Reviewer #1 (Public Review):

This paper provides valuable (and impressive) data on the geometry of cerebellar foliation among 56 species of mammals and gives novel insights into the evolution of cerebellar foliation and its relationship with the anatomy of the cerebrum. Thus far, the majority of the research on brain folding focuses on the cerebral cortex with little research on the cerebellum. The results from Heuer et al confirm that the evolution of the cerebellum and cerebrum follows a concerted fashion across mammals. Moreover, they suggest that both the cerebrum and cerebellum folding are explained by a similar mechanistic process.

1. Although I found the introduction well written, I think it lacks some information or needs to develop more on some ideas (e.g., differences between the cerebellum and cerebral cortex, and folding patterns of both structures). For example, after stating that "Many aspects of the organization of the cerebellum and cerebrum are, however, very different" (1st paragraph), I think the authors need to develop more on what these differences are. Perhaps just rearranging some of the text/paragraphs will help make it better for a broad audience (e.g., authors could move the next paragraph up, i.e., "While the cx is unique to mammals (...)").

2. Given that the authors compare the folding patterns between the cerebrum and cerebellum, another point that could be mentioned in the introduction is the fact that the cerebellum is convoluted in every mammalian species (and non-mammalian spp as well) while the cerebrum tends to be convoluted in species with larger brains. Why is that so? Do we know about it (check Van Essen et al., 2018)? I think this is an important point to raise in the introduction and to bring it back into the discussion with the results.

3. In the results, first paragraph, what do the authors mean by the volume of the medial cerebellum? This needs clarification.

4. In the results: When the authors mention 'frequency of cerebellar folding', do they mean the degree of folding in the cerebellum? At least in non-mammalian species, many studies have tried to compare the 'degree or frequency of folding' in the cerebellum by different proxies/measurements (see Iwaniuk et al., 2006; Yopak et al., 2007; Lisney et al., 2007; Yopak et al., 2016; Cunha et al., 2022). Perhaps change the phrase in the second paragraph of the result to: "There are no comparative analyses of the frequency of cerebellar folding in mammals, to our knowledge".

5. Sultan and Braitenberg (1993) measured cerebella that were sagittally sectioned (instead of coronal), right? Do you think this difference in the plane of the section could be one of the reasons explaining different results on folial width between studies? Why does the foliation index calculated by Sultan and Braitenberg (1993) not provide information about folding frequency?

6. Another point that needs to be clarified is the log transformation of the data. Did the authors use log-transformed data for all types of analyses done in the study? Write this information in the material and methods.

7. The discussion needs to be expanded. The focus of the paper is on the folding pattern of the cerebellum (among different mammalian species) and its relationship with the anatomy of the cerebrum. Therefore, the discussion on this topic needs to be better developed, in my opinion (especially given the interesting results of this paper). For example, with the findings of this study, what can we say about how the folding of the cerebellum is determined across mammals? The authors found that the folial width, folial perimeter, and thickness of the molecular layer increase at a relatively slow rate across the species studied. Does this mean that these parameters have little influence on the cerebellar folding pattern? What mostly defines the folding patterns of the cerebellum given the results? Is it the interaction between section length and area? Can the authors explain why size does not seem to be a "limiting factor" for the folding of the cerebellum (for example, even relatively small cerebella are folded)? Is that because the 'white matter' core of the cerebellum is relatively small (thus more stress on it)?

8. One caveat or point to be raised is the fact that the authors use the median of the variables measured for the whole cerebellum (e.g., median width and median perimeter across all folia). Although the cerebellum is highly uniform in its gross internal morphology and circuitry's organization across most vertebrates, there is evidence showing that the cerebellum may be organized in different functional modules. In that way, different regions or folia of the cerebellum would have different olivo-cortico-nuclear circuitries, forming, each one, a single cerebellar zone. Although it is not completely clear how these modules/zones are organized within the cerebellum, I think the authors could acknowledge this at the end of their discussion, and raise potential ideas for future studies (e.g., analyse folding of the cerebellum within the brain structure - vermis vs lateral cerebellum, for example). I think this would be a good way to emphasize the importance of the results of this study and what are the main questions remaining to be answered. For example, the expansion of the lateral cerebellum in mammals is suggested to be linked with the evolution of vocal learning in different clades (see Smaers et al., 2018). An interesting question would be to understand how foliation within the lateral cerebellum varies across mammalian clades and whether this has something to do with the cellular composition or any other aspect of the microanatomy as well as the evolution of different cognitive skills in mammals.

Reviewer #1 (Public Review):

This is a very elegant study of the dynamics of the longitudinal surface pH profile in growing Arabidopsis roots. The authors first present a new powerful method for the visualization of the surface pH profiles using the pH-sensitive fluorescent dye fluoresceine-5 (or 6)-sulfonic acid. This is an interesting new tool for studying surface pH in plants and perhaps other organisms. The main findings are that the presence of an alkaline band at the transition zone does not depend on AHA abundance (shown by immunolocalization) or activity shown by pharmacology (FC treatment) or by using plants expressing hyperactive, or PP2CD1- inhibited AHA2 or by using KO mutants aha2 or pp2c-d respectively. This band depends on auxin and AUX1-mediated auxin influx and rapid auxin response components AFB1 and CNGC14. The latter has a distribution along the root fitting the longitudinal surface pH zonation and are both required for it. Canonical auxin signaling (TIR) has more quantitative effects on the extent of the auxin-induced alkalinization. They also observe that the rapid auxin response module is constantly activated and inactivated as shown by the time-dependent variations in surface pH within the alkaline zone on both sides of the root and the rapid AUX1, AFB1, and CNGC14-dependent acidification of the upper surface and alkalinisation of the lower surface during gravitropic responses. Finally, they provide some evidence for the role of the rapid auxin responses in avoiding physical obstacles in the environment of the root.

The data look very sound. The originality of the approach used is the observation of dynamic responses at a second-to-minute time scale and to systematically correlate between the observed changes in the longitudinal surface pH profile with changes in growth rate. The manuscript is well-written with clear figures.

Reviewer #1 (Public Review):

This paper studies color vision in anemonefish. The central conclusion of the paper is that anemonefish use signals from their UV cones to discriminate colors that would not otherwise be distinguishable; this differs from other fish in which UV cones extend the range of wavelengths of sensitivity but do not add a dimension to color vision. The work fits into a rich history of studies investigating how color vision fits into an animal's ecological niche. My primary concerns regard the microspectrophotometry data from single cones and some aspects of the presentation of the behavioral data.

Microspectrophotometry<br /> The spectral properties of the cone types are a key issue for interpreting the results. These were measured using MSP, and fits are shown in Figure 2. The raw data shown in Fig. S1 appears more complicated than indicated in the main text. The templates miss the measurements across broad wavelength bands in each cone type. Particularly concerning is the high UV absorbance across cone types and the long-wavelength absorbance in the UV cone. It is not clear how this picture supports the relatively simple description of cone types and spectral sensitivities given in the main text and which forms the basis of the modeling.

Presentation<br /> The results are not presented in a straightforward way - at least for this reviewer. What is missing for me is a clear link between the psychometric curves in Figure 3A and the discrimination thresholds indicated in Figure 3B and Figure 4. Figure 3A is only discussed in the text on line 289 - after Figure 4 has been introduced and discussed. It would have been very helpful for me if the psychometric curves were first introduced and described, then the relation to Figure 3B was clearly indicated (perhaps with a single psychometric curve as an example). Similarly for Figure 4 the relationship between specific psychometric curves and the threshold plotted would be quite helpful. Currently it takes a careful reading to understand why being below the dashed line in Figure 4 is important.

RNL model<br /> The data is fit and interpreted in the context of the receptor noise limited model. The paragraph in the discussion about complementary color pairs suggests that this model is incorrect (text around line 332). Consideration of how the results depend on the RNL model is important, especially given the interpretation here.

Figure 3B<br /> This is the key figure in the paper. But several issues make seeing the data in this figure difficult. First, the important part of the figure is buried near the origin and hard to see. Can you show a surface that connects the thresholds in the different chromatic directions, or otherwise highlight the regions of discriminable and not discriminable colors?

Reviewer #1 (Public Review):

The technical approach is novel, exciting, and very carefully calibrated, and can certainly lead to many interesting downstream applications, e.g. enhanced throughput and consistency for screening purposes. Compared to traditional single-assay designs, this solution eliminates some sources of human error associated with manual dilution of reagents and reproducibility and facilitates the study of a wide spectrum of concentrations particularly at the low-concentration (below nanomolar), high-sensitivity range.

However, the study itself does not generate any fundamentally novel insights or new understanding of the biology or biophysics of the chemotactic response. It mainly reproduces previously measured trends in a more efficient and controlled manner. The novelty of the paper is purely in the technology, whereas the major weakness is that this new technology was not used to demonstrate or discover some new biological phenomenon.

Reviewer #1 (Public Review):

Croft, Pearce, and colleagues used a combination of spatial transcriptomics and analysis of publicly available scRNA-sequencing data of patients with PDAC to assess differences in the transcriptome of tumor proximal and distal stromal cells and associated these differences with survival data. Focusing on aSMA+ cancer-associated fibroblasts (CAFs), they find that tumor-proximal CAFs were defined by high expression of PDPN and Wnt ligands, and tumor-distal CAFs expressed inflammatory, complement, and Wnt inhibitor genes. While gene expression in relation to tumor distance did not per se correlate with clinical outcome, the authors find that individual genes within the tumor proximal and distal subsets were associated with increased or decreased survival. Based on this, the authors suggest a combination of targeting tumor-proximal CAFs defined by PDPN expression and inhibition of HIF-1a in the tumor distal stroma. Using an innovative approach to combine their spatial transcriptomics with single-cell transcriptomics data, the authors further identify an association between the expression of proximal CAF marker genes with elevated expression of complement and retinoic acid metabolism genes, and that complement genes were associated with increased survival.

While spatial differences in the abundance of different CAF subsets have been suggested before, the spatial transcriptomic data presented in this study provide a fresh look at CAF heterogeneity in PDAC and will be a useful resource for hypothesis generation and testing on the spatial regulation of CAF heterogeneity. However, there are major concerns associated with the interpretation of the data given the markers used to generate it, the association with single-cell data, and an overinterpretation of transcriptomic data, as detailed below.

1) The spatial transcriptomic data on fibroblasts relies on aSMA as a fibroblast marker. However, several studies in PDAC and other tumors have shown that there are subtypes of CAFs that do not (or only weakly) express aSMA, including inflammatory CAFs (iCAFs) and antigen-presenting CAFs (apCAFs) which therefore might have been missed by the authors. While there is no universal CAF marker, more recently the pancreatic cancer field has been using PDPN as a pan-CAF marker. aSMA is a classical myofibroblast marker across tissues and tumor types and the authors should state that they focused on myofibroblasts in their study.

2) While the association of the spatial data and single-cell data is innovative, it is flawed by the use of a single marker gene (DCN) to define the fibroblast population and the small number of cells (229) within this population. The authors need to corroborate their findings using a combination of fibroblast marker genes as well as other studies comprising a larger number of cells of fibroblast origin.

3) The authors find HIF1a expression to be associated with poor survival and enriched within the tumor-distal stroma. They interpret this as a reflection of the hypoxic environment of PDAC. However, the authors only ever look at HIF1a mRNA, but hypoxic regulation of HIF-1a occurs post-transcriptionally. Transcriptional regulation of HIF1a has been reported through pro-inflammatory cytokines and NFkB signaling. Therefore, the authors should either stain for HIF1a to confirm enrichment on the protein level or adjust their discussion to reflect the important biology behind HIF1a regulation.

4) The authors often misinterpret the correlations and associations they observe as causation and explanation - they should either adjust their language or perform experiments to show causation.

Reviewer #1 (Public Review):

In this manuscript, the authors present a computational framework based on Bayesian observer models that parameterises several different sources of noise and bias in perceptual decision-making. The authors show that these sources of suboptimality cannot be dissociated in many typical decision-making tasks. They present an analysis of two previously published sets of experimental data, where the experimental design should allow them to dissociate suboptimalities parameterised in the model. They fit various versions of the model including different suboptimalities and in general show that including the suboptimalities improves the fit, depending on whether the data is aggregated across participants or not.

The major strengths of the methods and results include 1. The clear theoretical delineation of different forms of suboptimalities may help to guide research understanding them on the behavioural and neural levels. 2. The attention to scientific rigor in model fitting, including the use of power analysis and corrections for the number of model parameters. 3. Clear figures that are helpful in understanding the model.

The major weaknesses of the methods and results include 1. The lack of model/parameter recovery analysis shows the extent to which the model can separate sources of suboptimality against some ground truth. 2. The lack of generalisability, where the model parameters can only be dissociated using specific experimental manipulations, and a large number of trials. 3. It is unclear to what extent the assumptions of the model (and its parameterisation) limit the realisability of the proposed computational framework.

The authors achieve their aim of outlining a computational framework that accounts for various sources of suboptimalities and shows some evidence that this model may be useful for making inferences about these suboptimalities given careful experimental manipulation.

The work adds to the movement toward delineating the specific sources of suboptimalities as opposed to capturing 'noise' and 'bias' as overarching variables, and the model may prove useful for other researchers. However, given the model requires special experimental tasks to dissociate the parameters, it is unclear how this model improves upon the traditional approach of designing experiments to dissociate sources of suboptimalities directly.

Reviewer #1 (Public Review):

Precise regulation of gamete fusion ensures that offspring will have the same ploidy as the parents. However, breaking this regulation can be useful for plant breeding. Haploid induction followed by chemical-induced genome doubling can be used to fix desirable genotypes, while triparental hybrids where two sperm cells with two different genotypes fertilize an egg cell can be advantageous for bypassing hybridization barriers to create interspecies hybrids with increased fitness. This manuscript follows up on a previous study from the same research group that used a clever high throughput polyspermy detection assay (HIPOD) to show that wild-type Arabidopsis naturally forms triparental hybrids at very low frequencies (less than 0.05% of progeny) and that these triparental hybrids can bypass dosage barriers in the endosperm (Nakel, et al., 2017). Mao and co-authors hypothesized that mutants that conferred polytubey, the attraction of multiple pollen tubes by mutant female gametophytes, would also increase the rate of triparental hybrids. They used a double mutant in the endopeptidase genes ECS1 and ECS2 which had previously been reported to induce supernumerary pollen tube attraction to test this hypothesis with their two-component HIPOD system in which one pollen donor constitutively expresses the mGAL4-VP16 transcription factor while the second pollen donor carries an herbicide resistance gene regulated by the GAL4-responsive UAS promoter. Triparental hybrids are detected as herbicide-resistant progeny from wild-type Arabidopsis flowers that have been pollinated by the two paternal genotypes. The authors convincingly show that the ecs1 ecs2-1 double mutant more than doubled the frequency of triparental, triploid hybrids in HIPOD crosses. They next tested the hypothesis that this increase in triparental hybrids was due to a gametophytic effect by using an ecs1-/- ecs2-1/ECS2 maternal parent in the HIPOD assay and testing whether the ecs2-1 mutant allele was preferentially inherited in triparental hybrids. The mutant allele was inherited at a much higher rate than expected, confirming their hypothesis.

The triparental hybrid results with the ecs1 ecs2 mutant were not that surprising since the presence of extra sperm cells gives more opportunities for triparental hybrids to form, especially if gamete fusion is misregulated. However, an unexpected result came when the authors used aniline blue staining to analyze the ecs1 ecs2 polytubey phenotype. They confirmed that the double mutant had increased levels of polytubey compared to wild-type ovules, but they also noticed that 13% of seeds were not developing normally. This phenotype was confirmed with a second ecs2 allele and was complemented with both ECS1 and ECS2 transgenes under their native promoters. Microscopic analysis revealed normal gametophyte morphology before fertilization, but 8% of pollinated ovules failed to develop an embryo and 7% failed to develop endosperm, suggesting single fertilization events. In a logical set of experiments, they followed up on this result by crossing ecs1 ecs2 with pollen carrying a fluorescent reporter that would be expressed in developing embryos and endosperm. In this experiment, they were again surprised. Some of the wild-type-looking seeds lacked a paternal contribution (i.e. no fluorescent signal from the paternal reporter construct) in the embryo. This prompted them to look more closely at the progeny, upon which they detected small plants that were haploid. They confirmed the haploid nature by chromosome spreads. Finally, they used interaccession crosses between ecs1 ecs2 (Col-0) and Landsberg to verify that haploid progeny only carried maternal alleles of markers on all five chromosomes, indicating that the ecs1 ecs2 genotype can induce maternal haploids.

This interesting study highlights the importance of following up on unexpected results. The conclusions are well-supported by the data and quite exciting. Paternal haploid inducers have been discovered in several species, but this is one of only two examples of maternal haploid induction. While the percentage of maternal haploids is very low, this phenomenon could be useful for plant breeding.

Weaknesses<br /> The data in the manuscript is intriguing, but the question of how the same mutant combination promotes the formation of both triploid and haploid progeny remains unanswered and is not thoroughly discussed, nor is any model suggested for how the ECS1/2 peptidases could play a role in regulating gamete fusion and/or repressing parthenogenesis. A second unanswered question is whether the maternal haploids are a result of failed plasmogamy or karyogamy between the egg and sperm leading to parthenogenesis or a result of paternal genome elimination after plasmogamy. In figure 3B, the authors attempted to test whether plasmogamy occurs between the male and female gametes in ecs1 ecs2 ovules by crosses with pollen that expresses a mitochondrial marker under control of the pRPS5a promoter which is active in sperm cells as well as embryos and endosperm of fertilized ovules. This experiment allowed them to detect sperm cells that had not fused with the egg and central cell at 2 days after pollination. They also counted the percentage of seeds that expressed the mitochondrial marker in both embryo and endosperm at 2 DAP and found that ecs1 ecs2 mutants had a 20% reduction of visible mitochondria in embryo sacs compared to wildtype. They conclude that the result indicates a potential plasmogamy defect. However, the dependability of this marker is questionable since only ~55% of wild-type seeds had detectable signal in the embryo and endosperm. The authors imply that this experiment could be used to test plasmogamy, but it is not clear how any conclusions related to the abnormal seed phenotype could be drawn from examining the rate of signal in both the embryo and endosperm. Since the mitochondrial marker was not expressed from a sperm-specific promoter, the fluorescent signal at 2DAP is likely due to new gene expression from pRPS5a in the fertilized embryo and endosperm, not an indication of the presence of sperm-derived mitochondria. Perhaps an earlier timepoint could be used as well as a sperm-specific promoter instead of pRPS5a to answer the question of whether plasmogamy is happening in the ecs1 ecs2 ovules.

Reviewer #1 (Public Reviews):

The article describes the development of a highly concentrated antibody formulation for MS-Hu6, a first-in-class FSH-blocking humanized antibody proposed for clinical use in osteoporosis, obesity, and Alzheimer's disease. The authors utilized various techniques, including protein thermal shift, size exclusion chromatography, and dynamic light scattering, to examine the stability and physiochemical properties of the formulated MS-Hu6 at concentrations ranging from 1 to 100 mg/mL. They found that the thermal, monomeric, and colloidal stability of the formulated MS-Hu6 was maintained at a concentration of 100 mg/mL, and the addition of L-methionine and disodium EDTA improved its long-term colloidal and thermal stability. The authors further confirmed the thermal stability of the formulation through Nano differential scanning calorimetry (DSC). They demonstrated that MS-Hu6's structural integrity was maintained through Circular Dichroism (CD) and Fourier Transform Infrared (FTIR) spectroscopy. The formulated MS-Hu6 displayed excellent thermal and colloidal stability even after three rapid freeze-thaw cycles and storage for more than 90 days at 4{degree sign}C and 25{degree sign}C. The authors concluded that they had developed a stable, manufacturable, and transportable MS-Hu6 formulation at an ultra-high concentration, meeting acceptable industry standards. The study's findings may serve as a resource for developing biologics formulation in academic medical centers.

Reviewer #1 (Public Review):

The authors solved cryoEM structural maps for the pLGIC homolog Alpo4 from an extreme thermophile worm in apo and CHAPS-bound conditions. The data appear to be of good quality and in addition to a first structural model of Alpo4 also provide several interesting observations. Notably, the detergent CHAPS was observed to occupy the orthosteric binding pocket where it induces a quaternary twist of extracellular relative to transmembrane domains opposite to that observed for activation in canonical pLGICs. Given recent advances in lipid modulation of pLGICs, this structural model of how a detergent can bind to the orthosteric site will be of interest. Additionally, a ring of 16' methionines forms a potential alternate pore gate to the 9' leucine ring in canonical pLGICs. Unfortunately, testing these hypotheses such as the alternate pore gate remains difficult due to the fact that the activating ligand for Alpo4 remains unknown. This also makes it hard to relate the observed changes upon CHAPS binding to the Alpo4 function. Nonetheless, the structures will aid in hypotheses for functional mechanisms of Alpo4 and pLGICs.

Reviewer #1 (Public Review):

This manuscript examines how subjects change their decision strategy in a visual motion change detection task between blocks of trials where they sought to detect stronger versus weaker signals. The authors hypothesized that decision bounds would be reduced for the weaker signal condition. While behavioral changes were reasonably consistent with this hypothesis, it was challenged by EEG measures that have been previously found to relate to decision variables. In particular, a Beta-band EEG measure suggested decision bounds being reduced for the stronger signal condition, in distinct contrast to the initial hypothesis. Based on this, the authors developed an alternative behavioral model that could explain behavioral adjustments while having decision bounds that were constrained by the putative signature of the decision variable derived from the EEG Beta amplitude. This alternative model has two central features: 1) Sensory evidence is referenced to an adjustable criterion before accumulation such that evidence below the criterion provides negative input to the decision variable. 2) There is a lower reflecting bound on the decision variable such that it cannot go to negative values.

This experiment makes a strong case for the benefit of reconciling behavioral modeling with ideas in the literature about neural signatures of the corresponding decision processes. In this work, the standard behavioral modeling and the EEG-derived putative signatures of the decision process did not initially agree. This is an important finding. The authors also go further. Something must give. Either the standard modeling approach for this type of task provides an incorrect account or the putative mapping of EEG Beta amplitude to the decision process is incorrect. The authors argue for the former. They build from the starting point that the neural signatures are correct and develop alternative behavioral models that they argue to be consistent with these while also explaining behavior. One of these models (described above) fits the data as well as the standard model. I think this approach and the resulting model are interesting, but I have concerns.

1) I think the authors should give greater consideration to the possibility that the relationship between the decision process and EEG-derived neural signatures - despite having a basis in previous results - is either incorrect in some ways, not the full story, or might not fully apply to this paradigm. The authors include important analyses that already suggest that point to some degree, such as the analysis of the raw Beta amplitude in Figure 4. I give the authors credit for including that analysis and the critical discussion they have surrounding it. I suggest that the authors should include further discussion on this general point. What is the main evidence for the relationship of Beta amplitude to decision bound? Are there any differences from the previous studies that might break the relationship in this paradigm? With the approach taken here so heavily dependent on that relationship, discussing how it might not be correct seems of utmost importance.

2) My biggest concern revolves around where the Beta amplitude measurement fits into the model. Figure 3 (neurally informed modeling) shows it informing an evidence-independent component that adds to the decision variable, and it is described as such in the methods. However, Beta amplitude is clearly affected by the evidence (Figure 2A). And in the results, it is described as best corresponding to a decision variable, which would include the influence of the sensory evidence. If Beta amplitude depends on evidence, then an adjustment of the evidence criterion would influence Beta amplitude, even during the ITI. So, I don't see how it can be properly used as the constraining factor for the evidence-independent urgency in neurally-informed modeling.

To illustrate my concern, consider the evidence criterion adjustment model. In this model, the average DV value during the ITI will be closer to the decision bound in the weaker signal condition compared to the stronger signal condition - that is how the model fits the false alarm rate differences. This should be reflected in Beta amplitude if the latter reflects the DV. However, the Beta-derived urgency is highest for the condition with the lowest false alarm rate and lowest for the condition with the highest false alarm rate. It seems there is an inescapable conclusion that Beta amplitude does not fully reflect the main behavioral-driving features of the DV in this paradigm, even in the criterion-adjustment model. That said, I do think the development and consideration of the criterion-adjustment model is an important contribution, even with this shortcoming.

Additional comments:

1) The criterion-adjustment model appears equivalent to one with a constant negative drift added to the decision variable in addition to the contribution of evidence (and a lower reflecting bound). If true, it seems that there could be an alternative equivalent account that does not involve regulation of the transfer of incoming evidence.

2) I understand why one of the model parameters (e.g., noise or bound) must be fixed, but I don't understand why the authors didn't keep it the same parameter across all models. Couldn't they have fixed the noise parameters for all the models? Having it different makes it difficult to compare parameter values across the models, especially because the fitted noise term in the neurally-informed models appears dramatically reduced compared to the fixed value of noise used for the bound-adjustment model. On the topic of parameters, can the leak parameter be reported in more intuitive units? It seems to be parameterized as a fraction leak per time step, and I couldn't easily find the time step used. Reporting the leak as a time constant would be immediately understandable.

Reviewer #1 (Public Review):

This paper provides compelling and clear analyses that show that the coding level (sparsity) of the granule-cell layer of a cerebellar-like network does not only change the dimensionality of the representation, but also the inductive bias of the network: Higher sparsity biases the network to learning more high-frequency representations. Depending on the dominant frequencies of the target function, different coding levels are therefore optimal. The results are important/fundamental to theories of cerebellar learning and speak to a relevant ongoing debate in cerebellar learning, but will be of interest to readers outside of cerebellar neurophysiology.

Reviewer #1 (Public Review):

This study combines in vitro somatic and dendritic recordings and computational modeling to study how cholinergic agonists modulate the response of CA1 pyramidal neurons to triangular current injections. The authors have previously used a similar approach (Upchurch, 2022, JNeuroscience) to show that CA1 neurons exhibit asymmetric AP firing (more firing on the upward ramp) in response to such current injections and that this effect is due to Na channel inactivation. The present work builds on these results by showing that cholinergic modulation changes this response, i.e., there is more firing on the downward part of the ramp. This change appears to require an intracellular Ca2+ concentration increase (mediated via IP3 and voltage-gated Ca2+ channels), which activates TRPM4 channels. In this scheme, cholinergic activity increases IP3, and the depolarizing current injection opens voltage-gated Ca2+ channels. This study will be of some interest to cellular neurophysiology experts working on the hippocampus.

1) This study claims that the triangular current injections recapitulate hippocampal place cell activity. However, it has been shown recently that the asymmetric firing of CA1 place cells is due to synaptic weight changes resulting from synaptic plasticity (e.g., Bittner et al., 2017). This suggests that the asymmetric firing of place cells is primarily the result of asymmetric synaptic input. Therefore, the authors should test whether carbachol similarly affects a synaptically driven membrane potential ramp. If this is not the case, the strong claim that this work has implications for place cell firing is not justified, in my opinion.

2) Along the same lines, it has been shown before that the precision of spike timing depends on the stimulation pattern in vitro (Mainen and Sejnowski, 1995). Constant stimuli led to imprecise AP firing trains, whereas current injections that included fluctuations resembling synaptic input generated spike trains that were more reliable and reproducible in terms of timing. This study concluded that a low intrinsic noise level in spike generation was essential in generating informative spike sequences. Following this pivotal work, the authors could add noise to their current stimulus and observe the effect on the AP firing patterns. If this is not possible, the authors should at least report the sweep-to-sweep variability for the data shown, e.g., in panels 1A2, 1B2, 1D2, and 1E2.

3) In most of the data presented in this manuscript, Carbachol appears to induce a 3 mV hyperpolarization and increase input resistance. As a result, the amount of current injected during Carbachol is drastically lower than during the controls. This should be emphasized more, and the input resistance should be quantified for each experimental condition. It should also be discussed whether this change in input resistance can account for the changes in the firing pattern observed. Finally, it should be clearly stated how the amount of the current injected was chosen for each cell, and data from a range of injected current ramps should be shown for each cell.

4) It remains unclear how the current result that TRPM4 channels can mediate the firing pattern change relates to the previous finding that the current injection evoked CA1 neuronal firing pattern is due to long-term Na channel inactivation.

5) Figure 8: Panel C is supposed to confirm the prediction from the model that the carbachol-mediated change of firing activity is related to intracellular Ca2+ domains. However, the example cell shown is depolarized to -52 mV, and there is no hyperpolarization following Carbachol. Is this an effect of the high concentration of BAPTA? Again, what was the current injected under this experimental condition?

Reviewer #1 (Public Review):

The manuscript studies the spontaneous contractions (SC) of the Hydra body wall and presents a detailed simple mathematical model of nutrient transport to hypothesize the role of SC on maintaining the microbiota. This work provides valuable insights on the functional im- plications of the SC and the increased nutrient update obtained from mixing the local fluid environment through body wall contractions.

Reviewer #1 (Public Review):

Brunetti et al. investigated the mechanism used by the SARS CoV-2 virus to infect CD4 T cells and the potential impact on the immune system by viral infection. They find that SARS CoV-2 infects CD4 helper T cells and not CD8 T cells present in the blood and bronchoalveolar fluid of infected patients. The ACE-2 receptor expression on T lymphocytes is less compared to epithelial and endothelial cells, but still, the virus is able to establish a productive infection of T lymphocytes by some alternative mechanism. The group also demonstrated that interaction between CD4 and SARS virus spike protein further enhances viral entry and infectivity as CD4 is acting as an auxiliary molecule for viral entry.

