Reviewer #2 (Public review):

Summary:

Egawa et al describe the developmental timeline of the assembly of nodes of Ranvier in the chick brainstem auditory circuit. In this unique system, the spacing between nodes varies significantly in different regions of the same axon from early stages, which the authors suggest is critical for accurate sound localization. Egawa et al set out to determine which factors regulate this differential node spacing. They do this by using immunohistological analyses to test the correlation of node spacing with morphological properties of the axons, and properties of oligodendrocytes, glial cells that wrap axons with the myelin sheaths that flank the nodes of Ranvier. They find that axonal structure does not vary significantly, but that oligodendrocyte density and morphology varies in the different regions traversed by these axons, which suggests this is a key determinant of the region-specific differences in node density and myelin sheath length. They also find that differential oligodendrocyte density is partly determined by secreted neuronal signals, as (presumed) blockage of vesicle fusion with tetanus toxin reduced oligodendrocyte density in the region where it is normally higher. Based on these findings, the authors propose that oligodendrocyte morphology, myelin sheath length, and consequently nodal distribution are primarily determined by intrinsic oligodendrocyte properties rather than neuronal factors such as activity.

Major comments:

(1) It is essential that the authors validate the efficiency of TeNT to prove that vesicular release is indeed inhibited, to be able to make any claims about the effect of vesicular release on oligodendrogenesis/myelination.

(2) Related to 1, can the authors clarify if their TeNT expression system results in the whole tract being silenced? It appears from Fig. 6 that their approach leads to sparse expression of TeNT in individual neurons, which enables them to measure myelination parameters. Can the authors discuss how silencing a single axon can lead to a regional effect in oligodendrocyte number?

(3) The authors need to fully revise their statistical analyses throughout and supply additional information that is needed to assess if their analyses are adequate:<br /> (3.1) the authors use a variety of statistical tests and it is not always obvious why they chose a particular test. For example, in Fig. 2G they chose a Kruskal-Wallis test instead of a two-way ANOVA or Mann-Whitney U test, which are much more common in the field. What is the rationale for the test choice?<br /> (3.2) in some cases, the choice of test appears wholly inappropriate. For example, in Fig. 3H-K, an unpaired t-test is inappropriate if the two regions were analysed in the same samples. In Fig. 5, was a t-test used for comparisons between multiple groups in the same dataset? If so, an ANOVA may be more appropriate.<br /> (3.3) in some cases, the authors do not mention which test was used (Fig 3: E-G no test indicated, despite asterisks; G/L/M - which regression test that was used? What does r indicate?)<br /> (3.4) more concerningly, throughout the results, data may have been pseudo-replicated. t-tests and ANOVAs assume that each observation in a dataset is independent of the other observations. In figures 1-4 and 6 there is a very large "n" number, but the authors do not indicate what this corresponds to. This leaves it open to interpretation, and the large values suggest that the number of nodes, internodal segments, or cells may have been used. These are not independent experimental units, and should be averaged per independent biological replicate - i.e. per animal (N).<br /> (3.5) related to the pseudo-replication issue, can the authors include individual datapoints in graphs for full transparency, per biological replicates, in addition or in alternative to bar-graphs (e.g. Fig. 5 and 6).

(4) The main finding of the study is that the density of nodes differs between two regions of the chicken auditory circuit, probably due to morphological differences in the respective oligodendrocytes. Can the authors discuss if this finding is likely to be specific to the bird auditory circuit?

(5) Provided the authors amend their statistical analyses, and assuming significant differences remain as shown, the study shows a correlation (but not causation) between node spacing and oligodendrocyte density, but the authors did not manipulate oligodendrocyte density per se (i.e. cell-autonomously). Therefore, the authors should either include such experiments, or revise some of their phrasing to soften their claims and conclusions. For example, the word "determine" in the title could be replaced by "correlate with" for a more accurate representation of the work. Similar sentences throughout the main text should be amended.

(6) The authors fail to introduce, or discuss, very pertinent prior studies, in particular to contextualize their findings with:<br /> (6.1) known neuron-autonomous modes of node formation prior to myelination, e.g. Zonta et al (PMID 18573915); Vagionitis et al (PMID 35172135); Freeman et al (PMID 25561543)<br /> (6.2) known effects of vesicular fusion directly on myelinating capacity and oligodendrogenesis, e.g. Mensch et al (PMID 25849985)<br /> (6.3) known correlation of myelin length and thickness with axonal diameter, e.g. Murray & Blakemore (PMID 7012280); Ibrahim et al (PMID 8583214); Hildebrand et al (PMID 8441812).<br /> (6.4) regional heterogeneity in the oligodendrocyte transcriptome (page 9, studies summarized in PMID 36313617)

Significance:

In our view the study tackles a fundamental question likely to be of interest to a specialized audience of cellular neuroscientists. This descriptive study is suggestive that in the studied system, oligodendrocyte density determines the spacing between nodes of Ranvier, but further manipulations of oligodendrocyte density per se are needed to test this convincingly.

Reviewer #3 (Public review):

Summary:

The authors have investigated the myelination pattern along the axons of chick avian cochlear nucleus. It has already been shown that there are regional differences in the internodal length of axons in the nucleus magnocellularis. In the tract region across the midline, internodes are longer than in the nucleus laminaris region. Here the authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons. However, the demonstration falls rather short of being convincing.

Major comments:

(1) The authors neglect the possibility that nodal cluster may be formed prior to myelin deposition. They have investigated stages E12 (no nodal clusters) and E15 (nodal cluster plus MAG+ myelin). Fig. 1D is of dubious quality. It would be important to investigate stages between E12 and E15 to observe the formation of pre-nodes, i.e., clustering of nodal components prior to myelin deposition.

(2) The claim that axonal diameter is constant along the axonal length need to be demonstrated at the EM level. This would also allow to measure possible regional differences in the thickness of the myelin sheath and number of myelin wraps.

(3) The observation that internodal length differs is explain by heterogeneity of sources of oligodendrocyte is not convincing. Oligodendrocytes a priori from the same origin remyelinate shorter internode after a demyelination event.

Significance:

The authors suggest that the difference in internodal length is attributed to heterogeneity of oligodendrocytes. In the tract region oligodendrocytes would contribute longer myelin internodes, while oligodendrocytes in the nucleus laminaris region would synthesize shorter myelin internodes. Not only length of myelin internodes differs, but also along the same axon unmyelinated areas between two internodes may vary. This is an interesting contribution since all these differences contribute to differential conduction velocity regulating ipsilateral and contralateral innervation of coincidence detector neurons.

Reviewer #1 (Public review):

Summary:

Ma & Yang et al. report a new investigation aimed at elucidating one of the key nutrients S. Typhimurium (STM) utilizes with the nutrient-poor intracellular niche within macrophage, focusing on the amino acid beta-alanine. From these data, the authors report that beta-alanine plays important roles in mediating STM infection and virulence. The authors employ a multidisciplinary approach that includes some mouse studies, and ultimately propose a mechanism by which panD, involved in B-Ala synthesis, mediates regulation of zinc homeostatisis in Salmonella.

Strengths and weaknesses:

The results and model are adequately supported by the authors' data. Further work will need to be performed to learn whether the Zn2+ functions as proposed in their mechanism. By performing a small set of confirmatory experiments in S. Typhi, the authors provide some evidence of relevance to human infections.

Impact:

This work adds to the body of literature on the metabolic flexibility of Salmonella during infection that enable pathogenesis.

Reviewer #3 (Public review):

Summary:

Salmonella is interesting due to its life within a compact compartment, which we call SCV or Salmonella containing vacuole in the field of Salmonella. SCV is a tight-fitting vacuole where the acquisition of nutrients is a key factor by Salmonella. The authors among many nutrients, focussed on beta-alanine. It is also known that Salmonella requires beta-alanine from many other studies. The authors have done in vitro RAW macrophage infection assays and In vivo mouse infection assays to see the life of Salmonella in the presence of beta-alanine. They concluded by comprehending that beta-alanine modulates the expression of many genes including zinc transporters which is required for pathogenesis.

Strengths:

Made a couple of knockouts in Salmonella and did transcriptomic to understand the global gene expression pattern

Weaknesses:

Transport of Beta-alanine to SCV is not yet elucidated. Is it possible to determine whether the Zn transporter is involved in B-alanine transport?

Beta-alanine can also be shuttled to form carnosine along with histidine. If beta-alanine is channelled to make more carnosine, then the virulence phenotypes may be very different.

Some amino acid transporters can be knocked out to see if beta-alanine uptake is perturbed. Like ArgT transport Arginine, and its mutation perturbs the uptake of beta-alanine. What is the beta-alanine concentration in the SCV? SCVS can be purified at different time points, and the Beta-alanine concentration can be measured

Reviewer #2 (Public Review):

Summary:

This paper describes a new approach to detecting directed causal interactions between two genes without directly perturbing either gene. To check whether gene X influences gene Z, a reporter gene (Y) is engineered into the cell in such a way that (1) Y is under the same transcriptional control as X, and (2) Y does not influence Z. Then, under the null hypothesis that X does not affect Z, the authors derive an equation that describes the relationship between the covariance of X and Z and the covariance of Y and Z. Violation of this relationship can then be used to detect causality.

The authors benchmark their approach experimentally in several synthetic circuits. In 4 positive control circuits, X is a TetR-YFP fusion protein that represses Z, which is an RFP reporter. The proposed approach detected the repression interaction in 2 of the 4 positive control circuits. The authors constructed 16 negative control circuit designs in which X was again TetR-YFP, but where Z was either a constitutively expressed reporter, or simply the cellular growth rate. The proposed method detected a causal effect in two of the 16 negative controls, which the authors argue is perhaps not a false positive, but due to an unexpected causal effect. Overall, the data support the potential value of the proposed approach.

Strengths:

The idea of a "no-causality control" in the context of detected directed gene interactions is a valuable conceptual advance that could potentially see play in a variety of settings where perturbation-based causality detection experiments are made difficult by practical considerations.

By proving their mathematical result in the context of a continuous-time Markov chain, the authors use a more realistic model of the cell than, for instance, a set of deterministic ordinary differential equations.

The authors have improved the clarity and completeness of their proof compared to a previous version of the manuscript.

Limitations:

The authors themselves clearly outline the primary limitations of the study: The experimental benchmark is a proof of principle, and limited to synthetic circuits involving a handful of genes expressed on plasmids in E. coli. As acknowledged in the Discussion, negative controls were chosen based on the absence of known interactions, rather than perturbation experiments. Further work is needed to establish that this technique applies to other organisms and to biological networks involving a wider variety of genes and cellular functions. It seems to me that this paper's objective is not to delineate the technique's practical domain of validity, but rather to motivate this future work, and I think it succeeds in that.

Might your new "Proposed additional tests" subsection be better housed under Discussion rather than Results?

I may have missed this, but it doesn't look like you ran simulation benchmarks of your bootstrap-based test for checking whether the normalized covariances are equal. It would be useful to see in simulations how the true and false positive rates of that test vary with the usual suspects like sample size and noise strengths.

It looks like you estimated the uncertainty for eta_xz and eta_yz separately. Can you get the joint distribution? If you can do that, my intuition is you might be able to improve the power of the test (and maybe detect positive control #3?). For instance, if you can get your bootstraps for eta_xz and eta_yz together, could you just use a paired t-test to check for equality of means?

The proof is a lot better, and it's great that you nailed down the requirement on the decay of beta, but the proof is still confusing in some places:

- On pg 29, it says "That is, dividing the right equation in Eq. 5.8 with alpha, we write the ..." but the next equation doesn't obviously have anything to do with Eq. 5.8, and instead (I think) it comes from Eq 5.5. This could be clarified.

- Later on page 29, you write "We now evoke the requirement that the averages xt and yt are stationary", but then you just repeat Eq. 5.11 and set it to zero. Clearly you needed the limit condition to set Eq. 5.11 to zero, but it's not clear what you're using stationarity for. I mean, if you needed stationarity for 5.11 presumably you would have referenced it at that step.

It could be helpful for readers if you could spell out the practical implications of the theorem's assumptions (other than the no-causality requirement) by discussing examples of setups where it would or wouldn't hold.

Reviewer #1 (Public review):

The manuscript by Rios et al. investigates the potential of GSK3 inhibition to reprogram human macrophages, exploring its therapeutic implications in conditions like severe COVID-19. The authors present convincing evidence that GSK3 inhibition shifts macrophage phenotypes from pro-inflammatory to anti-inflammatory states, thus highlighting the GSK3-MAFB axis as a potential therapeutic target. Using both GM-CSF- and M-CSF-dependent monocyte-derived macrophages as model systems, the study provides extensive transcriptional, phenotypic, and functional characterizations of these reprogrammed cells. The authors further extend their findings to human alveolar macrophages derived from patient samples, demonstrating the clinical relevance of GSK3 inhibition in macrophage biology.

The experimental design is sound, leveraging techniques such as RNA-seq, flow cytometry, and bioenergetic profiling to generate a comprehensive dataset. The study's integration of multiple model systems and human samples strengthens its impact and relevance. The findings not only offer insights into macrophage plasticity but also propose novel therapeutic strategies for macrophage reprogramming in inflammatory diseases.

Strengths:

(1) Robust Experimental Design: The use of both in vitro and ex vivo models adds depth to the findings, making the conclusions applicable to both experimental and clinical settings.

(2) Thorough Data Analysis: The extensive use of RNA-seq and gene set enrichment analysis (GSEA) provides a clear transcriptional signature of the reprogrammed macrophages.

(3) Relevance to Severe COVID-19: The study's focus on macrophage reprogramming in the context of severe COVID-19 adds clinical significance, especially given the relevance of macrophage-driven inflammation in this disease.

Weaknesses:

There are no significant weaknesses in the study.

Reviewer #1 (Public review):

Summary:

The topic of tumor-immune co-evolution is an important, understudied topic with, as the authors noted, a general dearth of good models in this space. The authors have made important progress on the topic by introducing a stochastic branching process model of antigenicity/immunogenicity and measuring the proportion of simulated tumors that go extinct. The model is extensively explored, and the authors provide some nice theoretical results in addition to simulated results.

Major comments

The text in lines 183-191 is intuitively and nicely explained. However, I am not sure all of it follows from the figure panels in Figure 2. For example, the authors refer to a mutation that has a large immunogenicity, but it's not shown how many mutations, or the relative size of the mutations in Figure 2. The same comment holds true for the claim that spikes also arise for mutations with low antigenicity.

Reviewer #2 (Public review):

Summary:

In this work, the authors developed a model of tumour-immune dynamics, incorporating stochastic antigenic mutation accumulation and escape within the cancer cell population. They then used this model to investigate how tumour-immune interactions influence tumour outcome and summary statistics of sequencing data.

Strengths:

This novel modeling framework addresses an important and timely topic. The authors consider the useful question of how bulk and single-cell sequencing may provide insights into the tumour-immune interactions and selection processes.

Weaknesses:

One set of conclusions presented in the paper is the presence of cyclic dynamics between effector/cancer cells, antigenicity, and immunogenicity. However, these conclusions are supported in the manuscript by two sample trajectories of stochastic simulations, and these provide mixed support for the conclusions (i.e. the phasing asynchrony described in the text does not seem to apply to Figure 2C). Similarly, the authors also find immune selection effects on the shape of the mutational burden in Figure 5 D/H using a qualitative comparison between the distributions and theoretical predictions in the absence of immune response. However the discrepancy appears quite small in panel D, and there are no quantitative comparisons provided to evaluate the significance. An analysis of the robustness of all the conclusions to parameter variation is missing. Lastly, the role of the Appendix results in the main messages of the paper is unclear.

Reviewer #1 (Public review):

Processing in the primary visual cortex (V1) of mice is not only based on sensory inputs but also strongly modulated by locomotion. In this study, Meier et al. ask whether neurons that are modulated by locomotion form clusters in V1. Their work is based on previous studies from their lab establishing a modularity in the organization of primary visual cortex based on M2-muscarinic-acetylcholine-receptor-positive patches and interpatches (Ji et al. 2015, D'Souza et al. 2019). In these studies, they have highlighted the clustering of specific visual pathways and inhibition. In the current study, they extend this modularity to motor inputs, confirming a clustering of locomotion modulated neurons but also show that these clusters overlap with the M2-negative interpatches of layer 1. Finally, they establish a blueprint for visual processing streams in V1, segregating projections to and from lateral visual areas (LM, AL, and RL) from projections to and from the lateral areas, including the visual area PM, the retrosplenial cortex (RSP), and the secondary motor area (MOs).

Conceptually, this study provides an important finding in the organization of locomotion-related signaling in primary visual cortex, which clearly has substantial implications for sensory processing in visual cortex. While the anatomical data are solid, the link to physiology is incomplete. In conclusion, there are numerous issues that leave the main findings in some doubt, so the authors have some work to do before I find this story convincing.

Major issues:

(1) The major results in this study rely on proper quantification of neuronal responses during resting and running. Recently, it has been reported that hemodynamic occlusion can strongly influence measurements of fluorescent changes using two-photon imaging (Yogesh et al. 2025, doi.org/10.1101/2024.10.29.620650). Since it is unclear whether there is an inherent bias in vasculature and hemodynamic occlusion in M2 patches and interpatches, a quantification of the effect of hemodynamic occlusion would be necessary. This control would ideally be done using mice with GFP expression to test if there is still a clustering of locomotion-modulated neurons that overlaps with M2-negative interpatches. Alternatively, the authors should at the very least quantify the vascularization in M2 patches and interpatches.

(2) To assess the effects, the authors use a correlation analysis for many of their findings (e.g., Figures 2b,c, 4j,k, ...). This, however, is inappropriate to assess the significance of the results. I suggest redoing all statistics with hierarchical bootstrap sampling (Saravanan et al. 2020, PMID: 33644783) or similar.

(3) The authors use two different measures to assess whether and to what extent a neuron is locomotion sensitive, the LMI and "locomotion-responsive". While the LMI is defined based on recording in the light and dark (Figure 2), the "locomotion-responsiveness" is defined only in the dark (Figure 3a,c,d). The link between the two measures should be clarified.

a) Additionally, Figure 2b shows higher average LMI for interpatches, but the locomotion-responsive fraction is similar in interpatches and patches (relative number of pairs in Figure 3c and Figure 3d). How do the authors explain this discrepancy?

b) How is the LMI calculated - based on the average or the maximum response over stimuli? One particular stimulus? If the LMI is defined for each stimulus separately, what is plotted in Figure 2b?

(4) In the last panels of Figures 4-7, the authors analyze the alignment of cell bodies with the M2 patches. While in superficial layers it might be straightforward to align the cell body locations with the M2 patches and interpatches in layer 1, this alignment does not appear to be trivial for deeper layers. The authors should provide additional material to convince the reader of the proper alignment.

(5) Related to point 4 above - Given the importance of a proper alignment of M2 patches with the in vivo imaging, the in vivo - ex vivo alignment should be more convincing than Figure 1 C-E. Measuring M2 patches in vivo (as the authors have tried to do) would have provided more solid evidence. Have the authors tried to remove the dura for their in vivo imaging to increase signal-to-noise? In any case, more examples of proper alignment are necessary.

(6) The authors state that locomotion selectively affects M2-/M2- pairs based on Figure 3c. However, to make this claim, there should be a significant difference between the correlation of stimulus-driven noise of M2-/M2- locomotion-responsive pairs and M2-/M2- locomotion-unresponsive pairs, AND no significant difference in the same analysis for M2+/M2+ pairs (i.e., testing the differences between the bars in Figure 3c and Figure 3d).

Reviewer #2 (Public review):

Summary:

Meier et al. explore the variability of locomotion-related modulations in mouse area V1. They present 4 major findings: V1 L2/3 neurons beneath M2- interpatches are more strongly locomotion-modulated than those beneath M2+ patches, while V1 L2/3 neurons are more strongly orientation tuned. They then use viral tracing to examine the relationship of M2- interpatches and M2+ patches with inputs from and outputs to HVOs, MO, RSP, and LP, and find evidence for different closed-loop subnetworks within L1; these relationships, however, are more complicated for cell bodies in L2/3. Finally, they also describe an overlap between M2- interpatches and SOM+ dendrites/axons.

Strengths:

The strength of the manuscript is the detailed anatomical quantification of closed-loop connectivity, and the description of the organizing principles of M2- interpatches and M2+ patches.

Weaknesses:

The major weakness of the manuscript is the lack of a direct connection between the functional and the anatomical data, and the somewhat puzzling effects observed in the analysis of noise correlations. The former issue might be alleviated by modelling, where the authors could explore the space of possibilities that could explain the functional data based on the anatomical connectivity. Some control analyses could be done, for the comparison of noise correlations.

Reviewer #3 (Public review):

The authors build on the large body of their previous research, which showed that the mouse primary visual cortex is organised into two types of clusters, M2+ and M2-, which exhibit distinct input patterns from thalamus and higher visual cortical areas and distinct visual tuning preferences. The current study reveals that a like-to-like projection from within-cluster neurons to the areas that provide feedback projections and, furthermore, that neurons in the M2- clusters are more strongly affected by non-visual signals about the locomotion of the animal.

The study adds fundamental insights to our understanding of the principles of cortical organisation and computation, specifically how the cortex integrates sensory and action-related signals.

While the tracing data are very convincing, data analysis should be strengthened to support the claims:

(1) The locomotion modulation index (LMI) compares the mean activity during running and not running but does not seem to account for differences between visual stimuli, so that the LMI could be influenced by the neuron's visual tuning rather than its sensitivity to locomotion, e.g. if the mouse was running more when the neuron's preferred stimulus was presented. Trials should first be averaged per stimulus, and then across stimuli. Alternatively, only the preferred stimulus could be considered.

The significance test (unpaired t-test) suffers from the same flaw. Instead an ANOVA (with stimulus parameter as factor) would resolve the problem, or testing whether fitting the data with two tuning curves (one per locomotion state) or a single curve results in a lower error (using cross-validation).

Given that there is evidence that specific visual stimuli can induce more or less running in mice, this issue is very important to account for behavioural differences across stimuli.

(2) All bars in Figure 2b show a lower LMI than the reported mean LMI of 0.19. This should be checked.

(3) Correlation tests: Pearson correlation is only meaningful when applied to continuous data. A more suitable test for discrete data like the M2 patch quantile is a rank test like Kendall's coefficient of rank correlation. This applies to data in Figure 2b,c, 4j,k, Figure 2 - Supplement 2,1a, etc.

(4) How OSI was determined should be clarified. Specifically, were R_pref and R_ortho the mean responses to the two opposite movement directions? Similarly, how was the half-width at half-maximum of orientation determined? From the fits in Figure 2a, it looks like the widths of both Gaussians can be different.

(5) The correlation measures in Figure 3 would greatly benefit from additional analyses to help interpretation of the results.

a) Correlations between neurons typically increase with increasing firing rates (e.g., de la Rocha J, Doiron B, Shea-Brown E, Josić K, Reyes A. 2007. Correlation between neural spike trains increases with firing rate. Nature 448:802-6. doi:10.1038/nature06028). Could the higher correlations in M2+ pairs (Figure 3a) be explained by higher firing rates in M2+ compared to M2- neurons?

b) To determine correlations in Figure 3a, trials during locomotion and stationarity were pooled. As locomotion impacts the firing rate of the neurons, it would be helpful to separate correlations between the two states, locomotion vs stationarity, so the measures reflect something closer to "noise correlations" rather than tuning to locomotion.

c) Similarly, in Figure 3b, I wonder whether the large correlations in M2- pairs are driven by locomotion rather than functional connectivity. As suggested in b, a better test of noise correlations would be to account for locomotion, i.e., separate trials by stimulus identity and locomotion state. To prevent conditions with few trials from having greater weight in the overall noise correlations, I suggest the authors first z-score responses per condition, then determine noise correlations across all trials (as explained in Renart et al., 2010).

d) Correlations in Figure 3a,b should be tested with an ANOVA and a control for multiple tests.

(6) In plots like Figure 4j-l, it would be very informative to show individual measures (per ROI and mouse) in addition to mean +- SEM. As the counts are low (<10) it wouldn't obstruct the plot.

(7) The caption of Figure 4l says that most retrogradely labelled cells are located in L2/3. However, the plot only shows data from L2/3 and a single section of L4, so one cannot compare it to other layers. Can the authors corroborate the claim with data from other layers?

(8) Methods:<br /> The authors should provide more details on the visual stimuli: What was the background on which gratings were presented? How long was the inter-stimulus interval? What was presented during the inter-stimulus interval? How large were gratings used to map tuning to SF, TF, and orientation?

Reviewer #1 (Public review):

Summary:

The authors use a sophisticated task design and Bayesian computational modeling to test their hypothesis that information generalization (operationalized as a combination of self-insertion and social contagion) in social situations is disrupted in Borderline Personality Disorder. Their main finding relates to the observation that two different models best fit the two tested groups: While the model assuming both self-insertion and social contagion to be present when estimating others' social value preferences fit the control group best, a model assuming neither of these processes provided the best fit to BPD participants.

Strengths:

The revisions have substantially strengthened the paper and the manuscript is much clearer and easier to follow now. The strengths of the presented work lie in the sophisticated task design and the thorough investigation of their theory by use of mechanistic computational models to elucidate social decision-making and learning processes in BPD.

Weaknesses:

Some critical concerns remain after the first revision, particularly regarding the use of causal language and the clarity of the hypotheses and results, specified in the points below.

(1) The authors frequently refer to their predictions and theory as being causal, both in the manuscript and in their response to reviewers. However, causal inference requires careful experimental design, not just statistical prediction. For example, the claim that "algorithmic differences between those with BPD and matched healthy controls" are "causal" in my opinion is not warranted by the data, as the study does not employ experimental manipulations or interventions which might predictably affect parameter values. Even if model parameters can be seen as valid proxies to latent mechanisms, this does not automatically mean that such mechanisms cause the clinical distinction between BPD and CON, they could plausibly also refer to the effects of therapy or medication. I recommend that such causal language, also implicit to expressions like "parameter influences on explicit intentional attributions", is toned down throughout the manuscript.

(2) Although the authors have now much clearer outlined the stuy's aims, there still is a lack of clarity with respect to the authors' specific hypotheses. I understand that their primary predictions about disruptions to self-other generalisation processes underlying BPD are embedded in the four main models that are tested, but it is still unclear what specific hypotheses the authors had about group differences with respect to the tested models. I recommend the authors specify this in the introduction rather than refering to prior work where the same hypotheses may have been mentioned.

(3) Caveats should also be added about the exploratory nature of the many parameter group comparisons. If there are any predictions about group differences that can be made based on prior literature, the authors should make such links clear.

(4) I'm not sure I understand why the authors, after adding multiple comparison correction, now list two kinds of p-values. To me, this is misleading and precludes the point of multiple comparison corrections, I therefore recommend they report the FDR-adjusted p-values only. Likewise, if a corrected p-value is greater than 0.05 this should not be interpreted as a result.

(5) Can the authors please elaborate why the algorithm proposed to be employed by BPD is more 'entropic', especially given both their self-priors and posteriors about partners' preferences tended to be more precise than the ones used by CON? As far as I understand, there's nothing in the data to suggest BPD predictions should be more uncertain. In fact, this leads me to wonder, similarly to what another reviewer has already suggested, whether BPD participants generate self-referential priors over others in the same way CON participants do, they are just less favourable (i.e., in relation to oneself, but always less prosocial) - I think there is currently no model that would incorporate this possibility? It should at least be possible to explore this by checking if there is any statistical relationship between the estimated θ_ppt^m and 〖p(θ〗_par |D^0).

"To note, social contagion under M3 was highly correlated with contagion under M1 (see Fig S11). This provides some preliminary evidence that trauma impacts beliefs about individualism directly, whereas trauma and persecutory beliefs impact beliefs about prosociality through impaired trait mentalising" - I don't understand what the authors mean by this, can they please elaborate and add some explanation to the main text?

Reviewer #2 (Public review):

Summary:

The paper investigates social-decision making, and how this changes after observing the behaviour of other people, in borderline personality disorder. The paper employs a task including three phases, the first where participants make decision on how to allocate rewards to oneself and to a virtual partner, the second where they observe the same task performed by someone else, and a third phase equivalent to phase one, but with a new partner. Using sophisticated computational modelling to analyse choice data, the study reports that borderline participants (versus controls) are more certain about their preferences in phase one, used more neutral priors and are less flexible during phase two, and are less influenced by partners in phase three.

Strengths:

The topic is interesting and important, and the findings are potentially intriguing. The computational methods employed is clever and sophisticated, at the cutting edge of research in the field.

Weaknesses:

The paper is not based on specific empirical hypotheses formulated at the outset, but, rather, it uses an exploratory approach. Indeed, the task is not chosen in order to tackle specific empirical hypotheses. This, in my view, is a limitation since the introduction reads a bit vague and it is not always clear which gaps in the literature the paper aims to fill. As a further consequence, it is not always clear how the findings speak to previous theories on the topic.

Reviewer #3 (Public review):

In this paper, the authors use a three-phase economic game to examine the tendency to engage in prosocial versus competitive exchanges with three anonymous partners. In particular, they consider individual differences in the tendency to infer about others' tendencies based on one's preferences and to update one's preferences based on observations of others' behavior. The study includes a sample of individuals diagnosed with borderline personality disorder and a matched sample of psychiatrically healthy control participants.

On the whole, the experimental design is well-suited to the questions and the computational model analyses are thorough, including modern model-fitting procedures. I particularly appreciated the clear exposition regarding model parameterization and the descriptive Table 2 for qualitative model comparison. In the revised manuscript, the authors now provide a more thorough treatment of examining group differences in computational parameters given that the best-fitting model differed by group. They also examine the connection of their task and findings to related research focusing on self-other representation and mentalization (e.g., Story et al., 2024).

The authors note that the task does not encourage competition and instead captures individual differences in the motivation to allocate rewards to oneself and others in an interdependent setting. The paper could have been strengthened by clarifying how the Social Value Orientation framework can be used to interpret the motivations and behavior of BPD versus CON participants on the task. Although the authors note that their approach makes "clear and transparent a priori predictions," the paper could be improved by providing a clear and consolidated statement of these predictions so that the results could be interpreted vis-a-vis any a priori hypotheses.

Finally, the authors have amended their individual difference analyses to examine psychometric measures such as the CTQ alongside computational model parameter estimate differences. I appreciate that these analyses are described as exploratory. The approach of using a partial correlation network with bootstrapping (and permutation) was interesting, but the logic of the analysis was not clearly stated. In particular, there are large group (Table 1: CON vs. BPD) differences in the measures introduced into this network. As a result, it is hard to understand whether any partial correlations are driven primarily by mean differences in severity (correlations tend to be inflated in extreme groups designs due to the absence of observation in middle of scales forming each bivariate distribution). I would have found these exploratory analyses more revealing if group membership was controlled for.

Reviewer #2 (Public review):

Summary:

Demonstrate the breadth of IgA response as determined by isolating individual antigen-specific B cells and generating mAbs in mice following intranasal immunization of mice with SARS-CoV2 Spike protein. The findings show that some IgA mAb can neutralize the virus, but many do not. Notable immunization with Wuhan S protein generates a weak response to the omicron variant.

Strengths:

Detailed analysis characterizing individual B cells with the generation of mAbs demonstrates the response's breadth and diversity of IgA responses and the ability to generate systemic immune responses.

Comments on Revision:

I have re-reviewed the paper and responses to my and other reviewers' comments. I feel the authors have adequately addressed my and other reviewer's comments.

Reviewer #1 (Public review):

Summary:

Goal: Find downstream targets of cmk-1 phosphorylation, identify one that also seems to act in thermosensory habituation, test for genetic interactions between cmk-1 and this gene and assess where these genes are acting in the thermosensory circuit during thermosensory habituation.

Methods: Two in vitro analyses of cmk-1 phosphorylation of C. elegans proteins. Thermosensory habituation of cmk-1 and tax-6 mutants and double mutants was assessed by measuring rate of heat evoked reversals (reversal probability) of C. elegans before and after 20s ISI repeated heat pulses over 60 minutes.

Conclusions: cmk-1 and tax-6 act in separate habituation processes primarily in AFD, that interact complexly, but both serve to habituate the thermosensory reversal response. They found that cmk-1 primarily acts in AFD and tax-6 primarily acts in RIM (and FLP for naïve responses). They also identified hundreds of potential cmk-1 phosphorylation substrates in vitro.

Strengths:

The effects size in the genetic data is quite strong and a large number of genetic interaction experiments between cmk-1 and tax-1 demonstrate a complex interaction.

A major concern concerning this manuscript was the assumption that the process they are observing is habituation. The two previously cited papers using this (or a very similar) protocol, Lia and Glauser 2020 and Jordan and Glauser 2023, both use the word 'adaptation' to describe the observed behavioral decrement. Jordan and Glauser 2023 does occasionally use the words 'habituation' or 'habituation-like' 10 times, however it uses 'adaptation' over 100 times. It is critical to distinguish habituation from sensory adaptation (or fatigue) in this thermal reversal protocol. These processes are often confused/conflated, however they are very different; sensory adaptation is a process that decreases how much the nervous system is activated by a repeated stimulus, therefore it can even occur outside of the nervous system. Habituation is a learning process where the nervous system responds less to a repeated stimulus, despite (at least part of the nervous system) the nervous system still being similarly activated by the stimulus. Habituation is considered an attentional process, while adaptation is due to fatigue of sensory transduction machinery. Control experiments such as tests for dishabituation (where application of a different stimulus causes recovery of the decremented response) or rate of spontaneous recovery (more rapid recovery after short inter-stimulus intervals) are required to determine if habituation or sensory adaptation are occurring. These experiments will allow the results to be interpreted with clarity; without them, it isn't actually clear what biological process is actually being studied. The authors have accepted this distinction and now correctly call the process adaptation.

While there was originally some discrepancy between the two in vitro phosphorylation experiments and the in silico predictions, the revision has cleared up the issues.<br /> Figure 3 -S1: This model has been adjusted to more closely fit the data.

The authors have expanded the discussion about the significance of the sites of cmk-1 and tax-6 function in the neural circuit.

Reviewer #2 (Public review):

Summary:

The reduction in a response to a specific stimuli after repeated exposures is called habituation. Alterations in habituation to noxious stimuli are associated with chronic pain in humans, however the underlying molecular mechanisms involved are not clear. This study uses the nematode C. elegans to study genes and mechanisms that underlie adaptation to a form of noxious stimuli based on heat, termed thermo-noxious stimuli. The authors previously showed that the Calcium/Calmodulin-dependent protein kinase (CMK-1) regulates thermo-nociceptive adaptation in the nematode C. elegans. Although CMK-1 is a kinase with many known substrates, the downstream targets relevant for thermo-nociceptive adaptation are not known. In this study, the authors use two different kinase screens to identify phosphorylation targets of CMK-1. One of the targets they identify is Calcineurin (TAX-6). The authors show that CMK-1 phosphorylates a regulatory domain of Calcineurin at a highly conserved site (S443). In a series of elegant experiments, the authors use genetic and pharmacological approaches to increase or decrease CMK-1 and Calcineurin signaling to study their effects on thermo-nociceptive adaptation in C. elegans. They also combine these various approaches to study the interactions between these two signaling proteins. The authors use specific promoters to determine in which neurons CMK-1 and Calcineurin function to regulate thermo-nociceptive adaptation. The authors propose a model based on their findings, illustrating that CMK-1 and Calcineurin act mostly in different neurons to antagonistically regulate adaptation to thermo-nociceptive stimuli in a complex manner.

Strengths:

- Given the conservation of adaptation across phylogeny, identifying genes and mechanisms that underlie nociceptive adaptation in C. elegans may be relevant for understanding chronic pain in humans.<br /> - The identification of canonical CaM Kinase phosphorylation motifs in the substrates identified in the CMK-1 substrate screen validates the screen.<br /> - The use of loss and gain of function approaches to study the effects of CMK-1 and Calcineurin on thermo-nociceptive responses and adaptation is elegant.<br /> - The ability to determine the cellular place of action of CMK-1 and Calcineurin using neuron specific promoters in the nematode is a clear strength of the genetic model system.

Weaknesses:

- The manuscript begins by identifying Calcineurin as a direct substrate of CMK-1 but ends by showing that CMK-1 and Calcineurin mostly act in different neurons to regulate nociceptive adaptation, thus the physiological relevance of CMK-1 phosphorylation of Calcineurin is not clear.

Reviewer #1 (Public review):

Summary:

This manuscript described a structure-guided approach to graft important antigenic loops of the neuraminidase to a homotypic but heterologous NA. This approach allows the generation of well-expressed and thermostable recombinant proteins with antigenic epitopes of choice to some extent. The loop-grafted NA was designated hybrid.

Strengths:

The hybrid NA appeared to be more structurally stable than the loop-donor protein while acquiring its antigenicity. This approach is of value when developing a subunit NA vaccine which is difficult to express. So that antigenic loops could be potentially grafted to a stable NA scaffold to transfer strain-specific antigenicity.

Reviewer #2 (Public review):

In their manuscript, Rijal and colleagues describe a 'loop grafting' strategy to enhance expression levels and stability of recombinant neuraminidase. The work is interesting and important.

Major points from first round of review:

(1) The authors overstress the importance of the epitopes covered by the loops they use and play down the importance of antibodies binding to the side, the edges, or the underside of the NA. A number of papers describing those mAbs are also not included.

(2) The rationale regarding the PR8 hybrid is not well described and should be described better.

(3) Figure 3B and 6C: This should be given as numbers (quantified), not as '+'.

(4) Figure 5A and 7A: Negative controls are missing.

(5) The authors claim that they generate stable tetramers. Judging from SDS-PAGE provided in Supplementary Figure 3B (BS3-crosslined), many different species are present including monomers, dimers, tetramers, and degradation products of tetramers. In line 7 for example there are at least 5 bands.

[Editors' note: the authors have appropriately responded to and addressed these points.]

Reviewer #1 (Public review):

Summary:

This manuscript by Kremer et al. characterizes the tissue-specific responses to changes in TFAM levels and mtDNA copy number in prematurely aging mice (polg mutator model). The authors find that overexpression of TFAM can have beneficial or detrimental effects depending on the tissue type. For instance, increased TFAM levels increase mtDNA copy number in the spleen and improve spleen homeostasis but do not elevate mtDNA copy number in the liver and impair mtDNA expression. Similarly, the consequences of reduced TFAM expression are tissue-specific. Reduced TFAM levels improve brown adipocyte tissue function while other tissues are unaffected. The authors conclude that these tissue-specific responses to altered TFAM levels demonstrate that there are tissue-specific endogenous compensatory mechanisms in response to the continuous mutagenesis produced in the prematurely aging mice model, including upregulation of TFAM expression, elevated mtDNA copy number, and altered mtDNA gene expression. Thus, the impact of genetically manipulating global TFAM expression is limited and there must be other determinants of mtDNA copy number under pathological conditions beyond TFAM.

Strengths:

Overall, this is an interesting study. It does a good job of demonstrating that given the multi-functional role of TFAM, the outcome of manipulating its activity is complex.

Weaknesses:

No major weaknesses noted. The authors have adopted all our suggestions to improve the clarity of the manuscript.

Reviewer #2 (Public review):

Summary:

This study by Kremer et al. investigates the impact of modulation of expression of TFAM, a key protein involved in mitochondrial DNA (mtDNA) packaging and expression, in mtDNA mutator mice, which carry random mtDNA mutations. While previous research suggested that increasing TFAM could counteract the pathological effects of mtDNA mutations, this study reveals that the effects of TFAM modulation are tissue-specific. These findings highlight the complexity of mtDNA copy number regulation and gene expression, emphasizing that TFAM alone is not the sole determinant of mtDNA levels in contexts where oxidative phosphorylation is impaired. Other factors likely play a significant role, underscoring the need for nuanced approaches when targeting TFAM for therapeutic interventions.

Strengths:

The data presented in the manuscript are of high quality and support the major conclusions.

Comments on revisions:

The authors have thoroughly addressed all the points raised during the first round of review. Their revisions effectively clarify key aspects of the manuscript, and the additional data and explanations have significantly improved the overall quality of the work. I believe the manuscript is now well-prepared for publication.

Reviewer #1 (Public review):

Summary:

In this paper Kawasaki et al describe a regulatory role for the PIWI/piRNA pathway in rRNA regulation in Zebrafish. This regulatory role was uncovered through a screen for gonadogenesis defective mutants, which identified a mutation in the meioc gene, a coiled-coil germ granule protein. Loss of this gene leads to redistribution of Piwil1 from germ granules to the nucleolus, resulting in silencing of rRNA transcription.

Strengths:

Most of the experimental data provided in this paper is compelling. It is clear that in the absence of meioc, PiwiL1 translocates in to the nucleolus and results in down regulation of rRNA transcription. the genetic compensation of meioc mutant phenotypes (both organismal and molecular) through reduction in PiwiL1 levels are evidence for a direct role for PiwiL1 in mediating the phenotypes of meioc mutant.

Weaknesses:

Questions remain on the mechanistic details by which PiwiL1 mediated rRNA down regulation, and whether this is a function of Piwi in an unperturbed/wildtype setting. There is certainly some evidence provided in support of the a natural function for piwi in regulating rRNA transcription (figure 5A+5B). However, the de-enrichment of H3K9me3 in the heterozygous (Figure 6F) is very modest and in my opinion not convincingly different relative to the control provided. It is certainly possible that PiwiL1 is regulating levels through cleavage of nascent transcripts. Another aspect I found confounding here is the reduction in rRNA small RNAs in the meioc mutant; I would have assumed that the interaction of PiwiL1 with the rRNA is mediated through small RNAs but the reduction in numbers do not support this model. But perhaps it is simply a redistribution of small RNAs that is occurring. Finally, the ability to reduce PiwiL1 in the nucleolus through polI inhibition with actD and BMH-21 is surprising. What drives the accumulation of PiwiL1 in the nucleolus then if in the meioc mutant there is less transcription anyway?

Despite the weaknesses outlined, overall I find this paper to be solid and valuable, providing evidence for a consistent link between PIWI systems and ribosomal biogenesis. Their results are likely to be of interest to people in the community, and provide tools for further elucidating the reasons for this link.

