cognitive/computational/neuroscience research community going forward.

Reviewer #2 (Public Review):

I found the study and findings important and largely convincing. While some of these observations might continue to refine with further sampling, the value of these data for what they already are, the novelty in the comparison, and the strength and importance of these results for our understanding of deep-sea marine ecosystems and variation thereof, are all exemplary.

My primary critique is the near-absence of statistical analyses in the current version of the manuscript that are necessary to support the many descriptive observations made with a more formal hypothesis testing framework, as well. Developing an appropriate framework for such analyses throughout the paper, including consideration of the multiple tests that will be performed. This is important for many reasons, including by providing a more formal sense of uncertainty in the conclusions to readers, given the understandable sampling limitations. Planning and conducting these analyses will require considerable work.

Reviewer #2 (Public Review):

This manuscript builds on previous work from the Pearson lab showing that one aspect of the trisomy 21 phenotype could be caused by an increase in the amount of pericentrin (PCNT), a component of the centrosome. The earlier work showed that the increase in PCNT is sufficient to reduce the frequency of ciliation in trisomy 21 cells and that the increased PCNT is often in the form of protein aggregates along microtubules proximal to the centrosome (in a preprint). Here they use several models with 3 or 4 copies of human chromosome 21 or the mouse equivalent to examine the defect in cilium formation at the level of specific proteins and the signaling function of the cilium. This is a substantial contribution that furthers the evidence for the authors' favored model of excess PCNT causing some form of pericentrosomal crowding that hinders the ability of other molecules, complexes, and/or vesicles to get to the right place at the right time. The work makes excellent use of cell lines and mice previously generated for the study of trisomy 21, making for well-controlled experiments in a situation where this is particularly important (only 1.5 x increased expression). One does wish that it were possible to do the PCNT depletion in more of the experiments than the single one shown, but that is understandable given the amount of work required and the uncertainty associated with RNAi depletion.

Reviewer #2 (Public Review):

Here, McKay, et al. describe a new automated system to feed killifish, and use it to explore dietary restriction effects on killifish lifespan and to develop an associative learning assay, two important goals in the KF/longevity field.

Fig. 1-2- The first figures focus on the design and evaluation of the feeding system. It appears that the feeding system works well and achieves what the authors set out to do.<br /> Fig. 3 explains the DR and overfeeding setup, and effects on growth and reproduction; demonstrates that the automated feeding system does achieve DR.<br /> Fig. 4 explains the DR setup and results on male and female KF, highlighting the fact that DR only extends the lifespan of males. This sex-specific effect seems somewhat surprising, and warrants further follow-up studies.<br /> Fig. 5 describes the associative learning assay, which is based on the ability of the fish to sense a red light and learn that it is associated with feeding. It is great that the authors have been able to develop a learning assay, which will no doubt become an important tool in the killifish researcher's arsenal, but additional experiments are necessary to increase the general impact of the work.

Overall, while the results seem sound, the current version of the manuscript may be pitched to a small audience (killifish researchers) who will benefit from the development of this methodology. Perhaps the paper could be re-structured to focus less on the methodology and more on the results, fleshing out the associative learning results even more (are there mutants that extend the length of associative learning? Does it require conserved genes? etc). Further exploration of the sex-specific effects of DR on lifespan (why does this only affect males) would also raise the general interest of the work, but both the DR and associative learning aspects of the paper would need to be studied quite a bit more to move this beyond a methods paper.

Reviewer #2 (Public Review):

In the current manuscript, Feng et al. investigate the mechanisms used by acute leukemia to get an advantage for the access to the hematopoietic niches at the expense of normal hematopoietic cells. They propose that B-ALLs hijack the niche by inducing the downmodulation of IL7 and CXCL12 by stimulating LepR+ MSCs through LTab/LTbR signaling. In order to prove the importance of LTab expression in B-ALL growth, they block LTab/LTbR signaling either through ligand/receptor inactivation or by using a LTbR-Ig decoy. They also show that CXCL12 and the DNA damage response induce LTab expression by B-ALL. They finally propose that similar mechanisms also favor the growth of acute myeloid leukemia.

Although the proposed mechanism is of particular interest, further experiments and controls are needed to strongly support the conclusions.

1/ Globally, statistics have to be revised. The authors have to include a "statistical analysis" section in the Material and Methods to explain how they proceeded and specify for each panel in the figure legend which tests they used according to the general rules of statistics.

2/ The setup of each experiment is confusing and needs to be detailed. Cell numbers are not coherent from one experiment to the other. As an example, there are discrepancies between Fig1 and Fig2. Based on the setup of the experiment in Fig.2 (Injection of B-ALL to mice followed by 2 injections of treatment every 5 days), mice have probably been sacrificed 12-14 days post leukemic cell injection. However, according to Fig.1, B cells and erythroid cells at this time point should be decreased >10 times while they are only decreased 2-4 times in Fig.2. This is also the case in Fig.4B-J or Fig.5D with even a lower decrease in B cells and erythroid cells despite a high number of leukemic cells. Please explain and give the end point for each experiment in each figure (main and supplemental).

3/ To formally prove that the observed effect is really due to LTab/LTbR signaling, the authors must perform further control experiments. LTbR signaling is better known for its positive role on lymphocyte migration. They cannot rule out by blocking LTbR signaling, that they inhibit homing of leukemic cells into the bone marrow through a systemic/peripheral effect, more than through an impaired crosstalk with BM LepR+ cells. They must confirm for inhibited/deficient LTbR signaling conditions, as compared to control, that similar B-ALL numbers home to the BM parenchyma at an early time point after injection. Furthermore, they cannot exclude that the effect on the expression of IL7 (and other genes), and consequently the effect on B cell numbers, is not simply due to the tumor burden. Indeed, B-ALL numbers/frequencies are different between control and inhibited/deficient signaling conditions at the time of analysis. The analyses should thus be performed at similar low and high tumor burden in the BM for both control and inhibited/deficient LTbR signaling conditions.

4/ LT/LTbR signaling is particularly known for its capacity to stimulate Cxcl12 expression. How do the authors explain that they see the opposite?

5/ The authors show that CXCL12 stimulates LTa expression in their cell line. They then propose that CXCR4 signaling in leukemic cells potentiates ALL lethality by showing that a CXCR4 antagonist reverses the decrease in IL7 and improves survival of the mice. This experiment is difficult to interpret. CXCL12 has been shown to be important for migration/retention of B-ALL in the BM and the decreased tumor burden is probably linked to a decreased migration more than an impaired crosstalk with LepR+ cells (see also point 3). If CXCL12 increases LTab expression, CXCR4 blockade should do the opposite. This result should be presented. The contradiction is that if B-ALLs induce a decrease in CXCL12 in the BM (in addition to IL7) and that CXCL12 regulates LTab levels, leukemic cells should be exhausted. Similarly, IL7 has been previously shown to stimulate LTab expression and B-ALL cells express the IL7R. Again, a decrease in IL7 should be unfavorable to B-ALL. How do they explain these discrepancies?

6/ In Supp 4A, RAG-/- mice are blocked at the pro-B cell stage and do not have pre-B cells. Please compare LTa and LTb expression by Artemis deficient pre-B cell to wt pre-B cells. In this experiment, the authors show that similarly to B-ALL artemis-/- pre-leukemic pre-B cells express high levels of LTab and induce IL7 downmodulation. Using mice deficient for LTbR in LepR+ cells, they show that IL7 expression is increased. However, in opposition to leukemic cells (see Figure 4F), pre-leukemic cells are increased in absence of LTab/LTbR signaling. Please explain this discrepancy. The authors use only one B-ALL model cell line for their demonstration (BCR-ABL expressing B-ALL). Another model should be used to confirm whether LTab/LTbR signaling does favor leukemic/pre-leukemic B cell growth.

7/ Pre-B cells are composed of large pre-B cells (pre-BCR+) and small pre-B cells (pre-BCR-). BCR-ABL B-ALL cells express the pre-BCR. What is the level of expression of LTa and LTb by each of these 2 subsets as compared to BCR-ABL B-ALL?

Reviewer #2 (Public Review):

This modeling paper looks at how single spikes in the cortex are able to evoke patterns of sequential neural response in the surrounding neural network, an effect observed in the visual cortex of turtles, rodents, and the middle temporal cortex of humans, and possibly generalizable across many other species and brain areas. The results are anchored by population recordings from the turtle cortex, recapitulating those data and exploring how single spikes might be able to have such an outsized effect on broad-scale neural activity. The authors aim to show which kinds of network connectivity support this kind of response.

The results reveal that sparse, but strong connections in a neural network are the necessary ingredient for the reliable triggering of network sequences by single spikes. Dense, but weaker networks can give rise to different sequences when triggered. One of the most intriguing results of the paper is the interaction of sequences triggered by different single spikes that are part of a strong, sparse sub-network. These concurrent sequences appear to be separable and potentially supported a wide repertoire of response states to very targeted and combinatorially expressive inputs.

The work is careful and well-executed and the work will be of interest to systems and computational neuroscientists. In particular, the work speaks to how to reliably trigger a wide array of broad-scale population sequence patterns. This could be important for signaling salient, complex external stimuli, especially in a dynamic environment. The work will also be of interest to the machine learning community working on recurrent neural networks and their computational capacity.

Reviewer #2 (Public Review):

Grasses develop morphologically unique stomata for efficient gas exchange. A key feature of stomata is the subsidiary cell (SC), which laterally flanks the guard cell (GC). Although it has been shown that the lateral SC contributes to rapid stomatal opening and closing, little is known about how the SC is generated from the subsidiary mother cell (SMC) and how the SMC acquires its intracellular polarity. The authors identified BdPOLAR as a polarity factor that forms a polarity domain in the SMC in a BdPAN1-dependent manner. They concluded that BdPAN1 and BdPOLAR exhibit mutually exclusive localization patterns within SMCs and that formative SC division requires both. Further mutant analysis showed that BdPAN1 and BdPOLAR act in SMC nuclear migration and the proper placement of the cortical division site marker BdTANGLED1, respectively. This study reveals a unique developmental process of grass stomata, where two opposing polarity factors form domains in the SMC and ensure asymmetric cell division and SC generation.

The findings of this study, if further validated, are novel and interesting. However, I feel that the data presented in the current manuscript do not fully support some crucial conclusions. The lack of dual-color images is the weakest point of this study. If it is technically impossible to add them, alternative analyses are needed to validate the main conclusions.

1. Is BdPOLAR-mVenus functional? Although the authors interpret that weak BdPOLAR-mVenus expression partially rescued the bdpolar mutant phenotype in Fig. S4D, the localization pattern visualized by BdPOLAR-mVenus may not be completely reliable with this partial rescue activity.<br /> 2. Regardless of the functionality of the tagged protein, the authors need to provide more information on their localization. For example, is there a difference in polarity pattern depending on expression level? Does overexpressed BdPOLAR-mVenus invade the BdPAN1 zone? In such cases, might the loss of BdPOLAR polarity in the bdpan1 mutant be a side effect of overexpression, not PAN1 exclusion? Does BdPOLAR expression (no tag) show a dose-dependent effect, similar to the mVenus-tagged protein?<br /> 3. A major conclusion of this study was that the polarity domains of BdPOLAR and BdPAN1 are mutually exclusive. However, not all the cells in the figures were consistent with this statement. For example, the BdPOLAR signals at the GMC/SMC interphase appear to match BdPAN1 localization (compare 0:03 s in Video 1 and 0:20 s in Video 2 [top cell]). The 3D rendered image in Fig. 2F shows that BdPOLAR is excluded near the GMC on the front side of the SMC, where BdPAN1 is not localized. Some cells did not exhibit polarization (Fig. 3A, bottom left; Fig. 3E, bottom left). The most convincing data are the dual-color images of these two proteins. Otherwise, a sophisticated image analysis is required to support this conclusion.<br /> 4. Another central conclusion was that BdPOLAR was excluded at the future SC division site, marked with BdTANGLED1. However, these data are also not very convincing, as such specific exclusion cannot be seen in some figure panels (e.g., Fig. 3A, bottom left; Fig. 3E, all three cells on the left). If dual-color imaging is not feasible, a quantitative image analysis is needed to support this conclusion.<br /> 5. I could not find detailed imaging conditions and data processing methods. Are Figs. 2B and 2E max-projection or single-plane images? If they are single-plane images, which planes of the SMC are observed? In addition, how were Figs. 2C and 2F rendered? (e.g., number of images, distance intervals, processing procedures). This information is important for data interpretations.<br /> 6. [Minor point] The authors should clearly describe where BdPAN1 is expressed and localized. Is it expressed in the GMC and localized at the GMC/SMC interface? Alternatively, is it expressed and localized in the SMC?

Reviewer #2 (Public Review):

In the paper by Chadwick et al., the authors identify the molecular determinants of CO2 tolerance in the human fungal pathogen Cryptococcus neoformans. The authors have screened a collection of deletion mutants to identify the genes that are sensitive at 37oC (host temperature) and elevated CO2 levels. The authors identified that the genes responsible for CO2 sensitivity are involved in the pathways responsible for thermotolerance mechanisms such as Calcineurin, Ras1-Cdc24, cell wall integrity, and the Regulator of Ace2 and Morphogenesis (RAM) pathways. Moreover, they identified that the mutants of the RAM pathway effector kinase Cbk1 were most sensitive to elevated temperature and CO2 levels. This study uncovers the previously unknown role of the RAM pathway in CO2 tolerance. Transcriptome data indicates that the deletion of CBK1 results in an alteration in the expression of CO2-related genes. To identify the potential downstream targets of Cbk1, the authors performed a suppressor screen and obtained the spontaneous suppressor mutants that rescued the sensitivity of cbk1 mutants to elevated temperature and CO2. Through this screen, the authors identified two suppressor groups that showed a modest improvement in growth at 37{degree sign}C and in presence of CO2.<br /> Interestingly, from the suppressor screen, the authors identified a previously known interactor of Cbk1 which is SSD1, and an uncharacterized gene containing a putative Poly(A)-specific ribonuclease (PARN) domain named PSC1 (Partial Suppressor of cbk1Δ) which acts downstream of Cbk1. Deletion of these two genes in cbk1 null mutants rescued the sensitivity to elevated CO2 levels and temperature but did not fully rescue the ability to cause disease in mice.

This study highlights the underappreciated role of the host CO2 tolerance and its importance in the ability of a fungal pathogen to survive and cause disease in host conditions. The authors claim to gain insight into the genetic components associated with carbon dioxide tolerance. The experimental results including the data presented, and conclusions drawn do justice to this claim. Overall, it is a well-written manuscript. However, some sections need improvement in terms of clarity and experimental design.

• One major drawback of the study is the virulence assay performed to test the ability of cbk1 mutants to cause the disease in the mouse model. The cbk1 null mutants are thermosensitive in nature. Using these mutants, establishing the virulence attributes in mice would undermine the mutants' ability to infect mice as they won't be able to survive at the host body temperature.

• The rationale for choosing the genes to test further is not clear in two instances in the study. a) From a list of 96 genes, how do the authors infer the pathways involved? Was any pathway analysis performed that helped them in shortlisting the pathways that they subsequently tested? A GO term analysis of the list of genes identified through the genetic screen would be more helpful to get an overview of the pathways involved in CO2 tolerance. b) The authors do not clearly mention why they chose only four genes to test for the CO2 sensitivity out of 16 downregulated genes identified from the nano string analysis.

• It would be more useful to the readers if the authors could also include a thorough analysis of the presence of the putative PARN domain-containing protein across various fungal species rather than mentioning that it is only observed in C. neoformans and S. pombe. Also, the authors may want to discuss the known role(s) of SSD1, if any, in pathogenic ascomycetous yeasts so that the proposed functional divergence is supported further.

Reviewer #2 (Public Review):

The authors re-analysed two previously published metagenomic datasets to test how diversity at the community level is associated with diversity at the strain level in the human gut microbiota. The overall idea was to test if the observed patterns would be in agreement with the "diversity begets diversity" (DBD) model, which states that more diversity creates more niches and thereby promotes further increase of diversity (here measured at the strain-level). The authors have previously shown evidence for DBD in microbiomes using a similar approach but focusing on 16S rRNA level diversity (which does not provide strain-level insights) and on microbiomes from diverse environments.

One of the datasets analysed here is a subset of a cross-sectional cohort from the Human Microbiome Project. The other dataset comes from a single individual sampled longitudinally over 18 months. This second dataset allowed the authors to not only assess the links between different levels of diversity at single timepoints, but test if high diversity at a given timepoint is associated with increased strain-level diversity at future timepoints.

Understanding eco-evolutionary dynamics of diversity in natural microbial communities is an important question that remains challenging to address. The paper is well-written and the detailed description of the methodological approaches and statistical analyses is exemplary. Most of the analyses carried out in this study seem to be technically sound.

The major limitation of this study comes with the fact that only correlations are presented, some of which are rather weak, contrast each other, or are based on a small number of data points. In addition, finding that diversity at a given taxonomic rank is associated with diversity within a given taxon is a pattern that can be explained by many different underlying processes, e.g. species-area relationships, nutrient (diet) diversity, stressor diversity, immigration rate, and niche creation by other microbes (i.e. DBD). Without experiments, it remains vague if DBD is the underlying process that acts in these communities based on the observed patterns.

Another limitation is that the total number of reads (5 mio for the longitudinal dataset and 20 mio for the cross-sectional dataset) is low for assessing strain-level diversity in complex communities such as the human gut microbiota. This is probably the reason why the authors only looked at one species with sufficient coverage in the longitudinal dataset.

Analyzing the effect of diversity at a given timepoint on strain-level diversity at a later timepoint adds an important new dimension to this study which was not assessed in the previous study about the DBD in microbiomes by some of the authors. However, only a single species was analysed in the longitudinal dataset and comparisons of diversity were only done between two consecutive timepoints. This dataset could be further exploited to provide more insights into the prevailing patterns of diversity.

Finally, the evidence that gene loss follows increase in diversity is weak, as very few genes were found to be lost between two consecutive timepoints, and the analysis is based on only a single species. Moreover, while positive correlation were found between overall community diversity and gene family diversity in single species, the opposite trend was observed when focusing on pathway diversity. A more detailed analysis (of e.g. the functions of the genes and pathways lost/gained) to explain these seemingly contrasting results and a more critical discussion of the limitations of this study would be desirable.

Reviewer #2 (Public Review):

The main analysis performed in the paper is to determine causal associations of 118 highly correlated lipid metabolites with coronary heart disease (CHD), using summary data from two genome-wide association studies, with 148 genetic variants identified for the exposures. A standard multivariable MR analysis is problematic in this case, as the genetic variants are not simultaneously relevant for all exposures, as clearly indicated by very low values of the conditional F-statistics. In order to reduce the multicollinearity problem, the use of (sparse) principal components techniques is proposed. For the summary data used here, this entails determining the (sparse) principal components from the matrix of the estimated univariate associations of the exposures and the genetic markers. This implicitly constructs linear combinations of the exposures. In a simulation study, this approach is shown to work well for determining whether an exposure has a causal association with the outcome. A conditional F-statistic is developed to evaluate the strength of relevance of the genetic markers for the principal components. In the application, these F-statistics show that instruments are jointly relevant for the transformed exposures. For the sparse methods, the transformed exposures are loaded on VLDL, LDL, and HDL traits, hence obtaining causal estimates for intervening on biologically meaningful pathways.

The dimension reduction techniques and the results obtained are very interesting. As the analysis is performed on summary statistics, the univariate associations are treated as data, on which to perform the principal components analysis. This could be explained more and contrasted with a standard PCA when one has all the individual-level data available.

Reviewer #2 (Public Review):

The authors addressed a timely and challenging topic, namely the role played by red blood cells (RBCs) and blood plasma in Covid-19 disease.

A remarkable feature reported here is that RBC from patients exhibits a notable morphological change, whereas when suspended in plasma control (healthy) exhibit normal shapes. Conversely, RBCs from healthy donors suspended in patients' plasma undergo similar morphological alteration as do RBCs of patients suspended in their plasma. Another important fact reported here is that RBCs affect plasma composition in a nontrivial way.

The data reported here cover a large panel of features, ranging from RBC morphological changes, plasma metabolites, and protein alteration, to collective RBC formation, in the form of clusters. They should constitute a precious enrichment of relevant information regarding the intricate response of organisms to the Covid-19 virus.

This work will be of the potential impact on the community aiming to decipher the multifactorial impacts of blood components on patients suffering Covid-19.

Reviewer #2 (Public Review):

This study models the fitness costs of loss-of-function mutations in a large cohort of a human database of 55,855 individuals. The modeling indicates different values for autosomal genes, X-linked genes, and those present in the pseudo-autosomal regions of the X and Y chromosomes. The study details the frequency of de novo mutations in zygotes and examined the relationship to a few specific genetic diseases. The authors have composed a well-written manuscript, have explicitly detailed their assumptions, and have noted caveats to interpretations. The results are a valuable documentation of the effects of loss-of-function mutations in humans.