By performing a technically impressive analysis of the infectivity of CD4 T cells isolated from healthy donors/controls invitro with the SARS CoV-2 virus and also by testing the infected population of CD4 cells invivo from COVID patients, the authors find that SARS CoV-2 infects CD4 T cells using Insitu Hybridization using probes against viral polymerase (RdRP), immunofluorescence and electron microscopy. Further, the authors also identified the region of SARS CoV-2 spike protein that interacts with CD4 by performing molecular docking analysis and found CD4 NTD interacts directly with the RBD region of the SARS Virus. The specific interaction between CD4 and SARS virus is further demonstrated by the use of anti-CD4 blocking antibodies and cell lines that over-express CD4 molecules. Interestingly by antibody inhibition of ACE-2 and camostat inhibition of TMPRSS2, the authors demonstrated that SARS CoV-2 infection of CD4 T cells requires ACE-2, TMPRSS2, and CD4. The data also show that viral infection of CD4 T cells leads to the expression of cytokines like 1L-10 that may impact cell viability and dampens immune response.

The experiments in the paper are well- performed and the conclusions of this paper are mostly well supported by data, but some aspects of the impact of viral infection of CD4 cells leading to lymphopenia need to be clarified and extended.<br /> A major weakness of the paper is the reference citations. Inconsistency in maintaining the citation style and numbering in the manuscript draft drastically impacts the readability. For example, the use of superscript format references in the introduction and results section and paraphrasing format in materials and methods could not make the readers identify the correct citations.

1. In this paper, the authors describe infection of CD4 T cells may lead to T cell death. Although the extended Fig. 9 suggests the expression of a multitude panel of gene expression, including apoptotic genes, the authors could not provide a piece of direct evidence to show how CD4 T cell death happens. What is the underlying mechanism of cell death? Is it by necrosis or apoptosis or pyroptosis? The exact mechanism of CD4 cell death needs to be discovered and adding control experiments to assess the exact mechanism of cell death would increase confidence in the presented results of the functional interaction of proteins (Ext Fig 9).

2. Some previous studies suggested that lymphocytopenia in COVID infection could be due to impaired T cell proliferation or extravasation of T cells into tissue. The exact mechanism for lymphocytopenia could be addressed by performing an animal experiment, but it would be interesting to see what are the author's opinion about other possibilities of lymphocytopenia.

3. The data on CREB-1 Ser 133 in Figure 4E is not sufficiently convincing. It is difficult to understand what is the difference between every three lanes within mock and SARS CoV-2 infection. There is a pCREB band in lane 5 (2nd lane of CoV-2), but not in the other two. Better data would have helped to substantiate the authors' conclusions.

Reviewer #1 (Public Review):

Guo et al. demonstrate that Metformin, a first-line anti-diabetic drug, significantly improves bone healing in diabetic mice. Mechanistically, they demonstrate that Metformin improves BMSC differentiation in T2D type 2 diabetic mice, potentially through an indirect mechanism. Overall the study is comprehensive and the effects of Metformin on bone healing are demonstrated by overwhelming data. The study further offers important information for management of the complications associated with type-2 diabetes. The weakness of the study is the lack of in-depth understanding of the mechanism underlying Metformin's effects on healing. The writing of the manuscript could also be improved for clarity and accuracy.

Reviewer #1 (Public Review):

In the manuscript the authors identify new players regulating cell state transitions. They show that heme biosynthesis is required for the naïve-to-primed pluripotency transition. In particular, they provide a link between heme biosynthesis inhibition and failure to activate TGFβ and MAPK pathways, two crucial regulators of the exit from the naïve pluripotent state. Heme biosynthesis inhibition increases the percentage of 2CLCs within the mESC population. The authors further show that this increased level of 2CLCs depends on the accumulation of succinate in non-mitochondrial cell compartments as a consequence of heme biosynthesis inhibition. Based on experiments using chemical inhibitors, the authors conclude that succinate acts in a paracrine and autocrine manner to enhance reprogramming of mESC into 2CLCs.

The observations provided by the authors are interesting and potentially relevant in the field of pluripotent cell state transitions. However, in the present state, neither the role of heme biosynthesis on FGF-ERK and TGF beta signalling and exit from naïve pluripotency nor the role of heme biosynthesis in controlling the 2CLC state are clear.

Below I list my main concerns:

1. The authors claim that the heme metabolic pathway is important for the naïve-to-primed transition. Interfering with its proper function appears to have developmental consequences. However, it is unclear how exactly this pathway is regulated during the exit from naïve pluripotency in WT cells. Hence the physiological relevance remains unclear. Are the levels of heme itself, or the mentioned 7 enzymes of the heme pathway regulated during differentiation?

2. The claim that "the roles of this [heme] biosynthetic pathway and this metabolite have never been studied in the context of pluripotency" is a bit misleading. It has been reported that HO1 is regulated by Oct4 (https://doi.org/10.1002/1873-3468.14138).

3. The link between heme biosynthesis and the TGFβ and MAPK pathways remains unclear. Is there any evidence for a direct link, or are these two observations simply linked through an altered cell state? Without further experiments it remains unclear whether the lack of proper Tgf beta and Fgf-ERK signalling activation are cause or consequence of the observed differentiation defects. Results must be discussed with this limitation in mind.

4. The fact that MEKi did not recapitulate the phenotype of SA treatment to prevent EpiLC differentiation should already be clarified in the results section. Moreover, the fact that SMAD inhibition seems to delay downregulation of naïve markers more than SA treatment, and the fact that SMAD inhibition combined with MEK inhibition seems weaker than SMAD inhibition alone seem counterintuitive and needs explanation. Can the authors attempt to titrate pathway inhibition to a similar level as observed in heme pathway deficient ESCs? Furthermore, can the differentiation defect be rescued upon overstimulation of Fgf-ERK and TGF beta to reach WT levels?

5. To better characterize the direct effect of heme biosynthesis inhibition it is necessary to in depth analyse any possible cell proliferation, viability of cell cycle defects after SA (or AA5) treatment. If there is an impact on cellular health, this needs to be reported and taken into careful consideration when interpreting results.

6. The authors claim that SA pre-treatment of mESC is able to enhance their differentiation ability into throphoblast like cells; however, they do not show statistically significant differences in terms of throphoblast expression markers between SA-treated and control cells (Supplemental Fig.2d). Furthermore, the variance of measurements in 2iL are not shown. Expression levels in TS cells or in trophoblast tissue must be used as control to judge the effect size. 2iL cells do also generate cells similar in morphology to 2iL+SA cells (Suppl Fig. 2b). Should these also be giant cells; this would be very surprising. Together, this makes drawing conclusions from these experiments impossible. If the statement that SA treatment expands the lineage potential of ESCs is made, it will need to be supported by appropriate and statistically strong data. A mere increase in some marker genes (which are not really specific for the TE lineage but also expressed in embryonic tissues) and statements based on morphology are no good support for this hypothesis.

7. What is the reason for calling the increase in 2C like cells 2C-like reprogramming? Is there any evidence that this is indeed a reprogramming event? There is no evidence for disruption of heme biosynthesis directly instructing cells to take on a 2CLC state, there is simply an increase in the Zscan4 expressing population, for a reason that remains unclear.

8. Addition of AA5 or SA results in absolutely striking changes in the epigenetic state of ESCs. H3K4me3, H3K27me3 and H3K9me3 together with 5mC levels appear drastically increased based on IF images in Supp. Fig. 5a. I wonder how physiological these levels are. How much data directly meaningful for 'normal' differentiation can be obtained from such a massively perturbed system? This needs to be appropriately acknowledged and discussed.

Reviewer #1 (Public Review):

In this manuscript, Dhekne et al. sought to identify modulators of LRRK2 activity. Thereto, the authors performed a FACS-based genome-wide pooled CRISPR/Cas9 screen in NIH-3T3 cells monitoring phosphorylation of the Rab GTPase Rab10 which is a well-characterized target of LRRK2. Besides Rab10 and LRRK2, this unbiased screen surprisingly uncovered another Rab GTPase, Rab12, as one of the most significant hits. Validation experiments with Rab12 knockout (KO) NIH-3T3 cells, the LRRK2 inhibitors MLi-2, and Rab12 KO mice unanimously confirmed the dependency of Rab10 phosphorylation on Rab12. Conversely, the authors used A549 LRKK2 and PPM1H KO cells to show that overexpression of Rab12 but not Rab29 which is another LRRK2 modulator leads to elevated phospho-Rab10 levels in a manner dependent on LRRK2 but insensitive to pathogenic mutations thereof. Moreover, the authors mapped E240 and S244 of LRRK2 as specific binding sites for Rab12 and showed that these residues are important to sustain full LRKK2 activity towards Rab10. Lastly, the authors showed that RAB12-dependent LRRK2 activation occurs under (patho)physiological stress conditions, namely endolysosomal membrane damage. Together, this comprehensive and elegant study of Dhekne and colleagues reveals exciting new mechanistic insights into the regulation of LRRK2 which have therapeutic potential for Parkinson's disease.

Reviewer #1 (Public Review):

For many years it has been understood that transposable elements (TEs) are an important source of natural variation. This is because, in addition to simple knockouts of genes, TEs carry regulatory sequences that can, and sometimes do, affect the expression of genes near the TEs. However, because TEs can be difficult to map to reference genomes, they have generally not been used for trait mapping. Instead, single nucleotide polymorphisms are widely used because they are easy to detect when using short reads. However, improvements in sequencing technology, as well as an increased appreciation of the importance of TEs to both linked to favorable alleles and are more likely to be causing the changes that make those alleles beneficial in a given environment. Further, because TE activity can occur after bottlenecks, they can provide polymorphisms in the absence of variation in point mutations.

In this manuscript, the authors carefully examine insertion polymorphisms in rice and demonstrate linkage to differences in expression. To do this, they used expression quantitative trait locus (eQTL) GWAS using TIPs as genetic markers to examine variation in 208 rice accessions. Because they chose to focus on genes that were expressed in at least 10% of the accessions, presumably because more rare variants would end up lacking statistical power. This is an understandable decision, but it says that recent insertions, such as the MITE elements detailed in a previous paper, would not be included. Importantly, although TIPs associated with differentially expressed genes are far less common than SNPs' traditional eQTLs, there were a significant number of eQTLs that showed linkage to TIPs but not to QTL.

The authors then show that of the eQTLs associated with both TIPs and SNPs, TIPs are more tightly linked to the eQTL, and are more likely to be associated with a reduction in expression, with variation in the effects of various TEs families supporting that hypothesis. Here and throughout, however, the distance of the TEs could be an important variable. It is also worth noting the relative numbers in order to assess the claim in the title of the paper. The total number of eQTL-TIPs is ten-fold less than the number of eQTL-SNPs, and, of the eQTLs that have both, there are a significant number of eQTL-TIPs that are not more tightly linked to the expression differences than the eQTL.

The authors show that eQTL-TIPs are more likely to be in the promoter-proximal region, but this may be due to insertion bias, which is well documented in DNA-type elements. Here and throughout the authors are careful to state that the data is consistent with the hypothesis that TEs are the cause of the change, but do not claim that the data demonstrate that they are.

Throughout the rest of the manuscript, the authors systematically build the case for a causal role for TEs by showing, for instance, that eQTL-TIPs show much stronger evidence for selection, with increased expression being more likely to be selected than decreased expression. The authors provide examples of genes most likely to have been affected by TE insertions.

Overall, the authors build a convincing case for TEs being an important source of regulatory information. I don't have any issues with the analysis, but I am concerned about the sweeping claims made in the title. Once you get rid of eQTLs that could be altered by either SNPs or TIPs and include only those insertions that show strong evidence of selection, the number of genes is reduced to only 30. And even in those cases, the observed linkage is just that, not definitive evidence for the involvement of TEs. Although clearly beyond the scope of this analysis, transgenic constructs with the TEs present or removed, or even segregating families, would have been far more convincing.

The fact that many of the eQTL-TIPs were relatively old is interesting because it suggests that selection in domesticated rice was on pre-existing variation rather than new insertions. This may strengthen the argument because those older insertions are less likely to be purged due to negative effects on gene expression. Given that the sequence of these TEs is likely to have diverged from others in the same family, it would have been interesting to see if selection in favor of a regulatory function had caused these particular insertions to move away from more typical examples of the family.

Reviewer #1 (Public Review):

In this manuscript the authors proposed a novel system by which they can suppress the expression of any gene of interest precisely and efficiently with a pre-validated, highly specific and efficient synthetic short-hairpin RNA. The idea of identifying potent artificial RNAi (ARTi) triggers is intriguing, and the authors successfully identify six ARTi with robust knockdown efficiency and limited to no off-target effects. As a proof-of-concept, the authors examined three oncology targets for validation, including EGFRdel19 (which already has a clinically approved drug for validation), KRASG12R (for which there are no in vivo compatible inhibitors yet) and STAG1 (which has a synthetic lethal interaction with recurrent loss-of-function mutations of STAG2). The authors demonstrated significant suppression of colony formation and in vivo tumor growth for all three oncology targets.

This novel system could serve as a powerful tool for loss-of-function experiments that are often used to validate a drug target. Not only this tool can be applied in exogenous systems (like EGFRdel19 and KRASG12R in this paper), the authors successfully demonstrated that ARTi can also be used in endogenous systems by CRISPR knocking in the ARTi target sites to the 3'UTR of the gene of interest (like STAG2 in this paper).

ARTi enables specific, efficient, and inducible suppression of these genes of interest, and can potentially improve therapeutic target validations. However, the system cannot be easily generalized as there are some limitations in this system:

• The authors claim in the introduction that CRISPR/Cas9-based methods are associated with off-target effects, however, the author's system requires the use CRISPR/Cas9 to knock out a given endogenous genes or to knock-in ARTi target sites to the 3' UTR of the gene of interest. Though the authors used a transient CRISPR/Cas9 system to minimize the potential off-target effects, the methods does not, as the authors acknowledge, eliminate the possibility of off-target effects.

• Instead of generating gene-specific loss-of-function triggers for every new candidate gene, the authors identified a universal and potent ARTi to ensure standardized and controllable knockdown efficiency. It seems this would save time and effort in validating each lost-of-function siRNAs/sgRNAs for each gene. However, users will still have to design and validate the best sgRNA to knock out endogenous genes or to knock in ARTi target sites by CRISPR/Cas9. The latter is by no-means trivial. Users will need to design and clone an expression construct for their cDNA replacement construct of interest, which will still be challenging for big proteins. This is not, as the authors point out, a replacement for other LOF methods, and there are other ways to achieve gene-specific regulation via, for example, degrons. However it is an effective orthogonal approach that many users may find compelling for their applications.

Reviewer #1 (Public Review):

This is a carefully performed and well documented study to indicate that the FUS protein interacts with the GGGGCC repeat sequence in Drosophila fly models, and the mechanism appears to include modulating the repeat structure and mitigating RAN translation. They suggest FUS, as well as a number of other G-quadruplex binding RNA proteins, are RNA chaperones, meaning they can alter the structure of the expanded repeat sequence to modulate its biological activities.Overall this is a nicely done study with nice quantitation.

Reviewer #1 (Public Review):

Based on a recent report of spontaneous and reversible remapping of spatial representations in the enthorhinal cortex (Low et al 2021), this study sets out to examine possible mechanisms by which a network can simultaneously represent a positional variable and an uncorrelated binary internal state. To this end, the authors analyse the geometry of activity in recurrent neural networks trained to simultaneously encode an estimate of position in a one-dimensional track and a transiently-cued binary variable. They find that network activity is organised along two separate ring manifolds. The key result is that these two manifolds are significantly more aligned than expected by chance, as previously found in neural recordings. Importantly, the authors show that this is not a direct consequence of the design of the model, and clarify scenarios by which weaker alignment could be achieved. The model is then extended to a two-dimensional track, and to more than two internal variables. The latter case is compared with experimental data that had not been previously analysed.

Strengths:

• rigorous and careful analysis of activity in trained recurrent neural networks

• particular care is taken to show that the obtained results are not a necessary consequence of the design of the model

• the writing is very clear and pleasant to read

• close comparison with experimental data

• extensions beyond the situations studied in experiments (two-dimensional track, more than two internal states)

Weaknesses:

• no major weaknesses

• (minor) the comparison with previous models of remapping could be expanded

Altogether the conclusions claimed by the authors seem to be strongly supported and convincing.

Reviewer #1 (Public Review):

This work by Gonzalez-Segarra et al. greatly extends previous research from the same group that identified ISNs as a key player in balancing nutrition and water ingestion. Using well-balanced sets of exploratory anatomical analyses and rigorous functional experiments, the authors identify and compile various peptidergic circuits that modulate nutrient and/or water ingestion. The findings are convincing and the experiments rigorous.

Strengths:

- The authors complement anatomically-reconstructed and functionally-validated neuronal connectivity with extensive and intensive morphological and synaptic reconstruction.

- Neurons and genes involved in specific components of feeding control are undoubtedly challenging, because numerous neurons and circuits redundantly and reciprocally regulate the same components of feeding behavior. This work dissociates how multiple, parallel and interconnected, peptidergic circuits (dilp3, CCHa2, CCAP) modulate sucrose and water ingestion, in tandem and in parallel.

- The authors address some of the incongruencies / discrepancies in current literature (IPCs) and try to provide explanations, rather than ignoring inconsistent findings.

Weaknesses:

- Is "function" of the ISNs to balance "nutrient need" or osmolarity? Balancing hemolymph osmolarity for physiological homeostasis is conceptually different from balancing thirst and hunger.

- The final schematic nicely sums up how the different peptidergic pathways might work together, but it is unclear which connections are empirically-validated or speculative. It would be informative to show which parts of the model are speculative versus validated. For example, does FAFB volume synapse = functional connectivity and not just anatomical proximity? A bulk of the current manuscript relies on "synapses of relatively high confidence" (according to Materials and methods: line 522). I recommend distinguishing empirically tested & predicted connections in the final schematic, and maybe reword/clarify throughout the manuscript as "predicted synaptic partners"

Reviewer #1 (Public Review):

There are a number of outstanding questions concerning how cohesin turnover on DNA is controlled by various accessory factors and how such turnover is controlled by post-translational modification. In this paper, Nasmyth et al. perform a series of AlphaFold structure predictions that aim to address several of these outstanding questions. Their structure predictions suggest that the release factor WAPL forms a ternary complex with PDS5 and SA/SCC3. This ternary complex appears to be able to bind the N-terminal end of SCC1, suggesting how formation of such a complex could stabilize an open state of the cohesin ring. Additional calculations suggest how the Eco/ESCO acetyltransferases and Sororin engage the SMC3 head domain and thereby protect against WAPL-mediated release.

This work thus demonstrates the power of AF prediction methods and how they can lead to a number of interesting and testable hypotheses that can transform our understanding of cohesin regulation. These findings require orthogonal experimental validation, but the authors argue convincingly that such validation should not be a pre-requisite to publication.

As the authors did not systematically include model confidence scores it is difficult for the reader to evaluate the reliability of the models obtained. The caveat is that many readers will by default assume that the presented models are correct, when in fact, some of them may score poorly and require careful assessment. As numerous readers will not be very familiar with the AF confidence scoring mechanisms, it would be important to include such metrics and indicate what these scores mean for the different interfaces (Acceptable, Medium and High confidence?). pLDDT and PAE plots should be included. When they report on a key interaction (E.g. WAPL-SCC1) they should indicate the key region (SCC1 N-terminus) on the PAE plot. False positives are always possible even with good scores, especially when many protein pairs are tried. It would therefore be important to also include a table showing the global scores for pTM and ipTM to summarise the confidence scores of interfaces.

It is exciting to see AF-multimer predictions being applied to cohesin. As some of the reported interactions are not universally conserved and some involve relatively small interfaces the possibility arises that these interfaces show poor or borderline confidence scores. As some of these interfaces map to mutants that have previously been obtained by hypothesis-free genetic screens and mutational analyses they appear nevertheless valid. Thus, an important point to make is that even interfaces that show modest confidence scores may turn out to be valid while others may be not. The authors therefore should emphasize that the proposed models are just predictions and that additional orthogonal validations are required.

Reviewer #1 (Public Review):

This paper presents extensive numerical simulations using a model that incorporates up to second-order epistasis to study the joint effects of microscopic epistasis and clonal interference on the evolutionary dynamics of a microbial population. Previous works that explicitly modeled microscopic epistasis typically assumed strong selection & weak mutation (SSWM), a condition that is generally not met in real-life evolutionary processes. Alternatively, another class of models coarse-grained the effects of microscopic epistasis into a generic distribution of fitness effects. The framework introduced in this paper represents an important advance with respect to these previous approaches, allowing for the explicit modeling of microscopic epistasis in non-SSWM scenarios. The modeling framework presented promises to be a valuable tool to study microbial evolution in silico.

Reviewer #1 (Public Review):

Ibar and colleagues investigate the function of spectrin in Drosophila wing imaginal discs and its effect on the Hippo pathway and myosin activity. The authors find that both βH-Spec and its canonical binding partner α-Spec reduce junctional localization of the protein Jub and thereby restrict Jub's inhibitory effect on Hippo signaling resulting in activation of the Hippo effector Yorkie regulating tissue shape and organ size. From genetic epistasis analysis and analysis of protein localization, the authors conclude that βH-Spec and α-Spec act independently in this regulation. The major point of this study is that the apical localization of βH-Spec and myosin is mutually exclusive and that the proteins antagonize each other's activity in wing discs. In vitro co-sedimentation assays and in silico structural modeling suggest that this antagonization is due to a competition of βH-Spec and myosin for F-actin binding.

The study's strengths are the genetic perturbation that is the basis for the epistasis analysis which includes specific knockdowns of the genes of interest as well as an elegant CRISPR-based overexpression system with great tissue specificity. The choice of the model for such an in-depth analysis of pathway dependencies in a well-characterized tissue makes it possible to identify and characterize quantitative differences between closely entangled and mutually dependent components. The method of quantifying protein localization and abundance is common for multiple figures which makes it easy to assess differences across experiments. The flow of experiments is logical and in general, the author's conclusions are supported by the presented data. The findings are very well embedded into the context of relevant literature and both confronting and confirming literature are discussed.

The study shows how components of the cytoskeleton are directly involved in the regulation of the mechanosensitive Hippo pathway in vivo and thus ultimately regulate organ size supporting previous data in other contexts. The molecular mechanism regulating myosin activity by out-competing it for F-actin binding has been observed for small actin-binding proteins such as cofilin but is a new mode for such a big, membrane-associated actin-binding protein. This may inspire future experiments in different morphogenetic contexts for the investigation of similar mechanisms. For example, the antagonistic activity of βH-Spec and myosin in this tissue context might help explain phenomena in other systems such as spectrin-dependent ratcheting of apical constriction during mesoderm invagination (as the authors discuss). Against the classical view, the work shows that βH-Spec can act independently of α-Spec. Together the results will be of interest to the cell biology community with a focus on the cytoskeleton and mechanotransduction.

Reviewer #1 (Public Review):

This manuscript by Mahlandt, et al. presents a significant advance in the manipulation of endothelial barriers with spatiotemporal precision, and in the use of optogenetics to manipulate cell signaling in vascular biology more generally. The authors establish the role of Rho-family GTPases in controlling the cytoskeletal-plasma membrane interface as it relates to endothelial barrier integrity and function, and adequately motivate the need for optogenetic tools for global and local signaling manipulation to study endothelial barriers.

Throughout the work, the optogenetic assays are conceptualized, described, and executed with exceptional attention to detail, particularly as it relates to potential confounding factors in data analysis and interpretation. Comparison across experimental setups in optogenetics is notoriously fraught, and the authors' control experiments and measurements to ensure equal light delivery and pathway activation levels across applications is very thorough. In demonstrating how these new opto-GEFs can be used to alter vascular barrier strength, the authors cleverly use fluorescent-labeled dextran polymers of different sizes and ECIS experiments to demonstrate the physiological relevance of BOEC monolayers to in vivo blood vessels. Of particular note, the resiliency of the system to multiple stimulation cycles and longer time course experiments is promising for use in vascular leakage studies.

Given that dozens of Rho GTPase-activating GEFs exist, expanded rationale for the selection of p63, ITSN1, and TIAM1 in the form of discussion and literature citations would be helpful to motivate their selection as protein effectors in the engineered tools. Extensive tool engineering studies demonstrate the superiority of iLID over optogenetic eMags or rapamycin-based chemogenetic tools for these purposes. However, as the utility of iLID and eMags has been demonstrated for manipulation of a variety of signaling pathways, the iSH-Akt demonstration does not seem necessary for these systems.

The demonstration of orthogonality in GTPase- and VE-cadherin-blocking antibody-mediated barrier function decreases and is compelling, even without full elucidation of the role of cell size or overlap in barrier strength. The discussion section presents a mature and thoughtful description of the limitations, remaining questions, and potential opportunities for the tools and technology developed in this work. Importantly, this manuscript demonstrates a commitment to scientific transparency in the ways in which the data are visualized, the methods descriptions, and the reagent and code sharing it presents, allowing others to utilize these tools to their full potential.

Joint Public Review:

In this study, Anthoney and coworkers continue an important, unique, and technologically innovative line of inquiry from the van Swinderen lab aimed at furthering our understanding of the different sleep stages that may exist in Drosophila. Here, they compare the physiological and transcriptional hallmarks of sleep that have been induced by two distinct means, a pharmacological block of GABA signaling and optogenetic activation of dorsal fan-shaped-body neurons. They first employ an incredibly impressive fly-on-the-ball 2-photon functional imaging setup to monitor neural activity during these interventions, and then perform bulk RNA sequencing of fly brains at different stages. These transcriptomic analyses leads them to (a) knocking out nicotinic acetyl-choline receptor subunits and (b) knocking down AkhR throughout the fly brain testing the impact of these genetic interventions on sleep behaviors in flies. Based on this work, the authors present evidence that optogenetically and pharmacologically induced sleep produces highly distinct brain-wide effects on physiology and transcription.

The study is of significant interest, is easy to read, and the figures are mostly informative. However there are features of the experimental design and the interpretation of results that diminish enthusiasm.

a - Conditions under which sleep is induced for behavioral vs neural and transcriptional studies

1) There is a major conceptual concern regarding the relationships between the physiological and transcriptomic effects of optogenetic and pharmacological sleep promotion, and the effects that these manipulations have on sleep behavior. The authors show that these two means of sleep-induction produce remarkably distinct physiological and transcriptional responses, however, they also show that they produce highly similar effects on sleep behavior, causing an increase in sleep through increases in the duration of sleep bouts. If dFB neurons were promoting active sleep, the sleep it produces should be more fragmented than the sleep induced by the drug, because the latter is supposed to produce quiet sleep. Yet both manipulations seem to be biasing behavior toward quiet sleep.

2) The authors show that the pharmacological block of GABA signaling and the optogenetic activation of dorsal fan-shaped-body neurons cause different responses on brain activity. Based on these recordings and the behavioral and brain transcriptomic data they then claim that these responses correspond to different sleep states and are associated with the expression and repression of a different constellation of genes. Nevertheless, neural activity in animals was recorded following short stimulations whereas behavioral and transcriptomic data were obtained following chronic stimulation. In this regard, it would be interesting to determine how the 12-hour pharmacological intervention they employed for their transcriptomic analysis changes neural activity throughout the brain - 12 hours will likely be too long for the open-cuticle preps, but an in-between time-point (e.g. 1h) would probably be equally informative.

b - Efficiency of THIP treatment under different conditions

1) There are no data to quantify how THIP alters food consumption. It is evident that flies consume it otherwise they would not show increased sleep. However, they may consume different amounts of food overall than the minus THIP controls. This might have an influence on the animal's metabolism, which could at least explain the fact that metabolism-related genes are regulated (Figure 5). Therefore, in the current state, it is not possible to be certain that gene regulation events measured in this experiment are solely due to THIP effects on sleep.

2) A similar problem exists in the sleep deprivation experiments. If flies are snapped every 20 seconds, they may not have the freedom to consume appropriate amounts of food, and therefore their consumption of THIP or ATR may be smaller than in non-sleep deprived controls. Thus, it would be crucial to know whether the flies that are sleep-deprived (i.e. shaken every 20 seconds for 12 hours) actually consume comparable amounts of food (and therefore THIP) as those that are undisturbed. If not, then perhaps the transcriptional differences between the two groups are not sleep-specific, but instead reflect varying degrees of exposure to THIP.

3) The authors should further discuss the slow action of THIP perfusion vs dFB activation, especially as flies only seem to fall asleep several minutes after THIP is being washed away. Is it a technical artifact? If not, it may not be unreasonable to hypothesize that THIP, at the concentration used, could prevent flies from falling asleep, and that its removal may lower the concentration to a point that allows its sleep-promoting action. The authors could easily test this by extending THIP treatment for another 4-5 minutes.

c - Comments regarding the behavioral assays

1) L319-322: the authors conclude that dFB stimulation and THIP consumption have similar behavioral effects on sleep. However, this is inaccurate as in Figure S1 they explain that one increases bout number in both day and night and the other one only during the day.

2) The behavioral definitions used for active and quiet sleep do not fit well with strong evidence that deep sleep (defined by lowered metabolic rates) is probably most closely associated with bouts of inactivity that are much longer than the >5min duration used here, i.e., probably 30min and longer (Stahl et al. 2017 Sleep 40: zsx084). Given that the authors are providing evidence that quiet sleep is correlated with changes in the expression of metabolism related genes, they should at least discuss the fact that reductions in metabolism have been shown to occur after relatively long bouts of inactivity and might reconsider their behavioral sleep analysis (i.e., their criteria for sleep state) with this in mind.

d - Comments regarding the recordings of neuronal activity

1) There is an additional concern regarding the proposed active and quiet sleep states that rest at the heart of this study. Here these two states in the fly are compared to the REM and NREM sleep states observed in mammals and the parallels between active fly sleep and REM and quiet fly sleep and NREM provide the framework for the study. The establishment of such parallel sleep states in the fly is highly significant and identifying the physiological and molecular correlates of distinct sleep stages in the fly is of critical importance to the field. However, the proposal that the dorsal fan shaped body (dFB) neurons promote active sleep runs counter to the prevailing model that these neurons act as a major site of sleep homeostasis. If quiet sleep were akin to NREM, wouldn't we expect the major site of sleep homeostasis in the brain to promote it? Furthermore, the authors state that the effects of dFB neuron excitation on transcription have "almost no overlap" (line 500) with the transcriptomic effects of sleep deprivation (Supplementary Table 3), which is not what would be expected if dFB neurons are tracking sleep pressure and promoting sleep, as suggested by a growing body of convergent work summarized on page four of the manuscript. Wouldn't the 10h excitation of the dFB neurons be predicted to mimic the effects of sleep deprivation if these neurons "...serve as the discharge circuit for the insect's sleep homeostat..." (line 60)? Shouldn't their prolonged excitation produce an artificial increase in sleep drive (even during sleep) that would favor deep, restorative sleep? How do the authors interpret their results with regard to the current prevailing model that dFB neurons act as a major site of sleep homeostasis? This study could be seen as evidence against it, but the authors do not discuss this in their Discussion.

