Reviewer #2 (Public review):

Recent work from the authors identified the synaptic changes and glial reaction that occur during exposure of a Drosophila odorant receptor neuron population to continued exposure of a stimulating odorant. This work markedly advanced our understanding of cellular response to critical periods. This current Advance manuscript carries that work forward and examines the non-autonomous responses to constant odorant exposure. The authors discover that the changes to ORN populations are not accompanied by changes to either PN dendrite or PN axon volume, nor are they concurrent with changes in postsynaptic PN structures. These changes are, however, notable, accompanied by changes in Ca2+ and voltage responses in ORNs. Importantly, this set of responses is specific to the Or42a ORNs (that are highly sensitive to the odorant in question, ethyl butyrate) and not the Or43b ORNs (which respond to ethyl butyrate, but not as drastically). Finally, the authors include connectomics analyses showing that Or43b and Or42a ORNs differ in their synaptic input/output relationships.

This is an excellent use of the Advance mechanism for the journal, as these are important follow-up findings for the parent story. The non-autonomous effects (or lack thereof) on PNs is an important part of the story, as is the functional response of Or42a ORNs and the differing response of similarly (but not identically) sensitive Or43b ORNs. The experiments are well-conceived, controlled, and conducted. Where the story falters a bit, though, is with the connectomics analysis. The authors show distinct differences between Or43b and Or42b ORN input-output relationships, and suggest that those differences may underlie the differences observed in their response to ethyl butyrate exposure during the critical period. This is certainly a possibility, but as it stands now, it is too disconnected to offer significant proof. There would have to be additional experiments to address this. Right now, the inclusion of the connectomics work feels like a distraction at best, and a complete non sequitur at worst. To be clear, the connectomics work is well done and I have no issues with its validity, but it is not helpful to the central thesis of the work. I would suggest the authors either remove it entirely or strongly rethink how it fits into the paper.

Major Concerns:

(1) The examination of PN axon terminals in the MB and LH is interesting, but it is only one possibility. Oftentimes, the volume of neurons remains constant with perturbation, while the synapse number is affected. Figure 1C and E would be greatly helped by examining synapse number (via Brp or Brp-Short) in the PN axons.

(2) The use of dlg1[4K] is a strong use of a new tool, but the result is surprising. The presynaptic ORN synapse number onto the PNs is notably changed, but that is not reflected in a postsynaptic PSD-95 change. That suggests a compensatory mechanism that the authors might explore. A good proportion of PN puncta should be postsynaptic to those ORNs, so why aren't they adjusted?

Reviewer #2 (Public review):

This study investigated changes in metabolic health across three genetically diverse mouse strains (NZO/HlLtJ, C57BL/6J mice, CAST/EiJ) that were fed either control or high-fat high-sucrose diets. The strength of this study is the depth of metabolic phenotyping, the use of both male and female mice, and the multi-tissue metabolic analysis, including metabolic and gene expression analysis in pancreatic islets, kidney, muscle, heart, liver, and adipose tissue.

Weaknesses include that only three mouse strains were included in this comparison, particularly given that similar comparisons have been published in the past and that the Jax lab has access to a wide range of mouse strains with diverse genetic backgrounds. Why were CAST mice included over (for example) BALB/c mice that are more commonly used in metabolic studies and are well known to show protection against diet-induced metabolic disease? Furthermore, the feeding regime was limited to 9 weeks, which may not be sufficient to evoke pronounced metabolic remodelling.

NZO mice are well known to develop obesity. However, only approximately 50% develop type 2 diabetes and beta-cell dysfunction. How were these mice selected in the study? The results state 'Most of the male NZO mice and a few female mice displayed overt diabetes', suggesting that all mice were included irrespective of their diabetic phenotype. More information on the rationale for this is required.

The transcriptomics data are presented in a convoluted way. As a reader, the main interest would be to determine the differences in diet-induced adaptations within each strain (e.g., why are CAST mice resistant to diet-induced metabolic defects?). However, the way Figure 4 is currently presented does not allow for this. Instead, the data are 'compressed' by looking at general changes in metabolic pathways between tissues in all three mouse strains. In addition, Figure 4E does not show the directionality of the responses within each pathway. For example, are the metabolism and inflammation pathways suppressed or activated? While more data is shown for adipose tissue, this is not sufficient.

Currently, the metabolic cage data are separated by diet within the main figures. However, given that the diet effect is the major comparison, this needs to be rearranged, and strain differences within each diet could be shown within the supplement.

The graphs lack labelling throughout to specify which lines/bars represent which strains and diets. This is particularly the case in the metabolic cage analysis.

Reviewer #2 (Public review):

This paper remarkably reveals the identification of plasma membrane repair proteins, revealing spatiotemporal cellular responses to plasma membrane damage. The study highlights a combination of sodium dodecyl sulfate (SDS) and lase for identifying and characterizing proteins involved in plasma membrane (PM) repair in Saccharomyces cerevisiae. From 80 PM, repair proteins that were identified, 72 of them were novel proteins. The use of both proteomic and microscopy approaches provided a spatiotemporal coordination of exocytosis and clathrin-mediated endocytosis (CME) during repair. Interestingly, the authors were able to demonstrate that exocytosis dominates early and CME later, with CME also playing an essential role in trafficking transmembrane-domain (TMD) containing repair proteins between the bud tip and the damage site.

Weaknesses/limitations:

(1) Why are the authors saying that Pkc1 is the best characterized repair protein? What is the evidence?

(2) It is unclear why the authors decided on the C-terminal GFP-tagged library to continue with the laser damage assay, exclusively the C-terminal GFP-tagged library. Potentially, this could have missed N-terminal tag-dependent localizations and functions and may have excluded functionally important repair proteins.

(3) The use of SDS and laser damage may bias toward proteins responsive to these specific stresses, potentially missing proteins involved in other forms of plasma membrane injuries, such as mechanical, osmotic, etc.). SDS stress is known to indirectly induce oxidative stress and heat-shock responses.

(4) It is unclear what the scale bars of Figures 3, 5, and 6 are. These should be included in the figure legend.

(5) Figure 4 should be organized to compare WT vs. mutant, which would emphasize the magnitude of impairment.

(6) It would be interesting to expand on possible mechanisms for CME-mediated sorting and retargeting of TMD proteins, including a speculative model.

Reviewer #2 (Public review):

Summary:

The authors aimed to investigate whether advanced microstructural diffusion MRI modeling using the SANDI framework could reveal clinically relevant tissue alterations in the subcortical structures of individuals with Huntington's disease (HD). Specifically, they sought to determine if SANDI-derived parameters-such as soma density, soma size, and extracellular diffusivity-could detect abnormalities in both manifest and premanifest HD stages, complement standard MRI biomarkers (e.g., volume, MD), and correlate with disease burden and motor impairment. Through this, they hoped to demonstrate the feasibility and added biological specificity of SANDI for early detection and characterization of HD pathology.

Strengths:

(1) Novelty and relevance:

This is, to the best of my knowledge, the first clinical deployment of SANDI in HD, offering more biophysically interpretable and specific imaging biomarkers than standard DTI or volumetric features.

(2) More specific microstructural insight: Traditional approaches have used volumetric features (e.g., striatal volume loss) or DTI metrics (like FA and MD), which are indirect and non-specific markers. They can indicate something is "wrong" but not what is wrong.

(3) SANDI parameters permit establishing clearer links with microstructure:

o Apparent soma density (fis): proxy for neuronal/glial cell body density.

o Apparent soma size (rs): reflects possible gliagl hypertrophy or neuronal shrinkage.

o Neurite density (fin): linked to dendritic/axonal integrity.

o Extracellular fraction and diffusivity: sensitive to edema, gliosis, and tissue loss.

In this way, a decrease in soma density can be related to neural loss (e.g., medium spiny neurons), and an increase in soma size and extracellular fraction could be related to glial reactivity (astrocytes, microglia). This enables differentiating between atrophy due to neuron loss vs reactive gliosis, which volumetrics or DTI cannot do.

(4) Integration of modalities: The inclusion of motor impairment (Q-Motor), HD-ISS staging, and multi-compartment diffusion modeling is a methodological strength.

(5) Early detection potential: SANDI metrics showed abnormalities in premanifest HD, sometimes even when volume loss was mild or absent. This suggests the potential for earlier, more sensitive biomarkers of disease progression.

(6) Predictive power: Regression models showed that SANDI metrics explained up to 63% of the variance in striatal volumes in HD. And this correlated strongly with motor impairment and disease burden (CAP100). This shows they are not just redundant with volume or DTI, but they are complementary and potentially more mechanistically meaningful.

Weaknesses:

Certain aspects of the study would benefit from clarification:

(1) Scanner and acquisition consistency: While HD data are from the WAND study, it is not clear whether controls were scanned on the same scanner or protocol. Given the use of model-derived metrics (especially SANDI), differences in scanner or acquisition could introduce confounds. Also, although it offers novel and biologically informative markers, widespread clinical translation still faces hurdles. For instance, the study used a 3T Connectom scanner (300mT/m gradients), which is not widely available. Reproduction of these results in standard 3T clinical scanners would be a great addition, in scenarios with lower resolution, less precise parameter recovery, and longer scans if SNR needs to be maintained.

(2) HD-ISS staging and group comparisons:<br /> a) Only 26-27 out of 56 gene-positive participants could be assigned HD-ISS stages, and none were classified into stages 0 or 4.

b) Visual overlap between stages 1 and 2 in behavioral and imaging features suggests that staging-based group separation may not be robust.

c) The above may lead to claims based on progression across HD-ISS stages to be overinterpreted or underpowered

(3) Regression modeling choices:<br /> a) SANDI metrics included in the models differ between HC and HD groups, reducing comparability.

b) The potential impact of multicollinearity (e.g., between fis and rs) is not discussed.

c) Beta coefficients could reflect model instability or parameter degeneracy rather than true biological effects.

These issues do not undermine the study's main conclusions, which effectively demonstrate the feasibility and initial clinical relevance of applying SANDI to HD. Nonetheless, addressing them more thoroughly would enhance the clarity and interpretability of the manuscript.

Reviewer #2 (Public review):

This manuscript from Hariharan, Shi, Viner, and Guan presents x-ray crystallographic structures of membrane protein MelB and HDX-MS analysis of ligand-induced dynamics. This work improves on the resolution of previously published structures, introduces further sugar-bound structures, and utilises HDX to explore in further depth the previously observed positive cooperatively to cotransported cation Na+. The work presented here builds on years of previous study and adds substantial new details into how Na+ binding facilitates melibiose binding and deepens the fundamental understanding of the molecular basis underlying the symport mechanism of cation-coupled transporters. However, the presentation of the data lacks clarity, and in particular, the HDX-MS data interpretation requires further explanation in both methodology and discussion.

Comments on Crystallography and biochemical work:

(1) It is not clear what Figure 2 is comparing. The text suggests this figure is a comparison of the lower resolution structure to the structure presented in this work; however, the figure legend does not mention which is which, and both images include a modelled water molecule that was not assigned due to poor resolution previously, as stated by the authors, in the previously generated structure. This figure should be more clearly explained.

(2) It is slightly unclear what the ITC measurements add to this current manuscript. The authors comment that raffinose exhibiting poor binding affinity despite having more sugar units is surprising, but it is not surprising to me. No additional interactions can be mapped to these units on their structure, and while it fits into the substrate binding cavity, the extra bulk of additional sugar units is likely to reduce affinity. In fact, from their listed ITC measurements, this appears to be the trend. Additionally, the D59C mutant utilised here in structural determination is deficient in sodium/cation binding. The reported allostery of sodium-sugar binding will likely influence the sugar binding motif as represented by these structures. This is clearly represented by the authors' own ITC work. The ITC included in this work was carried out on the WT protein in the presence of Na+. The authors could benefit from clarifying how this work fits with the structural work or carrying out ITC with the D59C mutant, or additionally, in the absence of sodium.

Comments on HDX-MS work:

While the use of HDX-MS to deepen the understanding of ligand allostery is an elegant use of the technique, this reviewer advises the authors to refer to the Masson et al. (2019) recommendations for the HDX-MS article (https://doi.org/10.1038/s41592-019-0459-y) on how to best present this data. For example:

(1) The Methodology includes a lipid removal step. Based on other included methods, I assumed that the HDX-MS was being carried out in detergent-solubilised protein samples. I therefore do not see the need for a lipid removal step that is usually included for bilayer reconstituted samples. I note that this methodology is the same as previously used for MelB. It should be clarified why this step was included, if it was in fact used, aka, further details on the sample preparation should be included.

(2) A summary of HDX conditions and results should be given as recommended, including the mean peptide length and average redundancy per state alongside other included information such as reaction temperature, sequence coverage, etc., as prepared for previous publications from the authors, i.e., Hariharan et al., 2024.

(3) Uptake plots per peptide for the HDX-MS data should be included as supporting information outside of the few examples given in Figure 6.

(4) A reference should be given to the hybrid significance testing method utilised. Additionally, as stated by Hageman and Weis (2019) (doi:10.1021/acs.analchem.9b01325), the use of P < 0.05 greatly increases the likelihood of false positive ΔD identifications. While the authors include multiple levels of significance, what they refer to as high and lower significant results, this reviewer understands that working with dynamic transporters can lead to increased data variation; a statement of why certain statistical criteria were chosen should be included, and possibly accompanied by volcano plots. The legend of Figure 6 should include what P value is meant by * and ** rather than statistically significant and highly statistically significant.

(5) Line 316 states a significant difference in seen in dynamics, how is significance measured here? There is no S.D. given in Table S4. Can the authors further comment on the potential involvement in solvent accessibility and buried helices that might influence the overall dynamics outside of their role in sugar vs sodium binding? An expected low rate of exchange suggests that dynamics are likely influenced by solvent accessibility or peptide hydrophobicity? The increased dynamics at peptides covering the Na binding site on overall more dynamic helices suggests that there is no difference between the dynamics of each site.

(6) Previously stated HDX-MS results of MelB (Hariharan et al., 2024) state that the transmembrane helices are less dynamic than polypeptide termini and loops with similar distributions across all transmembrane bundles. The previous data was obtained in the presence of sodium. Does this remove the difference in dynamics in the sugar-binding helices and the cation-binding helices? Including this comparison would support the statement that the sodium-bound MelB is more stable than the Apo state, along with the lack of deprotection observed in the differential analysis.

(7) Have the authors considered carrying out an HDX-MS comparison between the WT and the D59C mutant? This may provide some further information on the WT structure (particularly a comparison with sugar-bound). This could be tied into a nice discussion of their structural data.

(8) Have the authors considered utilising Li+ to infer how cation selectivity impacts the allostery? Do they expect similar stabilisation of a higher-affinity sugar binding state with all cations?

(9) MD of MelB suggests all transmembrane helices are reorientated during substrate translocation, yet substrate and cotransporter ligand binding only significantly impacts a small number of helices. Can the authors comment on the ensemble of states expected from each HDX experiment? The data presented here instead shows overall stabilisation of the transporter. This data can be compared to that of HDX on MFS sugar cation symporter XylE, where substrate binding induces a transition to OF state. There is no discussion of how this HDX data compares to previous MFS sugar transporter HDX. The manuscript could benefit from this comparison rather than a comparison to LacY. It is unlikely that there are universal mechanisms that can be inferred even from these model proteins. Highlighting differences instead between these transport systems provides broader insights into this protein class. Doi: 10.1021/jacs.2c06148 and 10.1038/s41467-018-06704-1.

(10) Additionally, the recent publication of SMFS data (by the authors: doi:10.1016/j.str.2022.11.011) states the following: "In the presence of either melibiose or a coupling Na+-cation, however, MelB increasingly populates the mechanically less stable state which shows a destabilized middle-loop C3." And "In the presence of both substrate and co-substrate, this mechanically less stable state of MelB is predominant.". It would benefit the authors to comment on these data in contrast to the HDX obtained here. Additionally, is the C3 loop covered, and does it show the destabilization suggested by these studies? HDX can provide a plethora of results that are missing from the current analysis on ligand allostery. The authors instead chose to reference CD and thermal denaturation methods as comparisons.

Reviewer #2 (Public review):

Summary:

Kern et al. investigated whether temporally delayed linear modeling (TDLM) can uncover sequential memory replay from a graph-learning task in human MEG during an 8-minute post-learning rest period. After failing to detect replay events, they conduct a simulation study in which they insert synthetic replay events, derived from each participant's localizer data, into a control rest period prior to learning. The simulations suggest that TDLM only reveals sequences when replay occurs at very high densities (> 80 per minute) and that individual differences in baseline sequenceness may lead to spurious and/or lackluster correlations between replay strength and behavior.

Strengths:

The approach is extremely well documented and rigorous. The authors have done an excellent job re-creating the TDLM methodology that is most commonly used, reporting the different approaches and parameters that they used, and reporting their preregistrations. The hybrid simulation study is creative and provides a new way to assess the efficacy of replay decoding methods. The authors remain measured in the scope/applicability of their conclusions, constructive in their discussion, and end with a useful set of recommendations for how to best apply TDLM in future studies. I also want to commend this work for not only presenting a null result but thoroughly exploring the conditions under which such a null result is expected. I think this paper is interesting and will be generally quite useful for the field, but I believe it also has a number of weaknesses that, if addressed, could improve it further.

Weaknesses:

The sample size is small (n=21, after exclusions), even for TDLM studies (which typically have somewhere between 25-40 participants). The authors address this somewhat through a power analysis of the relationship between replay and behavioral performance in their simulations, but this is very dependent on the assumptions of the simulation. Further, according to their own power analysis, the replay-behavior correlations are seriously underpowered (~10% power according to Figure 7C), and so if this is to be taken at face value, their own null findings on this point (Figure 3C) could therefore just reflect undersampling as opposed to methodological failure. I think this point needs to be made more clearly earlier in the manuscript. Relatedly, it would be very useful if one of the recommendations that come out of the simulations in this paper was a power analysis for detecting sequenceness in general, as I suspect that the small sample size impacts this as well, given that sequenceness effects reported in other work are often small with larger sample sizes. Further, I believe that the authors' simulations of basic sequenceness effects would themselves still suffer from having a small number of subjects, thereby impacting statistical power. Perhaps the authors can perform a similar sort of bootstrapping analysis as they perform for the correlation between replay and performance, but over sequenceness itself?

The task paradigm may introduce issues in detecting replay that are separate from TDLM. First, the localizer task involves a match/mismatch judgment and a button press during the stimulus presentation, which could add noise to classifier training separate from the semantic/visual processing of the stimulus. This localizer is similar to others that have been used in TDLM studies, but notably in other studies (e.g., Liu, Mattar et al., 2021), the stimulus is presented prior to the match/mismatch judgment. A discussion of variations in different localizers and what seems to work best for decoding would be useful to include in the recommendations section of the discussion. Second, and more seriously, I believe that the task design for training participants about the expected sequences may complicate sequence decoding. Specifically, this is because two images (a "tuple") are shown together and used for prediction, which may encourage participants to develop a single bound representation of the tuple that then predicts a third image (AB -> C rather than A -> B, B -> C). This would obviously make it difficult to i) use a classifier trained on individual images to detect sequences and ii) find evidence for the intended transition matrix using TDLM. Can the authors rule out this possibility?

Participants only modestly improved (from 76-82% accuracy) following the rest period (which the authors refer to as a consolidation period). If the authors assume that replay leads to improved performance, then this suggests there is little reason to see much task-related replay during rest in the first place. This limitation is touched on (lines 228-229), but I think it makes the lack of replay finding here less surprising. However, note that in the supplement, it is shown that the amount of forward sequenceness is marginally related to the performance difference between the last block of training and retrieval, and this is the effect I would probably predict would be most likely to appear. Obviously, my sample size concerns still hold, and this is not a significant effect based on the null hypothesis testing framework the authors employ, but I think this set of results should at least be reported in the main text. I was also wondering whether the authors could clarify how the criterion over six blocks was 80% but then the performance baseline they use from the last block is 76%? Is it just that participants must reach 80% within the six blocks *at some point* during training, but that they could dip below that again later?

Because most of the conclusions come from the simulation study, there are a few decisions about the simulations that I would like the authors to expand upon before I can fully support their interpretations. First, the authors use a state-to-state lag of 80ms and do not appear to vary this throughout the simulations - can the authors provide context for this choice? Does varying this lag matter at all for the results (i.e., does the noise structure of the data interact with this lag in any way?) Second, it seems that the approach to scaling simulated replays with performance is rather coarse. I think a more sensitive measure would be to scale sequence replays based on the participants' responses to *that* specific sequence rather than altering the frequency of all replays by overall memory performance. I think this would help to deliver on the authors' goal of simulating an "increase of replay for less stable memories" (line 246). On the other hand, I was also wondering whether it is actually necessary to use the real memory performance for each participant in these simulations - couldn't similar goals (with a better/more full sampling of the space of performance) be achieved with simulated memory performance as well, taking only the MEG data from the participant? Finally, Figure 7D shows that 70ms was used on the y-axis. Why was this the case, or is this a typo?

Because this is a re-analysis of a previous dataset combined with a new simulation study on that data aimed at making recommendations about how to best employ TDLM, I think the usefulness of the paper to the field could be improved in a few places. Specifically, in the discussion/recommendation section, the authors state that "yet unknown confounders" (line 295) lead to non-random fluctuations in the simulated correlations between replay detection and performance at different time lags. Because it is a particularly strong claim that there is the potential to detect sequenceness in the baseline condition where there are no ground-truth sequences, the manuscript could benefit from a more thorough exploration of the cause(s) of this bias in addition to the speculation provided in the current version. In addition, to really provide that a realistic simulation is necessary (one of the primary conclusions of the paper), it would be useful to provide a comparison to a fully synthetic simulation performed on this exact task and transition structure (in addition to the recreation of the original simulation code from the TDLM methods paper). Finally, I think the authors could do further work to determine whether some of their recommendations for improving the sensitivity of TDLM pan out in the current data - for example, they could report focusing not just on the peak decoding timepoint but incorporating other moments into classifier training.

Lastly, I would like the authors to address a point that was raised in a separate public forum by an author of the TDLM method, which is that when replays "happen during rest, they are not uniform or close". Because the simulations in this work assume regularly occurring replay events, I agree that this is an important limitation that should be incorporated into alternative simulations to ensure the lack of findings is not because of this assumption.

Reviewer #2 (Public review):

Summary:

The authors set out to characterise the GPCR family in choanoflagellates (and other unicellular holozoans). GPCRs are the most abundant gene family in many animal genomes, playing crucial roles in a wide range of physiological processes. Although they are known to evolve rapidly, GPCRs are an ancient feature of eukaryotic biology. Identifying conserved elements across the animal-protist boundary is therefore a valuable goal, and the increasing availability of genomes from non-animal holozoans provides new opportunities to explore evolutionary patterns that were previously obscured by limited taxon sampling. This study presents a comprehensive re-examination of GPCRs in choanoflagellates, uncovering examples of differential gene retention and revealing the dynamic nature of the GPCR repertoire in this group. As GPCRs are typically involved in environmental sensing, understanding how these systems evolved may shed light on how our unicellular ancestors adapted their signalling networks in the transition to complex multicellularity.

Strengths:

The paper combines a broad taxonomic scope with the use of both established and recently developed tools (e.g. Foldseek, AlphaFold), enabling a deep and systematic exploration of GPCR diversity. Each family is carefully described, and the manuscript also functions as an up-to-date review of GPCR classification and evolution. Although similar attempts of understanding GPCR evolution were done over the last decade, the authors build on this foundation by identifying new families and applying improved computational methods to better predict structure and function. Notably, the presence of Rhodopsin-like GPCRs in some choanoflagellates and ichthyosporeans is intriguing, even though they do not fall within known animal subfamilies. The computational framework presented here is broadly applicable, offering a blueprint for surveying GPCR diversity in other non-model eukaryotes (and even in animal lineages), potentially revealing novel families relevant to drug discovery or helping revise our understanding of GPCR evolution beyond model systems.

Weaknesses:

While the study contributes several interesting observations, it does not radically revise the evolutionary history of the GPCR family. However, in an era increasingly concerned with the reproducibility of scientific findings, this is arguably a strength rather than a weakness. It is encouraging to see that previously established patterns largely hold, and that with expanded sampling and improved methods, new insights can be gained-especially at the level of specific GPCR subfamilies. Then, no functional follow ups are provided in the model system Salpingoeca rosetta, but I am sure functional work on GPCRs in choanoflagellates is set to reveal very interesting molecular adaptations in the future.

Comments on the latest version:

The authors have done a good job answering my questions and suggestions.

Reviewer #2 (Public review):

Goal summary:

The authors sought to (i) demonstrate correlations between the dynamics of the dinoflagellate Alexandrium pacificum and the bacterim Vibrio atlanticus in natural populations, ii) demonstrate the occurrence of predation in laboratory experiments, iii) claim coordinated action by the predators in the predation process, iv) demonstrate that predation is induced by predator starvation, and v) test for effects of quorum sensing and iron-uptake genes on the predation process.

Strengths include:

(1) Data indicating correlated dynamics in a natural environment that increase the motivation for the study of in vitro interactions.

(2) Experimental design allowing clear inference of predation based on population counts of both prey and predators in addition to microscopy-based evidence.

(3) Supplementation of population-level data with molecular approaches to test hypotheses regarding possible involvement of quorum sensing and iron uptake in predation.

Weaknesses include:

(1) A lack of early, clear definitions for several important terms used in the paper, including 'predation', 'coordination' and 'coordinated action', 'group attack', and 'wolf-pack hunting', along with a corresponding lack of criteria for what evidence would warrant use of some of these labels. (For example, does mere simultaneity of attacks of an A. pacificum cell by many V. atlanticus cells constitute "coordination"? Or, as it seems to us, does coordination require some form of signalling between predator cells?)

(2) Absence of controls for cell density in the test for starvation effects on predatory behavior; unclear how the length of incubation affects the density of V. atlanticus cells.

(3) Lack of clarity in some of the methodological descriptions

Appraisal:

The authors convincingly achieve their aim of demonstrating that V. atlanticus can prey on A. pacificum, provide strongly suggestive evidence that such predation is induced by starvation, and clearly demonstrate that both iron availability and, correspondingly, the presence of genes involved in iron uptake, strongly influence the efficacy of predation. However, the evidence for starvation-induction of predation can be strengthened with cell-density controls; evidence for a social component to predation - positive interactions between attacking predators - is lacking.

Discussion of impact:

This paper will interest those interested in how microbial behaviour responds to environmental fluctuations, in particular predatory behaviour, but will do so more strongly if the evidence of starvation-induction of predation is strengthened. It will also interest those investigating bacteria-algae interactions and potential ecological controls of algal blooms. It has the potential to interest researchers of microbial cooperation, should the authors be able to provide any evidence of coordination between predator cells.

Reviewer #2 (Public review):

Summary:

This interesting study implicates the direct interaction between two multi-subunit complexes, known as the exocyst and septin complexes, in the function of both complexes during cytokinesis in fission yeast. While previous work from several labs had implicated roles for the exocyst and septin complexes in cytokinesis and cell separation, this study describes the importance of protein:protein interaction between these complexes in mediating the functions of these complexes in cytokinesis. Previous studies in neurons had suggested interactions between septins and exocyst complexes occur but the functional importance of such interactions was not known. Moreover, in baker's yeast where both of these complexes have been extensively studied - no evidence of such an interaction has been uncovered despite numerous studies which should have detected it. Therefore while exocyst:septin interactions appear to be conserved in several systems, it appears likely that budding yeast are the exception--having lost this conserved interaction.

Strengths:

The strengths of this work include the rigorous analysis of the interaction using multiple methods including Co-IP of tagged but endogenously expressed proteins, 2 hybrid interaction, and Alphafold Multimer. Careful quantitative analysis of the effects of loss of function in each complex and the effects on localization and dynamics of each complex was also a strength. Taken together this work convincingly describes that these two complexes do interact and that this interaction plays an important role in post Golgi vesicle targeting during cytokinesis.

Comments on revisions:

The authors have added substantial work to the revised manuscript, and it is much improved. In particular, the figures portraying the AlphaFold Multimer model of the exocyst:septin interactions are much clearer. I also appreciate the effort that went into modeling the fission yeast exocyst complex based on the yeast CryoEM structure in order to determine if the predicted interfaces with septins were likely to be surface accessible in the intact exocyst complex.

Reviewer #2 (Public review):

Summary:

The manuscript by Borghi and colleagues provides evidence that the combination of intermittent theta burst TMS stimulation and gamma transcranial alternating current stimulation (γtACS) targeting the precuneus increases long-term associative memory in healthy subjects compared to iTBS alone and sham conditions. Using a rich dataset of TMS-EEG and resting-state functional connectivity (rs-FC) maps and structural MRI data, the authors also provide evidence that dual stimulation increased gamma oscillations and functional connectivity between the precuneus and hippocampus. Enhanced memory performance was linked to increased gamma oscillatory activity and connectivity through white matter tracts.