Reviewer #2 (Public Review):

Interestingly, prior analysis of the 385A allele indicated a post-translational mechanism that led to instability of the protein and an ~50% reduction in protein concentrations and FAAH activity. In addition, FAAH degrades AEA, one of several known endocannabinoids, suggesting that FAAH is a significant part of the endocannabinoid signaling although there are several other endocannabinoids that are not affected by FAAH.

At the basal state, normal chow and home cage conditions, wild type mice were not different from homozygous mutant FAAH mice in terms of body weight and body composition. However, the FAAH mutants had reduced food intake that was compensated for by lower energy expenditure. This finding strongly suggests that compensatory mechanisms are in play during lifelong changes in strengths of AEA signaling.

The authors go on to perform increasingly shorter durations of manipulations of glucocorticoid manipulations (down to several hours) to examine the impact of the FAAH mutation. Thus, the authors are able to conclude that the FAAH mutation leads to acute changes of feeding.

Examination of the biochemical signaling pathway showed that AMPK activity is affected by GC/FAAH experimental manipulation although the relevance of the finding should be somewhat tempered by the later studies in hypothalamic AGRP neurons and FAAH since the measures were not neuron specific.

Finally, the authors examine the role of FAAH expression in hypothalamic AGRP neurons since their measures of AEA concentrations showed changes only in the hypothalamus after CORT treatments. Virally mediated knockdown of FAAH, using a AAV CRISPR/Cas9 single vector system, indicated that knockdown of FAAH in AGRP neurons is sufficient to recapitulate the authors' findings on GC-modulated feeding.

The data are convincing and settles the issue of variability in the evidence regarding the role of FAAH genetic variants in feeding.

Reviewer #2 (Public Review):

In this paper, the authors investigate the intriguing question of what orientation reference frame the visual selectivity of neurons in the IT cortex is expressed in - a world-centered gravitational one, or a retinal one? To address this, the authors physically rotate a monkey to dissociate a gravitational from a retinal reference frame. They find surprising and compelling evidence that many cells encode selectivity in a gravitational frame. The finding raises questions about whether the function of the IT cortex is solely object recognition, or whether it might play an important role in physical scene understanding.

In general, I found the paper clearly written, the analyses appropriate, and the results supportive of the conclusions. I think the work should spur new thinking about what the IT cortex is accomplishing. The notion that IT cells are receiving vestibular signals is likely to be unsettling for many who think of it as simply the endstage of a convolutional neural network.

Reviewer #2 (Public Review):

In this report, the authors evaluate the possibility that LEC neurons send direct projection onto MEC cells, thus revising the current model of LEC and MEC sending independent inputs to the DG, whose role is to eventually combine both inputs. They demonstrate that L2a SCs in the LEC that receive neocortical inputs, send collaterals to L1 MEC, thus identifying a new indirect route by which MEC neurons can integrate cortical information. Vandrey et al., show that L2a SCs in the LEC contact directly with both inhibitory and excitatory cells in the MEC, but superficial principal cells with a higher probability. Therefore, L2 LEC neurons can exert control of the MEC activity, thus shaping its inputs to the hippocampus. By controlling the firing activity in superficial MEC, this newly identified LEC-MEC connection may participate in the combination of spatial inputs with sensory and high-order signals and thus "provide a substrate for the integration of 'what' and 'where' components of episodic memories".

The manuscript is well-written and the experimental design is well-suited to answer the question. The data presented here is a thorough, well-explained, and detailed work describing a new communication route between the LEC and MEC.

Reviewer #2 (Public Review):

The study aims to characterize the role of lncRNA H19 in senescence and proposes a mechanism involving CTCF and the activation of p53. The authors suggest that H19 loss induces let7b-mediated repression of EZH2, which is a critical component in the regulation of senescence-associated genes. Additionally, the authors state that H19 is required for inhibition of senescence by the mTOR inhibitor rapamycin.

The experiments appear to be performed to a high standard, and the individual observations, and conclusions about the importance of the individual players in senescence appear solid. For example, the authors convincingly show that H19 decreases in expression in aged cells/tissues and that its knockdown leads to entry into senescence. These results are consistent with recent studies in other systems (e.g., ref 38). Also, the knockdown of CTCF convincingly leads to senescence. However, these observations are largely not very surprising/novel. The premise of the manuscript is a connection between these components into a particular "axis" that regulates entry into senescence. This connection between the different regulators studied (H19, CTCF, EZH2, p53), and in particular, their specificity, which is key to the proposed "axis" remains insufficiently supported, and many of the results, unfortunately, appear to be over-interpreted.

Major comments

1. In Figure 1, the authors claim that H19 levels are reduced during aging in vitro and in vivo and that H19 levels are maintained by rapamycin treatment. To state the connection between H19 and rapamycin and its relation to aging, there is a need to show what happens in "young" cells treated with rapamycin.

Furthermore, the authors state that H19 "is essential for the inhibitory effect of rapamycin on cellular senescence". There doesn't appear to be sufficient evidence to support such a claim; additional data emphasizing the direct connection between H19 and rapamycin is needed - e.g., show that in H19-null cells rapamycin does not affect senescence.

2. CTCF is a general regulator involved in various cellular processes and supporting progression through the cell cycle; therefore, its perturbation can lead to global effects on cell health that are not necessarily related to H19. The data shown in figure 2 is insufficient to indicate a direct correlation between CTCF and H19. This will require showing that mutating specifically the CTCF binding sites near H19 affects senescence.

The same applies to the connection between H19 and let-7b shown in Figure 5. It is not very surprising that let-7b, a general antagonist of proliferation, positively regulates senescence. Here as well, the direct connection to H19 is weak. Can the authors rescue the cells that enter senescence following H19 depletion by H19 expression? If so - is this rescue capacity lost when let-7 sites are mutated? Is it possible to rescue by expressing an artificial let-7 sponge instead of H19? Otherwise, let-7b could very well be another factor related to senescence and/or regulated, but not the main mediator of the effects of H19, or part of an axis that includes H19, as proposed in the manuscript.

3. In figures 2d,3f,5i/j the authors present only representative tracks and regions from CUT&Tag-experiments, and its not clear to what extent these changes are significant when considering genome-wide data, replicates etc., and so these data are uninterpretable. This is important, as these panels are used as evidence for specific connections between members of the axis. The authors should provide a statistical test for all the regions in the genome, based on replicates, and show that these changes are significant to use these data to support their model. Otherwise, the specific connection between CTCF and H19 remains weak, and the specific change in p53 regulation of CTCF in the context of senescence is not convincing. In any case, the number of replicates and the QC of the data should be presented, and the data should be made available to the reviewers.

4. The authors state in the Discussion that the mechanism that lead to decreased H19 expression as part of the senescence program consists of two phases: an acute response driven by p53 activation and a prolonged response dictated by the loss of CTCF. There doesn't appear to be enough evidence to support this claim, as the individual experiments don't measure any such bi-phasic phenomena.

Reviewer #2 (Public Review):

In this manuscript, Eyndhoven and colleagues develop an experimental and analytical setup to test the role of cell-intrinsic factors in guiding fate decisions to viral infections. The study is motivated by the observations that early antiviral response mediated by type 1 interferon (IFN-1) is not fully penetrant in response to virus, and is initiated only in 1-3% of the cells. Using a combination of IFN-1 reporter system, automated image segmentation, DNMT inhibitors, and Luria-Delbrück fluctuation test in a murine cell line model, the authors state that cell intrinsic factors guide IFN-1 response in rare cells. This response (measured with IRF7 translocation) is predetermined and heritable over several generations. Lastly, the authors report cell density effects on IFN-1 response, a phenomena the authors refer to as "quorum sensing", and rationalize their observations with an ODE-based mathematical model.

Overall, this is a well-designed, well-controlled, and timely study, given the rapidly increasing reports documenting heritable cell states that can guide fate choices in single cells. The manuscript has elegant experiments and is generally clear to follow and the figures are easy to understand. While the authors largely state what they find, some of their claims and terminology are not supported by their experiments. Additionally, many figures lacked scale bars, axis, labels, and detailed captions. The authors are also encouraged to cite a wider set of seminal studies, acknowledging their contributions to transient cell states guiding fate choices.

Reviewer #2 (Public Review):

This manuscript by Lehman et al. details the structural characterization of human Ferroportin, which builds on the previous structural characterisation of this protein. Here, through the use of synthetic nanobodies, the authors capture the protein in the outward-facing state that has been obtained previously, and a new conformation in an occluded state, information which would advance understanding of the Ferroportin transport mechanism. In addition, the authors capture Ferroportin in complex with the first clinical-stage Ferroportin inhibitor, Vamifeport, which provides insight that could be used to improve inhibitor efficacy to treat human disease. The structural data is very well supported by clear, well-executed, and informative binding and transport studies. These data reveal that the purified protein is functionally active, able to interact with the peptide-based inhibitor hepcidin in addition to Vamifeport and that hepcidin and vamifeport bind competitively. Site-directed mutagenesis and binding assays were used to convincingly validate the Vamifeport binding site.

Overall, the conclusions in this manuscript are well supported by the data, in particular those relating to inhibitor binding. However, as the authors point out, the occluded state captured here contains an unexpectedly large aqueous cavity compared to the size of the transported substrate. With this peculiar observation in mind, the requirement for the presence of Sy3 nanobody to capture this state and the positioning of the nanobody in between the 2 lobes of the transporter, raises the question of whether this conformation is physiologically relevant, or whether its formation is merely a consequence of Sy3 binding.

Reviewer #2 (Public Review):

This paper from the Fyodorov lab reports the isolation of a native protein complex of SUUR, a Drosophila SNF2-related factor, in a complex with Mdg4, an established chromatin boundary protein. The discovery of this native complex, called SUMM4, was enabled by the development of a mass spec-linked proteomic analysis of fractions from an unbiased, conventional multi-step chromatographic purification of low-abundance protein complexes. The authors validate the native interactions by co-immunoprecipitation and show further with recombinant proteins that SUUR displays ATPase activity, a property not previously shown, and which is stimulated by Mdg4. From a functional perspective, authors demonstrate that both components SUUR and Mdg4 mediate activities of the Drosophila gypsy insulator that blocks enhancer-promoter interactions and acts as a heterochromatin-euchromatin barrier, and moreover, has a role in the under-replication of intercalary heterochromatin.

Overall, this work is a substantial contribution to the field in two respects. First, it provides a new approach to the identification of novel native complexes that are of low abundance and difficult to isolate and identify by conventional biochemistry and mass spectrometry. Second, the interaction between Mdg4 and SUUR is novel and offers an ATP-driven pathway to be further investigated for understanding the mechanism of insulator (gypsy) function. Together, these advances are supported by the compelling quality and quantity of data. However, the paper does not read smoothly and can benefit from rewriting for readers who are not familiar with mass-spec proteomics or Drosophila biology.

Reviewer #2 (Public Review):

Kintscher et al present a nice study on the responses of Adora2a and D1R expressing cells in the tail of the striatum/amygdala transition zone during auditory fear conditioning. Overall the conclusions are that (1) D1R cells show plasticity in activity patterns during the task, with the emergence of tone/movement co-modulated cells; (2) Adora2a cells show less of such changes; (3) gain of function of activity does little where (4) loss of function of activity in each cell class has moderate effects on the learned behavior (i.e. freezing to the CS). There is a nice section on rabies tracing which maps inputs to both cell types which then motivates an analysis of insular cortex inputs onto both cell types and reveals that (5) CS/US pairing alters insular inputs to both cell types.

Overall the paper is well done and the conclusions are believable. Furthermore, this brain area is understudied yet potentially very important.

The analysis of the fluorescence transients is heavy handed. This leads to potential for error and could obscure what appear to be large differences that could be extracted more easily. In some instances, the data are interpreted too optimistically, especially that the silencing experiments implicate plasticity of the neurons rather than the need for activity.

Reviewer #2 (Public Review):

The authors present an R/Bioconductor package, scatterHatch, aimed at providing a novel framework for the creation of color-vision deficiency (CVD) accessible plots. The authors lay out that in increasingly common dimensionality reduction plots, like UMAPs and tSNEs, color tends to be the primary factor for distinguishing points of distinct groups. Although color palettes created with accessibility to CVDs in mind are often helpful, none adequately cater to all forms of CVD. Further, when too many colors are needed, even viewers with full-color vision may struggle. The authors lay out the current primary alternative to color, using point shape, which only works for sparse plotting regions, but most data points in UMAP and tSNE plots are not in sparse regions of the plot. All very true, thus demonstrating the need for a tool like scatterHatch, which can overlay hatch patterns both over regions in dense portions of a scatter plot, and also over points within automatically detected sparsely populated regions. The primary function of scatterHatch produces such plots from a given data frame and the names of columns to use for x, y, and color. The authors go on to demonstrate, with example figures, how the hatch patterns are indeed helpful in cases where color is not enough on its own. They demonstrate that the user can delineate custom hatch patterns, which gives flexibility to the user over how much to rely on hatch patterns versus color. Of particular note, the authors show how scatterHatch can be helpful for readers with monochromatic vision, a population that other visualization tools designed with CVD-accessibility in mind often still fail to aid.

Reviewer #2 (Public Review):

Rava et al. by creating a series of deletion mutants of tRNAs, rRNAs, and tRNA modifying enzymes, have shown the importance of gene copy number redundancy in rich media. Moreover, they successfully showed that having too many tRNAs in poor media can be harmful (for a subset of the examined tRNAs). Below, please find my comments regarding some of the methodologies, conclusions, and controls needed to stratify this manuscript's findings.

Figure 2 presents Rrel as a relative measurement (GRmut/GRwt). Therefore, I'm confused as to how Rrel can be negative, as shown in supplemental file 3 (statistics).<br /> Does Figure 3 show the mean of 4 biological replicates or technical replicates? It should be stated clearly in the legend of figure 3.

Do all strains (datapoint on figure 3 left panel) significantly perform better than the WT in nutrient downshift? Looking at supplemental file 3 I see this is not the case. Please mark the statistically significant points. I suggest giving each set a different symbol/shape and coloring the significant ones in red.

Another issue is that in the statistics of figure 2 (in supplemental file 3), positive values reflect cases where the mutant performs poorly compared to the WT, while in figure 3 the negative values indicate this. Such discrepancy is not very clear. And again, how can Rrel be negative?

Both axes say glycerol. What about galactose?

Lines 414-419: The authors state that "all but one had a growth rate that was comparable to WT (16 strains) or higher than WT (10 strains) after transitioning from rich to poor media (i.e. during a nutrient downshift, note data distribution along the x-axis in Fig 3; Supplementary file 3). In contrast, after a nutrient upshift, 11 strains showed significantly slower growth in one or both pairs of media, and only 2 showed significantly faster growth than WT (note data distribution along the y-axis in Fig 3; Supplementary file 3)".

Looking at the Rrel values when transitioning from TB to Glycerol and vice versa suggests no direction in the effect of reducing redundancy. During downshift, four strains perform better, and three strains perform worse than the WT. During upshift, four stains perform better, and six strains perform worse. Only during downshift and upshift from TB to Gal and vice versa give a strong signal.

The authors should write it clearly in the text because the effect is specific to that transition/conditions and not of general meaning is written in the text (e.g., transition from every rich to every poor media and vice versa). I am convinced that the authors see an actual effect when downshifting or upshifting from TB to galactose and vice versa. In that case, the conclusion is that redundancy is good or bad depending on the conditions one used and not as a general theme.

Also, this is true just for some tRNAs, so I don't think the conclusion is general regarding the question of redundancy.

Figures are indicated differently along the text. Sometimes they are written "figure X", sometimes FigX. Referring to the supplemental figures are also not consistent.<br /> Line 443-444: "In fact, 10 tRNAs were significantly upregulated in the poor medium relative to the rich medium".

This result contradicts the author's hypothesis. If redundancy is bad in poor media because the cells have more tRNAs than they need, the tRNAs level will be downregulated, not upregulated. How do the authors explain this?

Line 445-447: "In contrast (and as expected), all tested tRNA deletion strains had lower expression of focal tRNA isotypes in the rich medium (Fig 4B, left panel), showing that the backup gene copies are not upregulated sufficiently to compensate for the loss of deleted tRNAs".

It is great that the authors validated the expression in their strains. However, for accuracy, please indicate that it was done in four strains to avoid the impression that they did it in all the strains.

Finally, across the manuscript, the authors reveal that deleting some tRNAs or modifying enzymes can be deleterious in rich media or advantageous in poor media. However, I think this result and the conclusions derived from it could be more convincing if the authors would show in a subset of their strains that expressing the deleted tRNAs or modifying enzymes from a plasmid can rescue the phenotype.

Reviewer #2 (Public Review):

In this manuscript, Geisberg et al. present profiles of poly(A) site usage in cells with RNA Polymerase II variants transcribing at different elongation rates. It was known that transcript termination sites in cell populations occur as clusters at the 3'UTR of genes but how the choice of poly(A) site may be influenced by transcription elongation speed was not known.

The strength of their study involves using 3' READ technologies and data analyses that they have previously developed. A weakness of the study is that since the speed of elongation of Pol II is central for the data obtained and conclusions drawn, it would be important to actually measure the speed of elongation by the slow, fast, and wt Pol II used in these studies within the genes analyzed. Although the findings presented in this manuscript are not surprising, they are novel and contribute a missing piece of how the transcription machinery determines which poly(A) site to utilize at the end of genes.

Reviewer #2 (Public Review):

As shown in this study, the focal adhesion protein, kindlin-2, plays an essential role in liver function in that its genetic inactivation leads to severe liver fibrosis and death in young mice. This lethality is attributed to activation of TNF-mediated inflammation and caspase-8-dependent cell death since effects of kindlin-2 (Fermt2 gene) knockout can be reversed by genetic inactivation of TNF or caspase signaling. Evidence is also presented that kindlin-2 overexpression can have a mildly protective effect on acute liver toxicity. Overall, this work successfully connects kindlin-2 with normal liver function and raises the possibility that modulation of kindlin-2 could have therapeutic potential for treating liver disease.

On the other hand, the underlying mechanism explaining why kindlin-2 loss stimulates TNF, caspase 8, inflammation, and fibrosis is not explored. As a major component of focal adhesions via its interaction with integrins, kindlin2 has primary functions in regulating cell-ECM signaling and mechanotransduction. However, this study does not connect these known functions with the liver fibrosis and inflammation observed. For example, only cursory analysis is provided concerning the effects of kindlin-2 loss on hepatocyte-ECM interactions, cytoskeletal structure, or focal adhesion distribution. Also, the slightly protective effects of Kindlin-2 overexpression on D-galactosamine/LPS-induced liver toxicity and death are not connected to the rest of the study. Also, one might question whether extending mouse survival by approx. 3-4 hrs with kindlin-2 overexpression is a potentially clinically relevant finding.

Reviewer #2 (Public Review):

In this manuscript, Ruesseler and colleagues use a continuous task to examine how neural correlates of decision-making change when subjects face conditions with different durations and frequencies of occurrence of signals embedded in noise. The authors develop a novel task where subjects must report the direction of relatively sustained (3 or 5 s) signal changes in average coherence of a random dot kinetogram that are intermittent among relatively transient noise fluctuations (<1 s) of motion coherence that is continuous. Subjects adjust their behavior to changes in the duration of signal events and the frequency of their occurrence. The authors estimate a decay time constant of leaky integration of evidence based on the average coherence leading up to decision responses. Interestingly, there is considerable inter-subject variability in decay time constants even under identical conditions. In addition, the average time constants are shorter when signal periods occur more frequently as opposed to when they are more rare. The authors use EEG to find that a component of the Centroparietal Positivity (CPP) regressed to the magnitude of changes in the noise coherence is larger in conditions when the signal periods occur less frequently. Using a control condition, the authors show that this component of the CPP is not simply based on surprise because it is smaller for changes in motion coherence in irrelevant directions with matched statistics as the changes in relevant directions. The authors also find that a different component of the CPP related to the magnitude of the motion coherence co-varies with the inter-subject variability in decay time constants estimated from behavior.

Overall, the authors use a clever experimental design and approach to tackle an important set of questions in the field of decision-making. The manuscript is easy to follow with clear writing. The analyses are well thought-out and generally appropriate for the questions at hand. From these analyses, the authors have a number of intriguing results. So, there is considerable potential and merit in this work. That said, I have a number of important questions and concerns that largely revolve around putting all the pieces together. I describe these below.

1) Quite sensibly, the authors hypothesize that "decay time constant" for past evidence and "decision threshold" would be altered between the different task conditions. They find clear and compelling evidence of behavioral alterations with the conditions. They also have a method to estimate the decay time constant. However, it is unclear to what extent the decision threshold is changing between subjects and conditions, how that might affect the empirical integration kernel, and how well these two factors can together explain the overall changes in behavior.