2) Regarding the physiological effects of Gaboxadol, to what extent is the quieting induced by this drug reminiscent of physiology of the brains of flies spontaneously meeting the behavioral criterion for quiet sleep? Given the relatively high dose of the drug being delivered to the de-sheathed brain in the imaging experiments (at least when compared to the dose used in the fly food), one worries that the authors may be inducing a highly abnormal brain state that might bear very little resemblance to the deeply sleeping brain under normal conditions. As the authors acknowledge, it is difficult to compare these two situations. Comparing the physiological state of brains put to sleep by Gaboxadol and brains that have spontaneously entered a deep sleep state therefore seems critical.

3) There are some issues with Figure 3, in particular 3C-D. It is not clear whether these panels show representative traces or an average, however both the baseline activity and fluorescence are different between C and D, in particular in their amplitude. Therefore, it is difficult to attribute the differences between C and D to the stimulation itself or to the previously different baseline. In addition, the fact that flies with dFB activation seem to keep a basal level of locomotor activity whereas THIP-treated ones don't is quite striking, however it is not being discussed. Finally, the authors claim that the flies eventually wake up from THIP-induced sleep (L360-361), however there are no data to support this statement.

4) In Figure 4C, it is strange that the SEM is always exactly the same across the whole experiment. Readers should be aware that there might have been an issue when plotting the figure.

e - Comments regarding the transcript analyses

1) General comment: the title of this manuscript is inaccurate - the "transcriptome" commonly refers to the entirety of all transcripts in a cell/tissue/organ/animal (including genes that are not differentially expressed following their interventions), and it is therefore impossible to "engage two non-overlapping transcriptomes" in the same tissue. Perhaps the word "transcriptional programs" or transcriptional profiles" would be more accurate here?

2) Given the sensitivity of transcriptomic methods, there is a significant concern that the optogenetic experiments are not as well controlled as they could be. Given the need for supplemental all-trans retinal (ATR) for functional light gating of channelrhodopsins in the fly, it is convenient to use flies with Gal4-driven opsin that have not been given supplemental ATR as a negative control, particularly as a control for the effects of light. However, there is another critical control to do here. Flies bearing the UAS-opsin responder element but lacking the GAL4 driver and that have been fed ATR are critical for confirming that the observed effects of optogenetic stimulation are indeed caused by the specific excitation of the targeted neurons and not due to leaky opsin expression, or the effect of ATR feeding under light stimulation or some combination of these factors. Given the sensitivity of transcriptomic methods, it would be good to see that the candidate transcripts identified by comparing ATR+ and ATR- R23E10GAL4/UAS-Chrimson flies are also apparent when comparing R23E10GAL4/UAS-Chrimson (ATR+) with UAS-Chrimson (ATR+) alone.

3) Figures about qPCR experiments (5G and 6G) are problematic. First, whereas the authors seem satisfied with the 'good correspondence' between their RNA-seq and qPCR results, this is true for only ~9/19 genes in 5G and 2/6 genes in 6G. Whereas discrepancies are not rare between RNA-seq and qPCR, the text in L460-461 and 540-541 is misleading. In addition, it is unclear whether the n=19 in L458 refers to the number of genes tested or the number of replicates. If the qPCR includes replicates, this should be more clearly mentioned, and error bars should be added to the corresponding figures.

4) There is a lack of error bars for all their RNAseq and qPCR comparisons, which is particularly surprising because the authors went to great lengths and analyzed an applaudably large amount of independent biological replicates, yet the variability observed in the corresponding molecular data is not reported.

Reviewer #1 (Public Review):

Ritvo and colleagues present an impressive suite of simulations that can account for three findings of differentiation in the literature. This is important because differentiation-in which items that have some features in common, or share a common associate are less similar to one another than are unrelated items-is difficult to explain with classic supervised learning models, as these predict the opposite (i.e., an increase in similarity). A few of their key findings are that differentiation requires a high learning rate and low inhibitory oscillations, and is virtually always asymmetric in nature.

This paper was very clear and thoughtful-an absolute joy to read. The model is simple and elegant, and powerful enough to re-create many aspects of existing differentiation findings. The interrogation of the model and presentation of the findings were both extremely thorough. The potential for this model to be used to drive future work is huge. I have only a few comments for the authors, all of which are relatively minor.

1. I was struck by the fact that the "zone" of repulsion is quite narrow, compared with the zone of attraction. This was most notable in the modeling of Chanales et al. (i.e., just one of the six similarity levels yielded differentiation). Do the authors think this is a generalizable property of the model or phenomenon, or something idiosyncratic to do with the current investigation? It seems curious that differentiation findings (e.g., in hippocampus) are so robustly observed in the literature despite the mechanism seemingly requiring a very particular set of circumstances. I wonder if the authors could speculate on this point a bit-for example, might the differentiation zone be wider when competitor "pop up" is low (i.e., low inhibitory oscillations), which could help explain why it's often observed in hippocampus? This seems related a bit to the question about what makes something "moderately" active, or how could one ensure "moderate" activation if they were, say, designing an experiment looking at differentiation.

2. With real fMRI data we know that the actual correlation value doesn't matter all that much, and anti-correlations can be induced by things like preprocessing decisions. I am wondering if the important criterion in the model is that the correlations (e.g., as shown in Figure 6) go down from pre to post, versus that they are negative in sign during the post learning period. I would think that here, similar to in neural data, a decrease in correlation would be sufficient to conclude differentiation, but would love the authors' thoughts on that.

3. For the modeling of the Favila et al. study, the authors state that a high learning rate is required for differentiation of the same-face pairs. This made me wonder what happens in the low learning rate simulations. Does integration occur? This paradigm has a lot of overlap with acquired equivalence, and so I am thinking about whether these are the sorts of small differences (e.g., same-category scenes and perhaps a high learning rate) that bias the system to differentiate instead of integrate.

4. For the simulations of the Schlichting et al. study, the A and B appear to have overlap in the hidden layer based on Figure 9, despite there being no similarity between the A and B items in the study (in contrast to Favila et al., in which they were similar kinds of scenes, and Chanales et al., in which they were similar colors). Why was this decision made? Do the effects depend on some overlap within the hidden layer? (This doesn't seem to be explained in the paper that I saw though, so maybe just it's a visualization error?)

5. It seems as though there were no conditions under which the simulations produced differentiation in both the blocked and intermixed conditions, which Schlichting et al. observed in many regions (as the present authors note). Is there any way to reconcile this difference?

6. A general question about differentiation/repulsion and how it affects the hidden layer representation in the model: Is it the case that the representation is actually "shifted" or repelled over so it is no longer overlapping? Or do the shared connections just get pruned, such that the item that has more "movement" in representational space is represented by fewer units on the hidden layer (i.e., is reduced in size)? I think, if I understand correctly, that whether it gets shifted vs. reduce would depend on the strength of connections along the hidden layer, which would in turn depend on whether it represents some meaningful continuous dimension (like color) or not. But, if the connections within the hidden layer are relatively weak and it is the case that representations become reduced in size, would there be any anticipated consequences of this (e.g., cognitively/behaviorally)?

Reviewer #1 (Public Review):

The authors present a carefully controlled set of experiments that demonstrate an additional complexity for GPCR signalling in that endosomal signalling make be different when beta-arrestin is or isn't associated with a G protein-bound V2 vasopressin receptor. It uses state of the art biosensor-based approaches and beta-arrestin KO lines to assess this. It adds to a growing body of evidence that G proteins and beta-arresting can associate with GPCR complexes simultaneously. They also demonstrate the possibility that Gq might also be activated by the V2 receptor. My sense is one thing they may need to be considered is the possibility of such "megacomplexes" might actually involve receptor dimers or oligomers.

Reviewer #1 (Public Review):

In their research article, Sapiro et al. overcome the technical burden of low B. burgdorferi numbers during infection by physically enriching for spirochetes prior to RNA-sequencing/mass spectrometry. This technology, which has potential broad applications, was applied to B. burgdorferi-infected ticks, generating datasets for future studies.

Sapiro et al. addressed many of the reviewers' comments including the addition of experimental details, comparisons to other studies and some caveats to their approach. The manuscript has been significantly improved and I appreciate the efforts to address our critiques. There are a few remaining comments that the authors should consider before creating the final Version of Record.

The authors sought to develop technology for a transcriptomic analysis of B. burgdorferi directly from infected ticks. The methodology has exciting implications to better understand pathogen RNA profiles during specific infection timepoints, even beyond the Lyme spirochete. The authors demonstrate successful sequencing of the B. burgdorferi transcriptome from ticks and perform mass spectrometry to identify possible tick proteins that interact with B. burgdorferi. This technology and first dataset will be useful for the field. The study is limited in that no transcripts/proteins are followed-up by additional experiments and no biological interactions/infectious-processes are investigated.

Remaining critiques:

Experimental data regarding the sensitivity of this approach are missing. What is the limit of detection for this protocol? While the authors have stated that they were unable to sequence B. burgdorferi from unfed nymphs, the number of bacteria needed for antibody enrichment are not tested. The starting CFU in their infected nymphal ticks was also not reported (the authors only report reisolation data from 12 ticks). Page 18, line 458 the authors claim their approach "captured the vast majority" of Bb inside of the tick. Data are missing to demonstrate this. Understanding the limits of this approach will be critical for future applications, especially when using B. burgdorferi infected material with low bacterial burden.

The authors should clarify the term "genes" in the abstract and throughout the manuscript. I think they actually mean "open reading frames" or "annotated mRNAs".

More information regarding the efficacy of RNA-seq coverage is still warranted and lacking from the results, especially on page 6. The authors skip right to differential expression analysis without fully examining sequencing effectiveness. This is especially important given their development of a new technique. What was the numbers of detected genes for each sample? How is this affected by bacterial burden of the sample? What is the distribution of reads among tRNAs, mRNAs, UTRs, and sRNAs? How reproducible is the coverage for one gene across replicates? A few browser images of RNA-seq data (ex. of BAM files) across different genes would be useful to visualize the read coverage per gene.

Downregulated genes are largely ignored and should be commented on further.

Page 11, line 258-260: authors state Rpos, Rrp1, and RelBbu are the "three main Bb regulatory programs active in the tick." Yes, these three regulons have been well studied but there could be other uncharacterized regulatory programs. Please consider changing the language.

Reviewer #1 (Public Review):

This paper looks at nutrient-responsive Ca++ flux in islet cells of eight genetically diverse mouse strains. The investigators correlate Ca++ flux with insulin secretory capacity, demonstrating that calcium parameters in response to different nutrients are a better predictor of insulin secretory capacity than average calcium. They also correlate Ca++ flux with previously collected islet protein abundance followed by integration with human genome-wide association studies. This integration allows them to identify a sub-set of proteins that are both relevant to human islet function and that may play a causal role in regulating islet Ca++ oscillations. All data have been deposited in a searchable public database. There are many strengths to this paper. To my knowledge, this is the first work to assess the genetics of nutrient-responsive Ca++ flux in islets. Given the importance of Ca++ for beta cell insulin secretion, this work is of high importance. Investigators also use the founders of two powerful genetic mouse models: the diversity outbred and collaborative, opening up several avenues of future research into the genetics of Ca++ flux. By looking at multiple parameters of Ca++ flux, investigators are able to start to understand which parameters may be driving low or high insulin secretion. Integration with protein abundance and human GWAS has allowed identification of proteins with known roles in insulin secretory capacity, as well as several novel regulators, again opening up several avenues of future research. Finally, the public database is likely to be useful to multiple investigators interested in following up specific protein targets or in conducting future genetic studies. I found only minor weaknesses in this paper, mainly regarding clarity in certain areas. One specific area to be improved is Figure 4A, B where in addition to the heat maps, it would be useful to see regression plots that show the differences per sex and strain for the insulin secretion vs Ca++ parameters.

Reviewer #1 (Public Review):

The authors take on the challenge of defining the core nucleus for amyloid formation by polyglutamine tracts. This rests on the assertion that polyQ forms amyloid structures to the exclusion of all other forms of solids. Using their unique assay, deployed in yeast, the authors attempt to infer the size of the nucleus that templates amyloid formation by polyQ. Further, through a series of sequence titrations, all studied using a single type of assay, the authors converge on an assertion stating that a single polyQ molecule is the nucleus for amyloid formation, that 12-residues make up the core of the nucleus, that it takes ca. 60 Qs in a row to unmask this nucleation potential, and that polyQ amyloid formation belongs to the same universality class as self-poisoned crystallization, which is the hallmark of crystallization from polymer melts formed by large, high molecular weight synthetic polymers. Unfortunately, the authors have decided to lean in hard on their assertions without a critical assessment of whether their findings stand up to scrutiny. If their findings are truly an intrinsic property of polyQ molecules, then their findings should be reconstituted in vitro. Unfortunately, careful and rigorous experiments in vitro show that there is a threshold concentration for forming fibrillar solids. This threshold concentration depends on the flanking sequence context on temperature and on solution conditions. The existence of a threshold concentration defies the expectation of a monomer nucleus. The findings disagree with in vitro data presented by Crick et al., and ignored by the authors. Please see: https://doi.org/10.1073/pnas.1320626110. These reports present data from very different assays, the importance of which was underscored first by Regina Murphy and colleagues. The work of Crick et al., provides a detailed thermodynamic framework - see the SI Appendix. This framework dove tails with theory and simulations of Zhang and Muthukumar, which explains exactly how a system like polyQ might work (https://doi.org/10.1063/1.3050295). The picture one paints is radically different from what the authors converge upon. One is inclined to lean toward data that are gleaned using multiple methods in vitro because the test tube does not have all the confounding effects of a cellular milieu, especially when it comes to focusing on sequence-intrinsic conformational transitions of a protein. In addition to concerns about the limitations of the DAmFRET method, which based on the work of the authors in their collaborative paper by Posey et al., are being stretched to the limit, there is the real possibility that the cellular milieu, unique to the system being studied, is enabling transitions that are not necessarily intrinsic to the sequence alone. A nod in this direction is the work of Marc Diamond, which showed that having stabilized the amyloid form of Tau through coacervation, there is a large barrier that limits the loss of amyloid-like structure for Tau. There may well be something similar going on with the polyQ system. If the authors could show that their data are achievable in vitro without anything but physiological buffers one would have more confidence in a model that appears to contradict basic physical principles of how homopolymers self-assemble. Absent such additional evidence, numerous statements seem to be too strong. There are also several claims that are difficult to understand or appreciate.

Reviewer #1 (Public Review):

Secondary cell walls support vascular plants and conduct water throughout the plant body, but are also important resources for lignocellulosic feedstocks. Secondary cell wall synthesis is under complex transcriptional control, presumably because it must only be initiated after cell growth is complete. Here, the authors found that two Musashi-type RNA-binding proteins, MSIL2 and MSIL4 are redundantly required for secondary cell wall development in Arabidopsis. The plant phenotypes could be complemented by the wild-type version of either protein, but not by a MSIL4 version that carries mutations in the conserved RNA-binding domains, and the authors localized MSIL2 & 4 to stress granules, implicating the RNA-binding function of MSIL4 in the cell wall phenotype. Upon closer inspection, the secondary cell wall phenotypes included changes in vasculature morphology, and minor changes to lignin and hemicellulose (glucuronoxylan). While there were no changes to likely cell wall target genes in the transcriptome of msil2msil4 plants, proteomics experiments found glucuronoxylan biosynthesis components were upregulated in the mutants, and they detected an increase in substituted xylan via several methods. Finally, they documented MSIL4 binding to RNA encoding one of these targets, suggesting that MSIL2 and MSIL4 act to post-transcriptionally regulate glucuronoxylan modification. Altogether, this is a new mechanism by which cell wall composition could be regulated.

Overall, the manuscript is well-written, the data are generally high-quality, and the authors typically use several independent methods to support each claim. However, several important questions remain unanswered by this work in its current state and the model presented in Figure 7 is quite speculative. For example, the link between the striking plant phenotype and GXM misregulation is unclear since GXM overexpression doesn't alter plant phenotypes or lignin content (Yuan et al 2014 Plant Science), so misregulation of GXMs in msil2msil4 mutants clearly is not the whole story. It also remains to be determined why one particular secondary cell wall synthesis enzyme is regulated likely post-transcriptionally, while so much of the pathway is regulated at the transcriptional level. There are likely other targets for MSIL2- and MSIL4-mediated regulation since it seems that MSIL2 and MSIL4 are expressed in tissues that are not synthesizing secondary cell walls.

Reviewer #1 (Public Review):

This study presents a conceptual and analytical framework for tracking the impacts of human activities on freshwater ecosystems over time. It demonstrates the application of the framework to a 100-year record of community-level biodiversity, climate change, and chemical pollution from sediments cores of Lake Ring, Denmark. By reconstructing biodiversity using environmental DNA (eDNA) and pollutant inputs using mass spectrometry, the authors identify the taxonomic groups responding positively and negatively in different phases of the lake's environmental history. Furthermore, they identify the independent and additive effects of climate variables and pollutants on biodiversity throughout the 100-year record.

Strengths:

The advances in paired molecular and machine learning analyses are an important step towards a better understanding of 20th/21st century trajectories of biodiversity and pollution.

The finding that taxonomic groups so central to ecosystem assessment in Europe (i.e., diatoms) do not appear to respond to degradation or amelioration - providing at least a partial explanation as to why "ecological status" (as defined under the EU Water Framework Directive) has proved so difficult to improve.

The framework shows how both taxonomic and functional indicators can be used to better understand ecological degradation and recovery.

The identification of individual biocides and climate variables driving observed changes is a particular strength.

Limitations:

The analytical framework is not sufficiently explained in the main text.

The significance of findings in relation to functional changes is not clear. What are the consequences of enrichment of RNA transport or ribosome biogenesis pathways between pesticides and recovery stages, for example?

The impact of individual biocides and climate variables, and their additive effects, are assessed but there is no information offered on non-additive interactions (e.g., synergistic, antagonistic).

The level of confidence associated with results is not made explicit. The reader is given no information on the amount of variability involved in the observations, or the level of uncertainty associated with model estimates.

The major implications of the findings for regulatory ecological assessment are missed. Regulators may not be primarily interested in identifying past "ecosystem shifts". What they need are approaches which give greater confidence in monitoring outcomes by better reflecting the ecological impact of contemporary environmental change and ecosystem management. The real value of the work in this regard is that: (1) it shows that current approaches are inappropriate due to the relatively stable nature of the indicators used by regulators, despite large changes in pollutant inputs; (2) it presents some better alternatives, including both taxonomic and functional indicators; and (3) it provides a new reference (or baseline) for regulators by characterizing "semi-pristine" conditions.

Reviewer #1 (Public Review):

In this study, the authors use prospective sorting and microarray analyses, extended by single-cell RNA sequencing, in the neural stem cell niche of the subventricular zone (SVZ) to identify and refine a series of states along the continuum from quiescent neural stem cells to mature progeny. Of note, changes in the levels and subgroups of RNA splicing regulators are detailed across this continuum. Using in vitro proliferation and differentiation assays, coupled with in vivo engraftment of some prospectively sorted subsets, the authors argue that a stage they define as immature neuroblasts (iNBs) retain proliferative and multilineage differentiation capacity that is not seen in the mature neuroblast population, and is unexpected based on prior models for lineage progression in this system. This iNB stage is accompanied by a change in RNA splicing regulator expression, which is of interest due to the emerging roles for RNA processing and preferential translation within this niche.

These data complement several additional sc-RNAseq studies of this stem cell niche, and use a different, but similar, sorting strategy to isolate and profile subpopulations of stem/progenitor cells and neuroblast progeny. The claim that immature neuroblasts retain multipotency - the ability to generate glia and neurons - is surprising and somewhat controversial given that this has largely not been reported before under homeostatic conditions. Some factors to consider when interpreting these data are that the "immature neuroblast" populations are studied in some experiments using a transcriptional signature and a functional assay, namely the timing of reappearance of these cells after use of agents that kill rapidly dividing cells (in this case, radiation), leading to reconstitution of the lineage by previously quiescent stem cells. In a separate set of experiments, a tamoxifen-inducible labeling system is used in combination with cell-surface markers to prospectively isolate and study the differentiation potential of neuroblast populations that are assumed to be equivalent to those found in transcriptional experiments. It would be of interest in future to confirm that the exact sorted populations (using CD24/EGFR/DCX-CreERT2::CAG) have the same transcriptional profile as those studied in earlier experiments within the paper, and to confirm the purity of the sorted populations. Finally, while elegant use is made of engraftment of the sorted populations to study the differentiation and lineage potential of these immature neuroblasts, a remaining question is the relative abundance of each lineage (neurons/astrocytes/oligodendrocytes) produced by the engrafted cells - is production of glia rare, or common? Could this be due to factors such as alteration of lineage potential due to culture conditions, a disconnect between transcript expression and protein expression, or an incompletely purified starter population?

Overall, this manuscript presents an intriguing possible refinement of models for SVZ neurogenesis, and highlights the role of RNA splicing at specific stages in the lineage. It will be of interest to see if additional groups confirm these findings and whether multiplexed immunostaining, highly multiplexed flow cytometry, or other approaches focused at the proteomic level confirm and extend these findings, particularly given recent data in the developing brain that suggest transcript and protein levels are relatively poorly correlated in stem/progenitor populations.

A final point on terminology: "iNB", "A cells", and "D1/D2 cells" are all used in the manuscript to denote different stages along the continuum from TAP/C cells to mature neuroblasts; however, historically "D cells" refers to neuroblasts in the dentate gyrus, not those derived from the SVZ. In this case, the authors are exclusively studying SVZ-derived neuroblasts.

Reviewer #1 (Public Review):

This study by Park et al. describes an interesting approach to disentangle gene-environment pathways to cognitive development and psychotic-like experiences in children. They have used data from the ABCD study and have included PGS of EA and cognition, environmental exposure data, cognitive performance data and self-reported PLEs. Although the study has several strengths, including its large sample size, interesting approach and comprehensive statistical model, I have several concerns:

- The authors have included follow-up data from the ABCD Study. However, it is not very clear from the beginning that longitudinal paths are being explored. It would be very helpful if the authors would make their (analysis) approach clearer from the introduction. Now, they describe many different things, which makes the paper more difficult to read. It would be of great help to see the proposed path model in a Figure and refer to that in the Method.

- There is quite a lot of causal language in the paper, particularly in the Discussion. My advice would be to tone this down.

- It's a bit unclear to me why the authors chose the PEs phenotype as their outcome of interest. They mainly speak about child development and mental health in general in the Introduction, so why focus on PLE? Aren't genes and environments also relevant for other types of mental health problems? Relatedly, in the Discussion the authors seem to conflate PLEs with psychosis. There is a large body of literature highlighting the differences between PLEs and psychosis, and this should - in my opinion - be adjusted throughout the introduction and discussion.

- I feel that the limitation section is a bit brief, and can be developed further.

- I like that the assessment of CP and self-reports PEs is of good quality. However, I was wondering which 4 items from the parent-reported CBCL were used and how did they correlate with the child-reported PEs? And how was distress taken into account in the child self-reported PEs measurement? Which PEs measures were used?

- What was the correlation between CP and EA PGSs?

- Regarding the PGS: why focus on cognitive performance and EA? It should be made clearer from the introduction that EA is not only measuring cognitive ability, but is also a (genetic) marker of social factors/inequalities. I'm guessing this is one of the reasons why the EA PGS was so much more strongly correlated with PEs than the CP PGS. See the work bij Abdellaoui and the work by Nivard.

- Considering previous work on this topic, including analyses in the ABCD Study, I'm not surprised that the correlation was not very high. Therefore, I don't think it makes a whole of sense to adjust for the schizophrenia PGS in the sensitivity analyses, in other words, it's not really 'a more direct genetic predictor of PLEs'.

- How did the FDR correction for multiple testing affect the results?

Overall, I feel that this paper has the potential to present some very interesting findings. However, at the moment the paper misses direction and a clear focus. It would be a great improvement if the readers would be guided through the steps and approach, as I think the authors have undertaken important work and conducted relevant analyses.

Reviewer #1 (Public Review):

Park et al demonstrate that cells on either side of a BM-BM linkage strengthen their adhesion to that matrix using a positive feedback mechanism involving a discoidin domain receptor (DDR-2) and integrin (INA-1 + PAT-3). In response to its extracellular ligand (Collagen IV/EMB-9), DDR-2 is endocytosed and initiates signaling that in turn stabilizes integrin at the membrane. DDR-2 signaling operates via Ras/LET-60. This work's strength lies in its excellent in vivo imaging, especially of endogenously tagged proteins. For example, tagged DDR-2:mNG could be seen relocating from seam cell membranes to endosomes. I also think a second strength of this system is the ability to chart the development of BM-BM linkage over time based on the stages of worm larval development. This allows the authors to show DDR signaling is needed to establish linkage, rather than maintain it. It likely is relevant to many types of cells that use integrin to adhere to BM and left me pondering a number of interesting questions. For example: (1) Does DDR-2 activation require integrin? Perhaps integrin gets the process started and DDR-2 positively reinforces that (conversely is DDR-2 at the top of a linear pathway)? (2) In ddr-2(qy64) mutants, projections seem to form from the central portion of the utse cell. Does this reveal a second function for DDR-2, regulating perhaps the cytoskeleton? And (3) can you use the forward genetic tools available in C. elegans to find new genes connecting DDR-2 and integrin? The authors discuss these ideas in their response to the reviews, and I look forward to hearing about their future work on these questions.

I do see two areas where the manuscript could be improved. First, the authors rely on imprecise genetic methods to reach their conclusions (i.e. systemic RNAi, or expression of dominant negative constructs.) I think their conclusion would be stronger if they used tissue specific degradation to block ddr-2 function specifically in the utse or seam cells. Methods to do this are now regularly used in C. elegans and the authors have already developed the necessary tissue-specific promoters. Second, the manuscript is presented in the introduction as a study on formation and function of BM-BM linkage. However, their results actually demonstrate a mechanism by which cells adhere to BM. Since ddr-2 appears to function equally in both utse + seam cells (based on their dominant negative data), there are likely three layers of adhesion (utse-BM, BM-BM, BM-seam) and if any of those break down, you get a partially penetrant rupture phenotype. I pointed this out in my initial review, and after reading the revised manuscript, I do still feel the authors' introduction presents the paper as dealing with how basement membranes link together. But, I wonder if this might this be a question of terminology/language use? Maybe I am operating on a strict definition of linkage, and the authors use it more inclusively. What term(s) should we use to differentiate two basement membranes that are linked together, versus tissues that are connected through a basement membrane linkage? This is something that could be clarified in future publications.

These concerns do not undercut the significance of this work, which identifies an interesting mechanism cells use to strengthen adhesion during BM linkage formation. In fact, I am excited to read future papers detailing the connection between DDR-2 and integrin. But before undertaking those experiments the authors should be certain which cells require DDR-2 activity, and that should not be determined based solely on mis expression of a dominant negative.

Reviewer #1 (Public Review):

The authors examine signaling factors that differentiate parallel routes to activating phosphoinositide 3-kinase gamma (PI3Kγ). Dissecting the convergent pathways that control PI3Kγ activity is critical because PI3Kγ is a therapeutic target for treating inflammatory disease and cancer. Here, the authors employ a multipronged approach to reveal new aspects for how p84 and p101 pair with p110γ to activate the PI3Kγ heterodimer. The key instigator to this study is a previously reported inhibitory Nanobody, NB7. The hypothesized mechanism for NB7 allosteric inhibition of p84- p110γ was previously proposed to involve blockage of the Ras-binding domain. The authors revise the allosteric inhibition model based on meticulous profiling of various PI3Kγ complex interactions with NB7. In parallel, a cryo-EM-derived model of NB7 bound to the p110γ subunit convincingly reveals a Nanobody interaction pocket involving the helical domain and regulatory motifs of the kinase domain. This revelation shifts the focus to the helical domain, a known target of PKC phosphorylation. While the connections between NB7 interactions and the effects of PKC phosphorylation are sometimes tenuous, it could be argued that the Nanobody served as a tool to reveal the importance of the helical domain to p110γ regulation.

The sites of PKC-mediated p110γ helical domain phosphorylation were unexpectedly inaccessible in the available structural models. Nevertheless, mass spectrometry (MS)-based phosphorylation profiling indicates that PKC can phosphorylate the helical domain of p110γ and p84/p110γ (but not p101/p110γ) in vitro. The authors hypothesize that helical domain dynamics dictate susceptibility to PKC phosphorylation. To explore this notion, carefully executed, rigorous H/D exchange MS (HDX-MS) experiments were performed comparing phosphorylated vs. unphosphorylated p110γ. Notably, this design reveals more about the consequences of p110γ phosphorylation, rather than the mechanisms of p84/p101 promoting/resisting phosphorylation. Nevertheless, HDX-MS is very well suited to exploring secondary structure dynamics, and helical domain phosphorylation strikingly increases dynamics consistent with increased regional accessibility. The increased dynamics also nicely map to the pocket enveloped by the inhibitory NB7 Nanobody.

