Reviewer #1 (Public review):

[Editors' note: the authors have revised the work in response to the original reviews.]

Summary:

This paper describes experiments with alpha-synuclein (aS) with acetylated lysines (acK) at various positions. Their findings on how to use non-canonical amino acid (ncAA) mutagenesis to generate aS with acetylated lysines are valuable. The paper then continues with a range of experiments to characterise the acetylated alpha-synuclein constructs at different positions, with the aim of providing insights into which sites are relevant to disease or their function inside cells. The paper concludes these experiments with the suggestion that inhibiting the Zn2+-dependent histone deacetylase HDAC8 to potentially increase acetylation at lysine 80 may have therapeutic benefit. However, the relevance of most of these experiments is unclear, mainly as the filaments that form from these constructs are different from those observed in human disease (but see below for more details). Moreover, using the recombinantly produced acetylated versions of alpha-synuclein to normalise mass-spectrometry data, the authors themselves report that acetylation of alpha-synuclein does not differ between individuals with Parkinson's disease or healthy controls.

Strengths:

The authors report difficulties with chemical synthesis and then decide to make these constructs using non-canonical amino acid (ncAA) mutagenesis, which seems to work reasonably well (yields vary somewhat). In the Conclusion section, the authors report that they used these recombinant proteins to obtain quantitative insights into the levels of acetylation of lysines in individuals with PD versus healthy controls, for which they find no significant differences. This part of the work is valuable.

Weaknesses:

The authors then use circular dichroism to show that aSyn with acK at position 43 has less alpha-helical content. From this result, they deduce that "only this site could potentially perturb aS function in neurotransmitter trafficking", but no experiments on neurotransmitter trafficking were performed.

Reviewer #1 (Public review):

In the manuscript by Fabian-Fine et al., the authors employ neuroanatomy to investigate aquaporin-4 expression in cells they consider tanycytes and their supposed involvement in tau tangles and amyloid-beta plaques in the hippocampus. This study includes samples from three mice and two Alzheimer's disease (AD) patients.

My key concern and question is whether the cells presented in the manuscript are tanycytes. Tanycytes are specialized ependymoglial cells located in the circumventricular organs and are known to express specific markers. Importantly, they are not myelinated cells, which is a crucial distinction that the authors do not address.

Additionally, the methodologies described in the manuscript lack clarity and controls. For instance, the use of Cdh5-GCaMP882 mice is not adequately justified. It is unclear what these mice contribute to the study's objectives, particularly concerning the aim of investigating waste removal processes in the brain. Moreover, the rationale behind the purported "fluorophore uptake experiments" is unclear and appears to involve the uptake of fluorophore-labeled goat anti-rabbit secondary antibody, which seems implausible to me.

The hypotheses and claims presented in this manuscript are not sufficiently substantiated and are conceptually unclear. The notion that amyloid beta and tau proteins play structural roles in a hypothesized "tanycytes"-derived canal network is not sufficiently supported by the evidence. Furthermore, the study lacks rigorous data to convincingly establish the proposed interactions between these proteins and the processes of waste internalization.

In conclusion, due to conceptual and methodological issues, I consider the current evidence as inadequate to support the primary claims.

Reviewer #1 (Public review):

Summary:

This manuscript describes the development and validation of a low-cost device to identify viruses from saliva samples of animals non-invasively. This device was tested under laboratory conditions to assess whether viruses could be recovered in different environmental conditions and after different durations of time. The devices were then used to sample mice and cats in shelters to assess utility.

Strengths:

Sampling animals is cost-effective and highly labour-intensive, and this device has the potential to substantially improve surveillance. The device is relatively low-cost, and the authors demonstrate that the virus can be obtained from these filter papers after different durations of time and in different environmental conditions.

Weaknesses:

The authors do not discuss if different volumes were obtained from different animals (for example, due to different behaviours or attractiveness of the odour baits). Additionally, it appears the virus results were cross-validated using the serological status of the animals. While I am not an expert on FIV, there seems that there could be potential for different levels of viral shedding, and it would be more prudent to cross-validate against blood or another gold standard sample. Finally, the statistical analysis could be improved as there appear to be relatively few replicates and limited analysis conducted.