To be more specific, the authors state that the lower false alarm rates and slower reaction times for the LONG condition are consistent with a more cautious response threshold for LONG. The empirical integration kernels lead to the suggestion that the decay time constant is not changing between SHORT and LONG, while it is changing between FREQUENT and RARE. Does the lack of change in false alarm rate between FREQUENT and RARE imply no change in the decision threshold? Is this consistent with the behavior shown in Figure 2? I would expect that less decay in RARE would have led to more false alarms, higher detection rates, and faster RTs unless the decision threshold also increased (or there was some other additional change to the decision process). The CPP for motor preparatory activity reported in Fig. 5 is also potentially consistent with a change in the decision threshold between RARE and FREQUENT. If the decision threshold is changing, how would that affect the empirical integration kernel? These are important questions on their own and also for interpreting the EEG changes.

2) The authors find an interesting difference in the CPP for the FREQUENT vs RARE conditions where they also show differences in the decay time constant from the empirical integration kernel. As mentioned above, I'm wondering what else may be different between these conditions. Do the authors have any leverage in addressing whether the decision threshold differs? What about other factors that could be important for explaining the CPP difference between conditions? Big picture, the change in CPP becomes increasingly interesting the more tightly it can be tied to a particular change in the decision process.

I'll note that I'm also somewhat skeptical of the statements by the authors that large shifts in evidence are less frequent in the RARE compared to FREQUENT conditions (despite the names) - a central part of their interpretation of the associated CPP change. The FREQUENT condition obviously has more frequent deviations from the baseline, but this is countered to some extent by the experimental design that has reduced the standard deviation of the coherence for these response periods. I think a calculation of overall across-time standard deviation of motion coherence between the RARE and FREQUENT conditions is needed to support these statements, and I couldn't find that calculation reported. The authors could easily do this, so I encourage them to check and report it.

3) The wide range of decay time constants between subjects and the correlation of this with another component of the CPP is also interesting. However, in trying to interpret this change in CPP, I'm wondering what else might be changing in the inter-subject behavior. For instance, it looks like there could be up to 4 fold changes in false alarm rates. Are there other changes as well? Do these correlate with the CPP? Similar to my point above, the changes in CPP across subjects become increasingly interesting the more tightly it can be tied to a particular difference in subject behavior. So, I would encourage the authors to examine this in more depth.

Reviewer #2 (Public Review):

The authors set out to study whether there is altered liver regeneration under physiological homeostatic conditions depending on whether an experimental model is offered continuous feeding or intermittently fasted. They report, using a series of murine models in male mice, that hepatic adjustments to fasting/refeeding occur including hyperproliferation of pericentral hepatocytes during a period of relative liver enlargement. It is interesting to note that this occurs 1 week after daily fasting/feeding cycles and appears to occur very quickly following the reintroduction of food. During fasting, they show that the liver shrinks relative to body weight then, as demonstrated by a series of lineage tracing experiments, undergoes relative hyperproliferation, particularly by pericentral hepatocytes. This was shown using an Axin2-based reporter and additionally through zonal analysis or a confetti-multicolored reporter used to trace individual clones. This response appears stable then for upto 3 months. Ideally, additional data showing the liver and body weight individually would help to give an impression of whether the predominant effect is due to changes in body weight or liver weight but it appears implicit that there is an active contraction of liver and hepatocyte size and number during fasting. This is then followed by rapid growth upon refeeding, presumably without major changes in body weight.

It is not clear whether the length of fasting is critical and what the proliferative and metabolic state of the liver is immediately prior to refeeding. It is also unclear whether the relative expansion of pericentral hepatocytes results in an expansion of the pericentral zone or whether these hepatocytes then repopulate other zonal compartments of the liver. They do provide single-cell transcriptomic data which supports the expansion of pericentral transcripts, however, whether this represents a functionally advantageous liver metabolism and how this is achieved remains will be important questions for the future. The link changes to bile acids to altered expression of Cyp7A1, which suggests a role for altered bile acid metabolism in the fasting state. It would be interesting, in the future, to explore whether a liver-to-intestinal feedback loop exists utilising the altered hepatic bile acids occurring during fasting/refeeding to signal back to the intestine for example. This would also then potentially have implications for liver disease states including cholestatic liver diseases.

Mechanistically the authors use hepatocyte-targeted FGF receptor depletion (Klb) or Wnt/b-catenin transcription factor depletion (Tbx3), through efficient adeno-associated viral vector targeting to manipulate these axes combined with hepatocellular FGF overexpression. They demonstrate that the FGF receptor Klb is expressed throughout the lobule and that its global knockout results in the loss of the pericentral proliferative response in fasting/refeeding. It is interesting to note that with the loss of Klb particularly a senescence response occurs in the areas that previously underwent proliferation in response to IF. Similarly, the loss of Klb alters the metabolic rewiring which occurred during the IF response, unlike Tbx3 depletion. Tbx depletion was separately shown to result in a polyploidisation response within the normally diploid pericentral area, consistent with the previous report from this group.

Broadly the authors achieve their aim of both describing the effects resulting from fasting upon liver regenerative biology and also shedding significant insights mechanistically into this process. Overall, these results are highly provocative and raise important questions when interpreting murine studies. These include whether the experimental effect on liver pathophysiology might be explained by or influenced by altered dietary intake as a result of animal husbandry or animal pathology. It will also be interesting in the future what effect broader dietary modifications have on the liver, and other organs, physiologically. These would include but are not limited to a high-fat diet, altered microbiome, variable fasting, and background body habitus. It also has implications for what happens in response to fasting/refeeding during development and the longer-term adaptive responses to this.

Reviewer #2 (Public Review):

The manuscript by Luan et. al. describes the role of EHD2 in promoting breast tumor growth. They showed that EHD2 cytoplasmic staining predicts poor patient outcome. Both EHD2 KO or knockdown cells showed decreased cell migration/invasion abilities and significant reduction of tumor growth and metastasis in mice. The authors further showed that the levels of EHD2 and Cav1/2 correlate with each other. EHD2 KO cells showed defects on Ca2+ trafficking. Overexpressing the SOCE factor STIM1 partially rescued SOCE defects in EHD2 KO cells. Treatment of the SOCE inhibitor SKF96365 inhibited tumor cell migration in vitro and tumor growth in vivo.

Major strengths:<br /> The authors showed that EHD2 cytoplasmic levels predict patient survival and provided strong evidence that EHD2 knockout or knockdown inhibits tumor cell migration in vitro and tumor growth in vivo. The authors also showed that SKF96365, which inhibits SOCE, suppresses tumor growth in vivo.

Major weaknesses:<br /> The connection between EHD2 and SOCE is weak.

Reviewer #2 (Public Review):

Van der Goes et al recorded HD cells in the retrosplenial cortex and anterodorsal nucleus of the mouse during the rotation of a prominent visual cue. They describe the temporal coordination of the HD representation between the two structures, also in the dark condition. They provide evidence for a near-simultaneous realignment of the HD representation in the two structures (no consistent temporal offset during the cue shift). This finding is interesting and quite surprising, in light of the existing literature postulating a role of the retrosplenial cortex in a binding visual landmark and HD information. I am not sure whether the authors' conclusions are convincingly supported by the data.

Reviewer #2 (Public Review):

Volume-regulated anion channels (VRACs), comprised of the LRRC8 family of proteins, play important roles in cell volume regulation. Physiological LRRC8 channels are heteromeric assemblies of LRRC8A and LRRC8B-LRRC8E subunits. Previous structural studies have focused on homomeric channels, which do not recapitulate functional properties of native heteromeric channels. Thus, the molecular basis of physiological VRAC assembly and function remains unknown. In this study, Takahashi and colleagues present the single-particle cryo-electron microscopy structure of a functional LRRC8 chimera, which is composed of LRRC8C and a swapped intracellular loop from LRRC8A. Surprisingly, the chimeric channel forms a heptamer, in sharp contrast to the previously reported hexamers of homomeric and heteromeric LRRC8 channels. The findings of the chimeric channel are interesting. However, the physiological implication of this chimera is unclear, and the proposal that native LRRC8 channels are heptamers is not well supported.

Reviewer #2 (Public Review):

In the present study, the authors have combined calcium imaging and electrophysiology to systematically replicate the previously reported finding that the mechanical activation ion channel Piezo1 might also serve as a gut RNA sensor. The authors have employed multiple cell lines, knockout of endogenous Piezo1, and heterologous overexpression of Piezo1, Yoda1 as a positive control for chemical activation of Piezo1, and similar dosage of ssRNA used in the previous study, but clearly did not replicate the finding that ssRNA can specifically activate Piezo1. The experiments have been well designed and data quality is high. The data support the conclusion that Piezo1 is not a receptor for ssRNA in the gut.

Reviewer #2 (Public Review):

Yang et al. produced a transgenic mouse line (Syt1-TDT) that could be used for labeling both excitatory and inhibitory synaptic sites in cultured neurons and in vivo neurons. The strength of the current study is to provide a series of thorough analyses to claim the applicability of this mouse line in the relevant neuroscience research field(s). The weakness is the potential impact/usefulness of this mouse line. To strengthen the merit of this mouse line, the authors should present evidence showing its advantage over other similar genetic approaches.

Reviewer #2 (Public Review):

Giorgi Rossi et al measured in their paper the impact of COVID-19 pandemic on the main indicators used to assess the performance of national screening programs for cancers. As expected, they highlighted a significant reduction that changed during the different waves and also across geographical areas. The results of the study might be considered valid and representative as the study is relied on current data flows to assess the performance of screening programs. The paper also reports a complementary analysis on the factor associated to the access to screening that gives some more insights on the reasons behind the access. This second part of the work also relied on data collected at national level that anyway have some intrinsic limitations. Nevertheless, on the whole, the paper gives a useful contribution to the assessment of the disruption due to the pandemic that can be also used in the light of preparedness actions.

Reviewer #2 (Public Review):

Wen et al. developed a useful tool for causal network inference based on scRNA-seq data. The authors comprehensively benchmarked 9 feature selection and 9 causal discovery algorithms using both synthetic data and real scRNA-seq data. Their conclusions regarding the performance of these algorithms on synthetic data are solid and valuable. I believe this tool or platform has the potential to help biologists discover novel cell type-specific signaling pathways or gene regulatory events since there is no prior knowledge (such as known pathway annotations) as inputs. However, several major concerns below need to be addressed to improve the paper.

(1) Current validation of the inferred causal networks using real scRNA-seq datasets seems quite simple and is not sufficient to support the accuracy and reliability of results. Annotations from the STRING database do not contain directions of edges among genes or proteins. However, the edge direction in the inferred network is a crucial aspect to explain the causal relationships. Besides using "spike-in" data, a systematic validation of the inferred network, especially the edge directions, should be provided.

(2) In order to illustrate the novel discovery, CausalCell should be further compared to existing gene network construction methods based on scRNA-seq data such as SCENIC (Aibar et al. Nature Methods, 2017).

(3) The authors should also claim what type of the inferred causal network represent from the biological perspective (e.g. signaling networks or gene regulatory networks?).

(4) Besides edge direction, an important feature of CausalCell is the determination of edge sign (i.e. activation or inhibition). The authors should describe its related procedures.

(5) The authors did not provide an example of constructing a causal network between cells or cell types, although they mentioned its importance in the Abstract. Such intercellular network examples can distinguish the utility of CausalCell in single-cell data analysis from bulk data analysis.

(6) If the control dataset is available, it is currently not clear whether batch effects of the query and control datasets will be removed in the data pre-processing step. Differentially expressed genes cannot be selected correctly if batch effects exist.

Reviewer #2 (Public Review):

The authors use birth cohorts with extensive cognitive assessments and height measurements along with data on parental height and socioeconomic status. The authors estimate that the correlation between height and cognitive ability has approximately halved in the last 60 years.

Quantile regression results suggest that this is due to a stronger association between low cognitive ability and short stature in older cohorts, potentially due to environmental factors that cause both and that have been removed by improvements in the environment in the last 60 years.

While this is a plausible hypothesis, the evidence presented in the manuscript is unable to rule out alternative hypotheses, such as changes in assortative mating.

The results in the manuscript will be of interest to researchers investigating how genetics and environment lead to correlations between cognitive and physical/health traits, and to researchers interested in the relationship between social and health inequalities.

While my sense of the evidence presented is that there is fairly solid statistical evidence for a trend where the correlation between cognitive ability and height declines over time, there is no formal quantification of this trend nor measurement of the uncertainty in the trend.

Similarly, the quantile regression plots in Figure 2 appear to show a trend across the height deciles for the two oldest cohorts, but no quantification of how strong this is nor what uncertainty exists is calculated. Furthermore, if the apparent trend in the quantile regression plots is true, wouldn't this imply a non-linear association between height and cognitive ability for the older cohorts? Can this be seen in the scatterplots or in a non-linear regression?

I think the authors could have done more with their data to investigate the contribution of assortative mating to the observed trend. Looking at Figure S4, it looks like the correlation between mother's education and father's height in the 2001 cohort is substantially lower than for previous cohorts. While cognitive ability may not be available for parents, one could look at, for example, father's education and mother's height across the cohorts and see if there is a downward trend in correlation.

Reviewer #2 (Public Review):

The Tp53 gene is deemed as one of the most critical tumor suppressors in humans. Not surprisingly, the latter is found inactivated or mutated in the majority (if not all) of human cancers. The present study by Q. Li et al describes an attempt to predict the functional status of p53 in those tumors where no mutations on the DNA sequencing level were identified. To this end, the authors employed SVM models to train the algorithm for the detection of the 'p53 inactivation' features using normal and tumor tissues, respectively. It turned out that the 'p53 loss of function' phenotype was associated not with DNA methylation but rather with yet unknown mechanisms. Based on the fact that the p53LoF-containing tumors are similar to the p53 mutant-expressing ones with respect to platinum-based therapy, they subsequently used their SVM model on the glioblastoma samples to predict their chemosensitivity.

Reviewer #2 (Public Review):

The structure was solved in its resting (i.e. non-activated) form and was stabilized by adding an antibody that recognizes an extracellular epitope. The protein - the complex of NOX2-p22 bound to the antibody- was reconstituted from proteins expressed in human cells through baculovirus transduction. The cryoEM gridswere obtained by using nanodisc-embedded complexes. The structure clarifies the topology of the p22 subunit, showing that it comprises four transmembrane helices. Moreover, it confirms that the oxygen-reacting center is conserved among NOXs implying a similar mechanism for ROS generation. Furthermore, the 3D structure explains the effect of the many known disease-causing mutations. They mostly affect the active sites or the NOX2-p22 subunit-subunit interface. The cytosolic dehydrogenase domain is not as ordered in the cryoEM maps. Clearly, NOX2 is a highly dynamic protein where the cytosolic and membrane domains can enjoy considerable flexibility. This feature very likely underpins the mechanism of activation, which is triggered by the cytosolic subunits and remains to be understood. The manuscript suggests that the cytosolic subunits might stabilize the enzyme in the conformation that is capable of conducting electrons from the NADP-flavin site to the inner heme, thereby enabling catalysis.

Overall, this is great experimental work: the structure of NOX2 has been awaited for a long time. The data reported in this manuscript should probably be seen as the beginning of the NOX2 structural era. Indeed, a lot remains to be clarified, especially with regard to NADPH binding and the mechanism of enzyme activation. Along this line, the manuscript reads more as a preliminary report rather than a full-story manuscript. Beside this general concern, I do not have any specific comment about the presentation style: the manuscript is clearly written and nicely illustrated.

Reviewer #2 (Public Review):

In this manuscript, Lin et al. reveal a novel and fundamental discovery regarding the role of the EZH2/SULF1/cMET signaling pathway in regulating the disease progression of chondrosarcoma, a malignant cartilaginous bone tumor.

The significant strengths of the manuscript include identifying the EZH2-targeted genes in chondrosarcoma using EZH2-chromatin immunoprecipitation sequencing (ChIP-seq) and cDNA microarray profiling, deciphering the role of the EZH2/SULF1/cMET signaling pathway in regulating the progression of chondrosarcoma, verifying the therapeutic significance of this pathway using clinically used specific EZH2 and cMET pharmacological inhibitors in vitro and in vivo (in mouse tumor models), and demonstrating the clinical significance of the SULF1/cMET pathway in chondrosarcoma.

The significant weaknesses of the manuscript appear not noted. A minor drawback seems associated with the manuscript presentation.

In summary, I believe this manuscript's data well justify the authors' claims and conclusions, and this paper will significantly impact the field.

Reviewer #2 (Public Review):

Casillas-Espinosa et al. present a well-designed study to evaluate the validity of sodium-selenate treatment in chronic epilepsy. Previous studies from the same group identified increased phospho-tau in models of seizures and epilepsy, which can be pharmacologically addressed through activation of protein phosphatase 2A with sodium-selenate. Here the authors tested the effect of delayed treatment with sodium selenate in the post-KA SE rat TLE model. Sodium selenate stopped the progression of seizures during and beyond a 4-week treatment phase compared to Levitiracetam and vehicle-treated animals. Sodium selenate further improved cognitive and sensorimotor impairments. It also persistently reduced phospho-tau and increased PP2A protein expression, and reversed TLE-associated telomere-shortening. Finally, proteome and metabolome data from the model is discussed and provides initial insights into sodium selenate treatment's molecular consequences.

This study validates the use of sodium selenate as a promising pharmacological treatment in experimental TLE that reduces seizure burden and restores cognitive deficits and pathomolecular changes. The specific strength of the study is a clinically relevant treatment paradigm, starting when recurrent seizures are fully established, and the antiepileptogenic effect with a sustainable reduction in seizure burden even after discontinuation of treatment.

The conclusions of this paper are mostly well supported by data, but some aspects of the proteome and metabolome data analysis need to be clarified and extended. The molecular data appears to be the weakest part of this study and would have benefited from adjusted sample sizes to account for interindividual variability between animals and the complex multi-dimensional nature of the data.

Reviewer #2 (Public Review):

Summary:

This work presents a new machine-learning method, RaSP, to predict changes in protein stability due to point mutations, measured by the change in folding free energy ΔΔG.

The model consists of two coupled neural networks, a 3D self-supervised convolutional neural network that produces a reduced-dimensionality representation of the structural environment of a given residue, and a downstream supervised fully-connected neural network that, using the former network's structural representation as input, predicts the ΔΔG of any given amino-acid mutation. The first network is trained on a large dataset of protein structures, and the second network is trained using a dataset of the ΔΔG values of all mutants of 35 proteins, predicted by the biophysics-based method Rosetta.

The paper shows that RaSP gives good approximations of Rosetta ΔΔG predictions while being several orders of magnitude faster. As compared to experimental data, judging by a comparison made for a few proteins, RaSP and Rosetta predictions perform similarly. In addition, it is shown that both RaSP and Rosetta are robust to variations of input structure, so good predictions are obtained using either structures predicted by homology or structures predicted using AlphaFold2.

Finally, the usefulness of a rapid approach such as RaSP is clearly demonstrated by applying it to calculate ΔΔG values for all mutations of a large dataset of human proteins, for which this method is shown to reproduce previous findings of the overall ΔΔG distribution and the relationship between ΔΔG and the pathological consequences of mutations. The RaSP tool and the dataset of mutations of human proteins are shared.

Strengths:

The single main strength of this work is that the model developed, RaSP, is much faster than Rosetta (5 to 6 dex), and still produces ΔΔG predictions of comparable accuracy (as compared with Rosetta, and with the experiment). The usefulness of such a rapid approach is convincingly demonstrated by its application to predicting the ΔΔG of all single-point mutations of a large dataset of human proteins, for which using this new method they reproduce previous findings on the relationship between stability and disease. Such a large-scale calculation would be prohibitive with Rosetta. Importantly, other researchers will be able to take advantage of the method because the code and data are shared, and a google colab site where RaSP can be easily run has been set up. An additional bonus is that the dataset of human proteins and their RaSP ΔΔG predictions, annotated as beneficial/pathological (according to the ClinVar database) and/or by their allele frequency (from the gnomAD database) are also made available, which may be very useful for further studies.

Weaknesses:

The paper presents a solid case in support of the speed, accuracy, and usefulness of RaSP. However, it does suffer from a few weaknesses.

The main weakness is, in my opinion, that it is not clear where RaSP is positioned in the accuracy-vs-speed landscape of current ΔΔG-prediction methods. The paper does show that RaSP is much faster than Rosetta, and provides evidence that supports that its accuracy is comparable with that of Rosetta, but RaSP is not compared to any other method. For instance, FoldX has been used in large-scale studies of similar size to the one used here to exemplify RaSP. How does RaSP compare with FoldX? Is it more accurate? Is it faster? Also, as the paper mentions in the introduction, several ML methods have been developed recently; how does RaSP compare with them regarding accuracy and CPU time? How RaSP fares in comparison with other fast approaches such as FoldX and/or ML methods will strongly affect the potential usefulness and impact of the present work.