Strengths:

The combination of personalized repetitive TMS (iTBS) and gamma tACS is a novel approach to targeting the precuneus, and thereby, connected memory-related regions to enhance long-term associative memory. The authors leverage an existing neural mechanism engaged in memory binding, theta-gamma coupling, by applying TMS at theta burst patterns and tACS at gamma frequencies to enhance gamma oscillations. The authors conducted a thorough study that suggests that simultaneous iTBS and gamma tACS could be a powerful approach for enhancing long-term associative memory. The paper was well-written, clear, and concise.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigated how the type-I interferon response (ISG) and antigen presentation (AP) pathways are repressed in luminal breast cancer cells and how this repression can be overcome. They found that a STING agonist can reactivate these pathways in breast cancer cells, but it also does so in normal cells, suggesting that this is not a good way to create a therapeutic window. Depletion of ADAR and inhibition of KDM5 also activate ISG and AP genes. The activation of ISG and AP genes is dependent on cGAS/STING and the JAK kinase. Interestingly, although both ADAR depletion and KDM5 inhibition activate ISG and AP genes, their effects on cell fitness are different. Furthermore, KDM5 inhibitor selectively activates ISG and AP genes in tumor cells but not normal cells, arguing that it may create a larger therapeutic window than the STING agonist. These results also suggest that KDM5 inhibition may activate ISG and AP genes in a way different from ADAR loss, and this process may affect tumor cell fitness independently of the activation of ISG and AP genes.

The authors further showed that KDM5 inhibition increases R-loops and DNA damage in tumor cells, and XPF, a nuclease that cuts R-loops, is required for the activation of ISG and AP genes. Using H3K4me3 CUT&RUN, they found that KMD5 inhibition results in increased H3K4me3 not only at genes, but also at repetitive elements including SINE, LINE, LTR, telomeres, and centromeres. Using S9.6 CUT&TAG, they confirmed that R-loops are increased at SINE, LINE, and LTR repeated with increased H3K4me3. Together, the results of this study suggest that KMD5 inhibition leads to H3K4me3 and R-loop accumulation in repetitive elements, which induces DNA damage and cGAS/STING activation and subsequently activates AP genes. This provides an exciting approach to stimulate the anti-tumor immunity against breast tumors.

KDM5 inhibition activates interferon and antigen presentation genes through R-loops.

Strengths:

A new approach to make breast tumors "hot" for anti-tumor immunity.

Weaknesses:

Future in vivo studies are needed to show the effects of KDM5 inhibitors on the immunotherapy responses of breast tumors.

Comments on revised version:

The authors have adequately addressed my comments.

Reviewer #2 (Public review):

Summary:

In this study, the authors present the latest improvement of their previously published methods, pMAGIC and nMAGIC, which can be used to engineer mosaic gene expression in wild-type animals and in a tissue-specific manner. They address the main limitation of MAGIC, the lack of gRNA-marker transgenes, which has hampered the broader adoption of MAGIC in the fly community. To do so, they create an entire toolkit of gRNA markers for every Drosophila chromosome and test them across a range of different tissues and in the context of making Drosophila species hybrid mosaic animals. The study provides a significant and broadly useful improvement compared to earlier versions, as it broadens the use-cases for transgenic manipulation with MAGIC to virtually any subfield of Drosophila cell biology.

Strengths:

Major improvements to MAGIC were made in terms of clone induction efficiency and usability across the Drosophila model system, including wild-type genotypes and the use in non-melanogaster species.

Notably, mosaic mutants can now be created for genes residing on the 4th chromosome, which is exciting and possibly long-awaited by 4th chromosome gene enthusiasts.

Selection of the standard set of gRNA markers was done thoughtfully, using non-repetitive conserved and unique sequences.

The authors demonstrate that MAGIC can be used easily in the context of interspecific hybrids. I believe this is a great advancement for the Drosophila community, especially for evolutionary biologists, because this may allow for easy access to mechanistic, tissue-specific insight into the process of a range of hybrid incompatibilities, an important speciation process that is normally difficult to study at the level of molecular and cell biology.

In the same way, because it is not limited to usage in any particular genetic background, genome-wide MAGIC can be potentially used in wild-type genotypes relatively easily. This is exciting, especially because natural genetic diversity is rarely investigated more mechanistically and at the scale/resolution of cells or specific tissues. Now, one can ask how a particular naturally occurring allele influences cell physiology compared to another (control) while keeping the global physiological context of the particular genetic background largely intact.

Weaknesses:

It is not entirely clear how functionally non-critical regions were evaluated, besides that they are selected based on conservation of sequence between species. It may be useful to directly test the difference in viability or other functionally relevant phenotype for flies carrying different markers. Similarly, the frequency of off-targets could be investigated or documented in a bit more detail, especially if one of the major use-cases is meant for naturally derived, diverse genetic backgrounds. It is, at the moment, unclear how consistently the clones are induced for each new gRNA marker across different WT genetic backgrounds, for example, a set of DGRP genotypes, which could be highly useful information for future users.

Reviewer #2 (Public review):

Summary:

The manuscript by Leshem et al. presents a transcriptomic analysis of the developing human outflow tract (OFT) at embryonic and fetal stages using snRNAseq and spatial transcriptomics. Additionally, the authors analyze transcriptomic data from the adult aortic valve to compare embryonic and adult cell populations, aiming to identify persistent embryonic transcriptional signatures in adult cells. A total of 15 clusters were identified from the embryonic and fetal OFT samples, including three mesenchymal and four endothelial clusters. Using SCENIC analysis on the embryonic snRNAseq data, the authors identified GATA6 as a key regulator of valve precursor cells. Spatial transcriptomic analysis of four fetal OFT sections further revealed the spatial distribution of mesenchymal nuclei, smooth muscle cells, and valvular interstitial cells. Trajectory analysis identified two distinct developmental origins of fetal mesenchymal cells: the neural crest and the second heart field. Finally, the authors used snRNAseq data from the adult aortic valve to propose that embryonic transcriptional signatures persist in a subset of adult cells.

Strengths:

(1) The study offers a rich and detailed dataset, combining snRNA-seq and spatial transcriptomics in human embryonic and fetal OFT, which are challenging to obtain.

(2) The use of SCENIC and trajectory analysis adds mechanistic insight into cell lineage and regulatory programs during valve development.

(3) This study confirms GATA6 as a key regulator of valve precursor cells.

(4) Comparison between embryonic/fetal and adult datasets represents a novel attempt to trace persistence of developmental transcriptional programs.

Weaknesses:

(1) A major limitation is the lack of experimental validation to support key conclusions, particularly the claim of persistent embryonic transcriptional signatures in adult cells.

(2) The manuscript would benefit from a clearer discussion of how these results advance beyond previous studies in human heart and valve development.

(3) The comparison between embryonic and adult data is interesting, but would be more convincing with additional evidence supporting the proposed persistence of embryonic transcriptional signatures in adult cells.

Reviewer #2 (Public review):

Summary:

The authors tried deconvoluting, for the first time, the effect of various components of heart contraction on initial bacterial adhesion, which increases the risk of infective endocarditis. The proposed organoid platform might be used to develop and test novel therapeutic agents for infective endocarditis.

Strengths:

(1) Use of a broad range of methods: finite element methods, -omics, particle tracking, animal experiments to investigate the connections between contractility and infective endocarditis.

(2) Detailed procedure and supportive information, which will allow other groups to replicate the results and extend the application of the proposed organoid platform.

(3) Despite the complexity of the work reported, the manuscript is rather readable and understandable by non-specialists.

Weaknesses:

There is a minor issue with some of the vocabulary (e.g., magnificent amount of bacteria).

Reviewer #2 (Public review):

Summary:

The authors of this manuscript aim to investigate the formation of place fields (PFs) in hippocampal CA1 pyramidal cells. They focus on the role of behavioral time scale synaptic plasticity (BTSP), a mechanism proposed to be crucial for the formation of new PFs. Using in vivo two-photon calcium imaging in head-restrained mice navigating virtual environments, employing a classification method based on calcium activity to categorize the formation of place cells' place fields into BTSP, non-BTSP-like, and investigated their properties.

Strengths:

This work shows that place fields formation could induced by both BSTP and non-BSTP events, and it also provided a new and solid method to classify BTSP and non-BTSP place field formation using calcium image to the field. This work offers novel knowledge and new methods and factual evidence for other researchers in the field.

The method enabled the authors to reveal that while many PFs are formed by BTSP-like events, a significant number of PFs emerge with calcium dynamics that do not match BTSP characteristics, suggesting a diversity of mechanisms underlying PF formation. The characteristics of place fields under the first two categories are comprehensively described, including aspects such as formation timing, quantity, and width.

Weaknesses:

The authors have addressed the weaknesses in the revised version.

Reviewer #2 (Public review):

The manuscript identified Cyp17a2 as a master regulator of male-biased antiviral immunity in a sex chromosome-free model (zebrafish) challenging established immunological paradigms.

Strengths:

(1) The bifunctional role of Cyp17a2 (host-directed STING stabilization and virus-directed P degradation) represents a significant conceptual advance.

(2) First demonstration of K33 chains as a critical regulatory switch for both host defense proteins and viral substrates.

(3) Comprehensive validation across biological scales: organismal (survival, histopathology), cellular (transcriptomics, Co-IPs), and molecular (ubiquitination assays, site-directed mutagenesis).

(4) Functional conservation in cyprinids (zebrafish and gibel carp) strengthens biological significance.

Weaknesses:

(1) Colocalization analyses (Figures 4G, 6I, 9D) require quantitative metrics (e.g., Pearson's coefficients) rather than representative images alone.

(2) Figure 1 survival curves need annotated statistical tests (e.g., "Log-rank test, p=X.XX")

(3) Figure 2P GSEA should report exact FDR-adjusted *p*-values (not just "*p*<0.05").

(4) Section 2 overextends on teleost sex-determination diversity, condensing to emphasize relevance to immune dimorphism would strengthen narrative cohesion.

(5) Limited discussion on whether this mechanism extends beyond Cyprinidae and its implications for teleost adaptation.

Reviewer #2 (Public review):

Employing the MCF10 breast-cancer progression series, the authors integrate high-resolution Micro-C chromatin-conformation capture with RNA-seq and ChIP-seq to delineate the sequential reorganization of compartments, topologically associated domains (TADs), and long-range loops across benign, pre-neoplastic, and metastatic states, and couple these 3D alterations to gene expression and enhancer activity. Four principal findings emerge: (i) largely static chromatin frameworks still gate differential gene output, with up-regulated loci most affected; (ii) enhancer-promoter contact strength covaries with transcriptional amplitude; (iii) 127 genes gain expression concomitant with increased chromatin contacts; and (iv) progression-associated genes acquire altered histone marks at distal enhancers that remain tethered by stable loops. While the conclusions are broadly supported, methodological and analytical refinements are required.

(1) Model representativeness.<br /> The long-term culture-adapted MCF10 genome harbours extensive aneuploidies and translocations. Validation of key COL12A1/WNT5A loop dynamics in an independent breast-cancer line (e.g., MDA-MB-231, T47D) or in patient-derived organoids/PDX models would strengthen generalizability.

(2) The study remains purely correlative; no perturbation experiments are conducted to demonstrate causal roles of chromatin loops on gene expression. CRISPR interference (CRISPR-Cas9-KRAB/HDAC) or enhancer deletion/inversion should be applied to 3-5 pivotal loops (e.g., COL12A1, WNT5A) to test their impact on target-gene expression and cellular phenotypes (e.g., proliferation, migration).

(3) The manuscript lacks integration with clinical datasets. Integrate TCGA-BRCA data to assess whether elevated COL12A1/WNT5A expression associates with overall survival (OS) or distant metastasis-free survival (DMFS).

Reviewer #2 (Public review):

Summary:

Ueno et al. described substantial changes in the Afadin knockout retina. These changes include decreased numbers of rods and cones, an increased number of bipolar cells, and disrupted somatic and synaptic organization of the outer limiting membrane, outer nuclear layer, outer plexiform layer. In contrast, the number and organization of amacrine cells and retinal ganglion cells remain relatively intact. They also observed changes in ERG responses, RGC receptive fields and functions, and visual behaviors. The morphological and function characterization of retinal cell types and laminations is detailed and relatively comprehensive.

Reviewer #2 (Public review):

Summary:

This manuscript proposes that the use of a latent cause model for assessment of memory-based tasks may provide improved early detection in Alzheimer's Disease as well as more differentiated mapping of behavior to underlying causes. To test the validity of this model, the authors use a previously described knock-in mouse model of AD and subject the mice to several behaviors to determine whether the latent cause model may provide informative predictions regarding changes in the observed behaviors. They include a well-established fear learning paradigm in which distinct memories are believed to compete for control of behavior. More specifically, it's been observed that animals undergoing fear learning and subsequent fear extinction develop two separate memories for the acquisition phase and the extinction phase, such that the extinction does not simply 'erase' the previously acquired memory. Many models of learning require the addition of a separate context or state to be added during the extinction phase and are typically modeled by assuming the existence of a new state at the time of extinction. The Niv research group, Gershman et al. 2017, have shown that the use of a latent cause model applied to this behavior can elegantly predict the formation of latent states based on a Bayesian approach, and that these latent states can facilitate the persistence of the acquisition and extinction memory independently. The authors of this manuscript leverage this approach to test whether deficits in production of the internal states, or the inference and learning of those states, may be disrupted in knock-in mice that show both a build-up of amyloid-beta plaques and a deterioration in memory as the mice age.

Strengths:

I think the authors' proposal to leverage the latent cause model and test whether it can lead to improved assessments in an animal model of AD is a promising approach for bridging the gap between clinical and basic research. The authors use a promising mouse model and apply this to a paradigm in which the behavior and neurobiology are relatively well understood - an ideal situation for assessing how a disease state may impact both the neurobiology and behavior. The latent cause model has the potential to better connect observed behavior to underlying causes and may pave a road for improved mapping of changes in behavior to neurobiological mechanisms in diseases such as AD.<br /> The authors also compare the latent cause model to the Rescorla-Wagner model and a latent state model allowing for better assessment of the latent cause model as a strong model for assessing reinstatement.

Weaknesses:

I have several substantial concerns which I've detailed below. These include important details on how the behavior was analyzed, how the model was used to assess the behavior, and the interpretations that have been made based on the model.<br /> (1) There is substantial data to suggest that during fear learning in mice separate memories develop for the acquisition and extinction phases, with the acquisition memory becoming more strongly retrieved during spontaneous recovery and reinstatement. The Gershman paper, cited by the authors, shows how the latent causal model can predict this shift in latent causes by allowing for the priors to decay over time, thereby increasing the posterior of the acquisition memory at the time of spontaneous recovery. In this manuscript, the authors suggest a similar mechanism of action for reinstatement, yet the model does not appear to return to the acquisition memory after reinstatement, at least based on the simulation and examples shown in figures 1 and 3. More specifically, in figure 1, the authors indicate that the posterior probability of the latent cause, z_A (the putative acquisition memory), increases, partially leading to reinstatement. This does not appear to be the case as test 3 (day 36) appears to have similar posterior probabilities for z_A as well as similar weights for the CS as compared to the last days of extinction. Rather, the model appears to mainly modify the weights in the most recent latent cause, z_B - the putative the 'extinction state', during reinstatement. The authors suggest that previous experimental data have indicated that spontaneous recovery or reinstatement effects are due to an interaction of the acquisition and extinction memory. These studies have shown that conditioned responding at a later time point after extinction is likely due to a balance between the acquisition memory and the extinction memory, and that this balance can shift towards the acquisition memory naturally during spontaneous recovery, or through artificial activation of the acquisition memory or inhibition of the extinction memory (see Lacagnina et al. for example). Here the authors show that the same latent cause learned during extinction, z_B, appears to dominate during the learning phase of reinstatement, with rapid learning to the context - the weight for the context goes up substantially on day 35 - in z_B. This latent cause, z_B, dominates at the reinstatement test, and due to the increased associative strength between the context and shock, there is a strong CR. For the simulation shown in figure 1, it's not clear why a latent cause model is necessary for this behavior. This leads to the next point.

(2) The authors compared the latent cause model to the Rescorla-Wagner model. This is very commendable, particularly since the latent cause model builds upon the RW model, so it can serve as an ideal test for whether a more simplified model can adequately predict the behavior. The authors show that the RW model cannot successfully predict the increased CR during reinstatement (Appendix figure 1). Yet there are some issues with the way the authors have implemented this comparison:<br /> (2A) The RW model is a simplified version of the latent cause model and so should be treated as a nested model when testing, or at a minimum, the number of parameters should be taken into account when comparing the models using a method such as the Bayesian Information Criterion, BIC.<br /> (2B) The RW model provides the associative strength between stimuli and does not necessarily require a linear relationship between V and the CR. This is the case in the original RW model as well as in the LCM. To allow for better comparison between the models, the authors should be modeling the CR in the same manner (using the same probit function) in both models. In fact, there are many instances in which a sigmoid has been applied to RW associative strengths to predict CRs. I would recommend modeling CRs in the RW as if there is just one latent cause. Or perhaps run the analysis for the LCM with just one latent cause - this would effectively reduce the LCM to RW and keep any other assumptions identical across the models.<br /> (2C) In the paper, the model fits for the alphas in the RW model are the same across the groups. Were the alphas for the two models kept as free variables? This is an important question as it gets back to the first point raised. Because the modeling of the reinstatement behavior with the LCM appears to be mainly driven by latent cause z_B, the extinction memory, it may be possible to replicate the pattern of results without requiring a latent cause model. For example, the 12-month-old App NL-G-F mice behavior may have a deficit in learning about the context. Within the RW model, if the alpha for context is set to zero for those mice, but kept higher for the other groups, say alpha_context = 0.8, the authors could potentially observe the same pattern of discrimination indices in figure 2G and 2H at test. Because the authors don't explicitly state which parameters might be driving the change in the DI, the authors should show in some way that their results cannot simply be due to poor contextual learning in the 12 month old App NL-G-F mice, as this can presumably be predicted by the RW model. The authors' model fits using RW don't show this, but this is because they don't consider this possibility that the alpha for context might be disrupted in the 12-month-old App NL-G-F mice. Of course, using the RW model with these alphas won't lead to as nice of fits of the behavior across acquisition, extinction, and reinstatement as the authors' LCM, the number of parameters are substantially reduced in the RW model. Yet the important pattern of the DI would be replicated with the RW model (if I'm not mistaken), which is the important test for assessment of reinstatement.

(3) As stated by the authors in the introduction, the advantage of the fear learning approach is that the memory is modified across the acquisition-extinction-reinstatement phases. Although perhaps not explicitly stated by the authors, the post-reinstatement test (test 3) is the crucial test for whether there is reactivation of a previously stored memory, with the general argument being that the reinvigorated response to the CS can't simply be explained by relearning the CS-US pairing, because re-exposure the US alone leads to increase response to the CS at test. Of course there are several explanations for why this may occur, particularly when also considering the context as a stimulus. This is what I understood to be the justification for the use of a model, such as the latent cause model, that may better capture and compare these possibilities within a single framework. As such, it is critical to look at the level of responding to both the context alone and to the CS. It appears that the authors only look at the percent freezing during the CS, and it is not clear whether this is due to the contextual-US learning during the US re-exposure or to increased responding to the CS - presumably caused by reactivation of the acquisition memory. The authors do perform a comparison between the preCS and CS period, but it is not clear whether this is taken into account in the LCM. For example, the instance of the model shown in figure 1 indicates that the 'extinction cause', or cause z6, develops a strong weight for the context during the reinstatement phase of presenting the shock alone. This state then leads to increased freezing during the final CS probe test as shown in the figure. If they haven't already, I think the authors must somehow incorporate these different phases (CS vs ITI) into their model, particularly since this type of memory retrieval that depends on assessing latent states is specifically why the authors justified using the latent causal model. In more precise terms, it's not clear whether the authors incorporate a preCS/ITI period each day the cue is presented as a vector of just the context in addition to the CS period in which the vector contains both the context and the CS. Based on the description, it seemed to me that they only model the CRs during the CS period on days when the CS is presented, and thereby the context is only ever modeled on its own (as just the context by itself in the vector) on extinction days when the CS is not presented. If they are modeling both timepoints each day that the CS I presented, then I would recommend explicitly stating this in the methods section.

(4) The authors fit the model using all data points across acquisition and learning. As one of the other reviewers has highlighted, it appears that there is a high chance for overfitting the data with the LCM. Of course, this would result in much better fits than models with substantially fewer free parameters, such as the RW model. As mentioned above, the authors should use a method that takes into account the number of parameters, such as the BIC.

(5) The authors have stated that they do not think the Barnes maze task can be modeled with the LCM. Whether or not this is the case, if the authors do not model this data with the LCM, the Barnes maze data doesn't appear valuable to the main hypothesis. The authors suggest that more sophisticated models such as the LCM may be beneficial for early detection of diseases such as Alzheimer's, so the Barnes maze data is not valuable for providing evidence of this hypothesis. Rather, the authors make an argument that the memory deficits in the Barnes maze mimic the reinstatement effects providing support that memory is disrupted similarly in these mice. Although, the authors state that the deficits in memory retrieval are similar across the two tasks, the authors are not explicit as to the precise deficits in memory retrieval in the reinstatement task - it's a combination of overgeneralizing latent causes during acquisition, poor learning rate, over differentiation of the stimuli.

Reviewer #4 (Public review):

Summary

In this study, López-Jiménez and colleagues demonstrate the utility of using high-content microscopy in dissecting host and bacterial determinants that play a role in the establishment of infection using Shigella flexneri as a model. The manuscript nicely identifies that infection with Shigella results in a block to DNA replication and protein synthesis. At the same time, the host responds, in part, via the entrapment of Shigella in septin cages.

Strengths:

The main strength of this manuscript is its technical aspects. They nicely demonstrate how an automated microscopy pipeline coupled with artificial intelligence can be used to gain new insights regarding elements of bacterial pathogenesis, using Shigella flexneri as a model system. Using this pipeline enabled the investigators to enhance the field's general understanding regarding the role of septin cages in responding to invading Shigella. This platform should be of interest to those who study a variety of intracellular microbial pathogens.

Another strength of the manuscript is the demonstration - using cell biology-based approaches- that infection with Shigella blocks DNA replication and protein synthesis. These observations nicely dovetail with the prior findings of other groups. Nevertheless, their clever click-chemistry-based approaches provide visual evidence of these phenomena and should interest many.

Weaknesses:

There are two main weaknesses of this work. First, the studies are limited to findings obtained using a single immortalized cell line. It is appreciated that HeLa cells serve as an excellent model for studying aspects of Shigella pathogenesis and host responses. However, it would be nice to see that similar observations are observed with an epithelial cell line of intestinal, preferably colonic origin, and eventually, with a non-immortalized cell line, although it is appreciated that the latter studies are beyond the scope of this work.

The other weakness is that the studies are minimally mechanistic. For example, the investigators have data to suggest that infection with Shigella leads to an arrest in DNA replication and protein synthesis; however, no follow-up studies have been conducted to determine how these host cell processes are disabled. Interestingly, Zhang and colleagues recently identified that the Shigella OspC effectors target eukaryotic translation initiation factor 3 to block host cell translation (PMID: 38368608).

Reviewer #2 (Public review):

Summary:

This manuscript proposes a workflow for discovering and optimizing RNA aptamers, with application in the optimization of a SARS-CoV-2 RBD. The authors took a previously identified RNA aptamer, computationally docked it into one specific RBD structure, and searched for variants with higher predicted affinity. The variants were subsequently tested for RBD binding using gel retardation assays and competition with antibodies, and one was found to be a stronger binder by about three-fold than the founding aptamer.

Overall, this would be an interesting study if it were performed with truly high-affinity aptamers, and specificity was shown for RBD or several RBD variants.

Strengths:

The computational workflow appears to mostly correctly find stronger binders, though not de novo binders.

Weaknesses:

(1) Antibody competition assays are reported with RBD at 40 µM, aptamer at 5 µM, and a titration of antibody between 0 and 1.2 µg. This approach does not make sense. The antibody concentration should be reported in µM. An estimation of the concentration is 0-8 pmol (from 0-1.2 µg), but that's not a concentration, so it is unknown whether enough antibody molecules were present to saturate all RBD molecules, let alone whether they could have displaced all aptamers.

(2) These are not by any means high-affinity aptamers. The starting sequence has an estimated (not measured, since the titration is incomplete) KD of 110 µM. That's really the same as non-specific binding for an interaction between an RNA and a protein. This makes the title of the manuscript misleading. No high-affinity aptamer is presented in this study. If the docking truly presented a bound conformation of an aptamer to a protein, a sub-micromolar Kd would be expected, based on the number of interactions that they make.

(3) The binding energies estimated from calculations and those obtained from the gel-shift experiments are vastly different, as calculated from the Kd measurements, making them useless for comparison, except for estimating relative affinities.

Reviewer #2 (Public review):

Summary:

The study aimed to assess the associations between meteorological drivers and influenza is important although not new. The authors used only 6 years of surveillance data and deep learning models, combining distributed lag non-linear models (DLNM) with Bayesian-optimized LSTM neural networks for predictive modeling. The key interest in this area is to explore the subtropical locations, where influenza is less common and circulates year-round. The authors further claimed that such an association could be able to provide an early warning in the community. In this direction, the current manuscript has several scopes of improvements and clarification of the claims, as I list here.

Strengths:

Study design based on a prospective cohort to analyse the data for retrospective outcomes.

Weaknesses:

(1) The rationale of the study is not clearly stated.

(2) Several issues with methodological and data integration should be clarified.

(3) Validation of the models is not presented clearly.

(4) The claim for providing tools for 'early warning' was not validated by analysis and results.

Reviewer #2 (Public review):

Summary:

For centuries, humans have been developing methods to see ever smaller objects, such as cells and their contents. This has included studies of viruses and their interactions with host cells during processes extending from virion structure to the complex interactions between viruses and their host cells: virion entry, virus replication and virion assembly, and release of newly constructed virions. Recent developments have enabled simultaneous application of fluorescence-based detection and intracellular localization of molecules of interest in the context of sub-micron resolution imaging of cellular structures by electron microscopy.

The submission by Nahas et al., extends the state-of-the-art for visualization of important aspects of herpesvirus (HSV-1 in this instance) virion morphogenesis, a complex process that involves virus genome replication, and capsid assembly and filling in the nucleus, transport of the nascent nucleocapsid and some associated tegument proteins through the inner and outer nuclear membranes to the cytoplasm, orderly association of several thousand mostly viral proteins with the capsid to form the virion's tegument, envelopment of the tegumented capsid at a virus-tweaked secretory vesicle or at the plasma membrane, and release of mature virions at the plasma membrane.

In this groundbreaking study, cells infected with HSV-1 mutants that express fluorescently tagged versions of capsid (eYFP-VP26) and tegument (gM-mCherry) proteins were visualized with 3D correlative structured illumination microscopy and X-ray tomography. The maturation and egress pathways thus illuminated were studied further in infections with fluorescently tagged viruses lacking one of nine viral proteins.

Strengths:

This outstanding paper meets the journal's definitions of Landmark, Fundamental, Important, Valuable, and Useful. The work is also Exceptional, Compelling, Convincing, and Solid. The work is a tour de force of classical and state-of-the-art molecular and cellular virology. Beautiful images accompanied by appropriate statistical analyses and excellent figures. The numerous complex issues addressed are explained in a clear and coordinated manner; the sum of what was learned is greater than the sum of the parts. Impacts go well beyond cytomegalovirus and the rest of the herpesviruses, to other viruses and cell biology in general.

Comments on the latest version:

This is a very nice paper. The authors responded affirmatively to the suggestions and questions of the reviewers.

Reviewer #2 (Public review):

Summary:

In the study presented by Itani and colleagues it is shown that some strains of Aspergillus oryzae - especially those used industrially for the production of sake and soy sauce - develop hyphae with a significantly increased number of nuclei and cell volume over time. These thick hyphae are formed by branching from normal hyphae and grow faster and therefore dominate the colonies. The number of nuclei positively correlates with the thicker hyphae and also the amount of secreted enzymes. The addition of nutrients such as yeast extract or certain amino acids enhanced this effect. Genome and transcriptome analyses identified genes, including rseA, that are associated with the increased number of nuclei and enzyme production. The authors conclude from their data involvement of glycosyltransferases, calcium channels and the tor regulatory cascade in regulation of cell volume and number of nuclei. Thicker hyphae and an increased number of nuclei was also observed in high-production strains of other industrially used fungi such as Trichoderma reesei and Penicillium chrysogenum, leading to the hypothesis that the mentioned phenotypes are characteristic of production strains which is of significant interest for fungal biotechnology.