Ultimately, this study reveals an unexpected p110γ pocket that allows an engineered Nanobody to allosterically inhibit PI3Kγ complexes. The cryo-EM characterization of the interaction inspired an HDX-MS investigation of known sites of phosphorylation in the region. These insights could be linked to differences/convergences of p84 and p101 complex formation and activation of PI3Kγ, and future work may clarify these mechanisms further. The data presented herein will also be useful for broadening the target surface for future therapeutic developments. New allosteric connections between effector binding sites and post-translational modifications are always welcome.

Reviewer #1 (Public Review):

The authors sought to understand the neurocomputational mechanisms of how acute stress impacts human effortful prosocial behavior. Functional neuroimaging during an effort-based decision task and computational modeling were employed. Two major results are reported: 1) Compared to controls, participants who experienced acute stress were less willing to exert effort for others, with a more prominent effect for those who were more selfish; 2) More stressed participants exhibited an increase in activation in the dorsal anterior cingulate cortex and anterior insula that are critical for self-benefiting behaviour. The authors conclude that their findings have important insights into how acute stress affects prosociality and its associated neural mechanisms.

Overall, there are several strengths in this well-written manuscript. The experimental design along with acute stress induction procedures were well controlled, the data analyses were reasonable and informative, and the results from the computational modeling provide important insights (e.g., subjective values). Despite these strengths, there were some weaknesses regarding potential confounding factors in both the experimental design and methodological approach, including selective reporting of only some aspects of this complex dataset, and the interpretation of the observations. These detract from from the overall impact of the manuscript. In particular, the stress manipulation and pro-social task are both effortful, raising the possibility that stressed participants were more fatigued. Other concerns include the opportunity for social dynamics or cues during task administration, the baseline social value orientation (SVO) in each group, and the possibility of a different SVO in individuals with selfish tendencies. Finally, Figure 4 should specify whether the depicted prosocial choices include all five levels of effort.

Reviewer #1 (Public Review):

Wang et al., present a paper aiming to identify NALCN and TRPC6 channels as key mechanisms regulating VTA dopaminergic neuron spontaneous firing and investigating whether these mechanisms are disrupted in a chronic unpredictable stress model mouse.

Major strengths:

-This paper uses multiple approaches to investigate the role of NALCN and TRPC6 channels in VTA dopaminergic neurons.

Major weaknesses:

-The pharmacological tools used in this study are highly non-selective. Gd3+, used here to block NALCN is actually more commonly used to block TRP channels. 2-APB inhibits not only TRPC channels, but also TRPM and IP3 receptors while stimulating TRPV channels (Bon and Beech, 2013), while FFA actually stimulates TRPC6 channels while inhibiting other TRPCs (Foster et al., 2009).

Are the author's claims supported by the data?

-The multimodal approach including shRNA knockdown experiments alleviates much of the concern about the non-specific pharmacological agents. Therefore, the author's claim that NALCN is involved in VTA dopaminergic neuron pacemaking is well-supported.

-However, the claim that TRPC6 is the key TRPC channel in VTA spontaneous firing is somewhat, but not completely supported. As with NALCN above, the pharmacology alone is much too non-specific to support the claim that TRPC6 is the TRP channel responsible for pacemaking. However, unlike the NALCN condition, there is an issue with interpreting the shRNA knockdown experiments. The issue is that TRPC channels often form heteromers with TRPC channels of other types (Goel, Sinkins and Schilling, 2002; Strübing et al., 2003). Therefore, it is possible that knocking down TRPC6 is interfering with the normal function of another TRPC channel, such as TRPC7 or TRPC4.

-The claim that TRPC6 channels in the VTA are involved in the depressive-like symptoms of CMUS is supported.

- However, the connection between the mPFC-projecting VTA neurons, TRPC6 channels, and the chronic unpredictable stress model (CMUS) of depression is not well supported. In Figure 2, it appears that the mPFC-projecting VTA neurons have very low TRPC6 expression compared to VTA neurons projecting to other targets. However, in figure 6, the authors focus on the mPFC-projecting neurons in their CMUS model and show that it is these neurons that are no longer sensitive to pharmacological agents non-specifically blocking TRPC channels (2-APB, see above comment). Finally, in figure 7, the authors show that shRNA knockdown of TRPC6 channels (in all VTA dopaminergic neurons) results in depressive-like symptoms in CMUS mice. Due to the low expression of TRPC6 in mPFC-projecting VTA neurons, the author's claims of "broad and strong expression of TRPC6 channels across VTA DA neurons" is not fully supported. Because of the messy pharmacological tools used, it cannot be clamed that TRPC6 in the mPFC-projecting VTA neurons is altered after CMUS. And because the knockdown experiments are not specific to mPFC-projecting VTA neurons, it cannot be claimed that reducing TRPC6 in these specific neurons is causing depressive symptoms.

Impact:

It is valuable to compare pacemaking mechanisms in VTA and SNc neurons and this paper convincingly shows that NALCN contributes to VTA pacemaking, as it is known to contribute to SNc pacemaking. It also shows that TRPC6 channels in VTA dopamine neurons contribute to the depressive-like symptoms associated with CMUS.

It is important to note that the experiments presented in Figure 1 have all been previously performed in VTA dopaminergic neurons (Khaliq and Bean, 2010) including showing that low calcium increases VTA neuron spontaneous firing frequency and that replacement of sodium with NMDG hyperpolarizes the membrane potential.

Additional context:

-The authors explanation for the increase in firing frequency in 0 calcium conditions is that calcium-activated potassium channels would no longer be activated. However, there is a highly relevant finding that low calcium enhances the NALCN conductance through the calcium sensing receptor from Dejian Ren's lab (Lu et al., 2010) which is not cited in this paper. This increase in NALCN conductance with low calcium has been shown in SNc dopaminergic neurons (Philippart and Khaliq, 2018), and is likely a factor contributing to the low-calcium-mediated increase in spontaneous VTA neuron firing.

-One of the only demonstrations of the expression and physiological significance of TRPCs in VTA DA neurons was published by (Rasmus et al., 2011; Klipec et al., 2016) which are not cited in this paper. In their study, TRPC4 expression was detected in a uniformly distributed subset of VTA DA neurons, and TRPC4 KO rats showed decreased VTA DA neuron tonic firing and deficits in cocaine reward and social behaviors.

- Out of all seven TRPCs, TRPC5 is the only one reported to have basal/constitutive activity in heterologous expression systems (Schaefer et al., 2000; Jeon et al., 2012). Others TRPCs such as TRPC6 are typically activated by Gq-coupled GPCRs. Why would TRPC6 be spontaneously/constitutively active in VTA DA neurons?

-A new paper from the group of Myoung Kyu Park (Hahn et al., 2023) shows in great detail the interactions between NALCN and TRPC3 channels in pacemaking of SNc DA neurons.

References

Bon, R.S. and Beech, D.J. (2013) 'In pursuit of small molecule chemistry for calcium-permeable non-selective TRPC channels -- mirage or pot of gold?', British Journal of Pharmacology, 170(3), pp. 459-474. Available at: https://doi.org/10.1111/bph.12274.

Foster, R.R. et al. (2009) 'Flufenamic acid is a tool for investigating TRPC6-mediated calcium signalling in human conditionally immortalised podocytes and HEK293 cells', Cell Calcium, 45(4), pp. 384-390. Available at: https://doi.org/10.1016/j.ceca.2009.01.003.

Goel, M., Sinkins, W.G. and Schilling, W.P. (2002) 'Selective association of TRPC channel subunits in rat brain synaptosomes', The Journal of Biological Chemistry, 277(50), pp. 48303-48310. Available at: https://doi.org/10.1074/jbc.M207882200.

Hahn, S. et al. (2023) 'Proximal dendritic localization of NALCN channels underlies tonic and burst firing in nigral dopaminergic neurons', The Journal of Physiology, 601(1), pp. 171-193. Available at: https://doi.org/10.1113/JP283716.

Jeon, J.-P. et al. (2012) 'Selective Gαi subunits as novel direct activators of transient receptor potential canonical (TRPC)4 and TRPC5 channels', The Journal of Biological Chemistry, 287(21), pp. 17029-17039. Available at: https://doi.org/10.1074/jbc.M111.326553.

Khaliq, Z.M. and Bean, B.P. (2010) 'Pacemaking in dopaminergic ventral tegmental area neurons: depolarizing drive from background and voltage-dependent sodium conductances', The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 30(21), pp. 7401-7413. Available at: https://doi.org/10.1523/JNEUROSCI.0143-10.2010.

Klipec, W.D. et al. (2016) 'Loss of the trpc4 gene is associated with a reduction in cocaine self-administration and reduced spontaneous ventral tegmental area dopamine neuronal activity, without deficits in learning for natural rewards', Behavioural Brain Research, 306, pp. 117-127. Available at: https://doi.org/10.1016/j.bbr.2016.03.027.

Lu, B. et al. (2010) 'Extracellular calcium controls background current and neuronal excitability via an UNC79-UNC80-NALCN cation channel complex', Neuron, 68(3), pp. 488-499. Available at: https://doi.org/10.1016/j.neuron.2010.09.014.

Philippart, F. and Khaliq, Z.M. (2018) 'Gi/o protein-coupled receptors in dopamine neurons inhibit the sodium leak channel NALCN', eLife, 7. Available at: https://doi.org/10.7554/eLife.40984.

Rasmus, K. et al. (2011) 'Sociability is decreased following deletion of the trpc4 gene', Nature Precedings, pp. 1-1. Available at: https://doi.org/10.1038/npre.2011.6367.1.

Schaefer, M. et al. (2000) 'Receptor-mediated regulation of the nonselective cation channels TRPC4 and TRPC5', The Journal of Biological Chemistry, 275(23), pp. 17517-17526. Available at: https://doi.org/10.1074/jbc.275.23.17517.

Strübing, C. et al. (2003) 'Formation of novel TRPC channels by complex subunit interactions in embryonic brain', The Journal of Biological Chemistry, 278(40), pp. 39014-39019. Available at: https://doi.org/10.1074/jbc.M306705200.

Reviewer #1 (Public Review):

Li et al report that upon traumatic brain injury (TBI), Pvr signalling in astrocytes activates the JNK pathway and up-regulates the expression of the well-known JNK target MMP1. The FACS sort astrocytes, and carry out RNAseq analysis, which identifies pvr as well as genes of the JNK pathway as particularly up-regulated after TBI. They use conventional genetics loss of function, gain of function and epistasis analysis with and without TBI to verify the involvement of the Pvr-JNK-MMP1 signalling pathway.

The strengths are that multiple experiments are used to demonstrate that TBI in their hands damaged the BBB, induced apoptosis and increased MMP1 levels. The RNAseq analysis on FACS sorted astrocytes is nice and will be valuable to scientists beyond the confines of this paper. The functional genetic analysis is conventional, yet sound, and supports claims of JNK and MMP1 functioning downstream of Pvr in the TBI context.

However, the weaknesses are that novelty and insight are both rather limited, some data are incomplete and other data do not support some claims. Some approaches used lacked resolution and some experiments lacked rigour. The authors may wish to improve some of their data as this would make their case more convincing. Alternatively, they should remove unsupported claims.

Novelty and insight:<br /> Others had previously published that both JNK signalling and MMP1 were activated upon injury, in multiple contexts (as well as the articles cited by the authors, they should also see Losada-Perez et al 2021). That Pvr can regulate JNK signalling was also known (Ishimaru et al 2004). And it was also known that astrocytes can respond to injury by proliferating, both in larval ventral nerve cords and adult brains (Kato et al 2011; Losada-Perez et al 2016; Harrison et al 2021; Simoes et al 2022). The authors argue that the novelty of the work is the investigation of the response of astrocytes to TBI. However, this is of somewhat limited scope. The authors mention that Mmp1 regulates tissue remodelling, the inflammatory process and cancer. Exploring these functions further would have been an interesting addition, but the authors do not investigate what consequences the up-regulation of Mmp1 after injury has in repair or regeneration processes.

Incomplete or unconvincing data:<br /> The authors failed to detect PCNA-GFP and pH3 in brains after TBI and conclude that that TBI does not induce astrocyte proliferation. However, this is a surprising claim, as it would be rather different from all previous prevalent observations of cell proliferation induced by injury. Cell proliferation can be notoriously difficult to detect (ie due to timing and sample size), thus instead this raises doubts on the experimental protocol or execution.<br /> Others have previously reported: cells in S- phase using PCNA-GFP and other reporters (eg BrdU, EdU, FUCCI) in the intact adult brain (Kato et al 2009; Foo et al 2017; Li et al 2020; Fernandez-Hernandez et al 2013; Simoes et al 2022); that injury to the adult brain and VNC induces cell proliferation that can be detected with cell proliferation markers like BrdU, Myc, FUCCI and the mitotic marker pH3 (Kato et al 2009; Fernandez-Hernandez et al 2013; Losada-Perez et al 2021; Simoes et al 2022); and that injury to the brain and CNS induces glial proliferation in adult and larval brains/CNS, specifically of astrocytes (Kato et al 2011; Losada-Perez et al 2016; Fernandez-Hernandez et al 2013; Simoes et al 2022). Thus, the fact that they did not observe PCNAGFP+ cells in control, intact adult brains nor after TBI could suggest that they had technical, experimental difficulties. Detecting mitotic cells with anti-pH3 is difficult because M phase is very brief, but others have succeeded (Simoes et al 2022). Given that in all previous reports mentioned above cells were seen to proliferate after injury in the CNS, it would be rather surprising if no cell proliferation occurred after TBI. Resolving this conflicting result is important, as it could imply that TBI induces very different cellular responses from various other lesions or injury types. It is conceivably not impossible, but the most parsimonious start point would be that multiple injury types could cause equivalent responses in cells. Thus, the authors ought to consider whether technical or experimental design problems affected their experimental outcome instead.

Other claims not supported by data:<br /> (1) astrocyte hypertrophy, as the tools used do not have the resolution to support this claim;

(2) localisation of anti-Pvr to specific cells, as the images show uniform signal or background instead;

(3) astrocytes do not engulf cell debris after TBI, as the tools and images do not have the resolution to make this claim.

The authors could improve these data with alternative experiments to maintain the claims; alternatively, these unsupported claims should be removed.

Statistical analysis:<br /> The statistical analysis needs revising as it is wrong in multiple places. Revising the statistics will also require revision of the validity of the claims and adjusting interpretations accordingly.

Altogether, this is an interesting and valuable addition to the repertoire of articles investigating neuron-glia communication and glial responses to injury in the Drosophila central nervous system (CNS). It is good and important to see this research area in Drosophila grow. This community together is building a compelling case for using Drosophila and its unparalleled powerful genetics to investigate nervous system injury, regeneration and repair, with important implications. Thus, this paper will be of interest to scientists investigating injury responses in the CNS using Drosophila, other model organisms (eg mice, fish) and humans.

Reviewer #1 (Public Review):

Jackson and Giacomassi et al. investigated the impact of repeated topical application of the TLR-7/8 agonist R848, mimicking single-stranded RNA viral infection, on circulating monocytes. Interestingly in this murine model of skin inflammation, they find that there is a striking increase in vascular patrolling (Ly6Clow) monocytes in the blood. In the majority of inflammatory settings so far described, it is the classical (Ly6Chi) monocyte population that is augmented. They found that this Ly6Clow monocyte expansion occurred in response to stimulation by R848 at epithelial barrier surfaces (skin and gut) and not following systemic administration of R848. Of note, the Ly6Clow increase was not dependent on type I or type II IFNs or CCR2, all factors that are important for Ly6Chi monocyte expansion in response to life-threatening infections, such as Toxoplasma gondii. Positive factors driving Ly6Clow augmentation are not identified. Alterations to circulating monocytes may have implications for secondary infection as R848-treated animals were less susceptible to flu infection. This research furthers our understanding of how tissues and organs have distinct mechanisms of communication in response to inflammatory and infectious stimuli and the implications this can have on circulating immune populations.

The conclusions of this paper are generally well supported by the data presented, however, some aspects of the study need to be clarified or extended. Additionally, some of the findings could be better discussed in the context of the current literature.

1) CSF-1 availability is described, initially by Yona et al. (DOI: 10.1016/j.immuni.2012.12.001), as an important factor extending the half-life of Ly6Clow monocytes in circulation. Given the expansion of Ly6Clow monocytes and their upregulation of CD115 in circulation, it would have been relevant to measure CSF-1 to assess whether this may be a candidate factor for the phenotype observed.

2) The conclusion that the altered monocyte compartment enhances protection against secondary infection is underdeveloped. The key experiment presented involves treating animals with R848 and demonstrating that they have an altered response to flu infection. This approach does not specifically assess the importance of monocytes. From these studies, it is only possible to conclude there is an association between monocyte alterations and secondary infection.

Reviewer #1 (Public Review):

This study examines the effects of Ca2+ and NHE1 peptide binding on the conformation of CHP3, one of three related calcineurin-homologous proteins. One question that is addressed is whether Ca2+ binding triggers membrane association of the myristoyl group, a so-called "Ca2+-myristoyl switch". This is convincingly demonstrated to not be the case by the experiment in Figure 6B: unlike myristoylated recoverin, mCHP3 does not show enhanced association with liposomes. In the presence of a target peptide, however, myristoylation enhances membrane association. Curiously, this interaction is not Ca2+ dependent, but the membrane association of the non-myristoylated CHP3 is Ca2+-dependent.

My concerns with this study relate to physiological relevance. First, it is unclear if Ca2+ binding has a regulatory function in any of the CHP proteins. The authors state that CHP1 and CHP2 have Ca2+ binding affinities <100 nM, so these proteins are likely saturated with Ca2+ under all physiological conditions. On the other hand, CHP3 binds Ca2+ with a Kd of 8 micromolar (in the presence of physiological concentrations of Mg2+) so it will be largely unbound under most normal cellular concentrations of Ca2+ which are in the submicromolar range. Free Ca2+ rarely reaches 1 micromolar under non-pathological concentrations, and if it does, the fraction of CHP3 bound to Ca2+ should be estimated for context. Given these caveats, I am not convinced that experiments done with millimolar concentrations of Ca2+ (e.g., Figures 2, 3, 6) are physiologically informative.

Reviewer #1 (Public Review):

This is the most complete genomic overview of the epidemiology of Salmonella enterica serovar Typhi including close to 13,000 genmoes from multiple countries, clearly demonstrating the geographical differences in molecular epidemiology and antibiotic resistance traits. This database could serve as the global reference for the future with constant addition of new information.

This is a descriptive study, not providing fundamentally new mechanistic insights of the disease, but providing an overview of the global epidemiology of this bacterium.<br /> Open-ended questions remain the generalizability of the findings, which is linked to the completeness of the surveillance systems, as well as the linkage of genotypes to clinical disease presentation (severity) and of linkage of local antibiotic use and the prevalence of the different resistance traits.

Publication of these data will be very helpful for all those interested in the molecular epidemiology of Salmonella and may stimulate not-yet participating institutes to add information for future analyses. It may also stimulate investigators to use the data for deriving more insights in clinical disease presentations, associations with antibiotic use and input for mathematical modelling.

The (lenghty) introduction is textbook epidemiology of the emergence of antimicrobial resistance in Typhi.

Reviewer #1 (Public Review):

The manuscript by Mau et al. describes a sophisticated method to follow enhancer activity in both live embryos and fixed embryos in Tribolium. The authors identified putative enhancers via comparative ATAC-seq of embryos divided into different regions and at different developmental time points. As an experimental piece of work, this is excellent. However, the framing and presentation in this manuscript would need to be improved to avoid misrepresentation of existing ideas and over-interpretation of results. The manuscript would require significant re-writing. This can be done without additional data or analyses, but simply more careful writing.

The Introduction starts by setting up a straw-man argument, claiming that the assumption is that gene expression is set up as stable expression domains that undergo little or no subsequent change. I don't think that any current developmental biologist thinks this is true. The references used to support this claim are from the 1990s up to the early 2000s. There are numerous examples since then that show that developmental gene expression is dynamic as a rule.

The Introduction then continues as a rather detailed review of enhancers, Tribolium methodology, tools for identifying enhancers, and more. The Introduction cites 99 references, which seems excessive for what is essentially an experimental paper. Significant parts of the Introduction can be trimmed or removed. There is no need to mention all the tools available for Tribolium if they are not used in the described experiments. A thorough analysis of the advantages and disadvantages of different modes of ATAC-seq is also beyond the scope of the Introduction. The authors should explain why they chose the tools they chose without excessive background. Having said that, the Introduction actually overlooks a lot of significant work that is relevant to the subject of the paper. Specifically, the authors completely ignore all of the work on development in hemimetabolous insects such as Oncopeltus and Gryllus - the omission is glaring. There has been a lot of relevant work on dynamic gene expression patterns coming out of these species.

The experimental setup involves cutting embryos into three sections at two time points. The results then discuss differences in "space" and "time" but there is no discussion of the embryological meaning of these terms. What is happening at the two time points from a developmental perspective? What is the difference between the three sections? There is a lot of relevant development going on at these stages and important regional differences, which have been well-studied in Tribolium and in other insects but are not even mentioned.

In the preliminary results of the ATAC-seq analysis, it is clear that there are significant differences between the sections, which should come as no surprise, but fairly minor differences between the same section at the two time points. This could be because the two time points are pretty close together at a stage when there is a lot of repetitive patterning going on. A possible interpretation, which the authors don't mention because it goes against their main thesis, is that maybe most of the processes that are taking place at this stage are not dynamic enough to show up at the temporal resolution they have applied. This is worth at least a mention.

The authors link each accessible site to the nearest gene when looking at putative enhancer function. This is a risky assumption since there are many examples of enhancer sites that are far upstream or downstream of the target gene and often closer to an unrelated gene than to the target gene. The authors should at least acknowledge this problem with their functional annotation.

In the Discussion, the authors claim that contrary to how it may seem, the question they are addressing is not a "fringe problem". Once again, I think this is a straw man. No active researcher thinks that the question of dynamic regulation of gene expression during development is a fringe problem. On the contrary, most researchers will accept that this is one of the most interesting and important questions in current developmental biology.<br /> Perhaps the most significant problem with the manuscript is that it is all built around the premise of enhancer switching between dynamic enhancers and static enhancers. The authors find one site that is consistent with their prediction for a dynamic enhancer and one site - regulating a different gene - that is consistent with their prediction for a static enhancer and claim that they have provided support for their model. I think this claim is grossly exaggerated. They present data that can be seen as consistent with their model but are a long way from providing evidence for it.<br /> Like the Introduction, the Discussion includes long paragraphs (lines 450-480) that are more suitable for a review/hypothesis paper. The data presented in this manuscript has little relevance to the question of kinematic vs. trigger waves, and therefore there is no real reason for the question to be discussed here.

Reviewer #1 (Public Review):

This is a very interesting and timely manuscript investigating the roles of root-emitted secondary metabolites in mediating plant-soil feedback in a realistic and agricultural context (maize - wheat rotation). I find this article to be an important contribution to the field as the roles played by soil chemical legacies in mediating plant-soil feedbacks have been largely overlooked so far, particularly in the field. I found this manuscript to be extremely well-written and clear. I was impressed by the number of response variables measured by the authors to characterise how wheat plants responded to the soil legacies created by different maize genotypes.<br /> The article presents the results of a plant-soil feedback experiment in which two maize genotypes (wild type or benzoxazinoid-deficient bx1 mutant plant) conditioned field soil for one growing season. Monocultures of each genotype occupied alternate strips in the field. At the end of this conditioning phase, the authors analysed benzoxazinoids in the soil and found that the soil conditioned by WT maize was characterized by greater concentrations of several benzoxazinoids. In fact, most benzoxazinoids were below the detection limit for soil conditioned by bx1 mutant plants. These differences in soil chemical legacies were associated with differences in bacterial and fungal communities in the roots and rhizosphere of maize. Soon after the maize harvest, monocultures of three wheat varieties were grown in soil that was conditioned by either WT or bx1 maize plants. This factorial design allowed the authors to study the response of different wheat varieties to soil legacies created by maize genotypes that differ in their ability to produce and release benzoxazinoids into the soil. Although root and rhizosphere microbial communities were mainly driven by wheat genotype (and not by maize soil conditioning), soil conditioning effects on benzoxazinoid concentrations were still visible at the end of the feedback phase, but only for specific compounds (e.g. AMPO). In comparison to wheat grown on bx1-conditioned soil, the authors found that wheat plants grown on benzoxazinoid-conditioned soil had better emergence and were taller and more productive. In addition, benzoxazinoid soil conditioning reduced infestation by the cereal leaf beetle Oulema melanopus (particularly in one wheat variety) but did not affect weed pressure. The authors also found that wheat grown on benzoxazinoid-conditioned soil had more reproductive tillers, which led to greater grain yield (+4-5%). Grain quality, however, was not affected by maize soil conditioning.

I appreciated that the authors carefully interpreted the results of their experiment, although data analysis could be improved to take repeated measures within a plot into account. Overall, this is a compelling study, with rigorous and numerous measurements and state-of-the-art methods in plant/soil ecology. This study is unique in that it demonstrates the important role that soil chemical legacies can play in mediating plant-soil interactions and influencing the fitness of the following crop in a realistic agricultural setting. Therefore, I believe that this work will be of broad interest to plant and soil ecologists, as well as to agronomists.

Reviewer #1 (Public Review):

In this work Indurthy and Auerbach investigate the fundamental concept of how the energy of agonist binding is converted into the energy of the conformational change that opens the pore of the nicotinic acetylcholine receptor (nAChR). The conclusions are based on a very large pool of experimental data that are interpreted with great mechanistic insight.

Specifically, the authors define "efficacy" (eta) of a ligand as the fractional change in binding free energy between the open and the closed states of the channel. They construct a log-log scatter plot of efficacy vs. affinity which represents 23 different agonists acting on the WT receptor, plus a subset of the same agonists acting on various nAChR mutants. They go on to show that these largely scattered dots can be partitioned into 5 distinct clusters ("eta-classes") within which the dots are linearly arranged. They interpret these clusters in terms of a mechanistic gating model (the "catch&hold LFER model"), and suggest that a different model accounts for each different eta-class. Put in simple terms, the interpretation is that 5 different subtypes of gating isomerization exist for the nAChR, the choice among which depends on the agonist used.

These types of study are necessary to advance conceptual understanding in biophysics. I have some reservations regarding the mechanistic interpretation of the data set and the uniqueness of the proposed model.

1. One concern regards the clustering of the data sets in Fig. 5 into exactly 5 eta-classes. First, two clusters contain only two data points each. Second, the proposed "catch&hold LFER model" (Fig. 2) does not predict the existence of a discrete number of such eta-classes. How strong is the evidence that there are exactly 5 classes as opposed to a continuum of possible eta values.

2. The authors do not discuss the uniqueness of the proposed model. In fact, it seems to me that the existence of eta-classes might be explained just as well by an alternative model which assumes a single gating mechanism for the receptor, but distinct patterns of ligand-protein interactions for the different agonists. The pore opening-associated increase in agonist affinity is typically caused by a tightening of the substrate binding site (often called clamshell closure) which brings further protein side chains into the vicinity of the ligand, thereby allowing further ligand-protein interactions to form (or further strengthening interactions that exist also in the closed-pore state). Thus, at a first approximation, the ratio between binding free energies in the open- and closed-pore states reflects the ratio of the numbers (and strengths) of ligand-protein bonds in those two states.

As an illustration, consider the following simplified model for a channel and a given ligand. In the open-pore state the number of ligand-protein interactions is n(o), and all those interactions are comparably strong. Out of those interactions only a subset is formed in the closed-pore state, their number is n(c) (where n(c)<br /> The maximal possible values of n(c) and n(o) are determined by the number and spatial arrangement of protein chemical groups that surround the substrate binding site. On the other hand, depending on the number and arrangement of matching chemical groups on the ligand, different ligands will be able to "exploit" different subsets of these possible ligand-protein interactions, resulting in different values of eta. Furthermore, ligands for which the absolute values of n(o) are different, but the ratio n(c)/n(o) is similar, will form apparent "eta-classes", i.e., will be arranged on a "eta-plot" along a straight line. (See attached image file for a graphical representation of the model.)

This model would suggest that there is a single gating mechanism (i.e., the actual protein conformational change is similar regardless of which agonist is bound), but the relative stabilities of the ligand-bound closed and open states are agonist-dependent. Wouldn't such a mechanism equally well explain all the data shown? The authors should either acknowledge this possibility or discuss available structural or functional evidence to exclude it.

Reviewer #1 (Public Review):

This manuscript will interest cognitive scientists, neuroimaging researchers, and neuroscientists interested in the systems-level organization of brain activity. The authors describe four brain states that are present across a wide range of cognitive tasks and determine that the relative distribution of the brain states shows both commonalities and differences across task conditions.

The authors characterized the low-dimensional latent space that has been shown to capture the major features of intrinsic brain activity using four states obtained with a Hidden Markov Model. They related the four states to previously-described functional gradients in the brain and examined the relative contribution of each state under different cognitive conditions. They showed that states related to the measured behavior for each condition differed, but that a common state appears to reflect disengagement across conditions. The authors bring together a state-of-the-art analysis of systems-level brain dynamics and cognitive neuroscience, bridging a gap that has long needed to be bridged.