Reviewer #1 (Public review):

Summary:

This carefully executed study uncovers the functional relevance of curl signals that impinge on the retina every time an observer's gaze direction and movement direction are not aligned. This finding is important, highlighting the functional role of an abundant incidental signal (curl in retinal motion) that has thus far believed to be a nuisance that needs to be filtered out of the retinal motion stream. As such, the study forms an important contribution to the emerging recognition that incidental sensory signals are not a challenge to the sensorimotor system, but contain functionally relevant and effectively used visual signals. The study's evidence is compelling: A combination of psychophysical experiments and critical manipulations, control theory and neural modeling makes an internally consistent and biologically plausible case for the role of curl signals in estimating heading direction. The experimental and modeling results clearly go beyond previous studies and significantly advance our understanding of vision-based navigation.

Strengths:

The study has its strengths in the combination of psychophysical experiments and critical manipulations, control theory and neural modeling, which together make an internally consistent and biologically plausible case for the role of curl signals in estimating heading direction.

This study uncovers the functional relevance of curl signals that occur on the retina when an observer is moving and gaze is not straight ahead. The experimental and modeling results clearly go beyond previous studies and significantly advance our understanding of vision-based navigation.

Another clear strength is that the study uses tightly controlled experimental manipulation to provide strong test cases for the hypothesis that curl is used for visual navigation. These conditions are important to constrain the proposed model (and future models) of heading control.

The modeling is very clearly described and the modeling and analysis code is published and freely available. The authors go beyond a back-of-the-envelope control model and show how it might be implemented at the neural-circuit level. The model is biologically plausible.

Weaknesses:

I see no major weaknesses of the study. I expect it to inspire future research that extends these findings to a wider range of visual environments (including walking in natural scenes), motion speeds and kinds of movements.

Comments on revised version.

I have no additional comments for the authors.

Reviewer #1 (Public review):

Summary:

Poh and colleagues investigate dopamine signaling in the nucleus accumbens (ventromedial striatum) in rats engaged in several forms of go/no-go tasks, that differed in reward controllability (self-initiated reward seeking or cue-evoked/quasi-pavlovian), and in the specific timing of the action-reward contingencies. They analysis dopamine recordings made with fast scan cyclic voltammetry and find that dopamine signals vary most consistently to cues that signal a required action (go cues) vs cue signaling action withholding (no go cues). Through various analysis they report that dopamine signals align most clearly with action initiation and with the approach to the reward-delivery location. Collectively these data support aspects of a variety of frameworks related to accumbens dopamine signaling in movement, action vigor, approach, etc.

Strengths:

These studies use several task variants that consolidate a few different components of dopamine signal functions and allow for a broad comparison of many psychological and behavioral aspects. The behavioral analysis is detailed. These results touch on many previous findings, larger showing consistent results with past studies.

Weaknesses:

The paper is dense and could benefit from some revision to increase clarity of the figures, the methods and analysis. The inclusion of many tasks is a strength but also somewhat overshadows specific points in the data, which could be improved with some revision to focus. There is a lack of strong connection between some of the findings, which if revised would help to emphasize the impact of the work.

Reviewer #1 (Public review):

This study by Gangadharan and colleagues provides significant progress towards a quantitative biochemical mechanism for Stu2 polymerase activity. A key conceptual advance is the novel application of an enzyme-like model, initially developed for the actin polymerase Ena/VASP, to Stu2.

Strength:

New refined affinity measurements for a Stu2 TOG domain using Bio-layer interferometry show more than an order of magnitude higher affinity of TOG domains to tubulin compared to previously published reports.

The findings reinforce the "concentrating reactants" or, more specifically, for TOG-domain proteins, the "tubulin-shuttling antenna" model, compared to the "polarized unfurling" model, a more speculative structural hypothesis.

The manuscript builds upon a series of previous manuscripts that showcase the profound intellectual engagement with microtubule polymerization mechanisms by TOG-domain proteins from the Rice lab, a thought leader in microtubule polymerization for over a decade.

Minor weakness:

The affinity discrepancy is not fully resolved by side-by-side measurements, which seem to be not feasible as not all buffer conditions are compatible with all assays.

Reviewer #1 (Public review):

Summary:

The factors that create and maintain diversity in host-associated microbiomes remain poorly understood. A better understanding of these factors will help in the efforts to leverage the adaptive potential of the microbiome to help solve pressing problems in health and agriculture.

Experimental evolution provides a promising path forward as we can track the causes and consequences in the emergence of novel variants, but experimental evolution remains underutilized in host-microbiome interactions. Here, Gracia-Alvira utilizes a long-term experimental evolution study in Drosophila simulans under hot and cold temperature regimes to identify strain-level variation in an important fly bacterium, Lactiplantibacillus plantarum. They identify three strains of L. plantarum, which are most prevalent in their respective three temperature regimes, suggesting that these are locally adapted bacteria. Then, using a combination of genomics, in vitro, and in vivo, Gracia-Alvira et al attempt to understand the factors that led to the differentiation of the hot and cold L. plantarum and their impacts on the fly host.