Reviewer #2 (Public review):

Summary:

In this study, the authors report that Meioc is required to upregulate rRNA transcription and promote differentiation of spermatogonial stem cells in zebrafish. The authors show that upregulated protein synthesis is required to support spermatogonial stem cells' differentiation into multi-celled cysts of spermatogonia. Coiled coil protein Meioc is required for this upregulated protein synthesis and for increasing rRNA transcription, such that the Meioc knockout accumulates 1-2 cell spermatogonia and fails to produce cysts with more than 8 spermatogonia. The Meioc knockout exhibits continued transcriptional repression of rDNA. Meioc interacts with and sequesters Piwil1 to the cytoplasm. Loss of Meioc increases Piwil1 localization to the nucleolus, where Piwil1 interacts with transcriptional silencers that repress rRNA transcription.

Strengths:

This is fundamental study that expands our understanding of how ribosome biogenesis contributes to differentiation and demonstrates that zebrafish Meioc plays a role in this process during spermatogenesis. This work also expands our evolutionary understanding of Meioc and Ythdc2's molecular roles in germline differentiation. In mouse, the Meioc knockout phenocopies the Ythdc2 knockout, and studies thus far have indicated that Meioc and Ythdc2 act together to regulate germline differentiation. Here, in zebrafish, Meioc has acquired a Ythdc2-independent function. This study also identifies a new role for Piwil1 in directing transcriptional silencing of rDNA.

Comments on revisions:

Major and minor concerns were addressed in the revision.

Reviewer #3 (Public review):

Summary:

The paper describes the molecular pathway to regulate germ cell differentiation in zebrafish through ribosomal RNA biogenesis. Meioc sequesters Piwil1, a Piwi homolog, which suppresses the transcription of the 45S pre-rDNA by the formation of heterochromatin, to the perinuclear bodies.

Strong points:

The authors nicely provided the molecular evidence on the antagonism of Meioc to Piwil1 in the rRNA synthesis, which supported by the genetic evidence that the inability of the meioc mutant to enter meiosis is suppressed by the piwil1 heterozygosity. The authors nicely address my previous points.

Weak points:

Although the authors made an effort to revise the text. However, there are still some points that the authors need to check their text. Some of them are shown in "Minor points" below. I am sorry that some of them should have been pointed in my previous review.

Reviewer #1 (Public Review):

The study starts with the notion that in an AD-like disease model, ILC2s in the Rag1 knock-out were expanded and contained relatively more IL-5+ and IL-13+ ILC2s. This was confirmed in the Rag2 knock-out mouse model.

By using a chimeric mouse model in which wild-type knock-out splenocytes were injected into irradiated Rag1 knock-out mice, it was shown that even though the adaptive lymphocyte compartment was restored, there were increased AD-like symptoms and increased ILC2 expansion and activity. Moreover, in the reverse chimeric model, i.e. injecting a mix of wild-type and Rag1 knock-out splenocytes into irradiated wild-type animals, it was shown that the Rag1 knock-out ILC2s expanded more and were more active. Therefore, the authors could conclude that the RAG1 mediated effects were ILC2 cell-intrinsic.

Subsequent fate-mapping experiments using the Rag1Cre;reporter mouse model showed that there were indeed RAGnaïve and RAGexp ILC2 populations within naïve mice. Lastly, the authors performed multi-omic profiling, using single-cell RNA sequencing and ATAC-sequencing, in which a specific gene expression profile was associated with ILC2. These included well-known genes but the authors notably also found expression of Ccl1 and Ccr8 within the ILC2. The authors confirmed their earlier observations that in the RAGexp ILC2 population, the Th2 regulome was more suppressed, i.e. more closed, compared to the RAGnaïve population, indicative of the suppressive function of RAG on ILC2 activity. I do agree with the authors' notion that the main weakness was that this study lacks the mechanism by which RAG regulates these changes in ILC2s.

The manuscript is very well written and easy to follow, and the compelling conclusions are well supported by the data. The experiments are meticulously designed and presented. I wish to commend the authors for the study's quality.

Reviewer #2 (Public Review):

Summary:

The study by Ver Heul et al., investigates the consequences of RAG expression for type 2 innate lymphoid cell (ILC2) function. RAG expression is essential for the generation of the receptors expressed by B and T cells and their subsequent development. Innate lymphocytes, which arise from the same initial progenitor populations, are in part defined by their ability to develop in the absence of RAG expression. However, it has been described in multiple studies that a significant proportion of innate lymphocytes show a history of Rag expression. In compelling studies several years ago, members of this research team revealed that early Rag expression during the development of Natural Killer cells (Karo et al., Cell 2014), the first described innate lymphocyte, had functional consequences.

Here, the authors revisit this topic, a worthwhile endeavour given the broad history of Rag expression within all ILCs and the common use of RAG-deficient mice to specifically assess ILC function. Focusing on ILC2s and utilising state-of-the-art approaches, the authors sought to understand whether early expression of Rag during ILC2 development had consequences for activity, fitness, or function. Having identified cell-intrinsic effects in vivo, the authors investigated the causes of this, identifying epigenetic changes associated with the accessibility genes associated with core ILC2 functions.

The manuscript is well written and does an excellent job of supporting the reader through reasonably complex transcriptional and epigenetic analyses, with considerate use of explanatory diagrams. Overall I think that the conclusions are fair, the topic is thought-provoking, and the research is likely of broad immunological interest. I think that the extent of functional data and mechanistic insight is appropriate.

Strengths:

- The logical and stepwise use of mouse models to first demonstrate the impact on ILC2 function in vivo and a cell-intrinsic role. Initial analyses show enhanced cytokine production by ILC2 from RAG-deficient mice. Then through two different chimeric mice (including BM chimeras), the authors convincingly show this is cell intrinsic and not simply as a result of lymphopenia. This is important given other studies implicating enhanced ILC function in RAG-/- mice reflect altered competition for resources (e.g. cytokines).

- Use of Rag expression fate mapping to support analyses of how cells were impacted - this enables a robust platform supporting subsequent analyses of the consequences of Rag expression for ILC2.

- Use of snRNA-seq supports gene expression and chromatin accessibility studies - these reveal clear differences in the data sets consistent with altered ILC2 function.

- Convincing evidence of epigenetic changes associated with loci strongly linked to ILC2 function. This forms a detailed analysis that potentially helps explain some of the altered ILC2 functions observed in ex vivo stimulation assays.

- Provision of a wealth of expression data and bioinformatics analyses that can serve as valuable resources to the field.

Reviewer #1 (Public review):

Summary:

The authors aimed to classify hepatocellular carcinoma (HCC) patients into distinct subtypes using a comprehensive multi-omics approach. They employed an innovative consensus clustering method that integrates multiple omics data types, including mRNA, lncRNA, miRNA, DNA methylation, and somatic mutations. The study further sought to validate these subtypes by developing prognostic models using machine learning algorithms and extending the findings through single-cell RNA sequencing (scRNA-seq) to explore the cellular mechanisms driving subtype-specific prognostic differences.

Strengths:

(1) Comprehensive Data Integration: The study's integration of various omics data provides a well-rounded view of the molecular characteristics underlying HCC. This multi-omics approach is a significant strength, as it allows for a more accurate and detailed classification of cancer subtypes.

(2) Innovative Methodology: The use of a consensus clustering approach that combines results from 10 different clustering algorithms is a notable methodological advancement. This approach reduces the bias that can result from relying on a single clustering method, enhancing the robustness of the findings.

(3) Machine Learning-Based Prognostic Modeling: The authors rigorously apply a wide array of machine learning algorithms to develop and validate prognostic models, testing 101 different algorithm combinations. This comprehensive approach underscores the study's commitment to identifying the most predictive models, which is a considerable strength.

(4) Validation Across Multiple Cohorts: The external validation of findings in independent cohorts is a critical strength, as it increases the generalizability and reliability of the results. This step is essential for demonstrating the clinical relevance of the proposed subtypes and prognostic models.

Weaknesses:

(1) Inconsistent Storyline:<br /> Despite the extensive data mining and rigorous methodologies, the manuscript suffers from a lack of a coherent and consistent narrative. The transition between different sections, particularly from multi-omics data integration to single-cell validation, feels disjointed. A clearer articulation of how each analysis ties into the overall research question would improve the manuscript.

(2) Questionable Relevance of Immune Cell Activity Analysis:<br /> The evaluation of immune cell activities within the cancer cell model raises concerns about its meaningfulness. The methods used to assess immune function in the tumor microenvironment may not be fully appropriate, potentially limiting the insights gained from this part of the study.

(3) Incomplete Single-Cell RNA-Seq Validation:<br /> The validation of the findings using single-cell RNA-seq data appears insufficient to fully support the study's claims. While the authors make an effort to extend their findings to the single-cell level, the analysis lacks depth. A more comprehensive validation is necessary to substantiate the robustness of the identified subtypes.

(4) Figures and Visualizations:<br /> Several figures in the manuscript are missing necessary information, which affects the clarity of the results. For instance, the pathways in Figure 3A could be clustered to enhance interpretability, the blue bar in Figure 4A is unexplained, and Figure 4B is not discussed in the text. Additionally, the figure legend in Figure 7C lacks detail, and many figure descriptions merely repeat the captions without providing deeper insights.

(5) Appraisal of the Study's Aims and Results<br /> The authors have set out to achieve an ambitious goal of classifying HCC patients into distinct prognostic subtypes and validating these findings through both bulk and single-cell analyses. While the methodologies employed are innovative and the data integration comprehensive, the study falls short in fully achieving its aims due to inconsistencies in the narrative and incomplete validation. The results partially support the conclusions, but the lack of coherence and depth in certain areas limits the overall<br /> impact of the study.

(6) Impact on the Field<br /> If the identified weaknesses are addressed, this study has the potential to significantly impact the field of HCC research. The multi-omics approach combined with machine learning is a powerful framework that could set a new standard for cancer subtype classification. However, the current state of the manuscript leaves some uncertainty regarding the practical applicability of the findings, particularly in clinical settings.

(7) Additional Context<br /> For readers and researchers, this study offers a valuable look into the potential of integrating multi-omics data with machine learning to improve cancer classification and prognostication. However, readers should be aware of the noted weaknesses, particularly the need for more consistent narrative development and comprehensive validation of the methods. Addressing these issues could greatly enhance the study's utility and relevance to the community.

Comments on revisions:

The authors have addressed the reviewers' concerns effectively.

Reviewer #1 (Public review):

Summary:

The authors propose a new method to quantitatively assess morphogenetic processes during organismal development. They apply their method to ascidian morphogenesis and thus find that gastrulation is a two-step process.

The method applies to morphogenetic changes of surfaces. It consists of the following steps: first, surface deformations are quantified based on microscopy images without requiring cellular segmentation and tracking. This is achieved by mapping, at each time point, a polygonal mesh initially defined on a sphere to the surface of the embryo. The mapped vertices of this polygonal mesh then serve as (Lagrangian) markers for the embryonic surface. From these, one can infer the deformation of the surface, which can be expressed in terms of the strain tensor at each point of the surface. Changes in the strain tensor give the strain rate, which captures the morphogenetic processes. Second, at each time point, the strain rate field is decomposed in terms of spherical harmonics. Finally, the evolution of the weights of the various spherical harmonics in the decomposition is analysed via a wavelet analysis. The authors apply their workflow to ascidian development between 4 and 8.7 hpf. From their analysis they find clear indications for gastrulation and neurulation and identify two sub-phases of gastrulation, namely, endoderm invagination and 'blastophore closure'.

Strengths:

The combination of various tools allows the authors to obtain a quantitative description of the developing embryo without the necessity of identifying fiducial markers. Visual inspection shows that their method works well. Furthermore, this quantification then allows for an unbiased identification of different morphogenetic phases.

Weaknesses:

At times, the explanation of the method is hard to follow, unless the reader is already familiar with concepts like level-set methods or wavelet transforms. Furthermore, the software for performing the determination of Lagrangian markers or the subsequent spectral analysis does not seem to be available to the readers.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors proposed a method to quantitatively analyze 3D live imaging data of early developing embryos, using the ascidian development as an example. For this purpose, the previously proposed level set method was used to computationally track the temporal evolution of reference points introduced on the embryo surface. Then, from the obtained three-dimensional trajectories, the velocity field was obtained, from which the strain rate field was computed. The strain rate field was analyzed using spherical harmonics.

In this paper, the authors focused on the modes with lower order with real coefficients. The time evolution of these modes was analyzed using wavelet transforms. The results obtained by the pipeline reflected the developmental stages of ascidian embryos.

Strengths:

In this way, this manuscript proposes a pipeline of analyses combining various methods. The strength of this method lies in its ability to quantitatively analyze the deformation of the entire embryo without the requirement for cellular segmentation and tracking.

Weaknesses:

The mathematics behind this method is not straightforward to understand. The value of this method will be understood as analyses of real data using this method accumulate.

Comments on revised version:

I have reviewed the revised manuscript and the reply from the authors. All concerns have been addressed appropriately.

Reviewer #2 (Public review):

The revised manuscript by Genzoni et al. reports the striking discovery of a regulatory role for trophic eggs. Prior to this study, trophic eggs were widely assumed to play a nutritional role in the colony, but this study shows that trophic eggs can suppress queen development, and therefore, can play a role in regulating caste determination in specific social contexts. In this revised version of the manuscript, the authors have addressed many of the concerns raised in the first version regarding the lack of sufficient information and context in the Introduction and Discussion. I have several (mostly minor) comments I would like the authors to address:

Comments:

(1) The authors' experimental design is based on the comparison of a larva-only (control) versus larva+3 trophic eggs (treatment). The authors convincingly show that the larva plus 3 trophic eggs treatment has an inhibitory effect versus larva-only control. However, the authors should have also done a treatment composed of larva + 3 viable eggs to determine if the inhibitory effect observed on queens is specific to trophic eggs or whether it is an inhibitory effect of all eggs. This has had important mechanistic consequences, because if the inhibitory effect is specific to trophic eggs, it means there are specific inhibitory factors deposited in trophic eggs during oogenesis and the differences observed between trophic versus viable eggs are meaningful beyond just nutritional differences. If the inhibitory effect is a property of all eggs, then the inhibitory factor is dumped into all eggs and the differences observed between trophic and viable eggs are related to something else. In all cases, this reviewer is not necessarily asking that they perform this additional treatment, but the authors have to be clear in the text that they cannot claim that the inhibitory effect is specific to trophic eggs alone without doing this experiment.

(2) The other untested assumption the authors are making is that queen-laid trophic eggs would behave the same as worker-laid trophic eggs. This is apparent in the Discussion (line 422). They should instead highlight the interesting question of whether worker-laid trophic eggs would be similar in composition and have the same effect on caste as queen-laid eggs.

(3) To this reviewer, they are missing a crucial explanation in the discussion. As far as this reviewer knows, young queens produce a higher proportion of trophic eggs than older queens, meaning that trophic egg production decreases with age of the queen. This raises the possibility that trophic eggs may, in part, function to prevent the production of more virgin queens in young and immature colonies with small colony sizes. This would allow colonies to invest in producing more workers at a time when rapidly expanding the colony is crucial in young colonies' life. Production of trophic eggs, therefore, may have a dual function: one for nutrition and larval survival, and one in suppressing queen development in immature young colonies. It can be said then that trophic eggs can regulate / influence caste determination in specific social / life history contexts of the colony, rather than only proposing that trophic eggs are a constant attempt by the queen to manipulate her offspring. I prefer the superorganism explanation, but readers should at least hear explanations at the individual and superorganism scales as a way of explaining the authors' discovery that trophic eggs suppress further queen development.

(4) Why did the authors change the wording from caste "determination" to caste "differentiation." Determination is more appropriate because the trophic eggs do not affect morphogenesis of queens or workers, but rather the developmental switch between queens and workers.

(5) Khila and Abouheif (2008) is listed in the References but not cited in the text.

(6) On Line 70-81: "...may play a role in the regulation of body size" - I think the authors are trying to be broad in their language here since one study showed trophic eggs increased worker size but didn't induce queens, but this statement implies that the hypothesis is that trophic eggs act via body size to affect caste. Since the authors don't measure body size changes, only binary caste outcome, this is not the best way to set up the question. Could instead just conclude that previous work shows an effect on both caste and body size.

(7) Paragraph beginning line 432: this paragraph seems out of place, not well connected to previous parts of discussion. It introduces the term "egg cannibalism" without defining it - not clear if this is meant as a synonym for eating of trophic eggs, or broader (i.e., eating viable eggs also). Could either remove the paragraph, or better set up the context that egg-eating behaviour is common in ants, could have evolved for worker policing reasons and/or for nutritional exchange, trophic eggs (and potentially co-option of trophic eggs for caste determination functions) presumably evolved in this context of existing egg-eating behaviour.

(8) Line 41: Should read 'play an important part.

(9) Line 51: The food that was given is listed, but there is no information about the quantity of food given.

(10) Line 74: The paragraph states that queens were isolated for 16 hours per day. However, it lacks a clear reason for this specific duration. Why 16 hours? Could this isolation period have impacted egg quality or larval development?

(11) Line 76: The eggs were collected every 8 hours and then held for 10 days until hatching. This is a very long time for eggs to be held outside of the normal colony environment. This could have a large impact on the viability of the eggs, and the resulting larvae.

(12) Line 78: twice "that" in "suggested that that the larger castes"

(13) Lines 96-97: the following sentence is unclear: "The question mark indicates that it is unclear whether about the evidence for the production trophic eggs by queens and workers"

(14) Line 209: By simply stating "binomial GLMM," the authors are leaving out a crucial piece of information. Readers cannot fully understand how the model was fitted or how the coefficients should be interpreted without knowing the link function. Therefore, the critique is that for complete and replicable science, the link function must be reported.

Reviewer #1 (Public review):

Summary:

The authors' stated aim is to introduce so-called Minkowski tensors to characterize and quantify the shape of cells in tissues. The authors introduce Minkowski tensors and then define the p-atic order q_p, where p is an integer, as a cell shape measure. They also introduce a previously defined measure of p-atic order in the form of the parameter γ_p. The authors compute q_pp for data obtained by simulating an active vertex model and a multiphase field model, where they focus on p=2 and p=6 - nematic and hexatic order - as the two values of highest biological relevance. Based on their analysis, the authors claim that q₂ and q₆ are independent, that there is no crossover for the coarse-grained quantities, that the comparison of q_p for different values of p is not meaningful, and determine the dependence of the mean value of q₂ and q₆q₆ on cell activity and deformability. They then apply their method to data from MDCK monolayers and argue that the γ_p "fail to capture the nuances of irregular cell shapes".

Strength:

The work presents a set of parameters that are useful for analyzing cell shape.

Weaknesses:

The main weakness of the manuscript is that the points that the authors make are not sufficiently elaborated or supported by the data. Although they start out with Minkowski tensors, they eventually only consider the parameters q_p, which can be defined without any recourse to Minkowski tensors. Also, I dare to doubt that the average reader will benefit from the introduction to Minkowski tensors as it remains abstract and does not really go beyond repeating definitions. Eventually, for me, the work boils down to the statement that when you want to characterize (2d) cell shape, then it is better to take the whole cell contour instead of only the positions of the vertices of a polygon that approximates the full cell shape. By the way, for polygons, the q_p and γ_p should convey the same information as the vertex positions contain the whole geometric information.

Some statements made about the values of q_p are not supported by the data. For example, an independence of values of q₂ and q₆ cannot be inferred from Figure 7. Actually, Figure 8 points to some dependence between these values as the peaks of the pdfs move in the opposite direction as deformability and activity are changed. Figure 1 suggests that in general, larger cells have lower values of q_p for all p. Some more serious quantification should be obtained here.

The presented experimental data on MDCK cells is anecdotal.

Reviewer #2 (Public review):

Summary:

Orientational symmetries of cells and tissues play an important role in describing processes in development and disease, and the methods used to investigate them rely on the detection of cell shape. In this interesting and very timely manuscript by Lea Happel et al., Minkowski tensors are introduced to study the orientational symmetries of cells and set in comparison to existing shape descriptors, such as the shape function introduced by Armengol-Collado et al., which captures the orientational symmetry by the vertex positions of the polygonal shape of the cell. As an advantage, the Minkowski tensors consider the real cell shape with its arbitrary curvature of the cortex. Using computational models, such as the active vertex model and the multiphase field model, as well as experimental support with MDCK monolayers, the authors find that the orientational symmetries are independent of one another, as well as that they are dependent on the activity and deformability of the cells, resulting in a monotonic trend. A trend that has not been observed for the hexatic symmetry using the shape function. Together with the lack of hexatic-nematic crossover at the tissue scale, the authors suggest a reconsideration of findings from other shape descriptors. Taken together, the Minkowski tensors set a framework to investigate orientational symmetries at a single cell scale and how they may interplay in biological tissues.

Strengths:

The authors introduce the Minkowski tensors, which capture the p-atic orders of cells in tissues, considering their real shape instead of a polygonal approximation as reported for other shape descriptors in the literature. Thus, they do not depend on the vertex positions of the cells nor on the number of neighboring cells. The Minkowski tensors capture the dependence of the p-atic orders on the cell activity and deformability in a monotonic manner, which makes them a robust tool for quantifying p-atic orders at a single-cell scale, especially for rounded cells. The robustness has been tested by comparing the results of two computational model systems that simulate cell monolayers and whose results have been extended with experimental data. The Minkowski tensors have been used to explore the role of cell-cell adhesion and density in epithelial cells and have shown similar results to the shape function, a polygonal shape descriptor.

Weaknesses:

The authors point out the importance of studying the orientational order in biological systems. However, the current version of the manuscript lacks statistical information, a description of analysis methods, and experimental support. This support is needed to strengthen (i) the results of the two computational models and (ii) give weight to the authors' strong claim against other widely accepted shape descriptors capturing p-atic orders. The Minkowski tensors, which consider the real cell shapes, are reported to be a better method to investigate the p-atic orders of cells than the shape function introduced by Armengol-Collado et al. While there may be differences in the reported results coming from the two different approaches, both approaches show similar trends. As it stands, there is substantiated discussion as to why one method would be better than the other. The shape function, γ₆, may not be monotonic for great changes in cell activity and deformability, hinting at a potential weakness. In contrast to the shape function and results by Armengol-Collado et al. and Eckert et al., the coarse-grained Minkowski tensors do not capture the hexatic-nematic crossover at the tissue scale, applied here only to computational models. The cells simulated in the computational models have a similar size and the monolayer has a nearly regular pattern, which does not reflect the density variance in biological tissues. To strengthen the author's claim that there is no crossover at the tissue scale, experimental verification is essential. Further, the robustness of the Minkowski tensors seems to rely on determining the p-atic orders on the shape of individual cells in the tissue. However, when applying the shape descriptor to experimental systems, the p-atic orders are very low, perhaps too low for comparisons between different p-atic orders with meaningful conclusions.

Reviewer #3 (Public review):

Hapel et al. submit an article entitled “Quantifying the shape of cells - from Minkowski tensors to p-atic order”. The paper reports the p-actic quantitative method - established in physics - to extract cell shapes in experiments using phase contrast images of MDCK cells and simulations - vertex model and phase fields. The rationale of the quantification with adaptation of Minkowski tensors, as well as the detailed extraction of distributions of shapes and plots, distributions quantifying shapes are documented, with an emphasis on changes in cell shapes and their importance in epithelial dynamics.

Higher rank tensors are considered as well as representations with intuitive meanings and q_i orders and their potential correlations or absence of correlations. For example, q₂ and q₆, and statements about nematic and hexatic orders. A strong body of evidence is already reported in the papers of Armengol et al., quoted substantially in the paper, and the authors insist on an improvement thanks to the Minkowski tensors approach to challenge the former crossovers correlations statements.

Although the approach seems to present advantages, the paper does not appear sufficiently novel. Beyond the Armengol et al. paper, the advantages of this approach compared to the shear decomposition (from MPI-PKS Dresden) or the links joining centroids and its neighbours approach (MSC/Curie Paris) for example.

Reviewer #1 (Public review):

Summary:

In recent years, it has become increasingly evident how beautifully intricate IAC are at the nanoscale. Studies like the one presented here that shed light on the precise inner organisation of IAC are thus quite important and relevant in order to obtain a better in-depth understanding of IAC functioning and the contribution of different integrin subtypes to cell adhesive and mechanotransductive processes.

Interestingly, the authors found a distinct localisation of α5β1 and αVβ3 integrin nanoclusters within focal adhesion of human fibroblasts, with α5β1 integrin nanoclusters being at the periphery of IAC and αVβ3 integrin nanoclusters randomly distributed. Furthermore, a surprisingly high percentage of inactive integrins within IAC and relatively low spatial integrin colocalisation with adaptor proteins has been shown.

Strengths:

This is a very thoroughly performed STORM-based assessment of the nanodistribution of α5β1 and αVβ3 nanoclusters within IAC (and outside). The image quality is outstanding, and the authors have meticulously executed the experiments and the image analyses.

Weaknesses:

The only weakness is maybe that the manuscript remains descriptive. However, the high quality of the "description" of the nano-organisation of IAC by this scrupulous study is really important to better understand the inner workings of IAC. It provides a very solid foundation to look deeper into the (patho)physiological implications of this organisation, see recommendations (which are rather suggestions in this case).

Reviewer #2 (Public review):

Summary:

In this study, dual-color super-resolution microscopy analysis was performed to study the co-operation between integrins and focal adhesion proteins in human fibroblast cells. The study focused on two integrins which have been previously found to be mainly responsible for focal adhesions, namely α5β1 and αvβ3.

Specifically, the study tried to shed light on the nanoclustering of integrins in focal adhesions.

In the current study, more integrin nanoclusters were observed in focal adhesions compared to other cell-matrix adhesion structures. The study revealed that both α5β1 and αvβ3 form nanoclusters, and those appear segregated from each other. While αvβ3 nanoclusters organize randomly inside focal adhesions regardless of their activation state, α5β1 nanoclusters, and particularly the nanoclusters containing β1-integrin in active conformation, preferentially organized at the edges of focal adhesions. The nanoclusters formed by each integrin were similar in size.

Cytoplasmic adapter proteins appeared less in nanocluster assemblies, suggesting that integrin nanoclusters are also forming without the studied cytoplasmic adapter proteins (talin, vinculin, paxillin). Active integrins were identified with the help of conformation-specific antibodies, and this enabled us to study the colocalization between integrins and their cytoplasmic adapter proteins. This analysis revealed that activated integrins are strongly engaged with adapter proteins

Strengths:

The study stems from the thorough computational modelling of the nanoclusters, which enables quantification of the behavior of the clusters, including their mesoscale distribution.

The study strengthens the view that α5β1 and αvβ3 have specific functions in focal adhesions, α5β1 nanoclusters localizing preferentially on focal adhesion edges. The study also revealed that nanoclusters localized at the edges of focal adhesion were enriched for talin and paxillin but not for vinculin.

Analysis of adaptor protein nanoclusters (paxillin, talin, and vinculin) revealed that all adapter protein nanoclusters studied here close to active β1 nanoclusters are enriched on the focal adhesion edge region, whereas integrin adaptor nanoclusters far from active β1 appear to be more uniformly distributed.

Importantly, the current study suggests that integrin subtype-specific nanoclusters are not only present at an early stage of adhesion formation, but integrin nanoclusters remain segregated from each other also in mature focal adhesions, maintaining their sizes and number of molecules.

Interestingly, the study revealed that selected cytoplasmic adaptors (paxillin, talin, and vinculin), also form nanoclusters of similar size and number of single molecule localizations as the integrins, regardless of whether they locate inside or outside focal adhesions. The adapter nanoclusters are enriched in the focal adhesion "belt", colocalizing with the active α5β1 integrin nanoclusters.

Weaknesses:

The current study is highly dependent on the antibodies. It is possible that antibodies containing two binding sites for antigen influence the nanoscale organization (and also activation) of the receptors. Control experiments to study the possible contribution of antibodies to the measured outcome should be performed to verify the main findings. One possible approach could be to use fluorescently tagged integrins available. Alternatively, integrins (or adapter proteins) could be tagged with a small ligand and detected using a monovalent binder.

Only a limited number of integrin adapter proteins were investigated. Given the high number of identified adapter proteins, this is an understandable choice. However, it would be fascinating to understand if the nanoclusters of inactive integrins are dominantly bound with a certain adapter protein, such as tensin.

Reviewer #3 (Public review):

Summary:

In their study, the authors reveal using dual-color super-resolution STORM microscopy modality and immunolabeling in fixed adherent cells, that β1 and β3 integrins as well as adaptors (paxillin, talin and vinculin) are all organized in nanoclusters of similar size (50nm) and molecular density (20 copy number) inside FAs but also outside. Using activity-specific immunolabeling of β1 and β3 integrins, they revealed that active integrin subpopulations were both clustered but in distinct exclusive nano-aggregates in agreement with Spiess et al. (2018). Once more, the "active" integrin nanoclusters displayed similar properties in terms of size and molecular density, suggesting that molecular organization in nanoclusters is an intrinsic property of integrins in plasma membrane multimerizing independently of their location (inside or outside FAs), their level of activation, or their connection to the cytoskeleton. Then the authors followed up by analyzing at the mesoscale how these "universal" nanoclustered adhesive units are distributed spatially. Inspecting the surface density of nanoclusters revealed that the density of integrin nanoclusters in FAs was 5x larger, compared to integrin nanoclusters outside adhesions. Interestingly, whereas the density of total integrin nanoclusters was 2-4x larger than adaptor nanoclusters, the density of "active" integrin nanoclusters stoichiometrically matches that of talin and vinculin nanoclusters, and was slightly outnumbered by paxillin nanoclusters. These findings suggest that inside FAs, among the total number of integrin nanoclusters, the subset of "active" integrin nanoclusters could be engaged with "adaptor" nanoclusters on a 1:1 ratio. Using analysis of the nearest neighbor distance (NND) between distinct integrin clusters and each of the adaptors, the authors report that they found negligible spatial colocalization of integrins with these adaptor proteins and that spatial segregation is essentially determined by the density of nanoclusters within the FAs. As authors reported that α5β1 and αvβ3 do not intermix at the nanoscale, the authors finally highlighted how α5β1 and αvβ3 distinct nanoclusters are differently organized and segregated inside FAs. Adapting the NND analysis in order to inspect how far the nanoclusters are from the edges of FAs they are located in, authors revealed that α5β1 but not αvβ3 integrin nanoclusters are enriched on FA edges and that similar FA edge-enriched distribution for "active" α5β1 and adaptor protein nanoclusters was found for talin and paxillin but not vinculin. The latter results suggest that FA edges could constitute multiprotein hubs for enhanced colocalization and activation for α5β1 integrin nanoclusters and adaptors such as talin and paxillin. Unfortunately NND analysis could not confirm this enhanced colocalization hypothesis.

General Assessment:

While the study presents some valuable findings, it reads currently as a compilation of intriguing but preliminary observations derived primarily from a single methodology (dual-color STORM and DBSCAN clustering analysis). As the initial findings often lack confirmation through additional data analysis (such as the NND analysis the authors used), there's a critical necessity to bolster the methodological approach. This should involve replicating the main findings using alternative single-molecule super-resolution techniques (such as quantitative DNA-PAINT) or employing different clustering analytical tools (such as voronoi-tessellation). Furthermore, the manuscript feels incomplete, focusing solely on describing molecular organization without offering substantial insights into how these observations correlate with the regulation, activation, and functionality of integrins at the cellular level.

The manuscript presents extensive datasets and utilizes methodologies in which the investigators demonstrate expertise. Nevertheless, there's uncertainty regarding the novelty and broad appeal of the findings. For instance, the observation of integrin nanoclustering has been previously reported in several publications (e.g., Changede et al., Dev Cell 2015; Spiess et al., JCB 2018; Fujiwara et al., JCB 2023). Similarly, the accumulation of specific proteins at the periphery of FAs has been documented elsewhere (e.g., Sun et al., NCB 2016; Stubb et al., NatComm 2019; Nunes-Vicente TCB 2023), as well as the differential dynamic organization of α5β1 and αvβ3 integrins inside FAs (e.g., Rossier et al., NCB 2012). Beyond the universal organization of adhesive proteins, there's a need to identify novel insights that significantly advance the field. One potential avenue could involve pinpointing the molecular determinant controlling the FA edge enrichment of active α5β1 integrins and talin nanoclusters. For instance, could there be an interplay between α5β1 and αvβ3 integrin nanoclusters visible on one's organisation when suppressing the other using deletion (KO) or depletion (SiRNA)? Also, could KANK, which also exhibits enrichment and regulates talin activity (e.g., Sun et al., NCB 2016), play a role in this process? Identifying the molecular players that regulate even partially the mesoscale organization of nanoclusters of proteins would really benefit the breadth of this manuscript.

Echoing the previous concern, the manuscript described a novel and rather surprising finding related to molecular clustering of adhesion proteins. Indeed, the fact that nanoclusters exhibit uniform size and molecular density regardless of the protein type, location, or activation level is indeed surprising and raises many questions about the methodology used to assess molecular clustering. I feel that the description and characterization of integrin nanoclusters appear incomplete and need to be expanded by comparing different analytical strategies for protein clustering. Furthermore, a lack of the manuscript in its actual form concerns the quantification of integrin numbers inside the observed nanoclusters. I agree that the path from optical microscopy to protein stoichiometry quantification is hard and full of drawbacks. But the authors do not fully address these issues that are extremely important when discussing protein nanoclustering. This quantitative aspect should be discussed.

First, it is crucial for the authors to carefully examine and discuss in their manuscript whether there are any potential biases or limitations in the experimental techniques (dual-color STORM) or data analysis methods employed (DBSCAN). Second, the authors did not in the current manuscript, but should provide control samples to demonstrate the sensitivity and dynamic range of their experimental strategy.

In STORM images displayed in Figure S1, the authors highlighted localization clusters detected by DBSCAN as a signature for integrin nanoclusters. But the authors do not discuss the localization spots that were not detected by DBSCAN. Could they be individual integrins? And if so, they should also be considered as useful information? This brings me to another related technical question about how DBSCAN handles the case where fluorescent molecules are blinking. This is important as multiple emissions by a single fluorophore could be detected as a nanocluster of several molecules where it would be an artefact due to the photophysics of the fluorophore. Could the authors comment on these points?

Also, using isolated and stochastically physisorbed fluorophores (Ab coupled with activator /reporter pairs used in this study) on glass helped define the signature in STORM of a single isolated molecule. To obtain the signature of clustered fluorophores, the authors could use anti-donkey antibodies to cross-link those STORM-specifically labeled Ab as a means to artificially obtain clustered fluorophores. Ultimately, to avoid the bias effect of the glass surfaces on the photophysics of fluorophores and be in the same imaging conditions as for the described nanoclusters, the authors should use model systems composed of multimers of GFP vs. single GFP, immunolabeled with a GFP-binding monoclonal antibody. This will permit evaluation of the cluster signature obtained with DBSCAN analysis of STORM data for single vs. multimers of known stoichiometry. This would constitute an undisputable molecular stoichiometry ruler.

Due to the surprising finding of the nanoclusters' "universality", it is imperative for the authors to validate the findings through complementary methodologies and analytical tools. This should involve replication of results using alternative super-resolution techniques (quantitative DNA-PAINT) and exploring different clustering algorithms (Voronoï-Tesselation) to ensure the robustness and reliability of the observations.

Reviewer #1 (Public review):

Summary:

Early and accurate diagnosis is critical to treating N. fowleri infections, which often lead to death within 2 weeks of exposure. Current methods-sampling cerebrospinal fluid are invasive, slow, and sometimes unreliable. Therefore, there is a need for a new diagnostic method. Russell et al. address this need by identifying small RNAs secreted by Naegleria fowleri (Figure 1) that are detectable by RT-qPCR in multiple biological fluids including blood and urine. SmallRNA-1 and smallRNA-2 were detectable in plasma samples of mice experimentally infected with 6 different N. fowleri strains, and were not detected in uninfected mouse or human samples (Figure 4). Further, smallRNA-1 is detectable in the urine of experimentally infected mice as early as 24 hours post-infection (Figure 5). The study culminates with testing human samples (obtained from the CDC) from patients with confirmed N. fowleri infections; smallRNA-1 was detectable in cerebrospinal fluid in 6 out of 6 samples (Figure 6B), and in whole blood from 2 out of 2 samples (Figure 6C). These results suggest that smallRNA-1 could be a valuable diagnostic marker for N. fowleri infection, detectable in cerebrospinal fluid, blood, or potentially urine.

Strengths:

This study investigates an important problem, and comes to a potential solution with a new diagnostic test for N. fowleri infection that is fast, less invasive than current methods, and seems robust to multiple N. fowleri strains. The work in mice is convincing that smallRNA1 is detectable in blood and urine early in infection. Analysis of patient blood samples suggest that whole blood (but not plasma) could be tested for smallRNA-1 to diagnose N. fowleri infections.

Weaknesses:

(1) There are not many N. fowleri cases, so the authors were limited in the human samples available for testing. It is difficult to know how robust this biomarker is in whole blood (only 2 samples were tested, both had detectable smallRNA-1), serum (1 out of 1 sample tested negative), or human urine (presumably there is no material available for testing). This limitation is openly discussed in the last paragraph of the discussion section.

(2) There seems to be some noise in the data for uninfected samples (Figures 4B-C, 5B, and 6C), especially for those with serum (2E). While this is often orders of magnitude lower than the positive results, it does raise questions about false positives, especially early in infection when diagnosis would be the most useful. A few additional uninfected human samples may be helpful.

Reviewer #2 (Public review):

Summary:

The authors sought to develop a rapid and non-invasive diagnostic method for primary amoebic meningoencephalitis (PAM), a highly fatal disease caused by Naegleria fowleri. Due to the challenges of early diagnosis, they investigated extracellular vesicles (EVs) from N. fowleri, identifying small RNA biomarkers. They developed an RT-qPCR assay to detect these biomarkers in various biofluids.

Strengths:

(1) This study has a clear methodological approach, which allows for the reproducibility of the experiments.

(2) Early and Non-Invasive Diagnosis - The identification of a small RNA biomarker that can be detected in urine, plasma, and cerebrospinal fluid (CSF) provides a non-invasive diagnostic approach, which is crucial for improving early detection of PAM.

(3) High Sensitivity and Rapid Detection - The RT-qPCR assay developed in the study is highly sensitive, detecting the biomarker in 100% of CSF samples from human PAM cases and in mouse urine as early as 24 hours post-infection. Additionally, the test can be completed in ~3 hours, making it feasible for clinical use.

(4) Potential for Disease Monitoring - Since the biomarker is detectable throughout the course of infection, it could be used not only for early diagnosis but also for tracking disease progression and monitoring treatment efficacy.

(5) Strong Experimental Validation - The study demonstrates biomarker detection across multiple sample types (CSF, urine, whole blood, plasma) in both animal models and human cases, providing robust evidence for its clinical relevance.

(6) Addresses a Critical Unmet Need - With a >97% case fatality rate, PAM urgently requires improved diagnostics. This study provides one of the first viable liquid biopsy-based diagnostic approaches, potentially transforming how PAM is detected and managed.

Weaknesses:

(1) Limited Human Sample Size - While the biomarker was detected in 100% of CSF samples from human PAM cases, the number of human samples analyzed (n=6 for CSF) is relatively small. A larger cohort is needed to validate its diagnostic reliability across diverse populations.

(2) Lack of Pre-Symptomatic or Early-Stage Human Data - Although the biomarker was detected in mouse urine as early as 24 hours post-infection, there is no data on whether it can be reliably detected before symptoms appear in humans, which is crucial for early diagnosis and treatment initiation.

(3) Plasma Detection Challenges - While the biomarker was detected in whole blood, it was not detected in human plasma, which could limit the ease of clinical implementation since plasma-based diagnostics are more common. Further investigation is needed to understand why it is absent in plasma and whether alternative blood-based approaches (e.g., whole blood assays) could be optimized.

Reviewer #1 (Public review):

Summary:

The study investigates how neuropeptidergic signaling affects sleep regulation in Drosophila larvae. The authors first conduct a screen of CRISPR knock-out lines of genes encoding enzymes or receptors for neuropeptides and monoamines. As a result of this screen, the authors follow up on one hit, the hugin receptor, PK2-R1. They use genetic approaches, including mutants and targeted manipulations of PK2-R1 activity in insulin-producing cells (IPCs) to increase total sleep amounts in 2nd instar larvae. Similarly, dilp3 and dilp5 null mutants and genetic silencing of IPCs show increases in sleep. The authors also show that hugin mutants and thermogenetic/optogenetic activation of hugin-expressing neurons caused reductions in sleep. Furthermore, they show through imaging-based approaches that hugin-expressing neurons activate IPCs. A key finding is that wash-on of hugin peptides, Hug-γ and PK-2, in ex vivo brain preparations activates larval IPCs, as assayed by CRTC::GFP imaging. The authors then examine how the PK2-R1, hugin, and IPC manipulations affect adult sleep. Finally, the authors examine how Ca2+ responses through CRTC::GFP imaging in adult IPCs are influenced by the wash-on of hugin peptides. The conclusions of this paper are somewhat well supported by data, but some aspects of the experimental approach and sleep analysis need to be clarified and extended.

Strengths:

(1) This paper builds on previously published studies that examine Drosophila larval sleep regulation. Through the power of Drosophila genetics, this study yields additional insights into what role neuropeptides play in the regulation of Drosophila larval sleep.

(2) This study utilizes several diverse approaches to examine larval and adult sleep regulation, neural activity, and circuit connections. The impressive array of distinct analyses provides new understanding into how Drosophila sleep-wake circuitry in regulated across the lifespan.

(3) The imaging approaches used to examine IPC activation upon hugin manipulation (either thermogenetic activation or wash-on of peptides) demonstrate a powerful approach for examining how changes in neuropeptidergic signaling affect downstream neurons. These experiments involve precise manipulations as the authors use both in vivo and ex vivo conditions to observe an effect on IPC activity.

Weaknesses:

Although the paper does have some strengths in principle, these strengths are not fully supported by the experimental approaches used by the authors. In particular:

(1) The authors show total sleep amount over an 18-hour period for all the measures of 2nd instar larval sleep throughout the paper. However, published studies have shown that sleep changes over the course of 2nd instar development, so more precise time windows are necessary for the analyses in this study.