The strongest aspect of the study is its rigor. The authors use appropriate null models and examine multiple datasets (not used in the original analysis) to demonstrate that their findings replicate. Their thorough analysis convincingly supports their assertion that common states are present across a variety of conditions, but that different states may predict behavioural measures for different conditions. However, the authors could have better situated their work within the existing literature. It is not that a more exhaustive literature review is needed-it is that some of their results are unsurprising given the work reported in other manuscripts; some of their work reinforces or is reinforced by prior studies; and some of their work is not compared to similar findings obtained with other analysis approaches. While space is not unlimited, some of these gaps are important enough that they are worth addressing:

1. The authors' own prior work on functional connectivity signatures of attention is not discussed in comparison to the latest work. Neither is work from other groups showing signatures of arousal that change over time, particularly in resting state scans. Attention and arousal are not the same things, but they are intertwined, and both have been linked to large-scale changes in brain activity that should be captured in the HMM latent states. The authors should discuss how the current work fits with existing studies.<br /> 2. The 'base state' has been described in a number of prior papers (for one early example, see https://pubmed.ncbi.nlm.nih.gov/27008543). The idea that it might serve as a hub or intermediary for other states has been raised in other studies, and discussion of the similarity or differences between those studies and this one would provide better context for the interpretation of the current work. One of the intriguing findings of the current study is that the incidence of this base state increases during sitcom watching, the strongest evidence to date is that it has a cognitive role and is not merely a configuration of activity that the brain must pass through when making a transition.<br /> 3. The link between latent states and functional connectivity gradients should be considered in the context of prior work showing that the spatiotemporal patterns of intrinsic activity that account for most of the structure in resting state fMRI also sweep across functional connectivity gradients (https://pubmed.ncbi.nlm.nih.gov/33549755/ ). In fact, the spatiotemporal dynamics may give rise to the functional connectivity gradients (https://pubmed.ncbi.nlm.nih.gov/35902649/ ). HMM states bear a marked resemblance to the high-activity phases of these patterns and are likely to be closely linked to them. The spatiotemporal patterns are typically obtained during rest, but they have been reported during task performance (https://pubmed.ncbi.nlm.nih.gov/30753928/ ) which further suggests a link to the current work. Similar patterns have been observed in anesthetized animals, which also reinforces the conclusion of the current work that the states are fundamental aspects of the brain's functional organization.

Reviewer #1 (Public Review):

The goal of this study is to identify transcription factors that mediate stem cell transitions during differentiation. To achieve this, the authors examine the type II Drosophila neuroblast lineage, using single-cell RNA sequencing to examine all cell types in the type II lineage. There are known patterns of expression for neurons in this lineage, so they can identify clusters in their data set that are in the developmental state of transitioning from neuroblast to immature intermediate neuronal progenitor. They have outlined a set of expression criteria for transcription factors that are candidates for fine-tuning stem cell fate. They find that an isoform of Fruitless, called FruC, is a candidate transcription factor. Using microscopy and several genetic perturbation conditions the authors find that FruC is expressed in neuroblasts and can alter the number of cells in the lineage. To determine the mechanism that FruC uses to influence stemness the authors examine genomic occupancy of FruC, changes in histone modifications in FruC loss-of-function studies, and examination of DNA occupancy of proteins that function in chromatin modification. The authors argue that FruC functions to promote low-level H3K27me3 enrichment to maintain stemness based on comparisons across these data sets. The identification of transcription factors and the mechanisms used to maintain or differentiate stem cells is an important goal and is still a fundamental question in biology. The Drosophila model is poised for this type of analysis, given the knowledge of gene expression across cell fate that the authors use in this study.

Comments the authors should address:<br /> This is a valuable study that relies on several state-of-the-art genomic data sets to examine the mechanism that drives stemness. However, the authors should be using statical approaches to support their major conclusion regarding FruC and the role of H3K27me3. The study presents peak data in genome browser tracks of a handful of loci in the Notch pathway that show the pattern of reduced HK3K27me3 and not the other modifications they examine. However, it is not clear if the majority of FruC target genes in the genomic analyses have this pattern, though they argue they do. The major conclusion that FruC promotes a stem cell fate is based on the overlap between the list of genes they identify bound by FruC and the lists of genes that have changes in histone modifications (H3K27ac, H3K4me3, and H3K27me3). The limited use of statistical approaches to draw these conclusions is a weakness of the study. The authors do not use statistics to find changes in chromatin modification at loci, instead relying on 2-fold change calculations. Furthermore, the authors don't indicate if the genes with altered histone modification/binding peaks are significantly enriched (or not) with FruC targets, with no quantitative assessments of these data. The data in Figures 5,6 and S4 should have statistics/quantification to support the major conclusions of their study that FruC targets differ in H3K27me3, but not H3K27ac, and H3K4me3.

Reviewer #1 (Public Review):

This study optimized a protocol for analyzing microplastics (MPs) in bovine and human follicular fluid. The authors identified the most common plastic polymers in the follicular fluid and assessed the impact of polystyrene beads on bovine oocyte maturation based on the concentration of MPs in follicular fluid. The authors found a decrease in maturation rate in the presence of polystyrene beads and conducted proteomic analysis of oocytes treated with and without MPs, revealing protein alterations.

Strengths:<br /> • The optimization of the protocol for analyzing MPs in follicular fluid, which is important for future research in this area.<br /> • Investigating the effects of MPs on oocyte maturation and proteomic profiles is significant.

Weaknesses:<br /> • The effects of polystyrene beads on oocyte maturation and proteomic profiles are not directly demonstrated, and insufficient analysis is performed to support the claims made in the manuscript.<br /> • The use of polystyrene beads does not fully mimic the concentration and interaction of MPs in follicular fluid, which warrants careful interpretation and discussion.<br /> • A major weakness is the lack of mechanism. Determining the cause of meiotic arrest (decreased maturation rate) would be needed to strengthen the paper. Are spindle morphology, chromosome morphology/alignment and/or spindle assembly checkpoint mechanism perturbed in MPs-treated oocytes?<br /> • Functional assays to validate one or more of the pathways suggested by the proteomic analysis would be necessary to strengthen the paper.<br /> • The analysis of broken zona pellucida is not sufficiently convincing. Definitely the breakage of zona pellucida is most likely a result of oocyte denudation. However, this may indicate increased fragility of polystyrene beads-treated oocytes. Investigating cytoskeletal components in oocytes treated with or without polystyrene beads would strengthen this paper.<br /> • The percentage of degenerated oocytes in control group is abnormally high which raises concern that the oocytes are not healthy.<br /> • The small font size of the figures (such as Fig. 1C) affects the quality of the manuscript.<br /> • Finally, the authors should cite previous publications on the effects of MPs on female reproduction, as this is not a novel area of research, despite the use of different concentrations. For example, "Polystyrene microplastics lead to pyroptosis and apoptosis of ovarian granulosa cells via NLRP3/Caspase-1 signaling pathway in rats (DOI: 10.1016/j.ecoenv.2021.112012)".

Reviewer #1 (Public Review):

The authors performed a meta-analysis of GC concentrations and metabolic rates in birds and mammals. They found close associations for all studies showing a positive association between these two traits. As GCs have been viewed with close links to "stress," authors suggest that this overlooks the importance of metabolism and perhaps GC variation does not relate to "stress" per se but an increase in metabolism instead.

This is an important meta-analysis, as most researchers acknowledge the link between GCs and metabolism, metabolism is often overlooked in studies. The field of conservation physiology is especially focused on GCs being a "stress" hormone, which overlooks the importance of GCs in mediating energy balance, i.e., an animal that has high GC concentrations may not be doing that poorly compared to an animal with low GC concentrations, it might just be expending more energy, e.g., caring for young. The results, with overwhelming directionality and strong effect sizes, support the link for a positive association with these two variables.

My main concern lies in that most of the studies come from a few labs, therefore there may be limited data to test this relationship. I would include lab as a random effect to see how strong this effect might be. Furthermore, I would like to see a test of the directionality of the two variables. Authors suggest that changes in metabolism affect GC levels but likely changes in GC levels would affect metabolism. Why not look into studies that have altered GC levels experimentally and see the effect on metabolism? Based on the close link, authors suggest that GCs may not play a role outside of "stress" beyond the stressor's effect on metabolic rate. However, if they were to investigate manipulations of GCs on metabolic rate, the link may or may not be there, which would be interesting to look at. I firmly believe that GCs are tightly linked to metabolism; however, I also think that GCs have a range of effects outside of metabolism as well, depending on the course and strength of the stressor.

This work helps in the thinking that GCs are not the same as a "stress" hormone or labelling hormones with only one function. As hormones are naturally pleiotropic, the view of any one hormone being X is overly simplistic.

Reviewer #1 (Public Review):

The paper titled 'A dual function of the IDA peptide in regulating cell separation and modulating plant immunity at the molecular level' by Olsson Lalun et al., 2023 aims to understand how IDA-HAE/HSL2 signalling modulates immunity, a pathway that has previously been implicated in development. This is a timely question to address as conflicting reports exist within the field. IDL6/7 have previously been shown to negatively regulate immune signalling, disease resistance and stress responses in leaf tissue, however IDA has been shown to positively regulate immunity through the shedding of infected tissues. Moreover, recently the related receptor NUT/HSL3 has been shown to positively regulate immune signalling and disease resistance. This work has the potential to bring clarity to this field, however the manuscript requires some additional work to address these questions. This is especially the case as it contracts some previous work with IDL peptides which are perceived by the same receptor complexes.

Can IDA induce pathogen resistance? Does the infiltration of IDA into leaf tissue enhance or reduce pathogen growth? Previously it has been shown that IDL6 makes plants more susceptible. Is this also true for IDA? Currently cytoplasmic calcium influx and apoplastic ROS as overinterpreted as immune responses - these can also be induced by many developmental cue e.g. CLE40 induced calcium transients. Whilst gene expression is more specific is also true that treatment with synthetic peptides, which are recognised by LRR-RKs, can induce immune gene expression, especially in the short term, even when that is not there in vivo function e.g. doi.org/10.15252/embj.2019103894.

This paper shows that receptors other than hae/hsl2 are genetically required to induce defense gene expression, it would have been interesting to see what phenotype would be associated with higher order mutants of closely related haesa/haesa-like receptors. Indeed recently HSL1 has been shown to function as a receptor for IDA/IDL peptides. Could the triple mutant suppress all response? Could the different receptors have distinct outputs? For example for FRK1 gene expression the hae hsl2 mutant has an enhanced response. Could defence gene expression be primarily mediated by HSL1 with subfunctionalisation within this clade?

One striking finding of the study is the strong additive interaction between IDA and flg22 treatment on gene expression. Do the authors also see this for co-treatment of different peptides with flg22, or is this unique function of IDA? Is this receptor dependent (HAE/HSL1/HSL2)?

It is interesting how tissue specific calcium responses are in response to IDA and flg22, suggesting the cellular distribution of their cognate receptors. However, one striking observation made by the authors as well, is that the expression of promoter seems to be broader than the calcium response. Indicating that additional factors are required for the observed calcium response. Could diffusion of the peptide be a contributing factor, or are only some cells competent to induce a calcium response?<br /> It is interesting that the authors look for floral abscission phenotypes in cngc and rbohd/f mutants to conclude for genetic requirement of these in floral abscission. Do the authors have a hypothesis for why they failed to see a phenotype for the rbohd/f mutant as was published previously? Do you think there might be additional players redundantly mediating these processes?

Can you observe callose deposition in the cotyledons of the 35S::HAE line? Are the receptors expressed in native cotyledons? This is the only phenotype tested in the cotyledons.

Are flg22-induced calcium responses affected in hae hsl2?

Reviewer #1 (Public Review):

In this study, Le Moigne and coworkers shed light on the structural details of the Sedoheptulose-1,7-Bisphosphatase (SBPase) from the green algae Chlamydomonas reinhardtii. The SBPase is part of the Calvin cycle and catalyzes the dephosphorylation of sedoheptulose-1,7-bisphosphate (SBP), which is a crucial step in the regeneration of ribulose-1,5-bisphosphate (RuBP), the substrate for Rubisco. The authors determine the crystal structure of the CrSBPase in an oxidized state. Based on this structure, potential active site residues and sites of post-translational modifications are identified. Furthermore, the authors determine the CrSBPase structure in a reduced state revealing the disruption of a disulfide bond in close proximity to the dimer interface. The authors then use molecular dynamics (MD) to gain insights into the redox-controlled dynamics of the CrSBPase and investigate the oligomerization of the protein using small-angle X-ray scattering (SAXS) and size-exclusion chromatography. Despite the difference in oligomerization, disruption of this disulfide bond did not impact the activity of CrSBPase, suggesting additional thiol-dependent regulatory mechanisms modulating the activity of the CrSBPase.

The authors provide interesting new findings on a redox-mechanism that modulates the oligomeric behavior of the SBPase, however without investigating this potential mechanism in more detail. The conclusions of this manuscript are mostly supported by the data, but they should be more carefully evaluated in respect to what is known from other systems as e.g. the moss Physcomitrella patens. This is especially of interest, as SBPase was previously reported to be dimeric, whereas for FBPase a dimer/tetramer equilibrium has been observed.

1.) Given that PpSBPase has been already characterized in detail, the authors should provide a more rigorous comparison to the existing data on SBPases. This includes a more conclusive structural comparison but also the enzymatic assays should be compared to the findings from P. patens. Do the authors observe differences between the moss and the chlorophyte systems, maybe even in regard to the oligomerization of the SBPase?

2.) The authors should include the control experiments (untreated SBPase) and the assays performed with mutant versions of the SBPase, which are currently only mentioned in the text or not shown at all.

3.) The representation of the structure in figures (especially Figures 1 and 3) should be adjusted to match the author's statements. In Figure 1, the angle from which the structure is displayed changes over the entire figure making it difficult to follow especially as a non-structural biologist. Furthermore, important aspects of the structure mentioned in the text are not labeled and should be highlighted, by e.g. a close-up. Same holds true for Figure 3 that currently mostly shows redundant information.

4.) The authors state that mutation of C115 and C120 to serine destabilize the dimer formation, while more tetramer and monomer is formed. As the tetramer is essentially a dimer of dimers, the authors should elaborate how this might work mechanistically. In my opinion, dimer formation is a prerequisite for tetramer formation and the two mutations rather stabilize the tetramer instead of destabilizing the dimer.

Reviewer #1 (Public Review):

The authors used mathematical models to explore the mechanism(s) underlying the process of polar tube extrusion and the transport of the sporoplasm and nucleus through this structure. They combined this with experimental observations of the structure of the tube during extrusion using serial block face EM providing 3 dimensional data on this process. They also examined the effect of hyperosmolar media on this process to evaluate which model fit the predicted observed behavior of the polar tube in these various media solutions. Overall, this work resulted in the authors arriving at a model of this process that fit the data (model 5, E-OE-PTPV-ExP). This model is consistent with other data in the literature and provides support for the concept that the polar tube functions by eversion (unfolding like a finger of a glove) and that the expanding polar vacuole is part of this process. Finally, the authors provide important new insights into the bucking of the spore wall (and possible cavitation) as providing force for the nucleus to be transported via the polar tube. This is an important observation that has not been in previous models of this process.

Reviewer #1 (Public Review):

The goal of the authors is to use whole-exome sequencing to identify genomic factors contributing to asthenoteratozoospermia and male infertility. Using whole-exome sequencing, they discovered homozygous ZMYND12 variants in four unrelated patients. They examined the localization of key sperm tail components in sperm from the patients. To validate the findings, they knocked down the ortholog in Trypanosoma brucei. They further dissected the complex using co-immunoprecipitation and comparative proteomics with samples from Trypanosoma and Ttc29 KO mice. They concluded that ZMYND12 is a new asthenoteratozoospermia-associated gene, bi-allelic variants of which cause severe flagellum malformations and primary male infertility.

The major strengths are that the authors used the cutting-edge technique, whole-exome sequencing, to identify genes associated with male infertility, and used a new model organism, Trypanosoma brucei to validate the findings; together with other high-throughput tools, including comparative proteomics to dissect the protein complex essential for normal sperm formation/function. The major weakness is that limited samples could be collected from the patients for further characterization by other approaches, including western blotting and TEM.

In general, the authors achieved their goal and the conclusion is supported by their results. The findings not only provide another genetic marker for the diagnosis of asthenoteratozoospermia but also enrich the knowledge in cilia/flagella.

Joint Public Review:

In this study, Porter et al report on outcomes from a small, open-label, pilot randomized clinical trial comparing dornase-alfa to the best available care in patients hospitalized with COVID-19 pneumonia. As the number of randomized participants is small, investigators describe also a contemporary cohort of controls and the study concludes about a decrease of inflammation (reflected by CRP levels) after 7 days of treatment but no other statistically significant clinical benefit.

Suggestions to the authors:<br /> • The RCT does not follow CONSORT statement and reporting guidelines<br /> • The authors have chosen a primary outcome that cannot be at least considered as clinically relevant or interesting. After 3 years of the pandemic with so much research, why investigate if a drug reduces CRP levels as we already have marketed drugs that provide beneficial clinical outcomes such as dexamethasone, anakinra, tocilizumab and baricitinib.<br /> • Please provide in Methods the timeframe for the investigation of the primary endpoint<br /> • Why day 35 was chosen for the read-out of the endpoint?<br /> • The authors performed an RCT but in parallel chose to compare also controls. They should explain their rationale as this is not usual. I am not very enthusiastic to see mixed results like Figures 2c and 2d.<br /> • Analysis is performed in mITT; this is a major limitation. The authors should provide at least ITT results. And they should describe in the main manuscript why they chose mITT analysis.<br /> • It is also not usual to exclude patients from analysis because investigators just do not have serial measurements. This is lost to follow up and investigators should have pre-decided what to do with lost-to-follow-up.<br /> • In Table 1 I would like to see all randomized patients (n=39), which is missing. There are also baseline characteristics that are missing, like which other treatments as BAT received by those patients except for dexamethasone.<br /> • In the first paragraph of clinical outcomes, the authors refer to a cohort that is not previously introduced in the manuscript. This is confusing. And I do not understand why this analysis is performed in the context of this RCT although I understand its pilot nature.<br /> • Propensity-score selected contemporary controls may introduce bias in favor of the primary study analysis, since controls are already adjusted for age, sex and comorbidities.<br /> • The authors do not clearly present numerically survivors and non-survivors at day 34, even though this is one of the main secondary outcomes.<br /> • It is unclear why another cohort (Berlin) was used to associate CRP with mortality. CRP association with mortality should (also) be performed within the current study.

Reviewer #1 (Public Review):

Levy and Hasselmo investigated the representational codes of dorsal hippocampus neurons in episodic memory and spatial navigation. Specifically, how new learning affects previously acquired spatial memory. They asked if the hippocampal representational codes evolve in a different manner when two tasks governed by different rules are learnt in a single environment vs. when each rule is learnt in a separate environment. The two rules they used were based on the classical Packard & McGaugh (1996) experiment. In the original 1996 experiment there was a striatal-dependent response-based task vs. a hippocampal-dependent map-based task. In the current paper they either trained the two types of rules (response vs. map based) in two different contexts (Two-Maze), or in a single context (One-Maze). They found that the remapping of the second time in the response-based rule task was greater in the One-Maze variant of the experiment than in the Two-Maze variant, and they interpreted this by suggesting that in the One-Maze variant, the different intermediate map-based task interfered with the representation in the second response-based task, while in the Two-Maze variant no such interference occurred, and thus the hippocampal map remained more stable.

The results of this paper are well supported by data; however, we believe the conclusion of paper should be different than the conclusion the authors have arrived at.

Major issue:<br /> 1. The main claim is that a new behavioral rule in a familiar environment leads to an increase in the level of remapping of hippocampal activity when returning to the original rule in the same environment. However, we are worried that the result is not due to the interference by a different task in the same environment, but rather by the fact that the mouse spent more time in the environment, causing a larger representational drift. Consider, for example, the change in correlation in Figure 4E over days. In all cases, there is a maze-dependent reduction in correlation from day to day. This reduction continues in the One-maze case also when changing the rule, suggesting that what determined the larger reduction is the time spent in each context, and not the actual change of rule or behavior. Thus it is probable that the fact that the mouse was longer in the first maze in the one-maze variant was enough to create a difference in correlation. See also Khatib et al., bioRxiv, 2022 on the issue of context-dependent drift. To actually control for that, we suggest that the mouse spends twice the time in the first maze during the first Turn-Right session, in the Two-maze variant, and then the comparison will be more valid, by equating the amount of time spent in the first maze in-between comparisons, in the two types of experiments.

Additional points:<br /> 2. Figure 1.d: While behaving differently, is there a difference in the representation? (e.g. mouse 2 on 7th day showed in the beginning very bad behavior). What is the relation between the reduction in performance and the change in representation?<br /> 3. Figure 3.c: We suggest to get a better estimate of the significance of the effect here using shuffling. Specifically, it could be a good idea to distinguish between signal correlations (derived from the overlapping spatial fields) vs. noise correlations. To what extent are the correlations dependent on spatial overlap? It could be worthwhile to determine the type of correlation: Is it due to the fact that the maps are similar for overlapping place cells, or is there noise correlations between these cells?<br /> 4. Figure 4.a: What is the explanation for the reduction in correlation between days 5 and 6?<br /> 5. Supplementary Figure 2: Higher correlations in all arms - note the higher correlation in all arms in the Two-Maze vs. the One-Maze, suggesting again that the effect is related to the longer time in the context, and not so much to the rule-change.<br /> 6. Methods: the researchers note that the animals were previously used in a different study. This should be stated clearly also in the results.

Reviewer #1 (Public Review):

In their manuscript, Brischigliaro et al. show that the disruption of respiratory complex assembly results in Drosophila melanogaster results in the accumulation of respiratory supercomplexes. Further, they show that the change in the supercomplex abundance does not impact respiratory function suggesting that the main role of supercomplex formation is structural. Overall, the manuscript is well written and the results and conclusion are supported. The D. melanogaster system, in which the abundance of supercomplexes can be altered through the genetic disruption of the assembly of the individual complexes, will be important for the field to discover the role of the supercomplexes. This manuscript will be of broad interest to the field of mitochondrial bioenergetics. The findings are valuable and the evidence is convincing.

Strengths:

The system developed in which the relative levels of SCs can be varied will be extremely useful for studying SC physiology.

The experiments are clearly described and interpreted.

Weaknesses:

The statement in the abstract regarding low amounts of SCs in "insect tissues" needs further support or should be narrowed. I am only aware of detailed characterization of the mitochondrial SC composition from D. melanogaster, which is insufficient to make a broad statement about the large and diverse category of insects. This should be rewritten.

In the introduction (line 76) and discussion (line 283), the authors reference the CoQ binding sites in CI and CIII2 being "too far apart" to allow for substrate channeling. The distance between the active sites, though significant, is insufficient to rule out substrate channeling. A stronger argument arises from the fact that the CoQ sites of both CI and CIII2 are open to the membrane and that there are no clear barriers for the free exchange of CoQ with the membrane pool.

Line 195, the slight elevation in CI amounts referred to here, does not appear to be statistically significant and therefore should not be discussed a being altered relative to the control.

Figure 4H, the assignments of the observed larger bands seem incorrect. The largest band (currently assigned as SC I1+III2+IV1) represents too large of a shift for only the addition of CIV and the band currently assigned at SC I1+III2 appears to also contain CIV. The identity of these bands should be reevaluated and additional experiments are needed to definitively prove their identity. This uncertainty should be addressed experimentally or made more explicit in the text.

Line 302, the authors state that the structural basis for less SC in D. melanogaster is "due to a more stable association of the NDUFA11 subunit..." However, this would not result is a less stable SC association and only explains why NDUFA11 is more stably associated with CI in the absence of CIII2. The more likely structural reason for the observation of less SC in D. melanogaster is the N-terminal truncation of Dm-NDUFB4 relative to mammalian NDUFB4. This truncation results in the loss of a major SC interaction site between CI and CIII2 in the matrix.

Reviewer #1 (Public Review):

This article describes the development and refinement of an open-source software framework that is used to track how the COVID-19 pandemic impacted healthcare use in England over a range of key healthcare use indicators.

Important strengths of this study include the high coverage of 99% of practices in England, the development of health care indicators with the input of a clinical advisory group, extensive online documentation, and rigorous safeguards for the protection of patient confidentiality.

Perhaps the largest limitation is that only high-level descriptive data on the monthly volume of health outcomes are presented. It is not clear whether the system could be used to generate more fine-grained or stratified information, ex. weekly or daily data, or data stratified by important characteristics of practices or of patient characteristics. As such, the utility of the system for answering new scientific questions is unclear, and also what the utility and long-term potential uses of this system will be past the COVID-19 pandemic.

Reviewer #1 (Public Review):

In this study, Fang H et al. describe a potential pathway, ITGB4-TNFAIP2-IQGAP1-Rac1, that may involve in the drug resistance in triple negative breast cancer (TNBC). Mechanistically, it was demonstrated that TNFAIP2 bind with IQGAP1 and ITGB4 activating Rac1 and the following drug resistance. The present study focused on breast cancer cell lines with supporting data from mouse model and patient breast cancer tissues. The study is interesting. The experiments were well controlled and carefully carried out. The conclusion is supported by strong evidence provided in the manuscript. The authors may want to discuss the link between ITGB4 and Rac 1, between IQGAP1 and Rac1, and between TNFAIP2 and Rac1 as compared with the current results obtained. This is important considering some recent publications in this area (Cancer Sci 2021, J Biol Chem 2008, Cancer Res 2023).

Reviewer #1 (Public Review):

Current experimental work reveals that brain areas implicated in episodic and spatial memory have a dynamic code, in which activity representing familiar events/locations changes over time. This paper shows that such reconfiguration is consistent with underlying changes in the excitability of cells in the population, which ties these observations to a physiological mechanism.

Delamare et al. use a recurrent network model to consider the hypothesis that slow fluctuations in intrinsic excitability, together with spontaneous reactivations of ensembles, may cause the structure of the ensemble to change, consistent with the phenomenon of representational drift. The paper focuses on three main findings from their model: (1) fluctuations in intrinsic excitability lead to drift, (2) this drift has a temporal structure, and (3) a readout neuron can track the drift and continue to decode the memory. This paper is relevant and timely, and the work addresses questions of both a potential mechanism (fluctuations in intrinsic excitability) and purpose (time-stamping memories) of drift.

The model used in this study consists of a pool of 50 all-to-all recurrently connected excitatory neurons with weights changing according to a Hebbian rule. All neurons receive the same input during stimulation, as well as global inhibition. The population has heterogeneous excitability, and each neuron's excitability is constant over time apart from a transient increase on a single day. The neurons are divided into ensembles of 10 neurons each, and on each day, a different ensemble receives a transient increase in the excitability of each of its neurons, with each neuron experiencing the same amplitude of increase. Each day for four days, repetitions of a binary stimulus pulse are applied to every neuron.

The modeling choices focus in on the parameter of interest-the excitability-and other details are generally kept as straightforward as possible. That said, I wonder if certain aspects may be overly simple. The extent of the work already performed, however, does serve the intended purpose, and so I think it would be sufficient for the authors to comment on these choices rather than to take more space in this paper to actually implement these choices. What might happen were more complex modeling choices made? What is the justification for the choices that are made in the present work?

The two specific modeling choices I question are (1) the excitability dynamics and (2) the input stimulus. The ensemble-wide synchronous and constant-amplitude excitability increase, followed by a return to baseline, seems to be a very simplified picture of the dynamics of intrinsic excitability. At the very least, justification for this simplified picture would benefit the reader, and I would be interested in the authors' speculation about how a more complex and biologically realistic dynamics model might impact the drift in their network model. Similarly, the input stimulus being binary means that, on the single-neuron level, the only type of drift that can occur is a sort of drop-in/drop-out drift; this choice excludes the possibility of a neuron maintaining significant tuning to a stimulus but changing its preferred value. How would the use of a continuous input variable influence the results.

Result (1): Fluctuations in intrinsic excitability induce drift<br /> The two choices highlighted above appear to lead to representations that never recruit the neurons in the population with the lowest baseline excitability (Figure 1b: it appears that only 10 neurons ever show high firing rates) and produce networks with very strong bidirectional coupling between this subset of neurons and weak coupling elsewhere (Figure 1d). This low recruitment rate need may not necessarily be problematic, but it stands out as a point that should at least be commented on. The fact that only 10 neurons (20% of the population) are ever recruited in a representation also raises the question of what would happen if the model were scaled up to include more neurons.

Result (2): The observed drift has a temporal structure<br /> The authors then demonstrate that the drift has a temporal structure (i.e., that activity is informative about the day on which it occurs), with methods inspired by Rubin et al. (2015). Rubin et al. (2015) compare single-trial activity patterns on a given session with full-session activity patterns from each session. In contrast, Delamare et al. here compare full-session patterns with baseline excitability (E = 0) patterns. This point of difference should be motivated. What does a comparison to this baseline excitability activity pattern tell us? The ordinal decoder, which decodes the session order, gives very interesting results: that an intermediate amplitude E of excitability increase maximizes this decoder's performance. This point is also discussed well by the authors. As a potential point of further exploration, the use of baseline excitability patterns in the day decoder had me wondering how the ordinal decoder would perform with these baseline patterns.

Result (3): A readout neuron can track drift<br /> The authors conclude their work by connecting a readout neuron to the population with plastic weights evolving via a Hebbian rule. They show that this neuron can track the drifting ensemble by adjusting its weights. These results are shown very neatly and effectively and corroborate existing work that they cite very clearly.

Overall, this paper is well-organized, offers a straightforward model of dynamic intrinsic excitability, and provides relevant results with appropriate interpretations. The methods could benefit from more justification of certain modeling choices, and/or an exploration (either speculative or via implementation) of what would happen with more complex choices. This modeling work paves the way for further explorations of how intrinsic excitability fluctuations influence drifting representations.

Reviewer #1 (Public Review):

Qin et al. set out to investigate the role of mechanosensory feedback during swallowing and identify neural circuits that generate ingestion rhythms. They use Drosophila melanogaster swallowing as a model system, focusing their study on the neural mechanisms that control cibarium filling and emptying in vivo. They find that pump frequency is decreased in mutants of three mechanotransduction genes (nompC, piezo, and Tmc), and conclude that mechanosensation mainly contributes to the emptying phase of swallowing. Furthermore, they find that double mutants of nompC and Tmc have more pronounced cibarium pumping defects than either single mutants or Tmc/piezo double mutants. They discover that the expression patterns of nompC and Tmc overlap in two classes of neurons, md-C and md-L neurons. The dendrites of md-C neurons warp the cibarium and project their axons to the subesophageal zone of the brain. Silencing neurons that express both nompC and Tmc leads to severe ingestion defects, with decreased cibarium emptying. Optogenetic activation of the same population of neurons inhibited filling of the cibarium and accelerated cibarium emptying. In the brain, the axons of nompC∩Tmc cell types respond during ingestion of sugar but do not respond when the entire fly head is passively exposed to sucrose. Finally, the authors show that nompC∩Tmc cell types arborize close to the dendrites of motor neurons that are required for swallowing, and that swallowing motor neurons respond to the activation of the entire Tmc-GAL4 pattern.