Strengths:

This is an excellent use of experimental evolution to track the emergence of novelty in the microbiome. The genomic analyses are all solid and appropriate for the data sets. It is especially striking that the comparisons with the other, independent experimental evolution studies in different labs (and across continents between Portugal and South Africa) show a consistent response to temperature. Many have disregarded the microbiome as it is something that is too sensitive to seemingly innocuous variables (particularly in the fly microbiome), such that we cannot find generalities. However, this finding highlights the potential for experimental evolution to uncover these dynamics. The question of how strains emerge and are maintained is timely and is one of the key open questions in host-microbiome evolution currently.

Comments on revised version:

I thank the authors for their thoughtful responses to my concerns, and I appreciate the additional experiments to help resolve the questions about subspecies competition. The manuscript remains strongest in the genomic assessment of changes in the L. plantarum genomes, and it is striking and noteworthy that the isolates across multiple countries but same temperature conditions group together phylogenetically.

I appreciate the additional clarity also incorporated in this revision, but there are still a few key concerns that are unresolved about the microbial ecology described here. I will also note that I apologize if I missed something in the text as no line numbers were provided to point me to where the changes were incorporated in the revised manuscript.

(1) Competition has many different meanings and many different measurements (see Hart 2018 https://doi.org/10.1111/1365-2745.12954) -and incorporating the effects of competition in shaping an ecological community is, has been, and will continue to drive much research in community ecology. Measuring strain level competition is one of the major questions in host-associated microbiomes, and it is difficult-though there have been significant advances in doing so (see isogenic barcodes, e.g., Daniel 2024 doi: https://doi.org/10.1038/s41564-024-01634-9b, Ordon 2024 https://doi.org/10.1038/s41564-024-01619-8, as well as my previous suggestion to track the outcomes of competition). The inability to directly track and measure competition of the isolates remains a limitation of this manuscript. The authors' explanation of measuring competition is unusual, simplistic, and at times inconsistent.

They need to be crystal clear about their definitions, logic for making these inferences, and weaknesses in their approach. I think what the authors mean is that competition between the unevolved and C or H in their respective regimes leads to the decrease of the U clade over experimental evolution. But it is not clear how the authors are thinking about competition between C and H clades in the different temperatures.

The authors state that competition is inferred because changes in relative abundance across the time series-and this is unusual because there are alternative explanations that require no ecological interactions among sub-strains, as I described in my comments on the prior version. This is then combined with in vitro work that shows that the H and C clades can both grow in their mismatched temperature regimes-and thus I think it is to be inferred that because they can grow alone in vitro (and C isolates show lower growth than H isolates in hot temperature), then changes in the relative abundance over fly generations can be attributed to competitive interactions among C and H clades. But then the logic is inconsistent because then the authors just say that in vitro growth curves don't support the differences in relative abundance observed in the flies (lines 224-225). Then the authors argue is it about a combination of diet/sugar metabolism and temperature (line 373), which doesn't make any sense because temperature previously didn't matter (lines 224-225).

All of this is to say is that the authors need to make clear their logic to the readers-and explain these inconsistencies appropriately. To me, it suggests that there are clear methodological weaknesses that inhibit the ability to track competitive microbial dynamics. Because you can't really assess the microbial dynamics in vivo, it remains further unresolved why clade C isolates have such strong negative fitness effects on the fly but reach such high relative abundances in the C evolving flies. I find that this series of logical inconsistencies (and see my point #2) distracts from the important finding that the C and H clades evolved to utilize sugars differently from the U clade, which is an interesting finding!

(2) There are also inconsistencies in the patterns observed between the text and the figures. Some of this arises because the authors are not clear what comparisons they are making. For example, line 450 says that clade C outcompeted the other clades, which I presume means only in the cold temperature. Line 456 says that C and H isolates grow faster in the sugar-rich lab diet, but that is not really true because U and C have similar growth rates in Fig. 5, and U and H have similar growth rates in Fig. S4. The text about microbial load is a bit misleading (lines 271-273), as it is confusing that clade C is significantly higher load in both hot and cold temperatures (Fig. S6), which is counterintuitive given Fig. 4, 5, S4. But it is also overly speculative to say that these results suggest that fitness effects depend on microbial load of clade C without connecting the load to the fly fitness measures (and also given the inconsistency with the time series data from evolving lines). Please take care to more carefully phrase these statements to ensure the inference is supported by the experiment design (e.g., clarifying comparison) and statistics (e.g., ensuring agreement with what the figure shows).