(2) Previously published reports of sleep metrics in both Drosophila larvae and adults include the average number of sleep episodes (bout number) and the average length of sleep episodes (bout length). Neither of these metrics is included in the paper for either the larval sleep or adult sleep data. Not including these metrics makes it difficult for readers to compare the findings in this study to previously published papers in the established Drosophila sleep literature.

(3) Because Drosophila adult & larval sleep is based on locomotion, the authors need to show the activity values for the experiments supporting their key conclusions. They do show travel distances in Figure 2 - Figure Supplement 1, however, it is not clear how these distances were calculated or how the distances relate to the overall activity of individual larvae during sleep experiments. It is also concerning that inactivation of the PK2-R1-expressing neurons causes a reduction in locomotion speed. This could partially explain the increase in sleep that they observe.

(4) The authors rely on homozygous mutant larvae and adult flies to support many of their conclusions. They also rely on Gal4 lines with fairly broad expression in the Drosophila brain to support their conclusions. Adding more precise tissue-specific manipulations, including thermogenetic activation and inhibition of smaller populations of neurons in the study would be needed to increase confidence in the presented results. Similarly, demonstrating that larval development and feeding are not affected by the broad manipulations would strengthen the conclusions.

(5) Many of the experiments presented in this study would benefit from genetic and temperature controls. These controls would increase confidence in the presented results.

(6) The authors claim that their findings in larvae uncover the circuit basis for larval sleep regulation. However, there is very little comparison to published studies demonstrating that neuropeptides like Dh44 regulate larval sleep. Because hugin-expressing neurons have been shown to be downstream of Dh44 neurons, the authors need to include this as part of their discussion. The authors also do not explain why other neuropeptides in the initial screen are not pursued in the study. Given the effect that these manipulations have on larval sleep in their initial screen, it seems likely that other neuropeptidergic circuits regulate larval sleep.

Reviewer #2 (Public review):

Summary:

This study examines larval sleep patterns and compares them to sleep regulation in adult flies. The authors demonstrate hallmark sleep characteristics in larvae, including sleep rebound and increased arousal thresholds. Through genetic and behavioral analyses, they identify PK2-R1 as a key receptor involved in sleep modulation, likely via the HuginPC-IPC signaling pathway. Loss of PK2-R1 results in increased sleep, which aligns with previous findings in hugin knockout mutants. While the study presents significant contributions to the field, further investigation is needed to address discrepancies with earlier research and strengthen mechanistic claims.

Strengths:

(1) The study explores a relatively understudied aspect of sleep regulation, focusing on larval development.

(2) The use of an automated behavioral measurement system ensures precise quantification of sleep patterns.

(3) The findings provide strong genetic and behavioral evidence supporting the role of the HuginPC-IPC pathway in sleep regulation.

(4) The study has broader implications for understanding the evolution and functional divergence of sleep circuits.

Weaknesses:

(1) The manuscript does not sufficiently discuss previous studies, particularly concerning hugin mutants and their metabolic effects.

(2) The specificity of IPC secretion mechanisms is unclear, particularly regarding potential indirect effects on Dilp2.

(3) Alternative circuits, such as the HuginPC-DH44 pathway, require further consideration.

(4) Functional connectivity between HuginPC neurons and IPCs is not directly validated.

(5) Developmental differences in sleep regulatory mechanisms are not thoroughly examined.

Reviewer #3 (Public review):

Summary:

Sleep affects cognition and metabolism, evolving throughout development. In mammals, infants have fast sleep-wake cycles that stabilize in adults via circadian regulation. In this study, the author performed a genetic screen for neurotransmitters/peptides regulating sleep and identified the neuropeptide Hugin and its receptor PK2-R1 as essential components for sleep in Drosophila larvae. They showed that IPCs express Pk2-R1 and silencing IPCs resulted in a significant increase in the sleep amount, which was consistent with the effect they observed in PK2-R1 knock-out mutants. They also showed that Hugin peptides, secreted by a subset of Hugin neurons (Hug-PC), activate IPCs through the PK2-R1 receptor. This activation prompts IPCs to release insulin-like peptides (Dilps), which are implicated in the modulation of sleep. They showed that Hugin peptides induce a PK2-R1 dependent calcium (Ca²⁺) increase in IPCs, which they linked to the release of Dilp3, showing a connection between Hugin signaling to IPCs, Dilp3 release, and sleep regulation. Additionally, the activation of Hug-PC neurons reduced sleep amounts, while silencing them had the opposite effect. In contrast to the larval stage, the Hugin/PK2-R1 axis was not critical for sleep regulation in Drosophila adults, suggesting that this neuropeptidergic circuitry has divergent roles in sleep regulation across different stages of development.

Strengths:

This study used an updated system for sleep quantification in Drosophila larvae, and this method allowed precise measurement of larval sleep patterns which is essential for the understanding of sleep regulation.

The authors performed unbiased genetics screening and successfully identified novel regulators for larval sleep, Hugin and its receptor PK2-R1, making a substantial contribution to the understanding of neuropeptidergic control of sleep regulation.

They clearly demonstrated the mechanism by which Hugin-expressing neurons influence sleep through the activation of IPCs via PK2-R1 with Ca2+ responses and can modulate sleep.

Based on the demonstrated activation of PK2-R1 by the human Hugin orthologue Neuromedin U, research on human sleep disorders may benefit from the discoveries from Drosophila since sleep-regulating mechanisms are conserved across species.

Weaknesses:

The study primarily focused on sleep regulation in Drosophila larvae, showing that the Hugin/PK2-R1 axis is critical for larval sleep but not necessary for adult sleep. The effects of the Hugin axis in the adult are, however, incompletely explained and somewhat inconsistent. PK2-R1 knockout adults also display increased sleep, as does HugPC silencing, at least for daytime sleep. The difference lies in Dilp3/5 mutant animals showing decreased sleep and IPCs seemingly responding with reduced Dilp3 release to PK-2 treatment (Figure 6). It seems difficult to reconcile the author's conclusions regarding this point without additional data. It could be argued that PK2-R1 still regulates adult sleep, but not via Hugin and IPCs/Dilps.

Another issue might be that the authors show relative sleep levels for adults using Trikinetics monitoring. From the methods, it is not clear if the authors backcrossed their line to an isogenic wild-type background to normalize for line-specific effects on sleep. Thus, it is likely that each line has differences in total sleep time due to background effects, e.g., their Kir2.1 control line showed reduced sleep relative to the compared genotypes. This might limit the conclusions on the role of Hugin/PK2-R1 on adult sleep.

Reviewer #1 (Public review):

Summary:

Using highly specific antibody reagents for biological research is of prime importance. In the past few years, novel approaches have been proposed to gain easier access to such reagents. This manuscript describes an important step forward toward the rapid and widespread isolation of antibody reagents. Via the refinement and improvement of previous approaches, the Perrimon lab describes a novel phage-displayed synthetic library for nanobody isolation. They used the library to isolate nanobodies targeting Drosophila secreted proteins. They used these nanobodies in immunostainings and immunoblottings, as well as in tissue immunostainings and live cell assays (by tethering the antigens on the cell surface).

Since the library is made freely available, it will contribute to gaining access to better research reagents for non-profit use, an important step towards the democratisation of science.

Strengths:

(1) New design for a phage-displayed library of high content.

(2) Isolation of valuble novel tools.

(3) Detailed description of the methods such that they can be used by many other labs.

Weaknesses:

My comments largely concentrate on the representation of the data in the different Figures.

Reviewer #2 (Public review):

Summary:

In this study, the authors propose an alternative platform for nanobody discovery using a phage-displayed synthetic library. The authors relied on DNA templates originally created by McMahon et al. (2018) to build the yeast-displayed synthetic library. To validate their platform, the authors screened for nanobodies against 8 Drosophila secreted proteins. Nanobody screening has been performed with phage-displayed nanobody libraries followed by an enzyme-linked immunosorbent assay (ELISA) to validate positive hits. Nanobodies with higher affinity have been tested for immunostaining and immunoblotting applications using Drosophila adult guts and hemolymph, respectively.

Strengths:

The authors presented a detailed protocol with various and complementary approaches to select nanobodies and test their application for immunostaining and immunoblotting experiments. Data are convincing and the manuscript is well-written, clear, and easy to read.

Weaknesses:

On the eight Drosophila secreted proteins selected to screen for nanobodies, the authors failed to identify nanobodies for three of them. While the authors mentioned potential improvements of the protocol in the discussion, none of them have been tested in this manuscript.

The same comment applies to the experiments using membrane-tethered forms of the antigens to test the affinity of nanobodies identified by ELISA. Many nanobodies fail to recognize the antigens. While authors suggested a low affinity of these nanobodies for their antigens, this hypothesis has not been tested in the manuscript.

Improving the protocol at each step for nanobody selection would greatly increase the success rate for the discovery of nanobodies with high affinity.

Reviewer #1 (Public review):

Summary:

The authors wanted to better understand how the various septin-associated kinases contribute to septin organization and function in budding yeast. This question has been recently addressed by similar kinds of studies but there are still some open questions, particularly as regards to what extent the kinases may interact with and/or modify components of the contractile ring that drives cytokinesis.

Strengths:

This study uses sensitive imaging with good temporal and spatial resolution to monitor the localization of various proteins in living cells. Particularly informative is the use of a GFP/GFP-binding-protein "tethering" approach to ask if the requirement for one protein can be bypassed by physically tethering another protein to a third protein. Results from a yeast two-hybrid assay for measuring protein-protein interactions in vivo are buttressed by direct in vitro binding assays using purified proteins, which is important given the likelihood of "bridging" interactions between yeast proteins in the two-hybrid approach. The authors' conclusions are quite well supported by the data.

Weaknesses:

A control for non-specific binding is missing from the in vitro binding assay. The figures suffer sometimes from the very small text in the labels, which obscures understanding. Ultimately, while the study provides some interesting and novel insights, we still don't understand which phosphorylation events on which proteins are important for the events occurring at the molecular level, so the advance in knowledge is somewhat incremental.

Reviewer #2 (Public review):

Summary:

In this paper, Bhojappa et al. provide insights into the function of septin-related kinases Elm1, Gin4, Hsl1, and Kcc4 in septin organization and actomyosin ring (AMR) structure and constriction. Their findings are both corroborative of and complementary to previous related studies.

First, the authors provide a comparative analysis of the dynamic localization of these kinases at the bud neck, as well as a comparative analysis of defects in septin localization, splitting dynamics, AMR constriction rates, and cell morphology in kinase-deficient cells. They find that septin localization and splitting kinetics, as well as AMR constriction rates, are significantly perturbed in elm1∆ and gin4∆ mutants but remain largely unaffected in hsl1∆ and kcc4∆. A similar trend is observed in terms of cell morphology and viability.

Next, the authors focus on elm1∆ and gin4∆ cells, demonstrating that the residence time of the F-BAR protein Hof1 is significantly increased and defective in these mutants. Using yeast two-hybrid (Y2H) and in vitro binding assays, they show that the KA1 domain of Gin4 interacts with the F-BAR domain of Hof1, which may explain the cytokinesis-related functions of Elm1 and Gin4. Supporting this, they find that Gin4's role in septin localization, AMR constriction kinetics, and Hof1 bud neck localization is kinase-independent.

The authors then conduct a series of artificial tethering experiments given their bud neck localization is mostly interdependent. They first demonstrate that artificially tethering Gin4 to the bud neck rescues the morphology defects of elm1∆ cells, with the strongest rescue observed when Gin4 was forced to interact with Hsl1-an effect that was also kinase-independent. Additionally, artificial tethering of Hsl1 to the bud neck restores the morphology of elm1∆ cells in a KA1 domain-dependent manner, suggesting that Hsl1 functions downstream of Elm1 to maintain normal cell morphology. Consistently, artificial tethering of Elm1 to the bud neck in gin4∆ cells rescues morphology defects, as well as defects in Myo1 localization and AMR constriction, but only in the presence of full-length Hsl1. The rescue fails in the absence of Hsl1 or when using a version of Hsl1 lacking the KA1 domain, which supports the role of Hsl1 downstream to Elm1 in cytokinesis.

Strengths

Altogether, this study offers valuable insights into the mode of cytokinesis regulation mediated by the septin-related kinases, mainly Elm1, Gin4, and Hsl1, and would be an important contribution to the field of septins and cytokinesis after addressing current weaknesses.

Weaknesses

(1) When assessing rescue of the elm1∆ phenotype, it needs to become clearer whether only morphology or also cytokinesis and septin organization are rescued.

(2) The quantification of the microscopy data does not always match up with the example images, and it's not always clear how the authors quantitatively analyzed their data.

(3) The forced tethering data are key to the paper, but the lack of a summarizing table makes it difficult to grasp the full picture.

(4) Novel results and those confirming earlier results could be better distinguished.

Reviewer #3 (Public review):

Summary:

The study by Bhojappa et al. brings new and interesting elements about the stability of the septin ring and the crosstalk between septin and actomyosin ring assemblies. The study focuses on the four kinases associated with the septin ring, Elm1p, Gin4p, Hsl1p, and Kcc4p. Elm1 and Gin4 show strong knock-out phenotypes, whereas Hsl1p and Kcc4p show weak knock-out phenotypes. The Elm1p/Kccp1p and Gin4p/Hsl1p pairs show similar timing at the bud neck. While these kinases share redundant functions, Gin4 appears to have a unique interaction with the BAR domain protein Hof1, revealing a novel direct interaction between the septin and actomyosin rings. Interestingly, the kinase activity of Gin4 is not required for its role in septin organisation and AMR constriction. The last part of the manuscript shows an original protein tethering protocol used to show that Hsl1 and its membrane binding ability are required for phenotype rescue of gin4null cells.

Strengths:

The combination of genetics, cell imaging, and biochemical characterization of protein-protein interactions is attractive.

Weaknesses:

(1) Imaging and data analysis is the main weakness of this manuscript. The authors must avoid manual counting and selection when easy analysis software can be used to limit bias. Instead of presenting unclear statistics of "percentage phenotypes", they need to define clear metrics to offer meaningful phenotype analysis.

(2) This manuscript examines a very complex mechanism with four kinases of overlapping function using new data and existing literature. A clearer picture/model at the end of the manuscript that synthesizes the current knowledge would be beneficial.

Reviewer #1 (Public review):

Summary:

Using single-unit recording in 4 regions of non-human primate brains, the authors tested whether these regions encode computational variables related to model-based and model-free reinforcement learning strategies. While some of the variables seem to be encoded by all regions, there is clear evidence for stronger encoding of model-based information in the anterior cingulate cortex and caudate.

Strengths:

The analyses are thorough, the writing is clear, and the work is well-motivated by prior theory and empirical studies.

Weaknesses:

My comments here are quite minor.

The correlation between transition and reward coefficients is interesting, but I'm a little worried that this might be an artifact. I suspect that reward probability is higher after common transitions, due to the fact that animals are choosing actions they think will lead to higher reward. This suggests that the coefficients might be inevitably correlated by virtue of the task design and the fact that all regions are sensitive to reward. Can the authors rule out this possibility (e.g., by simulation)?

The explore/exploit section seems somewhat randomly tacked on. Is this really relevant? If yes, then I think it needs to be integrated more coherently.

Reviewer #2 (Public review):

Summary:

The authors investigate single-neuron activity in rhesus macaques during model-based (MB) and model-free (MF) reinforcement learning (RL). Using a well-established two-step choice task, they analyze neural correlates of MB and MF learning across four brain regions: the anterior cingulate cortex (ACC), dorsolateral PFC (DLPFC), caudate, and putamen. The study provides strong evidence that these regions encode distinct RL-related signals, with ACC playing a dominant role in MB learning and caudate updating value representations after rare transitions. The authors apply rigorous statistical analyses to characterize neural encoding at both population and single-neuron levels.

Strengths:

(1) The research fills a gap in the literature, which has been limited in directly dissociating MB vs. MF learning at the single unit level and across brain areas known to be involved in reinforcement learning. This study advances our understanding of how different brain regions are involved in RL computations.

(2) The study used a two-step choice task Miranda et al., (2020), which was previously established for distinguishing MB and MF reinforcement learning strategies.

(3) The use of multiple brain regions (ACC, DLPFC, caudate, and putamen) in the study enabled comparisons across cortical and subcortical structures.

(4) The study used multiple GLMs, population-level encoding analyses, and decoding approaches. With each analysis, they conducted the appropriate controls for multiple comparisons and described their methods clearly.

(5) They implemented control regressors to account for neural drift and temporal autocorrelation.

(6) The authors showed evidence for three main findings:<br /> a) ACC as the strongest encoder of MB variables from the four areas, which emphasizes its role in tracking transition structures and reward-based learning. The ACC also showed sustained representation of feedback that went into the next trial.<br /> b) ACC was the only area to represent both MB and MF value representations.<br /> c) The caudate selectively updates value representations when rare transitions occur, supporting its role in MB updating.

(7) The findings support the idea that MB and MF reinforcement learning operate in parallel rather than strictly competing.

(8) The paper also discusses how MB computations could be an extension of sophisticated MF strategies.

Weaknesses: o

(1) There is limited evidence for a causal relationship between neural activity and behavior. The authors cite previous lesion studies, but causality between neural encoding in ACC, caudate, and putamen and behavioral reliance on MB or MF learning is not established.

(2) There is a heavy emphasis on ACC versus other areas, but it is unclear how much of this signal drives behavior relative to the caudate.

(3) The role of the putamen is somewhat underexplored here.

(4) The authors mention the monkeys were overtrained before recording, which might have led to a bias in the MB versus MF strategy.

(5) The GLM3 model combines MB and MF value estimates but does not clearly mention how hyperparameters were optimized to prevent overfitting. While the hybrid model explains behavior well, it does not clarify whether MB/MF weighting changes dynamically over time.

(6) It was unclear from the task description whether the images used changed periodically or how the transition effect (e.g., in Figure 3) could be disambiguated from a visual response to the pair of cues.

Reviewer #1 (Public review):

In the manuscript entitled "Rtf1 HMD domain facilitates global histone H2B monoubiquitination and regulates morphogenesis and virulence in the meningitis-causing pathogen Cryptococcus neoformans" by Jiang et al., the authors employ a combination of molecular genetics and biochemical approaches, along with phenotypic evaluations and animal models, to identify the conserved subunit of the Paf1 complex (Paf1C), Rtf1, and functionally characterize its critical roles in mediating H2B monoubiquitination (H2Bub1) and the consequent regulation of gene expression, fungal development, and virulence traits in C. deneoformans or C. neoformans. Specially, the authors found that the histone modification domain (HMD) of Rtf1 is sufficient to promote H2B monoubiquitination (H2Bub1) and the expression of genes related to fungal mating and filamentation, and restores the fungal morphogenesis and pathogenicity defects caused by RTF1 deletion. These findings highlight the critical contribution of Rtf1's HMD to epigenetic regulation and cryptococcal virulence. This work will be of interest to fungal biologists and medical mycologists, particularly those studying fungal epigenetic regulation and fungal morphogenesis.

Comments on revisions:

The revised manuscript addresses all my previous concerns satisfactorily.

Reviewer #1 (Public review):

Summary:

The authors show for the first time that deleting GLS from rod photoreceptors results in the rapid death of these cells. The death of photoreceptor cells could result from loss of synaptic activity because of a decrease in glutamate, as has been shown in neurons, changes in redox balance, or nutrient deprivation.

Strengths:

The strength of this manuscript is that the author shows a similar phenotype in the mice when Gls was knocked out early in rod development or the adult rod. They showed that rapid cell death is through apoptosis, and there is an increase in the expression of genes responsive to oxidative stress.

Comments on revisions:

The authors addressed all of my concerns in their responses to reviewers.

Reviewer #2 (Public review):

Summary:

Photoreceptor neurons are crucial for vision, and discovering pathways necessary for photoreceptor health and survival can open new avenues for therapeutics. Studies have shown that metabolic dysfunction can cause photoreceptor degeneration and vision loss, but the metabolic pathways maintaining photoreceptor health are not well understood. This is a fundamental study that shows that glutamine catabolism is critical for photoreceptor cell health using in vivo model systems.

Strengths:

The data are compelling, and the consideration of potential confounding factors (such as glutaminase 2 expression) and additional experiments to examine the synaptic connectivity and inner retina added strength to this work. The authors were also careful not to overstate their claims, but to provide solid conclusions that fit the results and data provided in their study. The findings linking asparagine supplementation and the inhibition of the integrated stress response to glutamine catabolism within the rod photoreceptor cell are intriguing and innovative. Overall, the authors provide convincing data to highlight that photoreceptors utilize various fuel sources to meet their metabolic needs, and that glutamine is critical to these cells for their biomass, redox balance, function and survival.

Reviewer #3 (Public review):

Summary:

The authors explored the role of GLS, a glutaminase, which is an enzyme catalyzes the conversion of glutamine to glutamate, in rod photoreceptor function and survival. The loss of GLS was found to cause rapid autonomous death of rod photoreceptors.

Strengths:

Interesting and novel phenotype. Two types of cre-lines were rigorously used to knockout Gls gene in rods. Both of the conditional knockouts led to a similar phenotype, i.e. rod death. Histology and ERG were carefully done to characterize the loss of rods over specific ages. Necessary metabolomic study was performed and appreciated. Some rescue experiments were performed, and revealed possible mechanism.

Weaknesses:

No major weaknesses. Mechanism of GLS-loss induced rod death could be followed up in the future, and same for GLS's role in cones. Authors have addressed all minor points raised by this reviewer.

Reviewer #2 (Public review):

Summary:

The study investigates the potential influence of the response criterion on neural decoding accuracy in consciousness and unconsciousness, utilizing either simulated data or reanalyzing experimental data with post-hoc sorting data.

Strengths:

When comparing the neural decoding performance of Target versus NonTarget with or without post-hoc sorting based on subject reports, it is evident that response criterion can influence the results. This was observed in simulated data as well as in two experiments that manipulated the subject response criterion to be either more liberal or more conservative. One experiment involved a two-level response (seen vs unseen), while the other included a more detailed four-level response (ranging from 0 for no experience to 3 for a clear experience). The findings consistently indicated that adopting a more conservative response criterion could enhance neural decoding performance, whether in conscious or unconscious states, depending on the sensitivity or overall response threshold.

The uneven distribution of trails for Target (75%) and NonTarget (25%) was identified as a potential weakness in the initial review of this study. Nevertheless, we support the authors' assertion that their analysis methodology validates comparing liberal and conservative approaches. Future investigations could further explore differences between liberal and conservative on different ratios of Target vs NonTarget, particularly when the proportion of Target matches or falls below that of NonTarget.

Reviewer #1 (Public review):

Guo, Hue et al., is focused on understanding the epigenetic activity and functional dependencies for two different fusions found in spindle cell rhabdomyosarcoma, VGLL2::NCOA2 and TEAD1::NCOA2. They use a variety of models and methods; specifically, ectopic expression of the fusions in human 293T cells to perform RNAseq (both fusions), CUT&RUN (VGLL2::NCOA2) and BioID mass spec (both fusions). These data identify that the VGLL2::NCOA2 fusion has peaks that are enriched for TEAD motifs. Further, CPB/p300 CUT&RUN support an enrichment of binding sites and three TEAD targets in VGLL2::NCOA2 and TEAD1::NCOA2 expressing cells. They also functionally evaluate genetic and chemical dependencies (TEAD inhibition), and found this was only effective for the VGLL2::NCOA2 fusion, and not for TEAD1::NCOA2. Using complementary biochemical approaches, they suggest (with other supporting data) the fusions regulate TEAD transcriptional outputs via a YAP/TAZ independent mechanism. Further, they expand into a C2C12 myoblast model and show that TEAD1::NCOA2 is transforming in colony formation assays and in mouse allograft. These strategies for TEAD1-NCOA2 are consistent with previous published strategies using VGLL2::NCOA2. Importantly, they show that a CBP/p300 (a binding partner found in their BioID mass spec) small molecule inhibitor suppresses tumor formation using this mouse allograft model, and that the tumors are less proliferative, and have a reduction in transcriptional of three TEAD target genes. They complement in vivo data with biochemical approaches, and suggest this interface with p300 (for VGLL2::NCOA2) is through the NCOA2 fusion partner, as Co-IP in HEK293T with a mutant fusion that does not contain NCOA2 loses the association with endogenous p300. The data is interesting and suggests new biology for these fusion-oncogenes. However, the choice of 293T may limit the broad applicability of the findings. Strikingly, in 293T there was more transcriptional overlap with the VGLL2-NCOA2 fusion with the YAP5SA mutant than with TEAD1-NCOA2. Further, there is an additional opportunity to directly compare transcriptional profiles in 293T to the human disease and in the mouse allograft system to directly compare and discuss VGLL2-NCOA2 and TEAD1-NCOA2 histological differences or how A485 treatment may change the histology. Overall, the breadth of methods used in this study, and comparison of the two fusion-oncogene's biology is of interest to the fusion-oncogene, pediatric sarcoma, and epigenetic therapeutic targeting fields.

Reviewer #2 (Public review):

In the manuscript entitled "VGLL2 and TEAD1 fusion proteins drive YAP/TAZ-independent transcription and tumorigenesis by engaging p300", Gu et al. investigated two Hippo pathway-related gene fusion events (i.e., VGLL2-NCOA2, TEAD1-NCOA2) in spindle cell rhabdomyosarcoma (scRMS). They demonstrate that these fusion proteins activate Hippo downstream gene transcription independently of YAP/TAZ. Using BioID-based mass spectrometry analysis, the authors identify histone acetyltransferase CBP/p300 as a specific binding protein for VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins. Pharmacologically targeting p300 inhibits the fusion proteins-induced Hippo downstream gene transcription and tumorigenesis.

Overall, this work provides novel mechanistic insights into scRMS-associated gene fusions in tumorigenesis and reveals potential therapeutic targets for cancer treatment. The manuscript is well-written and easy to follow. Below are a few comments based on the revised study.

(1) While the study majorly focuses on Hippo downstream gene transcription, a significant portion of genes regulated by the VGLL2-NCOA2 and TEAD1-NCOA2 fusion proteins are non-Hippo downstream genes (Fig. 3). Further characterization of how both Hippo and non-Hippo downstream genes contribute to fusion proteins-induced oncogenesis would enhance our understanding of scRMS etiology.

(2) A potential limitation of this study is the reliance on overexpression approaches to investigate VGLL2-NCOA2 and TEAD1-NCOA2 fusion genes, which may not fully reflect pathological conditions in scRMS patients. Despite this, the significant study offers valuable mechanistic insights into fusion genes-induced scRMS and provides molecular foundation for developing targeted therapies.

Reviewer #1 (Public review):

Summary:

Giménez-Orenga et al. investigate the origin and pathophysiology of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and fibromyalgia (FM). Using RNA microarrays, the authors compare the expression profiles and evaluate the biomarker potential of human endogenous retroviruses (HERV) in these two conditions. Altogether, the authors show that HERV expression is distinct between ME/CFS and FM patients, and HERV dysregulation is associated with higher symptom intensity in ME/CFS. HERV expression in ME/CFS patients is associated with impaired immune function and higher estimated levels of plasma cells and resting CD4 memory T cells. This work provides interesting insights into the pathophysiology of ME/CFS and FM, creating opportunities for several follow-up studies.

Strengths:

(1) Overall, the data is convincing and supports the authors' claims. The manuscript is clear and easy to understand, and the methods are generally well-detailed. It was quite enjoyable to read.<br /> (2) The authors combined several unbiased approaches to analyse HERV expression in ME/CFS and FM. The tools, thresholds, and statistical models used all seem appropriate to answer their biological questions.<br /> (3) The authors propose an interesting alternative to diagnosing these two conditions. Transcriptomic analysis of blood samples using an RNA microarray could allow a minimally invasive and reproducible way of diagnosing ME/CFS and FM.

Weakness:<br /> (1) While this work makes several intriguing observations, some results will need to be validated in future studies using experimental approaches.

Reviewer #2 (Public review):

Summary:

Giménez-Orenga carried out this study to assess whether human endogenous retroviruses (HERVs) could be used to improve the diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Fibromyalgia (FM). To this end, they used the HERV-V3 array developed previously, to characterize the genome-wide changes in expression of HERVs in patients suffering from ME/CFS, FM or both, compared to controls. In turn, they present a useful repertoire of HERVs that might characterize ME/CFS and FM. For most part, the paper is written in a manner that allows a natural understanding of the workflow and analyses carried out, making it compelling. The figures and additional tables presents solid support for the findings. However, some statements made by the authors seem incomplete and would benefit by a more thorough literature review. Overall, this work will be of interest to the medical community seeking in better understanding the co-occurrence of these pathologies, hinting at a novel angle by integrating HERVs, which are often overlooked, into their assessment.

Strengths:

- The work is well-presented, allowing the reader to understand the overall workflow and how the specific aims contribute to filling the knowledge gap in the field.

- The analyses carried out to understand the potential impact on gene expression mediated by HERVs are in line with previous works, making it solid and robust in the context of this study.

Weaknesses:

- The authors claim to obtain genome-wide HERV expression profiles. However, the array used was developed using hg19, while the genomic analysis of this work are carried out using a liftover to hg38. It would improve the statement and findings to include a comparation of the differences in HERVs available in hg38, and how this could impact the "genome-wide" findings.

- The authors in some points are not thorough with the cited literature. Two examples are:<br /> (1) Lines 396-397 the authors say "the MLT1, usually found enriched near DE genes (Bogdan et al., 2020)". I checked the work by Bogdan, and they studied bacterial infection. A single work in a specific topic is not sufficient to support the statement that MLT1 is "usually" in close vicinity to differentially expressed genes. More works are needed to support this.<br /> (2) After the previous statement, the authors go on to mention "contributing to the coding of conserved lncRNAs (Ramsay et al., 2017)". First, lnc = long non-coding, so this doesn't make sense. Second, in the work by Ramsay they mention "that contributed a significant amount of sequence to primate lncRNAs whose expression was conserved", which is different to what the authors in this study are trying to convey. Again, additional work and a rephrasing might help to support this idea.

- When presenting the clusters, the authors overlook the fact that cluster 4 is clearly control-specific, and fail to discuss what this means. Could this subset of HERV be used as bona fide markers of healthy individuals in the context of these diseases? Are they associated with DE genes? What could be the impact of such associations?

Appraisals on aims:

The authors set specific questions and presented the results to successfully answer them. The evidence is solid, with some weaknesses discussed above that will methodologically strengthen the work.

Likely impact of work on the field:<br /> This work will be of interest to the medical community looking for novel ways to improve clinical diagnosis. Although future works with a greater population size, and more robust techniques such as RNA-Seq, are needed, this is the first step in presenting a novel way to distinguish these pathologies.

It would be of great benefit to the community to provide a table/spreadsheet indicating the specific genomic locations of the HERVs specific to each condition. This will allow proper provenance for future researchers interesting in expanding on this knowledge, as these genomic coordinates will be independent of the technique used (as was the array used here).

Comments on revisions:

When addressing the comments made in the previous round, there are some answers that lack substance and don't seem to be incorporated in the manuscript. For example, the authors say:

Authors' response: This is an important point. However, the low number of probes (less than 100) that were excluded from our analysis by lack of correspondence with hg38 among the 1,290,800 probesets was interpreted as insignificant for "genome-wide" claims. An aspect that will be explained in the revised version of this manuscript.

I checked the revised manuscript with tracked changes, and there doesn't seem to be an updated explanation to this. In which lines is this explained?

For the other response:

Authors' response: Using control DE HERV as bona fide markers of healthy individuals seems like an interesting possibility worth exploring. Control DE HERV (cluster 4) associate with DE genes involved in apoptosis, T cell activation and cell-cell adhesion (modules 1 and 6). The impact of which deserves further study.

I couldn't find an updated mention of this in the discussion.

Another point that I raised was regarding the decision of using an FDR of 0.1 instead of 0.05. The authors only speculate about the impacts in their answer, while I believe that this could have been rigorously addressed. Since this was done in R, and DE analysis are relatively fast, I don't see a reason as to why this part was not repeated and discussed accordingly.

For other analyses, there doesn't seem to be a problem with using 0.05 as threshold. Examples of this are the "Overrepresentation functional analysis", or the "Statistical analysis" part of the methods they say "we used a Fisher exact test to calculate p-value, considering enriched in the provided list if an adjusted p-value (FDR) was less than 0.05".

Just to make this point clear: I'm not asking the authors to repeat all the work using the 0.05 FDR threshold, but rather that they are aware and conscious about the impact of this, and give an idea to the audience on how it would change the DE numbers. This would put in perspective the findings to any future reader.

I think that most of the other answers to both my previous concerns and the other reviewer's concerns are ok. My last outstanding concern is that the probe coordinates apparently can't be shared, which undermines a lot this study reproducibility, and its use by future researches which won't be able to compare their results to this study.

Reviewer #3 (Public review):

Summary:

The authors find that HERV expression patterns can be used as new criteria for differential diagnosis of FM and ME/CFS and patient subtyping. The data are based on transcriptome analysis by microarray for HERVs using patient blood samples, followed by differential expression of ERVs and bioinformatic analyses. This is a standard and solid data processing pipeline, and the results are well presented and support the authors' claim.

Strengths:

It provides an innovative diagnostic approach using ERV profiles to subtype patients and distinguish FM and ME/CFS.

Comments on revisions:

This is a revised manuscript which addresses the comments well.

Reviewer #2 (Public review):

Summary:

This study uses in vivo multimodal high-resolution imaging to track how microglia and neutrophils respond to light-induced retinal injury from soon after injury to 2 months post-injury. The in vivo imaging finding was subsequently verified by ex vivo study. The results suggest that despite the highly active microglia at the injury site, neutrophils were not recruited in response to acute light-induced retinal injury.

Strengths:

An extremely thorough examination of the cellular-level immune activity at the injury site. In vivo imaging observations being verified using ex vivo techniques is a strong plus.

Weaknesses:

This paper is extremely long, and in the perspective of this reviewer, needs to be better organized. Update: Modifications have been made throughout, which has made the manuscript easier to follow.

Study weakness: though the finding prompts more questions and future studies, the findings discussed in this paper is potentially important for us to understand how the immune cells respond differently to different severity level of injury. The study also demonstrated an imaging technology which may help us better understand cellular activity in living tissue during earlier time points.

Comments on revisions:

I appreciate the thorough clarification and re-organization by the authors, and the messages in the manuscript are now more apparent. I recommend also briefly discussing limitations/future improvements in the discussion or conclusion.

Reviewer #3 (Public review):

Summary

This work investigated the immune response in the murine retina after focal laser lesions. These lesions are made with close to 2 orders of magnitude lower laser power than the more prevalent choroidal neovascularization model of laser ablation. Histology and OCT together show that the laser insult is localized to the photoreceptors and spares the inner retina, the vasculature and the pigment epithelium. As early as 1-day after injury, a loss of cell bodies in the outer nuclear layer is observed. This is accompanied by strong microglial proliferation to the site of injury in the outer retina where microglia do not typically reside. The injury did not seem to result in the extravasation of neutrophils from the capillary network, constituting one of the main findings of the paper. The demonstrated paradigm of studying the immune response and potentially retinal remodeling in the future in vivo is valuable and would appeal to a broad audience in visual neuroscience.

Strengths

Adaptive optics imaging of murine retina is cutting edge and enables non-destructive visualization of fluorescently labeled cells in the milieu of retinal injury. As may be obvious, this in vivo approach is a benefit for studying fast and dynamic immune processes on a local time scale - minutes and hours, and also for the longer days-to-months follow-up of retinal remodeling as demonstrated in the article. In certain cases, the in vivo findings are corroborated with histology.

The analysis is sound and accompanied by stunning video and static imagery. A few different sets of mouse models are used, a) two different mouse lines, each with a fluorescent tag for neutrophils and microglia, b) two different models of inflammation - endotoxin-induced uveitis (EAU) and laser ablation are used to study differences in the immune interaction.

One of the major advances in this article is the development of the laser ablation model for 'mild' retinal damage as an alternative to the more severe neovascularization models. This model would potentially allow for controlling the size, depth and severity of the laser injury opening interesting avenues for future study.

The time-course, 2D and 3D spatial activation pattern of microglial activation are striking and provide an unprecedented view of the retinal response to mild injury.

Weaknesses

Generalization of the (lack of) neutrophil response to photoreceptor loss - there is ample evidence in literature that neutrophils are heavily recruited in response to severe retinal damage that includes photoreceptor loss. Why the same was not observed here in this article remains an open question. One could hypothesize that neutrophil recruitment might indeed occur under conditions that are more in line with the more extreme damage models, for example, with a stronger and global ablation (substantially more photoreceptor loss over a larger area). This parameter space is unwieldy and sufficiently large to address the question conclusively in the current article, i.e. how much photoreceptor loss leads to neutrophil recruitment? By the same token, the strong and general conclusion in the title - Photoreceptor loss does not recruit neutrophils - cannot be made until an exhaustive exploration be made of the same parameter space. A scaling back may help here, to reflect the specific, mild form of laser damage explored here, for instance - Mild photoreceptor loss does not recruit neutrophils despite...

EIU model - The EIU model was used as a positive control for neutrophil extravasation. Prior work with flow cytometry has shown a substantial increase in neutrophil counts in the EIU model. Yet, in all, the entire article shows exactly 2 examples in vivo and 3 ex vivo (Figure 7) of extravasated neutrophils from the EIU model (n = 2 mice). The general conclusion made about neutrophil recruitment (or lack thereof) is built partly upon this positive control experiment. But these limited examples, especially in the case where literature reports a preponderance of extravasated neutrophils, raise a question on the paradigm(s) used to evaluate this effect in the mild laser damage model.

Overall, the strengths outweigh the weaknesses, provided the conclusions/interpretations are reconsidered.

Reviewer #1 (Public review):

The manuscript now compares the WNet3D quantitatively against other methods on all four datasets:

Figure 1b shows results on the mouse cortex dataset, comparing StarDist, CellPose, SegResNet, SwinUNetR against self-supervised (or learning-free methods) WNet3D and Otsu thresholding.

Figure 2b shows results on an unnamed dataset (presumably the mouse cortex dataset), comparing StarDist, CellPose, SegResNet, SwinUNetR with different levels of training data against WNet3D.

Figure 3 shows results on three datasets (Platynereis-ISH-Nuclei-CBG, Platynereis-Nuclei-CBG, and Mouse-Skull-Nuclei-CBG), comparing StarDist, CellPose against WNet3D and Otsu thresholding.

It is unclear whether the Otsu thresholding baseline was given the same post-processing as the WNet3D. Figure 1b shows two versions for WNet3D ("WNet3D - No artifacts" and "WNet3D"), but only one for Otsu thresholding. Given that post-processing (or artifact removal) seems to have a substantial impact on accuracy, the authors should clarify whether the Otsu thresholding results were treated in the same way and if Otsu thresholding was not post-processed. Figure 2a would also benefit from including the thresholding results (with and without artifact removal).

Reviewer #2 (Public review):

The authors have now addressed the most important points, and they include more comprehensive evaluation of their method and comparisons to other approaches for multiple datasets.

Some points would benefit from clarification:

- Figure 1B now compares "Otsu thresholding", "WNet 3D - No artifacts" and "WNet 3d". Why don't you also report the score for "Otsu thresholding - No Artifacts"? To my understanding this is a post-processing operation to remove small and very large objects, so it could easily be applied to the Otsu thresholding. Given the good results for Otsu thresholding alone (quite close F1-score to WNet 3d), it seems like DL might not really be necessary at all for this dataset and including "Otsu thresholding - No artifacts" would enable evaluating this point.

- CellPose and StarDist perform poorly in all the experiments performed by the authors. In almost all cases they underperform Otsu thresholding, which is in most cases on par with the WNet results (except for "Mouse Skull Nuclei CBG"). This is surprising and contradicts the collective expertise of the community: good CellPose and StarDist models can be trained for the 3D instance segmentation tasks studied here. Perhaps these methods were not trained in an optimal way. Seems unlikely that it is not possible to get much better CellPose or StarDist models for these tasks (current versions are on par or much worse than Otsu!), as I have applied both of these models successfully in similar settings. Specifically, it seems unlikely that the developers of CellPose or StarDist would obtain similarly poor scores on the same data (note I am not one of the developers).

The current experiments still highlight an interesting aspect: the problem of training / fine-tuning these methods correctly on new data and the technical challenges associated with this. But the reported results should by no means be taken as a fair assessment of the capabilities of StarDist or CellPose.

Please note that I did not have time to test the Napari plugin again, so I did not evaluate whether it improved in usability.

Reviewer #2 (Public review):

Summary:

The goal of the paper was to trace the transitions hippocampal microglia undergo along aging. ScRNA-seq analysis allowed the authors to predict a trajectory and hypothesize about possible molecular checkpoints, which keep the pace of microglial aging. E.g. TGF1b was predicted as a molecule slowing down the microglial aging path and indeed, loss of TGF1 in microglia led to premature microglia aging, which was associated with premature loss of cognitive ability. The authors also used the parabiosis model to show how peripheral, blood-derived signals from the old organism can "push" microglia forward on the aging path.

Strengths:

A major strength and uniqueness of this work is the in-depth single-cell dataset, which may be a useful resource for the community, as well as the data showing what happens to young microglia in heterochronic parabiosis setting and upon loss of TGFb in their environment.

Weaknesses:

All weaknesses were addressed during revision.

Overall:

In general, I think the authors did a good job following the initial observations and devised clever ways to test the emerging hypotheses. The resulting data are an important addition to what we know about microglial aging and can be fruitfully used by other researchers, e.g. those working on microglia in a disease context.

Comments on revisions:

All my comments were addressed.

Reviewer #1 (Public review):

Summary:

This study addresses the question of how task-relevant sensory information affects activity in motor cortex. The authors use various approaches to address this question, looking at single units and population activity. They find that there are three subtypes of modulation by sensory information at the single unit level. Population analyses reveal that sensory information affects the neural activity orthogonally to motor output. The authors then compare both single unit and population activity to computational models to investigate how encoding of sensory information at the single-unit level is coordinated in a network. They find that an RNN that displays similar orbital dynamics and sensory modulation to motor cortex also contains nodes that are modulated similarly to the three subtypes identified by the single unit analysis.

Strengths:

The strengths of this study lie in the population analyses and the approach of comparing single-unit encoding to population dynamics. In particular, the analysis in Figure 3 is very elegant and informative about the effect of sensory information on motor cortical activity. The task is also well designed to suit the questions being asked and well controlled.