Strengths:

The study is very comprehensive and involves application of divers state-of-the-art cell biological, biochemical and genetic methods. Overall, the data are properly controlled and analyzed, and the figures and movies are of excellent quality.The results are particularly interesting with regard to the elucidation of molecular mechanisms that regulate the size of fungal hyphae and the number of nuclei. For this, the authors have discovered a very good model: (regular) strains with a low number of nuclei and strains with high number of nuclei. Also, the results can be expected to be of interest for the further optimization of industrially relevant filamentous fungi.

In the revision the authors addressed all my comments and as a result produced an even stronger study.

Reviewer #2 (Public review):

To fuse, differentiated muscle cells must rearrange their cytoskeleton and assemble actin-enriched cytoskeletal structures. These actin foci are proposed to generate mechanical forces necessary to drive close membrane apposition and the fusion pore formation. While the study of these actin-rich structures has been conducted mainly in drosophila and in vertebrate embryonic development, the present manuscript present clear evidence this mechanism is necessary for fusion of adult muscle stem cells in vivo, in mice. The data presented here clearly demonstrate that ARP2/3 and SCAR/WAVE complexes are required for differentiating satellite cells fusion into multinucleated myotubes, during skeletal muscle regeneration.

Reviewer #2 (Public review):

Summary:

The study explores how single striatal projection neurons (SPNs) utilize dendritic nonlinearities to solve complex integration tasks. It introduces a calcium-based synaptic learning rule that incorporates local calcium dynamics and dopaminergic signals, along with metaplasticity to ensure stability for synaptic weights. Results show SPNs can solve the nonlinear feature binding problem and enhance computational efficiency through inhibitory plasticity in dendrites, emphasizing the significant computational potential of individual neurons. In summary, the study provides a more biologically plausible solution to single-neuron learning and gives further mechanical insights into complex computations at the single-neuron level.

Strengths:

The paper introduces a novel learning rule for training a single multicompartmental neuron model to perform nonlinear feature binding tasks (NFBP), highlighting two main strengths: the learning rule is local, calcium-based, and requires only sparse reward signals, making it highly biologically plausible, and it applies to detailed neuron models that effectively preserve dendritic nonlinearities, contrasting with many previous studies that use simplified models.

Reviewer #2 (Public review):

Summary:

In this study, Mondal and co-authors present the development of a computational model of homeostatic plasticity incorporating activity-dependent regulation of gating properties (activation, inactivation) of ion channels. The authors show that, similar to what has been observed for activity-dependent regulation of ion channel conductances, implementing activity-dependent regulation of voltage sensitivity participates in the achievement of a target phenotype (bursting or spiking). The results however suggest that activity-dependent regulation of voltage sensitivity is not sufficient to allow this and needs to be associated with the regulation of ion channel conductances in order to reliably reach target phenotype. Although the implementation of this biologically relevant phenomenon is undeniably relevant, a few important questions are left unanswered.

Strengths:

(1) Implementing activity-dependent regulation of gating properties of ion channels is biologically relevant.

(2) The modeling work appears to be well performed and provides results that are consistent with previous work performed by the same group.

Weaknesses:

(1) The main question not addressed in the paper is the relative efficiency and/or participation of voltage-dependence regulation compared to channel conductance in achieving the expected pattern of activity. Is voltage-dependence participating to 50% or 10%. Although this is a difficult question to answer (and it might even be difficult to provide a number), it is important to determine whether channel conductance regulation remains the main parameter allowing the achievement of a precise pattern of activity (or its recovery after perturbation).

(2) Another related question is whether the speed of recovery is significantly modified by implementing voltage-dependence regulation (it seems to be the case looking at Figure 3). More generally, I believe it would be important to give insights into the overall benefit of implementing voltage-dependence regulation, beyond its rather obvious biological relevance.

(3) Along the same line, the conclusion about how voltage-dependence regulation and channel conductance regulation interact to provide the neuron with the expected activity pattern (summarized and illustrated in Figure 6) is rather qualitative. Consistent with my previous comments, one would expect some quantitative answers to this question, rather than an illustration that approximately places a solution in parameter space.

Reviewer #2 (Public review):

Summary:

This preprint describes a practical and useful approach for labeling and tracking NPCs in situ. While useful applications including timelapse imaging, affinity purification, or proximity labeling are envisioned, addressing some outstanding technical questions would give a clearer picture of the sensitivity and temporal resolution of this approach.

Strengths:

Clever use of a fluorescently conjugated nanobody that binds directly to the core scaffold nucleoporin Nup84 with nanomolar affinity.

Weaknesses:

The decrease in nanobody labeling over 8 hours of chase period is interpreted to indicate that NPCs turn over during this time. However, it is also possible that the nanobody:Nup84 association is disrupted during mitosis by phosphorylation, other PTMs, or structural remodeling.

Reviewer #3 (Public review):

Summary:

In "Alternative Splicing Across the Tree of Life: A Comparative Study," the authors use rich annotation features from nearly 1,500 high-quality NCBI genome assemblies to develop a novel genome-scale metric, the Alternative Splicing Ratio, that quantifies the extent to which coding sequences generate multiple mRNA transcripts via alternative splicing (AS). This standardized metric enables cross-species comparisons and reveals clear phylogenetic patterns: minimal AS in prokaryotes and unicellular eukaryotes, moderate AS in plants, and high AS in mammals and birds. The study finds a strong negative correlation between AS and coding content, with genomes containing approximately 50% intergenic DNA exhibiting the highest AS activity. By integrating diverse lines of prior evidence, the study offers a cohesive evolutionary framework for understanding how alternative splicing varies and evolves across the tree of life.

Strengths:

By studying alternative splicing patterns across the tree of life, the authors systematically address an important yet historically understudied driver of functional diversity, complexity, and evolutionary innovation. This manuscript makes a valuable contribution by leveraging standardized, publicly available genome annotations to perform a global survey of transcriptional diversity, revealing lineage-specific patterns and evolutionary correlates. The authors have done an admirable job in this revised version, thoroughly addressing prior reviewer comments. The updated manuscript includes more rigorous statistical analyses, careful consideration of potential methodological biases, expanded discussion of regulatory mechanisms, and acknowledgment of non-adaptive alternatives. Overall, the work presents an intriguing view of how alternative splicing may serve as a flexible evolutionary strategy, particularly in lineages with limited capacity for coding expansion (e.g., via gene duplication). Notably, the identification of genome size and genic coding fraction thresholds (~20 Mb and ~50%, respectively) as tipping points for increased splicing activity adds conceptual depth and potential generalizability.

Weaknesses:

While the manuscript offers a broad comparative view of alternative splicing, its central message becomes diffuse in the revised version. The focus of the study is unclear, and the manuscript comes across as largely descriptive without a well-articulated hypothesis or explanatory evolutionary model. Although the discussion gestures toward adaptive and non-adaptive mechanisms, these interpretations are not developed early or prominently enough to anchor the reader. The negative correlation between alternative splicing and coding content is compelling, but the biological significance of this pattern remains ambiguous: it is unclear whether it reflects functional constraint, genome organization, or annotation bias. This uncertainty weakens the manuscript's broader evolutionary inferences.

Sections of the Introduction, particularly lines 72-90, lack cohesion and logical flow, shifting abruptly between topics without a clear structure. A more effective approach may involve separating discussions of coding and non-coding sequence evolution to clarify their distinct contributions to splicing complexity. Furthermore, some interpretive claims lack nuance. For example, the assertion that splicing in plants "evolved independently" seems overstated given the available evidence, and the citation regarding slower evolution of highly expressed genes overlooks counterexamples from the immunity and reproductive gene literature.

Presentation of the results is occasionally vague. For instance, stating "we conducted comparisons of mean values" (line 146) without specifying the metric undercuts interpretability. The authors should clarify whether these comparisons refer to the Alternative Splicing Ratio or another measure. Additionally, the lack of correlation between splicing and coding region fraction in prokaryotes may reflect a statistical power issue, particularly given their limited number of annotated isoforms, rather than a biological absence of pattern.

Finally, the assessment of annotation-related bias warrants greater methodological clarity. The authors note that annotations with stronger experimental support yield higher splicing estimates, yet the normalization strategy for variation in transcriptomic sampling (e.g., tissue breadth vs sequencing depth) is insufficiently described. As these factors can significantly influence splicing estimates, a more rigorous treatment is essential. While the authors rightly acknowledge that splicing represents only one layer of regulatory complexity, the manuscript would benefit from a more integrated consideration of additional dimensions, such as 3D genome architecture, e.g., the potential role of topologically associating domains in constraining splicing variation.

Reviewer #2 (Public review):

Summary:

The manuscript describes the results of an evolution experiment where Staphylococcus aureus was experimentally evolved via sequential exposure to an antibiotic followed by passaging through C. elegans hosts. Because infecting C. elegans via ingestion results in lysis of gut cells and an immune response upon infection, the S. aureus were exposed separately across generations to antibiotic stress and host immune stress. Interestingly, the dual selection pressure of antibiotic exposure and adaptation to a nematode host resulted in increased virulence of S. aureus towards C. elegans.

Strengths:

The data presented provide strong evidence that in S. aureus, traits involved in adaptation to a novel host and those involved in antibiotic resistance evolution are not traded off. On the contrary, they seem to be correlated, with strains adapted to antibiotics having higher virulence towards the novel host. As increased virulence is also associated with higher rates of haemolysis, these virulence increases are likely to reflect virulence levels in vertebrate hosts.

Weaknesses:

Right now, the results are presented in the context of human infections being treated with antibiotics, which, in my opinion, is inappropriate. This is because<br /> (1) exposure to the host and antibiotics was sequential, not simultaneous, and thus does not reflect the treatment of infection, and<br /> (2) because the site of infection is different in C. elegans and human hosts.

Nevertheless, the results are of interest; I just think the interpretation and framing should be adjusted.

Reviewer #2 (Public review):

Summary:

This study attempts to dissect the contributions of type I and type III IFNs to the antiviral response in chickens. The first part of the study characterises the generation of IFNAR and IFNLR KO chicken strains and describes basic differences. Four different viruses are then tested in chicken embryos, while the subsequent analysis of the antiviral response in vivo is performed with one influenza H3N1 strain.

Strengths:

Having these two KO chicken strains as a tool is a great achievement. The initial analysis is solid. Clear effect of IFNAR deficiency in in vivo infection, less so for IFNLR deficiency.

Weaknesses:

(1) The antibody induction by KLH immunisation: No data indicated whether or not this vaccination induces IFN responses in wt mice, so the effects observed may be due to steady-state differences or to differential effects of IFN induced during the vaccination phase. No pre-immune results are shown. The differences are relatively small and often found at only one plasma dilution - the whole of Figure 4 could be condensed into one or two panels by proper calculation of Ab titers - would these titres be significantly different? This, as all of the other in vivo experiments, has not been repeated, if I understand the methods section correctly.

(2) The basic conundrum here and in later figures is never addressed by the authors: Situations where IFN type 1 and 3 signalling deficiency each have an independent effect (i.e., Figure 4d) suggest that they act by separate, unrelated mechanisms. However, all the literature about these IFN families suggests that they show almost identical signalling and gene induction downstream of their respective receptors. How can the same signalling, clearly active here downstream of the receptors for IFN type 1 or type 3, be non-redundant, i.e., why does the unaffected IFN family not stand in? This is a major difference from the mouse studies, which showed a rather subtle phenotype when only one of the two IFN systems was missing, but a massive reduction in virus control in double KO mice (the correct primary paper should be quoted here, not only the review by McNab). Reasons could be a direct effect of IFNab on B cells and an indirect effect of IFNL through non-B cells, timing issues, and many other scenarios can be envisaged. The authors do not address this question, which limits the depth of analysis.

(3) In the one in vivo experiment performed with chickens, only one virus was tested; more influenza strains should be included, as well as non-influenza viruses.

(4) The basic conundrum of point 2 applies equally to Figure 6a; both KOs have a phenotype. Again in 6d, both IFNs appear to be separately required for Mx induction. An explanation is needed.

(5) Line 308, where are the viral titers you refer to in the text? The statement that the results demonstrate that excessive IFNab has a negative impact is overstretched, as no IFN measurements of the infected embryos are shown here.

(6) The in vivo infection is the most interesting experiment, and the key outcome here is that IFN type 1 is crucial for anti-H3N1 protection in chickens, while type 3 is less impactful. However, this experiment suffers from the different time points when chickens were culled, so many parameters are impossible to compare (e.g., weight loss, histopathology, IFN measurements, and more). Many of these phenomena are highly dynamic in acute virus infections, so disparate time points do not allow a meaningful comparison between different genotypes. What are the stats in 7b? Is the median rather than the mean indicated by the line? Otherwise, the lines appear in surprising places. SD must be shown, and I find it difficult to believe that there is a significant difference in weight, for e.g., IFNAR KO, unless maybe with a paired t test. What is the statistical test?

(7) Figures 7e,f: these comparisons are very difficult to interpret as the virus loads at these time points already differ significantly, so any difference could be secondary to virus load differences.

Reviewer #2 (Public review):

While the importance of asparagine in the differentiation and activation of CD8 T cells has been previously reported, its role in CD4 T cells remained unclear. Using culture media containing specific amino acids, the authors demonstrated that extracellular asparagine promotes CD4 T cell proliferation. Consistent with this, depletion of extracellular asparagine using PEG-AsnASE suppressed CD4 T cell activation. Proteomic analysis focusing on asparagine content revealed that, during the early phase of T cell activation, most asparagine incorporated into proteins is derived from extracellular sources. The authors further confirmed the importance of extracellular asparagine in vivo, demonstrating improved EAE pathology.

While the data are well organized and convincing, the mechanism by which asparagine deficiency leads to altered T cell differentiation remains unclear. It is also necessary to investigate the transporters involved in asparagine uptake. In particular, elucidating whether different T cell subsets utilize the same or distinct transport mechanisms would provide important insight into the immunoregulatory role of asparagine.

(1) The finding that asparagine supplementation promotes T cell proliferation under various amino acid conditions is highly significant. However, the concentration at which this effect occurs remains unclear. A titration analysis would be necessary to determine the dose-dependency of asparagine.

(2) The effects of asparagine deficiency occur during the early phase of T cell activation. Thus, it is likely that the transporters responsible for asparagine uptake are either rapidly induced upon activation or already expressed in the resting state. Since this is central to the focus of the manuscript, it is interesting to identify the transporter responsible for asparagine uptake during early T cell activation. A recent paper (DOI: 10.1126/sciadv.ads350) reported that macrophages utilize Slc6a14 to use extracellular asparagine. Is this also true for CD4+ T cells?

(3) Given that depletion of extracellular asparagine impairs differentiation of Th1 and Th17 cells, it is possible that TCR signaling is compromised under these conditions. This point should be investigated by targeting downstream signaling molecules such as Lck, ZAP70, or mTOR. Also, does it affect the protein stability of master transcription factors such as T-bet and RORgt?

(4) Is extracellular asparagine also important for the differentiation of helper T cell subsets other than Th1 and Th17, such as Th2, Th9, and iTreg?

(5) Asparagine taken up from outside the cell has been shown to be used for de novo protein synthesis (Figure 3E), but are there any proteins that are particularly susceptible to asparagine deficiency? This can be verified by performing proteome analysis, and the effects on Th1/17 subset differentiation mentioned above should also be examined.

(6) While the importance of extracellular asparagine is emphasized, Asns expression is markedly induced during early T cell activation. Nevertheless, the majority of asparagine incorporated into proteins appears to be derived from extracellular sources. Does genetic deletion of Asns have any impact on early CD4+ T cell activation? The authors indicated that newly synthesized Asns have little impact on CD8+ T cells in the Discussion section, but is this also true for CD4+ T cells? This could be verified through experiments using CRISPR-mediated Asns gene targeting or pharmacological inhibition.

Reviewer #2 (Public review):

Summary:

In this study, Xu et al. present a transcriptome-wide, single-base resolution map of RNA pseudouridine modifications across evolutionarily diverse bacterial species using an adapted form of BID-Seq. By optimizing the method for bacterial RNA, the authors successfully mapped modifications in rRNA, tRNA, and, importantly, mRNA across both exponential and stationary growth phases. They uncover evolutionarily conserved Ψ motifs, dynamic Ψ regulation tied to bacterial growth state, and propose functional links between pseudouridylation and bacterial transcript stability, translation, and RNA-protein interactions. To extend these findings, they develop a deep learning model that predicts pseudouridine sites from local sequence and structural features.

Strengths:

The authors provide a valuable resource: a comprehensive Ψ atlas for bacterial systems, spanning hundreds of mRNAs and multiple species. The work addresses a gap in the field - our limited understanding of bacterial epitranscriptomics, by establishing both the method and datasets for exploring post-transcriptional modifications.

Weaknesses:

The main limitation of the study is that most functional claims (i.e., translation efficiency, mRNA stability, and RNA-binding protein interactions) are based on correlative evidence. While suggestive, these inferences would be significantly strengthened by targeted perturbation of specific Ψ synthases or direct biochemical validation of proposed RNA-protein interactions (e.g., with Hfq). Additionally, the GNN prediction model is a notable advance, but methodological details are insufficient to reproduce or assess its robustness.

Reviewer #2 (Public review):

Summary:

In this manuscript, Thompson et al. investigate the impact of prior ATP exposure on later macrophage functions as a mechanism of immune training. They describe that ATP training enhances bactericidal functions, which they connect to the P2x7 ATP receptor, Nlrp3 inflammasome activation, and TWIK2 K+ movement at the cell surface and subsequently at phagosomes during bacterial engulfment. With stronger methodology, these findings could provide useful insight into how ATP can modulate macrophage immune responses, though they are generally an incremental addition to existing literature. The evidence supporting their conclusions is currently inadequate. Gaps in explaining methodology are substantial enough to undermine trust in much of the data presented. Some assays may not be designed rigorously enough for interpretation.

Strengths:

The authors demonstrate two novel findings that have sufficient rigor to assess:

(1) prolonged persistence of TWIK2 at the macrophage plasma membrane following ATP, and can translocate to the phagosome during particle engulfment, which builds upon their prior report of ATP-driven 'training' of macrophages.

(2) administering mice intra-nasal ATP to 'train' lungs to protect mice from otherwise fatal bacterial infection.

Weaknesses:

(1) Missing details from methods/reported data: Substantial sections of key methods have not been disclosed (including anything about animal infection models, RNA-sequencing, and western blotting), and the statistical methods, as written, only address two-way comparisons, which would mean analysis was improperly performed. In addition, there is a general lack of transparency - the methods state that only representative data is included in the manuscript, and individual data points are not shown for assays.

(2) Poor experimental design including missing controls: Particularly problematic are the Seahorse assay data (requires normalization to cell numbers to interpret this bulk assay - differences in cell growth/loss between conditions would confound data interpretation) and bacterial killing assays (as written, this method would be heavily biased by bacterial initial binding/phagocytosis which would confound assessment of killing). Controls need to be included for subcellular fractionating to confirm pure fractions and for dye microscopy to show a negative background. Conclusions from these assays may be incorrect, and in some cases, the whole experiment may be uninterpretable.

(3) The conclusions overstate what was tested in the experiments: Conceptually, there are multiple places where the authors draw conclusions or frame arguments in ways that do not match the experiments used. Particularly:<br /> a) The authors discuss their findings in the context of importance for AM biology during respiratory infection but in vitro work uses cells that are well-established to be poor mimics of resident AMs (BMDM, RAW), particularly in terms of glycolytic metabolism.<br /> b) In vivo work does not address whether immune cell recruitment is triggered during training.<br /> c) Figure 3 is used to draw conclusions about K+ in response to bacterial engulfment, but actually assesses fungal zymosan particles.<br /> d) Figure 5 is framed in bacterial susceptibility post-viral infection, but the model used is bacterial post-bacterial.<br /> e) In their discussion, the authors propose to have shown TWIK2-mediated inflammasome activation. They link these separately to ATP, but their studies do not test if loss of TWIK2 prevents inflammasome activation in response to ATP (Figure 4E does not use TWIK2 KO).

In summary, this work contains some useful data showing how ATP can 'train' macrophages. However, it largely lacks the expected level of rigor. For this work to be valuable to the field, it is likely to need substantial improvement in methods reporting, inclusion of missing assay controls, may require repeating key experiments that were run with insufficient methodology (or providing details and supplemental data to prove that methodology was sufficient), and should either add additional experiments that properly test their experimental question or rewrite their conclusions.

Reviewer #2 (Public review):

Summary:

The paper by Chao et al offers a reimplantation of the SpliceAI algorithm in PyTorch so that the model can more easily/efficiently be retrained. They apply their new implementation of the SpliceAI algorithm, which they call OpenSpliceAI, to several species and compare it against the original model, showing that the results are very similar and that in some small species pre-training on other species helps improve performance.

Strengths:

On the upside, the code runs fine and it is well documented.

Weaknesses:

The paper itself does not offer much beyond reimplementing SpliceAI. There is no new algorithm, new analysis, new data, or new insights into RNA splicing. There is not even any comparison to many of the alternative methods that have since been published to surpass SpliceAI. Given that some of the authors are well known with a long history of important contributions, our expectations were admittedly different. Still, we hope some readers will find the new implementation useful.

Update for the revised version:

The update includes mostly clarifications for tech questions/comments raised by the other two reviewers. There is no additional analysis/results that changes our above initial assessment of this paper's contribution.

Reviewer #2 (Public review):

Chalamalasetty et al. investigate the regulatory circuit of signaling molecules and transcription factors that drive the fate of neuromesodermal competent progenitors (NMCs). NMCs contribute to Sox2-positive spinal cord and Tbxt/Bra-expressing somitic mesoderm, and this choice is governed by the interplay between Wnt3a and Fgf signaling. The authors discovered that the transcription factors SP5 and SP8 participate in this process. Mouse genetics, in vivo development, and transcription factors profiling point to a model where SP5 and SP8 directly regulate Wnt3a expression to foster Tbxt-marked mesoderm formation at the expense of Sox2-marked neural ectoderm. Mechanistically, SP5/8 bind to an enhancer which the authors characterize: its activity depends on the presence of SP5, CDX2, TCF7, and TBXT binding sites, and it is activated only in primitive streak cells at E7.5, in NMP, and in caudal and somitic mesoderm, underscoring the tissue and stage-specific nature of this Wnt3a enhancer.

Moreover, the authors find that SP5/8 likely regulate the TCF7 association with the chromatin and compete for its binding to the TLE repressor.

The study is extensive, compelling, and well written. The combination of in vivo evidence with single-cell transcriptomics, transcription factors profiling, and in vitro regulatory element characterization is notable and builds a convincing picture of the action of SP5/SP8.

Here, I provide a series of comments and questions that, if addressed and clarified, could, in my opinion, improve the study.

(1) While Sp5 and Sp8 are both present in NMCs, their expression does not fully overlap. Sp5 is also detected in caudal and presomitic mesoderm, notochord and gut, while Sp8 overlaps with Sox2 in neural progenitors of the spinal cord and brain (Fig. 1D). Accordingly, Sp8 expression is also activated by the neural-promoting RA+Fgf. It is not easy for me to reconcile this non-fully overlapping expression pattern - and in particular the overlap of Sp8 and Sox2 - with the presumed redundancy (or similarity of function) described later. Sp5/8 dko NMCs show reduced Tbxt and expanded Sox2, indicating that SP8 also represses Sox2 or neural fate, an observation confirmed by Sp8 overexpression (Figure 4c). What is the explanation for this, and is the function of SP8 in Sox2-positive neural progenitors different from its Wnt3a-sustaining role in NMCs? Or what am I missing?

(2) I suggest that the authors show relevant ChIP-seq peaks in Figure 3 to lend credibility to the complicated overlapping Venn diagrams. I consider visual inspection of peak tracks as primary quality control of this type of experiment. A good choice could be the cis-regulatory elements at Sp5, Sp8, Tbxt, Cdx1, 2, 4 bound by TBXT and either CDX2, SP5, or SP8 (now referring to the Venn diagrams and the annotated peak table). On ChIP-seq visualization, in reference to Figures 5 and 7, I also suggest that the authors show the tracks of a negative control (IgG, non-related antibody, or better anti-flag in Sp5/8 dko). While I do not doubt the validity of these experiments, there are peaks in these figures bound by all factors tested that could be suspicious (even though, admittedly, they look like genuinely good TF peaks). A negative track would clearly show beyond any doubt that these are not suspect regions of positive unspecific signal caused by open chromatin, excessive cross-linking, or antibody cross-reaction.

(3) SP5 here is found as a direct inducer of Wnt3a expression, and accordingly positive regulator of Tbxt and mesoderm, caudal development. I find this in partial contradiction with a finding by the Willert group (PMID: 29044119). They show that "genes with an associated SP5 peak, such as SP5 itself, AXIN2, AMOTL2, GPR37, GSC, MIXL1, NODAL, and T, show significant upregulation in expression upon Wnt3a treatment in SP5 mutant cells". There, essentially, SP5 inhibits Wnt target genes. While the authors are aware of this and cite Huggins et al., I find that this deserves a better discussion addressing how opposite functions could be sustained in different contexts, if these really are different cellular contexts in the first place, or if this could result from different methodologies.

(4) The gastruloid experiment is nice, but I wonder whether there is any marker that the authors can use to show that other features of the gastruloids respond accordingly. For example, is the Sox2 expression domain expanded? And is there any unaffected marker to emphasize the specificity of the decreased Tbxt and Cdx2?

(5) SP5/8 seems to enhance the TCF7 occupancy at WRE. And then, SP5/8 appears to counteract the presence of TLE repressor associated with TCF7. While these two mechanisms are interesting, they are not necessarily interconnected. According to the still-established view, TCF7 should be associated with WRE even in the absence of the Wnt signal, when TLEs are also present on the locus. One could expect that SP5 competes with TLE, to decrease its presence on TCF7-bound loci, leaving the abundance of TCF7 binding unchanged. Yet, the authors also observe that the TCF7 association changes. What is the mechanism implied? Do they perhaps consider a TCF7L1 > TCF7 switch, and if so, what evidence exists for this?

(6) Along the same line as above, I wonder whether beta-catenin binding is also enhanced at these sites? Any TCF/LEF would require beta-catenin for gene upregulation.

(7) The authors write that "Small Tle peaks were identified at these WREs in WT cells, demonstrating that both repressive Tle and activating Tcf7 could be detected at active genes". However, ChIP-seq is a population assay, and it is possible - more plausible, in fact - that cells displaying TLE binding are not expressing the target genes.

Reviewer #2 (Public review):

In this study, Xiong et al. investigate whether rhythmic sampling - a process typically observed in the attended processing of visual stimuli - extends to task-irrelevant distractors. By using EEG with frequency tagging and multivariate pattern analysis (MVPA), they aimed to characterize the temporal dynamics of both target and distractor processing and examine whether these processes oscillate in time. The central hypothesis is that target and distractor processing occur rhythmically, and the phase relationship between these rhythms correlates with behavioral performance.

Major Strengths<br /> (1) The extension of rhythmic attentional sampling to include distractors is a novel and interesting question.<br /> (2) The decoding of emotional distractor content using MVPA from SSVEP signals is an elegant solution to the problem of assessing distractor engagement in the absence of direct behavioral measures.<br /> (3) The finding that relative phase (between 1 Hz target and distractor processes) predicts behavioral performance is compelling.

Major Weaknesses and Limitations<br /> (1) The central claim of 1 Hz rhythmic sampling is insufficiently validated. The windowing procedure (0.5s windows with 0.25s step) inherently restricts frequency resolution, potentially biasing toward low-frequency components like 1 Hz. Testing different window durations or providing controls would significantly strengthen this claim.<br /> (2) The study lacks a baseline or control condition without distractors. This makes it difficult to determine whether the distractor-related decoding signals or the 1 Hz effect reflect genuine distractor processing or more general task dynamics.<br /> (3) The pairwise decoding accuracies for distractor categories hover close to chance (~55%), raising concerns about robustness. While statistically above chance, the small effect sizes need careful interpretation, particularly when linked to behavior.<br /> (4) Neither target nor distractor signal strength (SSVEP amplitude) correlates with behavioral accuracy. The study instead relies heavily on relative phase, which-while interesting-may benefit from additional converging evidence.<br /> (5) Phase analysis is performed between different types of signals hindering their interpretability (time-resolved SSVEP amplitude and time-resolved decoding accuracy).

The authors largely achieved their stated goal of assessing rhythmic sampling of distractors. However, the conclusions drawn - particularly regarding the presence of 1 Hz rhythmicity - rest on analytical choices that should be scrutinized further. While the observed phase-performance relationship is interesting and potentially impactful, the lack of stronger and convergent evidence on the frequency component itself reduces confidence in the broader conclusions.