Second, this work being about presenting a new model, a notable weakness is that the model is not sufficiently described. I had to read a previous paper of 2017 on which this work builds to understand the self-supervised CNN used to model the structure, and even so, I still don't know which of 3 different 3D grids used in that original paper is used in the present work.

A third weakness is, I think, that a stronger case needs to be made for fitting RaSP to Rosetta ΔΔG predictions rather than experimental ΔΔGs. The justification put forward by the authors is that the dataset of Rosetta predictions is large and unbiased while the dataset of experimental data is smaller and biased, which may result in overfitting. While I understand that this may be a problem and that, in general, it is better to have a large unbiased dataset in place of a small biassed one, it is not so obvious to me from reading the paper how much of a problem this is, and whether trying to fix it by fitting the model to the predictions of another model rather than to empirical data does not introduce other issues.

Finally, the method is claimed to be "accurate", but it is not clear to me what this means. Accuracy is quantified by the correlation coefficient between Rosetta and RaSP predictions, R = 0.82, and by the Mean Absolute Error, MAE = 0.73 kcal/mol. Also, both RaSP and Rosetta have R ~ 0.7 with experiment for the few cases where they were tested on experimental data. This seems to be a rather modest accuracy; I wouldn't claim that a method that produces this sort of fit is "accurate". I suppose the case is that this may be as accurate as one can hope it to be, given the limitations of current experimental data, Rosetta, RaSP, and other current methods, but if this is the case, it is not clearly discussed in the paper.

Reviewer #2 (Public Review):

The antennae of insects are excellent sensors and are able to distinguish chemicals/compounds using odorant receptor proteins. Though many are promiscuous, several ORs are extremely specific and respond to only one or few related chemicals. In this study, the authors focus on two ORs from southern house mosquito, Culex quinquefasciatus namely OR10 and OR2, which respond to (show high specificity) skatole and indole respectively. Notably, these two compounds differ only by a methyl group raising the question how this is achieved. To address this question, the authors have chosen CquiOR10 (as it is more sensitive) for swapping the transmembrane domains (TMDs) from CquiOR2 and by performing heroic work, arrive at one single residue in one of the TMDs to explain the specificity in these ORs.

The major strengths of the manuscript include the careful design of the many different chimeric receptors (36 in total) and dissecting the importance of each TMD and zeroing on TMD2 as the specificity determinant, followed by zooming to a single residue in TMD2 that can change responsiveness of CquiOR10 to CquiOR2 and vice versa. This residue in TM2 is an alanine in CquiOR10, which when mutated to bulky residue becomes responsive to indole but when mutated to glycine remains specific to skatole and shows higher sensitivity. Similarly, mutating the equivalent residue in CquiOR2, Leucine 74 to a smaller residue makes this receptor now more responsive to skatole and making it more like CquiOR10.

Using RoseTTAfold and AlphaFold, the authors build models of CquiOR10 and CquiOR2, which gives them a platform to observe how ligands can bind using Rosettaligand both in native structures as well as mutants. They further ask how larger ligands or the methyl group at different position in the indole ring affects the response of the receptor, which follow a consistent trend on the key residue of Alanine 73. All these analysis allow authors to propose that the odorants or chemicals are accommodated/restricted due to the volume constraints by residues lining the cavity derived from the TMDs.

Reviewer #2 (Public Review):

In this communication by Motta and colleagues, the authors address the emerging role of the gut microbiome in degrading and detoxifying plant metabolites, using bees as a study system. The experiments are elegantly controlled, spanning in vitro and in vivo work that leverages the increasing tractability of bees and their microbial symbionts. This is evident in the extensive screening of Bifidobacterium, Bombilactobacillus, Lactobacillus, and Gilliamella relative to their susceptibility to amygdalin. This provided a foundation to pinpoint which strains can degrade the cyanogenic glycoside, the potential pathways underlying that process, and the key enzymes involved. The strain Bifidobacterium wkB204 displayed elevated expression of GH3, correlating to the ability of this microbe to degrade amygdalin in vitro. Expression of the GH3 in E. coli corroborated its putative role in the transformation of amygdalin to prunasin, consistent with the single inoculation effects of Bifidobacterium wkB204 into microbiota-deprived bees. These experiments collectively point to the importance of the bee microbiota for the consistent degradation of amygdalin. The findings are nicely contextualized relative to prior work on the gut microbiome and the metabolism of the cyanogenic glycoside, including efforts on bees and rats.

Reviewer #2 (Public Review):

In recent years the activity of cortical VIP+ interneurons in relation to learning and sensory processing has raised great interest and has been intensely investigated. The ability of VIP+ interneurons in the auditory cortex to respond to both reward and punishment was already reported a few years ago by some of the authors (Pi et al., 2013, Nature). However, this work importantly adds to their previous study demonstrating a largely similar and synchronous response of a large fraction of these interneurons across the neocortex to salient stimuli of different valence during the performance of an auditory discrimination task.

An additional strength of this study is the analysis and identification of the general pattern of VIP+ interneuron responses associated to specific behaviors in the different layers of the neocortex depth.

Interestingly, the authors also identified using cluster analysis 5 different classes of VIP+ interneurons, based on the dynamic of their responses, that were unequally distributed in distinct cortical areas.

This is a well performed study that took advantage of a cutting-edge imaging approach with high recording speed and good signal-to-noise ratio. Experiments are well performed and the data are properly analyzed and nicely illustrated. However, one shortcoming of this paper, in my opinion, is the "case report" structure of the data. Essentially for each neocortical area the activity of VIP+ interneurons was analyzed only in one animal. This limits the assessment of the stability of the response/recruitment of these interneurons. I appreciate the high number of recorded VIP+ interneurons per area/animal and I do understand that it would be excessively laborious to perform 3D random-access two-photon microscopy in several mice for each cortical area. On the other hand, it would be important to have some knowledge of the general variability of the responses of these neurons among animals.

In conclusion, despite the findings described in this manuscript being generally sound, additional experiments are recommended to further substantiate the conclusions.

Reviewer #2 (Public Review):

General description:<br /> This study elucidates how Advanced Glycation End-products (AGE), found in processed food and endogenously, drive food intake and cause some of the pathophysiological defects associated with metabolic disease. In their previous C. elegans study, the authors found that glod-4 mutants, animals that lack glyoxalase activity and thus accumulate AGEs, eat more and share some of the pathophysiological effects seen in metabolic disease. In this study, they identify a specific AGE, hydroimidazolone (MG-H1), that is sufficient to increase feeding, similar to what was previously observed in the glod-4 mutants. Gene expression studies then show expression changes in several neurotransmitter and eating genes, including the tyramine decarboxylase gene tdc-1 and its receptor. Measuring eating behaviors in animals carrying mutations in tyramine signalling genes they show that tyramine signaling system is required for the behavioral and pathophysiological effects of MG-H1. Finally, they show that the transcription factor elt-3 controls the expression of tyramine signaling components and thus is also required for the response to MG-H1.

Strength: Strengths of the paper include the elegant approach to study how toxic metabolites affect physiology and behavior in vivo. The logic behind the study is easy to follow and the paper is clearly written.

Weakness: The main weakness is that the genetic studies were generally only carried out with a single mutation that was not rescued. To corroborate the requirement of tdc-1 and elt-3 for the response to MG-H1, the results should be repeated either in a rescue strain or using a different allele. Some of the effects are subtle and there is the danger of them being caused by background mutations.

Impact: The occurrence of metabolites like AGEs in either processed food or endogenously is a topic that is not well investigated despite its general importance. In this study the authors show the functional consequences of a non-enzymatically generated metabolite and how it exerts its toxicity.

Reviewer #2 (Public Review):

This is an important and timely characterization of a diversity of behaviors male and female rats exhibit during the acquisition of Pavlovian fear conditioning in a conditioned suppression procedure. Using hand-scored video analysis and ethogram of nine different behaviors, the authors report that auditory conditioned stimuli that predict shock with high certainty evoke not only freezing, but a variety of other behaviors including locomotion, jumping, and rearing (in addition to suppressing reward-seeking). Auditory stimuli that were followed by shock on only some trials (uncertainty condition), were less likely to evoke freezing and did not lead to a suppression of port/cup-directed behaviors (reward seeking). There were subtle sex differences in the temporal profile of freezing behavior, but not in the properties of the other behaviors under study.

Ultimately, these findings point to the importance of task variables (eg., reward seeking in a conditioned suppression procedure) and shock probability in shaping an animal's defense repertoire under threat.

An important factor that this work does not resolve is how the magnitude of the threat/shock (and presumably the state of fear that it engenders) influences an animal's defensive topography. This report used a modest/weak footshock intensity that supported very low levels of tone-elicited freezing (<20%) - a stark contrast to the extant fear conditioning literature that typically reports much higher levels of freezing behavior.

Reviewer #2 (Public Review):

This paper by Sherratt et al. evaluated the performance of real-time predictions for COVID-19 submitted to the European COVID-19 Forecast Hub between March 8 2021 and March 7 2022. This large-scale multi-team multi-county collaboration collected short-term forecasts for COVID-19 from 26 teams generated for 32 countries in Europe, making this dataset one of the largest archives of real-time COVID-19 forecasts. The results indicate that ensemble models combining forecasts from individual models generally performs better than each individual model, and ensemble methods based on medians outperform the ones based on means. The comparison also shows that incident death forecasts are more reliable than incident case forecasts beyond two weeks into the future. The paper further included detailed discussions on several practical considerations in the operational use of forecasting models. These findings provide practical guides for generating real-time forecasts for infectious diseases and novel insights into coordinating international forecasting efforts during a public health emergency.

The conclusions of this paper are well supported by the data and analyses. A few aspects could be further discussed in the manuscript.

1. A parallel effort of real-time COVID-19 forecasting in the US (i.e., the US COVID-19 Forecast Hub) reported similar findings on the use of ensemble models. This study from Europe provides independent validation that shows the robustness of these findings. While both studies followed similar guidelines and used the same evaluation metrics (coverage and WIS), I believe there should be unique challenges associated with forecasting for multiple countries (as opposed to forecasting in a single country). As a result, it might be worthwhile to discuss those challenges and potential solutions to inform similar efforts in the future.

2. WIS is a strictly proper score for evaluating forecast performance; however, it must rely on a reference forecast model. This may create difficulties in interpreting forecast accuracy for the general public who may not understand the concept of WIS. For instance, what is a WIS score good enough to trust? The authors may want to include a simple metric (e.g., mean absolute error) as a supplement even though these metrics have some caveats. I presume the performance should be highly correlated using different evaluation metrics.

3. It might be helpful to elaborate more on the assumptions for near-term predictions in participating models (e.g., status quo, reactive change of transmission, etc.). Essentially all real-time predictions were generated based on assumptions, although sometimes those assumptions were not stated explicitly. For behavior-induced changing points (peaks or troughs), it might be challenging to predict using the status quo without considering a change in model states.

4. Data in the tables and figures were used to compare forecasts. It would be great to have a formal statistical test for comparing model performance, if possible.

Reviewer #2 (Public Review):

It is known that bacterial outer membrane proteins must interact with a variety of cellular factors to reach their final destination safely. There is considerable biochemical evidence in the literature (primarily from crosslinking studies) that these factors interact to promote the movement of client proteins and to prevent their aggregation or misfolding, but the details of the interactions are unknown. The authors showed that they could use a novel virtual screening method together with known crystal structures of individual factors to predict the three-dimensional structures of several pairs or groups of interacting factors (supercomplexes). The predicted supercomplex structures are both fascinating and compelling because they are consistent with the published results and they help to explain the mechanism by which the cellular factors promote outer membrane protein biogenesis. I think that this study will be of interest to a wide audience because it serves as a proof-of-concept that although Alpha Fold is incredibly useful for predicting the structures of protein monomers, more sophisticated applications can be used to successfully predict the structures of protein complexes which are often the workhorses of the cell. I have only two significant concerns. First, the authors focused on high confidence supercomplexes that have known biological significance. Their method also identified other high confidence supercomplexes, but they need to explain how they can distinguish predicted supercomplexes that have potential biological significance from those that are simply "false positives". Second, one of the proposed functional models does not seem to be consistent with the results of a previous study.

Reviewer #2 (Public Review):

Luongo et al. investigated the behavioural ability of 4 different species (macaque, mouse lemur, tree shrew and mouse) to segment figures defined by opponent motion, as well as different visual features from the background. With carefully designed experiments they convincingly make the point that figures that are not defined by textural elements (orientation or phase offsets, thus visible in a still frame) but purely by motion contrast, could not be detected by non-primate species. Interestingly it appears to be particularly motion contrast, since pure motion - figures moving on a static background - could be discriminated better, at least by mice.

This is highly interesting and surprising -- especially for a tree shrew, a diurnal, arboreal mammal, very closely related to primates and with a highly evolved visual system. It is also an important difference to take into account considering the multitude of studies on the mouse visual system in recent years.

The authors additionally present neuronal activity in mice, from three different visual cortical areas recorded with both electrophysiology and imaging. Their conclusions are mostly supported by the data, but some aspects of the recordings and data analysis need to be clarified and extended.

The main issues are outlined below roughly in order of importance:

1. The most worrying aspect is that, if I interpret their figures correctly, their recordings seem not very stable and this may account for many of the differences across the visual conditions. The authors do not report in which order the different stimuli were shown, their supplemental movie, however, makes it seem as though they were not recorded fully interleaved, but potentially in a block design with all cross1 positions recorded first, before switching to cross2 positions and then on to iso... If I interpret Figure 6a correctly, each line is the same neuron and the gray scale shows the average response rate for each condition. Many of these neurons, however, show a large change in activity between the cross1 and the cross2 block. Much larger than the variability within each block that should be due to figure location and orientation tuning. If this interpretation is correct, this would mean that either there were significant brain state changes (they do have the mice on a ball but don't report whether and how much the animals were moving) between the blocks or their recordings could be unstable in time. It would be good to know whether similar dramatic changes in overall activity level occur between the blocks also in their imaging data.

The same might be true for differences in the maps between conditions in figure 4. If indeed the recordings were in blocks and some cells stopped responding, this could explain the low map similarities. For example Cell 1 for the cross stimuli seems to be a simple ON cell, almost like their idealized cell in 3d. However, even though the exact texture in the RF and large parts of the surround for a large part of the locations is exactly identical for Cross1 and Iso2, as well as Cross2 and Iso1, the cells responses for both iso conditions appear to only be noise, or at least extremely noise dominated. Why would the cell not respond in a phase or luminance dependent manner here?

This could either be due to very high surround suppression in the iso condition (which cannot be judged within condition normalization) or because the cell simply responded much weaker due to recording instability or brain state changes. Without any evidence of significant visual responses, enough spikes in each condition and a stable recording across all blocks, this data is not really interpretable. Instability or generally lower firing rates could easily also explain differences in their decoding accuracy.

Similarly, it is very hard to judge the quality of their imaging data. They show no example field of views or calcium response traces and never directly compare this data to their electrophysiology data. It is mentioned that the imaging data is noisy and qualitatively similar, but some quantification could help convince the reader. Even if noisy, it is puzzling that the decoding accuracy should be so much worse with the imaging data: Even with ten times more included neurons, accuracy still does not even reach 30% of that of the ephys data. This could point to very poor data quality.

2. There is no information on the recorded units given. Were they spike sorted? Did they try to distinguish fast spiking and regular spiking units? What layers were they recorded from? It is well known that there are large laminar differences in the strength of figure ground modulation, as well as orientation tuned surround suppression. If most of their data would be from layer 5, perhaps a lack of clear figure modulation might not be that surprising. This could perhaps also be seen when comparing their electrophysiology data to the imaging data which is reportedly from layer 2/3, where most neurons show larger figure modulation/tuned surround suppression effects. There is, however, no report or discussion of differences in modulation between recording modalities.

3. There is an apparent discrepancy between Figure 5d and i. How can their modulation index be around -0.1 for cross (Figure 5d) - which would correspond to on average ~20% weaker responses to a figure than to background, when their PSTH (5i) shows an almost 50% increase of figure over ground. This positive figure modulation has also been widely reported in the literature (Schnabel, Kirchberger, Keller). Are there different populations of cells going into these analyses?

4. In a similar vein, it is not immediately clear why the average map correlation would be bigger for random cell pairs (~0.2, Fig 3g) than for the different conditions of the same cell (~0, Fig 5b). Could this be due to differences in recording modality (imaging in 3g and ephys in 5b)?

5. The maps in Figure 4 should show the location of the RF, because they cannot be interpreted without knowledge of the RF center and size. For example cell 4 in the iso 1 condition could be a border cell, or could respond to the center of the figure. It is impossible to deduce without knowledge of the location of the RF.

6. It could help the reader to discuss the interpretation of the map correlations in Fig 5 a and b in more detail. My guess is that negatively correlated maps (within cross or iso condition) could come from highly orientation tuned neurons, whereas higher correlation values point to more generally figure/contextually modulated cells (within this condition). While the distribution is far from bimodal, this does not rule out a population of nicely figured modulated cells at the high end of the distribution. It might not be necessary at the level of V1 that the figure modulation be consistent across all textures. It would not be surprising, if orientation contrast-defined, phase contrast-defined and motion contrast-defined figures could be signalled to higher areas by discrete populations of V1 or even LM cells.

7. Some of the behavioural results warrant a little more explanation or discussion, as well. In Figure 2h, the mice seem significantly better on the static version of the iso task, than on the moving one. If statistically significant, this should be discussed. Is this because the static frame was maximally phase offset? Then the figure would indeed be better visible better (bigger phase contrast in more frames) than in the moving condition.

Figure 2 and extended Figure 1c: why is the mouse lemur performing so poorly on average? It also appears to have biggest problems with the cross stimulus early on in training.

Tree shrews seem not to be able to memorize the textures as well as the mice do. Is this because of less deprivation/motivation? Or because of the bigger set of textures in training? This would make memorization harder and could thus lower their overall performance. The comparative aspects are very interesting but the absolute differences in performance could be discussed in more detail or explained better.

8. In Figure 7b, why wouldn't the explanation for the linear decodability in cross also hold for iso? There are phase offsets at the borders that simple cells should readily be able to resolve, just as in the case of orientation discontinuities. Could they make a surround phase model, similar to their surround orientation model, that could more readily capture the iso discontinuities?

Reviewer #2 (Public Review):

The authors trained two monkeys to perform a task that involved sequential (blocked) but unsignalled rules for discriminating the colour and shape of visual stimulus, by responding with a saccade to one of four locations. In rules 1 and 3, the monkeys made shape (rule 1) or colour (rule 3) discriminations using the same response targets (upper left / lower right). In rule 2, the monkeys made colour judgments using a unique response axis (lower left/upper right). The authors report behaviour, with a focus on time to relearn the rules after an (unsignalled) switch for each rule, discrimination sensitivity for partially ambiguous stimuli, and the effect of congruency. They compare the ability of models based on Q-learning, Bayesian inference, and a hybrid to capture the results.

The two major behavioural observations are (1) that monkeys re-learn faster following a switch to rule 2 (which occurs on 50% of blocks and involves a unique response axis), and (2) that monkeys are more sensitive to partially ambiguous stimuli when the response axis is unique, even for a matched feature (colour). These data are presented clearly and convincingly and, as far as I can tell, they are analysed appropriately. The former finding is not very surprising as rule 2 occurs most frequently and follows each instance of rule 1 or 3 (which is why the ideal observer model successfully predicts that the monkeys will switch by default to rule 2 following an error on rules 1 or 3) but it is nevertheless reassuring that this behaviour is observed in the animals. It additionally clearly confirms that monkeys track the latent state that denotes an uncued rule.

The latter finding is more interesting and seems to have two potential explanations: (i) sensitivity is enhanced on rule 2 because it is occurs more frequently; (ii) sensitivity is enhanced on rule 2 because it has a unique response axis (and thus involves less resource sharing/conflict in the output pathway).

The authors do not directly distinguish between these hypotheses per se but their modelling exercise shows that both results (and some additional constraints) can be captured by a hybrid model that combines Bayesian inference and Q learning, but not by models based on either principle alone. A Q-learning model fails to capture the latent state inference and/or the rule 2 advantage. The Bayesian inference model captures the rapid switches to rule 2 (which are more probable following errors on rule 1 and rule 3) but predicts matched discrimination performance for partially ambiguous stimuli on colour rules 2 and 3. This is because although knowing the most likely rule increases the probability of a correct response overall it does not increase discriminability and thus boosts the more ambiguous stimuli. I wondered whether it might be possible to explain this result with the addition of an attention-like mechanism that depends on the top-down inference about the rule. For example, greater certainty about the rule might increase the gain of discrimination (psychometric slope) in a more general way.