It is commendable that the authors compare single-unit to population modulation. The addition of the RNN model and perturbations strengthen the conclusion that the subtypes of individual units all contribute to the population dynamics.

Weaknesses:

The main weaknesses of the study lie in the categorization of the single units into PD shift, gain and addition types. The single units exhibit clear mixed selectivity, as the authors highlight. Therefore, the subsequent analyses looking only at the individual classes in the RNN are a little limited. Another weakness of the paper is that the choice of windows for analyses is not properly justified and the dependence of the results on the time windows chosen for single unit analyses is not assessed. This is particularly pertinent because tuning curves are known to rotate during movements (Sergio et al. 2005 Journal of Neurophysiology).

This study uses insights from single-unit analysis to inform mechanistic models of these population dynamics, which is a powerful approach, but is dependent on the validity of the single-cell analysis, which I have expanded on below.

I have clarified some of the areas that would benefit from further analysis below:

Task:

The task is well designed, although it would have benefited from perhaps one more target speed (for each direction). One monkey appears to have experienced one more target speed than the others (seen in Figure 3C). It would have been nice to have this data for all monkeys, although, of course, unfeasible given that the study has been concluded.

Single unit analyses:

The choice of the three categories (PD shift, gain addition) is not completely justified in a satisfactory way. It would be nice to see whether these three main categories are confirmed by unsupervised methods.

The decoder analyses in Figure 2 provide evidence that target speed modulation may change over the trial. Therefore, it is important to see how the window considered for the firing rate in Figure 1 (currently 100ms pre - 100ms post movement onset) affects the results. Whilst it is of course understandable that a window must be chosen and will always be slightly arbitrary, using different windows and comparing the results of two or three different sizes or timed windows would be more convincing that the results are not dependent on this particular window.

RNN:

Mixed selectivity is not analysed in the RNN, which would help to compare the model to the real data where mixed selectivity is common. The CCA and Procrustes analysis are a good start to validate the claim of similarity between RNN and neural dynamics, rather than allowing comparisons to be dominated by geometric similarities that may be features of the task. However, some of the disparity values for the Procrustes analysis are quite high, albeit below that of the shuffle. Maybe a comment about this in the text should be included. There is also an absence of alternate models to compare the perturbation model results to.

Reviewer #2 (Public review):

Summary:

In this manuscript, Zhang et al. examine neural activity in motor cortex as monkeys make reaches in a novel target interception task. Zhang et al. begin by examining the single neuron tuning properties across different moving target conditions, finding several classes of neurons: those that shift their preferred direction, those that change their modulation gain, and those that shift their baseline firing rates. The authors go on to find an interesting, tilted ring structure of the neural population activity, depending on the target speed, and find that 1) the reach direction has consistent positioning around the ring, and 2) the tilt of the ring is highly predictive of the target movement speed. The authors then model the neural activity with a single neuron representational model and a recurrent neural network model, concluding that this population structure requires a mixture of the three types of single neurons described at the beginning of the manuscript.

Strengths:

I find the task the authors present here to be novel and exciting. It slots nicely into an overall trend to break away from a simple reach-to-static-target tasks to better characterize the breadth of how motor cortex generates movements. I also appreciate the movement from single neuron characterization to population activity exploration, which generally serves to anchor the results and make them concrete. Further, the orbital ring structure of population activity is fascinating, and the modeling work at the end serves as a useful baseline control to see how it might arise.

Weaknesses:

While I find the behavioral task presented here to be excitingly novel, I find the presented analyses and results to be far less interesting than they could be. Key to this, I think, is that the authors are examining this task and related neural activity primarily with a single-neuron representational lens. This would be fine as an initial analysis, since the population activity is of course composed of individual neurons, but the field seems to have largely moved towards a more abstract "computation through dynamics" framework that has, in the last several years, provided much more understanding of motor control than the representational framework has. As the manuscript stands now, I'm not entirely sure what interpretation to take away from the representational conclusions the authors made (i.e. the fact that the orbital population geometry arises from a mixture of different tuning types). As such, by the end of the manuscript, I'm not sure I understand any better how motor cortex or its neural geometry might be contributing to the execution of this novel task.

Main Comments:

My main suggestions to the authors revolve around bringing in the computation through a dynamics framework to strengthen their population results. The authors cite the Vyas et al. review paper on the subject, so I believe they are aware of this framework. I have three suggestions for improving or adding to the population results:

(1) Examination of delay period activity: one of the most interesting aspects of the task was the fact that the monkey had a random-length delay period before he could move to intercept the target. Presumably, the monkey had to prepare to intercept at any time between 400 and 800 ms, which means that there may be some interesting preparatory activity dynamics during this period. For example, after 400ms, does the preparatory activity rotate with the target such that once the go cue happens, the correct interception can be executed? There is some analysis of the delay period population activity in the supplement, but it doesn't quite get at the question of how the interception movement is prepared. This is perhaps the most interesting question that can be asked with this experiment, and it's one that I think may be quite novel for the field--it is a shame that it isn't discussed.

(2) Supervised examination of population structure via potent and null spaces: simply examining the first three principal components revealed an orbital structure, with a seemingly conserved motor output space and a dimension orthogonal to it that relates to the visual input. However, the authors don't push this insight any further. One way to do that would be to find the "potent space" of motor cortical activity by regression to the arm movement and examine how the tilted rings look in that space. Presumably, then, the null space should contain information about the target movement. The ring tilt will likely be evident if the authors look at the highest variance neural dimension orthogonal to the potent space (the "null space")--this is akin to PC3 in the current figures, but it would be nice to see what comes out when you look in the data for it.

The authors attempt this sort of analysis in the supplement, alongside their dPCA results, but the results seem misinterpreted. The authors do identify one kind of output-potent space using the reach direction components of dPCA, and the reach directions are indeed aligned here. However, they then go on to interpret the target-velocity space as the output-null space, orthogonal to the potent space. There are two problems with this. 1) The target-velocity space is not necessarily orthogonal to the reach-direction space. This is a key aspect of dPCA--while the individual components within a particular marginalization space are orthogonal, the marginalization spaces themselves are not necessarily orthogonal unless they are forced to be (which the authors don't mention doing). 2) Even if the target-velocity space were orthogonal to the reach-direction space, it would not comprise the whole output-null space--such a null space would also include dimensions of neural population activity that have target-velocity/reach-direction interaction, which the authors show is a major component of neural population variance. Incidentally, the dPCA analysis the authors present shows what I would expect from their unsupervised results, but as it is written, the dPCA results are interpreted in a strange or potentially misleading way.

(3) RNN perturbations: as it's currently written, the RNN modeling has promise, but the perturbations performed don't provide me with much insight. I think this is because the authors are trying to use the RNN to interpret the single neuron tuning, but it's unclear to me what was learned from perturbing the connectivity between what seems to me almost arbitrary groups of neurons. It seems to me that a better perturbation might be to move the neural state before the movement onset to see how it changes the output. For example, the authors could move the neural state from one tilted ring to another to see if the virtual hand then reaches a completely different (yet predictable) target. Moreover, if the authors can more clearly characterize the preparatory movement, perhaps perturbations in the delay period would provide even more insight into how the interception might be prepared.

Reviewer #3 (Public review):

Summary:

This experimental study investigates the influence of sensory information on neural population activity in M1 during a delayed reaching task. In the experiment, monkeys are trained to perform a delayed interception reach task, in which the goal is to intercept a potentially moving target.

This paradigm allows the authors to investigate how, given a fixed reach end point (which is assumed to correspond to a fixed motor output), the sensory information regarding the target motion is encoded in neural activity.

At the level of single neurons, the authors find that target motion modulates the activity is three main ways: gain modulation (scaling of the neural activity depending on the target direction), shift (shift of the preferred direction of neurons tuned to reach direction), or addition (offset to the neural activity).

At the level of the neural population, target motion information was largely encoded along the 3rd PC of the neural activity, leading to a tilt of the manifold along which reach direction was encoded that was proportional to target speed. The tilt of the neural manifold was found to be largely driven by the variation of activity of the population of gain modulated neurons.

Finally, the authors study the behaviour of an RNN trained to generate the correct hand velocity given the sensory input and reach direction. The RNN units are found to similarly exhibit mixed selectivity to the sensory information, and the geometry of the « neural population » resembles that observed in the monkeys.

Overall, the experiment is well set up to address the question of how sensory information that is directly relevant to the behaviour but does not lead to a direct change in behavioural output modulates motor cortical activity.<br /> The finding that sensory information modulates the neural activity in M1 during motor preparation and execution is non trivial, given that this modulation of the activity must occur in the nullspace of the movement.<br /> The authors provide analyses at both the single neuron and the population level, leading to a relatively complete characterization of the effect of the target motion on neural activity.<br /> Additionally, they start exploring the link between the population geometry and the mixed selectivity of the single neurons in their RNN model. While they could be extended in future work, the analyses of the RNN provide a good starting point to address how exactly the task setup and constraints on the network shape the single neuron selectivity and the population geometry.

Reviewer #2 (Public review):

Summary:

The authors describe a "beads-on-a-string" (BOAS) immunogen, where they link, using a non-flexible glycine linker, up to eight distinct hemagglutinin (HA) head domains from circulating and non-circulating influenzas and assess their immunogenicity. They also display some of their immunogens on ferritin NP and compare the immunogenicity. They conclude that this new platform can be useful to elicit robust immune responses to multiple influenza subtypes using one immunogen and that it can also be used for other viral proteins.

Strengths:

The paper is clearly written. While the use of flexible linkers has been used many times, this particular approach (linking different HA subtypes in the same construct resembling adding beads on a string, as the authors describe their display platform) is novel and could be of interest.

Comments on revisions:

The authors have addressed most comments. Some mistakes/issues remain:

TI should be defined earlier on line 61 not on line 196

No legend for Figure 3E - it looks like this is where the authors did the first immunization with the "mix" to compare to the BOAs but strangely they do not mention this in the response to reviewers letter and only mention fig 6G and 7<br /> Maybe add "mix" to the title of Figure 3?

In Figure 6G they do show the response to the mix but do not mention it in the immunizations for that figure. Also weird because obviously the mix is not a NP while this figure addresses NP format.

Line 796 - pseudo viruses

The authors should add some clarification in the paper as they did in response to reviewers.

Reviewer #3 (Public review):

This work describes the tandem linkage of influenza hemagglutinin (HA) receptor binding domains of diverse subtypes to create 'beads on a string' (BOAS) immunogens. They show that these immunogens elicit ELISA binding titers against full-length HA trimers in mice, as well as varying degrees of vaccine mismatched responses and neutralization titers. They also compare these to BOAS conjugated on ferritin nanoparticles and find that this did not largely improve immune responses. This work offers a new type of vaccine platform for influenza vaccines, and this could be useful for further studies on the effects of conformation and immunodominance on the resulting immune response. 

Overall, the central claims of immunogenicity in a murine model of the BOAS immunogens described here are supported by the data. 

Strengths included the adaptability of the approach to include several, diverse subtypes of HAs. The determination of an optimal composition of strains in the 5-BOAS that overall yielded the best immune responses was an interesting finding and one that could also be adapted to other vaccine platforms. Lastly, as the authors discuss, the ease of translation to an mRNA vaccine is indeed a strength of this platform. 

One interesting and counter-intuitive result is the high levels of neutralization titers seen to vaccine-mismatched, group 2 H7 in the 5-BOAS group that differs from the 4-BOAS with the addition of a group 1 H5 RBD. At the same time, no H5 neutralization titers were observed for any of the BOAS immunogens, yet they were seen for the BOAS-NP. Uncovering where these immune responses are being directed and why these discrepancies are being observed would be informative future work. 

There are a few caveats in the data that should be noted: 

(1) 20 ug is a pretty high dose for a mouse and the majority of the serology presented is after 3 doses at 20 ug. By comparison, 0.5-5 ug is a more typical range (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6380945/, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9980174/). Also, the authors state that 20 ug per immunogen was used, including for the BOAS-NP group, which would mean that the BOAS-NP group was given a lower gram dose of HA RBD relative to the BOAS groups. 

(2) Serum was pooled from all animals per group for neutralization assays, instead of testing individual animals. This could mean that a single animal with higher immune responses than the rest in the group could dominate the signal and potentially skew the interpretation of this data. 

(3) In Figure S2, it looks like an apparent increase in MW by changing the order of strains here, which may be due to differences in glycosylation. Further analysis would be needed to determine if there are discrepancies in glycosylation amongst the BOAS immunogens and how those differ from native HAs. 

Comments on revisions:

The authors have addressed all concerns upon revision.

Reviewer #1 (Public review):

Summary:

Rossi et al. asked whether gait adaptation is solely a matter of slow perceptual realignment or if it also involves fast/flexible stimulus-response mapping mechanisms. To test this, they conducted a series of split-belt treadmill experiments with ramped perturbations, revealing behavior indicative of a flexible, automatic stimulus-response mapping mechanism.

Strengths:

(1) The study includes a perceptual test of leg speed, which correlates with the perceptual realignment component of motor aftereffects. This indicates that changes in motor performance are not fully accounted for by perceptual realignment.

(2) The study evaluates the possible contributions of explicit strategy using a framework (Tsay et al., 2024) and provides evidence for minimal strategy involvement in split-belt adaptation through subjective reports.

(3) The study incorporates qualitatively distinct, hypothesis-driven models of adaptation and proposes a new framework that integrates these mechanisms. Relatedly, the study considers a range of alternative models, demonstrating that perceptual recalibration and remapping uniquely explain the patterns of behavior and aftereffects, ruling out models that focus solely on a single process (e.g., PReMo, PEA, memory of errors, optimal feedback control) and others that do not incorporate remapping (dual rate state space models).

Reviewer #2 (Public review):

Recent findings in the field of motor learning have pointed to the combined action of multiple mechanisms that potentially contribute to changes in motor output during adaptation. A nearly ubiquitous motor learning process occurs via the trial-by-trial compensation of motor errors, often attributed to cerebellar-dependent updating. This error-based learning process is slow and largely unconscious. Additional learning processes that are rapid (e.g., explicit strategy-based compensation) have been described in discrete movements like goal-directed reaching adaptation. However, the role of rapid motor updating during continuous movements such as walking has been either under explored or inconsistent with those found during adaptation of discrete movements. Indeed, previous results have largely discounted the role of explicit strategy-based mechanisms for locomotor learning. In the current manuscript, Rossi et al. provide convincing evidence for a previously unknown rapid updating mechanism for locomotor adaptation. Unlike the now well-studied explicit strategies employed during reaching movements, the authors demonstrate that this stimulus-response mapping process is largely unconscious. The authors show that in approximately half of subjects, the mapping process appears to be memory based while the remainder of subjects appear to perform structural learning of the task design. The participants that learned using a structural approach had the capability to rapidly generalize to previously unexplored regions of the perturbation space.

One result that will likely be particularly important to the field of motor learning is the authors' quite convincing correlation between the magnitude of proprioceptive recalibration and the magnitude error-based updating. This result beautifully parallels results in other motor learning tasks and appears to provide a robust marker for the magnitude of the mapping process (by means of subtracting off the contribution of error-based motor learning). This is a fascinating result with implications for the motor learning field well beyond the current study.

A major strength of this manuscript is the large sample size across experiments and the extent of replication performed by the authors in multiple control experiments.

Finally, I commend the authors on extending their original observations via Experiment 2. While it seems that participants use a range of mapping mechanisms (or indeed a combination of multiple mapping mechanisms), future experiments may be able to tease apart why some subjects use memory versus structural mapping. A future ability to push subjects to learn structurally-based mapping rules has the potential to inform rehabilitation strategies.

Overall, the manuscript is well written, the results are clear, and the data and analyses are convincing.

Strengths:

(1) Convincing behavioral data supporting the existence of multiple learning processes during split-belt adaptation. Further convincing correlations typing the extent of forward-model based adaptation with proprioceptive recalibration.<br /> (2) The authors test a veritable "zoo" of prior motor learning models to show that these models do not account for their behavioral results.<br /> (3) The authors develop a convincing alternative model (PM-ReMap) that appears to account for their behavioral results by explicitly modeling forward-model based adaptation in parallel with goal remapping.

Reviewer #3 (Public review):

Summary:

In this work, Rossi et al. use a novel split-belt treadmill learning task to reveal distinct sub-components of gait adaptation. The task involved following a standard adaptation phase with a "ramp-down" phase that helped them dissociate implicit recalibration and more deliberate SR map learning. Combined with modeling and re-analysis of previous studies, the authors show multiple lines of evidence that both processes run simultaneously, with implicit learning saturating based on intrinsic learning constraints and SR learning showing sensitivity to a "perceptual" error. These results offer a parallel with work in reaching adaptation showing both explicit and implicit processes contributing to behavior; however, in the case of gait adaptation the deliberate learning component does not appear to be strategic but is instead a more implicit SR learning process.

The authors have done a commendable job responding to my comments and critiques. I have updated the S/W below to reflect that.

Strengths:

- The task design is very clever and the "ramp down" phase offers a novel way to attempt to dissociate competing models of multiple processes in gait adaptation<br /> - The analyses are thorough, as is the re-analysis of multiple previous data sets; the expanded modeling analyses are strong<br /> - The querying of perception of the different relative belt speeds is a very nice addition, allowing the authors to connect different learning components with error perception<br /> - The conceptual framework is compelling, highlighting parallels with work in reaching but also emphasizing differences, especially w/r/t SR learning versus strategic behaviors. Thus the discovery of an SR learning process in gait adaptation would be both novel and also help conjoin different siloed subfields of motor learning research.

Weaknesses:

- The expanded modeling analyses are useful although the SR process still seems somewhat mysterious (is it explicit/implicit? how exactly is it interacting with re-calibration?); however, understanding this system more could be a fruitful topic for future work<br /> - The sample size for the individual difference analysis is somewhat modest

Reviewer #1 (Public Review):

Summary:

Zhang et al. demonstrate that CD4+ single positive (SP) thymocytes, CD4+ recent thymic emigrants (RTE), and CD4+ T naive (Tn) cells from Cd11c-p28-flox mice, which lack IL-27p28 selectively in Cd11c+ cells, exhibit a hyper-Th1 phenotype instead of the expected hyper Th2 phenotype. Using IL-27R-deficient mice, the authors confirm that this hyper-Th1 phenotype is due to IL-27 signaling via IL-27R, rather than the effects of monomeric IL-27p28. They also crossed Cd11c-p28-flox mice with autoimmune-prone Aire-deficient mice and showed that both T cell responses and tissue pathology are enhanced, suggesting that SP, RTE, and Tn cells from Cd11c-p28-flox mice are poised to become Th1 cells in response to self-antigens. Regarding mechanism, the authors demonstrate that SP, RTE, and Tn cells from Cd11c-p28-flox mice have reduced DNA methylation at the IFN-g and Tbx21 loci, indicating 'de-repression', along with enhanced histone tri-methylation at H3K4, indicating a 'permissive' transcriptional state. They also find evidence for enhanced STAT1 activity, which is relevant given the well-established role of STAT1 in promoting Th1 responses, and surprising given IL-27 is a potent STAT1 activator. This latter finding suggests that the Th1-inhibiting property of thymic IL-27 may not be due to direct effects on the T cells themselves.

Strengths:

Overall the data presented are high quality and the manuscript is well-reasoned and composed. The basic finding - that thymic IL-27 production limits the Th1 potential of SP, RTE, and Tn cells - is both unexpected and well described.

Weaknesses from the original round of review:

A credible mechanistic explanation, cellular or molecular, is lacking. The authors convincingly affirm the hyper-Th1 phenotype at epigenetic level but it remains unclear whether the observed changes reflect the capacity of IL-27 to directly elicit epigenetic remodeling in developing thymocytes or knock-on effects from other cell types which, in turn, elicit the epigenetic changes (presumably via cytokines). The authors propose that increased STAT1 activity is a driving force for the epigenetic changes and resultant hyper-Th1 phenotype. That conclusion is logical given the data at hand but the alternative hypothesis - that the hyper-STAT1 response is just a downstream consequence of the hyper-Th1 phenotype - remains equally likely. Thus, while the discovery of a new anti-inflammatory function for IL-27 within the thymus is compelling, further mechanistic studies are needed to advance the finding beyond phenomenology.

Reviewer #2 (Public Review):

Summary:

Naïve CD4 T cells in CD11c-Cre p28-floxed mice express highly elevated levels of proinflammatory IFNg and the transcription factor T-bet. This phenotype turned out to be imposed by thymic dendritic cells (DCs) during CD4SP T cell development in the thymus [PMID: 23175475]. The current study affirms these observations, first, by developmentally mapping the IFNg dysregulation to newly generated thymic CD4SP cells [PMID: 23175475], second, by demonstrating increased STAT1 activation being associated with increased T-bet expression in CD11c-Cre p28-floxed CD4 T cells [PMID: 36109504], and lastly, by confirming IL-27 as the key cytokine in this process [PMID: 27469302]. The authors further demonstrate that such dysregulated cytokine expression is specific to the Th1 cytokine IFNg, without affecting the expression of the Th2 cytokine IL-4, thus proposing a role for thymic DC-derived p28 in shaping the cytokine response of newly generated CD4 helper T cells. Mechanistically, CD4SP cells of CD11c-Cre p28-floxed mice were found to display epigenetic changes in the Ifng and Tbx21 gene loci that were consistent with increased transcriptional activities of IFNg and T-bet mRNA expression. Moreover, in autoimmune Aire-deficiency settings, CD11c-Cre p28-floxed CD4 T cells still expressed significantly increased amounts of IFNg, exacerbating the autoimmune response and disease severity. Based on these results, the investigators propose a model where thymic DC-derived IL-27 is necessary to suppress IFNg expression by CD4SP cells and thus would impose a Th2-skewed predisposition of newly generated CD4 T cells in the thymus, potentially relevant in autoimmunity.

Strengths:

Experiments are well-designed and executed. The conclusions are convincing and supported by the experimental results.

Weaknesses from the original round of review:

The premise of the current study is confusing as it tries to use the CD11c-p28 floxed mouse model to explain the Th2-prone immune profile of newly generated CD4SP thymocytes. Instead, it would be more helpful to (1) give full credit to the original study which already described the proinflammatory IFNg+ phenotype of CD4 T cells in CD11c-p28 floxed mice to be mediated by thymic dendritic cells [PMID: 23175475], and then, (2) build on that to explain that this study is aimed to understand the molecular basis of the original finding.

In its essence, this study mostly rediscovers and reaffirms previously reported findings, but with different tools. While the mapping of epigenetic changes in the IFNg and T-bet gene loci and the STAT1 gene signature in CD4SP cells are interesting, these are expected results, and they only reaffirm what would be assumed from the literature. Thus, there is only incremental gain in new insights and information on the role of DC-derived IL-27 in driving the Th1 phenotype of CD4SP cells in CD11c-p28 floxed mice.

Altogether, the major issues of this study remain unresolved:

(1) It is still unclear why the p28-deficiency in thymic dendritic cells would result in increased STAT1 activation in CD4SP cells. Based on their in vitro experiments with blocking anti-IFNg antibodies, the authors conclude that it is unlikely that the constitutive activation of STAT1 would be a secondary effect due to autocrine IFNg production by CD4SP cells. However, this possibility should be further tested with in vivo models, such as Ifng-deficient CD11c-p28 floxed mice. Alternatively, is this an indirect effect by other IFNg producers in the thymus, such as iNKT cells? It is necessary to explain what drives the STAT1 activation in CD11c-p28 floxed CD4SP cells in the first place.

(2) It is also unclear whether CD4SP cells are the direct targets of IL-27 p28. The cell-intrinsic effects of IL-27 p28 signaling in CD4SP cells should be assessed and demonstrated, ideally by CD4SP-specific deletion of IL-27Ra, or by establishing bone marrow chimeras of IL-27Ra germline KO mice.

[Editors' note: The resubmitted paper was minimally revised, and many of the initial concerns remain unresolved.]

Reviewer #1 (Public review):

Summary:

The authors define the principles that, based on first principles, should be guiding the optimisation of trascription factors with intrinsically disordered regions (IDR). The first part of the study defines the following principles to optimize the binding affinities to the genome in the receiving region that is called the "antenna": (i) reduce the target to IDR-binding distance on the genome, (ii) optimise the distance betwee the DNA binding domain and the binding sites on the IDR to be as close as possible to the distance between their binding sites on the genome; (iii) keep the same number of binding sites and their targets and modulate this number with binding strength, reducing them with increased strenght; (iv) modulate the binding strenght to be above a threshold that depends on the proportion of IDR binding sites in the antenna. The second part defines the scaling of the seach time in function of key parameters such as the volume of the nucleus, and the size of the antenna, derived as a combination of 3D search of the antenna and 1D "octopusing" on the antenna. The third part focuses on validation, where the current results are compared to binding probabilith data from a single experiment, and new experiment are proposed to further validate the model as well as testing designed transcription factors.

Strengths:

The strength of this work is that it provides simple, interpretable and testable theoretical conclusions. This will allow the derived design principles to be understood, evaluated and improved in the future. The theoretical derivations are rigorous. The authors provides a comparison to experiments, and also propose new experiments to be performed in the future, this is a great value in the paper since it will set the stage and inspire new experimental techniques. Further, the field needs inspiration and motivations to develop these techniques, since they are required to benchmark the transcription factors designed with the methods presented in this paper, as well as to develop novel data based or in vivo methods that would greatly benefit the field. As such, this paper is a fundamental contribution to the field.

Weaknesses:

The model assumption that the interaction between the transcription factor and the DNA outside of the antenna region is negligible is probably too strong for many/most transcription factors, particularly in organisms with a longer genome than yeasts. The model presents many first principles to drive the design of transcription factor, but arguably, other principles and mechanisms might also play a role by being beneficial to the search and binding process. Specifically: (i) a role of the IDR in complex formation and cooperativity between multiple trascription factors, (ii) ability of the IDR to do parallel searching based on multiple DNA binding sites spaced by disordered regions, (iii) affinity of the IDR to specific compartmentalisations in the nucleus reducing the search time, etc. The paper would be improved by a discussion over alternative mechanisms.

Reviewer #2 (Public review):

Summary:

This is an interesting theoretical exploration of how a flexible protein domain, which has multiple DNA-binding sites along it, affects the stability of the protein-DNA complex. It proposes a mechanism ("octopusing") for protein doing a random walk while bound to DNA which simultaneously enables exploration of the DNA strand and stability of the bound state.

Strengths:

Stability of the protein-DNA bound state and the ability of the protein to perform 1d diffusion along the DNA are two properties of a transcription factor that are usually seen as being in opposition of each other. The octopusing mechanism is an elegant resolution of the puzzle of how both could be accommodated. This mechanism has interesting biological implications for the functional role of intrinsically disordered domains in transcription factor (TF) proteins. They show theoretically how these domains, if flexible and able to make multiple weak contacts with the DNA, can enhance the ability of the TF to efficiently find their binding site on the DNA from which they exert control over the transcription of their target gene. The paper concludes with a comparison of model predictions with experimental data which gives further support to the proposed model. Overall, this is an interesting and well executed theoretical paper that proposes an interesting idea about the functional role for IDR domains in TFs.

Weaknesses:

IDR domains are assumed flexible which I believe is not always the case. Also, I'm not sure how ubiquitous are the assumed binding sites on the DNA for multiple subdomains along the IDR. These assumptions though seem like interesting points of departure for further experiments.

Reviewer #1 (Public review):

Summary:

This paper uses state-of-the-art techniques to define the cellular composition and its complexity in two rodent species (mice and rats). The study is built on available datasets but extends those in a way that future research will be facilitated. The study will be of high impact for the study of metabolic control.

Strengths:

(1) The study is based on experiments that are combined with two exceptional data sets to provide compelling evidence for the cellular composition of the DVC.

(2) The use of two rodent species is very useful.

Weaknesses:<br /> There is no conceptual weakness, the performance of experiments is state-of-the-art, and the discussion of results is appropriate. One minor point that would further strengthen the data is a more distinct analysis of receptors that are characteristic of the different populations of neuronal and non-neuronal cells; this part could be improved. Currently, it is only briefly mentioned, e.g., line 585ff. See also lines 603ff; it is true that the previous studies lack some information about the neurotransmitter profile of cells, but combining all data sets should result in an analysis of the receptors as well, e.g. in the form of an easy-to-read table.

Reviewer #2 (Public review):

In this manuscript, Hes et al. present a comprehensive multi-species atlas of the dorsal vagal complex (DVC) using single-nucleus RNA sequencing, identifying over 180,000 cells and 123 cell types across five levels of granularity in mice and rats. Intriguingly, the analysis uncovered previously uncharacterized cell populations, including Kcnj3-expressing astrocytes, neurons co-expressing Th and Cck, and a population of leptin receptor-expressing neurons in the rat area postrema, which also express the progenitor marker Pdgfra. These findings suggest species-specific differences in appetite regulation. This study provides a valuable resource for investigating the intricate cellular landscape of the DVC and its role in metabolic control, with potential implications for refining obesity treatments targeting this hindbrain region.

In line with previous work published by the PI, the topic is of clear scientific relevance, and the data presented in this manuscript are both novel and compelling. Additionally, the manuscript is well-structured, and the conclusions are robust and supported by the data. Overall, this study significantly enhances our understanding of the DVC and sheds light on key differences between rats and mice.

I applaud the authors for the depth of their analysis. However, I have a few major concerns, comments, and suggestions that should be addressed.

(1) If I understand the methodology correctly, mice were fasted overnight and then re-fed for 2 hours before being sacrificed (lines 91-92), which occurred 4 hours after the onset of the light phase (line 111). This means that the re-fed animals had access and consequently consumed food when they typically would not. While I completely recognize that every timepoint has its limitations, the strong influence of the circadian rhythm on the DVC gene expression (highlighted by the work published by Lukasz Chrobok), and the fact that timing of food/eating is a potent Zeitgeber, might have an impact on the analysis and should be mentioned as a potential limitation in the discussion (along with citing Dr Chrobok's work). Could this (i.e., eating during a time when the animals are not "primed by their own circadian clock to eat" potentially explain why the meal-related changes in gene expression were relatively small?

(2) In the Materials and Methods section, LiCl is mentioned as one of the treatment conditions; however, very little corresponding data are presented or discussed. Please include these results and elaborate on the rationale for selecting LiCl over other anorectic compounds.

(3) The number of animals used differs significantly between species, which the authors acknowledge as a limitation in the discussion. Since the authors took advantage of previously published mouse data sets (Ludwig and Dowsett data sets), I wonder if the authors could compare/integrate any rat data set currently available in rats as well to partially address the sample size disparity.

(4) Dividing cells in AP vs NTS vs DMX clusters and analyzing potential species differences would significantly enhance the quality of the manuscript, given the partially diverse functions of these regions. This could be done by leveraging existing published datasets that employed spatial transcriptomics or more classical methodologies (e.g., PMID: 39171288, PMID: 39629676, PMID: 38092916). I would be interested to hear the authors' perspective on the feasibility of such an analysis.

(5) Given the manuscript's focus on feeding and metabolism, I believe a more detailed description and comparison of the transcription profile of known receptors, neurotransmitters, and neuropeptides involved in food intake and energy homeostasis between mice and rats would add value. Adding a curated list of key genes related to feeding regulation would be particularly informative.

Reviewer #3 (Public review):

Summary:

This manuscript from Cecilia H et al provides a compelling resource for single nuclei RNA sequencing data with an emphasis on facilitating the integration of future data sets across mouse and rat data sets.

Strengths:

There are also several interesting findings that are highlighted, even though without a functional assay the importance remains unclear. However, the manuscript properly addresses where conclusions are speculative.

As with other snRNA seq datasets the manuscript demonstrates convincingly an increased level of complexity, while other neuronal populations like Cck and Th neurons were reproduced. Several recent findings from other groups are well addressed and put into a new context, e.g., DMV expression of AgRP (and Hcrt) was found to result from non-coding sequences, co-localization of Cck/Th was identified in a small subset. These statements are informative.

The integration of rat data into the mouse data sets is excellent, and the comparison of cellular groups is very detailed, with interesting differences between mouse and rat data.<br /> All data sets are easily accessible and usable on open platforms, this will be an impactful resource for other researchers.

Weaknesses:

The data analysis seems incomplete. The title indicates the integration of mouse and rat data into a unified rodent dataset. But the discrepancy of animal numbers (30 mice vs. 2 rats) does not fit well with that focus.

On the other hand, the mouse group is further separated into different treatments to study genetic changes that are associated with distinct energy states of fed/fasting/refeeding responses. Yet, this aspect is not addressed in depth.

While the authors find transcriptional changes in all neuronal and non-neuronal cell types, which is interesting, the verification of known transcriptional changes (e.g., cFos) is unaddressed. cFos is a common gene upregulated with refeeding that was surprisingly not investigated, even though this should be a strong maker of proper meal-induced neuronal activation in the DMV. This is a missed opportunity either to verify the data set or to highlight important limitations if that had been attempted without success.

Additional considerations:

(1) The focus on transmitter classification is highlighted, but surprisingly, the well-accepted distinction of GABAergic neurons by Slc32a1 was not used, instead, Gad1 and Gad2 were used as GABAergic markers. While this may be proper for the DMV, given numerous findings that Gad1/2 are not proper markers for GABAergic neurons and often co-expressed in glutamatergic populations, this confound should have been addressed to make a case if and why they would be proper markers in the DMV.

(2) Figure S3 for anatomical localization of clusters is excellent, but several of the cluster gene names do not have a good signal in the DMV. Specifically, the mixed neurons that do not seem to have clear marker genes. What top markers (top 10?) were used to identify these anatomical locations? At least some examples should be shown for anatomical areas to support Figure S3.

(3) Page 15, lines 410-411: "...could not find clusters sharing all markers with our neuronal classes...". Are the authors trying to say that the DMV has more diverse neurons than other brain sites? It seems not too unusual that the hypothalamus is different from the brainstem. Maybe this could be stated more clearly, and the importance of this could be clarified.

(4) The finding of GIRK1 astrocytes is interesting, but the emphasis that this means these astrocytes are highly/more excitable is confusing. This was not experimentally addressed and should be put into context that astrocyte activation is very different from neuronal activation. This should be better clarified in the results and discussion.

(5) The Pdgfra IHC as verification is great, but images are not very convincing in distinguishing the 2 (mouse) or 3 (rat) classes of cells. Why not compare Pdgfra and HuC/D co-localization by IHC and snRNAseq data (using the genes for HuC/D) in the mouse and in the rat? That would also clarify how specific HuC/D is for DMV neurons, or if it may also be expressed in non-neuronal populations.

Reviewer #1 (Public review):

This was a clearly written manuscript that did an excellent job summarizing complex data. In this manuscript, Cuevas-Zuviría et al. use protein modeling to generate over 5,000 predicted structures of nitrogenase components, encompassing both extant and ancestral forms across different clades. The study highlights that key insertions define the various Nif groups. The authors also examined the structures of three ancestral nitrogenase variants that had been previously identified and experimentally tested. These ancestral forms were shown in earlier studies to exhibit reduced activity in Azotobacter vinelandii, a model diazotroph.

This work provides a useful resource for studying nitrogenase evolution. However, its impact is somewhat limited due to a lack of evidence linking the observed structural differences to functional changes. For example, in the ancestral nitrogenase structures, only a small set of residues (lines 421-431) were identified as potentially affecting interactions between nitrogenase components. Why didn't the authors test whether reverting these residues to their extant counterparts could improve nitrogenase activity of the ancestral variants?

Additionally, the paper feels somewhat disconnected. The predicted nitrogenase structures discussed in the first half of the manuscript were not well integrated with the findings from the ancestral structures. For instance, do the ancestral nitrogenase structures align with the predicted models? This comparison was never explicitly made and could have strengthened the study's conclusions.

Reviewer #2 (Public review):

This work aims to study the evolution of nitrogenanses, understanding how their structure and function adapted to changes in the environment, including oxygen levels and changes in metal availability.

The study predicts > 5000 structures of nitrogenases, corresponding to extant, ancestral, and alternative ancestral sequences. It is observed that structural variations in the nitrogenases correlate with phylogenetic relationships. The amount of data generated in this study represents a massive undertaking that is certain to be a resource for the community. The study also provides strong insight into how structural evolution correlates with environmental and biological phenotypes.

The challenge with this study is that all (or nearly all) of the quantitative analyses presented are based on RMSD calculations, many of which are under 2 angstroms. For all intents and purposes, two structures with RMSD < 2 angstroms could be considered 'structurally identical'. A lot of insight generated is based on minuscule differences in RMSD, for which it is not clear that they are significantly different. The suggestion would be to find a way to evaluate the RMSD metric and determine whether these values, as obtained for structures being compared, are reliable. Some options are provided in earlier studies: PMID: 11514933, PMID: 17218333, PMID: 11420449, PMID: 8289285 (and others).

It could also be valuable to focus more on site-specific RMSDs rather than Global RMSDs. The high conservation in the nitrogenases likely ensures that the global RMSDs will remain low across the family. Focusing on specific regions might reveal interesting differences between clades that are more informative regarding the evolution of structure in tandem with environment/time.

Reviewer #1 (Public review):

Summary:

The present study addresses whether physiological signals influence aperiodic brain activity with a focus on age-related changes. The authors report age effects on aperiodic cardiac activity derived from ECG in low and high-frequency ranges in roughly 2300 participants from four different sites. Slopes of the ECGs were associated with common heart variability measures, which, according to the authors, shows that ECG, even at higher frequencies, conveys meaningful information. Using temporal response functions on concurrent ECG and M/EEG time series, the authors demonstrate that cardiac activity is instantaneously reflected in neural recordings, even after applying ICA analysis to remove cardiac activity. This was more strongly the case for EEG than MEG data. Finally, spectral parameterization was done in large-scale resting-state MEG and ECG data in individuals between 18 and 88 years, and age effects were tested. A steepening of spectral slopes with age was observed, particularly for ECG and, to a lesser extent, in cleaned MEG data in most frequency ranges and sensors investigated. The authors conclude that commonly observed age effects on neural aperiodic activity can mainly be explained by cardiac activity.

Strengths:

Compared to previous investigations, the authors demonstrate effects of aging on the spectral slope in the currently largest MEG dataset with equal age distribution available. Their efforts of replicating observed effects in another large MEG dataset and considering potential confounding by ocular activity, head movements, or preprocessing methods are commendable and highly valuable to the community. This study also employs a wide range of fitting ranges and two commonly used algorithms for spectral parameterization of neural and cardiac activity, hence providing a comprehensive overview of the impact of methodological choices. The authors discuss their findings in-depth and give recommendations for the separation of physiological and neural sources of aperiodic activity.

Weaknesses:

While the study's aim is well-motivated and analyses rigorously conducted, it remains vague what is reflected in the ECG at higher frequency ranges that contributed to the confounding of the age effects in the neural data. However, the authors address this issue in their discussion.

Reviewer #2 (Public review):

As remains obvious from my previous reviews, I still consider this to be an important paper and that is final and publishable in its current state.

In that previous review, I revealed my identity to help reassure the authors that I was doing my best to remain unbiased because I work in this area and some of the authors' results directly impact my prior research. I was genuinely excited to see the earlier preprint version of this paper when it first appeared. I get a lot of joy out of trying to - collectively, as a field - really understand the nature of our data, and I continue to commend the authors here for pushing at the sources of aperiodic activity!

In their manuscript, Schmidt and colleagues provide a very compelling, convincing, thorough, and measured set of analyses. Previously I recommended that the push even further, and they added the current Figure 5 analysis of event-related changes in the ECG during working memory. In my opinion this result practically warrants a separate paper its own!

The literature analysis is very clever, and expanded upon from any other prior version I've seen.

In my previous review, the broadest, most high-level comment I wanted to make was that authors are correct. We (in my lab) have tried to be measured in our approach to talking about aperiodic analyses - including adopting measuring ECG when possible now - because there are so many sources of aperiodic activity: neural, ECG, respiration, skin conductance, muscle activity, electrode impedances, room noise, electronics noise, etc. The authors discuss this all very clearly, and I commend them on that. We, as a field, should move more toward a model where we can account for all of those sources of noise together. (This was less of an action item, and more of an inclusion of a comment for the record.)

I also very much appreciate the authors' excellent commentary regarding the physiological effects that pharmacological challenges such as propofol and ketamine also have on non-neural (autonomic) functions such as ECG. Previously I also asked them to discuss the possibility that, while their manuscript focuses on aperiodic activity, it is possible that the wealth of literature regarding age-related changes in "oscillatory" activity might be driven partly by age-related changes in neural (or non-neural, ECG-related) changes in aperiodic activity. They have included a nice discussion on this, and I'm excited about the possibilities for cognitive neuroscience as we move more in this direction.

Finally, I previously asked for recommendations on how to proceed. The authors convinced me that we should care about how the ECG might impact our field potential measures, but how do I, as a relative novice, proceed. They now include three strong recommendations at the end of their manuscript that I find to be very helpful.

As was obvious from previous review, I consider this to be an important and impactful cautionary report, that is incredibly well supported by multiple thorough analyses. The authors have done an excellent job responding to all my previous comments and concerns and, in my estimation, those of the previous reviewers as well.

Reviewer #3 (Public review):

Summary:

Schmidt et al., aimed to provide an extremely comprehensive demonstration of the influence cardiac electromagnetic fields have on the relationship between age and the aperiodic slope measured from electroencephalographic (EEG) and magnetoencephalographic (MEG) data.

Strengths:

Schmidt et al., used a multiverse approach to show that the cardiac influence on this relationship is considerable, by testing a wide range of different analysis parameters (including extensive testing of different frequency ranges assessed to determine the aperiodic fit), algorithms (including different artifact reduction approaches and different aperiodic fitting algorithms), and multiple large datasets to provide conclusions that are robust to the vast majority of potential experimental variations.

The study showed that across these different analytical variations, the cardiac contribution to aperiodic activity measured using EEG and MEG is considerable, and likely influences the relationship between aperiodic activity and age to a greater extent than the influence of neural activity.

Their findings have significant implications for all future research that aims to assess aperiodic neural activity, suggesting control for the influence of cardiac fields is essential.