If validated, the findings will advance our understanding of attentional dynamics and competition in complex visual environments. Demonstrating that ignored distractors can be rhythmically sampled at similar frequencies to targets has implications for models of attention and cognitive control. However, the methodological limitations currently constrain the paper's impact.

Additional Considerations<br /> • The use of EEG-fMRI is mentioned but not leveraged. If BOLD data were collected, even exploratory fMRI analyses (e.g., distractor modulation in visual cortex) could provide valuable converging evidence.<br /> • In turn, removal of fMRI artifacts might introduce biases or alter the data. For instance, the authors might consider investigating potential fMRI artifact harmonics around 1 Hz to address concerns regarding induced spectral components.

Comments on revisions:

The authors have addressed my previous points, and the manuscript is substantially improved. The key methodological clarifications have been incorporated, and the interpretation of findings has been appropriately moderated. I have no further major concerns.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors describe the production of a high-resolution connectome for the statocyst of a ctenophore nervous system. This study is of particular interest because of the apparent independent evolution of the ctenophore nervous system. The statocyst is a component of the aboral organ, which is used by ctenophores to sense gravity and regulate the activity of the organ's balancer cilia. The EM reconstruction of the aboral organ was carried out on a five-day-old larva of the model ctenophore Mnemiopsis leidyi. To place their connectome data in a functional context, the authors used high-speed imaging of ciliary beating in immobilized larvae. With these data, the authors were able to model the circuitry used for gravity sensing in a ctenophore larva.

Strengths:

Because of it apparently being the sister phylum to all other metazoans, Ctenophora is a particularly important group for studies of metazoan evolution. Thus, this work has much to tell us about how animals evolved. Added to that is the apparent independent evolution of the ctenophore nervous system. This study provides the first high-resolution connectomic analysis of a portion of a ctenophore nervous system, extending previous studies of the ctenophore nervous system carried out by Sid Tamm. As such, it establishes the methodology for high-resolution analysis of the ctenophore nervous system. While the generation of a connectome is in and of itself an important accomplishment, the coupling of the connectome data with analysis of the beating frequency of balancer cell cilia provides a functional context for understanding how the organization of the neural circuitry in the aboral organ carries out gravity sensing. In addition, the authors identified a new type of syncytial neuron in Mnemiopsis. Interestingly, the authors show that the neural circuitry controlling cilia beating in Mnemiopsis shares features with the circuitry that controls ciliary movement in the annelid Platynereis, suggesting convergent evolution of this circuitry in the two organisms. The data in this paper are of high quality, and the analyses have been thoroughly and carefully done.

Weaknesses:

The paper has no obvious weaknesses.

Reviewer #2 (Public review):

Summary:

This work presents a spiking network model of traveling waves at the whole-brain scale in the mouse neocortex. The authors use data from the Allen Institute to reconstruct connectivity between different neocortical sites. They then quantify macroscopic traveling waves following stimulation of all layer 4 neurons in the neocortex.

Strengths:

Overall, the results are interesting and shed new light on the dynamic organization of activity across the neocortex of the mouse. The paper uses realistic neuron models specifically fit to intracellular recordings, demonstrating that traveling waves occur in the mouse neocortex with both realistic connectivity and realistic single-neuron dynamics. The paper is also well-written in general. For these reasons, the authors have generally achieved their aims in this work.

Weaknesses:

(1) Description of Algorithm 1:<br /> While the Methods section clearly explains the density parameter \rho, the statement on line 358 concerning the "ideal" average number of connections is a little unclear. The authors should explicitly clarify that \rho is a free parameter that can be adjusted to balance computational feasibility (for a given set of computational resources) and biological fidelity.

(2) Lines 102-103:<br /> The \rho parameter used here results in approximately 300 connections per neuron on average. The authors should state clearly that the number of connections per cell is the key determinant of computational feasibility (cf. Morrison et al., Neural Computation, 2005). The authors should also review neuronal density and synaptic connectivity in the mouse neocortex and clearly reference density and connectivity in their model to the biological scales found in the mouse.

(3) Line 131:<br /> From the plots in Figure 2, it is not clear that the stimulus response is necessarily a rhythmic oscillation, in the sense of a single narrowband frequency.

(4) Line 217:<br /> The authors should clarify how these findings relate to the results from Mohajerani et al. (Nature Neuroscience, 2013) or differ from them.

(5) Line 230:<br /> Because higher temporal frequency activity also tends to be more spatially localized, a correlation between PGD and temporal frequency could be an inherent consequence of this relationship, rather than a meaningful result.

(6) Line 247-248:<br /> It is not clear that the algorithm for generating connections between neurons presented here really relates to those for community detection. For example, in the case of the Allen Institute data, the communities are essentially in the data already.

(7) Line 284-285:<br /> The relationship between conduction delay is more direct than this sentence suggests. Conduction delay is fundamentally determined by the time required for action potentials to propagate along axons, making it intrinsically linked to anatomical distance.

(8) Line 287-288:<br /> The authors suggest at this point that they do not have enough information to estimate time delays due to axonal conduction along white matter fibers. However, experimental data from white matter connections typically includes information about fiber length, which does enable estimating conduction delays. These estimations have been previously implemented for Allen Institute connectome data in the mouse (Choi and Mihalas, PLoS Comput Biology, 2019) and human connectome data (Budzinski et al., Physical Review Research, 2023).

(9) Lines 294-295:<br /> Several methods do exist for detecting and characterizing wave dynamics in three-dimensional data (Budzinski et al., Physical Review Research, 2023).

Reviewer #2 (Public review):

Summary:

Castanheira et al. investigate the role of spatial attention for planning during three maze navigation experiments (one new experiment and two existing datasets). Effective planning in complex situations requires the construction of simplified representations of the task at hand. The authors find that these mental representations (as assessed by conscious awareness) of a given stimulus are influenced by (spatially) surrounding stimuli. Individual participants varied in the degree to which attention influenced their task representations, and this attentional effect correlated with the sparsity of representations (as measured by the range of awareness reports across all stimuli). Spatially grouping task-relevant information on either the left or right side of the maze led to mental representations more similar to optimal representations predicted by the value-guided construal (VGC) model - a normative model describing a theoretical approach to simplifying complex task information. Finally, the authors propose an update to this model, incorporating an attentional spotlight component; the revised descriptive model predicts empirical task representations better than the original (normative) VGC model.

Strengths:

The novelty of this study lies in the proposal and investigation of a cognitive mechanism through which a normative model like value-guided construal can enable human planning. After proposing attention as this mechanism, the authors make concrete hypotheses about mismatches between the VGC predictions and real human behavior, which are experimentally validated. Thus, not only does this study describe a possible mechanism for simplification of task information for planning, but the authors also propose a descriptive model, revising VGC to incorporate this attentional component.

A strength of this paper is the variety of investigative approaches: analysis of existing data, novel experiment, and a computational approach to predict experimental findings from a theoretical model. Analyzing pre-existing datasets increases the size of the participant cohort and strengthens the authors' conclusions. Meanwhile, comparing the predictions of the existing normative model and the authors' own refined model is a clever approach to substantiate their claims. In addition, the authors describe several crucial controls, which are key to the interpretability of their results. In particular, the eye tracking results were critical.

In summary, this paper constitutes an important step toward a more complete understanding of the human ability to plan.

Weaknesses:

(1) There is a critical conceptual gap in the study and its interpretation, mainly due to the reliance on a self-report metric of awareness (rather than an objective measure of behavioral performance).

a. Awareness is tested by a 9-point self-report scale. It is currently unclear why awareness of task-irrelevant obstacles in this task would necessarily compromise optimal planning. There is no indication of whether self-reported awareness affects performance (e.g., navigation path distance, time to complete the maze, number of errors). Such behavioral evidence of planning would be more compelling.

b. Relatedly, it would have been more convincing to have an objective measure of awareness, for instance, how the presence or absence of a "task-irrelevant" obstacle affects performance (e.g., change navigation path distance or time to complete the maze), or whether participants can accurately recall the location of obstacles.

c. Consequently, I'm not sure that we can conclude that the spatial context does impact participants' ability to plan spatial navigation or to "incorporate task-relevant information into their construal". We know that the spatial context affects subjective (self-reported) awareness, but the authors do not present evidence that spatial context affects behavioral performance.

d. Another concern that may complicate interpretation is the following: Figure 3c shows improved VGC model predictions (steeper slope) for mazes with greater lateralization. However, there are notable outliers in these plots, where a high lateralization index does not correspond to good model performance. There is currently no discussion/explanation of these cases.

(2) I noticed an issue with clarity regarding task-relevance. It is currently not fully clear which obstacles are "task irrelevant". Also, the term is used inconsistently, sometimes conflating with "awareness". For example, in the "Attentional spotlight model of task representations" section, the authors state that "task-relevant information becomes less relevant when surrounded by task-irrelevant information". But they really mean that participants become less aware of those task-relevant obstacles. I assume task-relevance is an objective characteristic related to maze organization, not to a participant's construal. Indeed, the following paragraph provides evidence of model predictions of awareness.

(3) The behavioral paradigm has some distinct disadvantages, and the validity of the task is not backed up by behavioral data.

a. I understand the need for central fixation, but it also makes the task less naturalistic.

b. The task with its top-down grid view does not seem to mimic real human navigation. Though this grid may be similar to mental maps we form for navigation, the sensory stimuli corresponding to possible paths and to spatial context during real-life navigation are very different.

c. Behavioral performance is not reported, so it is unknown whether participants are able to properly complete the task. The task seems pretty difficult to navigate, especially when the obstacles disappear, and in combination with the central fixation.

d. There is no discussion of whether/how this navigation task generalizes to other forms of planning.

Reviewer #2 (Public review):

Summary:

This study presents a sophisticated molecular dissection of ribosome-associated complexes (RCs) in two well-defined cortical projection neuron subtypes (ScPN and CPN) during early postnatal development. The authors develop and optimize an rRNA immunoprecipitation-mass spectrometry (rRNA IP-MS) workflow to recover RCs from FACS-purified, retrogradely labeled neurons, achieving remarkable subtype specificity and biochemical resolution. Through proteomic profiling, they reveal both shared and distinct ribosome-associated proteins between ScPN and CPN, with a focus on non-core RC components and their potential functional relevance. The work advances our understanding of cell-type-specific translation regulation, moving beyond the transcriptome to explore the proteome-level complexity in neuronal subtypes.

Strengths:

This work stands out for its technical sophistication and innovation. The authors combine retrograde labeling, FACS purification, and an optimized rRNA IP-MS approach (low input) to isolate ribosome-associated complexes from highly specific neuronal subtypes in vivo, a challenging issue that they execute with impressive rigor. The methodological pipeline is both elegant and well-controlled, yielding high-quality, reproducible data. The depth of proteomic coverage is remarkable, with nearly all known cytoplasmic ribosomal proteins identified, along with hundreds of ribosome-associated proteins (RAPs), including translation factors, chaperones, and RNA-binding proteins. The analysis not only reveals shared components between ScPN and CPN RCs but also uncovers subtype-specific differences in associated proteins.

Particularly notable is the integration of this new proteomic dataset with previously published transcriptomic and ribosome footprinting data, which helps to validate the specificity and relevance of the findings. Overall, the clarity of the writing, the robustness of the data, and the transparency of the methods make this a strong and compelling contribution.

Weaknesses:

Despite the depth and high quality of the dataset, the study remains descriptive. While the identification of subtype-specific RC components is intriguing, the current version of the manuscript does not explore their functional roles or the biological consequences of their alterations. There is no perturbation, causal testing, in vitro or in vivo manipulation to demonstrate whether these proteins are necessary for ScPN or CPN identity, specific axonal targeting, metabolism, or synaptic function.

One important point highlighted by the authors in the discussion - and critical for establishing the subtype specificity of the identified proteins - is that some ribosomal complexes may be specialized for specific developmental stages, rather than exclusively for the subtype-specific needs of projection neuron development. The work presented here provides a valuable starting point for further investigation into such RC specialization. However, it will be essential to determine to what extent these RCs exhibit true subtype specificity, independently of their temporal maturation context.

As a result, key mechanistic insights remain a bit speculative. Although several of the identified proteins have known roles in processes like synaptogenesis or metabolism, their relevance to the specific neuronal subtypes under study is not experimentally addressed. That said, given its rich content and the comprehensive early postnatal dataset, the manuscript represents an extremely valuable resource for the community. While primarily exploratory, it lays a strong foundation for future functional studies aimed at uncovering the biological impact of the identified ribosomal complexes.

Reviewer #2 (Public review):

This manuscript describes a detailed model for bats flying together through a fixed geometry. The model considers elements which are faithful to both bat biosonar production and reception and the acoustics governing how sound moves in air and interacts with obstacles. The model also incorporates behavioral patterns observed in bats, like one-dimensional feature following and temporal integration of cognitive maps. From a simulation study of the model and comparison of the results with the literature, the authors gain insight into how often bats may experience destructive interference of their acoustic signals and those of their peers, and how much such interference may actually negatively effect the groups' ability to navigate effectively. The authors use generalized linear models to test the significance of the effects they observe.

The work relies on a thoughtful and detailed model which faithfully incorporates salient features, such as acoustic elements like the filter for a biological receiver and temporal aggregation as a kind of memory in the system. At the same time, the authors abstract features that are complicating without being expected to give additional insights, as can be seen in the choice of a two-dimensional rather than three-dimensional system. I thought that the level of abstraction in the model was perfect, enough to demonstrate their results without needless details. The results are compelling and interesting, and the authors do a great job discussing them in the context of the biological literature.

With respect to the first version of the manuscript, the authors have remedied all my outstanding questions or concerns in the current version. The new supplementary figure 5 is especially helpful in understanding the geometry.

Reviewer #2 (Public review):

Summary:

This manuscript documents the structure of the pharyngeal nervous system of the Drosophila larva. The authors wanted to achieve a detailed ultrastructural reconstruction of the gustatory sensory organs in the Drosophila pharynx. Using serial EM and the associated bioinformatics tools, they have achieved their goal. The paper is written clearly and illustrated beautifully with 3D models and annotated sections. The data will significantly enrich the field of Drosophila neurobiology.

Strengths:

Given the dataset, the findings presented are solid and will be an important work of reference for the future.

Weaknesses:

Previous work, including EM, on the pharyngeal sensory organ is not sufficiently referenced and used for comparison with the data presented in this study.

Reviewer #2 (Public review):

Summary:

The authors combined behavioral experiments, computational modeling, and functional magnetic resonance imaging (fMRI) to investigate the psychological and neural mechanisms underlying guilt, shame, and the altruistic behaviors driven by these emotions. The results revealed that guilt is more strongly associated with harm, whereas shame is more closely linked to responsibility. Compared to shame, guilt elicited a higher level of altruistic behavior. Computational modeling demonstrated how individuals integrate information about harm and responsibility. The fMRI findings identified a set of brain regions involved in representing harm and responsibility, transforming responsibility into feelings of shame, converting guilt and shame into altruistic actions, and mediating the effect of trait guilt on compensatory behavior.

Strengths:

This study offers a significant contribution to the literature on social emotions by moving beyond prior research that typically focused on isolated aspects of guilt and shame. The study presents a comprehensive examination of these emotions, encompassing their cognitive antecedents, affective experiences, behavioral consequences, trait-level characteristics, and neural correlates. The authors have introduced a novel experimental task that enables such a systematic investigation and holds strong potential for future research applications. The computational modeling procedures were implemented in accordance with current field standards. The findings are rich and offer meaningful theoretical insights. The manuscript is well written, and the results are clearly and logically presented.

Weaknesses:

In this study, participants' feelings of guilt and shame were assessed retrospectively, after they had completed all altruistic decision-making tasks. This reliance on memory-based self-reports may introduce recall bias, potentially compromising the accuracy of the emotion measurements.

In many behavioral economic models, self-interest plays a central role in shaping individual decision-making, including moral decisions. However, the model comparison results in this study suggest that models without a self-interest component (such as Model 1.3) outperform those that incorporate it (such as Model 1.1 and Model 1.2). The authors have not provided a satisfactory explanation for this counterintuitive finding.

The phrases "individuals integrate harm and responsibility in the form of a quotient" and "harm and responsibility are integrated in the form of a quotient" appear in the Abstract and Discussion sections. However, based on the results of the computational modeling, it is more accurate to state that "harm and the number of wrongdoers are integrated in the form of a quotient." The current phrasing misleadingly suggests that participants represent information as harm divided by responsibility, which does not align with the modeling results. This potentially confusing expression should be revised for clarity and accuracy.

In the Discussion, the authors state: "Since no brain region associated with social cognition showed significant responses to harm or responsibility, it appears that the human brain encodes a unified measure integrating harm and responsibility (i.e., the quotient) rather than processing them as separate entities when both are relevant to subsequent emotional experience and decision-making." However, this interpretation overstates the implications of the null fMRI findings. The absence of significant activation in response to harm or responsibility does not necessarily imply that the brain does not represent these dimensions separately. Null results can arise from various factors, including limitations in the sensitivity of fMRI. It is possible that more fine-grained techniques, such as intracranial electrophysiological recordings, could reveal distinct neural representations of harm and responsibility. The interpretation of these null findings should be made with greater caution.

Reviewer #2 (Public review):

Summary:

In this manuscript, Zhang and Speer examine changes in the spatial organization of synaptic proteins during eye specific segregation, a developmental period when axons from the two eyes initially mingle and gradually segregate into eye-specific regions of the dorsal lateral geniculate. The authors use STORM microscopy and immunostain presynaptic (VGluT2, Bassoon) and postsynaptic (Homer) proteins to identify synaptic release sites. Activity-dependent changes of this spatial organization are identified by comparing the β2KO mice to WT mice. They describe two types of synapses based on Bassoon clustering: the multiple active zone (mAZ) synapse and single active zone (sAZ) synapse. In this revision, the authors have added EM data to support the idea that mAZ synapses represent boutons with multiple release sites. They have also reanalyzed their data set with different statistical approaches.

Strengths:

The data presented is of good quality and provides an unprecedented view at high resolution of the presynaptic components of the retinogeniculate synapse during active developmental remodeling. This approach offers an advance to the previous mouse EM studies of this synapse because of the CTB label allows identification of the eye from which the presynaptic terminal arises.

Weaknesses:

While the interpretation of this data set is much more grounded in this second revised submission, some of the authors' conclusions/statements still lack convincing supporting evidence. In particular, the data does not support the title: "Eye-specific active zone clustering underlies synaptic competition in the developing visual system". The data show that there are fewer synapses made for both contra- and ipsi- inputs in the β2KO mice-- this fact alone can account for the differences in clustering. There is no evidence linking clustering to synaptic competition. Moreover, the findings of differences in AZ# or distance between AZs that the authors report are quite small and it is not clear whether they are functionally meaningful.

Reviewer #2 (Public review):

Summary:

In this work, Ganesh and colleagues use experimental data from Hi-C and from live-cell imaging to evaluate different polymer models of 3D genome organization in Drosophila based on both structural and dynamic properties. The authors consider several leading hypotheses, which are examined sequentially in increasing level of complexity - from the minimal Rouse polymer, to a model combining sequence-specific compartmentalization and loop-extrusion without extrusion blockers. They conclude that the combination of both compartmentalization and loop-extrusion gives the best agreement with the data. Their analysis also leads to concrete predictions about the processivity of cohesin loop extrusion in Drosophila, and a conclusion that the compartmental interaction strength is poised near criticality in the coil-globule phase space.

Strengths:

There is considerable interest in the field in understanding the mechanisms responsible for the 3D spatial organization genome and the dynamic movement of the genome, which has major implications for our understanding of long-range transcriptional regulation and other genome behaviors. The live-cell experimental work on which this study draws highlights the limitations of existing models to explain even the dynamic behaviors observed in the data, further exciting interest in further exploration. Therefore, this paper seeks to address an important gap in the field. The work is written in a well-organized, well-illustrated fashion. The text and figures are nicely integrated, easy to read, and explain challenging concepts with elegance and brevity in a manner that will be accessible to a broad audience.

Weaknesses:

The validity and utility of these conclusions are, in my view, substantially undermined by what appears to be unappreciated peculiarities of the live-cell data set that was used to constrain the model. The live-cell data comes from embryos were edited in a way that intentionally substantively changed both the 3D genome structure and dynamics specifically at the loci which are imaged, a case which is not at all explained by any of the models suggested nor acknowledged in the current work, nor compatible with the Hi-C data that simultaneously used to explain these models. As these ignored synthetic alterations have been previously shown to be determinative of transcriptional activity, the relevance of the author's work to transcriptional control (a prime motivation in the introduction) is unclear.

The agreement in 3D organization, as represented in chromosome-scale contact frequency heatmaps, is substantially less impressive than the agreement seen in prior work with similar models. This discrepancy appears to be due in part to the unappreciated effects of the mentioned in the previous limitation, as well as inappropriate choices in metrics used to evaluate agreement. It is also not particularly surprising that combining more models, with more free parameters, results in an improvement in the quality of fit.

Some major results, including both theoretical works and experimental ones, are ignored, despite their relevance to the stated objective of the work. The current manuscript and analysis could be improved substantially by a consideration of these works.

I describe these issues in more detail below.

Major issues:

(1) The genetic element "homie" is present in a subset of the data: The experimental data used in this analysis come from different fly lines, half of which have been edited explicitly to alter genome structure and consequent transcriptional behavior, yet the authors are trying to fit with a common model - a problem which substantially undermines the utility of the analysis.

Specifically, the authors evaluate the various models/simulations by comparing them to Hi-C from wildtype Drosophila embryos on the chromosome scale and 3D distances and dynamics from live cell imaging in genetically edited embryos, to a series of models in turn. The exercise fatally overlooks a critical fact, (admittedly not easily noticed in the work from Bruckner et al), that the fly embryos used for nearly all their analyses contain not only fluorescent labels, but also contain two copies of a powerful genetic sequence, "homie", known for its ability to dramatically change the 3D organization and dynamics of the genome. Whether or not the fluorescent labels themselves used in the study further alter structure and dynamics is not entirely clear (and will require further work beyond the scope of either study), but at least these fluorescent labels aren't known to dramatically affect 3D structure and dynamics the way homie is. The critical problem is that adding or removing the "homie", as shown in a collection of prior works I describe below in more detail, dramatically affects structure, dynamics, and gene expression. Whether or not the genome contains two distal cis-linked copies of homie fundamentally changes genome structure and dynamics, so to use one dataset which has this edit (the live-cell data) and one dataset which lacks it (the Hi-C data) is, in some sense, to guarantee failure of any model to match all the data.

If the authors had chosen instead to focus exclusively on the 'no homie' genetic lines in the Brukner data, they would have a much smaller dataset (just 2 distances), which would not cover all the length scales of interest, but it would at least be a dataset not known to be contradictory to the Hi-C. The two 'no homie' lines make much more plausible candidates for the sort of generalizable polymer dynamics these authors seek to explain, as will hopefully be made more clear by a brief review of what is known about homie. I next describe the published data that support these conclusions about how homie affects 3D genome spatial organization and dynamics:

What is "homie" and how does it affect 3D genome distances, dynamics, and gene expression?

The genetic element "homie" was named by James Jaynes' lab ( Fujioka...Jaynes 2009) in reference to its remarkable "homing" ability - a fascinating and still poorly understood biological observation that some genetic sequences from Drosophila, when cloned on plasmids and reintegrated into the genome with p-elements, had a remarkable propensity to re-integrate near their endogenous sequence, (Hama et al., 1990; Kassis, 2002; Taillebourg and Dura, 1999; Bender and Hudson, 2000; Fujioka...Jaynes 2009). By contrast, most genetic elements tend to incorporate at random across the genome in such assays (with some bias for active chromatin).

The Jaynes lab subsequently showed that flies carrying two copies of homie, one integrated in cis, ~140 kb distal from the endogenous element, formed preferential cis contacts with one another. Indeed, if a promoter and reporter gene were included at this distal integration site, the reporter gene would activate gene expression in the pattern normally seen by the gene, even-skipped. The endogenous copy of homie marks one border of ~16 kb mini-TAD which contains the even-skipped gene, (eve), and its developmental enhancers, so this functional interaction provides further evidence of physical proximity (as was also shown by 3C by Jaynes (Fujioka..., Schedl, Jaynes 2016), and later with elegant live imaging, by Jaynes and Gregor (Chen 2018)).

Critically, if either copy of homie is deleted or substantially mutated, the 3D proximity is lost (Fujioka 2016, Chen 2018, Bruckner 2023), and the expression of the transgene is dramatically reduced (at 58 kb) or lost. Given the author's motivation of understanding "E-P" interactions, the fact that the increased 3D proximity provided by homie is as essential for transcription as the promoter itself at the ~150 kb distance, underscores that these are not negligible changes.

These effects can be seen by plotting the data from Bruckner 2023, which includes data from labels with separations of 58 kb and ~150 kb "no homie" as well as homie. Unfortunately, the authors don't plot this data in the manuscript in the comparison of 3D distances, though the two-point MSD can be seen in Figure S13C, and laudably, the data is made public in a well-annotated repository on Zenodo, noted in the study. Note that the distance data in Figure S13 were filtered to exclude the transcriptionally off state, and are thus not the quantity the current authors are interested in. If they plot the published data for no homie, they will see the clear effect on the average 3D distance, R(s), and a somewhat stronger effect on the contact frequency P(s), which causes significant deviation from the trend-line followed by the homie-containing data.

(2) The agreement between the "best performing" simulations for all models and the Hi-C data is not on par with prior studies using similar approaches, apparently due to some erroneous choices in how the optimization is carried out:

Hi-C-comparison

The 'best fit' simulation Hi-C looks strikingly different from the biological data in all comparisons, with clearly lower agreement than other authors have shown using highly similar methods (e.g., Shi and Thirumalai 2023; Di Pierro et al. 2017; Nuebler et al. 2018; Esposito et al. 2022; Conte et al. 2022), among many others. I believe this results from a few issues with how the current authors select and evaluate the data in their work:

(a) Most works have used Pearson's correlation rather than Spearman's correlation when comparing simulation and Hi-C contact frequencies. Pearson's correlation is more appropriate when we expect the values to be linearly related, which they should be in this case, as they are constructed indeed to be measuring the same thing (contact frequency), just derived from two different methods. Spearman's correlation would have been justifiable for comparing how transcription output correlates with contact frequency. This may fix the bafflingly low correlations reported at lower adhesion values in Figure S2C.

(b) Choice of adhesion strengths - The Hi-C map comparison in Figure 3 strongly suggests that a much more striking visual agreement would have been achieved if much weaker (but still non-zero) homotypic monomer affinity had been selected. In the authors' simulation, the monomer state (A/B identity) strongly dominates polymer position, resulting in the visual appearance of an almost black-and-white checkerboard. The data, meanwhile, look like a weak checkerboard superimposed on the polymer.

(c) A further confounding problem is the aforementioned issue that the Hi-C data don't come from the edited cell lines, and that the interaction of the two Homie sites is vastly stronger than the compartment interactions of this region of the genome.

(3) Some important concepts from the field are ignored:

The crumpled/fractal globule model is widely discussed in the literature (including the work containing the data used in this study) - its exclusion from this analysis thus appears as a substantial gap/oversight:

A natural alternative to the much-discussed Rouse polymer model is the "crumpled polymer" (Grosberg et al. 1988; Grosberg 2016; Halverson et al. 2011; Halverson et al. 2011), also known as the "fractal globule" (Lieberman-Aiden et al. 2009; Mirny 2011; Dekker and Mirny 2016; Boettiger et al. 2016), much discussed for the way it captures the ⅓ scaling of R(s), found for much of the genome (or, equivalently, the -1 exponent of the probability of contact as a function of genome separation, P(s)). Given the 1/3rd scaling in the data, and the fact that the original authors highlighted the crumpled model in addition to the Rouse model, it seems that this comparison would be instructive and the lack of discussion an oversight. Moreover, while prior works (e.g., Buckner, Gregor, 2023) used some traditional simplifying assumptions to estimate the MSD and relaxation time scaling of this model, I believe a more rigorous analysis with explicit simulations (as in Figure 1 for the Rouse model) would be instructive for the crumpled polymer simulations. Note the crumpled globule is not necessarily the same as the globule in the coil-globule transition discussed here - it requires some assumptions about non-entanglement to stay trapped in the meta-stable state which has the 1/3rd R(s) scaling that is indicative of this model, and not the 1/2 exhibited by equilibrium globules (for s<< length of the polymer) and dilute polymers alike.