Strengths:<br /> -The authors rigorously quantify ingestion behavior to convincingly demonstrate the importance of mechanosensory genes in the control of swallowing rhythms and cibarium filling and emptying<br /> -The authors demonstrate that a small population of neurons that express both nompC and Tmc oppositely regulate cibarium emptying and filling when inhibited or activated, respectively<br /> -They provide evidence that the action of multiple mechanotransduction genes may converge in common cell types

Weaknesses:<br /> -A major weakness of the paper is that the authors use reagents that are expressed in both md-C and md-L but describe the results as though only md-C is manipulated<br /> -Severing the labellum will not prevent optogenetic activation of md-L from triggering neural responses downstream of md-L. Optogenetic activation is strong enough to trigger action potentials in the remaining axons. Therefore, Qin et al. do not present convincing evidence that the defects they see in pumping can be specifically attributed to md-C.<br /> -GRASP is known to be non-specific and prone to false positives when neurons are in close proximity but not synaptically connected. A positive GRASP signal supports but does not confirm direct synaptic connectivity between md-C/md-L axons and MN11/MN12.<br /> -As seen in Figure Supplement 2, the expression pattern of Tmc-GAL4 is broader than md-C alone. Therefore, the functional connectivity the authors observe between Tmc expressing neurons and MN11 and 12 cannot be traced to md-C alone

Overall, this work convincingly shows that swallowing and swallowing rhythms are dependent on several mechanosensory genes. Qin et al. also characterize a candidate neuron, md-C, that is likely to provide mechanosensory feedback to pumping motor neurons, but the results they present here are not sufficient to assign this function to md-C alone. This work will have a positive impact on the field by demonstrating the importance of mechanosensory feedback to swallowing rhythms and providing a potential entry point for future investigation of the identity and mechanisms of swallowing central pattern generators.

Reviewer #1 (Public Review):

In this preprint, Zhang et al. describe a new tool for mapping the connectivity of mouse neurons. Essentially, the tool leverages the known peculiar infection capabilities of Rabies virus: once injected into a specific site in the brain, this virus has the capability to "walk upstream" the neural circuits, both within cells and across cells: on one hand, the virus can enter from a nerve terminal and infect retrogradely the cell body of the same cell (retrograde transport). On the other hand, the virus can also spread to the presynaptic partners of the initial target cells, via retrograde viral transmission.

Similarly to previously published approaches with other viruses, the authors engineer a complex library of viral variants, each carrying a unique sequence ('barcode'), so they can uniquely label and distinguish independent infection events and their specific presynaptic connections, and show that it is possible to read these barcodes in-situ, producing spatial connectivity maps. They also show that it is possible to read these barcodes together with endogenous mRNAs, and that this allows spatial mapping of cell types together with anatomical connectivity.

The main novelty of this work lies in the combined use of rabies virus for retrograde labeling together with barcoding and in-situ readout. Previous studies had used rabies virus for retrograde labeling, albeit with low multiplexing capabilities, so only a handful of circuits could be traced at the same time. Other studies had instead used barcoded viral libraries for connectivity mapping, but mostly focused on the use of different viruses for labeling individual projections (anterograde tracing) and never used a retrograde-infective virus.

The authors creatively merge these two bits of technology into a powerful genetic tool, and extensively and convincingly validate its performance against known anatomical knowledge. The authors also do a very good job at highlighting and discussing potential points of failure in the methods.

Unresolved questions, which more broadly affect also other viral-labeling methods, are for example how to deal with uneven tropism (ie. if the virus is unable or inefficient in infecting some specific parts of the brain), or how to prevent the cytotoxicity induced by the high levels of viral replication and expression, which will tend to produce "no source networks", neural circuits whose initial cell can't be identified because it's dead. This last point is particularly relevant for in-situ based approaches: while high expression levels are desirable for the particular barcode detection chemistry the authors chose to use (gap-filling), they are also potentially detrimental for cell survival, and risk producing extensive cell death (which indeed the authors single out as a detectable pitfall in their analysis). This is likely to be one of the major optimisation challenges for future implementations of these types of barcoding approaches.

Overall the paper is well balanced, the data are well presented and the conclusions are strongly supported by the data. Impact-wise, the method is definitely going to be useful for the neurobiology research community.

Reviewer #1 (Public Review):

The manuscript describes an interesting experiment in which an animal had to judge a duration of an interval and press one of two levers depending on the duration. The Authors recorded activity of neurons in key areas of the basal ganglia (SNr and striatum), and noticed that they can be divided into 4 types.

The data presented in the manuscript is very rich and interesting, however, I am not convinced by the interpretation of these data proposed in the paper. The Authors focus on neurons of types 1 & 2 and propose that their difference encodes the choice the animal makes. However, I would like to offer an alternative interpretation of the data. Looking at the description of task and animal movements seen in Figure 1, it seems to me that there are 4 main "actions" the animals may do in the task: press right lever, press left lever, move left, and move right. It seems to me that the 4 neurons authors observed may correspond to these actions, i.e. Figure 1 shows that Type 1 neurons decrease when right level becomes more likely to be correct, so their decrease may correspond to preparation of pressing right lever - they may be releasing this action from inhibition (analogously Type 2 neurons may be related to pressing left lever). Furthermore, comparing animal movements and timing of activity of neurons of type 3 and 4, it seems to me that type 3 neurons decrease when the animal moves left, while type 4 when the animal moves right.

I suggest Authors analyse if this interpretation is valid, and if so, revise the interpretation in the paper and the model accordingly.

Joint Public Review:

In this work, Jain and colleagues have created two libraries of the AAV2 rep gene - either expressed separately from a strong heterologous promoter or embedded in the viral wild-type context - containing all possible single codon mutations. The libraries were cleverly made through a cloning process that ensured each mutant was attached to an exactly known 20-nt barcode included in each mutagenic oligo. This allowed the authors to confidently observe nearly all rep variants in their experiments, resulting in a comprehensive map between Rep protein variants and AAV production. Interrogation of these libraries identified several variants that improved AAV production, including mutations not observed in natural AAV isolates thus far, as independently verified through a conventional AAV vector production protocol. These benefits were also conserved across multiple natural AAV capsid variants including the heterologous AAV5 serotype.

While many other groups have previously created and interrogated individual point mutants of the AAV rep gene/protein or domain swapping mutants, this study is distinguished and excels by its degree of comprehensiveness and the complexity of the two complementary libraries. This reflects the next step in the field's efforts to better understand the natural biology of AAV and, as a result, to improve the production of recombinant AAV gene transfer vectors. Considering the rapidly increasing momentum of these vectors in the clinics and as approved drugs on the gene therapy market, and considering that the individual validation experiments reported in this work support the conclusions, this work including the reported resources and technologies is likely to have a critical impact on current and future research on AAV biology and vector development.

However, there are a few areas in which the study could be expanded for even greater impact. For instance, the authors may consider testing the selected rep variants in the context of a self-complementary AAV genome, which has different biology compared to the single-stranded genomes used in this study, and which is widely used granted its compatibility with the transgene of choice (which should be <2.5 kb). Likewise, it would be important to study the functionality of the selected rep variants with at least one AAV genome of regular size, considering that the two tested here seem rather unusual in length (2.9 kb, which is very small, or 5.0 kb, which is borderline large). Last but not least, despite the fact that the AAV2 ITRs are by far most commonly used in the field, it will also be interesting to test these rep variants in combination with ITRs derived from other AAV serotypes, considering that numerous groups have previously cloned and analyzed them, and that they can provide several benefits over the AAV2 ITRs.

Furthermore, in interpreting the results of this study, the reader should bear in mind that what has been measured and validated in this work is the production of intact genome-containing AAVs. Production is a precondition to functional AAVs that can transduce cells but is not equivalent to it. While the two are likely well correlated, further studies are needed to determine how well the effects of Rep protein variants on AAV production translate to their ability to then transduce cells. The more relevant property for gene therapy is the efficiency by which an AAV preparation transduces cells. For example, might production-enhancing Rep protein variants change the ratio of empty capsids to genome-containing capsids in a way that influences transduction efficiency of the corresponding AAV preparations? Does this influence reduce or enhance the production benefit? This particular scenario of empty capsid ratios influencing transduction represents a population effect that is not possible to capture in the multiplex assay, but it seems like a good idea to at least test transduction of some individual variants because transduction is the important function of AAV for gene therapy.

One additional aspect that may warrant further consideration is the assumption, as mentioned in Figure 2's legend, that synonymous mutations are neutral and can serve as controls for normalizing the production rate. However, Figures S5-6 and Figures S11-12 suggest that synonymous mutations are not necessarily neutral, as their distribution is similar to that of nonsynonymous mutations. Thus, a deeper examination of the impacts of synonymous mutations on the genotype-phenotype landscape could provide more nuanced insights into AAV2 rep gene function.

Reviewer #1 (Public Review):

In this study, the authors identify an insect salivary protein participating viral initiate infection in plant host. They found a salivary LssaCA promoting RSV infection by interacting with OsTLP that could degrade callose in plants. Furthermore, RSV NP bond to LssaCA in salivary glands to form a complex, which then bond to OsTLP to promote degradation of callose.

The story focus on tripartite virus-insect vector-plant interaction, and is interesting. However, the study is too simple and poor-conducted. The conclusion is also overstated due to unsolid findings.

Major comments:<br /> 1. The key problem is that how long the LssCA functioned for in rice plant. Author declared that LssCA had no effect on viral initial infection, but on infection after viral inoculation. It is unreasonable to conclude that LssCA promoted viral infection based on the data that insect inoculated plant just for 2 days, but viral titer could be increased at 14 day post-feeding. How could saliva proteins, which reached phloem 12-14 days before, induce enough TLP to degrade callose to promote virus infection? It was unbelievable.

2. Lines 110-116 and Fig. 1, the results of viruliferous insect feeding and microinjection with purified virus could not conclude the saliva factor necessary of RSV infection, because these two tests are not in parallel and comparable. Microinjection with salivary proteins combined with purified virus is comparable with microinjection with purified virus.

The second problem is how many days post viruliferous insect feeding and microinjection with purified virus did author detect viral titers? in Method section, authors declared that viral titers was detected at 7-14 days post microinjection. Please demonstrate the days exactly.

The last problem is that how author made sure that the viral titers in salivary glands of insects between two experiments was equal, causing different phenotype of rice plant. If not, different viral titers in salivary glands of insects between two experiments of course caused different phenotype of rice plant.

3. The callose deposition in phloem can be induced by insect feeding. In Fig. 5H, why was the callose deposition increased in the whole vascular bundle, but not phloem? Could the transgenic rice plant directional express protein in the phloem? In Fig.5, why was callose deposition detected at 24 h after insect feeding? In Fig. 6A, why was callose deposition decreased in the phloem, but not all the cells of the of TLP OE plant? Also in Fig.6A and B, expression of callose synthase genes was required.

Reviewer #1 (Public Review):

Bacteria can adapt to extremely diverse environments via extensive gene reprogramming at transcriptional and post-transcriptional levels. Small RNAs are key regulators of gene expression that participate in this adaptive response in bacteria, and often act as post-transcriptional regulators via pairing to multiple mRNA-targets.

In this study, Melamed et al. identify four E. coli small RNAs whose expression is dependent on sigma 28 (FliA), involved in the regulation of flagellar gene expression. Even though they are all under the control of FliA, expression of these 4 sRNAs peaks under slightly different growth conditions and each has different effects on flagella synthesis/number and motility. Combining RILseq data, structural probing, northern-blots and reporter assays, the authors show that 3 of these sRNAs control fliC expression (negatively for FliX, positively for MotR and UhpU) and two of them regulate r-protein genes from the S10 operon (again positively for MotR, and negatively for FliX). UhpU also directly represses synthesis of the LrhA transcriptional regulator, that in turn regulates flhDC (at the top of flagella regulation cascade). Based on RILseq data, the fourth sRNA (FlgO) has very few targets and may act via a mechanism other than base-pairing.

As r-protein S10 is also implicated in anti-termination via the NusB-S10 complex, the authors further hypothesize that the up-regulation of S10 gene expression by MotR promotes expression of the long flagellar operons through anti-termination. Consistent with this possible connection between ribosome and flagella synthesis, they show that MotR overexpression leads to an increase in flagella number and in the mRNA levels of two long flagellar operons, and that both effects are dependent on the NusB protein. Lastly, they provide data supporting a more general activating and repressing role for MotR and FliX, respectively, in flagellar genes expression and motility.

This study brings a lot of new information on the regulation of flagellar genes, from the identification of novel sigma 28-dependent sRNAs to their effects on flagella production and motility. It represents a considerable amount of work; the experimental data are clear and solid and support the conclusions of the paper. Even though mechanistic details underlying the observed regulations by MotR or FliX sRNAs are lacking, the effect of these sRNAs on fliC, several rps/rpl genes, and flagellar genes and motility is convincing.<br /> The connection between r-protein genes regulation and flagellar operons is exciting and raises a few questions. First, from the RILseq data, chimeric reads with mRNA for r-proteins (including rpsJ) are not restricted to the sigma 28-dependent sRNAs (e.g. rpsJ-sucD3'UTR, rpsF-DicF, rplN-DicF, rplK-ChiX, rplU-CyaR, rpsT-CyaR, rpsK-CyaR, rpsF-MicA...), suggesting that regulation of r-protein synthesis by sRNAs is not necessarily related to flagella/motility. Second, it would be interesting to know if the flagellar operons are more sensitive than other long operons to antitermination following MotR overexpression? In other words, does pMotR similarly affect antitermination in rrn or other long operons?

The general effect of pMotR or pFliX on the expression of multiple middle and late flagellar genes is also interesting even though the mechanism is not clear. While it may be difficult to fully address it, testing whether some of these regulatory events depend on the control of fliC and/or the S10 operon could be relevant (by analyzing the effects in strains deleted for fliC or nusB for instance).

Reviewer #1 (Public Review):

Cytotoxic agents and immune checkpoint inhibitors are the most commonly used and efficacious treatments for lung cancers. However their use brings two significant pulmonary side-effects; namely Pneumocystis jirovecii infection and resultant pneumonia (PCP), and interstitial lung disease (ILD). To observe the potential immunological drivers of these adverse events, Yanagihara et al. analysed and compared cells present in the bronchoalveolar lavage of three patient groups (PCP, cytotoxic drug-induced ILD [DI-ILD], and ICI-associated ILD [ICI-ILD]) using mass cytometry (64 markers). In PCP, they observed an expansion of the CD16+ T cell population, with the highest CD16+ T proportion (97.5%) in a fatal case, whilst in ICI-ILD, they found an increase in CD57+ CD8+ T cells expressing immune checkpoints (TIGIT+ LAG3+ TIM-3+ PD-1+), FCRL5+ B cells, and CCR2+ CCR5+ CD14+ monocytes. Given the fatal case, the authors also assessed for, and found, a correlation between CD16+ T cells and disease severity in PCP, postulating that this may be owing to endothelial destruction. Although n numbers are relatively small (n=7-9 in each cohort; common numbers for CyTOF papers), the authors use a wide panel (n=65) and two clustering methodologies giving greater strength to the conclusions. The differential populations discovered using one or two of the analytical methods are robust: whole population shifts with clear and significant clustering. These data are an excellent resource for clinical disease specialists and pan-disease immunologists, with a broad and engaging contextual discussion about what they could mean.

Strengths:<br /> • The differences in immune cells in BAL in these specific patient subgroups is relatively unexplored.<br /> • This is an observational study, with no starting hypothesis being tested.<br /> • Two analytical methods are used to cluster the data.<br /> • A relatively wide panel was used (64 markers), with particular strength in the alpha beta T cells and B cells.<br /> • Relevant biomarkers, beta-D-glucan and KL-6 were also analysed<br /> • Appropriate statistics were used throughout.<br /> • Numbers are low (7 cases of PCP, 9 of DI-ILD, and 9 of ICI-ILD) but these are difficult samples to collect and so in relative terms, and considering the use of CyTOF, these are good numbers.<br /> • Beta-D-glucan shows potential as a biomarker for PCP (as previously reported) whilst KL-6 shows potential as a biomarker for ICI-ILD (not reported before). Interestingly, KL-6 was not seen to be increased in DI-ILD patients.<br /> • Despite the relatively low n numbers and lack of matching there are some clear differentials. The CD4/CD8+CD16+HLA-DR+CXCR3+CD14- T cell result is striking - up in PCP (with EM CD4s significantly down) - whilst the CD8 EMRA population is clear in ICI-ILD and 'non-exhausted' CD4s, with lower numbers of EMRA CD8s in DI-ILD.<br /> • The authors identify 17/31 significantly differentiated clusters of myeloid cells, eg CD11bhi CD11chi CD64+ CD206+ alveolar macrophages with HLA-DRhi in PCP.<br /> • With respect to B cells, the authors found that FCRL5+ B cells were more abundant in patients with ICI-ILD compared to those with PCP and DI-ILD, suggesting these FCRL5+ B cells may have a role in irAE.<br /> • One patient's extreme CD16+ T cell (97.5% positive) and death, led the authors to consider CD16+ T cells as an indicator of disease severity in PCP. This was then tested and found to be correct.<br /> • Authors discuss results in context of literature leading them to suggest that CD16+ T cells may target endothelial cells and wonder if anti-complement therapy may be efficacious in PCP.<br /> • Great discussion on auto-reactive T cell clones where the authors suggest that in ICI-ILD CD8s may react against healthy lung, driving ILD.<br /> • An observation of CXCR3 in different CD8 populations in ICI-ILD and PCP lead the authors to hypothesise on the chemoattractants in the microenvironment.<br /> • Excellent point suggesting CD57 may not always be a marker of senescence on T cells - reflective of growing change within the community.<br /> • Well considered suggestion that FCRL5+ B cells may be involved in ICI-ILD driven autoimmunity.<br /> • The authors discuss the main weaknesses in the discussion and stress that the findings detailed in the paper "demonstrate a correlation rather than proof of causation".<br /> • Figures and legends are clear and pleasing to the eye.

Weaknesses:<br /> • This is an observational study, with no starting hypothesis being tested.<br /> • Only patients who were able to have a lavage taken have been recruited.<br /> • One set of analysis wasn't carried out for one subgroup (ICI-ILD) as PD1 expression was negative owing to the use of nivolumab.<br /> • Some immune cell subsets wouldn't be picked up with the markers and gating strategies used; e.g. NK cells.<br /> • Some immune cells would be disproportionately damaged by the storage, thawing and preparation of the samples; e.g. granulocytes.<br /> • Numbers are low (7 cases of PCP, 9 of DI-ILD, and 9 of ICI-ILD), sex, age and adverse event matching wasn't performed, and treatment regimen are varied and 'suspected' (suggesting incomplete clinical data) - but these are difficult samples to collect. These numbers drop further for some analyses e.g. T cell clustering owing to factors such as low cell number.<br /> • The disease comparisons are with each other, there is no healthy control.<br /> • Samples are taken at one time point.<br /> • The discussion on probably the stand out result - the CD16+ T cells in PCP - relies on two papers - leading to a slightly skewed emphasis on one paper on CD16+ cells in COVID. There are other papers out there that have observed CD16+ T cells in other conditions. It is also worth being in mind that given the markers used, these CD16+ T cell may be gamma deltas.<br /> • The discussion on ICI patient consistently showing increased PD1, could have been greater, as given the ICI is targeting PD1, one would expect the opposite as commented on, and observed, in the methods section.

Reviewer #1 (Public Review):

The authors took advantage of a large dataset of transcriptomic information obtained from parasites recovered from 35 patients. In addition, parasites from 13 of these patients were reared for 1 generation in vivo, 10 for 2 generations, and 1 for a third generation. This provided the authors with a remarkable resource for monitoring how parasites initially adapt to the environmental change of being grown in culture. They focused initially on var gene expression due to the importance of this gene family for parasite virulence, then subsequently assessed changes in the entire transcriptome. Their goal was to develop a more accurate and informative computational pipeline for assessing var gene expression and secondly, to document the adaptation process at the whole transcriptome level.

Overall, the authors were largely successful in their aims. They provide convincing evidence that their new computational pipeline is better able to assemble var transcripts and assess the structure of the encoded PfEMP1s. They can also assess var gene switching as a tool for examining antigenic variation. They also documented potentially important changes in the overall transcriptome that will be important for researchers who employ ex vivo samples for assessing things like drug sensitivity profiles or metabolic states. These are likely to be important tools and insights for researchers working on field samples.

One concern is that the abstract highlights "Unpredictable var gene switching....." and states that "Our results cast doubt on the validity of the common practice of using short-term cultured parasites......". This seems somewhat overly pessimistic with regard to var gene expression profiling and does not reflect the data described in the paper. In contrast, the main text of the paper repeatedly refers to "modest changes in var gene expression repertoire upon culture" or "relatively small changes in var expression from ex vivo to culture", and many additional similar assessments. On balance, it seems that transition to culture conditions causes relatively minor changes in var gene expression, at least in the initial generations. The authors do highlight that a few individuals in their analysis showed more pronounced and unpredictable changes, which certainly warrants caution for future studies but should not obscure the interesting observation that var gene expression remained relatively stable during transition to culture.

Consensus Public Review:

Ottenheimer et al., present an interesting study looking at the neural representation of value in mice performing a pavlovian association task. The task is repeated in the same animals using two odor sets, allowing a distinction between odor identity coding and value coding. The authors use state-of-the-art electrophysiological techniques to record thousands of neurons from 11 frontal cortical regions to conclude that 1) licking is represented more strongly in dorsal frontal regions, 2) odor cues are represented more strongly in ventral frontal regions, 3) cue values are evenly distributed across regions. They separately perform a calcium imaging study to track coding across days and conclude that the representation of task features increments with learning and remains stable thereafter.Overall, these conclusions are interesting and well supported by the data.

The authors use reduced-rank kernel regression to characterize the 5332 recorded neurons on a cell-by-cell basis in terms of their responses to cues, licks, and reward, with a cell characterized as encoding one of these parameters if it accounts for at least 2% of the observed variance (while at first this seemed overly lenient, the authors present analyses demonstrating low false-positives at this threshold and that the results are robust to different cutoffs).

Having identified lick, reward, and cue cells, the authors next select the 24% of "cue-only" neurons and look for cells that specifically encode cue value. Because the animal's perception of stimulus value can't be measured directly, the authors created a linear model that predicts the amount of anticipatory licking in the interval between odor cue and reward presentations. The session-average-predicted lick rate by this model is used as an estimate of cue value and is used in the regression analysis that identified value cells. (Hence, the authors' definition of value is dependent on the average amount of anticipatory behavior ahead of a reward, which indicates that compared to the CS+, mice licked around 70% as much to the CS50 and 10% as much to the CS-.) The claim that this is an encoding of value is strengthened by the fact that cells show similar scaling of responses to two odor sets tested. Whereas the authors found more "lick" cells in motor regions and more "cue" cells in sensory regions, they find a consistent percentage of "value" cells (that is, cells found to be cue-only in the initial round of analysis that is subsequently found to encode anticipatory lick rate) across all 11 recorded regions, leading to their claim of a distributed code of value.

In subsequent sections, the authors expand their model of anticipatory-licking-as-value by incorporating trial and stimulus history terms into the model, allowing them to predict the anticipatory lick rate on individual trials within a session. They also use 2-photon imaging in PFC to demonstrate that neural coding of cue and lick are stable across three days of imaging, supported by two lines of evidence. First, they show that the correlation between cell responses on all periods except for the start of day 1 is more correlated with day 3 responses than expected by chance (although the correlation is low, the authors attribute this to inherent limitations of the data), and that response for a given neuron is substantially better correlated with its own activity across time than random neurons. Second, they show that cue identity is able to capture the highest unique fraction of variance (around 8%) in day 3 cue cells across three days of imaging, and similarly for lick behavior in lick cells and cue+lick in cue+lick cells. Nonetheless, their sample rasters for all imaged cells also indicate that representations are not perfectly stable, and it will be interesting to see what *does* change across the three days of imaging.

Reviewer #1 (Public Review):

This work describes a novel high-throughput approach to diverse transgenesis which the authors have named TARDIS for Transgenic Arrays Resulting in Diversity of Integrated Sequences. The authors describe the general approach: the generation of a synthetic 'landing pad' for transgene insertion (as previously reported by this group) that has a split selection hygromycin resistance gene, meaning that only perfect integration with the insert confers resistance to the otherwise lethal hygromycin drug. The authors then demonstrate two possible applications of the technology: individually barcoded lineages for lineage tracing and transcriptional reporter lines generated by inserting multiple promoters. In both cases, the authors did a limited 'proof of concept' study including many important controls, showcasing the potential of the method. The conclusions for applications of this method in C. elegans are supported by the data and the authors discuss important observations and considerations. In the discussion, the discuss the potential application of the method beyond C. elegans, although this remains speculative at this point given that a nontrivial aspect of the success of the method in worms is the self-assembly of DNA into heritable extrachromosomal arrays (a feature that is absent in most other systems).

Reviewer #1 (Public Review):

The authors investigated state-dependent changes in evoked brain activity, using electrical stimulation combined with multisite neural activity across wakefulness and anesthesia. The approach is novel, and the results are compelling. The study benefits from in depth sophisticated analysis of neural signals. The effects of behavioral state on brain responses to stimulation are generally convincing.

It is possible that the authors' use of "an average reference montage that removed signals common to all EEG electrodes" could also remove useful components of the signal, which are common across EEG electrodes, especially during deep anesthesia. For example, it is possible (in fact from my experience I would be surprised if it is not the case) that under isoflurane anesthesia, electrical stimulation induces a generalized slow wave or a burst of activity across the brain. Subtracting the average signal will simply remove that from all channels. This does not only result in signals under anesthesia being affected more by the referencing procedure than during waking, but also will have different effects on different channels, e.g. depending on how strong the response is in a specific channel.

Reviewer #1 (Public Review):

The manuscript, "A versatile high-throughput assay based on 3D ring-shaped cardiac tissues generated from human induced pluripotent stem cell-derived cardiomyocytes" developed a unique culture platform with PEG hydrogel that facilitates the in-situ measurement of contractile dynamics of the engineered cardiac rings. The authors optimized the tissue seeding conditions, demonstrated tissue morphology with expressions of cardiac and fibroblast markers, mathematically modeled the equation to derive contractile forces and other parameters based on imaging analysis, and ended by testing several compounds with known cardiac responses.

To strengthen the paper, the following comments should be considered:

1. This paper provided an intriguing platform that creates miniature cardiac rings with merely thousands of CMs per tissue in a 96-well plate format. The shape of the ring and the squeezing motion can recapitulate the contraction of the cardiac chamber to a certain degree. However, Thavandiran et al (PNAS 2013) created a larger version of the cardiac ring and found the electrical propagation revealed spontaneous infinite loop-like cycles of activation propagation traversing the ring. This model was used to mimic a reentrant wave during arrhythmia. Therefore, it presents great concerns if a large number of cardiac tissues experience arrhythmia by geometry-induced re-entry current and cannot be used as a healthy tissue model. It would be interesting to see the impulse propagation/calcium transient on these miniature cardiac rings and evaluate the % of arrhythmia occurrence.

2. The platform can produce 21 cardiac rings per well in 96-well plates. The throughput has been the highest among competing platforms. The resulting tissues have good sarcomere striation due to the strain from the pillars. Now the emerging questions are culture longevity and reproducibility among tissues. According to Figure 1E, there was uneven ring formation around the pillar, which leads to the tissue thinning and breaking off. There is only 50% survival after 20 days of culture in the optimized seeding group. Is there any way to improve it? The tissues had two compartments, cardiac and fibroblast-rich regions, where fibroblasts are responsible for maintaining the attachment to the glass slides. Do the cardiac rings detach from the glass slides and roll up? The SD of the force measurement is a quarter of the value, which is not ideal with such a high replicate number. As the platform utilizes imaging analysis to derive contractile dynamics, calibration should be done based on the angle and the distance of the camera lens to the individual tissues to reduce the error. On the other hand, how reproducible of the pillars? It is highly recommended to mechanically evaluate the consistency of the hydrogel-based pillars across different wells and within the wells to understand the variance.

3. Does the platform allow the observation of non-synchronized beating when testing with compounds? This can be extremely important as the intended applications of this platform are drug testing and cardiac disease modeling. The author should elaborate on the method in the manuscript and explain the obtained results in detail.

4. The results of drug testing are interesting. Isoproterenol is typically causing positive chronotropic and positive inotropic responses, where inotropic responses are difficult to obtain due to low tissue maturity. It is inconsistent with other reported results that cardiac rings do not exhibit increased beating frequency, but slightly increased forces only. Zhao et al were using electrical pacing at a defined rate during force measurement, whereas the ring constructs are not.

Overall, the manuscript is well written and the designed platform presented the unique advantages of high throughput cardiac tissue culture. Besides the contractile dynamics and IHC images, the paper lacks other cardiac functional evaluations, such as calcium handling, impulse propagation, and/or electrophysiology. The culture reproducibility (high SD) and longevity (<20 days) still remain unsolved.