Weaknesses:

The authors have addressed the weaknesses of their study in their manuscript. Most alternative explanations for their results have been explored to ensure their conclusions are robust and are not explained by unexplored confounds. Minor potential weaknesses are:

(1) The number of electrodes used in the EEG analyses was on the lower side, and as such, the results do not confirm that the influence of ECG on the 1/f activity in the EEG is high even for higher density EEG montages where ICA may provide better performance at removing cardiac components (as noted by the authors). Having noted this potential weakness, I doubt the effects of cardiac activity can be completely mitigated with current methods, even in higher-density EEG montages.

(2) Head movements were used as a proxy for muscle activity. However, this may imperfectly address the potential influence of muscle activity on the slope in the EEG activity. As such, remaining muscle artifacts may have affected some of the results, particularly those that included high frequency ranges in the aperiodic estimate. Perhaps if muscle activity were left in the EEG data, it could have disrupted the ability to detect a relationship between age and 1/f slope in a way that didn't disrupt the same relationship in the cardiac data. However, I doubt this would reverse the overall conclusions given the number of converging results, including in lower frequency bands. The authors also note this potential weakness and suggest how future research might address it.

Reviewer #1 (Public review):

Summary:

In this paper Weber et al. investigate the role of 4 dopaminergic neurons of the Drosophila larva in mediating the association between an aversive high-salt stimulus and a neutral odor. The 4 DANs belong to the DL1 cluster and innervate non-overlapping compartments of the mushroom body, distinct from those involved in appetitive associative learning. Using specific driver lines for individual neurons, the authors show that activation of the DAN-g1 is sufficient to mimic an aversive memory and it is also necessary to form a high-salt memory of full strength, although optogenetic silencing of this neuron has only a partial phenotype. The authors use calcium imaging to show that the DAN-g1 is not the only DAN responding to salt. DAN-c1 and d1 also respond to salt, but they seem to play no role for the associative memory. DAN-f1, which does not respond to salt, is able to lead to the formation of a memory (if optogenetically activated), but it is not necessary for the salt-odor memory formation in normal conditions. However, when silenced together with DAN-g1, it enhances the memory deficit of DAN-g1. Overall, this work brings evidence of a complex interaction between DL1 DANs in both the encoding of salt signals and their teaching role in associative learning, with none of them being individually necessary and sufficient for both functions.

Strengths:

Overall, the manuscript contributes interesting results that are useful to understand the organization and function of the dopaminergic system. The behavioral role of the specific DANs is accessed using specific driver lines which allow to test their function individually and in pairs. Moreover, the authors perform calcium imaging to test whether DANs are activated by salt, a prerequisite for inducing a negative association to it. Proper genetic controls are carried across the manuscript.

Weaknesses:

The authors use two different approaches to silence dopaminergic neurons: optogenetics and induction of apoptosis. The results are not always consistent, but the authors discuss these differences appropriately. In general, the optogenetic approach is more appropriate as developmental compensations are not of major interest for the question investigated.

The physiological data would suggest the role of a certain subset of DANs in salt-odor association, but a different partially overlapping set is necessary in behavioral assays (with a partial phenotype). No manipulation completely abolishes the salt-odor association, leaving important open questions on the identity of the neural circuits involved in this behavior.

The EM data analysis reveals a non-trivial organization of sensory inputs into DANs, but it is difficult to extrapolate a link to the functional data presented in the paper.

Reviewer #2 (Public review):

Summary:

In this work the authors show that dopaminergic neurons (DANs) from the DL1 cluster in Drosophila larvae are required for the formation of aversive memories. DL1 DANs complement pPAM cluster neurons which are required for the formation of attractive memories. This shows the compartmentalized network organization of how an insect learning center (the mushroom body) encodes memory by integrating olfactory stimuli with aversive or attractive teaching signals. Interestingly, the authors found that the 4 main dopaminergic DL1 neurons act partially redundant, and that single cell ablation did not result in aversive memory defects. However, ablation or silencing of a specific DL1 subset (DAN-f1,g1) resulted in reduced salt aversion learning, which was specific to salt but no other aversive teaching stimuli tested. Importantly, activation of these DANs using an optogenetic approach was also sufficient to induce aversive learning in the presence of high salt. Together with the functional imaging of salt and fructose responses of the individual DANs and the implemented connectome analysis of sensory (and other) inputs to DL1/pPAM DANs this represents a very comprehensive study linking the structural, functional and behavioral role of DL1 DANs. This provides fundamental insight into the function of a simple yet efficiently organized learning center which displays highly conserved features of integrating teaching signals with other sensory cues via dopaminergic signaling.

Strengths:

This is a very careful, precise and meticulous study identifying the main larval DANs involved in aversive learning using high salt as a teaching signal. This is highly interesting because it allows to define the cellular substrates and pathways of aversive learning down to the single cell level in a system without much redundancy. It therefore sets the basis to conduct even more sophisticated experiments and together with the neat connectome analysis opens the possibility to unravel different sensory processing pathways within the DL1 cluster and integration with the higher order circuit elements (Kenyon cells and MBONs). The authors' claims are well substantiated by the data and balanced, putting their data in the appropriate context. The authors also implemented neat pathway analyses using the larval connectome data to its full advantage, thus providing network pathways that contribute towards explaining the obtained results.

Weaknesses:

Previous comments were fully addressed by the authors.

Reviewer #3 (Public review):

The study of Weber et al. provides a thorough investigation of the roles of four individual dopamine neurons for aversive associative learning in the Drosophila larva. They focus on the neurons of the DL-1 cluster which already have been shown to signal aversive teaching signals. But the authors go beyond the previous publications and test whether each of these dopamine neurons responds to salt or sugar, is necessary for learning about salt, bitter, or sugar, and is sufficient to induce a memory when optogenetically activated. In addition, previously published connectomic data is used to analyze the synaptic input to each of these dopamine neurons. The authors conclude that the aversive teaching signal induced by salt is distributed across the four DL-1 dopamine neurons, with two of them, DAN-f1 and DAN-g1, being particularly important. Overall, the experiments are well designed and performed, support the authors' conclusions, and deepen our understanding of the dopaminergic punishment system.

Strengths:

(1) This study provides, at least to my knowledge, the first in vivo imaging of larval dopamine neurons in response to tastants. Although the selection of tastants is limited, the results close an important gap in our understanding of the function of these neurons.<br /> (2) The authors performed a large number of experiments to probe for the necessity of each individual dopamine neuron, as well as combinations of neurons, for associative learning. This includes two different training regimen (1 or 3 trials), three different tastants (salt, quinine and fructose) and two different effectors, one ablating the neuron, the other one acutely silencing it. This thorough work is highly commendable, and the results prove that it was worth it. The authors find that only one neuron, DAN-g1, is partially necessary for salt learning when acutely silenced, whereas a combination of two neurons, DAN-f1 and DAN-g1, are necessary for salt learning when either being ablated or silenced.<br /> (3) In addition, the authors probe whether any of the DL-1 neurons is sufficient for inducing an aversive memory. They found this to be the case for two of the neurons, largely confirming previous results obtained by a different learning paradigm, parameters and effector.<br /> (4) This study also takes into account connectomic data to analyze the sensory input that each of the dopamine neurons receives. This analysis provides a welcome addition to previous studies and helps to gain a more complete understanding. The authors find large differences in inputs that each neuron receives, and little overlap in input that the dopamine neurons of the "aversive" DL-1 cluster and the "appetitive" pPAM cluster seem to receive.<br /> (5) Finally, the authors try to link all the gathered information in order to describe an updated working model of how aversive teaching signals are carried by dopamine neurons to the larva's memory center. This includes important comparisons both between two different aversive stimuli (salt and nociception) and between the larval and adult stages.

Reviewer #2 (Public review):

Summary:

Using the well-studied oxalate-microbiome-host system, the authors propose a novel conceptual and experimental framework for developing targeted bacteriotherapies using a three-phase pre-clinical workflow. The third phase is based on a 'complex system theoretical approach' in which multi-omics technologies are combined in independent in vivo and in vitro models to successfully identify the most pertinent variables that influence specific phenotypes in diet-host-microbe systems. The innovation relies on the third phase since phase I and phase II are the dominant approaches everyone in the microbiome field uses.

Strengths:

The authors used a multidisciplinary approach which included i] fecal transplant of two distinct microbial communities into Swiss-Webster mice (SWM) to characterize the host response (hepatic response-transcriptomics) and microbial activity (untargeted metabolomics of the stool samples) to different oxalate concentrations; 2] longitudinal analysis of the N. albigulia gut microbiome composition in response to varying concentrations of oxalate by shotgun metagenomics, with deep bioinformatic analyses of the genomes assembled; and 3] development of synthetic microbial communities around oxalate metabolisms and evaluation of these communities' activity into oxalate degradation in vivo.

Weaknesses:

This study presents a valuable finding on the oxalate-microbiome-host system using a multitude of approaches. Although the multidisciplinary approach allows for a unique perspective on the system and more robust conclusions, it is challenging for any authors to present all the data clearly and systematically in a conclusive way-especially when introducing unfamiliar concepts such as a complex systems theoretical approach.

Reviewer #1 (Public Review):

In this study, the authors build upon previous research that utilized non-invasive EEG and MEG by analyzing intracranial human ECoG data with high spatial resolution. They employed a receptive field mapping task to infer the retinotopic organization of the human visual system. The results present compelling evidence that the spatial distribution of human alpha oscillations is highly specific and functionally relevant, as it provides information about the position of a stimulus within the visual field.

Using state-of-the-art modeling approaches, the authors not only strengthen the existing evidence for the spatial specificity of the human dominant rhythm but also provide new quantification of its functional utility, specifically in terms of the size of the receptive field relative to the one estimated based on broad band activity.

Reviewer #2 (Public Review):

Summary:

In this work, Yuasa et al. aimed to study the spatial resolution of modulations in alpha frequency oscillations (~10Hz) within the human occipital lobe. Specifically, the authors examined the receptive field (RF) tuning properties of alpha oscillations, using retinotopic mapping and invasive electroencephalogram (iEEG) recordings. The authors employ established approaches for population RF mapping, together with a careful approach to isolating and dissociating overlapping, but distinct, activities in the frequency domain. Whereby, the authors dissociate genuine changes in alpha oscillation amplitude from other superimposed changes occurring over a broadband range of the power spectrum. Together, the authors used this approach to test how spatially tuned estimated RFs were when based on alpha range activity, vs. broadband activities (focused on 70-180Hz). Consistent with a large body of work, the authors report clear evidence of spatially precise RFs based on changes in alpha range activity. However, the size of these RFs were far larger than those reliably estimated using broadband range activity at the same recording site. Overall, the work reflects a rigorous approach to a previously examined question, for which improved characterization leads to improved consistency in findings and some advance of prior work.

Strengths:

Overall, the authors take a careful and well-motivated approach to data analyses. The authors successfully test a clear question with a rigorous approach and provide strong supportive findings. Firstly, well-established methods are used for modeling population RFs. Secondly, the authors employ contemporary methods for dissociating unique changes in alpha power from superimposed and concomitant broadband frequency range changes. This is an important confound in estimating changes in alpha power not employed in prior studies. The authors show this approach produces more consistent and robust findings than standard band-filtering approaches. As noted below, this approach may also account for more subtle differences when compared to prior work studying similar effects.

Original Weaknesses:

- Theoretical framing: The authors frame their study as testing between two alternative views on the organization, and putative functions, of occipital alpha oscillations: i) alpha oscillation amplitude reflects broad shifts in arousal state, with large spatial coherence and uniformity across cortex; ii) alpha oscillation amplitude reflects more specific perceptual processes and can be modulated at local spatial scales. However, in the introduction this framing seems mostly focused on comparing some of the first observations of alpha with more contemporary observations. Therefore, I read their introduction to more reflect the progress in studying alpha oscillations from Berger's initial observations to the present. I am not aware of a modern alternative in the literature that posits alpha to lack spatially specific modulations. I also note this framing isn't particularly returned to in the discussion. A second important variable here is the spatial scale of measurement. It follows that EEG based studies will capture changes in alpha activity up to the limits of spatial resolution of the method (i.e. limited in ability to map RFs). This methodological distinction isn't as clearly mentioned in the introduction, but is part of the author's motivation. Finally, as noted below, there are several studies in the literature specifically addressing the authors question, but they are not discussed in the introduction.

- Prior studies: There are important findings in the literature preceding the author's work that are not sufficiently highlighted or cited. In general terms, the spatio-temporal properties of the EEG/iEEG spectrum are well known (i.e. that changes in high frequency activity are more focal than changes in lower frequencies). Therefore, the observations of spatially larger RFs for alpha activities is highly predicted. Specifically, prior work has examined the impact of using different frequency ranges to estimate RF properties, for example ECoG studies in the macaque by Takura et al. NeuroImage (2016) [PubMed: 26363347], as well as prior ECoG work by the author's team of collaborators (Harvey et al., NeuroImage (2013) [PubMed: 23085107]), as well as more recent findings from other groups (Luo et al., (2022) BioRxiv: https://doi.org/10.1101/2022.08.28.505627). Also, a related literature exists for invasively examining RF mapping in the time-voltage domain, which provides some insight into the author's findings (as this signal will be dominated by low-frequency effects). The authors should provide a more modern framing of our current understanding of the spatial organization of the EEG/iEEG spectrum, including prior studies examining these properties within the context of visual cortex and RF mapping. Finally, I do note that the author's approach to these questions do reflect an important test of prior findings, via an improved approach to RF characterization and iEEG frequency isolation, which suggests some important differences with prior work.

- Statistical testing: The authors employ many important controls in their processing of data. However, for many results there is only a qualitative description or summary metric. It appears very little statistical testing was performed to establish reported differences. Related to this point, the iEEG data is highly nested, with multiple electrodes (observations) coming from each subject, how was this nesting addressed to avoid bias?

[Editors' note: the authors have addressed the original concerns.]

Reviewer #3 (Public Review):

Summary:

This study tackles the important subject of sensory driven suppression of alpha oscillations using a unique intracranial dataset in human patients. Using a model-based approach to separate changes in alpha oscillations from broadband power changes, the authors try to demonstrate that alpha suppression is spatially tuned, with similar center location as high broadband power changes, but much larger receptive field. They also point to interesting differences between low-order (V1-V3) and higher-order (dorsolateral) visual cortex. While I find some of the methodology convincing, I also find significant parts of the data analysis, statistics and their presentation incomplete. Thus, I find that some of the main claims are not sufficiently supported. If these aspects could be improved upon, this study could potentially serve as an important contribution to the literature with implications for invasive and non-invasive electrophysiological studies in humans.

Strengths:

The study utilizes a unique dataset (ECOG & high-density ECOG) to elucidate an important phenomenon of visually driven alpha suppression. The central question is important and the general approach is sound. The manuscript is clearly written and the methods are generally described transparently (and with reference to the corresponding code used to generate them). The model-based approach for separating alpha from broadband power changes is especially convincing and well-motivated. The link to exogenous attention behavioral findings (figure 8) is also very interesting. Overall, the main claims are potentially important, but they need to be further substantiated (see weaknesses).

Original Weaknesses:

I have three major concerns:

(1) Low N / no single subject results/statistics: The crucial results of Figure 4,5 hang on 53 electrodes from four patients (Table 2). Almost half of these electrodes (25/53) are from a single subject. Data and statistical analysis seem to just pool all electrodes, as if these were statistically independent, and without taking into account subject-specific variability. The mean effect per each patient was not described in text or presented in figures. Therefore, it is impossible to know if the results could be skewed by a single unrepresentative patient. This is crucial for readers to be able to assess the robustness of the results. N of subjects should also be explicitly specified next to each result.

(2) Separation between V1-V3 and dorsolateral electrodes: Out of 53 electrodes, 27 were doubly assigned as both V1-V3 and dorsolateral (Table 2, Figures 4,5). That means that out of 35 V1-V3 electrodes, 27 might actually be dorsolateral. This problem is exasperated by the low N. for example all the 20 electrodes in patient 8 assigned as V1-V3 might as well be dorsolateral. This double assignment didn't make sense to me and I wasn't convinced by the authors' reasoning. I think it needlessly inflates the N for comparing the two groups and casts doubts on the robustness of these analyses.

(3) Alpha pRFs are larger than broadband pRFs: first, as broadband pRF models were on average better fit to the data than alpha pRF models (dark bars in Supp Fig 3. Top row), I wonder if this could entirely explain the larger Alpha pRF (i.e. worse fits lead to larger pRFs). There was no anlaysis to rule out this possibility. Second, examining closely the entire 2.4 section there wasn't any formal statistical test to back up any of the claims (not a single p-value is mentioned). It is crucial in my opinion to support each of the main claims of the paper with formal statistical testing.

[Editors' note: the authors have addressed the original concerns.]

Reviewer #1 (Public review):

Astrocytes are known to express neuroligins 1-3. Within neurons, these cell adhesion molecules perform important roles in synapse formation and function. Within astrocytes, a significant role for neuroligin 2 in determining excitatory synapse formation and astrocyte morphology was shown in 2017. However, there has been no assessment of what happens to synapses or astrocyte morphology when all three major forms of neuroligins within astrocytes (isoforms 1-3) are deleted using a well characterized, astrocyte specific, and inducible cre line. By using such selective mouse genetic methods, the authors here show that astrocytic neuroligin 1-3 expression in astrocytes is not consequential for synapse function or for astrocyte morphology. They reach these conclusions with careful experiments employing quantitative western blot analyses, imaging and electrophysiology. They also characterize the specificity of the cre line they used. Overall, this is a very clear and strong paper that is supported by rigorous experiments. The discussion considers the findings carefully in relation to past work. This paper is of high importance, because it now raises the fundamental question of exactly what neuroligins 1-3 are actually doing in astrocytes. In addition, it enriches our understanding of the mechanisms by which astrocytes participate in synapse formation and function. The paper is very clear, well written and well illustrated with raw and average data.

Comments on revisions:

My previous comments have been addressed. I have no additional points to make and congratulate the authors.

Reviewer #2 (Public review):

In the present manuscript, Golf et al. investigate the consequences of astrocyte-specific deletion of Neuroligin (Nlgn) family cell adhesion proteins on synapse structure and function in the brain. Decades of prior research had shown that Neuroligins mediate their effects at synapses through their role in the postsynaptic compartment of neurons and their transsynaptic interaction with presynaptic Neurexins. More recently, it was proposed for the first time that Neuroligins expressed by astrocytes can also bind to presynaptic Neurexins to regulate synaptogenesis (Stogsdill et al. 2017, Nature). However, several aspects of the model proposed by Stogsdill et al. on astrocytic Neuroligin function conflict with prior evidence on the role of Neuroligins at synapses, prompting Golf et al. to further investigate astrocytic Neuroligin function in the current study. Using postnatal conditional deletion of Nlgn1-3 specifically from astrocytes in mice, Golf et al. show that virtually no changes in the expression of synaptic proteins or in the properties of synaptic transmission at either excitatory or inhibitory synapses are observed. Moreover, no alterations in the morphology of astrocytes themselves were found. To further extend this finding, the authors additionally analyzed human neurons co-cultured with mouse glia lacking expression of Nlgn1-4. No difference in excitatory synaptic transmission was observed between neurons cultured in the present of wildtype vs. Nlgn1-4 conditional knockout glia. The authors conclude that while Neuroligins are indeed expressed in astrocytes and are hence likely to play some role there, this role does not include any direct consequences on synaptic structure and function, in direct contrast to the model proposed by Stogsdill et al.

Overall, this is a strong study that addresses a fundamental and highly relevant question in the field of synaptic neuroscience. Neuroligins are not only key regulators of synaptic function, they have also been linked to numerous psychiatric and neurodevelopmental disorders, highlighting the need to precisely define their mechanisms of action. The authors take a wide range of approaches to convincingly demonstrate that under their experimental conditions, Nlgn1-3 are efficiently deleted from astrocytes in vivo, and that this deletion does not lead to major alterations in the levels of synaptic proteins or in synaptic transmission at excitatory or inhibitory synapses, or in the morphology of astrocytes. While the co-culture experiments are somewhat more difficult to interpret due to lack of a control for the effect of wildtype mouse astrocytes on human neurons, they are also consistent with the notion that deletion of Nlgn1-4 from astrocytes has no consequences for the function of excitatory synapses. Together, the data from this study provide compelling and important evidence that, whatever the role of astrocytic Neuroligins may be, they do not contribute substantially to synapse formation or function under the conditions investigated.

Reviewer #1 (Public review):

Summary:

The behavioral strategies underlying decisions based on perceptual evidence are often studied in the lab with stimuli whose elements provide independent pieces of decision-related evidence that can thus be equally weighted to form a decision. In more natural scenarios, in contrast, the information provided by these pieces is often correlated, which impacts how they should be weighted. Tardiff, Kang & Gold set out to study decisions based on correlated evidence and compare observed behavior of human decision makers to normative decision strategies. To do so, they presented participants with visual sequences of pairs of localized cues whose location was either uncorrelated, or positively or negatively correlated, and whose mean location across a sequence determined the correct choice. Importantly, they adjusted this mean location such that, when correctly weighted, each pair of cues was equally informative, irrespective of how correlated it was. Thus, if participants follow the normative decision strategy, their choices and reaction times should not be impacted by these correlations. While Tardiff and colleagues found no impact of correlations on choices, they did find them to impact reaction times, suggesting that participants deviated from the normative decision strategy. To assess the degree of this deviation, Tardiff et al. adjusted drift diffusion models (DDMs) for decision-making to process correlated decision evidence. These fits, and a comparison of different model variants revealed that participants considered correlations when weighing evidence, but did so with a slight underestimation of magnitude of this correlation. This finding made Tardiff et al. conclude that participants followed a close-to normative decision strategy that adequately took into account correlated evidence.

Strength:

The authors adjust a previously used experimental design to include correlated evidence in a simple, yet powerful way. The way it does so is easy to understand and intuitive, such that participants don't need extensive training to perform the task. Limited training makes it more likely that the observed behavior is natural and reflective of every-day decision-making. Furthermore, the design allowed the authors to make the amount of decision-related evidence equal across different correlation magnitudes, which makes it easy to assess whether participants correctly take account of these correlations when weighing evidence: if they do, their behavior should not be impacted by the correlation magnitude.

The relative simplicity with which correlated evidence is introduced also allowed the authors to fall back to the well-established DDM for perceptual decisions, that has few parameters, is known to implement the normative decision strategy in certain circumstances, and enjoys a great deal of empirical support. The authors show how correlations ought to impact these parameters, and which changes in parameters one would expect to see if participants mis-estimate these correlations or ignore them altogether (i.e., estimate correlations to be zero). This allowed them to assess the degree to which participants took into account correlations on the full continuum from perfect evidence weighting to complete ignorance. More specifically, the authors showed that a consistent mis-estimation of the correlation magnitude would not impact the fraction of correct choices (as they observe), but only the reaction times. With this, they could show that participants in fact performed rational evidence weighting if one assumed that they slightly underestimated the correlation magnitude.

Weaknesses:

While the authors convincingly demonstrate that the observed decision-making behavior seems to stem from a slight underestimation of the correlation magnitudes, their experimental paradigm did not allow them to determine the origin of this bias. Through additional analyses they rule out various possibilities, like the impact of a Bayesian prior on estimated correlations. Nonetheless, the authors provide no normative explanation of the observed bias.

A further minor weakness is that the authors only focus on a single normative aspect of the observed behavior, namely on whether participants optimally accumulate decision-related evidence across time. Another question is whether participants tune their decision boundaries to maximize reward rates or some other overall performance measures. While the authors discuss that the chosen diffusion models (DDMs) have the potential of also implementing normative decisions in the latter sense, the authors' analysis does not address this question in the context of their task.

Reviewer #2 (Public review):

This study by Tardiff, Kang & Gold seeks to i) develop a normative account of how observers should adapt their decision-making across environments with different levels of correlation between successive pairs of observations, and ii) assess whether human decisions in such environments are consistent with this normative model. The authors first demonstrate that, in the range of environments under consideration here, an observer with full knowledge of the generative statistics should take both the magnitude and sign of the underlying correlation into account when assigning weight in their decisions to new observations: stronger negative correlations should translate into stronger weighting (due to the greater information furnished by an anticorrelated generative source), while stronger positive correlations should translate into weaker weighting (due to the greater redundancy of information provided by a positively correlated generative source). The authors then report an empirical study in which human participants performed a perceptual decision-making task requiring accumulation of information provided by pairs of perceptual samples, under different levels of pairwise correlation. They describe a nuanced pattern of results with effects of correlation being largely restricted to response times and not choice accuracy, which could be captured through fits of their normative model (in this implementation, an extension of the well-known drift diffusion model) to the participants' behaviour while allowing for mis-estimation of the underlying correlations. An intriguing result is that the observed pattern of behavioural effects is best explained by a model in which observers marginally underestimated the level of correlation between the generative sources, and that this bias affects behaviour through effects on stimulus encoding that then shape how the evidence furnished by each stimulus sample is weighted in decision formation.

As the authors point out in their very well-written paper, appropriate weighting of information gathered in correlated environments has important consequences for real-world decision-making. Yet, while this function has been well studied for 'high-level' (e.g. economic) decisions, how we account for correlations when making simple perceptual decisions on well-controlled behavioural tasks has not been investigated. As such, this study addresses an important and timely question that will be of broad interest to psychologists and neuroscientists. The computational approach to arrive at normative principles for evidence weighting across environments with different levels of correlation is elegant, makes strong connections with prior work in different decision-making contexts, and should serve as a valuable reference point for future studies in this domain. The empirical study is well designed and executed, and the modelling approach applied to these data showcases an impressively deep understanding of relationships between different parameters of the drift diffusion model and its novel application to this setting. Another strength of the study is that it is preregistered.

In my view, any major weaknesses of the study have been well addressed by the authors during review. An outstanding question that arises from the current work and remains unanswered here is around the (normative?) origin of the correlation underestimates, and the present work lays a strong foundation from which to pursue this question in the future.

Reviewer #1 (Public review):

"Unraveling the Role of Ctla-4 in Intestinal Immune Homeostasis: Insights from a novel Zebrafish Model of Inflammatory Bowel Disease" generates a 14bp deletion/early stop codon mutation that is viable in a zebrafish homolog of ctla-4. This mutant exhibits an IBD-like phenotype, including decreased intestinal length, abnormal intestinal folds, decreased goblet cells, abnormal cell junctions between epithelial cells, increased inflammation, and alterations in microbial diversity. Bulk and single-cell RNA-seq show upregulation of immune and inflammatory response genes in this mutant (especially in neutrophils, B cells, and macrophages) and downregulation of genes involved in adhesion and tight junctions in mutant enterocytes. The work suggests that the makeup of immune cells within the intestine is altered in these mutants, potentially due to changes in lymphocyte proliferation. Introduction of recombinant soluble Ctla-4-Ig to mutant zebrafish rescued body weight, histological phenotypes, and gene expression of several pro-inflammatory genes, suggesting a potential future therapeutic route.

Strengths:

- Generation of a useful new mutant in zebrafish ctla-4<br /> - The demonstration of an IBD-like phenotype in this mutant is extremely comprehensive.<br /> - Demonstrated gene expression differences provide mechanistic insight into how this mutation leads to IBD-like symptoms.<br /> - Demonstration of rescue with a soluble protein suggests exciting future therapeutic potential<br /> - The manuscript is mostly well organized and well written.

Initial Weaknesses were addressed during review.

Reviewer #2 (Public review):

Summary:

The authors aimed to elucidate the role of Ctla-4 in maintaining intestinal immune homeostasis by using a novel Ctla-4-deficient zebrafish model. This study addresses the challenge of linking CTLA-4 to inflammatory bowel disease (IBD) due to the early lethality of CTLA-4 knockout mice. Four lines of evidence were shown to show that Ctla-4-deficient zebrafish exhibited hallmarks of IBD in mammals: 1) impaired epithelial integrity and infiltration of inflammatory cells; 2) enrichment of inflammation-related pathways and the imbalance between pro- and anti-inflammatory cytokines; 3) abnormal composition of immune cell populations; and 4) reduced diversity and altered microbiota composition. By employing various molecular and cellular analyses, the authors established ctla-4-deficient zebrafish as a convincing model of human IBD.

Strengths:

The characterization of the mutant phenotype is very thorough, from anatomical to histological and molecular levels. The finding effectively established ctla-4 mutants as a novel zebrafish model for investigating human IBD. Evidence from the histopathological and transcriptome analysis was very strong and supports a severe interruption of immune system homeostasis in the zebrafish intestine. Additional characterization using sCtla-4-Ig further probed the molecular mechanism of the inflammatory response, and provided a potential treatment plan for targeting Ctla-4 in IBD models.

Weaknesses:

To probe the molecular mechanism of Ctla-4, the authors used a spectrum of antibodies that target Ctla-4 or its receptors. The phenotype assayed was lymphocyte proliferation, while it was the composition rather than number of immune cells that was observed to be different in the scRNASeq assay. Although sCtla-4 has an effect of alleviating the IBD-like phenotypes, I found this explanation a bit oversimplified.

Comments on revised version:

The authors have sufficiently addressed all my concerns and I don't have further suggestions.

Reviewer #3 (Public review):

Summary:

Current study on the mutant zebrafish for IBD modeling is worth trying. The author provided lots of evidence, including histopathological observation, gut microflora, as well as intestinal tissue or mucosa cells' transcriptomic data. The multi-omic study has demonstrated the enteritis pathology at multi levels in zebrafish model.

Strengths:

The important immune checkpoint of Treg cells were knockout in zebrafish, and the enteritis were found then. It could be a substitution of mouse knockout model to investigate the molecular mechanism of gut disease.

Weaknesses:

(1) In Fig. 2I, as to the purple glycogen signals stained by PAS was ignored for the quantitative statistics. The purple stained area could be calculated by ImageJ.<br /> (2) Those characters in Fig. 3G are too small to recognize. It is suggested to adjusted this picture or just put it in the supplementation, with bigger size.<br /> (3) The tissue seems damaged for IgG ctrl in Fig. 8B. It is suggested to find another slice to present here.<br /> (4) Line 667 & 743: "16S rRNA sequencing" should be "16S rRNA gene sequencing". Please check this point throughout the text.

Reviewer #1 (Public review):

This work presents data from three species (mice, rats, and humans) performing an evidence accumulation task, that has been designed to be as similar as possible between species (and is based on a solid foundation of previous work on decision-making). The tasks are well-designed, and the analyses are solid and clearly presented - showing that there are differences in the overall parameters of the decision-making process between the species. This is valuable to neuroscientists who aim to translate behavioral and neuroscientific findings from rodents to humans and offers a word of caution for the field in readily claiming that behavioral strategies and computations are representative of all mammals. The dataset would be of great interest to the community and may be a source of further modelling of across-species behavior, but unfortunately, neither data or code are currently shared.

A few other questions remain, that make the conclusions of the paper a bit hard to assess:

(1) The main weakness is that the authors claim that all species rely on evidence accumulation as a strategy, but this is not tested against other models (see e.g. Stine et al. https://elifesciences.org/articles/55365): the fact that the DDM fits rather well does not mean that this is the strategy that each species was carrying out.

(2) In all main analyses, it is unclear what the effect is of the generative flash rate and how this has been calibrated between species. Only in Figure 6C do we see basic psychometric functions, but these should presumably also feature as a crucial variable dominating the accuracy and RTs (chronometric functions) across species. The very easy trials are useful to constrain the basic sensorimotor differences that may account for RT variability, e.g. perhaps the small body of mice requires them to move a relatively longer distance to trigger the response.

(3) The GLM-HMM results (that mice are not engaged in all trials) are very important, but they imply that mouse DDM fits may well be more similar to rats and humans if done only on engaged trials. Could it be that the main species differences are driven by different engagement state occupations?

(4) It would be very helpful if the authors could present a comprehensive overview (perhaps a table) of the factors that may be relevant for explaining the observed species differences. This may include contextual/experimental variables (age range (adolescent humans vs. mice/rats, see https://www.jax.org/news-and-insights/jax-blog/2017/november/when-are-mice-considered-old; reward source, etc) and also outcomes (e.g. training time required to learn the task, # trials per session and in total).

Reviewer #2 (Public review):

Summary:

Chakravarty et al. propose a 'synchronized framework' for studying perceptual decision-making (DM) across species -namely humans, rats, and mice. Although all species shared hallmarks of evidence accumulation, the results highlighted species-specific differences. Humans were the slowest and most accurate, rats optimized the speed-accuracy tradeoff to maximize the reward rate and mice were the fastest but least accurate. In addition, while humans were better fit by a classic DDM with fixed bounds, rodents were better fit by a DDM with collapsing bounds. While comparing behavioral strategies in evidence accumulation tasks across species is an important and timely question, some of the presented differences across species lack a clear interpretation and could be simply caused by differences in the task design. There is important information and analyses missing about the DDM and the other models used, which lowers the confidence and enthusiasm about the results.

Strengths:

The comparison of behavior across species, including humans and commonly used laboratory species like rats and mice, is a fundamental step in neuroscience to establish more informed links between animal experiments and human cognition. In this work, Chakravarty et al. analyze and model the behavior of three species during the same evidence accumulation task. They draw conclusions about the different strategies used in each case.

Weaknesses:

Novelty:<br /> While quite relevant, some parts of the work presented are more novel than others. That EA drives choice behavior and these choices can be described with a DDM have been shown before (see e.g. (Kane et al. 2023; Brunton et al. in 2013; Pinto et al 2018)). The novelty here mostly lies in the comparison of three species in the same task and in fitting the same exact model (close quantitative comparison of behavioral strategies). However, some of the differences lack a clear interpretation. For instance, the values of some of the DDM fitted parameters between the three species are not ordered "as expected" (e.g. non-decision time or DDM BIC). Other comparison results completely lack an explanation (e.g. rats' RT are near optimal while humans and mice are not). The aspect that I found most novel and exciting is the application of HMMs to each of the species. However, this part comes at the end of the paper and has been done without sufficient depth. There is almost no explanation for the results. I would suggest the authors bring up this part and move back to other aspects which are, in my opinion, less novel or interpretable (e.g. results around the optimality of RT).

Task design:<br /> Since there is no fixation, the response time (RT) reflects both the evidence integration time plus the motor time (stimuli are played until a response is given). This design makes it hard to compare RTs between species. While humans just had to press a button, rodents had to move their whole bodies from a central port to a side port. When comparing rats and mice, their difference in size relative to port distance could explain different RTs. This could for example explain the large difference in non-decision time (ndt) in Figure 3F between mice and rats. Are the measurements of the rat and the mouse boxes comparable? The authors should explain this difference more openly and discuss its implications when interpreting the results. The Methods should also provide information about the distance between ports for each species. I also strongly recommend including a few videos of rats and mice performing the task to have a sense of the movements involved in the task in each species.

(1) DDM

Goodness of fit:<br /> The authors conclude that the three species use an accumulation of evidence strategy because they can fit a DDM. However, there is little information about the goodness of these fits. They only show the RT distributions for one example subject (too small to distinguish whether the fit of the histograms is good or not). We suggest they make a figure showing in more detail the match of the RT distributions across subjects (e.g. they can compare RT quartiles for data and model for the entire group of subjects). Then they provide BIC which is a measure that depends on the number of trials. Were the number of trials matched across subjects/species? Could the authors provide a measure independent of the number of trials (e.g. cross-validated log-likelihood per trial)? Moreover, is this BIC computed only on the RTs, mouse responses, or both?

Overparameterization:<br /> The authors chose to include as DDM parameters the variability of the initial offset, the variability in non-decision time, and the variability of the drift rate. Having so many parameters with just one stimulus condition (80:20 ratio of flashes) may lead to unidentifiability problems as recognized previously (e.g. see M. Jones (2021) here osf.io/preprints/psyarxiv/gja3u). Their parameter recovery Supplementary Figure 3 shows that at least two of these variability parameters can not be recovered. I also couldn't find the values of these parameters for the fitted DDM. So I was wondering the extent to which adding these parameters improves the fits and is overall necessary.

Tachometric curves:<br /> The authors show increasing tachometric curves (i.e. Accuracy vs RT) and use this finding as proof of accumulation. They fit these curves using a GAAM with little justification or detail (in fact the GAAM seems to over-fit the data a bit). The authors do not say, however, that the other model used, i.e. the DDM, may not reproduce these increasing tachometric curves because "in its basic form", the DDM gives flat tachometric curves. Does the DDM fitted to the individual RT and choice data capture the monotonic increase observed in the tachometric curves?

Correct vs Error trials:<br /> In a similar line, the authors do not test the fitted DDM separately in correct vs error trials, which is a classical distinction that most DDMs can't capture. It would be good to know if: (1) the RT in the data of correct vs error responses are similar (quantified in panel Figure 2B because in 2E it is not clear) and (2) the same trend between correct and error RTs are observed in the fitted DDMs.

Urgency model:<br /> It is not clear how the urgency model used works. The authors cite Ditterich (2006), but in that paper, the urgency signal was applied to a race model with two decision variables: the urgency signal "accelerated" both DVs equally and sped up the race without favoring one DV versus the other. In a one-dimensional DDM, it is not clear where the urgency is applied. We assume it is applied in the direction of the stimulus, but then it is unclear how the urgency knows about the stimulus, which is what the DDM is trying to estimate in the first place. The authors should explain this model in greater detail and try to resolve this question.

Despite finding differences between species, the analyses seem mostly exploratory instead of hypothesis-driven. There is little justification for why differences in some DDM parameters across species would be expected.

(2) GLM and HMM

The GLM fits show nicely that humans, rats, and mice weigh differently the total provided evidence (Figures 6C-D). This may be because the internal noise in the accumulation of evidence is higher but also it could simply be because animals do not weigh the evidence that is presented when they are already moving towards the side ports. A parsimonious alternative to the "more noisy" species is simply that they only consider the first part of the stimulus. Extending the GLM to capture the differential weighting of each sequential sample (what is called the Psychophysical kernel, PK) should be straightforward and would provide a more fair comparison between species (i.e. perhaps the slope of the psychometric curves is not that different, once evidence is weighted in each species with its corresponding PK.

Choice Bias:<br /> Panel 3G (DDM starting point) shows that both rats and mice are slightly but systematically biased to the Left (x0 < 0.5). Panel 6D "Bias" seems to be showing the absolute value of the GLM bias parameter. It would be nice to (i) show the signed GLM bias parameter. (ii) Compare that the biases computed in the DDM and GLM are comparable across species and subjects; it looks like from the GLM they are comparable in magnitude across species whereas the in DDM they weren't (mice had a much bigger |x0| in the DDM), (iii) explain (or at least comment) on why animals show a systematic bias to one side.

Reviewer #3 (Public review):

Summary:

This study directly compares decision-making strategies between three species, humans, rats, and mice. Based on a new and common behavioral task that is largely shared across species, specific features of evidence accumulation could be quantified and compared between species. The authors argue their work provides a framework to study decision-making across species, which can be studied by the same decision models. The authors report specific features of decision-making strategies, such as humans having a larger decision threshold leading to more accurate responses, and rodents deciding under time pressure.

Strengths:

The behavioral task is set up in similar, comparable ways across species, allowing for employing the same decision models and directly comparing specific features of decision behavior. This approach is compelling since it is otherwise challenging to compare behavior between species. Data analysis is solid and does not only quantify features of classic drift-diffusion models, but also additional commonly applied behavior models or features such as win-stay/lose-shift strategies, reward-maximization behavior, and slow, latent changes in behavior strategies. This approach reveals some interesting species differences, which are a starting point to investigate species-specific decision strategies more deeply and could inform a broad set of past and future behavior studies commonly used in cognitive and neuroscience.

Weaknesses:

(1) The choice of the stimulus difficulty is unclear, as choosing a single, specific evidence strength (80:20) could limit model fitting performance and interpretation of psychometric curves. This could also limit conclusions about species differences since the perceptual sensitivity seems quite different between species. Thus, the 80:20 lies at different uncertainty levels for the different species, which are known to influence behavioral strategies. This might be addressed by exploiting the distribution of actually delivered flashes, but it remained unclear to me to what degree this is the case. Previous perceptual discrimination studies typically sample multiple evidence levels to differentiate the source of variability in choice behavior.

(2) The authors argue that their task is novel and that their task provides a framework to investigate perceptual decision-making. However, very similar, and potentially more powerful, perceptual decision-making tasks (e.g., using several evidence strength levels) have been used in humans, non-human primates, rats, mice, and other species. In some instances, analogous behavioral tasks, including studies using the same sensory stimulus, have been used across multiple species. While these may have been published in different papers, they have been conducted in some instances by the same lab and using the same analyses. Further, much of this work is not referenced here. This limits the impact of this work.

(3) The employed drift-diffusion model has many parameters, which are not discussed in detail. Results in Supplementary Figures 3-5 are not explained or discussed, including the interpretation that model recovery tests fail to recover some of the parameters (eg, Figures S3E, G). This makes the interpretation of such models more difficult.

(4) The results regarding potential reward-maximization strategies are compelling and connect perceptual and normative decision models. The results are however limited by the different inter-trial intervals and trial initiation times between species, which are shown in Figure S6. It's unclear to me how to interpret, for example, how the long trial initiation times in rats relate to a putative reward-maximizing strategy. This compares to the very low trial initiation times (ie, very 'efficient') of humans, even though they are 'too accurate' in terms of their sampling time. Reward-maximizing strategies seem difficult with such different trial times and in the absence of experimental manipulation.

Reviewer #1 (Public review):

Summary:

Sakelaris and Riecke used computational modeling to explore how neurogenesis and sequential integration of new neurons into a network support memory formation and maintenance. They focus on the integration of granule cells in the olfactory bulb, a brain area where adult neurogenesis is prominent. Experimental results published in recent years provide an excellent basis to address the question at hand by biologically constrained models. The study extends previous computational models and provides a coherent picture of how multiple processes may act in concert to enable rapid learning, high stability of memories, and high memory capacity. This computational model generates experimentally testable predictions and is likely to be valuable to understand the roles of neurogenesis and related phenomena in memory. One of the key findings is that important features of the memory system depend on transient properties of adult-born granule cells such as enhanced excitability and apoptosis during specific phases of the development of individual neurons. The model can explain many experimental observations and suggests specific functions for different processes (e.g., importance of apoptosis for continual learning). While this model is obviously a massive simplification of the biological system, it conceptualizes diverse experimental observations into a coherent picture, it generates testable predictions for experiments, and it will likely inspire further modeling and experimental studies. Nonetheless, there are issues that the authors should address.