(3) I understand the concern about focusing the reader on the L. plantarum strains. However, it should be clear to the readers that you did not examine the other parts of the microbiome, and that L. plantarum is often very rare in lab and wild fly populations. The data presented on Table S4 (cited line 552, I think citation at line 176 is incorrect) is confusing. If these were colonies picked and then identified, this should be explicit. If it is based off on colonies, then please clarify if this was sampled randomly or occurred when trying to enrich/focus on L. plantarum isolates. If the data was computational (e.g., Kraken to classify), then only taxa richness is not necessarily relevant, but please also include to the relative abundance of each taxa.

To me, this is relevant information to contextualize these results, particularly because you test this in both D. mel and D. simulans (apologies for the confusion over Mazzucco & Schlotterer 2021), and we have insight into how combinations of Lactobacillus and other taxa impact fitness (Gould PNAS 2018). If the results from D. melanogaster are not applicable to D. simulans, then the authors need to explain this. I understand if incorporating analysis of the broader microbiome is beyond the scope of this manuscript, but at least acknowledging the general rarity in Lactobacillus frequency in Drosophila microbiome and variation in fitness effects will more accurately contextualization these results.

One small point is that line 452 the citations are OK, but there are fly-specific examples to support this statement, like Gould PNAS 2018, Henry Proceedings B 2025.

Reviewer #1 (Public review):

Summary:

In this manuscript, Uphoff et al. propose a structural and mechanistic model in which the multidomain ECM protein SVEP1 enables Angiopoietin (ANG) binding to the orphan receptor TIE1, thereby promoting downstream receptor phosphorylation and signaling. Using AlphaFold-based modeling, the authors predict that the CCP20 domain of SVEP1 binds to TIE1, creating a composite surface that facilitates Angiopoietin association and TIE1 activation. The resulting ternary model (SVEP1-TIE1-ANG) offers a structural rationale for how SVEP1 converts TIE1 into a functional, ligand-responsive receptor. Additional models and biological assays suggest roles for other domains of SVEP1, such as CCP5-EGF-L7, although these interactions are predicted with low confidence. The authors interpret these findings as the first structural framework for how SVEP1 enables ANG-TIE1 signaling.

Strengths:

(1) The central hypothesis - that SVEP1 enables ANG binding to the orphan receptor TIE1 - is biologically compelling and addresses an important question in vascular biology.

(2) The AlphaFold-predicted ternary complex (SVEP1-TIE1-ANG) is plausible, high-confidence, and structurally consistent with prior functional data (e.g., poly-Ala scanning from Sato-Nishiuchi et al.).

(3) The authors' model offers a potential explanation for the previously observed role of SVEP1 in enhancing ANG signaling through TIE1 and may represent the first structural insight into TIE1's transition from orphan to ligand-activated receptor.

(4) The potential clinical implication - that a combinatorial ligand (ANG+SVEP1) can activate TIE1- could have translational relevance for vascular leak and inflammatory disease.

Comments on revised version:

The authors have adequately addressed my concerns.

Reviewer #1 (Public review):

Summary:

This manuscript describes a study examining the relationship between microsaccades and covert attention. This question has been widely investigated, with numerous studies showing that during sustained fixation, when subjects covertly attend to a peripheral stimulus, microsaccades tend to be biased toward the attended location. Here, the authors ask whether this microsaccade bias reflects a shift of covert attention or the maintenance of covert attention. They conclude that the bias is primarily driven by attention shifts, a finding that also helps reconcile the seemingly conflicting results of prior research, where the bias was questioned in paradigms that largely involved attention maintenance rather than shifting.

Strengths:

A large sample size was used.

Weaknesses:

The main weakness is that the authors' response does not adequately resolve concerns about the robustness of the microsaccade analyses. The newly reported event counts reveal that the number of microsaccades per participant is very low, especially in Experiment 2, and highly variable across subjects. Because the key analyses rely on proportions of microsaccades toward versus away from the attended location, estimates based on so few events are likely unstable and may not provide reliable subject-level measures.