Reviewer #1 (Public Review):

Kou and Kang et al. investigated the role of Notch-RBP-J signaling in regulating monocyte homeostasis. Specifically, they examined how a conditional knockout of Rbpj expression in monocytes through a Rbpjfl/fl Lyz2cre/cre mouse affects the homeostasis of Ly6Chi versus Ly6Clo monocytes. They discovered that Rbpj deficiency did not affect the percentage of Ly6Chi monocytes but instead, led to an accumulation of Ly6Clo monocytes in the peripheral blood. Using a comprehensive number of in vivo techniques to investigate the origin of this increase, the authors revealed that the accumulation of Rbpj deficient Ly6Clo monocytes was not due to an increase in bone marrow egress and that this defect was cell intrinsic. However, EdU-labelling and apoptosis assays revealed that this defect was not due to an increase in proliferation nor conversion of Ly6Chi to Ly6Clo monocytes. Interestingly, it was revealed that Rbpj deficient Ly6Clo monocytes had increased expression of CCR2 and ablation of CCR2 expression reversed the accumulation of these cells in the periphery. Lastly, they discovered that Rbpj deficiency also led to downstream effects such as an accumulation of Ly6Clo monocytes in the lung tissue and increased CD16.2+ interstitial macrophages, presumably due to dysregulated monocyte differentiation and function.

Their findings are interesting and highlight a previously unexplored mechanistic link between Notch-RBP-J signaling and CCR2 expression in monocyte homeostasis, providing further insight into the distinct pathways that regulate Ly6Chi vs Ly6Clo monocyte subsets individually.

The conclusions of this paper are mostly well substantiated from the experimental data. The strengths of this paper include the use of multiple conditional genetic knock out mouse models to explore their hypothesis and the use of sophisticated in vivo techniques to study the major mechanisms involved in monocyte homeostasis.

Reviewer #1 (Public Review):

The manuscript entitled, "Loss of PTPMT1 limits mitochondrial utilization of carbohydrates and leads to muscle atrophy and heart failure," by Zheng, et al., is focused on assessing the role of deletion of PTPMT1, a mitochondria-based phosphatase, in mitochondrial fuel selection. Authors show that the utilization of pyruvate, a key mitochondrial substrate derived from glucose, is inhibited, whereas fatty acid utilization is enhanced. Importantly, while the deletion of PTPMT1 does not impact development of skeletal muscle or heart, the metabolic inflexibility leads to muscular atrophy, heart failure, and sudden death. Mechanistically, authors claim that the prolonged substrate shift from carbohydrates to lipids causes oxidative stress and mitochondrial dysfunction, leading to accumulation of lipids and muscle cell and CM damage in the KO. Interestingly, PTPMT1 deletion from the liver or adipose tissue does not generate any local or systemic defects. Authors conclude that PTPMT1 plays an important role in maintaining mitochondrial flexibility and that the balanced utilization of carbohydrates and lipids is essential for skeletal muscle and heart. While interesting and authors did a ton of experiments for this project, several major concerns exist. First, because both the CKMM- and the MYHC-Cre express early, during development , it seems the effects of the deletion of PTPMT1 are more likely be specific to defects in muscle and cardiac development rather than postnatal, especially since loss of PTPMT1 affects mTOR activity; indeed, previous reports have shown that selective deletion of mTOR or raptor in skeletal muscle during embryonic development leads to a reduction in postnatal growth and the development of late-onset myopathy and premature death around 6 to 8 months of age. The effects of the deletion in muscle seem eerily similar and therefore likely mechanistically function the same -embryonically, as has been previously suggested. This is also true for cardiac abnormalities, where developmental defects can manifest in mice as they age after at least 3-4 months and decreased mTOR activity can lead to significant cardiac dysfunction and failure (similarly to the effects observed here by the authors). To prove one way or another, authors should show developmental data providing evidence that the effects are not occurring at this stage. It is a lot of work, but the right way to differentiate pre- vs post- development functions of PTPMT1 in the muscle and heart, otherwise cannot verify mechanistically what the precise cause for the phenotype may be. Authors could consider generating mice that have inducible Cre drivers. In addition, how is it that the effects of loss of PTPMT1 are similar between muscle and heart given the differences in energy usage and utilization between these two tissues? Increases in AMPK are usually associated with better metabolic outcomes, particularly in the heart. Increased AMPK activation has also been shown to help reduce fat storage, increase insulin sensitivity, reduce cholesterol/triglyceride production, and suppress chronic inflammation. In addition, increases in carnitines are associated with enhanced metabolic function. Carnitines facilitate transport of long-chain fatty acids into the mitochondrial matrix, triggering cardioprotective effects through reduced oxidative stress, inflammation and necrosis of cardiac myocytes. All of these factors are positive, so how do authors explain this discrepancy in their findings which suggest opposing outcomes- as above, I suggest the explanation is that it is due to developmental effects of deletion of PTPMT1.

Authors attribute much of the pathology in the muscle and heart due to increased lipid accumulation in these tissues; but how do authors explain how hearts and muscle have more fat when the mice are smaller than wt? Is there a difference in energy expenditure in the mice? What about changes in white fat, core temperature or browning of fat? Authors do not mechanistically prove that lipid accumulation is the cause of death in these animals. Rescue experiments should be considered.

Public Review:

In this manuscript, Karl et al. explore mechanisms underlying the activation of the receptor tyrosine kinase FGFR1 and stimulation of intracellular signaling pathways in response to FGF4, FGF8, or FGF9 binding to the extracellular domain of FGFR1. Quantitative binding experiments presented in the manuscript demonstrate that FGF4, FGF8, and FGF9 exhibit distinct binding affinities towards FGFRs. It is also proposed that FGF8 exhibits "biased ligand" characteristics that is manifested via binding and activation FGFR1 mediated by "structural differences in the FGF- FGFR1 dimers, which impact the interactions of the FGFR1 trans membrane helices, leading to differential recruitment and activation of the downstream signaling adapter FRS2".

Major points:

1. Previous studies have demonstrated that the variability of signal transduction stimulated by different FGF family members originates from their preferential activation of different members of the FGFR family (Ornitz et al., 1996). For example, it was previously shown that members of the FGF8 subfamily preferentially activate FGFR3c, whereas members of the FGF4 subfamily activate FGFR1c more potently than other FGFs. Moreover, it was shown that FGF18, a member of the FGF8 subfamily, preferentially binds to and activates the FGFR3c isoform. Indeed, this can be seen in the data shown in Figure 3 in this manuscript, where maximum levels of FGFR1 pY653/4 and pFRS2 are reached at different concentrations when stimulated with increasing concentrations of each ligand in HEK293T cells. In order to be sure that the 'biased agonist' described in this manuscript for FGF8 binding is not caused by binding preference towards different FGFR members, the authors should present data comparing cell signaling via FGFR3c stimulated by FGF4, FGF8, and FGF9.

2. It is well-established that FGFR signaling by canonical FGF family members including FGF4, FGF8, and FGF9 is dependent on interactions of heparin or heparan sulfate proteoglycans (HSPG) to the ligand the receptors. Differential contributions of heparin to cell signaling mediated by FGF4, FGF8, and FGF9 binding and activation of different FGFRs expressed in RCS cells as this cell express endogenous HSPG molecules. This question should be addressed by comparing cell signaling via FGFRs ectopically expressed in BAF/3 cells (which do not possess endogenous FGFRs and HSPG) stimulated by FGF4, FGF8, and FGF9 in the absence or presence of different heparin concentrations. This approach has been applied many times in the past to explore and establish the role of heparin in control of ligand induced FGFR activation. It is impossible to interpret the FGFR binding characteristics and cellular activates of FGF4, FGF8, and FGF9 in the absence of information about the role of heparin in their binding and activation.

3. It is not clear how some of the experimental data were analyzed. Blots in Figures 3A and 3B should include controls (total FGFR1 for pY653/4 and total FRS for pFRS2). How are the data shown in Figure 3C normalized? It does look like the level of phosphorylation was all normalized against the strongest signals irrespective of which ligand was used. Each data representing each ligand should be separately normalized.

4. In page 6, authors used the plot shown in Figure 3 for 'FGFR downregulation' to conclude that "the effect of FGF4 on FGFR1 downregulation is smaller when compared to the effects of FGF8 and FGF9. However, it is unclear how the data shown in the plot was normalized - none of the data seem to reach "1.0". Moreover, the plot seems to suggest that FGF4 can strongly downregulate FGFR as it can downregulate FGFR with higher potency.

5. The structural basis of FGFR1 ligand bias and the different dimeric configurations and interactions between the kinase domain of FGFR1 dimers are not warranted (Figure 6). In the absence of any structural experimental data of different forms of FGFR dimers stimulated by FGF ligands the model presents in the manuscript is speculative and misleading.

Reviewer #1 (Public Review):

I feel that this study has potentially high public health significance and should be made known to the public, especially the usefulness of a natural chemical product, oligomeric proanthocyanidins, in preventing SARS-CoV2 infection. The studies are very well designed, using the first 5 figures to compare carefully the effects of tannic acid, punicalagin, and oligomeric proanthocyanidins in disrupting the interaction of the virus with host cells and in inhibiting the enzymatic activity of transmembrane serine protease 2 required for viral entry. I am especially impressed by the work done in Figures 6 and 7 in which the investigators put their efforts into quantitating the amounts of oligomeric proanthocyanidins, tannic acid, and punicalagin present in the grape seed, peel, flesh as well as juice. I also appreciate the translational application in which the investigators prepared grape seed extract capsules (200 mg and 400 mg), recruited healthy human subjects to take these capsules once or twice, and showed that the sera from randomized human subjects taking grape seed extract capsules indeed exert does-dependent and time-dependent activities in suppressing the infection rate of various SARS-CoV2 variants using in vitro studies. The study in Figure 7 is indeed very well-designed and quite elegant. The manuscript is also well-written.

Reviewer #1 (Public Review):

The authors of this manuscript are interested in identifying the molecular mechanisms underlying antidepressant action. Though most antidepressants target the serotonin system, regulation of glutamate neurotransmission has been associated with rapid treatment response. Here the authors find that monoaminergic targeted antidepressants are associated in some patients with expression of a small nucleolar RNA that they go on to show results in alterations to glutamate neurotransmission in a mouse model. These data offer a molecular mechanism that can link traditional monoaminergic targeted antidepressants with glutamatergic regulation and could offer a new way to estimate the efficacy of these drugs.

Reviewer #1 (Public Review):

The authors generated detailed anatomical descriptions and images of the coronary vasculature of mice, quails, zebrafish, Japanese tree frogs, Japanese fire belly newt, African clawed frogs, salmon sharks, Japanese sleeper rays and bird-beak dogfish. Using this data, they are able to show anatomical similarities in the origination points of evolutionary distant vertebrates from the third to fourth brachial arch. Additionally, the authors highlight the additional presence of a coronary vascular plexuses as a unique amniote trait, since it is seen in quail and mice but not xenopus frogs. Based on the presence of the possible homologies, the authors propose that the early developmental amniotic coronary artery is a derived from the ancestral hypobrachial artery. The methods for labeling and imaging the cardiac vessels are robust and congruent with previous studies describing these structures in mice and zebrafish. The study also presents an intriguing hypothesis; however, it could benefit from a more expansive survey of vertebrate coronary diversity using an increased number of species and developmental time points. A more exhaustive surveying of vertebrate diversity is required to demonstrate that the coronary vasculature anatomy observed is from common ancestral states or novel adaptations. The author's claim that a primitive vascular plexus represents a novel amniote phenotype, is reasonable, but could benefit from further confirmation using additional species.

Reviewer #1 (Public Review):<br /> <br /> Lobanov et al. investigated the effects of spatial structure in microbial communities that interact via secreted metabolites. The work builds up on a previous theoretical model by the authors that considered well-mixed populations in which different bacterial species secrete and consume different sets of metabolites, and metabolites in turn modify the growth rates of species. The model considers communities that are periodically exposed to dilutions, and the authors focus on the regime in which bacterial densities do not reach saturation before the next dilution. Analyzing the stable outcome of these dynamics through comparison with well-mixed scenarios, the authors found that space can favor species richness, especially in the case of communities with prevalent facilitative interactions. This positive effect on species coexistence is also more pronounced in situations in which species produce more kinds of metabolites than they consume. On the other hand, the positive effects on coexistence can be reversed when bacterial dispersal becomes relevant over the timescale of the simulations, as well as in cases in which the diffusion of metabolites is too slow - which could even result in less coexistence than in well-mixed scenarios. These results add to an ongoing discussion on the different ways in which spatial effects can impact microbial community dynamics and species richness.

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

1) This is a model with many parameters and the manuscript should be clearer about how these parameters were used in different scenarios. It is probably a matter of rewriting the text, but I found it hard to understand which parameter values remained the same in scenarios with or without space, as well as how the strength of interactions was assigned, among a few other examples. In other cases, additional analysis (e.g. on how the spatial impact on coexistence depends on the average strength of interactions) would make the work more comprehensive.<br /> 2) To assess stable coexistence and richness, the authors use a criterium in which species have to be almost equally abundant (above 90% of the abundance of the fastest-growing species). It is not clear if the results would change significantly if potentially less abundant species would be classified as coexisting ones.<br /> 3) The majority of the results consider scenarios in which bacteria cannot disperse very effectively so bacterial dynamics is mostly driven by the growth of the initial populations at each region. Expanding on the analysis of higher dispersal rates would be valuable in order to analyze additional realistic scenarios of how bacteria grow and disperse in space.

Reviewer #1 (Public Review):

In this manuscript, Castrillon et al. analyze the heterogeneity of B cells exiting spontaneous germinal center reactions by scRNA-seq in a new mouse model of autoimmunity. In this model, they track the fate of wild-type Aid-Cre ERT2-EYFP B cells in the presence of 564 lgi B cells harboring a BCR specific for RNP. Throughout the manuscript, the authors compared the results obtained in the autoimmune model with those obtained after acute immunization with NP/OVA in Alum. They found extensive clonal overlap among dark/light zone germinal centers, memory B cells, and antibody-secreting cells (ASC). Within the ASC compartment, they found seven clusters. Through pseudotime analysis, they conclude the presence of two early ASC clusters, three intermediate ASC clusters, and two terminal ASC clusters. The two late ASCs have different patterns of gene expression (CD28, Itga4 among them), isotype expression (ASC_Late_1 mostly class-switched while ASC_Late_2 mostly IgM), and potentially different antibody-secreting capacity and metabolic program based on Ig counts and OXPHOS signature. Regarding memory B cells, they found four clusters of memory B cells with similar isotype expression (except for MemB2 which expresses more IgM) but different gene expression patterns (CD83, Fcrl5, Vim, Fcer2a). Finally, the authors found that FCRL5+ and CD23+ memory B cells are located in different areas of the spleen based on confocal microscopy analysis and their accessibility to blood after anti-CD45 iv administration. The data provided by the authors are very attractive and interesting. Yet, I found that the manuscript over relies on scRNA-seq. It will be important that authors back up some of their conclusions made from the scRNA-seq analysis with functional experiments, like measuring the differential antibody-secreting capacity of both terminal ASC subsets or profiling their metabolic status through one of the many metabolic techniques available.

Reviewer #1 (Public Review):

This manuscript reports new findings about the role of the glutamate transporter EAAC1 in controlling neural activity in the striatum. The significance is two-fold - it addresses gaps in knowledge about the functional significance of EAAC1, as well as provides a potential explanation for how EAAC1 mutations contribute to striatal hyperexcitability and OCD-associated behaviors. The manuscript is clearly presented, and the well-designed experiments are rigorously performed and analyzed. The main results showing that EAAC1 deletion increases the dendritic arbor of MSN D1 neurons and increases excitatory synaptic connectivity, as well as reduces D1-to-D1 mediated IPSCs are convincing. These results clearly demonstrate that EAAC1 deletion can alter excitatory and inhibitory synaptic function. Modelling the potential consequences for these changes on D1 MSN neural activity, and the behavior changes are interesting. Minor weaknesses include incomplete support for the conclusions about how EAAC1 regulates GABAergic transmission.

Reviewer #1 (Public Review):

This manuscript made use of a biologically realistic neuronal network model of cortico-basal ganglia-thalamic (CBGT) circuits and a cognitive drift-diffusion model (DDM) to account for both behavioural and functional neuroimaging (fMRI) data and to understand how change in reward contingency in the environment can affect different decision dynamics. They found that the rate of evidence accumulation was most affected, allowing explorative behaviour with a lower drift rate during likely contingency change and exploitative behaviour with a higher drift rate when contingency was likely similar. The multi-pronged approach presented in the manuscript is commendable. The biophysical model was sufficiently realistic with varying ramping firing rates of spiny projection neurons linked to the varying drift rates in the DDM. However, there are a few concerns regarding this work.

The model's cortical neurons had no contralateral encoding, unlike their neuroimaging data. Another concern with this work is that it was unclear why the spiking neuronal network model with so many model parameters was used to account for coarse-scale fMRI data - a simple firing-rate neural population model would perhaps do the work. Moreover, the activity dynamics of the fMRI were not shown. It would have been more rigorous to show the fMRI (BOLD) signals in different (particularly CBGT) brain regions and compare that with the CBGT model simulations.

The association between classier uncertainty and drift rate (by participants) was an order of magnitude difference between the simulated and actual participants (compare Figure 2E with Figure 4B). There was also a weak effect on human reaction times (Supp. Fig. 2).

There were only 4 human participants that performed the experiment - the results would perhaps be better with more human participants.

For such a complex biophysical computational model, there could perhaps have been more model predictions provided.

Overall, this work is interesting and could potentially be a good contribution in the area of computational modelling and neuroscience of adaptive choice behaviour.

Reviewer #1 (Public Review):

In this manuscript, Modi et al present a novel method to analyze brain oscillations. Traditional approaches are typically based on analyzing spectral features on individual oscillations (univariate methods) or the power and phase relationship between two oscillations (bivariate methods). The authors take a different, multivariate, approach to simultaneously analyze interactions between multiple oscillations. This is a better way to study dynamics interactions in a complex system than the more traditional 'reductionist' approach and, so far, few methods exist that allow such multivariate analysis of neural oscillations. The method is well demonstrated in the paper, including several application cases. Several aspects of the results need to be better characterized, a clear discussion of the caveats and limitations of the method is lacking and the advantages over existing methods need to be outlined more clearly. Provided these issues are corrected I believe this would be an important contribution to the field that may have multiple applications.

Reviewer #1 (Public Review):

This manuscript by Bohannon et al. continues a line of work from the Larsson laboratory with fundamental contributions describing the effects of polyunsaturated fatty acids (PUFAs) on the cardiac delayed rectifier potassium channel (IKs) formed by Kv7.1 and KCNE1 heteromers. Although the activating effect of PUFAs on these specific channels has been previously described, the authors now present a novel finding related to PUFAs containing large aromatic tyrosine head groups, showing significant activation effects on IKs, larger than other PUFAs previously studied. A combination of site-directed mutagenesis, electrophysiological and pharmacological approaches are used to dissect the different molecular mechanisms and sites involved in the functional interactions. The main conclusions are: 1) PUFA analogues with Tyr head groups are strong activators of the cardiac IKs channel by action on two previously described mechanisms: left-shift of the voltage-activation curve (by interaction with the voltage-sensor region of Kv7.1); and increased Gmax (by interacting with the pore region). 2) the underlying molecular interactions between PUFA and Kv7.1 are not cation-pi, as shown by the lack of effect of different chemical variations that disrupt the electrostatic surface potential. 3) the presence of electronegative groups on the aromatic ring favors increases in the maximal conductance. 4) the generation of a hydrogen bond with the -OH on the Tyr group seems to selectively impact on IKs voltage dependence of activation. 4) Kv7.1 sites involved in interactions with aromatic PUFAs are similar to the ones previously described for non-aromatic PUFAS, that is: R231 in S4 and K326 in S6. 5) residue T224 is newly identified as a potential site forming a hydrogen bond between the Tyr in the aromatic PUFA and Kv7.1.

The manuscript is very well written and structured. The experiments are solid and lead to mostly well-grounded conclusions. There are some aspects that would benefit from some clarification, which are mainly related to the different effects of the aromatic PUFA variants on IKs voltage dependence and/or conductance.

Reviewer #1 (Public Review):

In this study the authors investigate whether a presumably allosteric P2RX7 activating compound that they previously discovered reduces fibrosis in a bleomycin mouse model. They chose this particular model as publicly available mRNA data indicate that the P2XR7 pathway is downregulated in idiopathic pulmonary fibrosis patients compared to control individuals. The authors first demonstrate that two proxies of lung damage, Ashcroft score and collagen fibers, are significantly reduced in the bleomycin model on HEI3090 treatment. Additional data implicate specific immune cell infiltrates and cytokines, namely inflammatory macrophages and damped release of IL-17A, as potential mechanistic links between their compound and reduced fibrosis. Finally, the researchers transplant splenocytes from WT, NLRP3-KO, and IL-18-KO mice into animals lacking the P2XR7 receptor to specifically ascertain how the transplanted splenocytes, which are WT for P2XR7 receptor, respond to HEI3090 (a P2XR7 agonist). Based on these results, the authors conclude that HEI3090 enhanced IL-18 production through the P2XR7-NLRP3 inflammasome axis to dampen fibrosis.

These findings could be interesting to the field, as there are conflicting results as to whether NLRP3 activation contributes to fibrosis and if so, at what stage(s) (e.g., acute damage phase versus progression). However, major weaknesses of the study include inconsistent and small effect sizes in key outcomes used to measure fibrosis, small animal cohorts that do not empower results, and lack of key experimental controls. For example, damage indicators for the vehicle-treated mice transplanted with splenocytes of various genetic background are markedly different, and there are no statistical tests of these effects because the data are presented as separate graphs. Moreover, the fundamental assumption that HEI3090 acts specifically through the P2XR7 pathway in this model is questionable, as P2XR7 knockout mice are not included in the initial key experiments. These issues must be addressed as stimulating an inflammasome response might lead to pathogenic inflammation, which could counterproductively exacerbate fibrosis in the clinic and harm people.

Experimental concerns:

1. Ashcroft method quantification throughout is outdated and prone to bias. The methods describing quantification are lacking, and only include a citation: there should be mention of researcher blinding, etc. In general, please re-quantify using an automated classifier, and consider staining for additional markers of lung damage that are appropriate in the field.

2. For Figure 2, P2XR7 knockout mice, and an additional P2XR7 activator, should be included (e.g, A74003, AZ10606120, others), to support the hypothesis that HEI3090 acts through this pathway to alleviate fibrosis. Moreover, these data are especially important as the author's conclusions are directly opposed to a previous study demonstrating that the P2XR7 receptor is required for inflammation/fibrosis in this model system (PMID: 20522787). Two-way ANOVA or similar statistical tests on all groups should be examined to see whether genetic knockout of this DAMP receptor alone is protective or exacerbates fibrosis (e.g., comparing the vehicle-alone groups), and whether compounds exert a specific effect through this receptor.

3. Fig. 3A: Please show the individual IFN/IL-17A plots in the supplement, as a ratiometric result might mask variance. Moreover, please conduct a statistical test for the outlier in the HEI3090 condition (to potentially remove it), as this sole data point might skew the entire mean, causing the observed statistical difference between means despite a very modest change. If the results are still significant, please comment on effect size.

4. Fig. 3: How is IL-17A measured and what is the abbreviation GMFI?

5. Fig. 3E: It's unclear how the left and right figures align-it looks like the gates are 45.8 % and 25 %, respectively, but the means on the right are between 2-3%. Also, is this effect size (2 versus 3 %) significant biologically?

6. For Figure 4B-G, the Ashcroft scores for the vehicle mice treated with HEI3090 are at entirely different starting points following adoptive transfer of cells with different genetic background. In Fig. 1, WT mice have starting scores of around 3 following the induction of fibrosis, with a modest decrease of about 0.8 following HEI3090 treatment. Here, there is a much greater effect of the genetic background itself rather than the treatment, with the IL-18 knockout mice having a much lower baseline "vehicle" score (~1) compared to Fig. 1F (both of which are 14 day treatments). In fact, adoptive transfer of WT splenocytes start at a baseline of 1.8 here, which is much lower than Fig. 1F, and NLRP3-KO splenocytes score nearly the same as Fig. 1F following BLM treatment, with a modest reduction following treatment with HEI3090. Please analyze all of these groups together with appropriate multiple hypothesis testing to examine the effect of the genetic background, and please comment on why IL-18-knockout splenocytes might be protective at vehicle baseline while NLRP3-knockout splenocytes might exacerbate the phenotype at vehicle baseline.

7. The variance on Supplemental Figure 5C is quite large. These data have a decrease in mean Ashcroft score between untreated and HEI3090 treatment of around 0.8, which is similar to the WT mice in Figure 1. This is very concerning, as the underlying assumption is that KO of the protein required for HEI3090's on-target effect would completely ablate response, and this would be required for the subsequent adoptive transfer experiments in Figure 4. Please conduct power analysis, comment, and provide additional evidence (other than Ashcroft score).

8. Figure 4: Should quantify collagen fibers or have an additional quantitative metric for lung damage, as in Fig. 2C/J.

9. Figure 4: Should group the quantification of C/E/G and perform a 2-way Anova to assess effects of genetic background versus treatment.

10. Fig. 4H, Supplemental Fig. 6D: Is it reasonable to expect differences in IL-1beta and IL-18 in sera compared to in lung tissue itself?

Reviewer #1 (Public Review):

In this study, Satake and colleagues endeavored to explore the rates and patterns of somatic mutations in wild plants, with a focus on their relationship to longevity. The researchers examined slow- and fast-growing tropical tree species, demonstrating that slow-growing species exhibited five times more mutations than their fast-growing counterparts. The number of somatic mutations was found to increase linearly with branch length. Interestingly, the somatic mutation rate per meter was higher in slow-growing species, but the rate per year remained consistent across both species. A closer inspection revealed a prevalence of clock-like spontaneous mutations, specifically cytosine-to-thymine substitutions at CpG sites. The author suggested that somatic mutations were identified as neutral within an individual, but subject to purifying selection when transmitted to subsequent generations. The authors developed a model to assess the influence of cell division on mutational processes, suggesting that cell-division independent mutagenesis is the primary mechanism.

The authors have gathered valuable data on somatic mutations, particularly regarding differences in growth rates among trees. Their meticulous computational analysis led to fascinating conclusions, primarily that most somatic mutations accumulate in a cell-division independent manner. The discovery of a molecular clock in somatic mutations significantly advances our comprehension of mutational processes that may generate genetic diversity in tropical ecosystems. The interpretation of the data appears to be based on the assumption that somatic mutations can be effectively transmitted to the next generation unless negative selection intervenes. However, accumulating evidence suggests that plants may also possess "effective germlines," which could render the somatic mutations detected in this study non-transmittable to progeny. Incorporating additional analyses/discussion in the context of plant developmental biology, particularly recent studies on cell lineage, could further enhance this study.

Specifically, several recent studies address the topics of effective germline in plants. For instance, Robert Lanfear published an article in PLoS Biology exploring the fundamental question, "Do plants have a segregated germline?" A study in PNAS posited that "germline replications and somatic mutation accumulation are independent of vegetative life span in Arabidopsis." A phylogenetic-based analysis titled "Rates of Molecular Evolution Are Linked to Life History in Flowering Plants" discovered that "rates of molecular evolution are consistently low in trees and shrubs, with relatively long generation times, as compared with related herbaceous plants, which generally have shorter generation times." Another compelling study, "The architecture of intra-organism mutation rate variation in plants," published in PLoS Biology, detected somatic mutations in peach trees and strawberries. Although some of these studies are cited in the current work, a deeper examination of the findings in relation to the existing literature would strengthen the interpretation of the data.

Reviewer #1 (Public Review):

The manuscript by Hayes et al. explored the potential of combining chromosomal instability with macrophage phagocytosis to enhance tumor clearance of B16-F10 melanoma. However, the manuscript suffers from substandard experimental design, some contradictory conclusions, and a lack of viable therapeutic effects.

The authors suggest that early-stage chromosomal instability (CIN) is a vulnerability for tumorigenesis, CD47-SIRPa interactions prevent effective phagocytosis, and opsonization combined with inhibition of the CD47-SIRPa axis can amplify tumor clearance. While these interactions are important, the experimental methodology used to address them is lacking.

Reviewer #1 (Public Review):

The study by Ding et al demonstrated that microbial metabolite I3A reduced western diet induced steatosis and inflammation mice. They showed that I3A mediates its anti-inflammatory activities through AMP-activated protein kinase (AMPK)-dependent manner in macrophages. Translationally, they proposed that I3A could be a potential therapeutic molecule in preventing the progression of steatosis to NASH.

Major strengths<br /> • Authors clearly demonstrated that the Western Diet (WD)-induced steatosis and I3A treatment reduced steatosis and inflammation in pre-clinical models. Data clearly supports these statements.<br /> • I3A treatment rescued WD-altered bile acids as well partially rescued the metabolome, proteome in the liver.<br /> • I3A treatment reduced the levels of enzymes in fatty acid transport, de novo lipogenesis and β-oxidation<br /> • I3A mediates its anti-inflammatory activities through AMP-activated protein kinase (AMPK)-dependent manner in macrophages.

Minor Weakness<br /> Although data strongly support the notion that I3A reduced WD-induced steatosis and I3A treatment reduced steatosis and inflammation, the following concerns need to be addressed.<br /> • Authors suggested that I3A anti-inflammatory activities do not require AhR by using AhR-inhibitor in RAW cell lines. In the literature, studies do show that RAW cells do respond to AhR ligands such as TCDD and FICZ.<br /> • AhR-dependency needs to be confirmed by bone marrow derived macrophages isolated from AhR+/+ and AhR-/- or siRNA/ShRNA knockdown experiments.<br /> • Utilization of known AhR ligands as controls will strengthen the interpretation of the conclusions.