Strengths:

(1) The model can explain diverse experimental observations.

(2) The model directly represents the biological network.

Weaknesses:

As with many other models of biological networks, this model contains major simplifications.

Reviewer #2 (Public review):

Summary:

This is an excellent paper that demonstrates Computational Modeling at its best. The authors propose a mechanism to provide flexibility to learn new information while preserving stability in neural networks by combining structural plasticity and synaptic plasticity.

Strengths:

An intriguing idea, that is well embedded in experimental data.

The problem posed is real, the model uses data to be designed and implemented yet adds to the data novel and useful insight. The project proposes a parsimonious explanation for why neurogenesis can be better than classical plasticity and how stability versus flexibility can be solved with this approach.

Weaknesses:

No weaknesses were identified by this reviewer.

Reviewer #3 (Public review):

The manuscript is focused on local bulbar mechanisms to solve the flexibility-stability dilemma in contrast to long-range interactions documented in other systems (hippocampus-cortex). The network performance is assessed in a perceptual learning task: the network is presented with alternating, similar artificial stimuli (defined as enrichment) and the authors assess its ability to discriminate between these stimuli by comparing the mitral cell representations quantified by Fisher discriminant analysis. The authors use enhancement in discriminability between stimuli as a function of the degree of specificity of connectivity in the network to quantify the formation of an odor-specific network structure which as such has memory - they quantify memory as the specificity of that connectivity.

The focus on neurogenesis, excitability, and synaptic connectivity of abGCs is topical, and the authors systematically built their model, clearly stating their assumptions and setting up the questions and answers. In my opinion, the combination of latent dendritic representations, excitability, and apoptosis in an age-dependent manner is interesting and as the authors point out leads to experimentally testable hypotheses. I have however several concerns with the novelty of the work, the lack of referencing of previous work on granule cells-mitral cell interactions more generally, and the biological plausibility of the model that, in my opinion, should be further addressed to better contextualize the model.

(1) The authors find that a network with age-dependent synaptic plasticity outperforms one with constant age-independent plasticity and that having more GC per se is not sufficient to explain this effect. In addition, having an initial higher excitability of GCs leads to increased performance. To what degree the increased excitability of abGCs is conceptually necessarily independent of them having higher synaptic plasticity rates / fast synapses?

(2) The authors do not mention previous theoretical work on the specificity of mitral to granule cell interactions from several groups (Koulakov & Rinberg - Neuron, 2011; Gilra & Bhalla, PLoSOne, 2015; Grabska-Bawinska...Mainen, Pouget, Latham, Nat. Neurosci. 2017; Tootoonian, Schaefer, Latham, PLoS Comput. Biol., 2022), nor work on the relevance of top-down feedback from the olfactory cortex on the abGC during odor discrimination tasks (Wu & Komiyama, Sci. Adv. 2020), or of top-down regulation from the olfactory cortex on regulating the activity of the mitral/tufted cells in task engaged mice (Lindeman et al., PLoS Comput. Biol., 2024), or in naïve mice that encounter odorants (in the absence of specific context; Boyd, et al., Cell Rep, 2015; Otazu et al., Neuron 2015, Chae et al., Neuron, 2022). In particular, the presence of rich top-down control of granule cell activity (including of abGCs) puts into question the plausibility of one of the opening statements of the authors with respect to relying solely on local circuit mechanisms to solve the flexibility-stability dilemma. I think the discussion of this work is important in order to put into context the idea of specific interactions between the abGCs and the mitral cells.

(3) To what the degree of specific connectivity reflects a specific stimulus configuration, and is a good proxy for determining the stimulus discriminability and memory capacity in terms of temporal activity patterns (difference in latency/phase with respect to the respiration cycle, etc.) which may account to a substantial fraction of ability to discriminate between stimuli? The authors mention in the discussion that this is, indeed, an upper bound and specific connectivity is necessary for different temporal activity patterns, but a further expansion on this topic would help in understanding the limitations of the model.

(4) Reward or reward prediction error signals are not considered in the model. They however are ubiquitous in nature and likely to be encountered and shape the connectivity and activity patterns of the abGC-mitral cell network. Including a discussion of how the model may be adjusted to incorporate reward/error signals would strengthen the manuscript.

Specific Comments

(1) Lines 84-86; 507-509; Eq(3): Sensory input is defined by a basal parameter of MCs spontaneous activity (Sspontaneus) and the odor stimuli input (Siodor) but is not clear from the main text or methods how sensory inputs (glomerular patterns) were modeled.

(2) Lines 118-122: The used perceptual learning task explanation is done only in the context of the discriminability of similar artificial stimuli using the Fisher discriminant and "Memory" metric. A detailed description of the logic of the perceptual learning task methods and objective, taking into account Comment 1, would help to better understand the model.

(3) Rapid re-learning of forgotten odor pair is enabled by sensory-dependent dendritic elaboration of neurons that initially encoded the odors and the observed re-learning would occur even if neurogenesis was blocked following the first enrichment and even though the initial learning did require neurogenesis. When this would ever occur in nature? The re-learning of an odor period? Why is this highlighted in the study?

Reviewer #1 (Public review):

Summary:

In this paper, Thomas et al. set out to study seasonal brain gene expression changes in the Eurasian common shrew. This mammalian species is unusual in that it does not hibernate or migrate but instead stays active all winter while shrinking and then regrowing its brain and other organs. The authors previously examined gene expression changes in two brain regions and the liver. Here, they added data from the hypothalamus, a brain region involved in the regulation of metabolism and homeostasis. The specific goals were to identify genes and gene groups that change expression with the seasons and to identify genes with unusual expression compared to other mammalian species. The reason for this second goal is that genes that change with the season could be due to plastic gene regulation, where the organism simply reacts to environmental change using processes available to all mammals. Such changes are not necessarily indicative of adaptation in the shrew. However, if the same genes are also expression outliers compared to other species that do not show this overwintering strategy, it is more likely that they reflect adaptive changes that contribute to the shrew's unique traits.

The authors succeeded in implementing their experimental design and identified significant genes in each of their specific goals. There was an overlap between these gene lists. The authors provide extensive discussion of the genes they found.

The scope of this paper is quite narrow, as it adds gene expression data for only one additional tissue compared to the authors' previous work in a 2023 preprint. The two papers even use the same animals, which had been collected for that earlier work. As a consequence, the current paper is limited in the results it can present. This is somewhat compensated by an expansive interpretation of the results in the discussion section, but I felt that much of this was too speculative. More importantly, there are several limitations to the design, making it hard to draw stronger conclusions from the data. The main contribution of this work lies in the generated data and the formulation of hypotheses to be tested by future work.

Strengths:

The unique biological model system under study is fascinating. The data were collected in a technically sound manner, and the analyses were done well. The paper is overall very clear, well-written, and easy to follow. It does a thorough job of exploring patterns and enrichments in the various gene sets that are identified.

I specifically applaud the authors for doing a functional follow-up experiment on one of the differentially expressed genes (BCL2L1), even if the results did not support the hypothesis. It is important to report experiments like this and it is terrific to see it done here.

Comments on revised version:

This updated version of the paper is improved compared to its initial version. As such, the strengths remain the same as before, with a fascinating model system and an interesting research question. The earlier weaknesses related to overinterpretation of the data have been largely fixed by shortening the paper and adding appropriate caveats throughout. The paper now also includes a significance test for its overlap between gene lists. While this turned out to be negative (i.e., there is not more overlap between lists than expected by chance), reporting this result transparently has strengthened the paper.

Reviewer #2 (Public review):

Summary:

Shrews go through winter by shrinking their brain and most organs, then regrow them in the spring. The gene expression changes underlying this unusual brain size plasticity were unknown. Here, the authors looked for potential adaptations underlying this trait by looking at differential expression in the hypothalamus. They found enrichments for DE in genes related to the blood brain barrier and calcium signaling, as well as used comparative data to look at gene expression differences that are unique in shrews. This study leverages a fascinating organismal trait to understand plasticity and what might be driving it at the level of gene expression. This manuscript also lays the groundwork for further developing this interesting system.

Strengths:

One strength is that the authors used OU models to look for adaptation in gene expression. The authors also added cell culture work to bolster their findings.

Comments on revised version:

I think that the authors have made a strong revision. No other comments.

Reviewer #3 (Public review):

Summary:

In their study, the authors combine seasonal and comparative transcriptomics to identify candidate genes with plastic, canalized, or lineage-specific (i.e., divergent) expression patterns associated with an unusual overwintering phenomenon (Dehnel's phenomenon - seasonal size plasticity) in the Eurasian shrew. Their focus is on the shrinkage and regrowth of the hypothalamus, a brain region that undergoes significant seasonal size changes in shrews and plays a key role in regulating metabolic homeostasis. Through comparative transcriptomic analysis, they identify genes showing derived (lineage-specific), plastic (seasonally regulated), and canalized (both lineage-specific and plastic) expression patterns. The authors hypothesize that genes involved in pathways such as the blood-brain barrier, metabolic state sensing, and ion-dependent signaling will be enriched among those with notable transcriptomic patterns. They complement their transcriptomic findings with a cell culture-based functional assessment of a candidate gene believed to reduce apoptosis.

Strengths:

The study's rationale and its integration of seasonal and comparative transcriptomics are well-articulated and represent an advancement in the field. The transcriptome, known for its dynamic and plastic nature, is also influenced by evolutionary history. The authors effectively demonstrate how multiple signals-evolutionary, constitutive, and plastic-can be extracted, quantified, and interpreted. The chosen phenotype and study system are particularly compelling, as it not only exemplifies an extreme case of Dehnel's phenotype, but the metabolic requirements of the shrew suggest that genes regulating metabolic homeostasis are under strong selection.

Weaknesses:

The results of the expression patterns are quite compelling and a number of interesting downstream hypotheses are outlined; however, the interpretation of the role of each gene and pathway identified is speculative which dampens the overall impact of the work. That said, I commend the authors on functionally testing one of the differentially expressed genes. I also commend the inclusion of that negative result.

Reviewer #1 (Public review):

Summary

In this manuscript, De La Forest Divonne et al. build a repertory of hemocytes from adult Pacific oysters combining scRNAseq data with cytologic and biochemical analyses. Three categories of hemocytes were described previously in this species (i.e. blast, hyalinocyte and granulocytes). Based on scRNAseq data, the authors identified 7 hemocyte clusters presenting distinct transcriptional signatures. Using Kegg pathway enrichment and RBGOA, the authors determined the main molecular features of the clusters. In parallel, using cytologic markers, the authors classified 7 populations of hemocytes (i.e. ML, H, BBL, ABL, SGC, BGC, and VC) presenting distinct sizes, nucleus sizes, acidophilic/basophilic, presence of pseudopods, cytoplasm/nucleus ratio and presence of granules. Then, the authors compared the phenotypic features with potential transcriptional signatures seen in the scRNAseq. The hemocytes were separated in a density gradient to enrich for specific subpopulations. The cell composition of each cell fraction was determined using cytologic markers and the cell fractions were analysed by quantitative PCR targeting major cluster markers (two per cluster). With this approach, the authors could assign cluster 7 to VC, cluster 2 to H, and cluster 3 to SGC. The other clusters did not show a clear association with this experimental approach. Using phagocytic assays, ROS, and copper monitoring, the authors showed that ML and SGC are phagocytic, ML produces ROS, and SGC and BGC accumulate copper. Then with the density gradient/qPCR approach, the authors identified the populations expressing anti-microbial peptides (ABL, BBL, and H). At last, the authors used Monocle to predict differentiation trajectories for each subgroup of hemocytes using cluster 4 as the progenitor subpopulation.

The manuscript provides a comprehensive characterisation of the diversity of circulating immune cells found in Pacific oysters.

Strengths

The combination of scRNAseq, cytologic markers and gradient based hemocyte sorting offers an integrative view of the immune cell diversity.<br /> Hemocytes represent a very plastic cell population that has key roles in homeostatic and challenged conditions. Grasping the molecular features of these cells at the single-cell level will help understand their biology.<br /> This type of study may help elucidate the diversification of immune cells in comparative studies and evolutionary immunology.

Weaknesses

Several figures show inconsistency leading to erroneous conclusions and some conclusions are poorly supported. Moreover, the manuscript remains highly descriptive with limited comparison with the available literature.

Comments on revisions:

The authors replied to most comments.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors develop a novel method to infer ecologically-informative parameters across healthy and diseased states of the gut microbiota, although the method is generalizable to other datasets for species abundances. The authors leverage techniques from theoretical physics of disordered systems to infer different parameters - mean and standard deviation for the strength of bacterial interspecies interactions, a bacterial immigration rate, and the strength of demographic noise - that describe the statistics of microbiota samples from two groups-one for healthy subjects and another one for subjects with chronic inflammation syndromes. To do this, the authors simulate communities with a modified version of the Generalized Lotka-Volterra model and randomly-generated interactions, and then use a moment-matching algorithm to find sets of parameters that better reproduce the data for species abundances. They find that these parameters are different for the healthy and diseased microbiota groups. The results suggest, for example, that bacterial interaction strengths, relative to noise and immigration, are more dominant for microbiota dynamics in diseased states than in healthy states.

We think that this manuscript brings an important contribution that will be of interest in the areas of statistical physics, (microbiota) ecology, and (biological) data science. The evidence of their results is solid and the work improves the state-of-the-art in terms of methods. There are a few weaknesses that, in our opinion, the authors could address to further improve the work.

Strengths:

(1) Using a fairly generic ecological model, the method can identify the change in the relative importance of different ecological forces (distribution of interspecies interactions, demographic noise, and immigration) in different sample groups. The authors focus on the case of the human gut microbiota, showing that the data are consistent with a higher influence of species interactions (relative to demographic noise and immigration) in a disease microbiota state than in healthy ones.

(2) The method is novel, original, and it improves the state-of-the-art methodology for the inference of ecologically relevant parameters. The analysis provides solid evidence for the conclusions.

Weaknesses:

In the way it is written, this work might be mostly read by physicists. We believe that, with some rewriting, the authors could better highlight the ecological implications of the results and make the method more accessible to a broader audience.

Reviewer #2 (Public review):

Summary:

This valuable work aims to infer, from microbiome data, microbial species interaction patterns associated with healthy and unhealthy human gut microbiomes. Using solid techniques from statistical physics, the authors propose that healthy and unhealthy microbiome interaction patterns substantially differ. Unhealthy microbiomes are closer to instability and single-strain dominance; whereas healthy microbiomes showcase near-neutral dynamics, mostly driven by demographic noise and immigration.

Strengths:

A well-written article, relatively easy to follow and transparent despite the high degree of technicality of the underlying theory. The authors provide a powerful inferring procedure, which bypasses the issue of having only compositional data.

Weaknesses:

(1) This sentence in the introduction seems key to me: "Focusing on single species properties as species abundance distribution (SAD), fail to characterise altered states of microbiome." Yet it is not explained what is meant by 'fail', and thus what the proposed approach 'solves'.

(2) Lack of validation, following arbitrary modelling choices made (symmetry of interactions, weak-interaction limit, uniform carrying capacity).<br /> Inconsistent interpretation of instability. Here, instability is associated with the transition to the marginal phase, which becomes chaotic when interaction symmetry is broken. But as the authors acknowledge, the weak interaction limit does not reproduce fat-tailed abundance distributions found in data. On the other hand, strong interaction regimes, where chaos prevails, tend to do so (Mallmin et al, PNAS 2024). Thus, the nature of the instability towards which unhealthy microbiomes approach is unclear.

(3) Three technical points about the methodology and interpretation.<br /> a) How can order parameters h and q0 can be inferred, if in the compositional data they are fixed by definition?<br /> b) How is it possible that weaker interaction variance is associated with approach to instability, when the opposite is usually true?<br /> c) Having an idea of what the empirical data compares to the theoretical fits would be valuable.

Implications:

As the authors say, this is a proof of concept. They point at limits and ways to go forward, in particular pointing at ways in which species abundance distributions could be better reproduced by the predicted dynamical models. One implication that is missing, in my opinion, is the interpretability of the results, and what this work achieves that was missing from other approaches (see weaknesses section above): what do we learn from the fact that changes in microbial interactions characterise healthy from unhealthy microbiota? For instance, what does this mean for medical research?

Reviewer #3 (Public review):

Summary:

I found the manuscript to be well-written. I have a few questions regarding the model, though the bulk of my comments are requests to provide definitions and additional clarity. There are concepts and approaches used in this manuscript that are clear boons for understanding the ecology of microbiomes but are rarely considered by researchers approaching the manuscript from a traditional biology background. The authors have clearly considered this in their writing of S1 and S2, so addressing these comments should be straightforward. The methods section is particularly informative and well-written, with sufficient explanations of each step of the derivation that should be informative to researchers in the microbial life sciences who are not well-versed with physics-inspired approaches to ecology dynamics.

Strengths:

The modeling efforts of this study primarily rely on a disordered form of the generalized Lotka-Volterra (gLV) model. This model can be appropriate for investigating certain systems, and the authors are clear about when and how more mechanistic models (i.e., consumer-resource) can lead to gLV. Phenomenological models such as this have been found to be highly useful for investigating the ecology of microbiomes, so this modeling choice seems justified, and the limitations are laid out.

Weaknesses:

The authors use metagenomic data of diseased and healthy patients that were first processed in Pasqualini et al. (2024). The use of metagenomic data leads me to a question regarding the role of sampling effort (i.e., read counts) in shaping model parameters such as $h$. This parameter is equal to the average of 1/# species across samples because the data are compositional in nature. My understanding is that $h$ was calculated using total abundances (i.e., read counts). The number of observed species is strongly influenced by sampling effort, so it would be useful if the number of reads were plotted against the number of species for healthy and diseased subjects.

However, the role of sampling effort can depend on the type of data, and my instinct about the role that sampling effort plays in species detection is primarily based on 16S data. The dependency between these two variables may be less severe for the authors' metagenomic pipeline. This potential discrepancy raises a broader issue regarding the investigation of microbial macroecological patterns and the inference of ecological parameters. Often microbial macroecology researchers rely on 16S rRNA amplicon data because that type of data is abundant and comparatively low-cost. Some in microbiology and bioinformatics are increasingly pushing researchers to choose metagenomics over 16S. Sometimes this choice is valid (discovery of new MAGs, investigate allele frequency changes within species, etc.), sometimes it is driven by the false equivalence "more data = better". The outcome, though, is that we have a body of more-or-less established microbial macroecological patterns which rest on 16S data and are now slowly incorporating results from metagenomics. To my knowledge, there has not been a systematic evaluation of the macroecological patterns that do and do not vary by one's choice in 16S vs. metagenomics. Several of the authors in this manuscript have previously compared the MAD shape for 16S and metagenomic datasets in Pasqualini et al., but moving forward, a more comprehensive study seems necessary (2024).

References

Pasqualini, Jacopo, et al. "Emergent ecological patterns and modelling of gut microbiomes in health and in disease." PLOS Computational Biology 20.9 (2024): e1012482.

Reviewer #1 (Public review):

The manuscript by Liao et al investigates the mechanisms that induce ephrin expression in spinal cord lateral motor column (LMC) neurons to facilitate axon guidance into the dorsal and ventral limb. The authors show that Sp1 and its co-activators p300 and CBP are required to induce ephrin expression to modulate the responsiveness of motor neurons to external ephrin cues. The study is well done and convincingly demonstrates the role of Sp1 in motor neuron axon guidance.

Further discussion and clarification of some results would further improve the study.

(1) The mechanism that the authors propose (Figure 7) and is also supported by their data is that Sp1 induces ephrinA5 in LMCm and ephrinB2 in LMCl to attenuate inappropriate responses to external ephrins in the limb. Therefore, deletion of Sp1 should result in mistargeting of LMCl and LMCm axons, as shown in the mouse data, but no overt changes in the number of axons in the ventral and dorsal limb. From the mouse backfills, it seems that an equal number of LMCm/LMCl project into the wrong side of the limb. However, the chick data show an increase of axons projecting into the ventral limb in the Sp1 knockout. Is this also true in the mouse? The authors state that medial and lateral LMC neurons differ in their reliance on Sp1 function but that is not supported by the mouse backfill data (27% vs 32% motor neurons mistargeted). Also, the model presented in Figure 7 does not explain how Sp1 overexpression leads to axon guidance defects.

(2) The authors do not directly show changes in ephrin expression in motor neurons, either in chick or mouse, after Sp1 knockout, which is the basis of their model. The experiment in Figure 4G seems to be Sp1 overexpression rather than knockdown (as mentioned in the results) and NSC-34 cells may not be relevant to motor neurons in vivo. NSC-34 experiments are also not described in the methods.

(3) There is no information about how the RNA-sequencing experiment was done (which neurons were isolated, how, at what age, how many replicates, etc) so it is hard to interpret the resulting data.

(4) It is unclear why the authors chose to use a Syn1-cre driver rather than a motor neuron restricted cre driver. Since this is a broad neuronal cre driver, the behavioral defects shown in Figure 7 may not be solely due to Sp1 deletion in motor neurons. Are there other relevant neuronal populations that express Sp1 that are targeted by this cre-mediated deletion?

Reviewer #2 (Public review):

Summary:

This study shows that transcription factor Sp1 is required for correct ventral vs. dorsal targeting of limb-innervating LMC motor neurons using mouse and chick as model systems. In a wild-type embryo, lateral LMC axons specifically target dorsal muscles while medial LMC axons target ventral muscles. The authors convincingly show that this specificity is lost when Sp1 is knocked down or knocked out - axons of both lateral and medial LMC motor neurons project to both dorsal and ventral muscles in mutant conditions. The authors then conduct RNA-seq and ChIP experiments to show that Sp1 loss of function disrupts Ephrin-Epha receptor signaling pathway genes. These molecules are known to provide attractive or repulsive cues to guide LMC axons to their targets. The authors show that attraction/repulsion properties of medial and lateral LMC axons to specific Ephrin/Epha molecules are in fact disrupted in Sp1 mutants using ex vivo explant studies. Finally, the authors show that behaviors like coordinated movement and grip strength are also affected in Sp1 mutant mice. This study convincingly shows that Sp1 is important for correct circuit wiring of LMC neurons, and moves the field forward by elucidating a new level of transcriptional regulation required in this process. However, the claims made by the authors that the mode of Sp1-mediated regulation is through cis-attenuation of Epha activity is not well supported. These and additional strengths and weaknesses in approach and in data interpretation are discussed below.

Strengths:

(1) The study convincingly shows that wildtype levels of Sp1 are necessary for LMC axon targeting specificity. The combination of the following approaches is a strength:<br /> a) Both loss of function and gain of function experiments are performed for Sp1 and show complementary effects on the axon targeting phenotype.<br /> b) Retrograde labeling of LMC neurons from dorsal and ventral muscles shows that Sp1 mutants clearly lose the specificity of LMC axon targeting.<br /> c) The authors also use explant experiments to show that both loss of Sp1 and gain of Sp1 show clear changes in attraction and repulsion to specific ephrin and epha receptor molecules.<br /> d) The Sp1 loss and gain of function experiments are well controlled to show that the changes in axon wiring observed are not due to cell death, cell fate switches, or due to unequal numbers of medial and lateral LMC neurons being labeled in the experiments.

(2) It is also convincing that Sp1 requires cofactors p300 and CBP for its function. In the absence of these cofactors, the gain of function phenotypes of Sp1 are subdued.

Weaknesses:

(1) The robustness of RNAseq and ChIP experiments is difficult to judge as methods are not described. For example, it is unclear if RNAseq is performed on purified motor neurons or on whole spinal cords. This is an important consideration as Sp1 is a broadly expressed protein.

(2) The authors state that expression of Ephrin A5 and Ephrin B2 is reduced based on RNAseq data, however, it is not shown that this reduction occurs specifically in LMC neurons.

(3) The authors show Sp1 ChIP peaks at Ephrin B2 promoter, but nothing is mentioned about peaks at Eprin A5 or other types of signaling molecules like Sema7a, which are also differentially expressed in Sp1 mutants. There is also no mention of the correlation between changes in gene expression seen in RNAseq data and the binding profile of Sp1 seen in ChIP data, which could help establish the robustness of these datasets.

(4) The authors conclude that Sp1 functions by activating Ephrin A5 in medial LMC and Ephrin B2 in lateral LMC. The argument, as I understand it, is that this activation leads to cis attenuation of their respective Epha receptors and therefore targeting the correct muscle. Though none of the data presented go against this hypothesis, this hypothesis is also not fully supported. Specifically:<br /> a) It would be important to know that modulation of Sp1 expression leads to changes in EphrinA5 and B2 in LMC lateral/medial neurons.<br /> b) It would also be important to show that none of the other changes caused by Sp1 are responsible for axon mistargeting by performing rescue experiments with Ephrin A5 and Ephrin B2.<br /> c) To make the most convincing case, experiments showing increased or decreased cis-binding of Ephrin molecules with Epha receptors would be necessary. This study would still be compelling without this last experiment, but the language in the abstract would need to be modulated.

(5) All behavior experiments are done in a pan-neuronal knockout of Sp1. As Sp1 is broadly expressed in neurons, a statement describing whether and why the authors think the phenotypes arise from Sp1's function in LMC motor neurons would be helpful. Experimentally, rescue experiments in which Sp1 is restored in LMC neurons or motor neurons would also make this claim more convincing.

Reviewer #3 (Public review):

Summary:

This is a compelling study on the role of Sp1 in motor axon trajectory selection, demonstrating that Sp1 is both necessary and sufficient for correct axon guidance in the limb. Sp1 regulates ephrin ligand expression to fine-tune Eph/ephrin signaling in the lateral motor column (LMC) neurons.

Strengths:

The study integrates multiple approaches. These include in ovo electroporation in chick embryos, conditional knockout mouse models, transcriptomic analyses, and functional assays such as stripe assays and behavioral testing-to provide robust evidence for Sp1's role in axon guidance mechanisms. The manuscript is well-written and scientifically rigorous, and the findings are of broad interest to the developmental neuroscience community.

Weaknesses:

Some aspects of the manuscript could be improved to enhance clarity, ensure logical flow, and strengthen the impact of the findings.

Reviewer #1 (Public review):

In this study, Li et al et al. investigated the role of miR-283 in regulating cardiac aging and its potential contribution to age-related bradyarrhythmia. Using Drosophila as a model, the authors demonstrated that systemic overexpression or knockdown of miR-283 induced age-associated bradycardia. Notably, the study found that miR-283 knockdown in ventral-lateral neurons (LNvs), rather than in the heart, was sufficient to induce bradyarrhythmia, an effect the authors linked to the upregulation of miR-283 expression in both the brain and heart. The study also explored the beneficial impact of exercise on cardiac aging, showing that endurance training mitigated bradyarrhythmia, correlating with reduced miR-283 accumulation in the brain and myocardium.

The conclusions of this paper are mostly well supported by data; however, some concerns arise from the unexpected finding that bradyarrhythmia was triggered by miR-283 knockdown in LNvs rather than in the heart, suggesting a non-cell-autonomous mechanism. A more precise mechanistic explanation linking miR-283 dysregulation in LNvs to cardiac dysfunction would strengthen the study's conclusions. While the authors propose cwo as a potential target of miR-283, no functional experiments were conducted to confirm its role in mediating miR-283's effects. Additionally, it remains unclear whether reduced miR-283 levels in LNvs lead to accelerated aging rather than a cardiac-specific effect. Likewise, the potential influence of miR-283 on the circadian clock and its broader impact on aging warrant further investigation.

Major Comments:

(1) A significant concern arises from the unexpected outcome observed in miR-283 knockdown in LNvs, which suggests a non-cell-autonomous mechanism. Elucidating the mechanisms by which miR-283 deficiency leads to the observed phenotypes would provide a more comprehensive understanding of the study's implications.

(2) The authors propose cwo as a potential target of miR-283; however, no functional experiments were conducted to confirm its role in mediating miR-283's effects. Similarly, direct evidence demonstrating that cwo is a bona fide target of miR-283 in LNvs should be provided.

(3) It remains unclear whether miR-283 knockdown in LNvs results in accelerated aging rather than a cardiac-specific effect. This hypothesis is supported by observations that pdf>miR-283SP animals exhibit systemic premature senescence (elevated SA-β-gal activity in both the heart and brain), cardiac dysfunction, impaired climbing ability, and reduced lifespan.

(4) The finding that reduced miR-283 levels in LNvs lead to accelerated aging raises an important, yet unexplored, question: does miR-283 influence the circadian clock, thereby broadly affecting aging?

Two aspects of this question should be addressed:<br /> (a) Is the circadian rhythm disrupted in miR-283 knockdown experiments?<br /> (b) Do circadian rhythm defects impact aging?

(5) The authors state that miR-283 knockdown in LNvs led to bradyarrhythmia, which was mainly caused by miR-283 upregulation in the whole brain and heart. However, it is unclear which experiments support this conclusion. Could the authors clarify this point?

(6) Given that miR-283 expression varies with age, could the upregulation of miR-283 in both the brain and heart be a consequence of accelerated aging rather than a specific effect of miR-283 knockdown in LNvs?

(7) While the beneficial effects of exercise on cardiac function appear clear, the claim that this effect is mediated through miR-283 function in LNvs seems premature. The data suggest that exercise-induced improvement occurs in both wild-type and miR-283-SP animals, raising the possibility that exercise acts through a miR-283-independent mechanism.

Reviewer #2 (Public review):

Summary:

The manuscript presents findings that indicate a role in controlling Drosophila heart rate for a conserved miRNA (miR-238 in flies). Further, the manuscript localizes the relevant tissue for the function of this miRNA to a subset of neurons that are heavily involved in circadian regulation, thus presenting an interesting mechanistic link between the circadian system and heart rate. Either ubiquitous knockout or ubiquitous overexpression negatively impacts several aspects of heart performance, with a pronounced effect on heart rate. Interestingly, knockdowns in the heart itself are innocuous, but knockdown in LNvS neurons recapitulates the effect on heart rate. Authors use bioinformatics to identify the clockwork orange (cwo) gene as a potential target and validate that cwo expression is reduced when miR-238 is knocked down in LNvS neurons in vivo and also validate that cwo is regulated by miR-238 in cell culture luciferase assays. Exercise shows a modest ability to restore normal cwo expression and a trend toward an effect on survival, but shows a much stronger rescue of the heart rate phenotype.

Strengths:

Evidence is strong for the effect of miR-238 in pdf-positive neurons on the control of heart rate and for cwo as a downstream effector of miR-238.

Work to identify specific targets of miR-283 is well-done and successfully identified a key downstream regulator in cwo.

The potential mechanism using miR-238 to link circadian neurons to heart rate regulation is novel and exciting.

Weaknesses:

The evidence that this is related to normal aging is rather weak, and the effect of exercise on the observed parameters is small and not necessarily working through the miR-238/cwo mechanism.

The authors seem to be conflating two hypotheses in their interpretations. Is miR-283 working through circadian mechanisms or age-related mechanisms? While it is true that aging tends to reduce heart rate, I don't think that means that any intervention that reduces heart rate is causing "senescence". Similarly, reduced survival in miR-283 knockdown flies does not prove that miR-283 promotes healthy aging per se, just that miR-283 is required for health regardless of age.

Survival reduction is quite modest which does not necessarily support the idea that the bradycardia is causing major health issues or premature senescence for the flies. The interpretation of the longevity experiments throughout the manuscript seems overstated.

The study would benefit greatly from a direct test of the author's proposed pathway for exercise to improve bradycardia.

The statement in the discussion "inducing endurance exercise of anti gravity climbing in flies with miR-283 knockdown in LNvs can improve bradyarrhythmic features by decreasing brain miR-283 expression" is not fully supported by data in the paper. There is an association there, but it cannot be said to be the full cause (or even required) without doing more experiments

The summary figure includes both data-supported mechanistic relationships and mechanisms that are inferred or assumed.

Reviewer #1 (Public review):

Summary:

In this manuscript, Rosero and Bai examined how the well-known thermosensory neuron in C. elegans, AFD, regulates context-dependent locomotory behavior based on the tactile experience. Here they show that AFD uses discrete cGMP signalling molecules and independent of its dendritic sensory endings regulates this locomotory behavior. The authors also show here that AFD's connection to one of the hub interneurons, AIB, through gap junction/electrical synapses, is necessary and sufficient for the regulation of this context-dependent locomotion modulation.

Strengths:

This is an interesting paper showcasing how a sensory neuron in C. elegans can employ a distinct set of molecular strategies and different physical parts to regulate a completely distinct set of behaviors, which were not been shown to be regulated by AFD before. The experiments were well performed and the results are clear. However, there are some questions about the mechanism of this regulation. This reviewer thinks that the authors should address these concerns before the final published version of this manuscript.

Weaknesses:

(1) The authors argued about the role of prior exposure to different physical contexts which might be responsible for the difference in their locomotory behaviour. However, the worms in the binary chamber (with both non-uniformly sized and spaced pillars) experienced both sets of pillars for one hour prior to the assay and they were also free to move between two sets of environments during the assay. So, this is not completely a switch between two different types of tactile barriers (or not completely restricted to prior experience), but rather a difference between experiencing a more complex environment vs a simple uniform environment. They should rephrase their findings. To strictly argue about the prior experience, the authors need to somehow restrict the worms from entering the uniform assay zone during the 1hr training period.

(2) The authors here argued that the sensory endings of AFD are not required for this novel role of AFD in context-dependent locomotion modulation. However, gcy-18 has been shown to be exclusively localized to the ciliated sensory endings of AFD and even misexpression of GCY-18 in other sensory neurons also leads to localizations in sensory endings (Nguyen et. al., 2014 and Takeishi et. al., 2016). They should check whether gcy-18 or tax-2 gets mislocalized in kcc-3 or tax-1 mutants.

(3) MEC-10 was shown to be required for physical space preference through its action in FLP and not the TRNs (PMID: 28349862). Since FLP is involved in harsh touch sensation while TRNs are involved in gentle touch sensation, which are the neuron types responsible for tactile sensation in the assay arena? Does mec-10 rescue in TRNs rescue the phenotype in the current paper?

(4) The authors mention that the most direct link between TRNs and AFD is through AIB, but as far as I understand, there are no reports to suggest synapses between TRNs and AIB. However, FLP and AIB are connected through both chemical and electrical synapses, which would make more sense as per their mec-10 data. (the authors mentioned about the FLP-AIB-AFD circuit in their discussion but talked about TRNs as the sensory modality). mec-10 rescue experiment in TRNs would clarify this ambiguity.

(5) Do inx-7 or inx-10 rescue in AFD and AIB using cell-specific promoters rescue the behaviour?

(6) How Guanylyl cyclase gcy-18 function is related to the electrical synapse activity between AFD and AIB? Is AFD downstream or upstream of AIB in this context?

Reviewer #2 (Public review):

Summary:

The goal of the study was to uncover the mechanisms mediating tactile-context-dependent locomotion modulation in C. elegans, which represents an interesting model of behavioral plasticity. Starting from a candidate genetic screen focusing on guanylate cyclase (GCY) mutants, the authors identified the AFD-specific gcy-18 gene as essential for tactile-context-dependent locomotion modulation. AFD is primarily characterized as a thermo-sensory neuron. However, key thermosensory transduction genes and the sensory ending structure of AFD were shown here to be dispensable for tactile-context locomotion modulation. AFD actuates tactile-context locomotion modulation via the cell-autonomous actions of GCY-18 and the CNG-3 cyclic nucleotide-gated channel, and via AFD's connection with AIB interneurons through electrical synapses. This represents a potentially relevant synaptic connection linking AFD to the mechanosensory-behavior circuit.

Strengths:

(1) The fact that AFD mediates tactile-context locomotion modulation is new, rather surprising, and interesting.

(2) The authors have combined a very clever microfluidic-based behavioral assay with a large set of genetic manipulations to dissect the molecular and cellular pathways involved. Rescue experiments with single-copy transgenes are very convincing.

(3) The study is very clearly written, and figures are nicely illustrated with diagrams that effectively convey the authors' interpretation.

Weaknesses:

(1) Whereas GCY-18 in AFD and the AFD-AIB synaptic connection clearly play a role in tactile-context locomotion modulation, whether and how they actually modulate the mechanosensory circuit and/or locomotion circuit remains unclear. The possibility of non-synaptic communication linking mechanosensory neurons and AFD (in either direction) was not explored. Thus, in the end, we have not learned much about what GCY-18 and the AFD-AIB module are doing to actuate tactile context-dependent locomotion modulation.

(2) The authors only focused on speed readout, and we don't know if the many behavioral parameters that are modulated by tactile context are also under the control of AFD-mediated modulation.

(3) The AFD-AIB gap junction reconstruction experiment was conducted in an innexin double mutant background, in which the whole nervous system's functioning might be severely impaired, and its results should be interpreted with this limitation in mind.

Reviewer #3 (Public review):

Summary:

Rosero and Bai report an unconventional role of AFD neurons in mediating tactile-dependent locomotion modulation, independent of their well-established thermosensory function. They partially elucidate the signaling mechanisms underlying this AFD-dependent behavioral modulation. The regulation does not require the sensory dendritic endings of AFD but rather the AFD neurons themselves. This process involves a distinct set of cGMP signaling proteins and CNG channel subunits separate from those involved in thermosensation or thermotaxis. Furthermore, the authors demonstrate that AIB interneurons connect AFD to mechanosensory circuits through electrical synapses. They conclude that, beyond its primary function in thermosensation, AFD contributes to context-dependent neuroplasticity and behavioral modulation via broader circuit connectivity.

While the discovery of multifunctionality in AFD is not entirely unexpected, given the limited number of neurons in C. elegans (302 in total), the molecular and cellular mechanisms underlying this AFD-dependent behavioral modulation, as revealed in this study, provide valuable insights into the field.

Strengths:

(1) The authors uncover a novel role of AFD neurons in mediating tactile-dependent locomotion modulation, distinct from their well-established thermosensory function.

(2) They provide partial insights into the signaling mechanisms underlying this AFD-dependent behavioral modulation.

(3) The neural behavior assays utilizing two types of microfluidic chambers (uniform and binary chambers) are innovative and well-designed.

(4) By comparing AFD's role in locomotion modulation to its thermosensory function throughout the study, the authors present strong evidence supporting these as two independent functions of AFD.

(5) The finding that AFD contributes to context-dependent behavioral modulation is significant, further reinforcing the growing evidence that individual neurons can serve multiple functions through broader circuit connectivity.

Weaknesses:

(1) Limited Behavioral Assays: The study relies solely on neural behavior assays conducted using two types of microfluidic chambers (uniform and binary chambers) to assess context-dependent locomotion modulation. No additional behavioral assays were performed. To strengthen the conclusions, the authors should validate their findings using an independent method, at the very least by testing AFD-ablated animals and gcy-18 mutants with a second behavioral approach.

(2) Clarity in Behavioral Assay Methodology: The methodology for conducting the behavioral assays is unclear. It appears that worms were free to move between the exploration and assay zones, with no control over the duration each worm spent in either zone. This lack of regulation may introduce variability in tactile experience across individuals, potentially affecting the reproducibility and quantitativeness of the method. The authors should clarify whether and how they accounted for this variability.

(3) Potential Developmental and Behavioral Confounds in Mutant Analysis: Several neuronal mutant strains were used in this study, yet the effects of these mutations on development and general behavior (e.g., movement ability) were not discussed. Although young adult worms were used for behavioral assays, were they at similar biological ages? To rule out confounding factors, locomotion assays assessing movement ability should be conducted (see reference PMID 25561524).

(4) Definition and Baseline Measurements for Locomotion Categories: The finding that tax-4 and kcc-3 contribute to basal locomotion but not to context-dependent locomotion modulation is intriguing. The authors argue that distinct mechanisms regulate these two processes; however, the study does not clearly define the concepts of "basal locomotion" and "context-dependent locomotion," nor does it provide baseline measurements. A clear definition and baseline data are needed to support this conclusion.

Reviewer #1 (Public review):

Summary:

In this manuscript, Campbell et al. assess how intracranial theta-burst stimulation (TBS) applied to the basolateral amygdala in 23 epilepsy patients affects neuronal spiking in the medial temporal lobe and prefrontal cortex during a visual recognition memory task.

Strengths:

This is an incredibly rare dataset; collecting single-unit spiking data from behaving humans during active intracranial stimulation is a Herculaean task, with immense potential for translational studies of how stimulation may be applied to modulate biological mechanisms of memory. The authors utilize careful, high-quality methodology throughout (e.g. task design, spike recording and sorting, statistical analysis), providing high confidence in the validity of their findings.

Weaknesses:

(1) This is an exploratory study that doesn't explore quite enough. Critically, the authors make a point of mentioning that neuronal firing properties vary across cell types, but only use baseline firing rate as a proxy metric for cell type. This leaves several important explorations on the table, not limited to the following:<br /> a) Do waveform shape features, which can also be informative of cell type, predict the effect of stimulation?<br /> b) Is the autocorrelation of spike timing, which can be informative about temporal dynamics, altered by stimulation? This is especially interesting if theta-burst stimulation either entrains theta-rhythmic spiking or is more modulatory of endogenously theta-modulated units.<br /> c) The authors reference the relevance of spike-field synchrony (30-55 Hz) in animal work, but ignore it here. Does spike-field synchrony (comparing the image presentation to post-stimulation) change in this frequency range? This does not seem beyond the scope of investigation here.<br /> d) How does multi-unit activity respond to stimulation? At this somewhat low count of neurons (total n=156 included) it would be valuable to provide input on multi-unit responses to stimulation as well.<br /> e) Several intracranial studies have implicated proximity to white matter in determining the effects of stimulation on LFPs; do the authors see an effect of white matter proximity here?

(2) It is a little confusing to interpret stimulation-induced modulation of neuronal spiking in the absence of stimulation-induced change in behavior. How do the authors findings tell us anything about the neural mechanisms of stimulation-modulated memory if memory isn't altered? In line with point #1, I would suggest a deeper dive into behavior (e.g. reaction time? Or focus on individual sessions that do change in Figure 4A?) to make a stronger statement connecting the neural results to behavioral relevance.

(3) It is not clear to me why the assessment of firing rates after image onset and after stim offset is limited to one second - this choice should be more theoretically justified, particularly for regions that spike as sparsely as these.