While the fit in Figure 2 appears to get closer to the 1/3rd exponent (B= 0.32), this appears to be a largely coincidental allusion of agreement - the simulation data in truth shows a systematic deviation, returning to the 1/2 scaling for distances from 500 kb to whole chromosomes. This feature is not very evident as the authors restrict the analysis to only the few points available in the experimental data, though had they tested intervening distances I expect they would show log-log P(s) is nonlinear (non-powerlaw) for distances less than the typical loop length up to a few fold larger than the loop length, and thereafter returns to the scaling provided by the 'base' polymer behavior. This appears to be Rouse-like in these authors' model, with R(s) going like 1/2, even though the data are closer to 1/3rd, as indeed most published simulated P(s) curves based on loop extrusion - e.g., (Fudenberg et al. 2016; Nuebler et al. 2018). In this vein, it would be instructive to the readers if the authors would include additional predictions from the simulation on the plot that lie at genomic separation distances not tested in the data, to better appreciate the predictions.

Minor issues

(1) I think it is too misleading to only describe the experimental data from Brukner as "E-P" interactions from Drosophila. It is important to note somewhere that this is not an endogenous interaction with a functional role in Drosophila - it is a synthetic interaction between enhancers in the vicinity of the eve gene and a synthetic promoter placed at a variable distance away. The uniformity is elegant - (it is the same pair of elements being studied at all distances), but also provides limited scope for generalization as suggested by the current text. Moreover, the enhancers were not directly labeled; rather, the 3D position of nascent RNA transcribed from eve was tracked with an RNA-binding protein and used as a proxy for the 3D position of the enhancers. There is not an individual enhancer at the eve locus that interacts with the transgene, but rather a collection of enhancers is distributed at different positions throughout the entire TAD, which contains eve, and must form separate loops to reach eve. Indeed, it was previously reported that differences in the local position of these enhancers, relative to eve, affect their ability to interact with the distal reporter gene and the endogenous eve gene (Chen 2018). There is also reported competition between these enhancers and the distal gene, which further complicates the analysis (especially since the state of eve and of its enhancers varies among the different cells as a function of stripe position) - see Chen 2018. All of this is ignored in the current work, despite the assertion of the application to understanding E-P interaction. A detailed discussion of these issues is not necessary, but I fear that ignoring them entirely is to invite further confusion and error.

(2) I believe this sentence is overstated, given available data: " TAD borders are characterized by transitions between epigenetic states rather than by preferentially-bound CTCF [4, 23, 24]." Indeed, this claim has been repeatedly made in the literature as cited here. However, other data clearly demonstrate a strong enrichment of CTCF at TAD borders (and at epigenetic borders, which in Drosophila have a high correspondence with TAD borders, as the authors have already appropriately noted). See, for example, Figure 4 of Sexton Cell 2012, and compare to Figure 2 of Dixon 2012. Of minor note, CTCF peaks co-occupied by the Zinc Finger TF CP190 are more likely to be TAD borders than CTCF alone. How big a species-specific difference this is remains unclear, as it appears some mammalian CTCF-marked TAD boundaries may be co-occupied by additional ZNFs. While plenty of Drosophila TAD boundaries indeed lack CTCF, many are marked by CTCF, this is enriched relative to what would be expected by chance (or relative to the alignment of other TFs, like Twist or Eve with TAD boundaries), and it has been shown that CTCF loss is sufficient to remove a subset of these, see for example Figure 5 of (Kaushal et al. 2021) (though it is possible, most will require mutation of the all the border-associated factors that collectively bind many of the borders, dCTCF, CP190, mod(mdg4) and others).

(3) This assertion is overstated given available data: "Although TAD boundaries in Drosophila are often associated with insulator proteins [20], there is no direct evidence that these elements block LEFs in vivo. Therefore, we did not impose boundary constraints in our simulations; LEFs were allowed to move freely unless stalled by collisions with other LEFs, with the possibility of crossover.". Deletion of insulator in Drosophila that lie within a common epigenetic state leads to fusion of TADs (e.g., Mateo et al., 2019 - deletion of the CTCF-marked Fub insulator, in posterior tissues where both flanks of Fub are active; Kaushal, 2021, has examples as well). Loss of CTCF causes a small number of TADs to fuse as measured by Hi-C. This is far from 'direct evidence that insulators block LEFs' - as the authors have already noted, even the idea that cohesin extrudes loops in Drosophila in the first place is indeed controversial. However, LEF activity and stalling at insulators would provide a very natural explanation of why chromatin in a shared epigenetic state should form distinct TADs, and why these TADs should fuse upon insulator deletion. Justifying the lack of stalling sites based on empirical data is thus not very convincing to this reviewer. I believe it would be more apt to simply describe this as a simplifying assumption, rather than the above phrase, which may be misleading.

Reviewer #2 (Public review):

The findings in the current manuscript are interesting and valuable contributions to the fields of vascular biology and extracellular vesicle-related mechanisms. They suggest a potential role for smooth muscle cell-derived extracellular vesicles in presenting Type VI collagen to cells to orchestrate their migration, with proposed relevance to aberrant smooth muscle cell movements in the progression of atherosclerotic lesions. A wide range of assays are utilized to test various aspects of this working model, with the resulting data being largely solid and supporting several of the interpretations articulated by the authors. The revised manuscript has adequately addressed key weaknesses.

The authors present data suggesting a working model in which vascular smooth muscle cells (vSMCs) are stimulated by fibronectin (FN) to generate small extracellular vesicles (sEVs) that harbor Type VI Collagen (collagen VI). These collagen VI-associated sEVs are suggested to accumulate in the extracellular matrix (ECM) and influence cell migration and adhesion dynamics, potentially contributing to disease progression in atherosclerosis. Majors strengths of this manuscript include robust imaging data and the inclusion of human-derived samples in their analysis. The authors also make a reasonable attempt to provide data to support the potential existence of these mechanistic connections, though some minor questions remain regarding data interpretation. The authors largely achieved their aims of finding evidence consistent with their interpretations, and they have presented logical support for their conclusions while acknowledging important limitations and caveats to their current study. This work will likely have a sustained impact on the field of sEV biology and potential intersections with vascular biology, including their methodology e.g., imaging approaches. As biologists continue to explore the role of sEVs in physiological and pathological processes, this work raises an interesting aspect that must be considered more broadly, and that is, what is the role of sEVs that are ECM-associated and not necessarily internalized by recipient cells? Are there discrete mechanisms that govern their role in maintaining and/or disrupting normal physiological processes? This manuscript makes an attempt to address these unresolved yet critical questions.

Reviewer #2 (Public review):

In this paper, Dercon and colleagues report on affective changes related to components of reinforcement learning and on the effects of brief training in psychological distancing and participants' self-reported antidepressant use. About 1,000 participants were assessed online, with half randomized to a brief training in psychological distancing with reminders to distance during the subsequent reinforcement learning (RL) task. Participants completed a battery of psychiatric questionnaires and answered questions about medication use, with about 14% of participants reporting current antidepressant use. All participants completed the RL task and rated their happiness, confidence, engagement, and (at the end of each block of trials) fatigue throughout the task. Computational models were used to estimate trial-by-trial values of expected value and prediction error and to assess the effects of these values on self-reported affect. Participants' affect ratings decreased over time, and participants with higher psychiatric symptoms (particularly anxiety/depressive symptoms) showed lower baseline affect and greater decreases in affect. Participants randomized to the distancing intervention and who reported antidepressant use differed in their affective ratings: distancing reduced the reductions in happiness over time, while antidepressant use was related to higher baseline happiness. Distancing also reduced the effects of trial-level expected value on happiness, while antidepressant use was related to a more enduring effect of trial-level values on happiness.

Overall, this is an interesting paper with strong methods and an interesting approach. That psychiatric symptoms and cognitive distancing are related to affective ratings is not terribly novel; the relationship with antidepressant use is a bit more novel. The extension of the mood model to an RL task is a new contribution, as is the relationship of these effects with psychologically related manipulations.

One major concern is the inference that can be drawn from the two "treatments": one is a brief instruction in a component of psychotherapy, and one is ongoing use of medication. The former is not a treatment in and of itself, but a (presumably) active ingredient of one. How to interpret antidepressant use as measured is unclear, e.g., are the residual symptoms in these participants an early indicator of treatment resistance? Are these participants with better access to health care? Are they receiving antidepressants for a mental health issue?

There are some clarifications needed in the affect model as well.

Reviewer #2 (Public review):

Summary:

Burdge, Juhmka et al describe the development and validation of a new automated system for applying plantar stimuli in rodent somatosensory behavior tasks. This platform allows the users to run behavior experiments remotely, removing experimenter effects on animals and reducing variability in manual application of stimuli. The system integrates well with other automated analysis programs that the lab has developed, providing a complete package for standardizing behavior data collection and analysis. The authors present extensive validations of the system against manual stimulus application. Proof of concept studies also show how the system can be used to better understand the effect of experimenters on behavior and the effects of how stimuli are presented on the micro features of the animal withdrawal response.

Strengths:

If widely adopted, ARM has the potential to reduce variability in plantar behavior studies across and within labs and provide a means to standardize results. It provides a way to circumvent the confounds that humans bring into performing sensitive plantar behavior tests (e.g. experimenter odors, experince, physical abilities, variation in stimulus application, sex). Furthermore, it can be integrated with other automated platforms, allowing for quicker analysis and potentially automated stimulus delivery. The manuscript also presents some compelling evidence on the effects of stimulus application time and height on withdrawals, which can potentially help labs that are manually applying stimuli standardize applications. The system is well validated and the results are clear and convincingly presented. Claims are well supported by experimental evidence.

Weaknesses:

ARM seems like a fantastic system that could be widely adopted, a primary weakness is that it is not currently available to other labs. This will eventually be remedied as it is commercialised.

Reviewer #2 (Public review):

In this study, the authors aim to forecast the evolution of viral proteins by simulating sequence changes under a constraint of folding stability. The central idea is that proteins must retain a certain level of structural stability (quantified by folding free energy, ΔG) to remain functional, and that this constraint can shape and restrict the space of viable evolutionary trajectories. The authors integrate a birth-death population model with a structurally constrained substitution (SCS) model and apply this simulation framework to several viral proteins from HIV-1, SARS-CoV-2, and influenza.

The motivation to incorporate biophysical constraints into evolutionary models is scientifically sound, and the general approach aligns with a growing interest in bridging molecular evolution and structural biology. The authors focus on proteins where immune pressure is limited and stability is likely to be a dominant constraint, which is conceptually appropriate. The method generates sequence variants that preserve folding stability, suggesting that stability-based filtering may capture certain evolutionary patterns.

However, the study does not substantiate its central claim of forecasting. The model does not predict future sequences with measurable accuracy, nor does it reproduce observed evolutionary paths. Validation is limited to endpoint comparisons in a few datasets. While KL divergence is used to compare amino acid distributions, this analysis is only applied to a single protein (HIV-1 MA), and there is no assessment of mutation-level predictive accuracy or quantification of how well simulated sequences recapitulate real evolutionary paths. No comparison is made to real intermediate variants available from extensive viral sequencing datasets which gather thousands of sequences with detailed collection date annotation (SARS-CoV-2, Influenza, RSV).

The selection of proteins is narrow and the rationale for including or excluding specific proteins is not clearly justified.

The analyzed datasets are also under-characterized: we are not given insight into how variable the sequences are or how surprising the simulated sequences might be relative to natural diversity. Furthermore, the use of consensus sequences to represent timepoints is problematic, particularly in the context of viral evolution, where divergent subclades often coexist - a consensus sequence may not accurately reflect the underlying population structure.

The fitness function used in the main simulations is based on absolute ΔG and rewards increased stability without testing whether real evolutionary trajectories tend to maintain, increase, or reduce folding stability over time for the particular systems (proteins) that are studied. While a variant of the model does attempt to center selection around empirical ΔG values, this more biologically plausible version is underutilized and not well validated.

Ultimately, the model constrains sequence evolution to stability-compatible trajectories but does not forecast which of these trajectories are likely to occur. It is better understood as a filter of biophysically plausible outcomes than as a predictive tool. The distinction between constraint-based plausibility and sequence-level forecasting should be made clearer. Despite these limitations, the work may be of interest to researchers developing simulation frameworks or exploring the role of protein stability in viral evolution, and it raises interesting questions about how biophysical constraints shape sequence space over time.

Reviewer #2 (Public review):

Context and significance:

Distal renal tubular acidosis (dRTA) can be caused by mutations in a Cl-/HCO3- exchanger (kAE1) encoded by the SLC4A1 gene. The precise mechanisms underlying the pathogenesis of the disease due to these mutations are unclear, but it is thought that loss of the renal intercalated cells (ICs) that express kAE1 and/or aberrant autophagy pathway function in the remaining ICs may contribute to the disease. Understanding how mutations in SLC4A1 affect cell physiology and cells within the kidney, a major goal of this study, is an important first step to unraveling the pathophysiology of this complex heritable kidney disease.

Summary:

The authors identify a number of new mutations in the SLC4A1 gene in patients with diagnosed dRTA that they use for heterologous experiments in vitro. They also use a dRTA mouse model with a different SLC4A1 mutation for experiments in mouse kidneys. Contrary to previous work that speculated dRTA was caused mainly by trafficking defects of kAE1, the authors observe that their new mutants (with the exception of Y413H, which they only use in Figure 1) traffic and localize at least partly to the basolateral membrane of polarized heterologous mIMCD3 cells, an immortalized murine collecting duct cell line. They go on to show that the remaining mutants induce abnormalities in the expression of autophagy markers and increased numbers of autophagosomes, along with an alkalinized intracellular pH. They also reported that cells expressing the mutated kAE1 had increased mitochondrial content coupled with lower rates of ATP synthesis. The authors also observed a partial rescue of the effects of kAE1 variants through artificially acidifying the intracellular pH. Taken together, this suggests a mechanism for dRTA independent of impaired kAE1 trafficking and dependent on intracellular pH changes that future studies should explore.

Strengths:

The authors corroborate their findings in cell culture with a well-characterized dRTA KI mouse and provide convincing quantification of their images from the in vitro and mouse experiments.

Weaknesses:

The data largely support the claims as stated, with some minor suggestions for improving the clarity of the work. Some of the mutants induce different strengths of effects on autophagy and the various assays than others, and it is not clear why this is from the present manuscript, given that they propose pHi and the unifying mechanism.

Reviewer #2 (Public review):

Summary:

Endo et al. investigate the novel role of ubiquitin response upon lysosomal damage in activating cellular signaling for cell survival. The authors provide a comprehensive transcriptome and proteome analysis of aging-related cells experiencing lysosomal damage, identifying transcription factors involved in transcriptome and proteome remodeling with a focus on the NF-κB signaling pathway. They further characterized the K63-ubiquitin-TAB-TAK1-NF-κB signaling axis in controlling gene expression, inflammatory responses, and apoptotic processes.

Strengths:

In the aging-related model, the authors provide a comprehensive transcriptome and characterize the K63-ubiquitin-TAB-TAK1-NF-κB signaling axis. Through compelling experiments and advanced tools, they elucidate its critical role in controlling gene expression, inflammatory responses, and apoptotic processes.

Weaknesses:

The study lacks deeper connections with previous research, particularly:

• The established role of TAB-TAK1 in AMPK activation during lysosomal damage

• The potential significance of TBK1 in NF-κB signaling pathways

Comments on revisions:

The authors have successfully addressed all the raised questions and the manuscript is now significantly improved.

Reviewer #2 (Public review):

Summary:

Bushey et al. investigate the feasibility of using RubyACRs, specifically A1ACR1 and HfACR1 (described previously in (Govorunova et al., 2020)) as red-shifted inhibitory opsins in Drosophila melanogaster. The study employs a wide range of techniques to demonstrate successful neuronal inhibition. Electrophysiology experiments established that HfACR1 was most effective at hyperpolarizing cells, compared to A1ACR1 and GtACR1; both RubyACRs also appeared to be more effective than GtACR1 when the latter was actuated by green light. The authors further demonstrate successful neuronal inhibition using calcium imaging. RubyACRs were also shown to be useful in in vivo behavioral setups, specifically in spontaneous locomotion, associative learning, and courtship paradigms. In the courtship assay, in particular, the authors test multiple wavelengths of light at various light intensities, thus providing a rigorous analysis of the RubyACRs' efficacy under different light conditions.

Strengths:

The work provides the Drosophila field with a promising new tool. Red-shifted opsins are particularly advantageous in behavioral assays as red light penetrates the cuticle better than green or blue light, and provides less visual stimulation to the fly. It is also ideal for imaging as it allows for simultaneous optogenetic stimulation and GCamp imaging. A particular strength of the paper is the direct demonstration of RubyACR's capacity to inhibit neurons via electrophysiology and calcium imaging. Furthermore, inhibition effects in the three behavioral assays are strong and convincing. Given the apparent efficacy of RubyACRs and the advantages of a red-sensitive anion channelrhodopsin, this tool has great potential.

Weaknesses:

This work convincingly demonstrates the efficacy and potential utility of RubyACRs in Drosophila for imaging and behavior. However, the lethality/toxicity of RubyACRs is a relevant concern that should be addressed in-depth rather than glossed over, as it may pose a major obstacle to use. Discussing this issue in the present study will also help guide potential users and will set the stage for potential future efforts to ameliorate RubyACRs as optogenetic inhibitors.

Major concerns:

(1) Table 1 demonstrates high lethality in the RubyACRs compared to GtACR1. For example, in the MI04979-VGlut driver, GtACR1 expression resulted in 32.9% lethality, while HfACR1 expression resulted in 98.7% lethality. This lethality presents an obstacle to the potential adoption of this tool, and should be discussed in detail, rather than in passing. The authors might like to present "% lethality" rather than "% survived", as the former is more relevant when discussing the relative yield and health of flies that can be used in experiments.

(2) In Figure 3D, driver>opsin flies have lower locomotion during the baseline (i.e., dark) phase, compared to opsin-only controls or GtACR1 flies. For some comparisons, flies are walking around 10-fold slower. For example, in the case of VGlut-GAL4>HfACR1, test flies are walking at <1 mm/s, while "Empty" test flies are walking at ~10 mm/s. This suggests that, for these drivers, neuronal and/or network function is affected. It opens the possibility that the lethality and locomotor defects could be due to cell-autonomous toxicity. We ask the authors to provide a description of this effect in the Results and to discuss it in the Discussion. Relatedly, VGlut-GAL4>GtACR1 flies in red light exhibit a locomotion increase, but this data is not mentioned in the text. The use of differing scales for the Y-axes in these panels can be confusing when the reader is expected to compare velocity across different panels. It would be best if the y-axes were set to a single range, e.g., 0 to 12 mm/s.

(3) Lethality in broad drivers could result from cell-autonomous toxicity or neuronal dysfunction resulting from RubyACR expression. Ideally, the authors would address or even investigate the possible mechanisms of toxicity of the RubyACRs. Do cells and/or synapses expressing RubyACRs have normal morphology and function? For example, the authors could compare cell survival between flies with RubyACR expression and flies with a fluorescent protein with no opsin. The authors may also want to present lethality data for other, less broad drivers (such as MB320C, which was used for the associative memory assay) in order to demonstrate whether this problem is confined to broad drivers such as VGlut-GAL4, or if this is a problem with narrow drivers as well. If new experiments are not possible, these issues should at least be mentioned in the Discussion.

Minor concerns

(1) The specific method used for quantifying lethality is mentioned briefly in Table 1 but is not detailed in the Methods. The authors derive lethality by comparing to a sibling control group with either the opsin or the driver alone, but the opsin alone or driver alone may cause some lethality by themselves. We suggest the use of a viability assay, e.g. (Rockwell et al., 2019), which would give potential users a clearer picture of which developmental stage is most affected by opsin expression, as well as allow opsin-only, driver-only and experimental groups to be assessed separately (lethality would then be reported as the % of embryos that reach each stage of development, and eventually enclosure).

(2) For the calcium imaging analysis in Figure 2, the U-shaped curve observed for mean ΔF/F0 for A1ACR1 and HfACR1 may not be due to actual desensitization for the channels, as the authors suggest (lines 143-145), but may be due simply to a shifting baseline. The authors use the 5-s period preceding stimulation onset as F0, but in some cases (e.g., HfACR1 at 250 uW/mm2), calcium fluorescence rises above baseline and remains high post-stimulation (ΔF/F0 of +0.5, which we observe is the same magnitude as the ΔF/F0 of -0.5 observed during inhibition), thus affecting the ΔF/F0 for subsequent trials. The authors should discuss this incomplete recovery in the text, or (if available) use a static channel instead to provide a stable F0 for calculating ΔF/F0. Alternatively, if the authors wish to rigorously test the hypothesis that high light intensity indeed results in desensitization of these channels, they may consider using different flies for each light intensity or longer inter-stimulus intervals.

(3) For Figure 3C (Flybowl assay), the authors mention that "simply expressing the opsins decreased baseline locomotor activity compared to empty driver lines". However, the "Empty" controls in 3C appear to refer to opsin-only controls, not driver-only controls. The driver-only controls are not presented in the figure. The use of "empty" differs between the text and the figure, as the text refers to "empty" driver lines, while the figure uses "empty" to apparently refer to opsin-only controls. We recommend changing the terminology across all figures to be unambiguous, e.g., by using "opsin-only" or "driver-only" as opposed to the ambiguous "empty". In addition, the fact that opsin-only controls move less than driver-only controls may suggest some toxicity as a result of the opsin-only construct; this should be discussed further.

(4) Figures 4 and 5 lack the reporting of driver-only controls.

(5) Figures 3 and 4 lack positive controls; that is, the benchmarking of the efficacy of RubyACRs in their respective behavioral paradigms against a known inhibitor, e.g., GtACR1 with green light. To confirm that this GtACR1 transgene is functional, the authors could include GtACR1 with green light as a positive control for these two figures, as they have done for Figure 5-supplement 2 and 3.

(6) Several citations are missing. In their discussion, the authors highlight that shorter wavelengths of light are more attenuated by tissue (lines 278-281); this should be accompanied by the relevant citations (Inagaki et al., 2014). Similarly, the claim that behavioral experiments exhibit greater sensitivity to shorter wavelengths should be substantiated (lines 281-283).

References:

Govorunova EG, Sineshchekov OA, Li H, Wang Y, Brown LS, Spudich JL. 2020. RubyACRs, nonalgal anion channelrhodopsins with highly red-shifted absorption. Proc Natl Acad Sci U S A 117:22833-22840.

Inagaki HK, Jung Y, Hoopfer ED, Wong AM, Mishra N, Lin JY, Tsien RY, Anderson DJ. 2014. Optogenetic control of Drosophila using a red-shifted channelrhodopsin reveals experience-dependent influences on courtship. Nat Methods 11:325-332.

Rockwell AL, Beaver I, Hongay CF. 2019. A direct and simple method to assess Drosophila melanogaster's viability from embryo to adult. J Vis Exp e59996.

Reviewer #2 (Public review):

Summary:

In this paper, the authors have characterized Rv2577 as a Fe3+/Zn2+ -dependent metallophosphatase and a nucleomodulin protein. The authors have also identified His348 and Asn359 as critical residues for Fe3+ coordination. The authors show that the proteins encode for two nuclease localization signals. Using C-terminal Flag expression constructs, the authors have shown that the MmpE protein is secretory. The authors have prepared genetic deletion strains and show that MmpE is essential for intracellular survival of M. bovis BCG in THP-1 macrophages, RAW264.7 macrophages, and a mouse model of infection. The authors have also performed RNA-seq analysis to compare the transcriptional profiles of macrophages infected with wild-type and MmpE mutant strains. The relative levels of ~ 175 transcripts were altered in MmpE mutant-infected macrophages and the majority of these were associated with various immune and inflammatory signalling pathways. Using these deletion strains, the authors proposed that MmpE inhibits inflammatory gene expression by binding to the promoter region of a vitamin D receptor. The authors also showed that MmpE arrests phagosome maturation by regulating the expression of several lysosome-associated genes such as TFEB, LAMP1, LAMP2, etc. These findings reveal a sophisticated mechanism by which a bacterial effector protein manipulates gene transcription and promotes intracellular survival.

Strength:

The authors have used a combination of cell biology, microbiology, and transcriptomics to elucidate the mechanisms by which Rv2577 contributes to intracellular survival.

Weakness:

The authors should thoroughly check the mice data and show individual replicate values in bar graphs.

Reviewer #2 (Public review):

Summary:

The authors decomposed response times into component processes and manipulated the duration of these processes in opposing directions by varying contrast, and overall by manipulating speed-accuracy tradeoffs. They identify different processes and their durations by identifying neural states in time and validate their functional significance by showing that their properties vary selectively as expected with the predicted effects of the contrast manipulation. They identify 3 processes: stimulus encoding, attention orienting, and decision. These map onto classical event-related potentials. The decision-making component matched the CPP, and its properties varied with contrast and predicted decision-accuracy, while also exhibiting a burst not characteristic of evidence accumulation.

Strengths:

The design of the experiment is remarkable and offers crucial insights. The analysis techniques are beyond state-of-the-art, and the analyses are well motivated and offer clear insights.

Weaknesses:

It is not clear to me that the results confirm that there are only 3 processes, since e.g., motor preparation and execution were not captured. While the authors discuss this, this is a clear weakness of the approach, as other components may also have been missed. It is also unclear to what extent topographies map onto processes, since, e.g., different combinations of sources can lead to the same scalp topography.

Reviewer #3 (Public review):

Summary:

This manuscript by McDougal et al, demonstrates species-specific activities of diverse IFIT1 orthologs, and seeks to utilize evolutionary analysis to identify key amino acids under positive selection that contribute to antiviral activity of this host factor. While the authors identify amino acid residues important for antiviral activity of some orthologs, and propose a possible mechanism by which these residues may function, the significance or applicability of these findings to other orthologs is unclear. However, the subject matter is of interest to the field, and these findings contribute to the body of knowledge regarding IFIT1 evolution.

Strengths:

Assessment of multiple IFIT1 orthologs shows the wide variety of antiviral activity of IFIT1, and identification of residues outside of the known RNA binding pocket in the protein suggests additional novel mechanisms which may regulate IFIT1 activity.

Weaknesses:

Given that there appears to be very little overlap observed in orthologs that inhibited the viruses tested, it's possible that other amino acids may be key drivers of antiviral activity in these other orthologs. Thus, it's difficult to conclude whether the findings that residues 362/4/6 are important for IFIT1 activity can be broadly applied to other orthologs, or whether these are unique to human and chimpanzee IFIT1. While additional molecular studies of the impact of these mutations on IFIT1 function (e.g. impact on IFIT complex formation) would lend further insight, as it stands, these findings demonstrate a role for these residues in IFIT1 activity.

Reviewer #2 (Public review):

Summary:

The authors present a software package "aTrack" for identification of motion types and parameter estimation in single-particle tracking data. The software is based on maximum likelihood estimation of the time-series data given an assumed motion model and likelihood ratio tests for model selection. They characterized the performance of the software mostly on simulated data and showed that it is applicable to experimental data.

Strengths:

Although many tools exist in the single-particle tracking (SPT) field, this particular software package is developed using an innovative mathematical model and a probabilistic approach. It also provide inference of motion types, which are critical to answer biological questions in SPT experiments.

(1) The authors adopt a novel mathematical framework, which is unique in the SPT field.

(2) The authors have validated their method extensively using simulated tracks and compared to existing methods when appropriate.

(3) The code is freely available

Weaknesses:

The authors did a good job during the revision to address most of the weaknesses in my (as well as other reviewer's) first round of review. Nevertheless, the following issue is still not fully addressed.<br /> The hypothesis testing method presented here lacks rigorous statistical foundation. The authors improved on this point after the revision, but in their newly added SI section "Statistical Test", only justified their choices using "hand-waving" arguments (i.e. there is not a single reference to proper statistical textbooks or earlier works in this important section). I understand that sometimes mathematical rigor comes later after some intuition-guided choices of critical parameters seems to work, but nevertheless need to point it out as a remaining weakness.

Reviewer #2 (Public review):

Summary:

This manuscript presents the development and characterization of iGABASnFR2, a genetically encoded GABA sensor with markedly improved performance over its predecessor, iGABASnFR1. The study is comprehensive and methodologically rigorous, integrating high-throughput mutagenesis, functional screening, structural analysis, biophysical characterization, and in vivo validation. iGABASnFR2 represents a significant advancement in GABA sensor engineering and application in imaging GABA transmission in slice and in vivo. This is a timely and technically strong contribution to the molecular toolkit for neuroscience.

Strengths:

The authors apply a well-established sensor optimization pipeline and iterative engineering strategy from single-site to combinatorial mutants to engineer iGABASnFR2. The development of both positive and negative going variants (iGABASnFR2 and iGABASnFR2n) offers experimental flexibility. The structure and interpretation of the key mutations provide insights into the working mechanism of the sensor, which also suggest optimization strategies. Although individual improvements in intrinsic properties are incremental, their combined effect yields clear functional gains, enabling detection of direction-selective GABA release in the retina and volume-transmitted GABA signaling in somatosensory cortex, which were challenging or missed using iGABASnFR1.

Weaknesses:

With minor revisions and clarifications, especially regarding membrane trafficking, this manuscript will be a valuable resource for probing inhibitory transmission.