Reviewer #1 (Public Review):

This is an interesting study by Pinos and colleagues that examines the effect of beta carotene on atherosclerosis regression. The authors have previously shown that beta carotene reduces atherosclerosis progress and hepatic lipid metabolism, and now they seek to extend these findings by feeding mice a diet with excess beta carotene in a model of atherosclerosis regression (LDLR antisense oligo plus Western diet followed by LDLR sense oligo and chow diet). They show some metrics of lesion regression are increased upon beta carotene feeding (collagen content) while others remain equal to normal chow diet (macrophage content and lesion size). These effects are lost when beta carotene oxidase (BCO) is deleted. The study adds to the existing literature that beta carotene protects from atherosclerosis in general, and adds new information regarding regulatory T-cells. However, the study does not present significant evidence about how beta-carotene is affecting T-cells in atherosclerosis. For the most part, the conclusions are supported by the data presented, and the work is completed in multiple models, supporting its robustness. However there are a few areas that require additional information or evidence to support their conclusions and/or to align with the previously published work.

Specific additional areas of focus for the authors:<br /> The premise of the story is that b-carotene is converted into retinoic acid, which acts as a ligand of the RAR transcription factor in T-regs. The authors measure hepatic markers of retinoic acid signaling (retinyl esters, Cyp26a1 expression) but none of these are measured in the lesion, which calls into question the conclusion that Tregs in the lesion are responsible for the regression observed with b-carotene supplementation.

There does not appear to be a strong effect of Tregs on the b-carotene induced pro-regression phenotype presented in Figure 5. The only major CD25+ cell dependent b-carotene effect is on collagen content, which matches with the findings in Figure 1 +2. This mechanistically might be very interesting and novel, yet the authors do not investigate this further or add any additional detail regarding this observation. This would greatly strengthen the study and the novelty of the findings overall as it relates to b-carotene and atherosclerosis.

The title indicates that beta-carotene induces Treg 'expansion' in the lesion, but this is not measured in the study.

Reviewer #1 (Public Review):

In this study, Kim et al. investigated the mechanism by which uremic toxin indoxyl sulfate (IS) induces trained immunity, resulting in augmented pro-inflammatory cytokine production such as TNF and IL-6. The authors claim that IS treatment induced epigenetic and metabolic reprogramming, and the aryl hydrocarbon receptor (AhR)-mediated arachidonic acid pathway is required for establishing trained immunity in human monocytes. They also demonstrated that uremic sera from end-stage renal disease (ESRD) patients can generate trained immunity in healthy control-derived monocytes.

These are interesting results that introduce the important new concept of trained immunity and its importance in showing endogenous inflammatory stimuli-induced innate immune memory. Additional evidence proposing that IS plays a critical role in the initiation of inflammatory immune responses in patients with CKD is also interesting and a potential advance of the field. This study is in large part well done, but some components of the study are still incomplete and additional efforts are required to nail down the main conclusions.

Specific comments:<br /> 1) Of greatest concern, there are concerns about the rigor of these experiments, whether the interpretation and conclusions are fully supported by the data. 1) Although many experiments have been sporadically conducted in many fields such as epigenetic, metabolic regulation, and AhR signaling, the causal relationship between each mechanism is not clear. 2) Throughout the manuscript, no distinction was made between the group treated with IS for 6 days and the group treated with the second LPS (addressed below). 3) Besides experiments using non-specific inhibitors, genetic experiments including siRNA or KO mice should be examined to strengthen and justify central suggestions.<br /> 2) The authors showed that IS-trained monocytes showed no change in TNF or IL-6, but increased the expression levels of TNF and IL-6 in response to the second LPS (Fig. 1B). This suggests that the different LPS responsiveness in IS-trained monocytes caused altered gene expression of TNF and IL-6. However, the authors also showed that IS-trained monocytes without LPS stimulation showed increased levels of H3K4me3 at the TNF and IL-6 loci, as well as highly elevated ECAR and OCR, leading to no changes in TNF and IL-6. Therefore, it is unclear why or how the epigenetic and metabolic states of IS-trained monocytes induce different LPS responses. For example, increased H3K4me3 in HK2 and PFKP is important for metabolic rewiring, but why increased H3K4me3 in TNF and IL6 does not affect gene expression needs to be explained.<br /> 3) The authors used human monocytes cultured in human serum without growth factors such as MCSF for 5-6 days. When we consider the short lifespan of monocytes (1-3 days), the authors need to explain the validity of the experimental model.<br /> 4) The authors' ELISA results clearly showed increased levels of TNF and IL-6 proteins, but it is well established that LPS-induced gene expression of TNF and IL-6 in monocytes peaked within 1-4 hours and returned to baseline by 24 hours. Therefore, authors need to investigate gene expression at appropriate time points.<br /> 5) It is a highly interesting finding that IS induces trained immunity via the AhR pathway. The authors also showed that the pretreatment of FICZ, an AhR agonist, was good enough to induce trained immunity in terms of the expression of TNF and IL-6. However, from this point of view, the authors need to discuss why trained immunity was not affected by kynurenic acid (KA), which is a well-known AhR ligand accumulated in CKD and has been reported to be involved in innate immune memory mechanisms (Fig. S1A).<br /> 6) The authors need to clarify the role of IL-10 in IS-trained monocytes. IL-10, an anti-inflammatory cytokine that can be modulated by AhR, whose expression (Fig. 1E, Fig. 4D) may explain the inflammatory cytokine expression of IS-trained monocytes.<br /> 7) The authors need to show H3K4me3 levels in TNF and IL6 genes in all conditions in one figure. (Fig. 2B). Comparing Fig. 2B and Fig. S2B, H3K4me3 does not appear to be increased at all by LPS in the IL6 region.<br /> 8) The authors need to address the changes of H3K4me3 in the presence of MTA.<br /> 9) Interpretation of ChIP-seq results is not entirely convincing due to doubts about the quality of sequencing results. First, authors need to provide information on the quality of ChIP-seq data in reliable criteria such as Encode Pipeline. It should also provide representative tracks of H3K4me3 in the TNF and IL-6 genes (Fig. 2F). And in Fig. 2F, the author showed the H3K4me3 track of replicates, but the results between replicates were very different, so there are concerns about reproducibility. Finally, the authors need to show the correlation between ChIP-seq (Fig. 2) and RNA-seq (Fig. 5).<br /> 10) AhR changes in the cell nucleus should be provided (Fig. 4A).<br /> 11) Do other protein-bound uremic toxins (PBUTs), such as PCS, HA, IAA, and KA, change the mRNA expression of ALOX5, ALOX5AP, and LTB4R1? In the absence of genetic studies, it is difficult to be certain of the ALOX5-related mechanism claimed by the authors.<br /> 12) Fig.6 is based on the correlated expression of inflammatory genes or AA pathway genes. It does not clarify any mechanisms the authors claimed in the previous figures.

Reviewer #1 (Public Review):

The manuscript by Park et. al. examines the interaction of macrophages with SARS-CoV-2 spike protein and subsequent inflammatory reactions. The authors demonstrate that following intranasal delivery of spike it rapidly accumulates in alveolar macrophages. Inflammation associated with internalized spike recruits neutrophils to the lung, where they undergo a cell death process consistent with NETosis. The authors demonstrate that modifications spike to contain high mannose reduces uptake of spike protein and limits the inflammation induced. This finding could have implications on vaccine development, as vaccines containing modified spike could be safer and better tolerated.

The authors use a number of different techniques, including in vivo modeling, imaging, human and murine systems to interrogate their hypotheses. These systems provide robust supporting information for their conclusions. There are two key aspects from the current manuscript which would add key evidence. The authors suggest that neutrophils exposed to spike protein undergo a process of NETosis. To confirm this hypothesis inhibitors of NETosis should be used to demonstrate that the cell death is prevented. Additionally, vaccination of a murine model with the modified spike protein would add additional support to the conclusion that modified spike protein would be less inflammatory while maintaining its utility as a vaccine antigen.

Reviewer #1 (Public Review):

In the present work the authors explore the molecular driving events involved in the establishment of constitutive heterochromatin during embryo development. The experiments have been carried out in a very accurate manner and clearly fulfill the proposed hypotheses.

Regarding the methodology, the use of: i) an efficient system for conversion of ESCs to 2C-like cells by Dux overexpression; ii) a global approach through IPOTD that reveals the chromatome at each stage of development and iii) the STORM technology that allows visualization of DNA decompaction at high resolution, helps to provide clear and comprehensive answers to the conclusion raised.

The contribution of the present work to the field is very important as it provides valuable information on chromatin-bound proteins at key stages of embryonic development that may help to understand other relevant processes beyond heterochromatin maintenance.

The study could be improved through a more mechanistic approach that focuses on how SMARCAD1 and TOPBP1 cooperate and how they functionally connect with H3K9me3, HP1b and heterochromatin regulation during embryonic development. For example, addressing why topoisomerase activity is required or whether it connects (or not) to SWI/SNF function and the latter to heterochromatin establishment, are questions that would help to understand more deeply how SMARCAD1 and TOPBP1 operate in embryonic development.

Reviewer #1 (Public Review):

The paper from Hsu and co-workers describes a new automated method for analyzing the cell wall peptidoglycan composition of bacteria using liquid chromatography and mass spectrometry (LC/MS) combined with newly developed analysis software. The work has great potential for determining the composition of bacterial cell walls from diverse bacteria in high-throughput, allowing new connections between cell wall structure and other important biological functions like cell morphology or host-microbe interactions to be discovered. In general, I find the paper to be well written and the methodology described to be useful for the field. However, there are areas where the details of the workflow could be clarified. I also think the claims connecting cell wall structure and stiffness of the cell surface are relatively weak. The text for this topic would benefit from a more thorough discussion of the weak points of the argument and a toning down of the conclusions drawn to make them more realistic.

Specific points:

1) It was unclear to me from reading the paper whether or not prior knowledge of the peptidoglycan structure of an organism is required to build the "DBuilder" database for muropeptides. Based on the text as written, I was left wondering whether bacterial samples of unknown cell wall composition could be analyzed with the methods described, or whether some preliminary characterization of the composition is needed before the high-throughput analysis can be performed. The paper would be significantly improved if this point were explicitly addressed in the main text.

2) The potential connection between the structure of different cell walls from bifidobacteria and cell stiffness is pretty weak. The cells analyzed are from different strains such that there are many possible reasons for the change in physical measurements made by AFM. I think this point needs to be explicitly addressed in the main text. Given the many possible explanations for the observed measurement differences (lines 445-448, for example), the authors could remove this portion of the paper entirely. Conclusions relating cell wall composition to stiffness would be best drawn from a single strain of bacteria genetically modified to have an altered content of 3-3 crosslinks.

Reviewer #1 (Public Review):

In this manuscript, "Diminishing neuronal acidification by channelrhodopsins with low proton conduction" by Hayward and colleagues, the authors report on the properties of novel optogenetic tools, PsCatCh2.0 and ChR2-3M, that minimize photo-induced acidification. The authors point out that acidification is an undesirable side-effect of many optogenetic approaches that could be minimized using the new tools. ChRs are known to acidify cells, while Arch are known to alkalize cells. This becomes particularly important when optical stimulation is prolonged and pH changes can become significant. pH is known to affect neuronal excitability, vesicular release, and more. To develop novel optogenetic tools with minimal proton conductances, the authors combined channelrhodopsin stimulation with a red-shifted pH sensor to measure pH during optogenetic stimulation. The authors report that optogenetic activation of CheRiff caused slow cellular acidification. 150 seconds of illumination caused a 3-fold increase in protons or approximately a 0.6 unit pH change that returned to baseline very slowly. They also found that pH changes occurred more rapidly, and recovered more rapidly, in dendrites. The authors go on to robustly characterize PsCatCh2.0 and ChR2-3M in terms of their proton conductances, photocurrent, kinetics, and more. They convincingly show that these constructs induced reduced acidification while maintaining robust photocurrents. In sum, this manuscript shows important findings that convincingly characterizes 2 optogenetic tools that have reduced pH artifacts that may be of broad interest to the field of neuroscience research and optogenetic therapies.

Reviewer #1 (Public Review):

The blood-brain barrier separates neural tissue from blood-borne factors and is important for maintaining central nervous system health and function. Endothelial cells are the site of the barrier. These cells exhibit unique features relative to peripheral endothelium and a unique pattern of gene expression. There remains much to be learned about how the transcriptome of brain endothelial cells is established in development and maintained throughout life.

The manuscript by Sadanandan, Thomas et al. investigates this question by examining transcriptional and epigenetic changes in brain endothelial cells in embryonic and adult mice. Changes in transcript levels and histone marks for various BBB-relevant transcripts, including Cldn5, Mfsd2a and Zic3 were observed between E13.5 and adult mice. To perform these experiments, endothelial cells were isolated from E13.5 and adult mice, then cultured in vitro, then sequenced. This approach is problematic. It is well-established that brain endothelial cells rapidly lose their organotypic features in culture (https://elifesciences.org/articles/51276). Indeed, one of the primary genes investigated in this study, Cldn1, exhibits very low expression at the transcript level in vivo but is strongly upregulated in cultured ECs.

(https://elifesciences.org/articles/36187 ; https://markfsabbagh.shinyapps.io/vectrdb/)

This undermines the conclusions of the study.

An additional concern is that for many experiments, siRNA knockdowns are performed without validation of the efficacy of the knockdown.

Some experiments in the paper are promising, however. For example, the knockout of HDAC2 in endothelial cells resulting in BBB leakage was striking. Investigating the mechanisms underlying this phenotype in vivo could yield important insights.

https://www.frontiersin.org/articles/10.3389/fpsyg.2020.01358/full

The Pastoral Origin of Semiotically Functional Tonal Organization of Music Nikolsky, A. 2020

Reviewer #1 (Public Review):

The paper describes a robotic system that can be used for prolonged recording of forced activity in crawling Drosophila larvae. This is mostly intended to be a proof of principle description of a tool potentially useful for the community. The system - whose value lies completely in its reproducibility and adoption - is only superficially described in the paper, but a more detailed description is made available through Github, along with the software used for the collection and analysis of data.

There is good, convincing evidence this can work as some sort of "larval conveyor belt", used to artificially prolong food crawling behaviour in the animals. More could be said about the ecological implications of the assay (for instance: how relevant is it to an animal's natural behaviour? Does the system introduce artifactual distortions in the analysis, driven by the fact that animals crawl greater distances than they would normally crawl in nature? Will this extensive activity affect their development to pupation or adulthood?).

Reviewer #1 (Public Review):

This manuscript provides a structural analysis of bushy cells in the mouse cochlear nucleus. The analysis uses volume electron microscopy techniques to describe bushy cell-auditory nerve synapses and bushy cell dendrites. The analysis takes a morphological analysis of bushy cells to a new level, and the computational modeling is well done. The models are used to predict busy cell behavior, which leads to a major concern. The authors make reasonable assertions, but all of these need to be validated by electrophysiological studies before they can be treated as fact. Instead, they should be treated as predictions. For example, in the conclusions from the model section, that endbulb size does not strictly predict synaptic efficacy should be modified from an assertion to a prediction.

Reviewer #1 (Public Review):

Ichinose et al., utilize a mixture of cultured hippocampal neurons and non-neuronal cells to identify the role of the transmembrane protein teneurin-2 (TEN-2) in the formation of inhibitory synapses along the dendritic shaft. First, they identify distinct clusters of gephyrin that are either actin-rich, microtubule-rich or contain neither actin nor microtubules and find that TEN-2 is enriched in microtubule-rich gephyrin clusters. This leads the authors to hypothesize that TEN-2 recruits microtubules (MTs) through the plus end binding protein EB1 when successfully matched with a pre-synaptic partner, and perform a variety of experiments to test this hypothesis. The authors then extend this finding to state quite strongly throughout the paper, including in the title, that TEN-2 acts as a signpost for the unloading of cargo from motor proteins without providing any supporting evidence. They use previous work to justify this conclusion, but without actual experiments to back up the claim, it seems like a reach.

The strength of the paper lies in the various lines of evidence that the authors employ to assess the role of TEN-2 in MT recruitment and synaptogenesis. They have also been very thorough in validating the expression and functionality of various knock-in constructs, knock-down vectors and antibodies that were generated during the study. However, there are some discrepancies in the findings that have not been addressed satisfactorily, as well as some instances where the data presented is not of sufficient quality to support the conclusions derived from them.<br /> 1. The emphasis placed on the clustering analysis presented in figure 1 and the two associated supplementary figures is puzzling, since the conclusion derived from the results presented would be that Neuroligin 2 (NLGN2) is the strongest candidate to test for a relationship to MT recruitment at inhibitory post synapses. Instead, the authors cite prior evidence to exclude NLGN2 from subsequent analysis and choose to focus on TEN2 instead.<br /> 2. It is difficult to reach the same conclusion as the authors from the images and intensity plot shown on Figure 2 E and F. While there seems to be an obvious reduction in expression levels between the TEN2N-L and TEN2TM constructs, neither seem to co-localize with EB1.<br /> 3. The authors mimic the activity of TEN-2 at the inhibitory post synapse in non-neuronal cells by immobilizing HA- tagged TEN constructs in COS-7 cells as a proxy for synaptic partner matching. Using this model, they find that by immobilizing TEN2N-L, which contains EB1 binding motifs, MTs are excluded from the cell periphery (Figure 3D). This contradicts their conclusion that MTs are recruited through EB1 by TEN-2 on synaptic partner matching. Later in the paper, when they use the same TEN2N-L construct as a dominant negative in neuronal cells, they find that MTs are recruited the membrane, even if TEN2N-L is not immobilized by synaptic partner matching (Figure 6C). Taken together, these findings call into question the sequence of events driven by TEN-2 during synaptogenesis.<br /> 4. It is unclear how the authors could conclude that TEN-2 is at the semi-periphery (?) of inhibitory post synapses from the STORM data that is presented in the paper. Figure 4D and 4F show comparisons of Bassoon and TEN-2 localization vs TEN-2 and gephyrin, but the image quality is not sufficient to adequately portray a strong distinction in the distance of center of mass, which is also only depicted for the TEN2-Gephyrin pair and not the TEN2-Bassoon pair in Figure 4J.<br /> 5. The authors do not satisfactorily explain why gephyrin appears to have completely disappeared in the TEN2N-L condition (Figure 6A), instead of appearing uniformly distributed as one would expect if MTs are indiscriminately recruited to the membrane by the dominant negative construct that remains unanchored.<br /> 6. In a similar critique to that of Figure 2E and F, the distinction that the authors wish to portray between the effect of TEN2TM and TEN2N-L constructs on EGFP-TEN-2 and MAP2 colocalization (Figure 6 E and F) appear to be driven by a difference in overall expression levels of EGFP-TEN2 rather that a true difference in localization of TEN-2 and MTs.

Reviewer #1 (Public Review):

Wu et al. sought to investigate the biological role of GPR110 in modulating hepatic lipid metabolism. The authors demonstrate a pathological role of GPR110 in promoting hepatic steatosis and generalized metabolic syndrome in a mouse model of diet-induced obesity. Furthermore, the authors identify enhanced SCD1 expression as an underlying mechanism promoting GPR110-induced metabolic dysfunction. Finally, the authors provide clinically relevant human data demonstrating a positive correlation between GPR110 expression and degree of hepatic steatosis. The strengths of this study include the rigorous design and execution of experiments, the utilization of gain and loss of function as well as pharmacological and genetic approaches, and the clinically relevant human data presented. The claims are supported by robust data. These findings have the potential to impact the field of metabolism in general, given their findings indicate targeting GPR110 can reverse diet induced obesity and metabolic syndrome. Only minor weaknesses were noted in regard to further interpretation of the data.

Reviewer #1 (Public Review):

The first defined that FAM76B inhibited the NF-κB-mediated inflammation by modulating translocation of hnRNPA2B1 to cytosol, where hnRNPA2B1 bound to IKK and released active NFkB that translocated into nuclear and initiated inflammation.

Reviewer #1 (Public Review):

The authors have investigated the effect of aerobic exercise on the decline in cerebral blood flow and cognitive function in old mice. Using appropriately two-photon microscopy and optical coherence tomography they found that aerobic exercise restored capillary blood flow and oxygenation in the white matter more than in the grey matter in old female mice. Interestingly, this aerobic exercise also ameliorated cognitive performance in these mice. The data obtained strongly supports the hypothesis and supports the conclusion of the study. Nevertheless, it would be important to compare the effects of aerobic exercise in old mice to its effects in young animals. It will be also interesting to know if the protective effect of exercise is similar in male mice.

This work brings new insights into the comprehension of the age-associated changes in cerebral microcirculation and in the protective effects of aerobic exercise.

Reviewer #1 (Public Review):

The manuscript by Huang, Li, et al. describes the identification of variants in the gene coding for p31 comet, a protein required for silencing the spindle assembly checkpoint or SAC, in women with recurrent pregnancy loss upon IVF. In three families mutations affecting splicing or expression of full-length protein were identified. The authors show that oocytes of the patients arrest in meiosis I, are most likely to fail to inactivate the SAC without a fully functional p31 comet. Indeed, the metaphase I arrest occurring in mouse oocytes upon overexpression of Mad2 can be rescued by overexpression of wild-type p31 comet, but not a truncated version. Injection of wt p31 comet into 6 human oocytes from one patient rescued the meiosis I arrest.

Main points:

The fact that inactivation of the SAC is required for anaphase I onset in human oocytes is not novel. Biallelic mutations of TRIP13 were shown to lead to the same phenotype (Zhang et al. Am J. Hum Gen., 2020).

No new mechanistic insights are obtained.

The authors propose a role for female fertility, however, also a male patient with a p31 comet variant is sterile.

The fact that the C-terminus of p31 comet is required for interaction with Mad2 and hence, turning off the SAC, is already known.

Reviewer #1 (Public Review):

This work reports an important demonstration of how to predict the mutational pathways to antimicrobial resistance (AMR) emergence, particularly in the enzyme DHFR (dihydrofolate reductase). Epistasis, or non-additive effects of mutations due to their background dependence, is a major confounding factor in the predictability of protein evolution, including proteins that confer antimicrobial resistance. In the first approach, they used the Rosetta to predict the mutant DHFR-drug binding affinity and the resulting selection coefficient, which then became inputs to a population genetics model. In the second approach, they use the observed clinical/environmental frequency of the variants to estimate the selection coefficient. Overall, this work is a compelling demonstration that a mechanistic model of the fitness landscape could recapitulate AMR evolution; however, considering that the number of mutations and pathways is small, a more compelling description of the robustness of the results and/or limitations of the model is needed.

Major strengths:<br /> 1. This is a compelling multi-disciplinary work that combines a mechanistic fitness landscape of DHFR (previously articulated in literature and cited by the authors), Rosetta to determine the biophysical effects of mutations, and a population genetics model.<br /> 2. The study takes advantage of extensive data on the clinical/environmental prevalence of DHFR mutations.<br /> 3. Provides a careful review of the surrounding literature.

Major weakness:<br /> 1. Considering that the number of mutations and pathways being recapitulated is rather small, I would suggest a more detailed description of the robustness of the results. For example:<br /> a. Please report the P-value for the correlation of the predicted DDG_{binding, theory} and DDG_{binding, experimental}. If interested in showing the correct assignment of mutational effects, perhaps use a contingency matrix to derive a P-value.<br /> b. Although the DDG_binding calculation in Rosetta seems to converge (Appendix figures 3 and 4), I do not think the DDG values before equilibration should be included in the final DDG estimate. In practice, there is a "burn in" number of runs where the force field optimizes the calculation to account for potential clashes in the structure, etc. This is particularly important since the starting structures are modeled from homology. Consequently, the distributions of DDG that include the equilibration runs are multimodal (Appendix figure 2), which means that calculating an average may be inappropriate.<br /> 2. The geographical areas over which the mutational pathways are independently estimated are not isolated, allowing for the potential that an AMR variant in one region arose due to "migration" from another area. For example, the S58R-S117N is the most frequent double mutant of PvDHFR in geographically proximate Southern/Southeastern Asia (Fig. 4). To a certain extent, similar mutational patterns occur for PfDHFR in Southern/Southeastern Asia (Fig. 3). Although accounting for mutant migration in the model may be beyond the scope of the study, a clear argument for the validity of the "isolated island" assumption is needed.

Reviewer #1 (Public Review):

This work develops new and improved methods for tracking and quantifying yeast cells in time-lapse microscopy. Overall, the manuscript presents exceedingly clever solutions to many practical data analysis problems that arise in microfluidics, some of which may be useful in other image analysis settings.

I find the manuscript is at times very dense and technical and is missing context for a general audience. Hard to know what are the most important contributions, and the authors assume the reader is familiar with many details of their previous work/field. Claims are made with little explanation, context or scientific logic.

Reviewer #1 (Public Review):

Extracellular vesicles (EVs) are emerging as important mediators of cell-to-cell signaling. In this paper the authors aim to demonstrate that Stranded at second (Sas), a Drosophila cell surface protein, binds to dArc1 and Ptp10D to mediate intercellular transport of dArc1 via EVs. dArc1 protein has been shown to form virus-like capsids that carry dArc1 mRNA from neurons to muscle, but little is known about this new intercellular communication pathway. Similarly, not much is known generally about how EVs are targeted to specific cell types, or how specific EV cargo can be delivered. Thus, this work is of interest to cell biologists and neuroscientists. However, the jumbled description of the results and general lack of rigor of experiments diminish the impact and interpretability of the conclusions. Moreover, almost all experiments rely on gain-of-function and over-expression of Sas, thus the relevance to normal physiological signaling is unclear.

Major strengths:<br /> 1. The data showing that Sas is released into EVs and delivered to cells is strong.<br /> 2. The EM data showing Sas localization to EVs is clear.

Major weaknesses:<br /> The description of the results omits some data in the figures and is not in a logical order. This made it hard to read and follow. There is also a lack of rigor and quantification in some experiments. Specifically:

1. Figure 2: Description of dArc1 putative capsids is absent from the results section (2f,g) until describing fig 4 data (line 362). Given that there is no immuno-EM labeling of dArc1 protein, it is not clear if Sas and dArc1 are localized to the same EVs. Nor is it clear if the double membrane EVs are actually EVs that contain capsids. Overall, the EM data lacks quantification. How many EVs on average show Sas labeling? How many EVs have double membranes? The dense protein staining surrounding EVs seems unusual, is this due to artifact of the purification? EV kits are generally non-specific and isolate non-EV membranes, corroboration using ultracentrifugation or size exclusion chromatography methods would be beneficial. SAS-FL overexpression results in more EVs, which confounds subsequent experiments suggesting that Sas targets EVs to specific cell types/regions.

2. Figure 3: There are no data showing the expression of Sas in SG cells using the GAL4 lines. Is this expression restricted to just SG cells? The results jump from a-b to f-g. c-e are out of order. The quantification in g should be broken into two and paired with the actual data (c-e, and f). It is not clear how the quantification in g was performed. How many WBs were analyzed? There seems to be a bubble in the first lane of f, which would preclude quantification. Why is d not quantified and there seems to be an overall increase in background staining in e that is not specific to discs. The source data files are not labelled and these data should be incorporated into annotated supplemental figures. Is transfer in a-b due to Ptp10D? How many WBs were quantified in g?

3. Figure 4: C and d, IP data has no inputs for IPs, no sizing markers, and no IgG controls for antibody specificity. These data would also be more convincing if done with FL Sas and included co-Ips from cell lysates.

4. In general, the WBs in the figures show very white backgrounds with high contrast, which suggests the images may have been manipulated. Total protein controls are also missing.

5. Figure 5: Ashley et al (Cell 2018) showed that dArc1 mRNA transfer required the 3'UTR so it is puzzling that the authors used heterologous UTRs. The results using FISH on endogenous dArc1 mRNA are dramatic. The authors should show definitively that their probe does not pick up over-expressed dArc1.

6. Many of the conclusions would be strengthened by the loss of function experiments, especially showing a requirement for Sas in dArc1 transfer.

Reviewer #1 (Public Review):

The authors of this manuscript address the question of whether vagal and sacral neural crest make distinct contributions to the enteric nervous system (ENS). The ENS regulates intestinal motility and many intestinal homeostatic functions; mutations in genes involved in ENS development lead to defects that can range from mild to catastrophic. The best studied of the ENS neuropathies is Hirschsprung disease, which is thought to result from failure of vagal neural crest cells to migrate properly into the distal intestine to differentiate into ENS neurons and glia. However, sacral neural crest cells are known to contribute to the distal ENS and have to migrate a considerably shorter distance. Thus, understanding whether there are distinct vagal and sacral contributions to the ENS provides insights into basic ENS biology as well as the basis of human disease. Previous transplantation and ablation studies have already revealed that vagal and sacral neural crest have differing ENS developmental potentials, although this has not been directly correlated with discrete cell types. Here the authors combine single cell RNA sequencing and a viral lineage tracing technique that is new to avians to gain insight into the different ENS cell types generated by vagal and sacral neural crest along the length of the intestine. They find that vagal and sacral neural crest exhibit distinct transcriptional profiles and contribute both similar and different progeny to the ENS. For example, both vagal and sacral crest contribute to progenitor cells, connective tissue and neurons, but most secretomotor neurons are vagal crest-derived whereas most adrenergic neurons and melanocytes in the distal intestine are sacral-crest derived. The authors also suggest a role of the local environment in determining the fate of vagal and sacral derivatives. The data presented in this manuscript provide a multitude of hypotheses about similarities and differences between vagal and sacral derived ENS cells. However, a shortcoming of the manuscript is that all of these hypotheses remain untested.

Reviewer #1 (Public Review):

In this project, the authors used a single-cell RNA sequencing technique, created a cell atlas of normal and diseased human anterior cruciate ligaments of 49,356 cells from 8 patients, explored the variations of the cell subtypes' spatial distributions, and found their associations with ligamental degeneration. Using the single-cell RNA sequencing data, the authors identified fibroblast subsets unique to normal and diseased tissues, revealed two processes of acute and chronic disease outcome in ligamental degeneration and found immune cell and stromal cell subclusters changed the extracellular matrix in ligament and contributed to the disease. Combined with spatial transcriptome sequencing, they found the spatial distribution of immune and stromal cells associated with the disease and demonstrated cell-cell communications among endothelial cells, macrophages, and fibroblasts.