The authors propose a hybrid model in which there is an implicit assumption that the response axis defines the rule. The model infers the latent state like an ideal observer but learns the stimulus-response mappings by trial and error. This means that the monkeys are obliged to constantly re-learn the response mappings along the shared response axis (for rules 1/3) but they remain fixed for rule 2 because it has a unique response axis. This model can capture the two major effects, and for free captures the relative performance on congruent and incongruent trials (those trials where the required action is the same, or different, for given stimuli across rules) on different blocks.

I found the author's account to be plausible but it seemed like there might be other possible explanations for the findings. In particular, having read the paper I remained unclear as to whether it was the sharing of response axis per se that drove the cost on rule 3 relative to 2, or whether it was only because of the assumption that response axis = rule that was built into the authors' hybrid model. It would have been interesting to know, for example, whether a similar advantage for ambiguous stimuli on rule 2 occurred under circumstances where the rule blocks occured randomly and with equal frequency (i.e. where there was response axis sharing but no higher probability); or even whether, if the rule was explicitly signalled from trial to trial, the rule 2 advantage would persist in the absence of any latent state inference at all (this seems plausible; one pointer for theories of resource sharing is this recent review: https://www.cell.com/trends/cognitive-sciences/fulltext/S1364-6613(21)00148-0?_returnURL=https%3A%2F%2Flinkinghub.elsevier.com%2Fretrieve%2Fpii%2FS1364661321001480%3Fshowall%3Dtrue). No doubt these questions are beyond the scope of the current project but nevertheless it felt to me that the authors' model remained a bit tentative for the moment.

Reviewer #2 (Public Review):

In this manuscript, the authors investigated the mechanism by which glycine prevents cell membrane rupture. They found that deficiency of NINJ1 (the key executioner of plasma membrane rupture by forming oligomers) phenocopies the cytoprotection of glycine during lytic cell death, and glycine treatment inhibits the oligomerization of NINJ1. Based on these observations, they claimed that glycine executes its inhibitory effect on cell lysis by targeting and inactivating NINJ1. This study addresses an important subject, because how glycine prevents cell membrane rupture is not understood and the literature is full of the implausible conclusion that it works as an osmoprotectant and that pyroptotic cell rupture is secondary to osmotic changes in cells undergoing pyroptosis, even though the gasdermin pore is very large and should allow the free passage of ions and many small proteins.

Reviewer #2 (Public Review):

This manuscript provides additional data about how smell is encoded by insects. The study includes both new experimental measurements and simulations. At present, there are questions about whether simulations are appropriately performed to support experimental measurements.

The main experimental finding reported here is that the same olfactory receptor neurons (ORN) can respond with different temporal dynamics to different odorants. This finding is of interest. However, it is very important to discuss whether the differences in temporal dynamics can be explained by differences in how this odorant is carried by air, as has been described here: https://pubmed.ncbi.nlm.nih.gov/23575828/.

There are several questions that need to be addressed regarding the simulations part of the manuscript.

1) There is a mismatch between the number of ORNs used in the model and in the insect system studied.

2) The demonstration in Figure 5 that motif switching improves odor classification includes motif switching for a given odorant, which is not observed experimentally.

3) The methodology for estimating neural temporal dynamics needs to be corrected to apply to the natural stimuli used here.

Reviewer #2 (Public Review):

This fascinating study describes a possible effect of cancer-generated microvesicles on fibroblasts. Microvesicles from a particularly metastatic line promote more contractile and proliferative fibroblasts, and there is a key role for at least one microvesicle factor - the crosslinking enzyme Transglutaminase-2. A wide range of studies help identify and elucidate these effects, but a few aspects remain unclear.

1. MV- has more crosslinking TGM2 but also less MMP14 degradation, and so ECM is more stable either way. The authors should describe any other factors that would give a similar effect as these. The authors should address: do other genes change with TGM2 knockdown; does MMP14 change? If the latter changes, does it have a more important role than TGM2?

2. Perhaps the cleanest and important study of MV effects is in Fig.6j,k, but it shows in vivo differences that are barely significant or not significant, and compares to 'SF' serum free media as a control. Are serum components detected in Mass Spec? If so, wouldn't this suggest a serum supplemented media is a better control? The serum is usually from another species, which is a further (xenogenic) concern that motivates care and discussion about dose -- especially given the high frequency of injection. Also, is there a survival difference for the mice?

Reviewer #2 (Public Review):

The goal of the study by Rutherford and colleagues was to characterize functional, structural, and molecular changes at the highly specialized cochlear inner hair cell (IHC) - spiral ganglion neuron (SGN) ribbon synapse in GluA3 AMPA receptor subunit knockout mice (GluA3KO). Previous work by the authors demonstrated that 2-month-old GluA3KO mice experienced impaired auditory processing and changes in synaptic ultrastructure at the SGN - bushy cell synapse, the next synapse in the auditory pathway.

In the present study, the authors investigated whether GluA3 is required for ribbon synapse formation and physiology in 5-week-old mice using a series of functional and light- and electron microscopy imaging approaches. While deletion of GluA3 AMPAR subunit did not affect hearing sensitivity at this age, the authors reported that cochlear ribbon synapses exhibited changes in the molecular composition of AMPARs and pre- and postsynaptic ultrastructural alterations. Specifically, the authors demonstrated that GluA3KO ribbon synapses exhibit i) a global reduction in postsynaptic AMPARs, which is also reflected by smaller AMPAR arrays, ii) a reduction in GluA2 and an increase in GluA4 protein expression at individual postsynaptic sites, and iii) changes in the dimensions and morphology of the presynaptic specialization ("ribbon") and in the size of synaptic vesicles. These reported structural changes are linked to the side of innervation with respect to the IHC modiolar-pillar axis.

The results presented by the authors are conceptually very interesting as the data support the notion that potentially detrimental changes in the molecular composition of a sensory synapse can be compensated to sustain synaptic function to a certain extent during development. The conclusions of the study are mostly well supported by the data, but some experimental details or control experiments are missing or need to be clarified to allow a full assessment.

1. The authors tested which GluA isoforms are expressed in SGNs of GluA3KO mice and reported that only GluA2 and GluA4, and not GluA1, receptor subunits are present in the cochlear. It is, however, a bit difficult to understand why immunolabelling for GluA1 was only performed on brainstem sections (Fig. 1B right) and not in the cochlear to probe for postsynaptic localization at ribbon synapses as it was done for the other isoforms (Fig. 2 and 6) given that GluA3KO IHCs exhibited a larger number of ribbons that lacked GluA2 and 3 (lone or 'orphaned' ribbons; Fig. 6B). It is also not clear why immunolabelling for GluA2 and 4 was performed to probe for expression of these receptor subunits on SGN cell bodies in the cochlear spiral ganglion. Which neurons are expected to synapse onto these somata?

2. The authors state in the text that GluA3 expression is completely abolished in GluA3KO IHCs, however, there appears to still be a faint punctate immunofluorescence signal visible when an antibody directed against GluA3 was used (Fig. 2C). Providing additional information on the specificity of this (and the other) antibodies used in the study would be helpful.

3. The authors reported changes in the volume of the presynaptic ribbon and postsynaptic density surface area in GluA3KO KO animals. The EM data as presented are however not sufficiently convincing.<br /> i) There appears to be a mismatch between the EM data shown in Fig. 3 and 4 and the information in the text with respect to the number of data points in the plots and the reported number of reconstructed synapses. This raises several questions with respect to the analysis. For instance, it is unclear whether certain synapses were reconstructed but excluded from the analysis. If so, what were the exclusion criteria?<br /> ii) The authors compare PSD surface areas in reconstructions from 3D serial sections, but for some of the shown reconstructions (i.e. Fig. 3A' and B' and 4B'), it appears as if PSDs were only incompletely reconstructed.

4. The immunolabelling experiments shown in Fig. 2 and 6 are of very high quality and the quantitative analysis of the light microscopy data (Fig. 6-9) is clearly very detailed, but slightly difficult to interpret the way it is presented. Specifically, it is unclear how the number of synapses per IHC (Fig. 6B) and the separation into modiolar and pillar side (Fig. 8) was achieved based on the shown images without the outlines of individual cells being visible.

5. Adding more detailed information about important parameters (mean, N/n, SD/SEM) and the statistical tests used for the individual comparisons presented in the Figures would help strengthen the confidence in the presented data.

6. In general, the authors report a series of molecular and structural changes in IHCs and reach the conclusion that GluA3 subunits may have a role in "trans-synaptically" determining or organizing the architecture of both the pre- and post-synapse. However, some of the arguments are very speculative and many of the claims are not supported by experimental data presented in the paper. The authors should consider to also compare their findings to studies that investigated ultrastructural changes of AMPAR subunit knockouts in other synapse types, and discuss alternative interpretations (e.g. homeostatic changes).

Reviewer #2 (Public Review):

The manuscript by Lefebvre et al. investigates how the tissue-scale spatial organization of protein evolves during germ band extension. The key question is whether changes in the localization of important features such as pair-rule gene (PRG) stripes and apical myosin orientation can be explained purely via passive advection without the need for additional regulatory mechanisms. In the case of the PRG, as well as TLRs, their data strongly suggests the answer is yes: the authors show that the deformation of the characteristic stripe pattern closely matches that predicted by advecting the initial pattern in a velocity field extracted from the observed tissue flow. By contrast, the authors find that anisotropic myosin orientation cannot be explained purely in terms of the local velocity field, in particular the fact that myosin remains robustly oriented with the DV axis. This leads the authors to postulate that myosin orientation is continually re-established via a static source aligned with said axis, which dominates over re-orientation due to advection. A simple model of myosin reorientation is developed from this hypothesis, which produces qualitatively similar relationships between orientation and local vorticity to that seen both in WT and in several mutants.

The strongest feature of this paper is illustrated by the results in Figure 2. The result it presents, which the authors summarize as "PRGs flow with tissue while myosin does not," is a very nice application of recent advances in using toto microscopy for embryonic systems to extract and quantify whole embryo expression patterns and flow fields, which are needed information for this kind of result. Tissue flow is a complicated, active process, and identifying which parts of the dynamics can be sufficiently explained by passive transport can tremendously simplify the conceptual challenges of germ band extension and related tissue movements found during neurulation or organogenesis. The resistance the authors found that myosin exhibits to re-orientation is likewise very interesting because it implies that information about global geometry (the direction of the DV axis) is somehow maintained at the cellular level throughout the convergent extension.

The principle weakness in this manuscript is the vagueness of the proposed static source mechanism and the lack of direct evidence for it in experiments. The FRAP experiments performed here suggest that binding/unbinding happens on the right timescale to play a role in anisotropy maintenance, but if the principle question is 'how does myosin remain oriented along the DV axis' then the static source hypothesis just kicks the can down the road to ask 'how does the static source remain oriented along the DV axis'? The minimal model the authors employ has the benefit that it lets them relate angular deviation to vorticity, at the cost that it is agnostic to the form and nature of the source term, so it cannot be used to extract useful constraints. This said the evidence provided regarding the connection between vorticity and binding rates to myosin deflection is sufficient indirect evidence of the hypothesized mechanism that I suspect it will be of interest to a good number of people interested in epithelial morphogenesis.

Reviewer #2 (Public Review):

This manuscript by Winter et al represents an analysis of the function of the ATAD1 gene in cancer. At present, the manuscript makes a number of interesting observations, with strong experimental support. First, the authors show that tumors with PTEN deletions frequently have additional mutations in ATAD1, and that prostate tumors with both mutations are associated with a shorter period of survival. Second, tumors lacking ATAD1 are more sensitive to proteotoxic stress, based in part on an increased tendency to apoptosis. Third, the ATAD1 protein interacts with BIM, and interactions with BIM contribute in part to an increased tendency to apoptosis. Fourth, ATAD1 and MARCH5 have at least moderate synthetic sick/lethal interactions; together with other data, this suggests they control the release of BIM from the OMM, contributing to its degradation. Overall, the data suggest that tumors with ATAD1 deletions may be particularly vulnerable to drugs that induce proteotoxic stress, suggesting new potential therapeutic regimens, which would be a valuable contribution to the field. The level of data presented here is already substantial; however, some additional experiments to support the authors' contentions would strengthen the work. Some claims about the mechanism are overstated given the current body of data and should be qualified.

Reviewer #2 (Public Review):

In this work, the authors were trying to prove the model that the fungal pathogen Fusarium oxysporum f. sp. lycopersici (Fol) utilizes the acetyltransferase FolArd1 to induce the acetylation of the K167 residue of the effector protein FolSvp1. This acetylation prevents the K152, K258 and K284 ubiquitination-mediated degradation of FolSvp1 in Fol, and meanwhile inhibits the K167 ubiquitination-mediated degradation of FolSvp1 in tomato plants. In the host plants, FolSvp1 interacts specifically with the apoplastic defense protein SlPR1 and translocates it to the nucleus, which suppresses the SlPR1-derived CAPE1 peptide-induced fungal resistance. Overall, the experiments were well designed and the large amount of data justified most of their conclusions. The work sheds novel insight into the virulence mechanisms of fungal effectors by showing that acetylation modification can stabilize a fungal effector, which is able to mis-localize a key defense protein to dampen the host immunity.

There are two issues that need to be addressed.

1. As far as I know, the apoplastic PR1 proteins may have a fungicide activity. When the authors tested the interaction between FolSvp1 and SlPR1 in Nicotiana benthamiana by BiFC, both apoplastic and nuclear interactions could be detected. Therefore, the authors should discuss the possibilities whether the binding of FolSvp1 to SlPR1 remained in the apoplast can inhibit (i) its anti-Fol activity and (ii) the cleavage of SlPR1 to produce the CAPE1 peptide. In other words, although translocating SlPR1 to the nucleus by FolSvp1 is effective for suppressing CAPE1 production, this may not be the only way.

2. The FolSvp1 produced in N. benthamiana was using the SlPR1 signal peptide and lacked the acetylation modification. It is possible that the acetylation of FolSvp1 can affect the interaction affinity or localization between FolSvp1 and SlPR1. The K167Q mutation of FolSvp1 might not be able to faithfully mimic the K167 acetylation.

Reviewer #2 (Public Review):

This study seeks to determine how neuronal glycolysis is coupled to electrical activity. Previous studies had found that glycolytic enzymes cluster within nerve terminals (in C. elegans) during activity. Furthermore, the glucose transporter GLUT4 is recruited to synaptic surface during activity. The authors previously showed that Ca2+ does not stimulate glycolysis in active neurons. Here, the authors show that the cytosolic Na+, not Ca2+, and the activity of the Na/K pump drive glycolysis. However, it is important to note that in this study, glycolysis was examined in the soma, not nerve terminals, where some of the previous studies were conducted. A few other caveats in the interpretation of the findings are listed below:

1. The NADH/NAD ratio is used throughout as the only measurement reflecting glycolytic flux.<br /> 2. It has been hypothesized that the close association of glycolytic enzymes with ion transporters (such as the Na+/K+ pump) is meant to provide localized ATP to power these pumps. How does bulk glycolysis (monitored with NADH/NAD ratio) relate to localized/compartmentalized glycolysis?<br /> 3. Related to point 2, most of the peredox measurements in the paper have been made at baseline, in the absence of electrical activity. Therefore, it is not clear how the findings relate to activity-driven glycolysis.<br /> 4. The finding that inhibition of SERCA during stimulation actually elevates cytosolic NADH level argues against Na+ being the only ion that regulates glycolysis.<br /> 5. The finding that "SBFI ΔF/F transients were longer in duration than the RCaMP LT transient" does not necessarily mean that Na+ elevation lasts longer than Ca2+ in the cell. This could be an artefact of the SBFI on/off rate relative to RCaMP. In fact, prolonged elevation of cytosolic Na+ would make neurons refractive to depolarization in AP trains.

Reviewer #2 (Public Review):

The authors applied an innovative and very interesting approach based on different -omics platforms to study the biological post-mortem transformations of human bone. Despite the study being a proof-of-concept, because of the small number of collected samples and the lack of external validation, the methodology is promising. The study will have a strong impact on the field and present the state-of-art of -omics sciences.

Reviewer #2 (Public Review):

The goal of this study was to understand the molecular mechanism of how transcription factor DUX4, which has a role in cancer, inhibits the induction of genes stimulated by interferon-gamma. The authors achieved this goal, and their results mostly support their conclusions. They found that DUX4, in their experimental model, interacts with STAT1, thereby decreasing STAT1 and Pol-II recruitment to sites of gene transcription.

The present study has many strengths: The topic is of broad interest, the findings are novel and intriguing, the experiments are well-designed and controlled, the data, with one exception, is carefully interpreted, and the manuscript is very well-written.

Two major weaknesses were identified. One is that all experiments, except Figure 6, rely on one experimental setup, which is a human skeletal muscle cell line with an integrated doxycycline-inducible transgene. The concern is that both the treatment of cells with the drug doxycycline and the fact that signaling pathways could be disrupted in this (immortalized?) cell line could lead to artifacts that skew results. Indeed, results in Figure 4C indicate that total STAT1 is completely localized in the nucleus even prior to interferon stimulation when it should be in the cytoplasm. The other weakness is the use of the DUX4-C-terminal-domain (DUX4-CTD) mutant for the majority of the mechanistic experiments. The concern here is that although the phenotype of ISG repression is observed in this truncated mutant, important regulatory domains could be missing that modulate the interaction with STAT1 or other proteins. Is the NLS added after the flag tag identical to the endogenous NLS? Related, I disagree with the interpretation of Figure 4C that "this interaction happens within the nuclei of DUX4-CTC expressing cells". The interaction could happen prior to STAT1 shuttling to the nucleus.

Reviewer #2 (Public Review):

To test how oxytocin impacts the brain and the psychological, neural, and hormonal response to touch, the authors tested human females during two counterbalanced fMRI sessions wherein females were stroked on the arm or the palm, by a real-world romantic partner or a stranger, while blood levels of oxytocin and cortisol were collected at multiple time points.

This combination of measures, and the number of hypotheses that could be tested with them, is remarkable - virtually unheard of. This impressive, difficult, and more ecological design than is typical for the field is a major strength of the study, which allowed the authors to test many important hypotheses concurrently and to show contextual effects that could not otherwise be observed. The only potential drawback perhaps is that with such a large design, including many measures, the authors produced so many significant interactions and results that it could be hard for the casual reader to appreciate the importance of each.

The authors supported their hypothesis that oxytocin effects are context-sensitive, as they found a key interaction wherein experiencing the partner first increased oxytocin for the partner relative to when they came first the OT levels were low but then increased if they were preceded by the partner (excepting one timepoint). Cortisol responses (which reflect hormonal stress) were also higher when the stranger came first than when he was preceded by the partner). In addition, touch was experienced more positively on the arm than on the palm, supporting the role of c-fibers in conveying specifically felt responses to warm, tender touch.

These data indicate significant context sensitivity with real-world implications. For example, experiencing warm touch on the arm can make us more receptive to other people in subsequent encounters. Conversely, when strangers try to approach and get close to us "out of the blue" people experience this as stressful, which reduces the pleasantness of the interaction and may reduce trust in the moment...perhaps even subsequently.

This research is critical to the basic science of neurohormonal modulation, given that most of this research occurs in rodents or in simplified studies in humans, usually through intranasal oxytocin administration with unclear impacts on circulating levels in the brain and blood. Oxytocin in particular has suffered from oversimplification as the "love drug" - wherein people assume that it always renders people more loving and trusting. The reality is more complex, as they showed, and these demonstrations are needed to clarify for the field and the public that neurohormones adaptively shift with the context, location, and identity of the social partner in an adaptive way. These results also help us understand the many null effects of oxytocin on trusting strangers in human neuroeconomic studies. In a modern world that is characterized by significant loneliness, interactions with strangers and outsiders, and touch-free digital interactions, our ability to understand the human need for genuine social contact and how it impacts our response to outsiders (welcomed in versus a source of stress) is critical to human health and the wellbeing of individuals and society.

Reviewer #2 (Public Review):

In their manuscript, Wiest and colleagues focus on testing two primary hypotheses. The first is that the aperiodic exponent from the intracranial EEG / LFP reflects to population EI balance, and the second is that Parkinson's disease is specifically associated with reduced inhibition-concomitant excessive excitation-in the STN.

To accomplish this, they make use of data from 24 patients with Parkinson's disease who have undergone surgery to implant a deep brain stimulator as part of the treatment of their disease. These patients provide a rare opportunity to record high signal-to-noise EEG/LFP data directly from the human brain. These data are complemented by an additional dataset collected from eight 6-OHDA-lesioned rats, which provide a model of Parkinson's disease. The rat data includes both single-unit spiking activity, which allows Wiest and colleagues to examine periods of relatively high- or low-firing as a proxy for excitatory tone, as well as LFP data which allows them to bridge to the human data and more directly test their first hypothesis that the aperiodic exponent reflects EI balance.

Overall this is a very strong paper. The cross-species approach is especially convincing, and the methods are well-implemented and sound. The authors use appropriate analysis tools and statistical methods, and their inferences are clear, but measured. Their results are convincing, and the potential for aperiodic activity to serve as a potential physiologically interpretable index of Parkinsonian state.