Reviewer #2 (Public review):

Summary:

This study investigated how listeners adapt to and utilize statistical properties of different acoustic spaces to improve speech perception. The researchers used repetitive TMS to perturb neural activity in DLPFC, inhibiting statistical learning compared to sham conditions. The authors also identified the most effective room types for the effective use of reverberations in speech in noise perception, with regular human-built environments bringing greater benefits than modified rooms with lower or higher reverberation times.

Strengths:

The introduction and discussion sections of the paper are very interesting and highlight the importance of the current study, particularly with regard to the use of ecologically valid stimuli in investigating statistical learning. However, they could be condensed into parts. TMS parameters and task conditions were well-considered and clearly explained.

Weaknesses

(1) The Results section is difficult to follow and includes a lot of detail, which could be removed. As such, it presents as confusing and speculative at times.

(2) The hypotheses for the study are not clearly stated.

(3) Multiple statistical models are implemented without correcting the alpha value. This leaves the analyses vulnerable to Type I errors.

(4) It is confusing to understand how many discrete experiments are included in the study as a whole, and how many participants are involved in each experiment.

(5) The TMS study is significantly underpowered and not robust. Sample size calculations need further explanation (effect sizes appear to be based on behavioural studies?). I would caution an exploratory presentation of these data, and calculate a posteriori the full sample size based on effect sizes observed in the TMS data.

Reviewer #2 (Public review):

Summary:

The study by Rowley and Sedigh-Sarvestani presents modeling data suggesting that map reversals in mouse lateral extrastriate visual cortex do not coincide with areal borders, but instead represent borders between subregions within a single area V2. The authors propose that such an organization explains the partial coverage in higher-order areas reported by Zhuang et al., (2017). The scheme revisits an organization proposed by Kaas et al., (1989), who interpreted the multiple projection patches traced from V1 in the squirrel lateral extrastriate cortex as subregions within a single area V2. Kaas et al's interpretation was challenged by Wang and Burkhalter (2007), who used a combination of topographic mapping of V1 connections and receptive field recordings in mice. Their findings supported a different partitioning scheme in which each projection patch mapped a specific topographic location within single areas, each containing a complete representation of the visual field. The area map of mouse visual cortex by Wang and Burkhalter (2007) has been reproduced by hundreds of studies and has been widely accepted as ground truth (CCF) (Wang et al., 2020) of the layout of rodent cortex. In the meantime, topographic mappings in marmoset and tree shew visual cortex made a strong case for map reversals in lateral extrastriate cortex, which represent borders between functionally diverse subregions within a single area V2. These findings from non-rodent species raised doubts about whether during evolution, different mammalian branches have developed diverse partitioning schemes of the cerebral cortex. Rowley and Sedigh-Sarvestani favor a single master plan in which, across evolution, all mammalian species have used a similar blueprint for subdividing the cortex.

Strengths:

The story illustrates the enduring strength of science in search of definitive answers.

Weaknesses:

To me, it remains an open question whether Rowley and Sedigh-Sarvestani have written the final chapter of the saga. A key reason for my reservation is that the areas the maps used in their model are cherry-picked. The article disregards published complementary maps, which show that the entire visual field is represented in multiple areas (i.e. LM, AL) of lateral extrastriate cortex and that the map reversal between LM and AL coincides precisely with the transition in m2AChR expression and cytoarchitecture (Wang and Burkhalter, 2007; Wang et al., 2011). Evidence from experiments in rats supports the gist of the findings in the mouse visual cortex (Coogan and Burkhalter, 1993).

(1) The selective use of published evidence, such as the complete visual field representation in higher visual areas of lateral extrastriate cortex (Wang and Burkhalter, 2007; Wang et al., 2011) makes the report more of an opinion piece than an original research article that systematically analyzes the area map of mouse visual cortex we have proposed. No direct evidence is presented for a single area V2 with functionally distinct subregions.

(2) The article misrepresents evidence by commenting that m2AChR expression is mainly associated with the lower field. This is counter to published findings showing that m2AChR spans across the entire visual field (Gamanut et al., 2018; Meier et al., 2021). The utility of markers for delineating areal boundaries is discounted, without any evidence, in disregard of evidence for distinct areal patterns in early development (Wang et al., 2011). Pointing out that markers can be distributed non-uniformly within an area is well-familiar. m2AChR is non-uniformly expressed in mouse V1, LM and LI (Ji et al., 2015; D'Souza et al., 2019; Meier et al., 2021). Recently, it has been found that the patchy organization within V1 plays a role in the organization of thalamocortical and intracortical networks (Meier et al., 2025). m2AChR-positive patches and m2AChR-negative interpatches organize the functionally distinct ventral and dorsal networks, notably without obvious bias for upper and lower parts of the visual field.

(3) The study has adopted an area partitioning scheme, which is said to be based on anatomically defined boundaries of V2 (Zhuang et al., 2017). The only anatomical borders used by Zhuang et al. (2017) are those of V1 and barrel cortex, identified by cytochrome oxidase staining. In reality, the partitioning of the visual cortex was based on field sign maps, which are reproduced from Zhuang et al., (2017) in Figure 1A. It is unclear why the maps shown in Figures 2E and 2F differ from those in Figure 1A. It is possible that this is an oversight. But maintaining consistent areal boundaries across experimental conditions that are referenced to the underlying brain structure is critical for assigning modeled projections to areas or sub-regions. This problem is evident in Figure 2F, which is presented as evidence that the modeling approach recapitulates the tracings shown in Figure 3 of Wang and Burkhalter (2007). The dissimilarities between the modeling and tracing results are striking, unlike what is stated in the legend of Figure 2F.

(4) The Rowley and Sedigh-Sarvestani find that the partial coverage of the visual field in higher order areas shown by Zhuang et al (2017) is recreated by the model. It is important to caution that Zhuang et al's (2017) maps were derived from incomplete mappings of the visual field, which was confined to -25-35 deg of elevation. This underestimates the coverage we have found in LM and AL. Receptive field mappings show that LM covers 0-90 deg of azimuth and -30-80 elevation (Wang and Burkhalter, 2007). AL covers at least 0-90 deg of azimuth and -30-50 deg of elevation (Wang and Burkhalter, 2007; Wang et al., 2011). These are important differences. Partial coverage in LM and AL underestimates the size of these areas and may map two projection patches as inputs to subregions of a single area rather than inputs to two separate areas. Complete, or nearly complete, visual representations in LM and AL support that each is a single area. Importantly, both areas are included in a callosal-free zone (Wang and Burkhalter, 2007). The surrounding callosal connections align with the vertical meridian representation. The single map reversal is marked by a transition in m2AChR expression and cytoarchitecture (Wang et al., 2011).

(5) The statement that the "lack of visual field overlap across areas is suggestive of a lack of hierarchical processing" is predicated on the full acceptance of the mappings by Zhuang et al (2017). Based on the evidence reviewed above, the reclassification of visual areas proposed in Figure 1C seems premature.

(6) The existence of lateral connections is not unique to rodent cortex and has been described in primates (Felleman and Van Essen, 1991).

(7) Why the mouse and rat extrastriate visual cortex differ from those of many other mammals is unclear. One reason may be that mammals with V2 subregions are strongly binocular.

Reviewer #2 (Public review):

Summary:

Building on previous models of multisensory integration (including their earlier correlation-detection framework used for non-spatial signals), the author introduces a population-level Multisensory Correlation Detector (MCD) that processes raw auditory and visual data. Crucially, it does not rely on abstracted parameters, as is common in normative Bayesian models," but rather works directly on the stimulus itself (i.e., individual pixels and audio samples). By systematically testing the model against a range of experiments spanning human, monkey, and rat data - the authors show that their MCD population approach robustly predicts perception and behavior across species with a relatively small (0-4) number of free parameters.

Strengths:

(1) Unlike prior Bayesian models that used simplified or parameterized inputs, the model here is explicitly computable from full natural stimuli. This resolves a key gap in understanding how the brain might extract "time offsets" or "disparities" from continuously changing audio-visual streams.

(2) The same population MCD architecture captures a remarkable range of multisensory phenomena, from classical illusions (McGurk, ventriloquism) and synchrony judgments, to attentional/gaze behavior driven by audio-visual salience. This generality strongly supports the idea that a single low-level computation (correlation detection) can underlie many distinct multisensory effects.

(3) By tuning model parameters to different temporal rhythms (e.g., faster in rodents, slower in humans), the MCD explains cross-species perceptual data without reconfiguring the underlying architecture.

(4) The authors frame their model as a plausible algorithmic account of the Bayesian multisensory-integration models in Marr's levels of hierarchy.

Weaknesses:

What remains unclear is how the parameters themselves relate to stimulus quantities (like stimulus uncertainty), as is often straightforward in Bayesian models. A theoretical missing link is the explicit relationship between the parameters of the MCD models and those of a cue combination model, thereby bridging Marr's levels of hierarchy.

Likely Impact and Usefulness

The work offers a compelling unification of multiple multisensory tasks-temporal order judgments, illusions, Bayesian causal inference, and overt visual attention-under a single, fully stimulus-driven framework. Its success with natural stimuli should interest computational neuroscientists, systems neuroscientists, and machine learning scientists. This paper thus makes an important contribution to the field by moving beyond minimalistic lab stimuli, illustrating how raw audio and video can be integrated using elementary correlation analyses.

Reviewer #2 (Public review):

Summary:

This manuscript is a technical report on a new model of early neurogenesis, coupled to a novel platform for genetic screens. The model is more faithful than others published to date, and the screening platform is an advance over existing ones in terms of speed and throughput.

Strengths:

It is novel and useful.

Weaknesses:

The novelty of the results is limited in terms of biology, mainly a proof of concept of the platform and a very good demonstration of the hierarchical interactions of the top regulators of GRNs.

The value of the manuscript could be enhanced in two ways:

(1) by showing its versatility and transforming the level of neural tube to midbrain and hindbrain, and looking at the transcriptional hierarchies there.

(2) by relating the patterning of the organoids to the situation in vivo, in particular with the information in reference 49. The authors make a statement "To compare our findings with in vivo gene expression patterns, we applied the same approach to published scRNA-seq data from 4-week-old human embryos at the neurula stage" but it would be good to have a more nuanced reference: what stage, what genes are missing, what do they add to the information in that reference?

Reviewer #2 (Public review):

Summary:

The manuscript by Gitanjali Roy et al. applies deep transfer learning (DEGAS) to assign patient-level disease attributes (metadata) to single cells of T2D and non-diabetic patients, including obese patients. This led to the identification of a singular cluster of T2D-associated β-cells; and two subpopulations of obese- β-cells derived from either non-diabetic or T2D donors. The objective was to identify novel and established genes implicated in T2D and obesity. Their final goal is to validate their findings at the protein level using immunohistochemistry of pancreas tissue from non-diabetic and T2D organ donors.

Strengths:

This paper is well-written, and the findings are relevant for β-cell heterogeneity in T2D and obesity.

Weaknesses:

The validation they provide is not sufficiently strong: no DLK1 immunohistochemistry is shown of obese patient-derived sections. Additional presumptive relevant candidates from this transcriptomic analysis should be screened for, at the protein level.

Comments on revisions:

The authors have largely addressed my comments. No further experiments are requested.

Reviewer #2 (Public review):

Summary:

The authors are trying to understand why certain mutants of O-GlcNAc transferase (OGT) appear to cause developmental disorders in humans. As an important step towards that goal, the authors generated a mouse model with one of these mutations that disrupts OGT activity. They then go on to test these mice for behavioral differences, finding that the mutant mice exhibit some signs of hyperactivity and differences in learning and memory. They then examine alterations to the structure of the brain and skull, and again find changes in the mutant mice that have been associated with developmental disorders. Finally, they identify proteins that are up- or down-regulated between the two mice as potential mechanisms to explain the observations.

Strengths:

The major strength of this manuscript is the creation of this mouse model, as a key step in beginning to understand how OGT mutants cause developmental disorders. This line will prove important for not only the authors but other investigators as well, enabling the testing of various hypotheses and potentially treatments. The experiments are also rigorously performed, and the conclusions are well supported by the data.

Weaknesses:

The only weakness identified is a lack of mechanistic insight. However, this certainly may come in the future through more targeted experimentation using this mouse model.

Reviewer #2 (Public review):

Summary:

Zhou, Sajid et al. present a study investigating the STN involvement in signaled movement. They use fiber photometry, implantable lenses, and optogenetics during active avoidance experiments to evaluate this. The data are useful for the scientific community, and the overall evidence for their claims is solid, but many aspects of the findings are confusing and seemingly contradictory. For example, STN activity increases with contraversive turning in the fiber photometry experiments, but optogenetic stimulation of the STN evokes ipsiversive turning. While the authors present a huge collection of data, it is somewhat difficult to extract the key information and the meaningful implications resulting from this data.

Strengths:

The study is comprehensive in using many techniques, stimulation powers, frequencies, and configurations.

Weaknesses:

Here are the specific weaknesses of the paper.

(1) Vglut2 isn't a very selective promoter for the STN. Did the authors verify every injection across brain slices to ensure the para-subthalamic nucleus, thalamus, lateral hypothalamus, and other Vglut2-positive structures were never infected?

(2) The authors say in the methods that the high vs low power laser activation for optogenetic experiments was defined by the behavioral output. This is misleading, and the high vs low power should be objectively stated and the behavioral results divided according to the power used, not according to the behavioral outcome.

(3) In the fiber photometry experiments exposing mice to the range of tones, it is impossible to separate the STN response to the tone from the STN response to the movement evoked by the tone. The authors should expose the mouse to the tones in a condition that prevents movement, such as anesthetized or restrained, to separate out the two components.

(4) The claim 'STN activation is ideally suited to drive active avoids' needs more explanation. This claim comes after the fiber photometry experiments during active avoidance tasks, so there has been no causality established yet.

(5) The statistical comparisons in Figure 7E need some justification and/or clarification. The 9 neuron types are originally categorized based on their response during avoids, then statistics are run showing that they respond differently during avoids. It is no surprise that they would have significantly different responses, since that is how they were classified in the first place. The authors must explain this further and show that this is not a case of circular reasoning.

(6) The authors show that neurons that have strong responses to orientation show reduced activity during avoidance. What are the implications of this? The author should explain why this is interesting and important.

(7) It is not clear which conditions each mouse experienced in which order. This is critical to the interpretation of Figure 9 and the reduction of passive avoids during STN stimulation. Did these mice have the CS1+STN stimulation pairing or the STN+US pairing prior to this experiment? If they did, the stimulation of the STN could be strongly associated with either punishment or with the CS1 that predicts punishment. If that is the case, stimulating the STN during CS2 could be like presenting CS1+CS2 at the same time and could be confusing.

(8) The experiments in Figure 10 are used to say that STN stimulation is not aversive, but they only show that STN stimulation cannot be used as punishment in place of a shock. This doesn't mean that it is not aversive; it just means it is not as aversive as a shock. The authors should do a simpler aversion test, such as conditioned or real-time place preference, to claim that STN stimulation is not aversive. This is particularly surprising as previous work (Serra et al., 2023) does show that STN stimulation is aversive.

(9) In the discussion, the idea that the STN encodes 'moving away' from contralateral space is pretty vague and unsupported. It is puzzling that the STN activates more strongly to contraversive turns, but when stimulated, it evokes ipsiversive turns; however, it seems a stretch to speculate that this is related to avoidance. In the last experiments of the paper, the axons from the STN to the GPe and to the midbrain are selectively stimulated. Do these evoke ipsiversive turns similarly?

(10) In the discussion, the authors claim that the STN is essential for modulating action timing in response to demands, but their data really only show this in one direction. The STN stimulation reliably increases the speed of response in all conditions (except maximum speed conditions such as escapes). It seems to be over-interpreting the data to say this is an inability to modulate the speed of the task, especially as clear learning and speed modulation do occur under STN lesion conditions, as shown in Figure 12B. The mice learn to avoid and increase their latency in AA2 vs AA1, though the overall avoids and latency are different from controls. The more parsimonious conclusion would be that STN stimulation biases movement speed (increasing it) and that this is true in many different conditions.

(11) In the discussion, the authors claim that the STN projections to the midbrain tegmentum directly affect the active avoidance behavior, while the STN projections to the SNr do not affect it. This seems counter to their results, which show STN projections to either area can alter active avoidance behavior. What is the laser power used in these terminal experiments? If it is high (3mW), the authors may be causing antidromic action potentials in the STN somas, resulting in glutamate release in many brain areas, even when terminals are only stimulated in one area. The authors could use low (0.25mW) laser power in the terminals to reduce the chance of antidromic activation and spatially restrict the optical stimulation.

(12) Was normality tested for data prior to statistical testing?

(13) Why are there no error bars on Figure 5B, black circles and orange triangles?

Push

Push factor

Civilizing indigenous groups

Perhaps a blueprint for what woukd eventually happen in america?

Push

Reviewer #2 (Public review):

Summary:

Li et al. investigated the potential anti-ageing role of 17α-Estradiol on the hypothalamus of aged rats. To achieve this, they employed a very sophisticated method for single-cell genomic analysis that allowed them to analyze effects on various groups of neurons and non-neuronal cells. They were able to sub-categorize neurons according to their capacity to produce specific neurotransmitters, receptors, or hormones. They found that 17α-Estradiol treatment led to an improvement in several factors related to metabolism and synaptic transmission by bringing the expression levels of many of the genes of these pathways closer or to the same levels to those of young rats, reversing the ageing effect. Interestingly, among all neuronal groups, the proportion of Oxytocin-expressing neurons seems to be the one most significantly changing after treatment with 17α-Estradiol, suggesting an important role of these neurons on mediating its anti-ageing effects. This was also supported by an increase in circulating levels of oxytocin. It was also found that gene expression of corticotropin-releasing hormone neurons was significantly impacted by 17α-Estradiol even though it was not different between aged and young rats, suggesting that these neurons could be responsible for side effects related to this treatment. This article revealed some potential targets that should be further investigated in future studies regarding the role of 17α-Estradiol treatment in aged males.

Strengths:

• The single nucleus mRNA sequencing is a very powerful method for gene expression analysis and clustering. The supervised clustering of neurons was very helpful in revealing otherwise invisible differences between neuronal groups and helped identify specific neuronal populations as targets.

• There is a variety of functions used that allowed the differential analysis of a very complex type of data. This led to a better comparison between the different groups in many levels.

• There were some physiological parameters measured such as circulating hormone levels that helped the interpretation of the effects of the changes in hypothalamic gene expression.

Weaknesses:

• One main control group is missing from the study, the young males treated with 17α-Estradiol.

• Even though the technical approach is a sophisticated one, analyzing the whole rat hypothalamus instead of specific nuclei or subregions makes the study weaker.

• Although the authors claim to have several findings, the data fail to support these claims.

• The study is about improving ageing but no physiological data from the study demonstrated such claim with the exception of the testes histology which was not properly analyzed and was not even significantly different between the groups.

• Overall, the study remains descriptive with no physiological data to demonstrate that any of the effects on hypothalamic gene expression is related to metabolic, synaptic or other function.

Comments on revisions:

The authors revised part of the manuscript to address some of the reviewers' comments. This improved the language and the text flow to a certain extent. They also added an additional analysis including glial cells. However, they failed to address the main weaknesses brought up by the reviewers and did not add any experimental demonstration of their claims on lifespan expansion induced by 17α-estradiol in rats (the cited study does not include lifespan in rats). In addition, they insisted i keeping parts in the discussion that are not directly linked to any of the papers' findings.

Reviewer #2 (Public review):

Summary:

In this manuscript, Farber and colleagues have performed single cell RNAseq analysis on bone marrow derived stem cells from DO Mice. By performing network analysis, they look for driver genes that are associated with bone mineral density GWAS associations. They identify two genes as potential candidates to showcase the utility of this approach.

Strengths:

The study is very thorough and the approach is innovative and exciting. The manuscript contains some interesting data relating to how cell differentiation is occurring and the effects of genetics on this process. The section looking for genes with eQTLs that differ across the differentiation trajectory (Figure 4) was particularly exciting.

Weaknesses:

The manuscript is, in parts, hard to read due to the use of acronyms and there are some questions about data analysis that still need to be addressed.

Comments on revisions:

Dillard et al have made several improvements to their manuscript.

(1) We previously asked the authors to determine whether any cell types were enriched for BMD-related traits since the premise of the paper is that 'many genes impacting BMD do so by influencing osteogenic differentiation or ... adipogenic differentiation'. Given the potential for the cell culture method to skew the cell type distribution non-physiologically, it is important to establish which cell types in their assay are most closely associated with BMD traits. The new CELLECT analysis and Figure 1E address this point nicely. However, it would still be nice to see the correlations between these cell types and BMD traits in the mice as this would provide independent evidence to support their physiological importance more broadly.

(2) Shortening the introduction.

(3) Addressing limitations that arise from not accounting for founder genome SNPs when aligning scRNA-seq data.

(4) The main take-away of this paper is, to us, the development of a single cell approach to studying BMD-related traits. It is encouraging that the cells post-culture appear to be representative of those pre-culture (supplemental figure 3).

However, the authors seem to have neglected several comments made by both reviewers. While we share the authors' enthusiasm for the single cell analytical approach, we do not understand their reluctance to perform further statistical tests. We feel that the following comments have still not been addressed:

(1) The manuscript still contains the following:

"To provide further support that tradeSeq-identified genes are involved in differentiation, we performed a cell type-specific expression quantitative trait locus (eQTL) analysis for each mesenchymal cell type from the 80 DO mice. We identified 563 genes (eGenes) regulated by a significant cis-eQTL in specific cell types of the BMSC-OB scRNA-seq data (Supplementary Table S14). In total, 73 eGenes were also tradeSeq-identified genes in one or more cell type boundaries along their respective trajectories (Supplementary Table S9)."

The purpose of this paragraph is to convince readers that the eGenes approach aligns with the tradeSeq approach (and that their approach can therefore be trusted). It is essential that such claims are supported by statistical reasoning. Given that it would be very simple to perform permutation/enrichment analyses to address this point, and both reviewers requested similar analyses, we do not understand the author's reluctance here. Otherwise, this section should be rewritten so that it does not imply that the identification of these genes provides support for their approach.

(2) Given that a central purpose of this manuscript is to establish a systematic workflow for identifying candidate genes, the manuscript could still benefit from more explanation as to why the authors chose to highlight Tpx2 and Fgfrl1. Tpx2 does already have a role in bone physiology through the IMPC. The authors should comment on why they did not explore Kremen1, for instance, as this gene seems important for the transition to both OB1 and 2.

A final minor comment is that it would be very helpful if the authors could indicate if the DDGs in Table 1 are also eGenes for the relevant cell type. This is much more meaningful than looking through GTEx.

Reviewer #2 (Public review):

Summary:

This study introduces SemReps-8K, a large multimodal fMRI dataset collected while subjects viewed natural images and matched captions, and performed mental imagery based on textual cues. The authors aim to train modality-agnostic decoders--models that can predict neural representations independently of the input modality - and use these models to identify brain regions containing modality-agnostic information. They find that such decoders perform comparably or better than modality-specific decoders and generalize to imagery trials.

Strengths:

(1) The dataset is a substantial and well-controlled contribution, with >8,000 image-caption trials per subject and careful matching of stimuli across modalities - an essential resource for testing theories of abstract and amodal representation.

(2) The authors systematically compare unimodal, multimodal, and cross-modal decoders using a wide range of deep learning models, demonstrating thoughtful experimental design and thorough benchmarking.

(3) Their decoding pipeline is rigorous, with informative performance metrics and whole-brain searchlight analyses, offering valuable insights into the cortical distribution of shared representations.

(4) Extension to mental imagery decoding is a strong addition, aligning with theoretical predictions about the overlap between perception and imagery.

Weaknesses:

While the decoding results are robust, several critical limitations prevent the current findings from conclusively demonstrating truly modality-agnostic representations:

(1) Shared decoding ≠ abstraction: Successful decoding across modalities does not necessarily imply abstraction or modality-agnostic coding. Participants may engage in modality-specific processes (e.g., visual imagery when reading, inner speech when viewing images) that produce overlapping neural patterns. The analyses do not clearly disambiguate shared representational structure from genuinely modality-independent representations. Furthermore, in Figure 5, the modality-agnostic encoder did not perform better than the modality-specific decoder trained on images (in decoding images), but outperformed the modality-specific decoder trained on captions (in decoding captions). This asymmetry contradicts the premise of a truly "modality-agnostic" encoder. Additionally, given the similar performance between modality-agnostic decoders based on multimodal versus unimodal features, it remains unclear why neural representations did not preferentially align with multimodal features if they were truly modality-independent.

(2) The current analysis cannot definitively conclude that the decoder itself is modality-agnostic, making "Qualitative Decoding Results" difficult to interpret in this context. This section currently provides illustrative examples, but lacks systematic quantitative analyses.

(3) The use of mental imagery as evidence for modality-agnostic decoding is problematic. Imagery involves subjective, variable experiences and likely draws on semantic and perceptual networks in flexible ways. Strong decoding in imagery trials could reflect semantic overlap or task strategies rather than evidence of abstraction.

The manuscript presents a methodologically sophisticated and timely investigation into shared neural representations across modalities. However, the current evidence does not clearly distinguish between shared semantics, overlapping unimodal processes, and true modality-independent representations. A more cautious interpretation is warranted. Nonetheless, the dataset and methodological framework represent a valuable resource for the field.

Reviewer #2 (Public review):

Summary:

This study develops a joint epidemiological and population genetic model to infer variant-specific effective reproduction numbers Rt and growth advantages of SARS-CoV-2 variants using US case counts and sequence data (Jan 2021-Mar 2022). For this, they use the commonly used renewal equation framework, observation models (negative binomial with zero inflation and Dirichlet-multinomial likelihoods, both to account for overdispersion). For the parameterization of Rt, again, they used a classic cubic spline basis expansion. Additionally, they use Bayesian Inference, specifically SVI. I was reassured to see the sensitivity analysis on the generation time to check effects on Rt.

This is an incredibly robust study design. Integrating case and sequence data enables estimation of both absolute and relative variant fitness, overcoming limitations of frequency-only or case-only models. This reminds me of https://www.medrxiv.org/content/10.1101/2023.01.02.23284123v4.full

I also really appreciated the flexible and interpretable parameterization of the renewal equations with splines. But I may be biased since I really like splines!

The approach is justified, however, it has some big limitations. Specifically, there are some notable weaknesses, that I detail below.

(1) The model does not account for demographic stochasticity or transmission overdispersion (superspreading), which are known to affect SARS-CoV-2 dynamics and can bias Rt, especially in low incidence or early introduction phases.

(2) While the authors explore the sensitivity of generation time, the reliance on fixed generation time parameters (with some adjustments for Delta/Omicron) may still bias results

(3) There is no explicit adjustment for population immunity, which limits the ability to disentangle intrinsic variant fitness (even though the model allows for inclusion of covariates - this to me is one of two major flaws in the study.

(4) The second major flaw in my opinion is that there is no hierarchical pooling across states - each state is modeled independently. A hierarchical Bayesian model could borrow strength across states, improving estimates for states with sparse data and enabling more robust inference of shared variant effects.

I would strongly recommend the following things in order of priority, where the first two points I consider critical.

(1) Implement a hierarchical model for variant growth advantages and Rt across states.

(2) Include time-varying covariates for vaccination rates, prior infection, and non-pharmaceutical interventions directly. This would help disentangle intrinsic variant transmissibility from changes in population susceptibility and behavior.

(3) Extend the renewal model to a stochastic or branching process framework that explicitly models overdispersed transmission.

(4) It would be good to allow for multiple seeding events per variant and per state. This can be informed by phylogeography in a minimum effort way and would improve the accuracy of Rt.

(5) By now, I don't think it will be a surprise that addressing sampling bias is standard, reweighting sequence data or comparing results with independent surveillance data to assess the impact of non-representative sequencing.

Reviewer #2 (Public review):

This important paper studies the problem of learning from feedback given by sources of varying credibility. The convincing combination of experiment and computational modeling helps to pin down properties of learning, while opening unresolved questions for future research.

Summary:

This paper studies the problem of learning from feedback given by sources of varying credibility. Two bandit-style experiments are conducted in which feedback is provided with uncertainty, but from known sources. Bayesian benchmarks are provided to assess normative facets of learning, and alternative credit assignment models are fit for comparison. Some aspects of normativity appear, in addition to possible deviations such as asymmetric updating from positive and negative outcomes.

Strengths:

The paper tackles an important topic, with a relatively clean cognitive perspective. The construction of the experiment enables the use of computational modeling. This helps to pinpoint quantitatively the properties of learning and formally evaluate their impact and importance. The analyses are generally sensible, and advanced parameter recovery analyses (including cross-fitting procedure) provide confidence in the model estimation and comparison. The authors have very thoroughly revised the paper in response to previous comments.