(4) This work coincides with another example of human intracranial stimulation investigating the effect on firing rates (doi: https://doi.org/10.1101/2024.11.28.625915). Given how incredibly rare this type of work is, I think the authors should discuss how their work converges with this work (or doesn't).

(5) What information does the pseudo-population analysis add? It's not totally clear to me.

Reviewer #2 (Public review):

Summary:

This study presents a valuable characterization of the effects of intracranial theta-burst stimulation of the basolateral amygdala on single units spiking activity in several areas in the human brain, associated with memory processing. It is written clearly and concisely, allowing readers to fully understand the analysis used.

The authors used a visual recognition memory task previously employed by their group to characterize the effects of basolateral amygdala stimulation upon memory consolidation (Inman et al, 2018). This current report is an interesting analysis to complement the results reported in the 2018 paper.

Strengths:

Rare combination of human neurophysiology and behavior -<br /> The type of experiment performed in the manuscript, which contains both neurophysiological data, behavior, and a deep brain stimulation intervention (DBS), is incredibly rare, takes many years to accomplish with tight collaboration between clinical and research teams. Our understanding of spiking dynamics of human neurons is very limited, and this report is an important piece in the puzzle that allows DBS to be used in future interventions that will benefit patients' health.

Multiple brain areas included -<br /> It's important to note that the report analyzes brain areas with which the Amygdala has extensive connections (Fig. 1A) - Hippocampus, OFC, Amygdala, ACC. It seems that neurons in all these areas were modulated by the stimulation, except the ACC, in which firing rates were so low, that only a handful of neurons were included in the analysis. This is an important demonstration that low amplitude stimulation (even when reduced to 0.5mA) can travel far and wide across the human brain.

The experiment is cleverly designed to tease apart responses due to visual stimuli (image presentation) and electrical stimulation. Authors suggest that the units modulated by stimulation are largely distinct from those responsive to image offset during trials without stimulation. The subpopulation that responds strongly also tends to have a higher baseline of firing rate. It's important to add that the chosen modulation index is more likely to be significant in neurons with higher firing rates.

Weaknesses:

Readers can benefit from understanding with more details the locations chosen for stimulation - in light of previous studies that found differences between effects based on proximity to white matter (For example - PMID 32446925, Mohan et al, Brain Stimul. 2020 and PMID 33279717 Mankin et al Brain Stimul. 2021).

Reviewer #1 (Public review):

This is an interesting manuscript aimed at improving the transcriptome characterization of 52 C. elegans neuron classes. Previous single-cell RNA seq studies already uncovered transcriptomes for these, but the data are incomplete, with a bias against genes with lower expression levels. Here, the authors use cell-specific reporter combinations to FACS purify neurons and bulk RNA sequencing to obtain better sequencing depth. This reveals more rare transcripts, as well as non-coding RNAs, pseudogenes, etc. The authors develop computational approaches to combine the bulk and scRNA transcriptome results to obtain more definitive gene lists for the neurons examined.

To ultimately understand features of any cell, from morphology to function, an understanding of the full complement of the genes it expresses is a pre-requisite. This paper gets us a step closer to this goal, assembling a current "definitive list" of genes for a large proportion of C. elegans neurons. The computational approaches used to generate the list are based on reasonable assumptions, the data appear to have been treated appropriately statistically, and the conclusions are generally warranted. I have a few issues that the authors may choose to address:

(1) As part of getting rid of cross-contamination in the bulk data, the authors model the scRNA data, extrapolate it to the bulk data and subtract out "contaminant" cell types. One wonders, however, given that low expressed genes are not represented in the scRNA data, whether the assignment of a gene to one or another cell type can really be made definitive. Indeed, it's possible that a gene is expressed at low levels in one cell, and high levels in another, and would therefore be considered a contaminant. The result would be to throw out genes that actually are expressed in a given cell type. The definitive list would therefore be a conservative estimate, and not necessarily the correct estimate.

(2) It would be quite useful to have tested some genes with lower expression levels using in vivo gene-fusion reporters to assess whether the expression assignments hold up as predicted. i.e. provide another avenue of experimentation, non-computational, to confirm that the decontamination algorithm works.

(3) In many cases, each cell class would be composed of at least 2 if not more neurons. Is it possible that differences between members of a single class would be missed by applying the cleanup algorithms? Such transcripts would be represented only in a fraction of the cells isolated by scRNAseq, and might then be considered not real.

(4) I didn't quite catch whether the precise staging of animals was matched between the bulk and scRNAseq datasets. Importantly, there are many genes whose expression is highly stage-specific or age-specific so even slight temporal differences might yield different sets of gene expression.

(5) To what extent does FACS sorting affect gene expression? Can the authors provide some controls?

Reviewer #2 (Public review):

Summary:

This study from the CenGEN consortium addresses several limitations of single-cell RNA (scRNA) and bulk RNA sequencing in C. elegans with a focus on cells in the nervous system. scRNA datasets can give very specific expression profiles, but detecting rare and non-polyA transcripts is difficult. In contrast, bulk RNA sequencing on isolated cells can be sequenced to high depth to identify rare and non-polyA transcripts but frequently suffers from RNA contamination from other cell types. In this study, the authors generate a comprehensive set of bulk RNA datasets from 53 individual neurons isolated by fluorescence-activated cell sorting (FACS). The authors combine these datasets with a previously published scRNA dataset (Taylor et al., 2021) to develop a novel method, called LittleBites, to estimate and subtract contamination from the bulk RNA data. The authors validate the method by comparing detected transcripts against gold-standard datasets on neuron-specific and non-neuronal transcripts. The authors generate an "integrated" list of protein-coding expression profiles for the 53 neuron sub-types, with fewer but higher confidence genes compared to expression profiles based only on scRNA. Also, the authors identify putative novel pan-neuronal and cell-type specific non-coding RNAs based on the bulk RNA data. LittleBites should be generally useful for extracting higher confidence data from bulk RNA-seq data in organisms where extensive scRNA datasets are available. The additional confidence in neuron-specific expression and non-coding RNA expands the already great utility of the neuronal expression reference atlas generated by the CenGEN consortium.

Strengths:

The study generates and analyzes a very comprehensive set of bulk RNA datasets from individual fluorescently tagged transgenic strains. These datasets are technically challenging to generate and significantly expand our knowledge of gene expression, particularly in cells that were poorly represented in the initial scRNA-seq datasets. Additionally, all transgenic strains are made available as a resource from the Caenorhabditis Elegans Genetics Center (CGC).

The study uses the authors' extensive experience with neuronal expression to benchmark their method for reducing contamination utilizing a set of gold-standard validated neuronal and non-neuronal genes. These gold-standard genes will be helpful for benchmarking any C. elegans gene expression study.

Weaknesses:

The bulk RNA-seq data collected by the authors has high levels of contamination and, in some cases, is based on very few cells. The methodology to remove contamination partly makes up for this shortcoming, but the high background levels of contaminating RNA in the FACS-isolated neurons limit the confidence in cell-specific transcripts.

The study does not experimentally validate any of the refined gene expression predictions, which was one of the main strengths of the initial CenGEN publication (Taylor et al, 2021). No validation experiments (e.g., fluorescence reporters or single molecule FISH) were performed for protein-coding or non-coding genes, which makes it difficult for the reader to assess how much gene predictions are improved, other than for the gold standard set, which may have specific characteristics (e.g., bias toward high expression as they were primarily identified in fluorescence reporter experiments).

The study notes that bulk RNA-seq data, in contrast to scRNA-seq data, can be used to identify which isoforms are expressed in a given cell. However, no analysis or genome browser tracks were supplied in the study to take advantage of this important information. For the community, isoform-specific expression could guide the design of cell-specific expression constructs or for predictive modeling of gene expression based on machine learning.

Reviewer #3 (Public review):

The manuscript by Barrett et al. "Integrating bulk and single cell RNA-seq refines transcriptomic profiles of individual C. elegans neurons" presents a comprehensive approach to integrating bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to refine transcriptomic profiles of individual C. elegans neurons. The study addresses the limitations of scRNA-seq, such as the under-detection of lowly expressed and non-polyadenylated transcripts, by leveraging the sensitivity of bulk RNA-seq. The authors deploy a computational method, LittleBites, to remove non-neuronal contamination in bulk RNA-seq, that aims to enhance specificity while preserving the sensitivity advantage of bulk sequencing. Using this approach, the authors identify lowly expressed genes and non-coding RNAs (ncRNAs), many of which were previously undetected in scRNA-seq data.

Overall, the study provides high-resolution gene expression data for 53 neuron classes, covering a wide range of functional modalities and neurotransmitter usage. The integrated dataset and computational tools are made publicly available, enabling community-driven testing of the robustness and reproducibility of the study. Nevertheless, while the study represents a relevant contribution to the field, certain aspects of the work require further refinement to ensure the robustness and rigor necessary for peer-reviewed publication. Below, I outline the areas where improvements are needed to strengthen the overall impact and reliability of the findings.

(1) The study relies on thresholding to determine whether a gene is expressed or not. While this is a common practice, the choice of threshold is not thoroughly justified. In particular, the choice of two uniform cutoffs across protein-encoding RNAs and of one distinct threshold for non-coding RNAs is somewhat arbitrary and has several limitations. This reviewer recommends the authors attempt to use adaptive threshold-methods that define gene expression thresholds on a per-gene basis. Some of these methods include GiniClust2, Brennecke's variance modeling, HVG in Seurat, BASiCS, and/or MAST Hurdle model for dropout correction.

(2) Most importantly, the study lacks independent experimental validation (e.g., qPCR, smFISH, or in situ hybridization) to confirm the expression of newly detected lowly expressed genes and non-coding RNAs. This is particularly important for validating novel neuronal non-coding RNAs, which are primarily inferred from computational approaches.

(3) The novel biology is somewhat limited. One potential area of exploration would be to look at cell-type specific alternative splicing events.

(4) The integration method disproportionately benefits neuron types with limited representation in scRNA-seq, meaning well-sampled neuron types may not show significant improvement. The authors should quantify the impact of this bias on the final dataset.

(5) The authors employ a logit transformation to model single-cell proportions into count space, but they need to clarify its assumptions and potential pitfalls (e.g., how it handles rare cell types).

(6) The LittleBites approach is highly dependent on the accuracy of existing single-cell references. If the scRNA-seq dataset is incomplete or contains classification biases, this could propagate errors into the bulk RNA-seq data. The authors may want to discuss potential limitations and sensitivity to errors in the single-cell dataset, and it is critical to define minimum quality parameters (e.g. via modeling) for the scRNAseq dataset used as reference.

(7) Also very important, the LittleBites method could benefit from a more intuitive explanation and schematic to improve accessibility for non-computational readers. A supplementary step-by-step breakdown of the subtraction process would be useful.

(8) In the same vein, the ROC curves and AUROC comparisons should have clearer annotations to make results more interpretable for readers unfamiliar with these metrics.

(9) Finally, after the correlation-based decontamination of the 4,440 'unexpressed' genes, how many were ultimately discarded as non-neuronal?<br /> a) Among these non-neuronal genes, how many were actually known neuronal genes or components of neuronal pathways (e.g., genes involved in serotonin synthesis, synaptic function, or axon guidance)?<br /> b) Conversely, among the "unexpressed" genes classified as neuronal, how many were likely not neuron-specific (e.g., housekeeping genes) or even clearly non-neuronal (e.g., myosin or other muscle-specific markers)?

(10) To increase transparency and allow readers to probe false positives and false negatives, I suggest the inclusion of:<br /> a) The full list of all 4,440 'unexpressed' genes and their classification at each refinement step. In that list flag the subsets of genes potentially misclassified, including:<br /> - Neuronal genes wrongly discarded as non-neuronal.<br /> - Non-neuronal genes wrongly retained as neuronal.<br /> b) Add a certainty or likelihood ranking that quantifies confidence in each classification decision, helping readers validate neuronal vs. non-neuronal RNA assignments.<br /> This addition would enhance transparency, reproducibility, and community engagement, ensuring that key neuronal genes are not erroneously discarded while minimizing false positives from contaminant-derived transcripts.

Reviewer #1 (Public review):

Summary:

The authors investigate the role of different specific dopaminergic neurons in the mushroom body of Drosophila larvae for learning and innate behavior. All the tested neurons are thought to be involved in punishment learning. The authors discover that artificial activation of single DANs in training leads to safety learning, but not punishment learning. Furthermore, activation of single DANs can lead to changes in locomotion behavior, which can affect light preference. The authors provide a deeper understanding of the functional diversity of single dopamine neurons; however, it is unclear how translatable these findings are to learning experiments with real punishment stimuli.

Strengths:

The authors attempt to disentangle what kind of memories are formed with the activation of different dopamine neurons - safety learning, and punishment learning, will the US be required to test for recall or not? They do indeed find differences and the results will be of interest to the learning and memory community.

Interestingly, optogenetic activation of a single DAN during training leads to safety memory, but not punishment memory. Furthermore, DAN activation also affects innate locomotion, and the authors can show that optogenetic activation of different DANs affects locomotion differently.

Weaknesses:<br /> All experiments in the manuscript use optogenetic activation of DANs, thus it is not clear what kind of memories are formed. Several stimuli can be used as punishment, such as electric shock, salt, bitter, and light - it is not clear what kind of memory the authors investigate here. The findings could be discussed in the context of what DANs respond to. Furthermore, studies in adults and larvae showed that most DANs can code for both valences - etc., aversive DANs can be activated by punishment, and inhibited by reward. Thus, safety learning might be a result of a decrease in activity in DANs during odor presentation. The authors also do not discuss possible feedback loops from MBONs to DANs across compartments. Could such connections allow for safety learning in larvae?

The authors show that artificial activation with different light intensities can form different memories and that increasing the light intensity sometimes leads to no memories. Also, using different optogenetic tools reveals different results. This again raises the question of how applicable the results will be for learning with real stimuli. Is there a natural stimulus that only induces safety learning, but no punishment learning?<br /> The authors provide a detailed behavioral analysis of locomotion behavior; however, the detailed analysis seems unnecessary for that dataset. Modulation of speed and bending rate has been described before with simpler methods (specifically for MBONs). The revealed locomotion phenotypes probably affect larval locomotion during memory recall with light activation, thus the authors should show that larvae are potentially able to move during light-on memory tests.

Reviewer #2 (Public review):

Summary:

This study provides valuable context for ongoing research on the role of dopamine in memory and locomotion. DANs have been a fascinating area of study due to their complexity, and this work dissects specific DANs, exploring their roles in different memory-related behaviors while offering some explanations. The discussions provided by the authors effectively situates the study in the broader field of learning, memory, DAN circuitry and behavioral computation in insect brains. The study achieves what it sets out to and it does so unequivocally. The experiments were elegantly designed, leaving little room for doubt in the study's claims. However, the study lacks context regarding the molecular pathways underlying these results. While it strengthens current knowledge by providing robust evidence, it does little to explore the molecular mechanisms behind these effects.

Strengths:

(1) Experiment design is one of the strengths of this study. The experiments are thorough and cover the length and breadth of the core findings of the study. Although a lot of work has already been done in studying the role of dopamine in memory and locomotion, the dissection of the functions of distinct DANs in larvae has been done meticulously with well-structured experiments.<br /> (2) This study fits quite nicely into the puzzle of memory, especially in the context of Dopamine. Previous studies in *Drosophila* adults have shown the opposing roles of DANs in locomotion depending on the context of DAN activation. This study drives that point home for larvae, providing conclusive evidence in that regard.<br /> (3) The use of clear figures and simple language is one of the strengths of this paper. The figures are comprehensive, complete and manage to narrate the story by themselves. The flow of information is smooth. The simple and effective language used maintains scientific rigor while remaining accessible to those new to the field. A pleasant read.

Weaknesses:<br /> (1) The authors have done a great job at structuring the figures. But some main figures would benefit from including the controls instead of placing them in supplementary.<br /> (2) The paper would benefit from a deeper discussion regarding molecular mechanisms underlying their results. It would be interesting to see what the authors think about different Dopamine receptors and how they relate to the findings of this paper.<br /> (3) Throughout the paper, the authors have been clear and comprehensive, but in some cases, further explanation of their choices were missing. For example, the choice to compare bending and tail velocity over other parameters within the same clusters is unclear.

Reviewer #3 (Public review):

Summary

Across species, dopamine release carries out seemingly diverse functions, like reinforcing memories and regulating locomotion and flight. However, whether distinct dopaminergic neurons (DANs) are allocated for each function is not clear. In this study, Toshima et al. have used the numerically simple organization of the Drosophila larval brain to answer this question. They use optogenetic activation to systematically stimulate a small set of DANs, individually and collectively, and study the effect on diverse functions such as memory formation, retrieval, and locomotion. They find that singly or collectively, DL1 DANs can induce punishment and/or safety memory formation and retrieval. DANs can even gate the expression of memory. Finally, the same DANs also modulate locomotion in the larvae. The authors speculate that dopaminergic neurons in other species may also share such overlapping functions. Their findings are nicely summarised in Figure 9.

Strengths

The study comprehensively activates the neurons in the DL1 cluster in a systematic manner. Individual and collective stimulation of the Dl1 DANs has been conducted to assess the induction and gating of aversive punishment memory, safety memory, and acute locomotion.

Specific adult Drosophila DANs are known to induce dual behaviors and functions. The same MP1/y1pedc DANs are recognized for gating appetitive memory expression and representing aversive teaching signals downstream of sensory stimuli such as electric shocks, bitter tastes, and heat. Neurons in the PPL1 cluster regulate adult flight and food-seeking behavior. The authors deserve credit for conducting an organized examination of dopaminergic neuron functions in larvae, which makes their findings more comparable and facilitates the proposal of a holistic model.

They have provided substantial evidence for their findings and frequently presented replicated behavioral data sets. They have been transparent about results that were difficult to explain. Additionally, they have provided an impressive body of supporting data to strengthen their main findings.

Weaknesses

The larvae exhibit directed locomotory action to express punishment or safety memory. If the larvae did not move, we would not be able to assess memory function. Hence, functional activation of DANs could result in one action, which seems like two different functions of memory expression and locomotion. It can also be argued that activation of DANs represents a teaching signal to the KCs, and then eventually, downstream of the MBONs, it results in locomotion modulation. Hence, the seeming functional diversity could be a function of different downstream neuronal pathways and not molecular context-dependent diversity inside dopaminergic neurons. The authors should address this possibility or point out the fallacy in the above argument.

The finding that activation of TH-GAL4 conveys aversive valence and R58E02-GAL4 conveys appetitive valence seems redundant (Figure 6). I understand they say this in the context of locomotion. However, they may not have mentioned similar findings in adults. In adults, artificial activation of DANs covered by the same GAL4 lines acts as aversive and appetitive teaching signals for memory formation. These references should be cited appropriately in the results and discussion if not currently included.

The evidence for the role of dopamine (Figure 7) can be bolstered by using other available RNAi lines against TH. A valium20 vector-based shRNA line is recommended. The current evidence is based mainly on non-specific pharmacological intervention with 3IY.

Reviewer #1 (Public review):

Summary:

The authors sequenced 888 individuals from the 1000 Genomes Project using the Oxford Nanopore long-read sequencing method to achieve highly sensitive, genome-wide detection of structural variants (SVs) at the population level. They conducted solid benchmarking of SV calling and systematically characterized the identified SVs. While short-read sequencing methods, including those used in the 1000 Genomes Project, have been widely applied, they exhibit high accuracy in detecting single nucleotide variants (SNVs) and small insertions and deletions but have limited sensitivity for SV detection. This study significantly enhances SV detection capabilities, establishing it as a valuable resource for human genetic research. Furthermore, the authors constructed an SV imputation panel using the generated data and imputed SVs in 488,130 individuals from the UK Biobank. They then conducted a proof-of-principle genome-wide association study (GWAS) analysis based on the imputed SVs and selected traits within the UK Biobank. Their findings demonstrate that incorporating SV-GWAS analysis provides additional insights beyond conventional GWAS frameworks focusing on SNVs, particularly in improving fine mapping.

Strengths:

The authors constructed a high-sensitivity reference panel of genome-wide SVs at the population level, addressing a critical gap in the field of human genetics. This resource is expected to significantly advance research in human genetics. They demonstrated the imputation of SVs in individuals from the UK Biobank using this panel and conducted a proof-of-concept SV-based GWAS. Their findings highlight a novel and effective strategy for integrating SVs into GWAS, which will facilitate the analysis of human genetic data from the UK Biobank and other datasets. Their conclusions are supported by comprehensive analyses.

Weaknesses:

(1) Although the authors employ state-of-the-art analytical approaches for the identification of SVs, the overall accuracy remains suboptimal, as indicated by an F1 score of 74.0%, particularly in tandem repeat regions. To enhance accuracy, it would be beneficial to explore alternative SV detection methods or develop novel approaches. Given the value of the reference panel and the fact that improved SV accuracy would lead to more precise SV imputation and GWAS results, investing effort in methodological refinement is highly encouraged.

(2) From the Methods section, it appears that the authors employed Beagle for both the "leave-one-out" imputation and the UK Biobank imputation. It would be better to explicitly clarify this in the Results section and provide a detailed description of the corresponding procedures and parameters in the Methods section for both analyses, as this represents a key aspect of the study. Additionally, Beagle is not specifically designed for SV imputation, the imputation quality of SVs is generally lower than that of SNVs. Exploring strategies to improve SV imputation, such as developing a novel method with reference panel data, may enhance performance. It is also important to assess how this reduced imputation quality may influence GWAS results. For instance, it would be useful to examine whether associated SVs exhibit higher imputation quality and whether SVs with lower quality are less likely to achieve significant association signals. In addition, the lower imputation quality observed for INV, DUP, and BND variants (Figure 3) may be due to their greater lengths (Figure 2). It is better to investigate the relationship between SV length and imputation quality.

(3) All examples presented in the manuscript focus on SVs that overlap with genes. It may also be valuable to investigate SVs that do not overlap with genes but intersect with enhancer regions. SVs can contribute to disease by altering regulatory elements, such as enhancers, which play a crucial role in gene expression. Including such analyses would further demonstrate the utility of SV-GWAS and provide deeper insights into the functional impact of SVs.

(4) The data availability link currently provides only a VCF file ("sniffles2_joint_sv_calls.vcf.gz") containing the identified SVs. It would be beneficial for the authors to make all raw sequencing data (FASTQ files) and key processed datasets (such as alignment results and merged SV and SNV files) available. Providing these resources would enable other researchers to develop improved SV detection and imputation methods or conduct further genetic analyses. Furthermore, establishing a dedicated website for data access, along with a genome browser for SV visualization, could significantly enhance the impact and accessibility of the study. Additionally, all code, particularly the SV imputation pipeline accompanied by a detailed tutorial, should be deposited in a public repository such as GitHub. This would support researchers in imputing SVs and conducting SV-GWAS on their own datasets.

Reviewer #2 (Public review):

Summary:

The authors aimed to develop a novel and efficient method for SV detection, utilizing data from the 1000 Genomes Project (1KGP) for modeling and calibration. This method was subsequently validated using UK population data and applied to identify structural variants associated with specific disease phenotypes.

Strengths:

Third-generation single-molecule sequencing data offers several advantages over traditional high-throughput sequencing methods, particularly due to its long-read lengths, which provide valuable insights into significant forms of genomic variation. The authors have developed an efficient method for detecting structural variations and optimizing the utilization of genomic data. We hope that this method will continue to be refined, enabling researchers to more effectively leverage long-read data, high-throughput data, or even a synergistic combination of both.

Weaknesses:

Although this research contributes to our ability to more effectively utilize long-length and high-throughput data, there are some key issues that need to be addressed in terms of analyzing the specific results as well as writing the article.

Reviewer #3 (Public review):

Summary:

This study successfully identified genetic loci associated with various traits by generating large-scale long-read sequencing data from a diverse set of samples. This study is significant because it not only produces large-scale long-read genome sequencing data but also demonstrates its application in actual genetics research. Given its potential utility in various fields, this study is expected to make a valuable contribution to the academic community and to this journal. However, there are several critical aspects that could be improved. Below are specific comments for consideration.

Strengths:

Producing high-quality, large-scale variant datasets and imputation datasets

Weaknesses:

(1) Data availability

Currently, it appears that only the Genomic Lens SV Panel is available on the webpage described in the Data Availability section. It is unclear whether the authors intend to release the raw sequencing data. Since the study utilized samples from the 1000 Genomes Project, there should be no restriction on making the data publicly accessible. Given this, would the authors consider making the raw sequencing reads publicly available? If so, NCBI SRA or EBI ENA would be the most appropriate repositories for data deposition. I strongly encourage the authors to consider public data release.

Additionally, accessing the Genomic Lens SV Panel data does not seem straightforward. The manuscript should provide a more detailed description of how researchers can access and utilize these data. In my opinion, the best approach would be to upload the variant data (VCF files) to a public database such as the European Variation Archive (EVA) hosted by EBI.

I strongly request that the authors publicly deposit the variant data. At a minimum:

a) The joint genotype data for all 888 samples from the 1000 Genomes Project must be publicly available.<br /> b) For the UK Biobank samples, at least allele frequency data should be disclosed.

Since eLife has a well-established data-sharing policy, compliance with these guidelines is essential for publication in this journal.

(2) Long-read sequencing data quality

While the manuscript presents N50 read length and mean or median read base quality for each sample in a table, it would be highly beneficial to visualize these data in figures as well. A violin plot or similar visualization summarizing these distributions would significantly improve data presentation.

Notably, the base quality of ONT long-read sequencing data appears lower than expected. This may be attributed to the use of pore version 9.4.1, but the unexpectedly low base quality still warrants attention. It would be helpful to include a small figure within Figure 2 to illustrate this point. A visual representation of read length distribution and base quality distribution would strengthen the manuscript.

(3) Variant detection precision, recall, and F1 score

This study focuses on insertions and deletions (indels) {greater than or equal to}50 bp, but it remains unclear how well variants <50 bp are detected. I am particularly interested in the precision, recall, and F1 score for variants between 5-49 bp.

While ONT base quality is relatively low, single-base variants are challenging to analyze, but variants {greater than or equal to}5 bp should still be detectable as their read accuracy is still approximately 90%, making analysis feasible. Given that Sniffles supports the detection of variants as small as 1 bp, I strongly encourage the authors to conduct an additional analysis.

A simple two-category classification (e.g., 5-49 bp and {greater than or equal to}50 bp) should suffice. Additionally, a comparative analysis with HiFi and short-read sequencing data would be highly valuable. If possible, I strongly recommend that all detected variants {greater than or equal to}5 bp be made publicly available as VCF files.

(4) Assembly-based methods

Given the low read accuracy and low sequencing depth in this dataset, it is understandable that genome assembly is challenging. However, the latest high-quality human genome datasets-such as those produced by the Human Pangenome Reference Consortium (HPRC)-demonstrate that assembly-based approaches provide significant advantages, particularly for resolving complex and long structural variants.

Since HPRC data also utilize 1000 Genomes Project samples, it would be highly informative to compare the accuracy of ONT sequencing in this study with HPRC's assembly-based genome data. The recent publication on 47 HPRC samples provides a valuable reference for such a comparison. Given its relevance, the authors should consider providing a comparative analysis with HPRC data.

References:

(1) A draft human pangenome reference<br /> https://www.nature.com/articles/s41586-023-05896-x

(2) The Human Pangenome Project: a global resource to map genomic diversity<br /> https://www.nature.com/articles/s41586-022-04601-8

(3) A pangenome reference of 36 Chinese populations<br /> https://www.nature.com/articles/s41586-023-06173-7

(4) Long-read sequencing of 3,622 Icelanders provides insight into the role of structural variants in human diseases and other traits<br /> https://www.nature.com/articles/s41588-021-00865-4

(5) Increased mutation and gene conversion within human segmental duplications<br /> https://www.nature.com/articles/s41586-023-05895-y

(6) Structural polymorphism and diversity of human segmental duplications<br /> https://www.nature.com/articles/s41588-024-02051-8

(7) Highly accurate Korean draft genomes reveal structural variation highlighting human telomere evolution<br /> https://academic.oup.com/nar/article/53/1/gkae1294/7945385

Joint Public Review:

Pannexin (Panx) hemichannels are a family of heptameric membrane proteins that form pores in the plasma membrane through which ions and relatively large organic molecules can permeate. ATP release through Panx channels during the process of apoptosis is one established biological role of these proteins in the immune system, but they are widely expressed in many cells throughout the body, including the nervous system, and likely play many interesting and important roles that are yet to be defined. Although several structures have now been solved of different Panx subtypes from different species, their biophysical mechanisms remain poorly understood, including what physiological signals control their activation. Electrophysiological measurements of ionic currents flowing in response to Panx channel activation have shown that some subtypes can be activated by strong membrane depolarization or caspase cleavage of the C-terminus. Here, Henze and colleagues set out to identify endogenous activators of Panx channels, focusing on the Panx1 and Panx2 subtypes, by fractionating mouse liver extracts and screening for activation of Panx channels expressed in mammalian cells using whole-cell patch clamp recordings. The authors present a comprehensive examination with robust methodologies and supporting data that demonstrate that lysophospholipids (LPCs) directly Panx-1 and 2 channels. These methodologies include channel mutagenesis, electrophysiology, ATP release and fluorescence assays, and molecular modelling. Mouse liver extracts were initially used to identify LPC activators, but the authors go on to individually evaluate many different types of LPCs to determine those that are more specific for Panx channel activation. Importantly, the enzymes that endogenously regulate the production of these LPCs were also assessed along with other by-products that were shown not to promote pannexin channel activation. In addition, the authors used synovial fluid from canine patients, which is enriched in LPCs, to highlight the importance of the findings in pathology. Overall, we think this is likely to be an important study because it provides strong evidence that LPCs can function as activators of Panx1 and Panx2 channels, linking two established mediators of inflammatory responses and opening an entirely new area for exploring the biological roles of Panx channels. This study provides an excellent foundation for future studies and importantly provides clinical relevance.

[Editors' note: this paper has been through two rounds of review and revisions, available here: https://sciety.org/articles/activity/10.1101/2023.10.23.563601]

Reviewer #1 (Public review):

Summary:

This study aims to understand the malaria antigen-specific cTfh profile of children and adults living in malaria holoendemic area. PBMC samples from children and adults were unstimulated or stimulated with PfSEA-1A or PfGARP in vitro for 6h and analysed by a cTfh-focused panel. Unsupervised clustering and analysis on cTfh was performed. The main conclusions are: A) the children cohort has a more diverse (cTfh1/2/17) recall responses compared to adults (mainly cTfh17) and, B) Pf-GARP stimulates better cTfh17 responses in adults, thus a promising vaccine candidate.

Strengths:

This study is, in general, well-designed and with excellent data analysis. The use of unsupervised clustering is a nice attempt to understand the heterogeneity of cTfh cells.

Weaknesses:

The authors have provided additional data in Supplementary Figures 14-16. However, I remain concerned about whether cTfh cells are truly responding to antigen stimulation. In Supplementary Figure 15A-F, the IFNg responses appear as expected, SEB elicits the strongest response, as it stimulates bulk T cells, and the staining is promising, showing a clear distinction between IFNg+ and IFNg- populations. However, in Supplementary Figure 15I-N, the IL-21 secretion assay is concerning. The FACS plots make it difficult to distinguish IL-21+ from IL-21- cells, raising concerns about the validity of this analysis. Additionally, in panel J, the responses to PfSEA-1A or PfGARP appear even greater than those to SEB stimulation. In PBMCs, only a small percentage of T cells should be specific to a particular antigen. How can the positive control (SEB) produce a weaker response than stimulation with a specific antigen? This suggests that the IL-21 secretion assay may not have worked, making the authors' interpretation unreliable.

I also have similar concerns about the IL-4 secretion in Sup Figure 16. First, the FACS plot shows that appear double-positive for IL-21 and IL-4, so it suggests the staining may be due to autofluorescence rather than true cytokine signals. Also in B-C the responses of SEB stimulation is generally weaker than stimulated by one antigen, further questioning the reliability of the IL-4 assay. In summary, I am not convinced that the in vitro antigen stimulation assay worked as intended. Consequently, the manuscript's claims regarding PfSEA-1A- and PfGARP-specific cTfh responses are not sufficiently supported by the presented data.

Reviewer #3 (Public review):

Summary:

The goal of this study was to carry out an in-depth granular and unbiased phenotyping of peripheral blood circulating Tfh specific to two malaria vaccine candidates, PfSEA-1A and PfGARP, and correlate these with age (children vs adults) and protection from malaria (antibody titers against Plasmodium antigens.) Authors further attempted to identify any specific differences of the Tfh responses to these two distinct malaria antigens.

Strengths:

The authors had access to peripheral blood samples from children and adults living in a malaria-endemic region of Kenya. The authors studied these samples using in vitro restimulation in the presence of specific malaria antigens. Authors generated a very rich data set from these valuable samples using cutting-edge spectral flow cytometry and a 21-plex panel that included a variety of surface markers, cytokines and transcription factors.

Update following first revision (R1) of the manuscript:

The authors have made a great effort to comprehensively address comments raised by the reviewers. In particular, clearly showing expression of ICOS and Bcl6 on CXCR5+ cells greatly strengthens the case for defining these cells as Tfh-like circulatory lymphocytes (cTfh).

Weaknesses:

Update following first revision (R1) of the manuscript:

Unfortunately, my main concern remains. As it stands, the study is not really on antigen-specific T cells, but rather on the overall CD4 T cell compartment plus or minus antigenic stimulation. Although authors used an in vitro restimulation strategy with malaria antigens, they do not focus on cells de-novo expressing activation markers as a result of restimulation, neither they use tetramers to detect antigen-specific T cells. Moreover, their data shows that the number of CXCR5+ CD4 T cells de-novo expressing activation markers and/or cytokines as a result of their in vitro restimulation is negligible, even when using a prototypic superantigen (SEB).

Thus, no antigen-specific CXCR5+ CD4 T cells could be analysed with the data that the authors provide in this manuscript.

Reviewer #4 (Public review):

Summary:

This manuscript is a descriptive study of circulating T follicular helper (cTfh) responses to PfSEA -1A or PfGARP (targets of new antimalaria vaccine candidates) in PBMCs from a convenience sample of children (7 yrs of age) and adults living in a malaria holo endemic Kenya using multiparameter flow cytometry and clustering analysis. This cell type promotes B cell production of long-lived antimalarial antibodies to provide protection against malaria. They find that children had a wider cTFH cytokine and TF profile cellular response in comparison to adults who responded to both antigens but had a narrower response profile.

Strengths:

Carefully done study, very detailed, nice summary model at the end of the paper. The revision provides requested clarification on a number of issues, including CD40L expression which was not differentially expressed between groups. They add additional data into the supplemental files, including IL4 and IL21 data by presenting the cytoplots.

Weaknesses:

To know the significance of these cTfh cells for long-term protection of malaria requires functional and transfer experiments in animal models which is outside the scope of this work.

Reviewer #1 (Public review):

This is a comprehensive study that sheds light on how Wag31 functions and localises in mycobacterial cells. A clear link to interactions with CL is shown using a combination of microscopy in combination with fusion fluorescent constructs, and lipid specific dyes. Furthermore, studies using mutant versions of Wag31 shed light on the functionalities of each domain in the protein. My concerns/suggestions for the manuscript are minor:

(1) Ln 130. A better clarification/discussion is required here. It is clear that both depletion and overexpression have an effect on levels of various lipids, but subsequent descriptions show that they affect different classes of lipids.<br /> (2) The pulldown assays results are interesting, but the links are tentative.<br /> (3) The authors may perhaps like to rephrase claims of effects lipid homeostasis, as my understanding is that lipid localisation rather than catabolism/breakdown is affected.

In response to the above reviews the authors have made the required changes in the revised manuscript.

Reviewer #2 (Public review):

Summary:

Kapoor et. al. investigated the role of the mycobacterial protein Wag31 in lipid and peptidoglycan synthesis and sought to delineate the role of the N- and C- terminal domains of Wag31. They demonstrated that modulating Wag31 levels influences lipid homeostasis in M. smegmatis and cardiolipin (CL) localisation in cells. Wag31 was found to preferentially bind CL-containing liposomes, and deleting the N-terminus of the protein significantly decreased this interaction. Novel interactions between Wag31 and proteins involved in lipid metabolism and cell wall synthesis were identified, suggesting that Wag31 recruits proteins to the intracellular membrane domain by direct interaction.

Strengths:

(1) The importance of Wag31 in maintaining lipid homeostasis is supported by several lines of evidence.<br /> (2) The interaction between Wag31 and cardiolipin, and the role of the N-terminus in this interaction was convincingly demonstrated.

Weakness:

(1) Interactome analysis with truncated versions of the proteins could not be performed in M. smegmatis due to protein instability.

Reviewer #3 (Public review):

Summary:

This manuscript describes the characterization of mycobacterial cytoskeleton protein Wag31, examining its role in orchestrating protein-lipid and protein-protein interactions essential for mycobacterial survival. The most significant finding is that Wag31, which directs polar elongation and maintains the intracellular membrane domain, was revealed to have membrane tethering capabilities.

Strengths:

The authors provided a detailed analysis of Wag31 domain architecture, revealing distinct functional roles: the N-terminal domain facilitates lipid binding and membrane tethering, while the C-terminal domain mediates protein-protein interactions. Overall, this study offers a robust and new understanding of Wag31 function.

Weaknesses:

The authors did not address some of the comments. The following concerns should be addressed.

• As far as I can tell, authors did not address my prior comments on Line 270, which is Line 280 in the revised manuscript: the N-terminal region is important for lipid homeostasis, but the statement in Line 270, "the maintenance of lipid homeostasis by Wag31 is a consequence of its tethering activity" requires additional proof. Please indicate the page and line numbers in the revised manuscript so that I can identify the specific changes the authors made.

• Since this pull-down assay was conducted by mixing E. coli lysate expressing Wag31 and Msm lysate expression Wag31 interactors like MurG, it is possible that the interactions are not direct. Authors acknowledge that this is a valid point, and indicated that they "will describe this caveat in the revised manuscript". I have difficulty finding where this revision was made. Please indicate the page and line numbers.

Reviewer #1 (Public review):

Summary:

In this study, the authors re-analyzed a public dataset (Rademaker et al, 2019, Nature Neuroscience) which includes fMRI and behavioral data recorded while participants held an oriented grating in visual working memory (WM) and performed a delayed recall task at the end of an extended delay period. In that experiment, participants were pre-cued on each trial as to whether there would be a distracting visual stimulus presented during the delay period (filtered noise or randomly-oriented grating). In this manuscript, the authors focused on identifying whether the neural code in retinotopic cortex for remembered orientation was 'stable' over the delay period, such that the format of the code remained the same, or whether the code was dynamic, such that information was present, but encoded in an alternative format. They identify some timepoints - especially towards the beginning/end of the delay - where the multivariate activation pattern fails to generalize to other timepoints, and interpret this as evidence for a dynamic code. Additionally, the authors compare the representational format of remembered orientation in the presence vs absence of a distracting stimulus, averaged over the delay period. This analysis suggested a 'rotation' of the representational subspace between distracting orientations and remembered orientations, which may help preserve simultaneous representations of both remembered and viewed stimuli. Intriguingly, this rotation was a bit smaller for Expt 2, in which the orientation distractor had a greater behavioral impact on the participants' behavioral working memory recall performance, suggesting that more separation between subspaces is critical for preserving intact working memory representations.

Strengths:

(1) Direct comparisons of coding subspaces/manifolds between timepoints, task conditions, and experiments is an innovative and useful approach for understanding how neural representations are transformed to support cognition

(2) Re-use of existing dataset substantially goes beyond the authors' previous findings by comparing geometry of representational spaces between conditions and timepoints, and by looking explicitly for dynamic neural representations

(3) Simulations testing whether dynamic codes can be explained purely by changes in data SNR are an important contribution, as this rules out a category of explanations for the dynamic coding results observed

Weaknesses:

(1) Primary evidence for 'dynamic coding', especially in early visual cortex, appears to be related to the transition between encoding/maintenance and maintenance/recall, but the delay period representations seem overall stable, consistent with some previous findings. However, given the simulation results, the general result that representations may change in their format appears solid, though the contribution of different trial phases remains important for considering the overall result.

(2) Converting a continuous decoding metric (angular error) to "% decoding accuracy" serves to obfuscate the units of the actual results. Decoding precision (e.g., sd of decoding error histogram) would be more interpretable and better related to both the previous study and behavioral measures of WM performance.

Reviewer #2 (Public review):

Summary:

In this work, Degutis and colleagues addressed an interesting issue related to the concurrent coding of sensory percepts and visual working memory contents in visual cortices. They used generalization analyses to test whether working memory representations change over time, diverge from sensory percepts, and vary across distraction conditions. Temporal generalization analysis demonstrated that off-diagonal decoding accuracies were lower than on-diagonal decoding accuracies, regardless of the presence of intervening distractions, implying that working memory representations can change over time. They further showed that the coding space for working memory contents showed subtle but statistically significant changes over time, potentially explaining the impaired off-diagonal decoding performance. The neural coding of sensory distractions instead remained largely stable. Generalization analyses between target and distractor codes showed overlaps but were not identical. Cross-condition decodings had lower accuracies compared to within-condition decodings. Finally, within-condition decoding revealed more reliable working memory representations in the condition with intervening random noises compared to cross-condition decoding using a trained classifier on data from the no-distraction condition, indicating a change in the VWM format between the noise distractor and no-distractor trials.

Strengths:

This paper demonstrates a clever use of generalization analysis to show changes in the neural codes of working memory contents across time and distraction conditions. It provides some insights into the differences between representations of working memory and sensory percepts, and how they can potentially coexist in overlapping brain regions.

Comments on revisions:

I appreciate the authors' efforts in addressing my previous concerns. The inclusion of additional analyses and data has strengthened the paper. I have no further concerns.

Reviewer #1 (Public review):

Summary:

The authors investigate ligand and protein-binding processes in GPCRs (including dimerization) by the multiple walker supervised molecular dynamics method. The paper is interesting and it is very well written.

Strengths:

The authors' method is a powerful tool to gain insight on the structural basis for the pharmacology of G protein-coupled receptors.