Reviewer #2 (Public Review):

This work presents valuable evidence of the connection between Huntingtin's (HTT) phosphorylation state and the recruitment of Kif1A in the axonal anterograde trafficking of synaptic vesicles precursors (SVPs). In brief, the authors describe how phosphorylation of HTT in Serine 421 determines the recruitment of the anterograde molecular motor Kif1A to SVPs, increasing their rate of transport along the axons to the synapse. This conclusion is substantiated by the measured impact of HTT phosphorylation on motor skills learning ability.

The study presents a variety of investigative angles, combining both ex vitro and in vivo approaches. The use of custom microfluidics chamber to recreate neuronal circuits is a point of strength as it allows for in depth analysis of the transport phenotype. This tool could be a very useful tool for the community to explore for a variety of similar studies. The use of mouse models also adds credibility to the physiological importance of the findings.<br /> The evidence presented supports the claims, though more emphasis could be added to the explanation and mechanisms behind how an increased transport dynamic of SVPs due to HTT phosphorylation, results in a detrimental effect on motor skill learning. This finding is perhaps the most critical as it reiterates the importance of balance in SVPs transport and highlights how the system is finely regulated and sensitive to both down and upregulation. This fine tuning might ensure the presence of the proper quantity of SVs at synapses to guarantee an effective synaptic function.

This works adds an important angle to role of HTT phosphorylation, which could open new avenues of treatment for HTT disease based on the manipulation of HTT phosphorylation state.

Reviewer #2 (Public Review):

Ines Lago-Baldaia et al. investigate the connection between transcriptional and morphological diversity of glial cells. This is an important question to answer in the glial biology field and has been amplified by recent advances in single-cell sequencing. It remains unclear if transcriptional diversity that is often reported in scRNA analysis equates to morphologically distinct glia. To explore the correlation between transcriptome and morphology, the authors utilize the strength of the Drosophila model system to demonstrate that although morphotypes of glia can be identified in the nervous system, the morphotypes do not correlate with a distinct transcriptional profile. Overall, the paper is well written and the conclusion matches the results that are presented. This work will be an important contribution to the glial biology field.

Reviewer #2 (Public Review):

What the authors were trying to explore is very interesting with translational potential toward glaucoma treatment. They used a topical dexamethasone (dex) induced mouse model showing ocular hypertension and a culture model using human TM cells treated with tBHP to induce TM oxidative stress. Their results suggested that metformin protected TM cells from cytoskeletal destruction by enhancing the integrin/ROCK pathway and alleviated elevated IOP in the mouse model. However, the provided simulative results were vague and the research needs extra experimental data to support its conclusion.

Reviewer #2 (Public Review):

The work of Iyer et al. uses a computational approach to investigate how cells using multiple tiers of processing and multiple parallel receptor types allow more accurate reading of position from a noisy signal. Authors find that combining signaling and non-signaling types of receptors together with additional feedback increases the accuracy of positional readout against extrinsic noise that is conveyed in the morphogen signal. Further, extending the number of layers of signal processing counteracts the intrinsic stochasticity of the signal reading and processing steps. The mathematical formulation of the model is general but comprehensive in the way it handles the difference between branches and tiers for the processing of channels with feedbacks. The results of the model are presented from simple one-branch and one-tier architecture to two-branch and two-tier architecture with feedbacks. Interestingly authors find that adding more tiers results in only very small improvements in the accuracy of positional readout. The model is tested against a perturbation experiment that impairs one of the signaling branches in the Drosophila wing disc, but the comparison is only qualitative as further experiment-oriented work is planned in a separate paper.

Strengths

There is a clear statement of objectives, model, and how the model is evaluated. In particular, the objective is to find what number of receptor types and their concentrations for a given number of tiers and feedback types is resulting in the most accurate positional readout. The employed optimization procedure is capable to find signalling architectures that result in one cell diameter positional precision for most of the tissue with 3-4 cells at the tissue end that is most distant to the morphogen source. This demonstrates that employing additional complexity in signal processing results in a very accurate positional readout, which is comparable with estimates of positional precision obtained in other developmental systems (Petkova et al., Cell 2019, Zagorski et al., Science 2017).

The optimal signalling architectures indicate that both signalling (specific) and non-signalling (non-specific) receptors affect the precision of positional readout, but the contributions of each type of these receptors are qualitatively different. Even slight perturbation of signalling receptors drives the system out of optimum, resulting in a decrease in positional precision. In contrast, the non-signalling receptors could accommodate much larger perturbations. This observation could provide a biophysical explanation for how cross-talk between different morphogen species could be realized in a way that positional precision is kept at the optimum when morphogen signaling undergoes extrinsic and intrinsic perturbations.

Last, the model formulation allows to specifically address perturbations of signalling and feedbacks, that could be explored to validate model predictions experimentally in Drosophila wing disc, but also in other developmental tissues. The authors present a proof-of-concept by obtaining consistent results of variation of output profiles in two-tier two-branch architectures with non-signaling branch removed and intensity profiles of Wg in wing disc where the CLIC/GEEC endocytic pathway was perturbed.

Weaknesses

The list of model parameters is long including more than 20 entries for two-tier two-branch architectures. This is expected, as the aim of the model is to describe the sophisticated signalling architecture mimicking the biological system. However, this also makes it very challenging or impossible to provide guiding principles or understanding of the system behaviour for the complete space of signalling architectures that optimize positional readout. Although, the employed optimization procedure finds solutions that exhibit very high positional accuracy, there is only very limited notion how these solutions depend on variation of different parameters. The authors do not address the following question, whether these solutions correspond to broad global optima in the space of all solutions, or were rather fine-tuned by the optimization procedure and are quite rare.

It is unclear how contributions from the intrinsic noise affect the system behaviour compared to contributions from extrinsic noise. In principle, the two-branch one-tier architecture results in an already very accurate positional readout across the tissue. The adding of another tier seems to provide only a very weak improvement over a one-tier solution. It is possible that contributions from intrinsic noise for the investigated signalling architectures are only mildly affecting the system compared with contributions from extrinsic noise. Hence, it is difficult to assess whether the claim of reducing intrinsic noise by adding another tier is supported by the presented data, as the contributions from intrinsic noise could overall very weakly affect the positional readout.

Reviewer #2 (Public Review):

Hwang et al take an unconventional approach to address a longstanding problem in the field of Wnt signaling and cancer: the mechanism of beta-catenin nuclear import. The authors introduce expression of Xenopus beta-catenin in budding yeast, a heterologous model system that does not harbor any known Wnt signaling components but carries highly conserved nuclear transport machinery. They find that GFP-tagged beta-catenin is actively transported to the yeast nucleus in a Ran-GEF-dependent process, indicating NTR-dependent transport. An elegant rapamycin treatment-dependent Anchor-Away method is applied to systematically inhibit 10 budding yeast NTRs, for which orthologues exist in human cells. Significant and specific inhibition of beta-catenin nuclear import is identified when Kap104 (orthologue of Kapbeta2/Transportin-1 (TNPO1) was anchored to the plasma membrane. Furthermore, nuclear import depends on a PY-like NLS sequence in the beta-catenin C-terminus, which was shown to mediate a direct interaction with TNPO1. A role of the vertebrate paralogs tnpo1/2 and the PY-like NLS was confirmed in Xenopus, using double axis formation assays, and in mouse and human cell lines, combining tnpo1/2 depletion with nuclear localization and reporters for beta-catenin dependent transcription. Finally, the authors provide proof that responses of MEF cells to Wnt3a or human beta-catenin overexpression can be inhibited by treatment with a TNPO1/2 blocking peptide (M9M).

Overall, the results of this study provide a valuable addition to the longstanding and ongoing discussions on the mechanisms of beta-catenin nuclear import. The conclusions are based on a well-focused and solid set of experiments and are confirmed across species in a diverse set of model systems, and findings are discussed against the state of the field. Although the identified TNPO1/2-dependent beta-catenin nuclear import pathway was shown to be a target for peptide-based inhibitory strategies, these findings remain to be confirmed in relevant (colorectal) cancer model systems in which levels of beta-catenin are inappropriately enhanced and inhibition of its nuclear entry is most warranted.

Reviewer #2 (Public Review):

This manuscript puts forward a new idea that topography in neural networks helps to remove noise from inputs. The neural network consists of multiple stages. At each stage, the network is structured to be balanced in terms of the strength of inhibitory and excitatory signals. Because of topography, the networks become "dis-balanced" and receive more recurrent excitatory signals locally for those regions that receive strong initial inputs. This leads to error correction. The main weakness in the manuscript is that the approach will only work for inputs that are constant-in-time. It is important to acknowledge this limitation in both the title and throughout the manuscript.

Reviewer #2 (Public Review):

Overall, I think that the screen or mutants in the Arabidopsis flowering pathway and its outcome are biologically interesting and important. The authors show that FIO1 methylates U6 snRNA and not (or rarely) mRNA. However, subsequent to this, the results are entirely from bioinformatics of RNAseq data from the derived mutants; there are no further experiments performed, either to confirm or test newly-derived hypotheses. Furthermore, the main hypothesis, that 5'SS pos.+4 identity is critical for sensitivity to U6 N6-methylation, was already described in yeast S. pombe, based on data from mutants in the pombe ortholog Mtl16. Minimally, the conclusions based on bioinformatics should be confirmed with experimental data. In addition, there are examples throughout the manuscript where the authors state results or conclusions without providing any data; this is not acceptable and data supporting these assertions must be included.

Reviewer #2 (Public Review):

Pulmonary neuroendocrine cells (PNECs) are known to monitor oxygen levels in the airway and can serve as stem cells that repair the lung epithelium after injury. Due to their rarity, however, their functions are still poorly understood. To identify potential sensory functions of PNECs, the authors have used single-cell RNA-sequencing (scRNA-seq) to profile hundreds of mouse and human PNECs. They report that PNECs express over 40 distinct peptidergic genes, and over 150 distinct combinations of these genes can be detected. Receptors for these neuropeptides and peptide hormones are expressed in a wide range of lung cell types, suggesting that PNECs may have mechanical, thermal, acid, and oxygen sensory roles, among others. However, since some of these cognate receptors are not expressed in the lung, PNECs may also have systemic endocrine functions. Although these data are largely descriptive, the results represent a significant resource for understanding the potential roles of PNECs in normal biology as well as in pulmonary diseases and cancer and are likely to be relevant for understanding neuroendocrine cells in other tissue contexts.

However, there are several aspects of the data analysis that are unclear and require clarification, most notably the definition of a neuroendocrine cell (points #1 and #2 below).

1. Figure S1 shows the sorting strategy used for isolation of putative PNECs from Ascl1CreER/+; Rosa26ZsGreen/+ mice, and distinguishes neuroendocrine cells defined as ZsGreen+ EpCAM+ and "neural" cells defined as ZsGreen+ EpCAM-; the figure legend also refers to the ZsGreen+ EpCAM- cells as "control" cells. However, the table shown in panel D indicates that the NE population combines 112 ZsGreen+ EpCAM+ cells together with 64 ZsGreen+ EpCAM- cells to generate the 176 cells used for subsequent analyses. Why are these ZsGreen+ EpCAM- cells initially labeled as neural or control, but are then defined as neuroendocrine? If these do not express an epithelial marker, can they be rigorously considered as neuroendocrine?

2. Similarly, in the human scRNA-seq analysis, how were PNECs defined? The methods description states that these cells were identified by their expression of CALCA and ASCL1, but does not indicate whether they also expressed epithelial markers.

3. The presentation of sensitivity and specificity in Figure 1 is confusing and potentially misleading. According to Figure 1B, Psck1 and Nov are two of the top-ranked differentially expressed genes in PNECs with respect to both sensitivity and specificity. However, the specificity of these two genes appears to be lower than that of Scg5, Chgb, and several other genes, as suggested in Figure 1C and Figure S1E. In contrast, Chgb appears to have higher specificity and sensitivity than Psck1 in Figures 1C and E but is not shown in the list of markers in Figure 1B.

4. The expression of serotonin biosynthetic genes in mouse versus human PNECs deserves some comment. The authors fail to detect the expression of Tph1 and Tph2 in any of the mouse PNECs analyzed, but TPH1 is expressed in 76% of the human PNECs (Table S8). Is it possible that Tph1 and Tph2 are not detected in the mouse scRNA-seq data due to gene drop-out? If serotonin signaling by mouse PNECs is due to protein reuptake, as implied on p. 5, is there a discrepancy between serotonin expression as detected by smFISH versus immunostaining?

5. The smFISH and immunostaining analyses are often presented without any indication of the number of independent replicate samples analyzed (e.g., Figure 2B, Figure 3F, G).

6. It would be helpful to provide a statistical analysis of the similarities and differences shown in the graphs in Figures 1E and G.

Reviewer #2 (Public Review):

The authors introduce a model based on textual data for predicting odor properties of a mixture of chemicals. Modelling approach is relevant to olfactory scientists and experimental neuro-scientists.

Work is relevant because it unifies and studies multiple mixture odor datasets, achieving satisfactory results. Work is novel because modelling for mixture datasets is scarce, this work introduces a grounded approach for modeling such data. Model is directly interpretable since it relies on a linear model (lasso) to build mapping between features (metric learning).

The authors's evidence supports most of the conclusions of the work with some room for improvement.

This work can be of the many in the future trying to further modelling approaches for mixture data.

Reviewer #2 (Public Review):

The study by Sommer et al. applies alphafold to the CHESS selection of transcripts with the goal of generating predicted 3D protein structures and a quality measure of folding, the pLDDT score. From these data, the authors build up a database for result exploration. In addition, they provide examples to underline this approach. Examples include proteins, where the authors propose the pLDDT score as a measure of presumed superior biological functionality over other isoforms. The authors also use the generated data to propose novel functionally relevant isoforms, e.g. in the mouse.

The study is based on the elegant idea to aid genome annotation through 3D structure prediction. This is a very powerful approach that allows large-scale data generation for functional interpretation. This approach appears technically sound and well executed (although I may miss details not being a protein expert). However, in my opinion, the authors could make more use of the potential of their approach. From the big-data start, they seem to directly restrict themselves to interesting examples. I am missing a global analysis that shows the bigger picture of their results. Given that they have generated structures from 90,415 isoforms, each associated with a pLDDT score, conservation scores, length, expression levels and other quantifiable data listed on page 18. I would wish for a comprehensive analysis of these data and their potential before applying the focus on a few (admittedly very nice) examples.

Furthermore, one of the weak spots of such an analysis is the relationship between foldability and functional relevance. Disordered regions would imply reduced relevance due to poor pLDDT scores, which may be a misleading conclusion. While this may be a problem difficult to solve with their approach, I think this still needs to be addressed and discussed throughout the paper and particularly as part of the global analysis, not just in the context of examples.

As a minor point, I would like to motivate the authors to be more explicit with some quantifications. For example, when focusing on proteins < 500 aa long, what does this mean in relation to what they are not representing in their analysis? How many isoforms will they miss? Is there going to be a bias (e.g. against scaffolding proteins, kinases like ATM, etc.)?

Overall, I consider the idea of the paper very elegant and well executed, yet focusing too much on trees, while I, as a reader, would like to know more about the forest.

Reviewer #2 (Public Review):

In this work, Li, Dorajoo, and colleagues use national Singaporean data to demonstrate the associations of previously published polygenic risk scores (PRS) for 4 cancers (breast, prostate, colorectal, and lung) with incident cases over 20 years of follow-up. Using available PRS for the four cancers from the Polygenic Score Catalog, they used recommended metrics to evaluate the distribution, discrimination, risk association, and calibration of the PRS. Although the PRS were derived from predominantly European populations, the authors confirmed all PRS-disease associations in this ethnic Chinese population, with per-standard deviation effect sizes ranging from hazard ratio 1.17 for lung cancer to 1.73 for prostate cancer.

The strengths of this work include the use of an apparently unbiased national population with 20 years of follow-up and near-complete outcomes ascertainment. The authors use state-of-the-art methods for genotyping, imputation, and PRS construction, and they use recently published PRS reporting standards to evaluate the PRS and organize the presentation of their work. Although the authors used an unbiased approach to their initial selection of PRS to evaluate (all 1,706 entries with <10,000 predictors in the PGS Catalog at the time), a significant weakness is the lack of detail in how the final 110 cancer PRS were selected for evaluation. Notable absences from these 110 are the PRS from the largest prostate cancer GWAS to date (PGS000662) and a Chinese-specific lung cancer GWAS (PGS000070). The latter absence is particularly notable as the authors report poorest performance of the lung cancer PRS they did evaluate.

Nonetheless, this work confirms prior observations of imperfect portability of PRS derived in one population to another, particularly of different genetic ancestry. The practical consequences of this performance differential will depend on the proposed use of the PRS. One important distinction the authors rightly point out is whether a PRS is intended for individual- or population-level application. The authors do not quantify the potential consequences of applying these PRS to the Singaporean population in different use cases (e.g., screening programs based on PRS), but interested readers will be able to use these findings to make such projections on their own.

Reviewer #2 (Public Review):

In this work, the authors attempt to resolve an apparent paradox in human locomotor development. Previous works have reported that neonates exhibit highly variable movement, which is believed to be important for driving exploration-based motor skill learning. Yet, other recent studies have also demonstrated that locomotor behaviors of newborn babies are generated by a very small number of invariant motor primitives that may underpin stereotypical innate motor behaviors. Indeed, as infants acquire the ability to walk independently, the number of motor primitives tends to increase while the overall motor variability decreases. Hinnekens et al. propose that this apparent paradox can be explained by following the variability of the activations of the motor primitives (or motor modules) as the locomotor behaviors of infants mature. The authors collected bilateral EMGs from infants longitudinally at 3 time points (from ~4 days old to walking onset) and used a well-known machine learning algorithm (non-negative matrix factorization) to extract both spatial and temporal motor modules, along with their activations, from the EMGs. They found that at birth, the cycle-to-cycle activations of the small number of modules were highly variable. But as the infants developed into toddlers, while the number of motor modules increased, their activations across cycles also became less variable. The authors conclude that early motor exploration is driven by the variable activation of a small number of motor modules, which would later fractionate into more modules that are more stably recruited across step cycles.

STRENGTHS:

Overall, this work is a valuable addition to the growing literature on the development of motor modules. It not only emphasizes how motor variability is a hallmark of typical motor development, but also suggests the relatively new concept that development-related motor variability originates from the variable activations of early motor modules. Indeed, recent works have proposed that in human adults, the motor variability that drives early motor skill learning may likewise originate from the variable recruitment of motor modules. With this work, it may become possible to conceptually unite the provenance of motor variability that drives both early development and adult learning under the modularity framework. The authors are also commended for their huge effort in collecting this very valuable data from newborn infants and following them with multiple recording sessions till their walking onset. The demonstration of the same longitudinal trend in variability and modules in two different motor behaviors (stepping and kicking) is also highly appreciated.

WEAKNESSES:

The analysis of EMGs relies on a model of motor modules that assumes that multi-muscle activities across step cycles are generated by the variable activations of fixed spatial modules and fixed temporal modules (line 511); thus, by design, after the identification of the spatial (w_j in equation 511) and temporal (w_i(t) in equation 511) modules, the only variable that is adjustable for explaining motor variability is the modules' activation coefficient (a_ijs in equation 511). But it is possible that the observed EMG or kinematic variability may be equally, if not better, accounted for by the cycle-to-cycle variation of the spatial and/or the temporal modules themselves. In fact, the variances of any combination of w_j, w_i(t), and a_ijs may all contribute to EMG variability, even though with the present model, the variance of w_j and w_i(t) are not considered. Therefore, the conclusion that motor variability is generated by variable activations of fixed modules can only be argued based on how well a single model (i.e., line 511) describes the data, rather than by excluding other alternatives (but equally legitimate a priori) models with perhaps less explanatory power. Notably, recent works (e.g., Cheung et al., 2020, IEEE-OJEMB; Berger, d'Avella et al., 2022, JNP) have shown or implied that the variability of the spatial/temporal modules themselves, in addition to their activation coefficients, may be a source of learning-related motor variability.