Weaknesses:

The authors acknowledge the potential for cognitive load and the interleaved task structure to play a meaningful role in the results, though leave this for future work. This is entirely reasonable, but remains a limitation in our ability to generalize the results. Broadly, some of the results obtain in cases where the extent of generalization is not always addressed and remains uncertain.

Reviewer #2 (Public review):

Summary:

This article addresses a very pertinent question: what are the computational mechanisms underlying risky behaviour in patients who have attempted suicide? In particular, it is impressive how the authors find a broad behavioural effect whose mechanisms they can then explain and refine through computational modeling. This work is important because, currently, beyond previous suicide attempts, there has been a lack of predictive measures. This study is the first step towards that: understanding the cognition on a group level. This is before being able to include it in future predictive studies (based on the cross-sectional data, this study by itself cannot assess the predictive validity of the measure).

Strengths:

(1) Large sample size.

(2) Replication of their own findings.

(3) Well-controlled task with measures of behaviour and mood + precise and well-validated computational modeling.

Weaknesses:

I can't really see any major weakness, but I have a few questions:

(1) I can see from the parameter recovery that the parameters are very well identified. Is it surprising that this is the case, given how many parameters there are for 90 trials? Could the authors show cross-correlations? I.e., make a correlation matrix with all real parameters and all fitted parameters to show that not only the diagonal (i.e., same data is the scatter plots in S3) are high, but that the off-diagonals are low.

(2) Could the authors clarify the result in Figure 2B of a correlation between gambling rate and suicidal ideation score, is that a different result than they had before with the group main effect? I.e., is your analysis like this: gambling rate ~ suicide ideation + group assignment? (or a partial correlation)? I'm asking because BSI-C is also different between the groups. [same comment for later analyses, e.g. on approach parameter].

(3) The authors correlate the impact of certain rewards on mood with the % gambling variable. Could there not be a more direct analysis by including mood directly in the choice model?

(4) In the large online sample, you split all participants into S+ and S-. I would have imagined that instead, you would do analyses that control for other clinical traits. Or, for example, you have in the S- group only participants who also have high depression scores, but low suicide items.

Reviewer #2 (Public review):

Summary:

The function of neural circuits relies heavily on the balance of excitatory and inhibitory inputs. Particularly, inhibitory inputs are understudied when compared to their excitatory counterparts due to the diversity of inhibitory neurons, their synaptic molecular heterogeneity, and their elusive signature. Thus, insights into these aspects of inhibitory inputs can inform us largely on the functions of neural circuits and the brain.

Endophilin A1, an endocytic protein heavily expressed in neurons, has been implicated in numerous pre- and postsynaptic functions, however largely at excitatory synapses. Thus, whether this crucial protein plays any role in inhibitory synapse, and whether this regulates functions at the synaptic, circuit, or brain level remains to be determined.

The three remaining concerns are:

(1) The use of one-way ANOVA is not well justified.

(2) The use of superplots to show culture to culture variability would make it more transparent.

(3) Change EEN1 in Figure 8B to EndoA1.

Comments on revised version:

The authors addressed the concerns adequately.

Reviewer #2 (Public review):

The study by Chaguza et al. presents a novel perspective on pneumococcal growth kinetics, suggesting that the overall genetic background of Streptococcus pneumoniae, rather than specific loci, plays a more dominant role in determining growth dynamics. Through a genome-wide association study (GWAS) approach, the authors propose a shift in how we understand growth regulation, differing from earlier findings that pinpointed individual genes, such as wchA or cpsE, as key regulators of growth kinetics. This study highlights the importance of considering the cumulative impact of the entire genetic background rather than focusing solely on individual genetic loci.

The study emphasizes the cumulative effects of genetic variants, each contributing small individual impacts, as the key drivers of pneumococcal growth. This polygenic model moves away from the traditional focus on single-gene influences. Through rigorous statistical analyses, the authors persuasively advocate for a more holistic approach to understanding bacterial growth regulation, highlighting the complex interplay of genetic factors across the entire genome. Their findings open new avenues for investigating the intricate mechanisms underlying bacterial growth and adaptation, providing fresh insights into bacterial pathogenesis.

Strengths:

This study exemplifies a holistic approach to unraveling key factors in bacterial pathogenesis. By analyzing a large dataset of whole-genome sequences and employing robust statistical methodologies, the authors provide strong evidence to support their main findings. Which is a leap forward from previous studies focused on a relatively smaller number of strains. Their integration of genome-wide association studies (GWAS) highlights the cumulative, polygenic influences on pneumococcal growth kinetics, challenging the traditional focus on individual loci. This comprehensive strategy not only advances our understanding of bacterial growth regulation but also establishes a foundation for future research into the genetic underpinnings of bacterial pathogenesis and adaptation. The amount of data generated and corresponding approaches to analyze the data are impressive as well as convincing. The figures are convincing and comprehensible too. The revised version of the manuscript, after the addition and including explanations, is more convincing and acceptable.

Weaknesses:

This study suggests evidence that the genetic background significantly influences bacterial growth kinetics. However, the absence of experimental validation remains a critical limitation. Although the authors acknowledge in their response to reviewers that bench-experiments were beyond the scope of this work and are planned, this gap of experimental validation weakens the current conclusions. Demonstrable validation will be essential to corroborate the associations identified through the GWAS approach. Future experimental efforts will be critical to substantiate these findings and to deepen our understanding of the genetic determinants governing bacterial growth dynamics.

Reviewer #2 (Public review):

Summary:

Govorunova et al present three new anion opsins that have potential applications silencing neurons. They identify new opsins by scanning numerous databases for sequence homology to known opsins, focusing on anion opsins. The three opsin identified, are uncommonly fast, potent, and are able to silence neuronal activity. The authors characterize numerous parameters of the opsins and compare these opsins to the existing and widely used GtACR opsins.

Strengths:

This paper follows the tradition of the Spudich lab, presenting and rigorously characterizing potentially valuable opsins. Furthermore, they explore several mutations of the identified opsin that may make these opsins even more useful for the broader community. The opsins AnsACR and FtACR are particularly notable having extraordinarily fast onset kinetics that could have utility in many domains. Furthermore, the authors show AnsACR is useable in multiphoton experiments having a peak photocurrent in a commonly used wavelength. Overall, the author's detailed measurements and characterization make for an important resource - both presenting new opsins that may be important for future experiment, and providing characterizations to expand our understanding of opsin biophysics in general.

Reviewer #2 (Public review):

Summary:

This study makes a significant contribution to understanding the microenvironment of megakaryocytes (MKs) in the bone marrow, identifying an extracellular matrix (ECM) cage structure that influences MK localization and maturation. The authors provide compelling evidence for the presence of this ECM cage and its role in MK homeostasis, employing an array of sophisticated imaging techniques and molecular analyses.

The authors have addressed most of the concerns raised in the previous review, providing clarifications and additional data that strengthen their conclusions

More broadly, this work adds to a growing recognition of the ECM as an active participant in haematopoietic cell regulation in the bone marrow microenvironment. This work could pave the way to future studies investigating how the megakaryocytes' ECM cage affects their function as part of the haematopoietic stem cell niche, and by extension, influences global haematopoiesis.

Reviewer #2 (Public review):

Summary

This study provides new insights into organ morphogenesis using the Drosophila salivary gland (SG) as a model. The authors identify a requirement for sulfation in regulating lumen expansion, which correlates with several effects at the cellular level, including regulation of intracellular trafficking and the organization of Golgi, the aECM and the apical membrane. In addition, the authors show that the ZP proteins Dumpy (Dpy) and Pio form an aECM regulating lumen expansion. Previous reports already pointed to a role for Papss in sulfation in SG and the presence of Dpy and Pio in the SG. Now this work extends these previous analyses and provides more detailed descriptions that may be relevant to the fields of morphogenesis and cell biology (with particular focus on ECM research and tubulogenesis). This study nicely presents valuable information regarding the requirements of sulfation and the aECM in SG development.

Strengths:

- The results supporting a role for sulfation in SG development are strong. In addition, the results supporting the involvement of Dpy and Pio in the aECM of the SG, their role in lumen expansion, and their interactions, are also strong.

- The authors have made an excellent job in revising and clarifying the many different issues raised by the reviewers, particularly with the addition of new experiments and quantifications. I consider that the manuscript has improved considerably.

- The authors generated a catalytically inactive Papss enzyme, which is not able to rescue the defects in Papss mutants, in contrast to wild type Papss. This result clearly indicates that the sulfation activity of Papss is required for SG development.

Reviewer #2 (Public review):

Summary:

The study is well-conducted, revealing that NPY1, with previously less-characterized molecular functions, can suppress pid mutant phenotypes with a phosphorylation-based mechanism. Overexpression of NPY1 (NPY1-OE) results in PIN phosphorylation at unique sites and bypasses the requirement for PID for this event. Conversely, a C-terminal deleted form of NPY1 (NPY1-dC) fails to rescue pid despite promoting a certain phospho-profile in PIN proteins.

Strengths:

(1) The careful genetic analyses of pid suppression by NPY1-OE and the inability of NPY1dC to do the same.

(2) Phospho-proteomics approaches reveal that NPY1-OE induces phosphorylation of PINs at non-canonical sites, independent of PID.

Weaknesses:

(1) The native role of NPY1 is not tested by phospho-proteomics in loss-of-function npy1 mutants. Such analysis would be crucial to demonstrate that NPY1 is required for the observed phosphorylation events.

(2) The functional consequences of the newly identified phosphorylation sites in PINs remain speculative. Site-directed mutagenesis (phospho-defective and phospho-mimetic) would help clarify their physiological roles.

(3) The kinase responsible for NPY1-mediated phosphorylation remains unidentified. Since NPY1 is a non-kinase protein, a model involving recruitment of partner kinases (e.g., PIN-phosphorylating kinases other than PID) should be considered or discussed.

Reviewer #2 (Public review):

Summary:

Using C. elegans as a model, the authors present an interesting story demonstrating a new regulatory connection between olfactory neurons and the digestive system. Mechanistically, they identified key factors (NSY-1, STR-130 et.al) in neurons, as well as critical 'signaling factors' (INS-23, DAF-2) that bridge different cells/tissues to execute the digestive shutdown induced by poor-quality food (Staphylococcus saprophyticus, SS).

Strengths:

The conclusions of this manuscript are mostly well supported by the experimental results shown.

Weaknesses:

The authors have done a nice job in addressing my comments.

Reviewer #2 (Public review):

Summary:

The authors aimed to uncover what role, if any, the UFD1/NPL4 complex might play in innate immune responses of the nematode C. elegans. The authors find that loss of the complex renders animals more sensitive to both pathogenic and non-pathogenic bacteria. However, there appears to be a complex interplay with known innate immune pathways since loss of UFD1/NPL4 actually results in increased survival of animals lacking the canonical innate immune pathways.

Strengths:

The authors perform robust genetic analysis to exclude and include possible mechanisms by which the UFD1/NPL4 pathway acts in the innate immune response.

Weaknesses:

The argument that the loss of the UFD1/NPL4 complex triggers a response that mimics that of an intracellular pathogen is not thoroughly investigated. Additionally, the finding of a role of the GATA transcription factor, ELT-2, in this response is suggestive, but experiments showing sufficiency in the context of loss of the UFD1/NPL4 complex need to be explored.

Comments on revisions:

The authors have performed several control experiments for their RNAi based experiments and also tested the requirement for xbp-1s in their paradigm. The findings and their interpretations are acceptable.

Reviewer #3 (Public review):

Summary:

This study investigates the role of the host protein RBMX2 in regulating the response to Mycobacterium bovis infection and its connection to epithelial-mesenchymal transition (EMT), a key pathway in cancer progression. Using bovine and human cell models, the authors have wisely shown that RBMX2 expression is upregulated following M. bovis infection and promotes bacterial adhesion, invasion, and survival by disrupting epithelial tight junctions via the p65/MMP-9 signaling pathway. They also demonstrate that RBMX2 facilitates EMT and is overexpressed in human lung cancers, suggesting a potential link between chronic infection and tumor progression. The study highlights RBMX2 as a novel host factor that could serve as a therapeutic target for both TB pathogenesis and infection-related cancer risk.

Strengths:

The major strengths lie in its multi-omics integration (transcriptomics, proteomics, metabolomics) to map RBMX2's impact on host pathways, combined with rigorous functional assays (knockout/knockdown, adhesion/invasion, barrier tests) that establish causality through the p65/MMP-9 axis. Validation across bovine and human cell models and in clinical tissue samples enhances translational relevance. Finally, identifying RBMX2 as a novel regulator linking mycobacterial infection to EMT and cancer progression opens exciting therapeutic avenues.

Weaknesses:

There are a few minor weaknesses like grammatical errors, spelling mistakes. Also, the manuscript is too dense; improving the narratives in the Results and Discussion section could help readers follow the logic of the experimental design and conclusions.

Reviewer #2 (Public review):

Aldridge et al. demonstrate the important role of IL-27 in limiting emergency myelopoiesis in response to Toxoplasma gondii infection. Interestingly, IL-27 acts specifically at the level of early haematopoietic progenitors, inducing STAT signalling, which, in this case, dampens proliferation and preserves HSC fitness.

They used different mouse genetic models such as HSC lineage tracing, IL27 and IL27R-deficient mice to show that :

HSCs actively participate in emergency myelopoiesis during Toxoplasma gondii infection.

The absence of IL27 and IL27R increases monocyte progenitors and monocytes, mainly inflammatory monocytes CCR2hi.

At steady state, loss of IL27 impairs HSC fitness as competitive transplantation shows long-term engraftment deficiency of IL27 BM cells. This impairment is exacerbated after infection.

IL27 is produced by various BM and other tissue cells at steady state and its expression increases with infection, mainly by increasing the number of monocytes producing it.

This article highlights a new mechanism that acts directly at the level of early hematopoietic cells to limit over-inflammation during infection.

Reviewer #3 (Public review):

Summary:

Chatzis et al showed that β-glucan trained macrophages have decreased phagocytic activity of apoptotic tumor cells and that is accompanied by lower levels of secreted IL-1β using mouse model.

Strengths:

This finding has potential impact on designing new cancer immunotherapeutic approaches by targeting macrophage efferocytosis.

The concerns have been addressed.

Reviewer #2 (Public review):

Hawes et al. investigated the role of striatal neurons in the patch compartment of the dorsal striatum. Using Sepw1-Cre line, the authors combined a modified version of the light/dark transition box test that allows them to examine locomotor activity in different environmental valence with a variety of approaches, including cell-type-specific ablation, miniscope calcium imaging, fiber photometry, and opto-/chemogenetics. First, they found ablation of patchy striatal neurons resulted in an increase in movement vigor when mice stayed in a safe area or when they moved back from more anxiogenic to safe environments. The following miniscope imaging experiment revealed that a larger fraction of striatal patchy neurons was negatively correlated with movement speed, particularly in an anxiogenic area. Next, the authors investigated differential activity patterns of patchy neurons' axon terminals, focusing on those in GPe, GPi, and SNr, showing that the patchy axons in SNr reflect movement speed/vigor. Chemogenetic and optogenetic activation of these patchy striatal neurons suppressed the locomotor vigor, thus demonstrating their causal role in the modulation of locomotor vigor when exposed to valence differentials. Unlike the activation of striatal patches, such a suppressive effect on locomotion was absent when optogenetically activating matrix neurons by using the Calb1-Cre line, indicating distinctive roles in the control of locomotor vigor by striatal patch and matrix neurons. Together, they have concluded that nigrostriatal neurons within striatal patches negatively regulate movement vigor, dependent on behavioral contexts where motivational valence differs.

The strengths of this work include the use of multiple experimental approaches, including genetic/viral ablation of patch neurons, miniscope single-cell imaging, as well as projection-specific recording of axonal activity by fiber photometry, and causal manipulation of the neurons by chemogenetic and optogenetics. Although similar findings were reported previously, the authors' results will be of value owing to multiple levels of investigation. In my view, this study will add to the important literature by demonstrating how patch (striosomal) neurons in the striatum controls movement vigor.

Reviewer #2 (Public review):

Summary:

Park et al. has made a tool for spatiotemporally restricted knockout of the astrocytic GABA transporter GAT3 leveraging CRISPR/Cas9 and viral transduction in adult mice, and evaluated the effects of GAT3 on neural encoding of visual stimulation.

Strengths:

This concise manuscript leverages state-of-the-art gene CRISPR/Cas9 technology for knocking out astrocytic genes. This has to a little degree been preformed previously in astrocytes and represents an important development in the field. Moreover they utilize in vivo two-photon imaging of neural responses to visual stimuli as a readout of neural activity, in addition to validating their data with ex vivo electrophysiology. Lastly, they use advanced statistical modeling to analyze the impact on GAT3 knockout. Overall, the study comes across as rigorous and convincing.

Weaknesses:

Adding the following experiments would potentially have strengthened the conclusions and helped interpret the findings, although may be considered outside the scope of this manuscript, and be pursued in future work:

(1) Neural activity is quite profoundly influenced by GAT3 knockout. Corroborating these relatively large changes to neural activity with in vivo electrophysiology of some sort as an additional readout would have strengthened the conclusions.

(2) Given the quite large effects on neural coding in visual cortex assessed with jRGECO imaging it would have been interesting the mouse groups could have been subjected to behavioral testing assessing the visual system.

Reviewer #2 (Public review):

This manuscript explores the mechanisms underlying cerebral cortical folding using a combination of physical modelling, computational simulations, and geometric morphometrics. The authors extend their prior work on human brain development (Tallinen et al., 2014; 2016) to a comparative framework involving three mammalian species: ferrets (Carnivora), macaques (Old World monkeys), and humans (Hominoidea). By integrating swelling gel experiments with mathematical differential growth models, they simulate sulcification instability and recapitulate key features of brain folding across species. The authors make commendable use of publicly available datasets to construct 3D models of fetal and neonatal brain surfaces: fetal macaque (ref. [26]), newborn ferret (ref. [11]), and fetal human (ref. [22]).

Using a combination of physical models and numerical simulations, the authors compare the resulting folding morphologies to real brain surfaces using morphometric analysis. Their results show qualitative and quantitative concordance with observed cortical folding patterns, supporting the view that differential tangential growth of the cortex relative to the subcortical substrate is sufficient to account for much of the diversity in cortical folding. This is a very important point in our field, and can be used in the teaching of medical students.

Brain folding remains a topic of ongoing debate. While some regard it as a critical specialization linked to higher cognitive function, others consider it an epiphenomenon of expansion and constrained geometry. This divergence was evident in discussions during the Strüngmann Forum on cortical development (Silver et al., 2019). Though folding abnormalities are reliable indicators of disrupted neurodevelopmental processes (e.g., neurogenesis, migration), their relationship to functional architecture remains unclear. Recent evidence suggests that the absolute number of neurons varies significantly with position-sulcus versus gyrus-with potential implications for local processing capacity (e.g., https://doi.org/10.1002/cne.25626). The field is thus in need of comparative, mechanistic studies like the present one.

This paper offers an elegant and timely contribution by combining gel-based morphogenesis, numerical modelling, and morphometric analysis to examine cortical folding across species. The experimental design - constructing two-layer PDMS models from 3D MRI data and immersing them in organic solvents to induce differential swelling - is well-established in prior literature. The authors further complement this with a continuum mechanics model simulating folding as a result of differential growth, as well as a comparative analysis of surface morphologies derived from in vivo, in vitro, and in silico brains.

I offer a few suggestions here for clarification and further exploration:

Major Comments

(1) Choice of Developmental Stages and Initial Conditions

The authors should provide a clearer justification for the specific developmental stages chosen (e.g., G85 for macaque, GW23 for human). How sensitive are the resulting folding patterns to the initial surface geometry of the gel models? Given that folding is a nonlinear process, early geometric perturbations may propagate into divergent morphologies. Exploring this sensitivity-either through simulations or reference to prior work-would enhance the robustness of the findings.

(2) Parameter Space and Breakdown Points

The numerical model assumes homogeneous growth profiles and simplifies several aspects of cortical mechanics. Parameters such as cortical thickness, modulus ratios, and growth ratios are described in Table II. It would be informative to discuss the range of parameter values for which the model remains valid, and under what conditions the physical and computational models diverge. This would help delineate the boundaries of the current modelling framework and indicate directions for refinement.

(3) Neglected Regional Features: The Occipital Pole of the Macaque

One conspicuous omission is the lack of attention to the occipital pole of the macaque, which is known to remain smooth even at later gestational stages and has an unusually high neuronal density (2.5× higher than adjacent cortex). This feature is not reproduced in the gel or numerical models, nor is it discussed. Acknowledging this discrepancy-and speculating on possible developmental or mechanical explanations-would add depth to the comparative analysis. The authors may wish to include this as a limitation or a target for future work.

(4) Spatio-Temporal Growth Rates and Available Human Data

The authors note that accurate, species-specific spatio-temporal growth data are lacking, limiting the ability to model inhomogeneous cortical expansion. While this may be true for ferret and macaque, there are high-quality datasets available for human fetal development, now extended through ultrasound imaging (e.g., https://doi.org/10.1038/s41586-023-06630-3). Incorporating or at least referencing such data could improve the fidelity of the human model and expand the applicability of the approach to clinical or pathological scenarios.

(5) Future Applications: The Inverse Problem and Fossil Brains

The authors suggest that their morphometric framework could be extended to solve the inverse growth problem-reconstructing fetal geometries from adult brains. This speculative but intriguing direction has implications for evolutionary neuroscience, particularly the interpretation of fossil endocasts. Although beyond the scope of this paper, I encourage the authors to elaborate briefly on how such a framework might be practically implemented and validated.

Conclusion

This is a well-executed and creative study that integrates diverse methodologies to address a longstanding question in developmental neurobiology. While a few aspects-such as regional folding peculiarities, sensitivity to initial conditions, and available human data-could be further elaborated, they do not detract from the overall quality and novelty of the work. I enthusiastically support this paper and believe that it will be of broad interest to the neuroscience, biomechanics, and developmental biology communities.

Note: The paper mentions a companion paper [reference 11] that explores the cellular and anatomical changes in the ferret cortex. I did not have access to this manuscript, but judging from the title, this paper might further strengthen the conclusions.

Reviewer #2 (Public review):

Summary:

The authors aim to quantify passive muscle forces in the legs of Drosophila, and test the hypothesis that these forces would be sufficient to support body weight in small insects. They take advantage of the genetic tools available in Drosophila, and use a combination of genetic silencing (optogenetic inactivation of motor neurons), kinematic measurements, and simulations using OpenSim. This integrative toolkit is used to examine the role of passive torques across multiple leg joints. They find that passive forces are weaker than expected - in particular, passive forces were found to be too weak to support the body weight of the fly. This challenges previous scaling assumptions derived from studies in larger insects and has potential implications for our understanding of motor control in small animals.

Strengths:

The primary strength of this work lies in its integration of multiple analyses. By pulling together simulations, kinematic measurements from high-resolution videos, and genetic manipulation, they are able to overcome limitations of past studies. In particular, optogenetic manipulation allowed for measurements to be made in whole animals, and the modeling component is valuable because it both validates experimental findings and elucidates the mechanism behind some of the observed dynamic consequences (e.g., the rapid fall after motor inactivation). The conclusions made in the study are well-supported by the data and could have an impact on a number of fields, including invertebrate neurobiology and bioinspired design.

Weaknesses:

While (as mentioned above) the study's conclusions are well-supported by the results and modeling, limitations arise because of the assumptions made. For instance, using a linear approximation may not hold at larger joint angles, and future studies would benefit from accounting for nonlinearities. Future studies could also delve into the source of passive forces, which is important for more deeply understanding the anatomical and physical basis of the results in this study. For instance, assessments of muscle or joint properties to correlate stiffness values with physical structure might be an area of future consideration

Reviewer #2 (Public review):

Summary:

Based on MRI data of the ferret (a gyrencephalic non-primate animal, in whom folding happens postnatally), the authors create in vitro physical gel models and in silico numerical simulations of typical cortical gyrification. They then use genetic manipulations of animal models to demonstrate that cortical thickness and expansion rate are primary drivers of atypical morphogenesis. These observations are then used to explain cortical malformations in humans.

Strengths:

The paper is very interesting and original, and combines physical gel experiments, numerical simulations, as well as observations in MCD. The figures are informative, and the results appear to have good overall face validity.

Weaknesses:

On the other hand, I perceived some lack of quantitative analyses in the different experiments, and currently, there seems to be rather a visual/qualitative interpretation of the different processes and their similarities/differences.

Ideally, the authors also quantify local/pointwise surface expansion in the physical and simulation experiments, to more directly compare these processes. Time courses of eg, cortical curvature changes, could also be plotted and compared for those experiments.

I had a similar impression about the comparisons between simulation results and human MRI data. Again, face validity appears high, but the comparison appeared mainly qualitative.

I felt that MCDs could have been better contextualized in the introduction.

Reviewer #2 (Public review):

Summary:

Foik et al. studied the regulation of the fro operon in response to HOCl, an oxidant derived from immune cells, especially neutrophils. They use a transcriptional fusion of YFP to the froA promoter in an mCherry-expressing P. aeruginosa strain to determine fro-induction under the microscope. They use this system to study fro expression in medium, in the presence of neutrophils and macrophages, neutrophil-conditioned medium, and several chemical stimuli, including NaCl, HOCl, hydrogen peroxide, nitric acid, hydrochloric acid, and sodium hydroxide. They also use a corneal infection model to demonstrate that froA is upregulated in P. aeruginosa 20 h post-infection and perform transcriptional analyses in WT and a froR mutant in response to HOCl.

Strengths:

Their data clearly shows that HOCl is a strong inducer of the fro Operon. The addition of HOCl-quenching chemicals together with HOCl abrogates the response. They also show that a froR mutant is more susceptible to HOCl than WT. Their transcriptomic data reveal genes under control of the FroR/FroI sigma factor/anti sigma factor system.

Weaknesses:

Although the presented evidence is mostly solid, some of their findings need to be evaluated more carefully; explaining the rationale behind some of the experiments might enhance the article, and some of the models proposed by the authors seem far-fetched, as outlined below:

(1) In line 76 the authors claim "Relative to P. aeruginosa that were incubated in host cell-free media, P. aeruginosa in close proximity to human neutrophils or that were engulfed in mouse macrophages appeared to increase fro expression (Fig. 1C)". Counting bacterial cells in Figure 1C shows that 1 in 17 bacteria (5.8%) induce the froA-promotor in media in the absence of immune cells, while 4 in 72 bacteria (only 5.5%) do the same in the presence of neutrophils. Contrary to the authors' claims, it appears that P. aeruginosa actually decreases fro-expression in close proximity to neutrophils. There is a slight increase in fro-expression in bacteria co-incubated with macrophages (3 in 21, or 14.3%). A more rigorous statistical analysis might substantiate the authors' claim, but, as is, the claim "neutrophils increase fro expression" is untenable.

(2) The authors should explain the rationale behind some of the chemicals used. Why did they use nitric acid? Especially at these high concentrations, a strong acid such as nitric acid might have a significant influence on the medium pH. I understand that the medium is phosphate-buffered, but 25 mM nitric acid in an unbuffered medium would shift the pH well below 2. Similar considerations apply to hydrochloric acid and sodium hydroxide.

(3) In line 187, the authors state that "It is possible that oxidized methionine increases fro expression" and they suggest a model to that effect in Figure 5D. It is unclear why the authors singled out methionine sulfoxide, since a number of other things get oxidized by HOCl. In line 184, the authors state, in the same vein, that "HOCl oxidizes methionine residues 100-fold more rapidly than other cellular components". The authors should state which other cellular compounds they are referring to. Certainly not cysteine and other thiols, which react equally fast and are highly abundant in the cell: P. aeruginosa contains 340 µM GSH, 140 µM CoA-SH (https://doi.org/10.1074/jbc.RA119.009934) plus free cysteine and cysteines in proteins (based on codon usage, 1.34% of amino acids in proteins are cysteine, while methionine is only slightly more present at 2.10%, although a number of starting methionines are removed from mature proteins).

(4) Overall (and this is probably not addressable with the authors' data), some very interesting questions remain unanswered: what is the molecular mechanism of fro-induction? How is the FroR/FroI system modulated by HOCl? Does the system sense free or protein-bound methionine-sulfoxide? Are certain methionine residues in these proteins directly oxidized by HOCl? Many "HOCl-sensing" proteins are also modified at cysteine residues or amino groups; could those play a role? And lastly: what is the connection between shear/fluid flow and HOCl, or are these totally separate mechanisms of fro-induction?