Reviewer #2 (Public review):

The study by Deganutti and co-workers is a methodological report on an adaptive sampling approach, multiple walker supervised molecular dynamics (mwSuMD), which represents an improved version of the previous SuMD.<br /> Case-studies concern complex conformational transitions in a number of G protein Coupled Receptors (GPCRs) involving long time-scale motions such as binding-unbinding and collective motions of domains or portions. GPCRs are specialized GEFs (guanine nucleotide exchange factors) of heterotrimeric Gα proteins of the Ras GTPase superfamily. They constitute the largest superfamily of membrane proteins and are of central biomedical relevance as privileged targets of currently marketed drugs.<br /> MwSuMD was exploited to address:

a) binding and unbinding of the arginine-vasopressin (AVP) cyclic peptide agonist to the V2 vasopressin receptor (V2R);<br /> b) molecular recognition of the β2-adrenergic receptor (β2-AR) and heterotrimeric GDP-bound Gs protein;<br /> c) molecular recognition of the A1-adenosine receptor (A1R) and palmotoylated and geranylgeranylated membrane-anchored heterotrimeric GDP-bound Gi protein;<br /> d) the whole process of GDP release from membrane-anchored heterotrimeric Gs following interaction with the glucagon-like peptide 1 receptor (GLP1R), converted to the active state following interaction with the orthosteric non-peptide agonist danuglipron.

The revised version has improved clarity and rigor compared to the original also thanks to the reduction in the number of complex case studies treated superficially.<br /> The mwSuMD method is solid and valuable, has wide applicability and is compatible with the most world-widely used MD engines. It may be of interest to the computational structural biology community.<br /> The huge amount of high-resolution data on GPCRs makes those systems suitable, although challenging, for method validation and development.<br /> While the approach is less energy-biased than other enhanced sampling methods, knowledge, at the atomic detail, of binding sites/interfaces and conformational states is needed to define the supervised metrics, the higher the resolution of such metrics is the more accurate the outcome is expected to be. Definition of the metrics is a user- and system-dependent process.

Reviewer #3 (Public review):

Summary:

In the present work Deganutti et al. report a structural study on GPCR functional dynamics using a computational approach called supervised molecular dynamics.

Strengths:

The study has the potential to provide novel insight into GPCR functionality. An example is the interaction between D344 and R385 identified during the Gs coupling by GLP-1R. However, validation of the findings, even computationally through for instance in silico mutagenesis study, is advisable.

Weaknesses:

No significant advance of the existing structural data on GPCR and GPCR/G protein coupling is provided. Most of the results are reproductions of the previously reported structures.

Reviewer #1 (Public review):

Summary:

Activated male Plasmodium gametocytes undergo very rapid nuclear division, while keeping the nuclear envelope intact. There is interest in how events inside the nucleus are co-ordinated with events in the parasite cytoplasm, to ensure that each nucleus is packaged into a nascent male gamete.

This manuscript by Zeeshan et al describes the organisation of a nuclear membrane bridging protein, SUN1, during nuclear division. SUN1 is expected from studies in other organisms to be a component of a bridging complex (LINC) that connects the inner nuclear membrane to the outer nuclear membrane, and from there to the cytoplasmic microtubule-organising centres, the centrosome and the basal body.

The authors show that knockout of the SUN1 in gametocytes leads to severe disruption of the mitotic spindle and failure of the basal bodies to segregate. The authors show convincingly that functional SUN1 is required for male gamete formation and subsequent oocyst development.

The authors identified several SUN1-interacting proteins, thus providing information about the nuclear membrane bridging machinery.

Strengths:

The authors have used state of the art imaging, genetic manipulation and immunoprecipitation approaches.

Weaknesses:

Technical limitations of some of the methods used make it difficult to interpret some of the micrographs.

From studies in other organisms, a protein called KASH is a critical component the bridging complex (LINC). That is, KASH links SUN1 to the outer nuclear membrane. The authors undertook a gene sequence analysis that reveals that Plasmodium lacks a KASH homologue. Thus, further work is needed to identify the functional equivalent of KASH, to understand bridging machinery in Plasmodium.

Comments on revised version:

The authors have addressed the comments and suggestions that I provided as part of a Review Commons assessment.

Reviewer #2 (Public review):

Zeeshan et al. investigate the function of the protein SUN1, a proposed nuclear envelope protein linking nuclear and cytoplasmic cytoskeleton, during the rapid male gametogenesis of the rodent malaria parasite Plasmodium berghei. They reveal that SUN1 localises to the nuclear envelope (NE) in male and female gametes and show that the male NE has unexpectedly high dynamics during the rapid process of gametogenesis. Using expansion microscopy, the authors find that SUN1 is enriched at the neck of the bipartite MTOC that links the intranuclear spindle to the basal bodies of the cytoplasmic axonemes. Upon deletion of SUN1, the basal bodies of the eight axonemes fail to segregate, no spindle is formed, and emerging gametes are anucleated, leading to a complete block in transmission. By interactomics the authors identify a divergent allantoicase-like protein, ALLAN, as a main interaction partner of SUN1 and further show that ALLAN deletion largely phenocopies the effect of SUN1.

Overall, the authors use an extensive array of fluorescence and electron microscopy techniques as well as interactomics to convincingly demonstrate that SUN1 and ALLAN play a role in maintaining the structural integrity of the bipartite MTOC during the rapid rounds of endomitosis in male gametogenesis.

Two suggestions for improvement of the work remain:

(1) Lipidomic analysis of WT and SUN1-knockout gametocytes before and after activation resulted in only minor changes in some lipid species. Without statistical analysis, it remains unclear if these changes are statistically significant and not rather due to expected biological variability. While the authors clearly toned down their conclusions in the revised manuscript, some phrasings in the results and the discussion still suggest that gametocyte activation and/or SUN1-knockout affects lipid composition. Similarly, some phrases suggest that SUN1 is responsible for the observed loops and folds in the NE and that SUN1 KO affects the NE dynamics. Currently, I do not think that the data supports these statements.

(2) It is interesting to note that ALLAN has a much more specific localisation to basal bodies than SUN1, which is located to the entire nuclear envelope. Knock out of ALLAN also exhibits a milder (but still striking) phenotype than knockout of SUN1. These observations suggest that SUN1 has additional roles in male gametogenesis besides its interaction with ALLAN, which could be discussed a bit more.

This study uses extensive microscopy and genetics to characterise an unusual SUN1-ALLAN complex, thus providing new insights into the molecular events during Plasmodium male gametogenesis, especially how the intranuclear events (spindle formation and mitosis) are linked to the cytoplasmic separation of the axonemes. The characterisation of the mutants reveals an interesting phenotype, showing that SUN1 and ALLAN are localised to and maintain the neck region of the bipartite MTOC. The authors here confirm and expand the previous knowledge about SUN1 in P. berghei, adding more detail to its localisation and dynamics, and further characterise the interaction partner ALLAN. Given the evolutionary divergence of Plasmodium, these results are interesting not only for parasitologists, but also for more general cell biologists.

Reviewer #1 (Public review):

Summary:

This paper contains what could be described as a "classic" approach towards evaluating a novel taste stimuli in an animal model, including standard behavioral tests (some with nerve transections), taste nerve physiology, and immunocytochemistry of taste cells of the tongue. The stimulus being tested is ornithine, from a class of stimuli called "kokumi" (in terms of human taste); these kokumi stimuli appear to enhance other canonical tastes, increasing what are essentially hedonic attributes of other stimuli. The mechanism for ornithine detection is thought to be GPRC6A receptors expressed in taste cells. The authors showed evidence for this in an earlier paper with mice; this paper evaluates ornithine taste in a rat model, and comes to a similar conclusion, albeit with some small differences between the two rodent species.

Strengths:

The data show effects of ornithine on taste/intake in laboratory rats: In two-bottle and briefer intake tests, adding ornithine results in higher intake of most, but all not all stimuli tested. Bilateral chorda tympani (CT) nerve cuts or the addition of GPRC6A antagonists decreased or eliminated these effects. Ornithine also evoked responses by itself in the CT nerve, but mainly at higher concentrations; at lower concentrations it potentiated the response to monosodium glutamate. Finally, immunocytochemistry of taste cell expression indicated that GPRC6A was expressed predominantly in the anterior tongue, and co-localized (to a small extent) with only IP3R3, indicative of expression in a subset of type II taste receptor cells.

Weaknesses:

As the authors are aware, it is difficult to assess a complex human taste with complex attributes, such as kokumi, in an animal model. In these experiments they attempt to uncover mechanistic insights about how ornithine potentiates other stimuli by using a variety of established experimental approaches in rats. They partially succeed by finding evidence that GPRC6A may mediate effects of ornithine when it is used at lower concentrations. In the revisions they have scaled back their interpretations accordingly. A supplementary experiment measuring certain aspects of the effects of ornithine added to Miso soup in human subjects is included for the express purpose of establishing that the kokumi sensation of a complex solution is enhanced by ornithine. This (supplementary) experiment was conducted with a small sample size, and though perhaps useful, these preliminary results do not align particularly well with the animal experiments. It would be helpful to further explore human taste of ornithine in a larger and better-controlled study.

Reviewer #2 (Public review):

Summary:

The authors used rats to determine the receptor for a food-related perception (kokumi) that has been characterized in humans. They employ a combination of behavioral, electrophysiological, and immunohistochemical results to support their conclusion that ornithine-mediated kokumi effects are mediated by the GPRC6A receptor. They complemented the rat data with some human psychophysical data. I find the results intriguing, but believe that the authors overinterpret their data.

Strengths:

The authors provide compelling evidence that ornithine enhances the palatability of several chemical stimuli (i.e., IMP, MSG, MPG, Intralipos, sucrose, NaCl, quinine). Ornithine also increases CT nerve responses to MSG. Additionally, the authors provide evidence that the effects of ornithine are mediated by GPRC6A, a G-protein-coupled receptor family C group 6 subtype A, and that this receptor is expressed primarily in fungiform taste buds. Taken together, these results indicate that ornithine enhances the palatability of multiple taste stimuli in rats, and that the enhancement is mediated, at least in part, within fungiform taste buds. This finding could stand on its own. The question of whether ornithine produces these effects by eliciting kokumi-like perceptions (see below) should be presented as speculation in the Discussion section.

Weaknesses:

I am still unconvinced that the measurements in rats reflect the "kokumi" taste percept described in humans. The authors conducted long-term preference tests, 10-min avidity tests and whole chorda tympani (CT) nerve recordings. None of these procedures specifically model features of "kokumi" perception in humans, which (according to the authors) include increasing "intensity of whole complex tastes (rich flavor with complex tastes), mouthfulness (spread of taste and flavor throughout the oral cavity), and persistence of taste (lingering flavor)." While it may be possible to develop behavioral assays in rats (or mice) that effectively model kokumi taste perception in humans, the authors have not made any effort to do so. As a result, I do not think that the rat data provide support for the main conclusion of the study--that "ornithine is a kokumi substance and GPRC6A is a novel kokumi receptor."

Why are the authors hypothesizing that the primary impacts of ornithine are on the peripheral taste system? While the CT recordings provide support for peripheral taste enhancement, they do not rule out the possibility of additional central enhancement. Indeed, based on the definition of human kokumi described above, it is likely that the effects of kokumi stimuli in humans are mediated at least in part by the central flavor system.

The authors include (in the supplemental data section) a pilot study that examined the impact of ornithine on variety of subjective measures of flavor perception in humans. The presence of this pilot study within the larger rat study does not really make sense. If the human studies are so important, as the authors state, then why did the authors relegate them to the supplemental data section? Usually one places background and negative findings in this section of a paper. Accordingly, I recommend that the human data be published in a separate article.

Reviewer #3 (Public review):

Summary:

In this study the authors set out to investigate whether GPRC6A mediates kokumi taste initiated by the amino acid L-ornithine. They used Wistar rats, a standard laboratory strain, as the primary model and also performed an informative taste test in humans, in which miso soup was supplemented with various concentrations of L-ornithine. The findings are valuable and overall the evidence is solid. L-Ornithine should be considered to be a useful test substance in future studies of kokumi taste and the class C G protein coupled receptor known as GPRC6A (C6A) along with its homolog, the calcium-sensing receptor (CaSR) should be considered candidate mediators of kokumi taste. The researchers confirmed in rats their previous work on Ornithine and C6A in mice (Mizuta et al Nutrients 2021).

Strengths:

The overall experimental design is solid based on two bottle preference tests in rats. After determining the optimal concentration for L-Ornithine (1 mM) in the presence of MSG, it was added to various tastants including: inosine 5'-monophosphate; monosodium glutamate (MSG); mono-potassium glutamate (MPG); intralipos (a soybean oil emulsion); sucrose; sodium chloride (NaCl; salt); citric acid (sour) and quinine hydrochloride (bitter). Robust effects of ornithine were observed in the cases of IMP, MSG, MPG and sucrose; and little or no effects were observed in the cases of sodium chloride, citric acid; quinine HCl. The researchers then focused on the preference for Ornithine-containing MSG solutions. Inclusion of the C6A inhibitors Calindol (0.3 mM but not 0.06 mM) or the gallate derivative EGCG (0.1 mM but not 0.03 mM) eliminated the preference for solutions that contained Ornithine in addition to MSG. The researchers next performed transections of the chord tympani nerves (with sham operation controls) in anesthetized rats to identify a role of the chorda tympani branches of the facial nerves (cranial nerve VII) in the preference for Ornithine-containing MSG solutions. This finding implicates the anterior half-two thirds of the tongue in ornithine-induced kokumi taste. They then used electrical recordings from intact chorda tympani nerves in anesthetized rats to demonstrate that ornithine enhanced MSG-induced responses following the application of tastants to the anterior surface of the tongue. They went on to show that this enhanced response was insensitive to amiloride, selected to inhibit 'salt tastant' responses mediated by the epithelial Na+ channel, but eliminated by Calindol. Finally they performed immunohistochemistry on sections of rat tongue demonstrating C6A positive spindle-shaped cells in fungiform papillae that partially overlapped in its distribution with the IP3 type-3 receptor, used as a marker of Type-II cells, but not with (i) gustducin, the G protein partner of Tas1 receptors (T1Rs), used as a marker of a subset of type-II cells; or (ii) 5-HT (serotonin) and Synaptosome-associated protein 25 kDa (SNAP-25) used as markers of Type-III cells.

At least two other receptors in addition to C6A might mediate taste responses to ornithine: (i) the CaSR, which binds and responds to multiple L-amino acids (Conigrave et al, PNAS 2000), and which has been previously reported to mediate kokumi taste (Ohsu et al., JBC 2010) as well as responses to Ornithine (Shin et al., Cell Signaling 2020); and (ii) T1R1/T1R3 heterodimers which also respond to L-amino acids and exhibit enhanced responses to IMP (Nelson et al., Nature 2001). These alternatives are appropriately discussed and, taken together, the experimental results favor the authors' interpretation that C6A mediates the Ornithine responses. The authors provide preliminary data in Suppl. 3 for the possibility of co-expression of C6A with the CaSR.

In the Discussion, the authors consider the potential effects of kokumi substances on the threshold concentrations of key tastants such as glutamate, arguing that extension of taste distribution to additional areas of the mouth (previously referred to as 'mouthfulness') and persistence of taste/flavor responses (previously referred to as 'continuity') could arise from a reduction in the threshold concentrations of umami and other substances that evoke taste responses. This concept may help to design future experiments.

Weaknesses:

The authors point out that animal models pose some difficulties of interpretation in studies of taste and raise the possibility in the Discussion that umami substances may enhance the taste response to ornithine (Line 271, Page 9).

The status of one of the compounds used as an inhibitor of C6A, the gallate derivative EGCG, as a potential inhibitor of the CaSR or T1R1/T1R3 is unknown. It would have been helpful to show that a specific inhibitor of the CaSR failed to block the ornithine response.

It would have been helpful to include a positive control kokumi substance in the two bottle preference experiment (e.g., one of the known gamma glutamyl peptides such as gamma-glu-Val-Gly or glutathione), to compare the relative potencies of the control kokumi compound and Ornithine, and to compare the sensitivities of the two responses to C6A and CaSR inhibitors.

Reviewer #1 (Public review):

Summary:

This manuscript explores the transcriptional landscape of high-grade serous ovarian cancer (HGSOC) using consensus-independent component analysis (c-ICA) to identify transcriptional components (TCs) associated with patient outcomes. The study analyzes 678 HGSOC transcriptomes, supplemented with 447 transcriptomes from other ovarian cancer types and noncancerous tissues. By identifying 374 TCs, the authors aim to uncover subtle transcriptional patterns that could serve as novel drug targets. Notably, a transcriptional component linked to synaptic signaling was associated with shorter overall survival (OS) in patients, suggesting a potential role for neuronal interactions in the tumor microenvironment. Given notable weaknesses like lack of validation cohort or validation using other platforms (other than the 11 samples with ST), the data is considered highly descriptive and preliminary.

The study reveals significant findings by identifying a transcriptional component (TC121) associated with synaptic signaling, which is linked to shorter survival in patients with high-grade serous ovarian cancer, highlighting the potential role of neurons in the tumor microenvironment. However, the evidence could be strengthened by experimental validation to confirm the functional roles of key genes within TC121 and further exploration of its spatial aspects, including deeper analysis of neuronal and synaptic and other neuronal gene expression.

Strengths:

Innovative Methodology:<br /> The use of c-ICA to dissect bulk transcriptomes into independent components is a novel approach that allows for the identification of subtle transcriptional patterns that may be overshadowed in traditional analyses.

Comprehensive Data Integration:<br /> The study integrates a large dataset from multiple public repositories, enhancing the robustness of the findings. The inclusion of spatially resolved transcriptomes adds a valuable dimension to the analysis.

Clinical Relevance:<br /> The identification of a synaptic signaling-related TC associated with poor prognosis highlights a potential new avenue for therapeutic intervention, emphasizing the role of the tumor microenvironment in cancer progression.

Weaknesses:

Mechanistic Insights:<br /> While the study identifies TCs associated with survival, it provides limited mechanistic insights into how these components influence cancer progression. Further experimental validation is necessary to elucidate the underlying biological processes.

Generalizability:<br /> The findings are primarily based on transcriptomic data from HGSOC. It remains unclear how these results apply to other subtypes of ovarian cancer or different cancer types.

Innovative Methodology:<br /> Requires more validation using different platforms (IHC) to validate the performance of this bulk derived data. Also, the lack of control on data quality is a concern.

Clinical Application:<br /> Although the study suggests potential drug targets, the translation of these findings into clinical practice is not addressed. Probably given lack of some QA/QC procedures it'll be hard to translate these results. Future studies should focus on validating these targets in clinical settings.

Reviewer #2 (Public review):

Summary:

Consensus-independent component analysis and closely related methods have previously been used to reveal components of transcriptomic data which are not captured by principal component or gene-gene coexpression analyses.

Here, the authors asked whether applying consensus-independent component analysis (c-ICA) to published high-grade serous ovarian cancer (HGSOC) microarray-based transcriptomes would reveal subtle transcriptional patterns which are not captured by existing molecular omics classifications of HGSOC.

Statistical associations of these (hitherto masked) transcriptional components with prognostic outcomes in HGSOC would lead to additional insights into underlying mechanisms and, coupled with corroborating evidence from spatial transcriptomics, are proposed for further investigation.

This approach is complementary to existing transcriptomics classifications of HGSOC.

The authors have previously applied the same approach in colorectal carcinoma (for example, Knapen et al. (2024) Commun. Med).

Strengths:

Overall, this study describes a solid data-driven description of c-ICA-derived transcriptional components that the authors identified in HGSOC microarray transcriptomics data, supported by detailed methods and supplementary documentation.

The biological interpretation of transcriptional components is convincing based on (data-driven) permutation analysis and a suite of analyses of association with copy-number, gene sets, and prognostic outcomes.<br /> The resulting annotated transcriptional components have been made available in a searchable online format.

For the highlighted transcriptional component which has been annotated as related to synaptic signalling, the detection of the transcriptional component among 11 published spatial transcriptomics samples from ovarian cancers is compelling and supports the need for further mechanistic follow-up.

Further comments:

This revised version includes a suite of comparisons between the c-ICA-derived components and existing published transcriptomic/genomic-based classifications of ovarian cancers. Newly described components will require experimental validation, as acknowledged by the authors.

Here, the authors primarily interpret the c-ICA transcriptional components as a deconvolution of bulk transcriptomics due to the presence of cells from tumour cells and the tumour microenvironment.<br /> In this revised version, the authors additionally investigate their TC scores in single cells from a published HGSOC single-cell RNAseq dataset, highlighting examples of TC scores within and between cell types.

c-ICA is not explicitly a deconvolution method with respect to cell types: the transcriptional components do not necessarily correspond to distinct cell types, and may reflect differential dysregulation within a cell type. This application of c-ICA for the purpose of data-driven deconvolution of cell populations is distinct from other deconvolution methods which explicitly use a prior cell signature matrix.

Reviewer #1 (Public review):

The aim of this study was a better understanding of the reproductive life history of acoels. The acoel Hofstenia miamia, an emerging model organism, is investigated; the authors nevertheless acknowledge and address the high variability in reproductive morphology and strategies within Acoela.

The morphology of male and female reproductive organs in these hermaphroditic worms is characterised through stereo microscopy, immunohistochemistry, histology, and fluorescent in situ hybridization. The findings confirm and better detail historical descriptions. A novelty in the field is the in situ hybridization experiments, which link already published single-cell sequencing data to the worms' morphology. An interesting finding, though not further discussed by the authors, is that the known germline markers cgnl1-2 and Piwi-1 are only localized in the ovaries and not in the testes.

The work also clarifies the timing and order of appearance of reproductive organs during development and regeneration, as well as the changes upon de-growth. It shows an association of reproductive organ growth to whole body size, which will be surely taken into account and further explored in future acoel studies. This is also the first instance of non-anecdotal degrowth upon starvation in H. miamia (and to my knowledge in acoels, except recorded weight upon starvation in Convolutriloba retrogemma [1]).

Egg laying through the mouth is described in H. miamia for the first time as well as the worms' behavior in egg laying, i.e. choosing the tanks' walls rather than its floor, laying eggs in clutches, and delaying egg-laying during food deprivation. Self-fertilization is also reported for the first time.

The main strength of this study is that it expands previous knowledge on the reproductive life history traits in H. miamia and it lays the foundation for future studies on how these traits are affected by various factors, as well as for comparative studies within acoels. As highlighted above, many phenomena are addressed in a rigorous and/or quantitative way for the first time. This can be considered the start of a novel approach to reproductive studies in acoels, as the authors suggest in the conclusion. It can be also interpreted as a testimony of how an established model system can benefit the study of an understudied animal group.

The main weakness of the work is the lack of convincing explanations on the dynamics of self-fertilization, sperm storage, and movement of oocytes from the ovaries to the central cavity and subsequently to the pharynx. These questions are also raised by the authors themselves in the discussion. Another weakness (or rather missing potential strength) is the limited focus on genes. Given the presence of the single-cell sequencing atlas and established methods for in situ hybridization and even transgenesis in H. miamia, this model provides a unique opportunity to investigate germline genes in acoels and their role in development, regeneration, and degrowth. It should also be noted that employing Transmission Electron Microscopy would have enabled a more detailed comparison with other acoels, since ultrastructural studies of reproductive organs have been published for other species (cfr e.g. [2],[3],[4]). This is especially true for a better understanding of the relation between sperm axoneme and flagellum (mentioned in the Results section), as well as of sexual conflict (mentioned in the Discussion).

(1) Shannon, Thomas. 2007. 'Photosmoregulation: Evidence of Host Behavioral Photoregulation of an Algal Endosymbiont by the Acoel Convolutriloba Retrogemma as a Means of Non-Metabolic Osmoregulation'. Athens, Georgia: University of Georgia [Dissertation].<br /> (2) Zabotin, Ya. I., and A. I. Golubev. 2014. 'Ultrastructure of Oocytes and Female Copulatory Organs of Acoela'. Biology Bulletin 41 (9): 722-35.<br /> (3) Achatz, Johannes Georg, Matthew Hooge, Andreas Wallberg, Ulf Jondelius, and Seth Tyler. 2010. 'Systematic Revision of Acoels with 9+0 Sperm Ultrastructure (Convolutida) and the Influence of Sexual Conflict on Morphology'.<br /> (4) Petrov, Anatoly, Matthew Hooge, and Seth Tyler. 2006. 'Comparative Morphology of the Bursal Nozzles in Acoels (Acoela, Acoelomorpha)'. Journal of Morphology 267 (5): 634-48.

Reviewer #2 (Public review):

Summary:

While the phylogenetic position of Acoels (and Xenacoelomorpha) remains still debated, investigations of various representative species are critical to understanding their overall biology.

Hofstenia is an Acoels species that can be maintained in laboratory conditions and for which several critical techniques are available. The current manuscript provides a comprehensive and widely descriptive investigation of the productive system of Hofstenia miamia.

Strengths:

(1) Xenacoelomorpha is a wide group of animals comprising three major clades and several hundred species, yet they are widely understudied. A comprehensive state-of-the-art analysis on the reprodutive system of Hofstenia as representative is thus highly relevant.

(2) The investigations are overall very thorough, well documented, and nicely visualised in an array of figures. In some way, I particularly enjoyed seeing data displayed in a visually appealing quantitative or semi-quantitative fashion.

(3) The data provided is diverse and rich. For instance, the behavioral investigations open up new avenues for further in-depth projects.

Weaknesses:

While the analyses are extensive, they appear in some way a little uni-dimensional. For instance the two markers used were characterized in a recent scRNAseq data-set of the Srivastava lab. One might have expected slightly deeper molecular analyses. Along the same line, particularly the modes of spermatogenesis or oogenesis have not been further analysed, nor the proposed mode of sperm-storage.

Reviewer #1 (Public review):

Summary:

The authors report the role of a novel gene Aff3ir-ORF2 in flow induced atherosclerosis. They show that the gene is anti-inflammatory in nature. It inhibits the IRF5 mediated athero-progression by inhibiting the causal factor (IRF5). Furthermore, authors show a significant connection between shear stress and Aff3ir-ORF2 and its connection to IRF5 mediated athero-progression in different established mice models which further validates the ex vivo findings.

Strengths:

(1) Adequate number of replicates were used for this study.<br /> (2) Both in vitro and in vivo validation was done.<br /> (3) Figures are well presented<br /> (4) In vivo causality is checked with cleverly designed experiments

Weaknesses:

(1) Inflammatory proteins must be measured with standard methods e.g ELISA as mRNA level and protein level does not always correlate.<br /> (2) RNA seq analysis has to be done very carefully. How does the euclidean distance correlate with the differential expression of genes. Do they represent neighborhood? If they do how does this correlation affect the conclusion of the paper?<br /> (3) Volcano plot does not indicate q value of the shown genes. It is advisable to calculate q value for each of the genes which represents the FDR probability of the identified genes.<br /> (4) GO enrichment was done against Global gene set or local geneset? Authors should provide more detailed information about the analysis.<br /> (5) If the analysis was performed against global gene set. How does that connect with this specific atherosclerotic microenvironment?<br /> (6) what was the basal expression of genes and how does the DGE (differential gene expression) values differ?<br /> (7) How did IRF5 picked from GO analysis? was it within 20 most significant genes?<br /> (8) Microscopic studies should be done more carefully? There seems to be a global expression present on the vascular wall for Aff3ir-ORF2 and the expression seems to be similar like AFF3 in fig 1.

Comments on Revision:

The authors have adequately addressed my concerns.

Reviewer #2 (Public review):

Summary:

The authors recently uncovered a novel nested gene, Aff3ir, and this work sets out to study its function in endothelial cells further. Based on differences in expression correlating with areas of altered shear stress, they investigate a role for the isoform Aff3ir-ORF2 in endothelial activation and development of atherosclerosis downstream of disturbed shear stress. Using a knockout mouse model and in vivo overexpression experiments, they demonstrate a strong potential for Aff3ir-ORF2 to alleviate atherosclerosis. They find that Aff3ir-ORF2 interacts with the pro-inflammatory transcription factor IRF5 and retains it in the cytoplasm, hence preventing upregulation of inflammation-associated genes. The data expands our knowledge of IRF5 regulation which could be relevant to researchers studying various inflammatory diseases as well as adding to our understand of atherosclerosis development.

Strengths:

The in vivo data is convincing using immunofluorescence staining to assess AFF3ir-ORF2 expression, a knockout mouse model, overexpression and knockdown studies and rescue experiments in combination with two atherosclerotic models to demonstrate that Aff3ir-ORF2 can lessen atherosclerotic plaque formation in ApoE-/- mice.

Weaknesses:

The effect on atherosclerosis is clear and there is sufficient evidence to conclude that this is the result of reduced endothelial cell activation. However, other cell types such as smooth muscle cells or macrophages could be contributing to the effects observed. The mouse model is a global knockout and the shRNA knockdowns (Fig. 5) and overexpression data in Figure 2 are not cell type-specific. Only the overexpression construct in Figure 6 uses an ICAM-2 promoter construct, which drives expression in endothelial cells, though leaky expression of this promoter has been reported in the literature.

The in vitro experiments are solidly executed, but most experiments are performed in mouse embryonic fibroblasts (MEFs) and results extrapolated to endothelial cell responses. However, several key experiments are repeated in HUVEC, thereby making a solid case that Aff3ir-ORF2 can regulate IRF5 in both MEFs and HUVEC. It is important to note that the sequence of AFF3ir-ORF2 is not conserved in humans and lacks an initiation codon, hence the regulatory pathway is not conserved. However, the overexpression studies in HUVEC suggest that mouse AFF3ir-ORF2 can also regulate human IRF5 and hence the mechanism retains relevance for possible human health interventions.

Overall, the paper succeeds in demonstrating a link between Aff3ir-ORF2 and atherosclerosis. The study shows a functional interaction between Aff3ir-ORF2 and IRF5 in embryonic fibroblasts, but makes a solid case that this mechanism is relevant for atherosclerosis development via endothelial cell activation.

Reviewer #1 (Public review):

Summary:

Shihabeddin et al. used bioinformatic and molecular biology tools to study the unique regeneration of rod photoreceptors in a zebrafish model. The authors identified a few transcription factors that seem to play an important role in this process.

Strengths:

This manuscript is well prepared. The topic of this study is an interesting and important one. Bioinformatics clues are interesting.

Weaknesses:

Considering the importance of the mechanism, the knockdown experiments require further validation. The authors over-emphasized this study's relevance to RP disease (i.e. patients and mammals are not capable of regeneration like zebrafish). They under-explained this regeneration's relevance or difference to normal developmental process, which is pretty much conserved in evolution.

Reviewer #2 (Public review):

This is an interesting and important work from Shihabeddin et al, to identify master regulators for rod photoreceptor regenerations in a zebrafish model of Retinitis Pigmentosa. Building on their scRNA-seq data, Shihabeddin et al dissected the progenitor cell types and performed trajectory analyses to predict transcription factors that apparently drive the progenitor proliferation and differentiation into rod photoreceptors. Their analyses predicted e2f1, e2f2, and e2f3 as critical drivers of progenitor proliferation, Prdm1a as a driver of rod photoreceptor differentiation, and SP1 as a driver of rod photoreceptor maturation. Genetic experiments provide clear support for the roles of e2fs in progenitor proliferation. It's also apparent from Figure 8 that prdm1 knockdown appears to cause a decrease in rhodopsin expression. By colocalizing BrdU and Retp1, the authors inferred that the apparent "new rods" (which exhibit mixed BrdU and Retp1 signal) are decreased with prdm1, providing further support. Overall I found the work to be interesting, rigorous, and informative for the community.

I have a few suggestions for the authors to consider:

(1) Perhaps the authors can consider explaining why the Prdm1a knock-down cells would have a higher Retp1 signal per cell in Fig 9B. Is this a representative picture? This appears to contradict Figure 8's conclusion, although I could tell that the number of Retp1+ cells in the ONL appears to be lower.

(2) The authors noted "Surprisingly, the knockdown of prdm1a resulted in a significantly higher number of rhodopsin-positive cells in the INL (p=0.0293)", while it appears in Figure 9B, 9C that the difference is 2 cells vs 0 in a rightly broader field. It seems to be too strong of a statement for this effect.

(3) It appears to this reviewer that the proteomic data didn't reveal much in line with the overall hypothesis or the mechanism, and it's unclear why the authors went for proteomics rather than bulk RNA-seq or ChIP-seq for a transcription factor knock-down experiment. Overall this is a minor point.

Reviewer #3 (Public review):

Summary:

This study uses a combination of single-cell RNA-Seq to globally profile changes in gene expression in adult P23H transgenic zebrafish, which show progressive rod photoreceptor degeneration, along with age-matched controls. As expected, mitotically active retinal progenitors are identified in both conditions, the increased number of both progenitors and immature rods are observed. DrivAER-mediated gene regulatory network analysis in retinal progenitors, photoreceptor precursors, and mature rod photoreceptors respectively identified e2f1-3, prdm1a, and sp1 as top predicted transcriptional regulators of gene expression specific to these cell types. Finally, morpholino-mediated knockdown of these transcription factors led to expected defects in proliferation and rod differentiation.

Strengths:

Overall, this is a rigorous study that is convincingly executed and well-written. The data presented here will be a useful addition to existing single-cell RNA-Seq datasets obtained from regenerating zebrafish retina.

Weaknesses:

Multiple similar studies have been published and it is something of a missed opportunity in terms of identifying novel mechanisms of rod photoreceptor regeneration. Several other recent studies have used both single-cell RNA and ATAC-Seq to analyze gene regulatory networks that regulate neurogenesis in zebrafish retina following acute photoreceptor damage (Hoang, et al. 2020; Celloto, et al. 2023; Lyu, et al. 2023; Veen, et al 2023) or in other genetic models of progressive photoreceptor dystrophy such cep290 mutants (Fogerty, et al. 2022).

The gene regulatory network analysis here would also benefit from the addition of matched scATAC-Seq data, which would allow the use of more powerful tools such as Scenic+ (Bravo and de Winter, et al. 2023). It would also benefit from integration with single-cell multiome data from developing retinas (Lyu, et al. 2023). The genes selected for functional analysis here are all either robustly expressed in retinal progenitor cells (ef1-3 and aurka) or in developing rods (prdm1a), so it is not really surprising that defects are observed. Identification of factors that selectively regulate rod photoreceptor regeneration, rather than those that regulate both development and regeneration, would provide additional novelty. This would also potentially allow the use of animal mutants for candidate genes, rather than exclusively relying on morphant analysis, which may have off-target effects.

The description of the time points analyzed is vague, stating only that "fish from 6 to 12 months of age were analyzed". Since photoreceptor degeneration is progressive, it is unclear how progenitor behavior changes over time, or how the gene expression profile of other cell types such as microglia, cones, or surviving rods is altered by disease progression. Most similar studies address this by analyzing multiple time points from specific ages or times post-injury.

Reviewer #1 (Public review):

Summary:

The objective of this research is to understand how the expression of key selector transcription factors, Tal1, Gata2, Gata3, involved in GABAergic vs glutamatergic neuron fate from a single anterior hindbrain progenitor domain is transcriptionally controlled. With suitable scRNAseq, scATAC-seq, CUT&TAG, and footprinting datasets, the authors use an extensive set of computational approaches to identify putative regulatory elements and upstream transcription factors that may control selector TF expression. This data-rich study will be a valuable resource for future hypothesis testing, through perturbation approaches, of the many putative regulators identified in the study. The data are displayed in some of the main and supplemental figures in a way that makes it difficult to appreciate and understand the authors' presentation and interpretation of the data in the Results narrative. Primary images used for studying the timing and coexpression of putative upstream regulators, Insm1, E2f1, Ebf1, and Tead2 with Tal1 are difficult to interpret and do not convincingly support the authors' conclusions. There appears to be little overlap in the fluorescent labeling, and it is not clear whether the signals are located in the cell soma nucleus.

Strengths:

The main strength is that it is a data-rich compilation of putative upstream regulators of selector TFs that control GABAergic vs glutamatergic neuron fates in the brainstem. This resource now enables future perturbation-based hypothesis testing of the gene regulatory networks that help to build brain circuitry.

Weaknesses:

Some of the findings could be better displayed and discussed.

Reviewer #2 (Public review):

Summary:

In the manuscript, the authors seek to discover putative gene regulatory interactions underlying the lineage bifurcation process of neural progenitor cells in the embryonic mouse anterior brainstem into GABAergic and glutamatergic neuronal subtypes. The authors analyze single-cell RNA-seq and single-cell ATAC-seq datasets derived from the ventral rhombomere 1 of embryonic mouse brainstems to annotate cell types and make predictions or where TFs bind upstream and downstream of the effector TFs using computational methods. They add data on the genomic distributions of some of the key transcription factors and layer these onto the single-cell data to get a sense of the transcriptional dynamics.

Strengths:

The authors use a well-defined fate decision point from brainstem progenitors that can make two very different kinds of neurons. They already know the key TFs for selecting the neuronal type from genetic studies, so they focus their gene regulatory analysis squarely on the mechanisms that are immediately upstream and downstream of these key factors. The authors use a combination of single-cell and bulk sequencing data, prediction and validation, and computation.

Weaknesses:

The study generates a lot of data about transcription factor binding sites, both predicted and validated, but the data are substantially descriptive. It remains challenging to understand how the integration of all these different TFs works together to switch terminal programs on and off.

Reviewer #1 (Public review):

Summary:

The authors demonstrate impairments induced by a high cholesterol diet on GLP-1R dependent glucoregulation in vivo as well as an improvement after reduction in cholesterol synthesis with simvastatin in pancreatic islets. They also map sites of cholesterol high occupancy and residence time on active versus inactive GLP-1Rs using coarse-grained molecular dynamics (cgMD) simulations, and screened for key residues selected from these sites and performed detailed analyses of the effects of mutating one of these residues, Val229, to alanine on GLP-1R interactions with cholesterol, plasma membrane behaviour, clustering, trafficking and signalling in pancreatic beta cells and primary islets, and describe an improved insulin secretion profile for the V229A mutant receptor.

These are extensive and very impressive studies indeed. I am impressed with the tireless effort exerted to understand the details of molecular mechanisms involved in the effects of cholesterol for GLP-1 activation of its receptor. In general, the study is convincing, the manuscript well written and the data well presented. Some of the changes are small and insignificant which makes one wonder how important the observations are. For instance, in Figure 2E (which is difficult to interpret anyway because the data are presented in per cent, conveniently hiding the absolute results) does not show a significant result of the cyclodextrin except for insignificant increases in basal secretion. That is not identical to impairment of GLP-1 receptor signaling!

To me the most important experiment of them all is the simvastatin experiment, but the results rest on very few numbers and there is a large variation. Apparently, in a previous study using more extensive reduction in cholesterol the opposite response was detected casting doubt on the significance of the current observation. I agree with the authors that the use of cyclodextrin may have been associated with other changes in plasma membrane structure than cholesterol depletion at the GLP-1 receptor. The entire discussion regarding the importance of cholesterol would benefit tremendously from studies of GLP-1 induced insulin secretion in people with different cholesterol levels before and after treatment with cholesterol-lowering agents. I suspect that such a study would not reveal major differences.

Comments on revisions: The authors have responded well to my criticism.

Reviewer #2 (Public review):

Summary:

In this manuscript the authors were providing a proof of concept that they can identify and mutate a cholesterol-binding site of a high-interest class B receptor, the GLP-1R, and functionally characterize the impact of this mutation on receptor behavior in the membrane and downstream signaling with the intent that similar methods can be useful to optimize small molecules that as ligands or allosteric modulators of GLP-1R can improve the therapeutic tools targeting this signaling system.

Strengths:

The majority of results on receptor behavior are elucidated in INS-1 cells expressing the wt or mutant GLP-1R, with one experiment translating the findings to primary mouse beta-cells. I think this paper lays a very strong foundation to characterize this mutation and does a good job discussing how complex cholesterol-receptor interactions can be (ie lower cholesterol binding to V229A GLP-1R, yet increased segregation to lipid rafts). Table 1 and Figure 9 are very beneficial to summarize the findings. The lower interaction with cholesterol and lower membrane diffusion in V229A GLP-1R resembles the reduced diffusion of wt GLP-1R with simv-induced cholesterol reductions, by presumably decreasing the cholesterol available to interact with wt GLP-1R. The effects of this mutation are not due to differences in Ex-4:recepotor affinity. I think this paper will be of interest to many physiologists who may not be familiar with many of the techniques used in this paper and the authors largely do a good job explaining the goals of using each method in the results section. While not necessary for this paper, a comparison of islet cholesterol content after this cholesterol diet vs the more typical 60% HFD used in obesity research would be beneficial for GLP-1 physiology research broadly to take these findings into consideration with model choice.

Weaknesses:

There are no obvious weaknesses in this manuscript and overall, I believe the authors achieved their aims and have demonstrated the importance of cholesterol interactions on GLP-1R functioning in beta-cells.

Certainly many follow-up experiments are possible from these initial findings and of primary interest is how this mutation affects insulin homeostasis in vivo under different physiological conditions. One of the biggest pathologies in insulin homeostasis in obesity/t2d is an elevation of baseline insulin release (as modeled in Fig 1E) that renders the fold-change in glucose stimulated insulin levels lower and physiologically less effective. Future work by the authors may determine the effects of the GLP-1R V229A mutation on insulin secretion responses under diet-induced metabolic stress conditions. Furthermore, the authors may additionally investigate if V229A would have the same impact in a different cell type, especially in neurons, with implications in the regulation of satiation, gut motility, and especially nausea, which are of high translational interest.

The comparison is drawn in the discussion between this mutation and ex4-phe1 to have biased agonism towards Gs over beta-arrestin signaling. Ex4-phe1 lowered pica behavior (a proxy for nausea) in the authors previously co-authored paper on ex4-phe1 (PMID 29686402) and drawing a parallel for this mutation or modification of cholesterol binding to potentially mitigate nausea is a novel direction.

Reviewer #1 (Public review):

Summary:

The authors examine CD8 T cell selective pressure in early HCV infection using. They propose that after initial CD8-T mediated loss of virus fitness, in some participants around 3 months after infection, HCV acquires compensatory mutations and improved fitness leading to virus progression.

Strengths:

Throughout the paper, the authors apply well-established approaches in studies of acute to chronic HIV infection for studies of HCV infection. This lends rigor the to the authors' work.