Reviewer #2 (Public Review):

Anti-VEGF treatment is currently used to treat patients with pathological retinal angiogenesis, but finding the underlying cause of increased VEGF is a challenge for the field. Wang and colleagues determined the role played by the amino acid transporter, SLC38A5, in retinal angiogenesis. They showed that Slc38a5 mRNA was enriched in retinal blood vessels versus neural retina, supporting previous single cell data that they reanalyzed here. In mouse models of human Retinopathy of Prematurity (ROP; Lrp5-/- and Ndp-/-) with decreased blood vessels, they showed a decrease in SLC38A5 protein. As both LRP5 and NDP encode proteins that work through the Wnt signaling pathway, the authors showed that both Slc38a5 mRNA and protein levels are controlled by Wnt agonists and antagonists in human endothelial cell cultures. They further showed that Slc38a5 transcription is affected by Wnt signaling by performing luciferase assays on putative Wnt binding regions that they identified 5' of the Slc38a5 gene. To further characterize the role of SLC38A5 in vivo, they injected a validated si-RNA into mouse eyes and found that formation of retinal vasculature layers was significantly impaired, which they also showed in Slc38a5 knockout mice. Using another mouse model of Retinopathy of Prematurity (oxygen-induced retinopathy), they find that Slc38a5 is required during pathological angiogenesis, and using in vitro cell culture studies show that it is required for endothelial cell viability, migration and tubular formation via its role in transporting glutamine. In part, they find that this may be through the regulation of angiogenesis-promoting receptor, VEGFR2. The authors performed an impressive series of experiments both in vitro and in vivo in studying the role of SLC38A5 in retinal angiogenesis. Their final model also does a nice job of summarizing their manuscript.

While the overall conclusions are supported by the data, some aspects of image acquisition and data analysis need to be clarified and extended.

Reviewer #2 (Public Review):

The present manuscript revisits the perennial (and important) question of which role the right IFG (rIFG) plays exactly in response inhibition. It does so using a stop-signal task in a patient group with lesions focused on rIFG, as well as a matched healthy control group, along with a group of control patients with lesions outside of the rIFG, and again a matched healthy control group. The behavioral data are analyzed with a novel parametric modeling approach that allows characterizing the distributions of Go RTs as well as the stop-signal reaction time (SSRT). Crucially, in the present form, it also accounts for so-called trigger failures, a long-known (but nearly equally long mostly ignored) phenomenon describing the failure to even initiate an inhibitory process (rather than the latency of this process being too long to succeed). Not accounting for trigger failures is known to inflate SSRT, and conceptually, they have been linked more to attentional processes than specifically to response inhibition. Here it is shown that behavioral deficits in rIFG patients are more strongly related to trigger failures than to the SSRT. This is elegantly complemented by the EEG data, where it is shown that mid-frontal beta bursts are strongly reduced in the rIFG group, but not in the others. Finally, it is shown that these mid-frontal beta bursts lead to corresponding beta bursts over the motor cortex. Importantly, this is also still the case for the rIFG patient group on successful stop trials where such mid-frontal beta bursts happened.

The present work has many strong elements. The use of a targeted patient group, with additional control groups, gets this research closer to causality than e.g. a pure EEG study could. The employed methods (computational modeling, beta bursts) are all cutting-edge and very appropriate, and the results form a coherent story, which is interpreted appropriately. The manuscript is also clear, yet very succinct, which at times might come at some cost towards following the details of the analysis and results, in particular, and some additional analyses might further strengthen the authors' claims. For example, there seems to be no reference to a traditional, non-parametric SSRT estimate, the size of the reduction of which by accounting for trigger failures might be a better metric of how central accounting for trigger failures is, rather than the five-fold TF increase in this group over the others (all of which have very low percentages, which put also a manifold increase into perspective). Maybe also more generally, the conceptual distinction between initiation and actual implementation of inhibition could be further sharpened, including with reference to the residual SSRT group effect from the parametric analysis, which is still quite sizable.

Given its innovative approach and important findings, the present results will undoubtedly have a major impact on the field of response inhibition, which is also relevant to the clinical domain.

Reviewer #2 (Public Review):

This report highlights the unexpected off-target presence of Cre in the mouse epididymis under conditions where specific Cre activity was only expected in the brain or adipose tissue. The use of a modified CLARITY protocol to provide visual demonstration of Cre in the caput epididymis was complemented and strengthened by supplementary data from fluorescent microscopy. However, the apparent '2-phase' expression between the distal and proximal portions of the caput was not further elaborated upon.

Through a series of technically challenging studies involving parabiosis and serum/exosome transfer experiments, there was some evidence that off-target expression involved the circulatory system. However, the lack of consistent outcomes suggests that this is not a robust effector process, so the precise reasons for the off-target expression remain unknown.

This study raises more questions than uncovered answers, and the conclusions are somewhat speculative (and correctly so). We are not closer to understanding why there is off-target Cre expression, nor why it is limited to the epididymis. It is not apparent how, and if, this unexpected observation holds any implications on past research reliant on Cre-recombination if those studies do not focus on the male reproductive tract, or the animal's health/behaviour is not affected. However, there is initial evidence (albeit less robust than desired) to support the authors' claim of distal organ-to-organ signalling, consistent with previous reports. Overall, this study currently speaks more so to the technology, rather than systems biology.

Reviewer #2 (Public Review):

Despite the long history of the study of topo II, the role of its long CTD in vitro and in vivo has remained poorly understood. The current manuscript provides solid lines of evidence that the intrinsically disordered CTD modulates topo II's enzymatic activities through LLPS. The experiments reported here were properly performed, and the conclusions are largely supported by the data presented, thereby making them an excellent contribution to the field. The current manuscript contains some weaknesses, though. The phosphatase treatment experiments are weak (Figure 4), and the role of phosphorylation on topo II-mediated LLPS remains unclear. The experiments using human topo IIs are also weak (Figure 6): the potential differences between topo IIa and topo IIb have not been rigorously tested or properly discussed. Most importantly, the difference in the catalytic mode between the full-length and CTD-lacking topo II needs to be tested and described more convincingly along with quantitative data (Figure 5).

Reviewer #2 (Public Review):

Congratulations on producing a very nice study. Your study aims to examine the morphological diversity of different mammalian limb elements, with the ultimate goal seemingly to test expectations based on the different timing of development of the limb bones. There's a lot to like: the sample size is impressive, the methods seem appropriate and sound, the results are interesting, the figures are clear, and the paper is very well written. You find greater diversity and integration in distal limb segments compared to proximal elements, and this may be due to the developmental timing and/or functional specialization of the limb segments. These are interesting results and conclusions that will be of interest to a broad readership. And the large dataset will likely be valuable to future researchers who are interested in mammalian limb morphology and evolution. I have one major concern with how you frame your discussion and conclusions, which I explain below. But I think you can address this issue with some text edits.

Major concern - is developmental timing the best hypothesis?

You discuss two potential drivers for the relatively greater diversity in distal elements: 1) later development and 2) greater functional specialization. Your data doesn't allow you to fully test these two hypotheses (e.g. you don't have detailed evo-devo data to infer developmental constraints), and I think you realize this - you use phrases like "consistent with the hypothesis that ...". You seem to compromise and conclude that both factors (development + function) are likely driving greater autopod diversity (e.g. Lines 302-306). Being unable to fully test these hypotheses weakens the impact of your conclusions, making them a bit more speculative, but otherwise, it isn't a critical issue.

But my concern is that you seem to favor developmental factors over functional factors as the primary drivers of your results, and that seems backwards to me. For instance, early in the Abstract (Line 32) and early in the Discussion (Line 201) you mention that your results are consistent with the developmental timing hypothesis, but it's not until later in the Abstract or Discussion that you mention the role of functional diversity/specialization/selection. The problem with favoring the development hypothesis is that your integration results seem to contradict that hypothesis, at least based on your prediction in the Introduction (Line 126; although you spend some of the Discussion trying to make them compatible). Later in the paper, you acknowledge that functional specialization (rather than developmental factors) might be a better explanation for the integration results (Lines 282-284, 345-347), but, again, this is only after discussions about developmental factors.

When you first start discussing functional diversity, you say, "high integration in the phalanx and metacarpus, possibly favoured the evolution of functionally specialized autopod structures, contributing to the high variation observed in mammalian hand bones." (Line 282). This implies that integration led to functional diversity in the autopod. But I'd flip that: I think the functional specialization of the hand led to greater integration. Integration does not result solely from genetic/developmental factors. It can also result from traits evolving together because they are linked to the same function. From Zelditch & Goswami (2021, Evol. & Dev.): "Within individuals, integration is customarily ascribed to developmental and/or functional interdependencies among traits (Bissell & Diggle, 2010; Cheverud, 1982; Wagner, 1996) and modularity is thus due to their developmental and/or functional independence."

In sum, I think your results capture evidence of greater functional specialization in hands relative to other segments. You're seeing greater 1) disparity and 2) integration in hands, and both of those are expected outcomes of greater functional specialization. In contrast, I think it's harder to fit your results to the developmental timing hypothesis. Thus, I recommend that throughout the paper (Abstract, Intro, Discussion) you flip your discussion of the two hypotheses and start with a discussion on how functional specialization is likely driving your results, and then you can also note that some results are consistent with the development hypothesis. You could maintain most of your current text, but I'd simply rearrange it, and maybe add more discussion on functional diversity to the Intro.

Or, if you disagree and think that there's more support for the development hypothesis, then you need to make a better case for it in the paper. Right now, it feels like you're trying to force a conclusion about development without much evidence to back it up.

Limitations of the dataset

Using linear measurements is fine, but they mainly just capture simple aspects of the elements (lengths and widths). You should acknowledge in your paper the limitations of that type of data. For example, the deltoid tuberosity of the humerus can vary considerably in size and shape among mammals, but you don't measure that structure. The autopod elements don't have a comparable process, meaning that if you were to measure the deltoid tuberosity then you'd likely see a relative increase in humerus disparity (although my guess is that it'd still be well below that of the autopod). And you omit the ulna from your study, and its olecranon process varies considerably among taxa and its length is a very strong correlate of locomotor mode. In other words, your finding of the greatest disparity in the hand might be due in part to your choice of measurements and the omission of measurements of specific processes/elements. I recommend that you add to your paper a brief discussion of the limitations of using linear measurements and how you might expect the results to change if you were to include more detailed measurements and/or more elements.

Reviewer #2 (Public Review):

The authors have recorded the activity of neurons in the rat substancia nigra pars reticulata (SNr) while animals performed a version of a stop-signal task. The goal of this study is to investigate and describe the contribution of SNr in proactive inhibitory control. By examining single-cell responses as well as population activity, the authors show that increasing the probability of stop signal trials induces several changes in SNr responses. First, specific populations of SNr neurons increase their activity during proactive, direction-specific inhibition. At the population level, neurons are biased away from the side of the movement that has to be potentially inhibited. Second, during proactive inhibition, neuron activity is more variable, both at the single-cell and population levels. Finally, the authors show that animals' outcome history influences both firing rates and variability of neuron responses in the current trial. Especially, neural variability is increased following a failure to inhibit a movement.

Strengths<br /> The manuscript provides an interesting and timely insight into the role of the basal ganglia output nucleus in movement initiation control. The paper is often clearly and concisely written (although see one issue related to this below). One of the main strengths of the work is to allow an interesting comparison with recent work by the same team, aimed at investigating the responses of another basal ganglia nucleus (GPe) in the same task, using similar analyses (this comparison is not extensively exploited in the discussion section though). Another potential strength is the use of different analysis scales. The authors investigated single-unit responses as well as population "trajectories" in the neural state space. This is an interesting option that could have been better motivated, given that the two approaches assume quite different brain operations.

Weaknesses<br /> The analyses and results sometimes lack clarity and details. For instance, and unless I missed the information, it is not clearly stated whether "maybe-stop" trial analyses only include Go trials or if (failed) Stop trials are also considered. Moreover, quite complicated figures are often described very briefly in the main text. Methods are also often too succinctly described, and sometimes refer to a previous publication (Gu et al., 2020) that readers did not necessarily read.<br /> There are some points that the authors might need to discuss more. Especially, a global picture of the role of the different basal ganglia nuclei during movement control would have been appreciated. Also, the authors monitored the activity of the rat basal ganglia output. We would have appreciated more information regarding the impact of this output activity on SNr target areas, as compared to their previous work that focused on GPe for instance. Another example concerns the observation that SNr activity is elevated during active inhibition regardless of the firing rate pattern before movement (increase or decrease). As noted by the authors themselves, this is inconsistent with the classical role assigned to the basal ganglia output nucleus (i.e. a decrease in activity promotes movement). Despite that this observation is of potential interest to readers working on the basal ganglia, it is not discussed.

Reviewer #2 (Public Review):

This manuscript by Einarsson and colleagues in the Andersson lab examined how genetic variability across a population impacts both gene expression and promoter architecture in a human population. The authors generate new CAGE data in 108 lymphoblastoid cell lines (LCLs). The authors' analysis is focused on defining how DNA sequence and promoter architecture correlate with population-variation in expression across this cohort. In general, there is a lot that I like about this manuscript: The dataset will be an extremely valuable resource for the genomics community. Furthermore, the biological findings are often thoughtful and potentially interesting and significant for the community. The analysis is generally very strong and is clearly conducted by a lab that has a lot of expertise in this area. My main concerns are centered around the often unwarranted implication that DNA sequence or promoter features cause differences in variation at different genes.

Reviewer #2 (Public Review):

The authors used a cell based system to investigate how expression of disease-associated Seipin glycosylation mutants (ngSeipin) impact on endoplasmic reticulum (ER) homeostasis. In particular, they focus their attention on SERCA, previously shown to interact genetically and biochemically with Seipin. They show that endogenous SERCA interacts with both overexpressed WT and ngSeipin. Using reporters monitoring calcium levels in the cytosol and ER lumen, it is shown that overexpression of ngSeipin (but not WT seipin) results in lower ER calcium levels, increase ER stress and eventually apoptosis. Based on the analysis of several Seipin mutants, the authors conclude that the toxicity of ngSeipin requires oligomerization (via the luminal domain) and the presence of its C-terminal domain. It is proposed that low ER calcium resulting from inhibition of SERCA by ngSeipin is a key event in Seipinopathies.

Despite the large amount of data presented, these not always lay support to the main conclusions of the study. Critical flaws are:

1- All conclusions are based on experiments where Seipin is overexpressed to levels are are unlikely to be physiological, even in the disease context. Importantly, as shown at several points (for example Figure 3), the effects of ngSeipin are drastically different depending of the expression levels.

2- The conclusions about ngSeipin aggregation are unjustified. The PLA assay is not suitable to assess protein aggregation or to distinguish between aggregation and oligomerization.

3- The effects of ngSeipin on UPR activation or calcium levels are modest, in particular considering that the levels to which it is overexpressed in relation to endogenous Seipin (see for example Figure 1Ec or 3Ac).

Reviewer #2 (Public Review):

This study investigated a substantial set of camelid nanobodies for their characteristics when expressed in mammalian cells as intrabodies. Intrabodies have a variety of important research, diagnostic and therapeutic uses, and nanobodies have several inherent characteristics that make them amenable for use as intrabodies. While a substantial number of nanobodies have been developed that are effective as intrabodies, a systematic study of the suitability of a set of otherwise unrelated nanobodies for this purpose has not been performed. As such, the molecular characteristics of what may make an nanobody suitable for use as an intrabody have not been defined. This study addresses this gap in knowledge by FP-tagging a set of 75 nanobodies selected from among those whose structure has been solved. The study uses live cell imaging to evaluate expression of these nanobodies when expressed in mammalian cells. These results are used in bioinformatics analyses to define key amino acids positions in the nanobodies that distinguish those that have high level expression in diffuse cytoplasmic pattern that is consistent with expression in a stable, soluble form. These analyses inform mutagenesis to phenoconvert poorly expressed nanobodies into those with improved expression. The outcome is a set of rules that can be used by investigators to predict the likely characteristics of a nanobody with a given sequence when expressed in cells as an intrabody. The strengths of the study is the elegant and rational manner it is pursued by the iterative application of bioinformatics analyses of nanobody sequences, cell biological assays of expression as intrabodies and mutagenesis. This study has great value to the field as nanobodies gain increased use as intrabodies. The weakness is the lack of a quantitative analysis of expression levels and solubility, with all of the results based on a subjective visual determination of the appearance of the FP-tagged nanobody in expressing cells. Moreover, steady-state appearance is used to infer active processes of aggregation and clearance. Another weakness is that the study presumes that the steady-state expression levels of FP-tagged nanobodies are determined solely by posttranslational stability/solubility, and not by differences in transfection levels, transcription, and translation. Lastly, the study implies that the set studied here is representative of nanobodies in general and the results are transferable across all nanobodies. While the study still has substantial value in spite of these weaknesses, the manuscript would be greatly improved by explicitly stating these limitations of the study.

Reviewer #2 (Public Review):

RPA is a ssDNA binding protein that functions as a hub protein to recruit more than three dozen enzyme onto DNA to coordinate almost all DNA metabolic roles. There are two specific protein interaction domain OB-F and the wh domain. NMR and crystallographic studies have solved the structure of OB-F bound to peptides from various target interactors. Nevertheless, these cognate binding sequences are not conserved. To decipher if there are unique binding modes during such interactions, Wu et. al., use a strategy to tether these target peptides to OB-F using a flexible linker and have solved the structure of complexes with peptides from HelB, ATRIP, RMI1, WRN and BLM. The high-resolution structures presented by Wu et. al. showcase key interactions between RPA70N and peptides of binding partners. These findings add to similar knowledge from several other such structures that have been previously reported. The authors also suggest multivalency where multiple OB-F domains can be bound by a single peptide or a cluster of peptides. This leads to a model where such an interaction can stabilize RPA nucleoprotein filaments and better recruit interacting partners. However, such a model assumes that OB-F is floating around freely accessible to interact with other proteins. This assumption is incorrect as in most of these cases (like in Rad52-RPA interaction) there are other inter and intra protein interactions that need to be accounted for and are ignored here. Ideally, interaction studies with full length proteins provide better mechanistic understanding of interactions between proteins.

Reviewer #2 (Public Review):

The work presented in this manuscript details an analysis of the partitioning of low copy plasmids under the control of the ParABS system in bacteria. Using a high throughput imaging set up they were able to track the dynamics of the partition complex of one to a few plasmids over many cell cycles. The work provides an impressive amount of quantitative data for this chemo-mechanical system. Using this data, the paper sought to clarify whether the dynamics of plasmids is due to regular positioning or noisy oscillations around a mean position. They supplement their experimental work with an intuitive model that combines elements of previous modelling efforts. Their model relies on diffusion of the ParA substrate on the nucleoid with the dynamics of the ParB partition complex being driven by the underlying elastic force due to the nucleoid on which the substrate is tethered. Their model dynamics depend on two parameters, the ratio of the length over which the substrate can explore to the characteristic length of the space and the ratio of stimulated to non-stimulated hydrolysis rates of the substrate. If the length ratio is large, ParA can fully explore the space before interacting with the ParB complex leading to balanced fluxes and regular positioning. If it gets reduced, for example by lengthening the cell, oscillations can emerge as fluxes of substrates become imbalanced and a net force can pull the partition complex.

Strengths:<br /> Given the large amount of data, the observations unambiguously show that one particular ParABS system under the conditions studied is carrying out regular positioning of plasmids. The model synthesizes prior work into a nice intuitive picture. These model parameters can be fit to the data leading to estimates of molecular kinetic parameters that are reasonable and in line with other observations. Lining up the experimental observations with the phase space of the model suggests that the system is poised on the edge of oscillations, allowing for the system to have regular positioning with low resource consumption.

Weaknesses:

However, despite the correspondence of the simulated results with the experimental findings, other explanations are not completely ruled out. The paper emphasizes that ParA diffusion/hopping on the nucleoid is essential for the establishment of regular positioning and that without it, only oscillations were possible. Prior simulation efforts, that the paper cites, which include ParA diffusion and mixing in the cytosol but no diffusion on the nucleoid have shown that regular positioning is possible and that oscillations could get triggered as the system lengthened. Thus ParA hopping is not a necessity for regular positioning (as claimed in the paper), but very well might be needed for the given kinetic parameters of the system studied here.

The paper also presents experimental results for a second ParABS system (pB171) that is more likely to show oscillations. They attribute the greater likelihood of oscillations for pB1717 being due to ParA exploring a smaller space than the F plasmid system that showed regular positioning. This is pure conjecture and the paper does not provide any evidence that this is the reason. Thus it is hard to conclude if oscillations may not be due to other factors.

Reviewer #2 (Public Review):

The authors use a conditional Lox/Cre knock-out system to test and confirm the essentiality of glycerophosphodiester phosphodiesterase (GDPD) for blood-stage parasites and a key role in mobilizing choline from precursor lysophosphocholine (LPC) for parasite phospholipid synthesis. Prior works had identified serum LPC as the key choline source for parasites, localized this enzyme in parasites, and suggested an essential function in releasing choline, but this key function had remained untested in parasites. This manuscript critically advances mechanistic understanding of parasite phospholipid metabolism and its essentiality for blood-stage Plasmodium and identifies a potential new drug target.

Overall, this study is well constructed and rigorously performed, and the data provide strong support for the central conclusions about GDPD essentiality and functional contribution to parasite phosphocholine metabolism. The observation that exogenous choline largely rescues parasites from lethal deletion of GDPD is especially compelling evidence for a critical and dominant role in choline mobilization. A few aspects of the paper, however, are not fully supported by the current data and/or need clarification.