Reviewer #2 (Public review):

Summary:

These studies investigate the phenotypic variability and roles of neutrophils in tuberculosis (TB) susceptibility by using a diverse collection of wild-derived inbred mouse lines. The authors aimed to identify new phenotypes during Mycobacterium tuberculosis infection by developing, infecting, and phenotyping 19 genetically diverse wild-derived inbred mouse lines originating from different geographic regions in North America and South America. The investigators achieved their main goals, which were to show that increasing genetic diversity increases the phenotypic spectrum observed in response to aerosolized M. tuberculosis, and further to provide insights into immune and/or inflammatory correlates of pulmonary TB. Briefly, investigators infected wild-derived mice with aerosolized M. tuberculosis and assessed early infection control at 21 days post-infection. The time point was specifically selected to correspond to the period after infection when acquired immunity and antigen-specific responses manifest strongly, and also early susceptibility (morbidity and mortality) due to M. tuberculosis infection has been observed in other highly susceptible wild-derived mouse strains, some Collaborative Cross inbred strains, and approximately 30% of individuals in the Diversity Outbred mouse population. Here, the investigators normalized bacterial burden across mice based on inoculum dose and determined the percent of immune cells using flow cytometry, primarily focused on macrophages, neutrophils, CD4 T cells, CD8 T cells, and B cells in the lungs. They also used single-cell RNA sequencing to identify neutrophil subpopulations and immune phenotypes, elegantly supplemented with in vitro macrophage infections and antibody depletion assays to confirm immune cell contributions to susceptibility. The main results from this study confirm that mouse strains show considerable variability to M. tuberculosis susceptibility. Authors observed that enhanced infection control correlated with higher percentages of CD4 and CD8 T cells, and B cells, but not necessarily with the percentage of interferon-gamma (IFN-γ) producing cells. High levels of neutrophils and immature neutrophils (band cells) were associated with increased susceptibility, and the mouse strain with the most neutrophils, the MANC line, exhibited a transcriptional signature indicative of a highly activated state, and containing potentially tissue-destructive, mediators that could contribute to the strain's increased susceptibility and be leveraged to understand how neutrophils drive lung tissue damage, cavitation, and granuloma necrosis in pulmonary TB.

Strengths:

The strengths are addressing a critically important consideration in the tuberculosis field - mouse model(s) of the human disease, and taking advantage of the novel phenotypes observed to determine potential mechanisms. Notable strengths include,

(1) Innovative generation and use of mouse models: Developing wild-derived inbred mice from diverse geographic locations is innovative, and this approach expands the range of phenotypic responses observed during M. tuberculosis infection. Additionally, the authors have deposited strains at The Jackson Laboratory making these valuable resources available to the scientific community.

(2) Potential for translational research: The findings have implications for human pulmonary TB, particularly the discovery of neutrophil-associated susceptibility in primary infection and/or neutrophil-mediated disease progression that could both inform the development of therapeutic targets and also be used to test the effectiveness of such therapies.

(3) Comprehensive experimental design: The investigators use many complementary approaches including in vivo M. tuberculosis infection, in vitro macrophage studies, neutrophil depletion experiments, flow cytometry, and a number of data mining, machine learning, and imaging to produce robust and comprehensive analyses of the wild-derives d strains and neutrophil subpopulations in 3 weeks after M. tuberculosis infection.

Weaknesses:

The manuscript and studies have considerable strengths and very few weaknesses. One minor consideration is that phenotyping is limited to a single limited-time point; however, this time point was carefully selected and has a strong biological rationale provided by investigators. This potential weakness does not diminish the overall findings, exciting results, or conclusions.

Reviewer #2 (Public review):

Pinon and colleagues have developed a Vessel-on-Chip model showcasing geometrical and physical properties similar to the murine vessels used in the study of systemic infections. The vessel was created via highly controllable laser photoablation in a collagen matrix, subsequent seeding of human endothelial cells, and flow perfusion to induce mechanical cues. This model could be infected with Neisseria meningitidis as a model of systemic infection. In this model, microcolony formation and dynamics, and effects on the host were very similar to those described for the human skin xenograft mouse model (the current gold standard for systemic studies) and were consistent with observations made in patients. The model could also recapitulate the neutrophil response upon N. meningitidis systemic infection.

The claims and the conclusions are supported by the data, the methods are properly presented, and the data is analyzed adequately. The most important strength of this manuscript is the technology developed to build this model, which is impressive and very innovative. The Vessel-on-Chip can be tuned to acquire complex shapes and, according to the authors, the process has been optimized to produce models very quickly. This is a great advancement compared with the technologies used to produce other equivalent models. This model proves to be equivalent to the most advanced model used to date (skin xenograft mouse model). The human skin xenograft mouse model requires complex surgical techniques and has the practical and ethical limitations associated with the use of animals. However, the Vessel-on-chip model is free of ethical concerns, can be produced quickly, and allows to precisely tune the vessel's geometry and to perform higher resolution microscopy. Both models were comparable in terms of the hallmarks defining the disease, suggesting that the presented model can be an effective replacement of the animal use in this area. In addition, the Vessel-on-Chip allows to perform microscopy with higher resolution and ease, which can in turn allow more complex and precise image-based analysis.

A limitation of this model is that it lacks the multicellularity that characterizes other similar models, which could be useful to research disease more extensively. However, the authors discuss the possibilities of adding other cells to the model, for example, fibroblasts. It is also not clear whether the technology presented in the current paper can be adopted by other labs. The methodology is complex and requires specialized equipment and personnel, which might hinder its widespread utilization of this model by researchers in the field.

This manuscript will be of interest for a specialized audience focusing on the development of microphysiological models. The technology presented here can be of great interest to researchers whose main area of interest is the endothelium and the blood vessels, for example, researchers on the study of systemic infections, atherosclerosis, angiogenesis, etc. This manuscript can have great applications for a broad audience and it can present an opportunity to begin collaborations, aimed at answering diverse research questions with the same model.

Reviewer #2 (Public review):

Summary:

Schmid & colleagues test an interesting hypothesis that V1 neurons might act as theta-tuned filters to incoming sensory information, and thereby influence downstream processing and detection performance.

Strengths:

The authors report that circular stimuli elicit theta oscillations in V1 single units and population activity. They also report that the phase of the theta oscillations influences performance in a change detection task.

Weaknesses:

The results are reported in terms of specific stimulus sizes. To truly reflect general-purpose spatial computations in the primary visual cortex, it will be important to establish a relationship between stimulus size and receptive field size.

I have several major concerns that I would like the authors to address:

(1) First paragraph of Results: The results are presented at very specific stimulus sizes: 0.3-degree, 1-degree, 4-degree, and so on. A key missing piece of information is the size of the receptive fields (RFs) that were recorded from. A related missing information is at what eccentricity these RFs were recorded from. Since there is nothing magical about a 1-degree stimulus, any general-purpose computation in the primary visual cortex has to establish a relationship between RF size and stimulus size.

(2) Second paragraph of Results: The authors state that "specific stimulus sizes consistently induced strong theta rhythmic activity: 1{degree sign} in MUA and 2{degree sign} in LFP". What is the interpretation of these specific sizes? Given that the LFP and MUAe reflect different aspects of neural activity, how does one interpret the discrepancy?

(3) Third paragraph of Results: Again related to (1), what is the relationship between the stimulus size that elicited the largest theta peaks and RF size at the population level? (1)-(3) taken together, there seems to be an opportunity to reveal something more fundamental about V1 processing that the authors might have missed here.

(4) Change detection task: It was not clear to me whether the timing of the luminance change, which varied from 500ms to 1500ms, was drawn from an exponential distribution or a uniform distribution. Only an exponential distribution has the property of a flat hazard function, which will be important to establish that the animal could not anticipate the timing of the upcoming change.

(5) Figure 3D: Have the authors tried to fit the data separately for each animal? There seems to be an inconsistency in the results between the 2 animals. The circular data points ('AL') seem positively correlated, similar to the overall trend, but the diamond data points ('DP') seem to have a negative slope.

Reviewer #2 (Public review):

Sasaki et al. use a combination of live-cell biosensors and patch-clamp electrophysiology to investigate the effect of membrane potential on the ERK MAPK signaling pathway, and probe associated effects on proliferation. This is an effect that has long been proposed, but a convincing demonstration has remained elusive, because it is difficult to perturb membrane potential without disturbing other aspects of cell physiology in complex ways. The time-resolved measurements here are a nice contribution to this question, and the perforated patch clamp experiments with an ERK biosensor are fantastic - they come closer to addressing the above difficulty of perturbing voltage than any prior work. It would have been difficult to obtain these observations with any other combination of tools.

However, there are still some concerns as detailed in specific comments below:

Specific comments:

(1) All the observations of ERK activation, by both high extracellular K+ and voltage clamp, could be explained by cell volume increase (more discussion in subsequent comments). There is a substantial literature on ERK activation by hypotonic cell swelling (e.g. https://doi.org/10.1042/bj3090013, https://doi.org/10.1002/j.1460-2075.1996.tb00938.x, among others). Here are some possible observations that could demonstrate that ERK activation by volume change is distinct from the effects reported here:

i) Does hypotonic shock activate ERK in U2OS cells?

ii) Can hypotonic shock activate ERK even after PS depletion, whereas extracellular K+ cannot?

iii) Does high extracellular K+ change cell volume in U2OS cells, measured via an accurate method such as fluorescence exclusion microscopy?

iv) It would be helpful to check the osmolality of all the extracellular solutions, even though they were nominally targeted to be iso-osmotic.

(2) Some more details about the experimental design and the results are needed from Figure 1:

i) For how long are the cells serum-starved? From the Methods section, it seems like the G1 release in different K+ concentration is done without serum, is this correct? Is the prior thymidine treatment also performed in the absence of serum?

ii) There is a question of whether depolarization constitutes a physiologically relevant mechanism to regulate proliferation, and how depolarization interacts with other extracellular signals that might be present in an in vivo context. Does depolarization only promote proliferation after extended serum starvation (in what is presumably a stressed cell state)? What fraction of total cells are observed to be mitotic (without normalization), and how does this compare to the proliferation of these cells growing in serum-supplemented media? Can K+ concentration tune proliferation rate even in serum-supplemented media?

(3) In Figure 2, there are some possible concerns with the perfusion experiment:

i) Is the buffer static in the period before perfusion with high K+, or is it perfused? This is not clear from the Methods. If it is static, how does the ERK activity change when perfused with 5 mM K+? In other words, how much of the response is due to flow/media exchange versus change in K+ concentration?

ii) Why do there appear to be population-average decreases in ERK activity in the period before perfusion with high K+ (especially in contrast to Fig. 3)? The imaging period does not seem frequent enough for photobleaching to be significant.

(4) Figure 3 contains important results on couplings between membrane potential and MAPK signaling. However, there are a few concerns:

i) Does cell volume change upon voltage clamping? Previous authors have shown that depolarizing voltage clamp can cause cells to swell, at least in the whole-cell configuration:

https://www.cell.com/biophysj/fulltext/S0006-3495(18)30441-7 . Could it be possible that the clamping protocol induces changes in ERK signaling due to changes in cell volume, and not by an independent mechanism?

ii) Does the -80 mV clamp begin at time 0 minutes? If so, one might expect a transient decrease in sensor FRET ratio, depending on the original resting potential of the cells. Typical estimates for resting potential in HEK293 cells range from -40 mV to -15 mV, which would reach the range that induces an ERK response by depolarizing clamp in Fig. 3B. What are the resting potentials of the cells before they are clamped to -80 mV, and why do we not see this downward transient?

(5) The activation of ERK by perforated voltage clamp and by high extracellular K+ are each convincing, but it is unclear whether they need to act purely through the same mechanism - while additional extracellular K+ does depolarize the cell, it could also be affecting function of voltage-independent transporters and cell volume regulatory mechanisms on the timescales studied. To more strongly show this, the following should be done with the HEK cells where there is already voltage clamp data:

i) Measure resting potential using the perforated patch in zero-current configuration in the high K+ medium. Ideally this should be done in the time window after high K+ addition where ERK activation is observed (10-20 minutes) to minimize the possibility of drift due to changes in transporter and channel activity due to post-translational regulation.

ii) Measure YFP/CFP ratio of the HEK cells in the high K+ medium (in contrast to the U2OS cells from Fig. 2 where there is no patch data).

iii) The assertion that high K+ is equivalent to changes in Vmem for ERK signaling would be supported if the YFP/CFP change from K+ addition is comparable to that induced by voltage clamp to the same potential. This would be particularly convincing if the experiment could be done with each of the 15 mM, 30 mM, and 145 mM conditions.

(6) Line 170: "ERK activity was reduced with a fast time course (within 1 minute) after repolarization to -80 mV." I don't see this in the data: in Fig. 3C, it looks like ERK remains elevated for > 10 min after the electrical stimulus has returned to -80 mV

Comments on revisions:

The authors have done a good job addressing the comments on the previous submission.

Reviewer #2 (Public review):

Summary:

Human histone H3K36 methyltransferase Setd2 has been previously shown to be a tumor suppressor in lung and pancreatic cancer. In this manuscript by Mack et al., the authors first use a mouse KRASG12D-driven lung cancer model to confirm in vivo that Setd2 depletion exacerbates tumorigenesis. They then investigate the enzymatic regulation of the Setd2 SET domain in vitro, demonstrating that H2A, H3, or H4 acetylation stimulates Setd2-SET activity, with specific enhancement by mono-acetylation at H3K14ac or H3K27ac. In contrast, histone ubiquitination has no effect. The authors propose that H3K27ac may regulate Setd2-SET activity by facilitating its binding to nucleosomes. This work provides insight into how cross-talk between histone modifications regulates Setd2 function.

Comments on revisions:

(1) Regarding New Figure 2F lane 1, please reference PMID: 33972509 Fig 4D bottom. Setd2-SET is a well-known robust K36 trimethylase. Why, under the authors' conditions, do WT nucleosomes show a significant amount of K36me1 and K36me2 accumulation, whereas K36me3 is not as pronounced? As a comparison, the authors should also report the evidence for the efficiency of each chemical modification that generates K36 methylation mimic.

(2) The bottom panel of Figure 2B does not match the top one; the number of repeats should be indicated in the figure legends.

(3) In Figure 4E, the differences between Setd2-bound WT and acetylated nucleosomes are minimal, as judged by both the decreasing trend of unbound nucleosomes and the increasing trend of bound fractions. This experiment needs to be quantified based on multiple repeats.

Reviewer #2 (Public review):

Summary:

In this manuscript by Wolfson et al., various adeno-associated viruses (AAVs) were delivered to mice to assess the cardiac-specificity, injury border-zone cardiomyocyte transduction rate, and temporal dynamics in the goal to find better AAVs for gene therapies targeting the heart. The authors delivered tissue regeneration enhancer elements (TREEs) controlling luciferase expression and used IVIS imaging to examine transduction in the heart and other organs. They found that luciferase expression increased in the first week after injury when using AAV9-TREE-Hsp68 promoter, waning to baseline levels by 7 weeks. However, AAV9 vectors transduced the liver, which was significantly reduced by using an AAV.cc84 liver de-targeting capsid. The authors then performed in vivo screening of AAV9 capsids and found AAV-IR41 to preferentially transduce injured myocardium when compared to AAV9. Finally, the authors combined TREEs with AAV-IR41 to show improved luciferase expression compared to AAV9-TREE at 7, 14 and 21 days after injury.

Overall, this manuscript provides insights into TREE expression dynamics when paired with various heart-targeting capsids, which can be useful for researchers studying ischemic injury of murine hearts. While the authors have shown the success of using AAV9-TREEs in porcine hearts, it is unknown whether the expression dynamics would be similar in pigs or humans, as mentioned in the limitations.

Strengths:

Important contribution to the AAV gene therapy literature.

Comments on revised version:

My concerns have been adequately addressed.

Reviewer #3 (Public review):

Summary:

In this work, Yamada, Brandani and Takada have developed a mesoscopic model of the interacting proteins in the postsynaptic density. They have performed simulations, based on this model and using the software ReaDDy, to study the phase separation in this system in 2D (on the membrane) and 3D (in the bulk). They have carefully investigated the reasons behind different morphologies observed in each case, and have looked at differences in valency, specific/non-specific interactions and interfacial tension.

Strengths:

The simulation model is developed very carefully, with strong reliance on binding valency and geometry, experimentally measured affinities, and physical considerations like the hydrodynamic radii. The presented analyses are also thorough, and great effort has been put into investigating different scenarios that might explain the observed effects.

Weaknesses:

The biggest weakness of the study, in my opinion, has been a lack of more in-depth and quantitative physical insights about phase separation theories. In the revised version, the authors have added text to point the interested reader to the respective theories, and have included a qualitative assessment of their findings in the light of said theories. This better positions their discussion. I still believe the role of entropic effects need more attention, which can be the subject of future studies.

The authors have revised their Introduction and added text to the Discussion, to enrich their view on the attractive and repulsive forces as well as mixing entropy. This version better covers the physics of phase separation.

I appreciate the added discussion about the different diffusive behavior in the membrane in contrast to the bulk (i.e. the Saffman-Delbrück model). This paves the way for future studies, including realistic kinetics of the studied system.

Reviewer #2 (Public review):

Summary:

In the study presented by Itani and colleagues it is shown that some strains of Aspergillus oryzae - especially those used industrially for the production of sake and soy sauce - develop hyphae with a significantly increased number of nuclei and cell volume over time. These thick hyphae are formed by branching from normal hyphae and grow faster and therefore dominate the colonies. The number of nuclei positively correlates with the thicker hyphae and also the amount of secreted enzymes. The addition of nutrients such as yeast extract or certain amino acids enhanced this effect. Genome and transcriptome analyses identified genes, including rseA, that are associated with the increased number of nuclei and enzyme production. The authors conclude from their data involvement of glycosyltransferases, calcium channels and the tor regulatory cascade in regulation of cell volume and number of nuclei. Thicker hyphae and an increased number of nuclei was also observed in high-production strains of other industrially used fungi such as Trichoderma reesei and Penicillium chrysogenum, leading to the hypothesis that the mentioned phenotypes are characteristic of production strains which is of significant interest for fungal biotechnology.

Strengths:

The study is very comprehensive and involves application of divers state-of-the-art cell biological, biochemical and genetical methods. Overall, the data are properly controlled and analyzed, figures and movies are of excellent quality.<br /> The results are particularly interesting with regard to the elucidation of molecular mechanisms that regulate the size of fungal hyphae and their number of nuclei. For this, the authors have discovered a very good model: (regular) strains with a low number of nuclei and strains with high number of nuclei. Also, the results can be expected to be of interest for the further optimization of industrially relevant filamentous fungi.

In the revision the authors addressed all my comments and as a result produced an even stronger study.

Reviewer #2 (Public review):

Summary:

The authors investigate the behavior of oncogenic cells in mammary and bronchial epithelia. They observe that individual oncogenic cells are preferentially excluded from the mammary epithelium, but they remain integrated in the bronchial epithelium. They also observe that clusters of oncogenic cells form a compact cluster in the mammary epithelium, but they disperse in the bronchial epithelium. The authors demonstrate experimentally and in the vertex model simulations that the difference in observed behavior is due to the differential tension between the mutant and wild-type cells due to a differential expression of actin and myosin.

Strengths:

(1) Very detailed analysis of experiments to systematically characterize and quantify differences between mammary and bronchial epithelia.

(2) Detailed comparison between the experiments and vertex model simulations to identify the differential cell line tension between the oncogenic and wild-type cells as one of the key parameters that are responsible for the different behavior of oncogenic cells in mammary and bronchial epithelia

Weaknesses:

(1) It is unclear what the mechanistic origin of the shape-tension coupling is, which is used in the vertex model, and how important that coupling is for the presented results. The authors claim that the shape-tension coupling is due to the anisotropic distribution of stress fibers when cells are under external stress. It is unclear why the stress fibers should affect an effective line tension on the cell boundaries and why the stress fibers should be sensitive to the magnitude of the internal isotropic cell pressure. In experiments, it makes sense that stress fibers form when cells are stretched. Similar stress fibers form when the cytoskeleton or polymer networks are stretched. It is unclear why the stress fibers should be sensitive to the magnitude of internal isotropic cell pressure. If all the surrounding cells have the same internal pressure, then the cell would not be significantly deformed due to that pressure, and stress fibers would not form. The authors should better justify the use of the shape-tension coupling in the model and also present simulation results without that coupling. I expect that most of the observed behavior is already captured by the differential tension, even if there is no shape-tension coupling.

(2) The observed difference of shape indices between the interfacial and bulk cells in simulations in the absence of differential line tension is concerning. This suggests that either there are not enough statistics from the simulations or that something is wrong with the simulations. For all presented simulation results, the authors should repeat multiple simulations and then present both averages and standard deviations. This way, it would be easier to determine whether the observed differences in simulations are statistically significant.

(3) The authors should also analyze the cell line tension data in simulations and make a comparison with experiments.

Reviewer #2 (Public review):

Summary:

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, well embedded in experimental data.

Authors have done a great job addressing reviewers' concerns

Weaknesses:

None

Reviewer #2 (Public review):

In this study, Fontana et al. develop a paradigm for associative conditioning by pairing exposure to an alarm substance with a novel tank. Exposure to conspecific alarm substance (CAS) in the novel tank triggers freezing and what they characterize as evasive swimming behaviour, which is subsequently seen in a re-exposure to the novel tank without the CAS present. Importantly, these states are identified via automated processes, including postural tracking and a random forest classification process, which could be very useful tools for subsequent studies.

In their experiments, they focus on the differences in behaviour among strains of zebrafish (both males and females), and among individual zebrafish. For males and females of different strains, they find some differences, though the clearest message seems to be that the most robust measure of the behaviour in response to both the CAS and in the memory trials is the freezing behaviour, while evasive behaviour is more variable. and not always seen. This may relate to their observation of significant "evasiveness" in vehicle control experiments (discussed further below).

Moving on to individual variation from within this multi-strain male/female dataset, they first examine transition matrices between states and find tthat his is not dramatically altered by stimulus exposure. They then use clustering to identify 4 different "classes" of zebrafish that differ in their expression (or not) of two types of behaviour: freezing and/or evasive behaviour. They show that over the three exposure epochs of the experiment, this classification is somewhat stable in an individual fish, though many fish change their behaviour - e.g., evading + freezing -> only freezing.

In the final set of experiments, the authors move beyond behavioural analyses and perform whole-brain cFos mapping of these individual zebrafish. They perform analyses aimed at identifying correlations between individual behavioural expression and the number of cFos-positive cells in different brain regions. Using partial least squares analysis, they find areas associated with two types of behavioural contrasts, which differ in their weighting of different behavioural expression during the Memory trials. Covariation and network structure analysis within different classes of larvae also find some differences in covariation among brain areas, providing hypotheses as to underlying network effects that may govern the expression of freezing and/or evasive behavior in the memory trial phases.

Overall, I find this to be an interesting study that employs state of the are methods of behavioural analyses and whole-brain cFos analyses, but I am left a little bit confused as to what the take home message is and what can be concluded from this complex study that mixes in analyses of strain, sex, and individuality within a quite complex assay with multiple behavioural parameters.

My suggestions are as follows:

(1) My first concern relates to the claim in the abstract that "We found that fear memory behavior fell into four distinct groups: non-reactive, evaders, evading freezers, and freezers".

In my opinion, the "freezing" aspect is well supported as being both triggered by the CAS and for memory effect upon re-exposure to the tank, but I am less convinced about the "evasive" behaviour. In Figure 2, it appears that "evasiveness" is generally not increased in both the Exposure or Memory phases for many groups, and in Figure 5, it appears that "evasiveness" is expressed by nearly 50% of the fish in the pre-exposure condition before CAS addition and in all phases in the vehicle condition. Therefore, it appears that most of the expression of this behaviour is independent of any memory-based effect.

(2) My second concern relates to the claim in the abstract that "background strain and sex influenced how fish respond to CAS, with males more likely to increase evasive behaviors than females and the TU strain more likely to be non-reactive."

My understanding, based on the introduction and on the methods, is that it is likely important that the CAS be prepared from conspecifics of the same strain and sex, and for this reason, they prepared different CAS specific for each strain and each sex. Therefore, the "CAS" that is applied is necessarily different for each condition, and I am concerned about if the differences observed could relate more to variation in the quality, purity, concentration, etc. of the specific CAS samples for different groups, rather than their reactivity to the substance or their ability to form memories based on such experiences.

(3) My third concern relates to the interpretation of the cFos data.

As I mentioned above, I feel as though the behavioural analysis is perhaps more complex than is warranted via the inclusion of evasiveness, and I wonder if the conclusions from the experiments would be simpler if analyzed only from the perspective of freezing.

But considering the presented analyses: while I dont think there is anything wrong with the partial least squares approach and the network analyses, I am concerned that the simple messaging in the text does not reflect the complexity of this analysis combining different weightings of different behavioural characteristics in a behavioural contrast, or covariations among many regions and what such analyses mean at the level of brain function. For these reasons, I feel like statements along the lines of "Behavioral variation is driven by differences in the activity of brain regions outside the telencephalon, such as the cerebellum, preglomerular nuclei, preoptic area and hypothalamus" are not well supported.

Reviewer #2 (Public review):

Summary:

Overall, the work is exceptionally well done and controlled and the results properly and appropriately interpreted. While several of the approaches, while powerful, are somewhat indirect (i.e., following gene expression via ribosomal profiling) additional experiments utilizing traditional gene expression assays added in revision combine to ultimately provide a compelling answer to the main questions being asked.

The key finding from this work is the discovery that Apj1 regulates Hsf1 attenuation in a manner that includes Hsp70. That finding is strongly supported by the experimental data. While it would be ideal to also demonstrate Apj1-controlled differential binding of Ssa1/2 to Hsf1 at either the N- or C-terminal binding sites during attenuation, the Hsp70-Hsf1 interactions are difficult to reproducibly assess in cell extracts and are likely beyond the scope of this study. However, this work paves the way in the future for potential biochemical reconstitution assays that could elucidate both Hsp70-Hsf1 interactions as well as the distinct JDP-Hsf1 interactions reported here.

This discovery raises additional new questions about JDP specificity in HSR regulation and the role of JDPs in navigating protein aggregation and sensing of proteostatic challenge in the nucleus, thus advancing the field and opening new, exciting avenues for exploration.

Reviewer #2 (Public review):

This manuscript describes experiments characterising how malaria parasites respond to physiologically relevant heat-shock conditions. The authors show, quite convincingly, that moderate heat-shock appears to increase cytoadherance, likely by increasing trafficking of surface proteins involved in this process.

While generally of a high quality and including a lot of data, I have a few small questions and comments, mainly regarding data interpretation.

(1) The authors use sorbitol lysis as a proxy for trafficking of PSAC components. This is a very roundabout way of doing things and does not, I think, really show what they claim. There could be a myriad of other reasons for this increased activity (indeed, the authors note potential PSAC activation under these conditions). One further reason could be a difference in the membrane stability following heat shock, which may affect sorbitol uptake, or the fragility of the erythrocytes to hypotonic shock. I really suggest that the authors stick to what they show (increased PSAC) without trying to use this as evidence for increased trafficking of a number of non-specified proteins that they cannot follow directly.

(2) Supplementary Figure 6C/D: The KAHRP signal does not look like it should. In fact, it doesn't look like anything specific. The HSP70-X signal is also blurry and overexposed. These pictures cannot be used to justify the authors' statements about a lack of colocalisation in any way.

(3) Figure 6: This experiment confuses me. The authors purport to fractionate proteins using differential lysis, but the proteins they detect are supposed to be transmembrane proteins and thus should always be found associated with the pellet, whether lysis is done using equinatoxin or saponin. Have they discovered a currently unknown trafficking pathway to tell us about? Whilst there is a lot of discussion about the trafficking pathways for TM proteins through the host cell, a number of studies have shown that these proteins are generally found in a membrane-bound state. The authors should elaborate, or choose an experiment that is capable of showing compartment-specific localisation of membrane-bound proteins (protease protection, for example).

(4) The red blood cell contains, in addition to HSP70-X, a number of human HSPs (HSP70 and HSP90 are significant in this current case). As the name suggests, these proteins non-specifically shield exposed hydrophobic domains revealed upon partial protein unfolding following thermal insult. I would thus have expected to find significantly more enrichment following heat shock, but this is not the case. Is it possible that the physiological heat shock conditions used in this current study are not high enough to cause a real heat shock?

Reviewer #2 (Public review):

Summary:

The C-terminal region of EB1 is responsible for protein-protein interactions, thereby recruiting the binding partners of EB1 to microtubules; the coiled-coil region (EBH) and the acidic tail are critical for their binding partners. The authors demonstrated by using NMR that the binding mode of EBH with the SxIP motif, which is a two-step process termed "dock-and-lock". The ITC analysis supports the results obtained from NMR. The initial version of the manuscript contained ambiguities on the ITC data; however, the results of the revised manuscript are convincing and support the two-step binding model.

Strength:

The authors propose a novel model of "dock-and-lock" by using multiple methods of NMR, ITC and cell biology.