1. GDPD localization<br /> a) The authors conclude that GDPD is localized to the parasitophorous vacuole (PV) and parasite cytoplasm (lines 114-115), which is consistent with the prior 2012 Klemba paper. However, the data in the present paper (Figures 2A and 2E) only seem to support cytoplasmic localization but don't obviously suggest a population in the PV, in part because no co-staining with a PV marker is shown. The legend for Fig. 2E indicates staining with the PV marker, SERA5, but such co-stain is not shown in the figures or figure supplements. This data should ideally be included and described.

b) How do the authors explain cytoplasmic localization for GDPD? This protein contains an N-terminal signal peptide, which can account for secretion to the PV but would contradict a cytoplasmic population. The 2012 Klemba paper suggested that internal Met19 might provide an alternate site for translation initiation without a signal peptide and thus result in cytoplasmic localization. Some discussion of this ambiguity, its relation to understanding GDPD function, and a possible path to resolve experimentally seem necessary, especially as the authors suggest from data in Fig. 7 that this enzyme may have functions beyond choline mobilization, which may relate to distinct forms in different sub-cellular compartments.

2. The phenotypes depicted by representative microscopy images in panel 4E (especially for choline rescue) should be supported by population-level analysis by flow cytometry or microscopy of many parasites to establish generality.

3. The analysis in the last results section (starting on line 296) seems preliminary.<br /> a) For panel 7B, a population analysis of many parasites, with appropriate statistics, is important to establish a generalizable defect beyond the single image currently provided.

b) The data here would seem to be equally explained by an alternative model that GDPD∆ parasites are competent to form gametocytes but their developmental stall (due to choline deficiency) prevents progression to gametocytogenesis. The authors speculate that GDPD may play other roles in phospholipid metabolism beyond choline mobilization that are essential for gametocytogenesis. Their model, if correct, predicts that a GDPD deletion clone from +RAP treatment that is rescued by exogenous choline should not form gametocytes. Testing this prediction would be important to strongly support the conclusion of broader roles for GDPD in sexual development beyond choline mobilization.

Reviewer #2 (Public Review):

The authors have used well-characterized Drosophila intestinal epithelium as a model to investigate the potentially harmful effect of Btk Cry toxins on organisms that are not susceptible to the toxins. The experiments are well-designed, precisely performed, and appropriately assessed. Therefore, the presented results are in support of the authors' claims and conclusions. Additionally, the manuscript is written well to convey the message to a wide audience.

Reviewer #2 (Public Review):

In this study, the authors investigate the ubiquitin-mediated mechanisms underlying erythroid maturation. They first investigated proteome changes of CD34+ cells and HUDEP2 cells (an immortalized CD34+-derived line) which can be induced to undergo differentiation into different erythroblast stages. They identified that protein members of the E3 ubiquitin ligase complex called CTLH complex were globally increased during differentiation. They also found that the expression of several E2 enzymes including UBE2H, which partners with the CTLH complex, increase in later stages of erythroid maturation. Interestingly, they found that the 2 subunits of the CTLH complex, RanBP9 and RanBP10 which are structurally very similar, display opposite changes of expression, with RanBP9 decreasing and RanBP10 increasing during differentiation. They then show that both RanBP9 and RanBP10 can support complex formation in vitro and result in ubiquitin transfer competent complexes using ubiquitination with a model substrate peptide in vitro.

In the second part of the study, they created CRISPR-Cas9 knock out of UBE2H and the CTLH complex subunit MAEA in HUDEP2 cells to investigate the effect on proteome changes and erythroid cell differentiation. They found that both UBE2H and MAEA knockout cells display pronounced proteome-wide changes in erythroid-specific factors. They also show that the knockout of UBE2H and MAEA cause aberrant differentiation, with accelerated maturation, altogether suggesting that these 2 factors are required to maintain cells in progenitor state. Finally, they identify that MAEA expression is required to maintain UBE2H expression and that this regulation occurs at the post-translational level.

The authors clearly demonstrate that the CTLH complex and its associated E2 enzyme play important roles in erythroid differentiation. They also generated a wealth of data that document erythroid differentiation and point out very interesting co-regulatory mechanisms regarding ubiquitin machineries underlying this process. Notably, the authors identify an intriguing regulation of two CTLH complex members, RanBP9 and RanBP10 during erythroid maturation that correlates with, and suggests that the replacement of RanBP9 and RanBP10 during the process may be involved in regulating pathways that lead to erythroid maturation.

Unfortunately, while the above-mentioned regulation of the two CTLH complex members, RanBP9 and RanBP10 is suggested to play a role in erythroid maturation, it is not investigated further. It is genuinely surprising that the authors did not investigate the proteome of the RanBP9 and RanBP10 knockout HUDEP2 cells they generated, to figure out the effect the differential expression of these factors on erythrocyte development.

Instead, the study changes direction to focus on another CTLH complex subunit, MAEA, and how that subunit may function to regulate the expression of UBE2H, the E2 enzyme associated with the CTLH complex, in a manner seemingly independent of the other complex members. Overall, the work is interesting and advance our knowledge of the erythroid differentiation process, but there are some main issues including over-interpretation of data and experimental issues limiting data interpretation that would need to be addressed or the authors would need to revise their conclusions since as it stands now, some of the conclusions are not supported by the data.

Reviewer #2 (Public Review):

The authors aim to analyze and describe the neuroanatomy of the Late Jurassic sauropod Europasaurus holgeri. This is done by scanning with microCT both adult and juvenile specimens.

The authors successfully report in detail the overall anatomy of the Europasaurus braincase, as well as morphological characteristics so far undescribed in this taxon. Precociality in juveniles is suggested and also well-supported. Comparisons made with other sauropods are considered appropriate and clear.

Aspects of reproductive and social behavior in this taxon are deduced from the estimated auditory capabilities. They are not investigated in detail and more details regarding these aspects would be welcomed in the discussion.

Images in the manuscript are well-presented and clear, supporting adequately the description. Slicing of the CT data is sufficiently clear although a "polishing" of the final renders in some cases would be appreciated. Again, it is not necessary, since images are clear enough, but only suggested.

Reviewer #2 (Public Review):

Taking advantage of the high molecular order of the Drosophila flight muscle, Schueder, Mangeol et al. leverage small (<4 nm) original nanobodies, tailored coupling to fluorophores, and DNA-PAINT resolution capabilities, to map the nanoarchitecture of two titin homologs, Sallismus and Projectin.

Using a toolkit of nanobodies designed to bind to specific domains of the two proteins (described in the companion article "A nanobody toolbox to investigate localisation and dynamics of Drosophila titins" ), Schueder, Mangeol et al position these domains within the sarcomere with <5nm resolution, and demonstrate that the N-ter of Sallismus overlaps with the C-ter of Projectin at the A-band/I-band interface. They propose this architecture may help to anchor Sallismus to the muscle, thus supporting flight muscle function while ensuring muscle integrity.

This study nicely extends previous work by Szikora et al, and precisely dissect the the sarcomeric geography of Sallismus and Projectin. From these results, the authors formulate specific functional hypotheses regarding the organization of flight muscles and how these are tuned to the mechanical constraints they undergo.

Although they remain descriptive in essence, the conclusions of the paper are well supported by the experimental results.

Reviewer #2 (Public Review):

This clinical trial is conducted to pursue short course DAA therapy. For an ultra-short course to work, it has to be simple, equally efficacious to established treatments, and requires no additional workup (like genotyping, IL28B, HCV VL determination, etc after initiation of therapy as shown in Liu et al.). This is because our aim is to simplify therapy to treat most people, especially those who are not engaged in care.<br /> This work struggles to achieve these goals, as the to the SVR for short-course therapy is unacceptably low. The authors' conclusion that treat short first and then you can treat those who fail again does not appear to achieve these goals, as realistically, it is difficult to re-engage marginalized population from an elimination perspective. The ideal is to treat them in one attempt.

Reviewer #2 (Public Review):

Despite the fact that Igf2 and H19 are the two best-studied imprinted genes in (mouse) development, substantial gaps in knowledge continue to exist as to their independent functions in normal and pathological situations, including in the imprinting disorders BWS and SRS to which they are linked. Here, using three established mouse models in a clever way, the authors are able to dissect the impact of overexpression or depletion of Igf2 and H19 independently of each other. This sophisticated use of mouse genetic models is a major achievement in trying to discern the precise impact of these genes on the development of particular cell types and tissues.

The authors report the placenta and heart as the two most severely affected organs in these mouse models. What remains unclear is if these phenotypes are causally linked, such that - for example - endothelial cell dysfunction causes the placental phenotypes or, conversely, trophoblast dysfunction causes the heart phenotypes. The "placenta-heart axis" is repeatedly referred to; however, unlike in the use of the term in the current manuscript, this term is more commonly used to describe situations where the placenta is causative of heart phenotypes, as first established in the Pparg mutation. Along these lines, it would be instrumental to establish in at least one of the mouse models under investigation here, if any of the heart or placental defects are secondary to gene function in a cell type outside of the affected organ itself.

The transcriptomic analysis of E12.5 endocardial cushion cells in the various mouse models is informative in the extraction of Igf2- and H19-specific gene functions. This analysis raises a few questions: In Fig. 6D, a huge sex effect is obvious with many more DEGs in female embryos compared to males. How can this be explained given that Igf2/H19 reside on Chr7 and do not primarily affect gene expression on the X chromosome? Is any chromosomal bias observed in the genomic distribution of DEGs? A separate issue is raised by Fig. 6E that shows a most dramatic dysregulation of a single gene in the delta3.8/hIC1 "rescue" model. Interestingly, this gene is Shh. Hence, these embryos should exhibit some dramatic skeletal abnormalities or other defects linked to sonic hedgehog function.

The placental analysis needs to be strengthened. Placentas should be consistently positioned with the decidua facing up, and the chorionic plate down. The placentas in Fig. 3F are sectioned at an angle and the chorionic plate is missing. These images must be replaced with better histological sections. The CD34 staining has not worked and does not show any fetal vasculature, in particular not in the WT sample. The "thrombi" highlighted in Fig. 4E are well within the normal range, to make the point that these are persistent abnormalities more thorough measurements would need to be performed (number, size, etc).

The statement that H19 is disproportionately contributing to the labyrinth phenotype (lines 154/155) is not warranted as Igf2 expression is reduced to virtually nothing in these mice. Even though there is more H19 in the labyrinth than in the junctional zone, the phenotype may still be driven by a loss of Igf2.<br /> Given the quasi Igf2-null situation in +/hIC1 mice, is the glycogen cell type phenotype recapitulated in these mice, and how do glycogen numbers compare in the other mouse models?

How do delta3.8/+ and delta3.8/hIC1 mice with a VSD survive? Is it resolved some time after birth such that heart function is compatible with postnatal viability? And more importantly, do H19 expression levels correlate with phenotype severity on an individual basis?

Reviewer #2 (Public Review):

Copeland et al. set out to assess the impact of HIF1 activation - either through glycolysis or pharmacological inhibition of prolyl hydroxylase (PHD) - in primary fibroblasts and smooth muscle cells. The goal was to compare the metabolic responses between these two states, and with the scores of papers studying metabolic responses to hypoxia in cancer cells. Using a combination of metabolomics, metabolic flux analysis, and gene expression studies, they surprisingly find that hypoxia induces the expected activation of glycolytic genes, but fails to induce some of the classical metabolic responses reported in cancer cells, including glucose uptake and lactate secretion. Lactate secretion is enhanced by PHD expression, but this is reversed by hypoxia. The authors further find that hypoxia induces the expression of MYC, and they use gain and loss of function experiments to show that MYC is involved in the reduction of lactate secretion by hypoxia. The paper's strengths are the detailed and quantitative analysis of metabolic responses in two different models of non-transformed cell proliferation, as well as the combination of gain- and loss-of-function analyses of MYC. Relative limitations include the use of a single chemical compound to activate HIF-1 in normoxia, and the lack of an explanation for why MYC is induced in hypoxia but not in normoxic HIF activation, or why MYC's effects are so different here than what is usually observed in cancer cells. If validated, the findings of the paper would add complexity to the mechanisms by which cells respond to hypoxia; to date, these responses have mostly been studied in cancer cells, and the new data suggest that non-transformed cells respond quite differently. The findings also suggest that MYC's effects on metabolism are determined in part by the cell state, as a large amount of existing data indicates that MYC drives lactate secretion in cancer cells.

Reviewer #2 (Public Review):

The purpose of this study was to evaluate the transcription factor NF-KB, a common transcription factor that is thought to mediate muscle atrophy, in the setting of a rotator cuff injury. Unlike many other models of atrophy such as hind limb suspension, aging, and neurologic injury, the tenotomy model represents a unique mechanical change where the muscle is acutely unloaded from the bone, which is relevant for rotator cuff injuries as well as achilles tendon ruptures.

The premise of the study was that NF-kB, a known central regulator of muscle atrophy, would be a central mediator of this process in a tenotomy model as well. The study hypothesized that NF-kB inhibition would reduce atrophy in a rotator cuff model through atrogene-independent mechanisms, a hypothesis that is well supported by literature in other models.

Using gain of function and loss of function NF-kB inhibitors, the IKKB family, to evaluate this pathway after a tenotomy model. The results were rigorously approached with appropriate timelines and controls, and the analyses were well done. Surprisingly, the study found that NF-kB did not appear to be an important regulator of tenotomy-induced atrophy, which they did an excellent job of exploring in detail with their gain/loss of function mice, and by looking at cellular changes, protein changes, and architectural changes after rotator cuff injury. They did find that autophagy, which was more pronounced in male mice, was a sex-dependent mechanism that seemed to regulate atrophy.

The primary strength of this paper is a rigorous approach to 'negative' data. Did the authors definitively prove that NF-kB has no role in the tenotomy-induced atrophy? Probably not entirely, since there are limitations of the mouse model and the knockdown mice. There cannot be complete elimination of load since mice heal with some scar tissue, and the knockdown is not complete elimination. However, even with these limitations, this presents important findings that tenotomy, which induces mechanical unloading of the muscle-tendon unit, provides a unique biomechanical environment for the muscle to undergo atrophy, which warrants a more in-depth look given that these injuries are unique and extremely common. It must be mentioned that the results are entirely supported by their data and that even though the model is not 'perfect' it truly supports that NF-kB has a limited role in atrophy. The sex-mediated differences based on autophagy are a secondary hypothesis and are interesting but possibly less clinically relevant based on the differences shown.

The important next step for this group and others is to evaluate the 'how and why' of tenotomy atrophy if not through NF-kB. Is it that there are many redundant processes that the muscle may have to circumnavigate the NF-kB pathway given that it is so ubiquitous that the authors didn't see a difference? Could it be differences in axial vs appendicular muscle? Or should there be a closer look at the mechanosensors in the muscle cells to determine if there are other key drivers of atrophy? Regardless, this paper shows that tenotomy-induced muscle atrophy is unique, and supports the conclusion that muscle has many ways to atrophy based on the injury it undergoes.

Reviewer #2 (Public Review):

Does our proprioceptive system try to recognize our own actions?

Proprioception is our sense of the motion and posture of our own body. This sixth sense uses signals from receptors in the joints, tendons, muscles, and skin that measure forces and degrees of extension. These receptors enable us to sense, for example, the posture of our body as we wake from sleep. They also provide feedback signals that help us precisely control our limbs, for example during handwriting.

Feedback is thought to be essential to motor control, enabling the controller in our brains to rapidly adapt to the unexpected. The unexpected may include changes in the environment (like something pushing our hand that we didn't see coming), changes in our bodies (such as muscle fatigue or injury), and shortcomings of the motor program (such as a lack of precision or a badly planned limb trajectory). Feedback can come from vision and even audition, but proprioception provides an essential additional feedback path that informs us directly about the motion and posture of our limbs, and any forces on them.

How does feedback control work in the human motor system? I want to write a 'k', but there are forces on my limbs resulting from the friction of chalk on this particular blackboard. Also, my muscles are recovering from tennis practice this morning, and I haven't used chalk on a blackboard in years.

If the goal is to write a 'k', I have some flexibility. I am committed, not to a precise trajectory, but to a more abstractly defined objective: to write a legible 'k'. This suggests that feedback processing should evaluate to what extent I am succeeding at the action, not at tracing out a particular trajectory. Does what I'm actually doing look like writing a 'k'?

In a new paper, Sandbrink et al. (pp2022) report on simulations of the human musculoskeletal system and neural network models that suggest that the tuning properties of neurons in the somatosensory cortex (S1) can be explained by assuming that the objective of the proprioceptive system is to recognize the action being performed.

They used recorded traces of a person writing lower-case letters to simulate the responses of muscle spindles sensing the lengths and velocities of muscles in the human arm as would be present if the hand was moved passively along these trajectories. The physical simulation uses a 3D model of the human arm with two parameters for the direction of the upper arm and two more for the direction of the lower arm. These four parameters are inferred by inverse kinematics from the hand trajectories tracing each letter in a variety of vertical and horizontal planes. A 3D muscle model then enables the authors to compute the expected spindle responses that reflect the lengths and velocities of 25 relevant upper arm muscles.

The authors then trained neural network models of proprioceptive processing that took the simulated muscle spindle signals as input. The neural net architectures included one that first integrates information over the muscle spindles and then across time ("spatial-temporal"), one that integrated across muscle spindles and time simultaneously ("spatiotemporal") and a recurrent long-short-term-memory model.<br /> Each architecture was trained on two objectives: to decode the trajectory (i.e. the position of the hand tracing a letter as a function of time) or to recognize the action (i.e. the letter being traced). The two objectives correspond to two hypotheses about the function of proprioceptive processing: To inform the feedback controller about either the current position of the hand or the letter being drawn.

The models trained to recognize the action developed tuning more consistent with what is known about the tuning of neurons in the primary somatosensory cortex in primates. In particular, direction tuning with roughly equal numbers of units preferring each direction emerged in the middle layers of the neural network models trained to recognize the action, similar to what has been observed in primate neural recordings. Direction tuning is already present in the muscle-spindle signals, but the spindle signals do not uniformly represent the directions.

The task-optimization approach to neural network modeling is inspired by work in vision, where neural networks trained on the task of image classification explained responses to novel images in populations of neurons in the inferior temporal cortex. This result suggested a tentative answer to the why question: Why do inferior temporal neurons exhibit the response profiles and representational geometry they exhibit? Because their function (or one of their functions) is to recognize the objects in the images. Here, similarly, the authors address a why question with task-optimized neural network models: Why do somatosensory cortical neurons exhibit the types of tuning that have been reported in the literature?

The function of proprioception, of course, is not for the brain to recognize which letter it is trying to write. It already knows that. The function is to sense how the current trajectory - the actual, not the intended one - differs from, say, a legible "k" (if that was the intention), and to map from that difference to a modification vector that will improve the outcome.<br /> Why is action decoding relevant for performing the action? A key reason may be that the goal is not to produce a fixed trajectory, but to produce a legible 'k'. A legible 'k' is not a single trajectory, but a class of trajectories containing an infinity of viable solutions. If someone nudged my arm while writing, adaptive feedback control should not attempt to return me to the originally intended trajectory, but to a new trajectory that traces the most legible 'k' that is still in the cards, which may be a different style of 'k' than I originally intended.

The paper contributes a useful data set for training models and a qualitative comparison of models to real neurons in terms of tuning properties. It would be good, in follow-up studies, to directly test to what extent each of the models can quantitatively predict either single-neuron responses or population representational geometries, as has been done in vision, and to perform statistical comparisons between models.

Importantly, this paper develops the idea of combining simulations body and brain, of the musculoskeletal system, and the processing of control-related signals in the nervous system, which provides a very exciting direction for future research.

Strengths

• The paper introduces a highly original research program that marries simulation of the musculoskeletal system and neural network modelling to predict neural representations in the proprioceptive pathway.<br /> • The authors performed an architecture search and trained multiple instances of different neural network architectures with each of the two objectives.<br /> • The paper includes comprehensive analyses of the proprioceptive representations from the simulated muscle-spindle signals through the layers of the models. These analyses characterize unit tuning, linear decodability, and representational similarity.<br /> • The results suggest an explanation for the direction tuning with a roughly uniform distribution of the units' direction preferences that has been reported previously for neurons in the primate primary somatosensory (S1) cortex.<br /> • If the simulated muscle-spindle data set, models and analysis code were shared along with the published paper, this work could form the basis for quantitative model evaluation and further model development.

Weaknesses

• The models are qualitatively evaluated by comparison of model unit tuning to what is known about the tuning of neurons in the somatosensory cortex. Follow-up studies should quantitatively evaluate the models by inferential analyses of their ability to predict measured responses.<br /> • The two training objectives differ in multiple respects, making it difficult to assess what the necessary requirements are for the emergence of representations similar to primate S1. Decoding the hand position may be too simple, but what about decoding velocity, or trajectory descriptors such as curvature? There may be a middle ground between trajectory decoding and action recognition that also leads to the emergence of tuning properties as found in primate S1.