A second major concern is that several additional analyses introduced in the revision appear to suffer from the same limitation. The permutation analyses and angle-partition analyses may give the impression of statistical rigor, but if the underlying averages are based on very few microsaccadic events, the resulting probabilities are difficult to interpret. Further subdividing already sparse data into narrower angular bins likely makes the estimates even less reliable.

A third concern is that the authors have not fully addressed issues related to microsaccade detection and fixation control. The presence of very small-amplitude events with relatively high velocities raises the possibility that some detected microsaccades may be artifacts. The authors also did not implement the requested exclusion of microsaccades smaller than 5 arcmin or the suggested reanalysis using stricter fixation criteria. These omissions leave open the possibility that the reported effects are influenced by detection errors.

A fourth weakness is that some of the requested analyses or clarifications were addressed only superficially. The comparison with Brandolani et al. remains minimal, despite being highly relevant to interpreting whether the observed microsaccade-direction effect is transient or sustained. Similarly, the gaze-density plots do not show the raw gaze-position distributions that were requested and may therefore be misleading, because difference maps cannot determine whether subjects were actually fixating centrally.

Overall, the revision raises additional concerns rather than resolving the original ones. The main conclusions remain insufficiently supported unless the authors can demonstrate that the effects are robust at the individual-subject level, based on adequate numbers of microsaccadic events, reliable detection criteria, and appropriate controls for fixation behavior.

Reviewer #1 (Public review):

In this article, the authors investigate how glutamate transporter function regulates excitability and synaptic coding in T-stellate cells in the mouse ventral cochlear nucleus. They test this in acute brain slices using whole-cell electrophysiology and artificially raise the relative local concentration of glutamate via pharmacological inhibition of transporter proteins. The main finding is that when sub-saturating doses of DL-TBOA are applied, cells become much more sensitive to synaptic input, diminishing the normally high fidelity of EPSP-spike coupling in these neurons. Notably, high-frequency stimulation in the presence of DL-TBOA reveals a large and slowly decaying AMPA receptor component that underlies persistent/rebound firing in earlier recordings. These effects are not seen in other ventral cochlear neurons, suggesting that rapid glutamate clearance in T-stellate cells, particularly, is important for auditory intensity coding. Overall, these experiments are well-performed, and the findings are robust, though there are some aspects that could be expanded to make the work more impactful. These include a better understanding of the relative contribution of neuronal vs glial transporters and an ability to separate the relative contributions of tonic glutamate concentrations in the cleft vs changes in membrane potential in action potential output. Additionally, there were some minor issues of clarity in both the figure presentation and the main text language that should be addressed.

Major Points:

(1) Given the dramatic effect of saturating DL-TBOA on tonic leak/RMP and that the sub-maximal concentration used in most of the experiments still varied between 25-50 uM, Figure 1 would be strengthened substantially by a dose-response curve. Ideally, 5 or 6 concentrations, plotting the effect on tonic current or RMP increase.

(2) Examining the contribution of glial (EAAT1/2) vs. neuronal (EAAT3) transporters (Fig 8) is intriguing but comes across as incomplete here, especially given the small number of recordings. Using a different non-selective EAAT inhibitor (TFB-TBOA) to chase the EAAT1/2 blocker combo seems like an odd choice, given that you have already characterized the effects of DL-TBOA well. One could also try a lower concentration (~50-100 nM) of TFB-TBOA since it is somewhat selective itself for glial EAAT1/2. Given the data presented, neuronal transporters (presumably EAAT3) appear to dominate the rapid clearance of glutamate at this synapse, but this point isn't emphasized or explored sufficiently.

(3) Separating the effects of depolarization vs. glutamate clearance was never explored. What effect does depolarizing the cell ~10 mV in control conditions (i.e., without TBOA) have on AP number/fidelity during synaptic stimulation experiments? The authors state that submaximal DL-TBOA generally causes no more than a 5 mV change in RMP, but tonic depolarization could also influence spike fidelity. This experiment could demonstrate that the increase in excitability during/after stimulation is not due to increased engagement of voltage-gated channels.

Joint Public Review:

Summary:

This manuscript couples a 32-parameter model with simulation-based inference (SBI) to identify parameter changes that can compensate for three canonical hyperexcitability perturbations (interneuron loss, recurrent-excitatory sprouting, and intrinsic depolarisation). The study demonstrates a careful implementation of SBI and offers a practical ranking of "compensatory levers" that could, in principle, guide therapeutic strategies for epilepsy and related network disorders.

Strengths:

(1) By analysing three mechanistically distinct hyper-excitable regimes within the same modelling and inference framework, the work reveals how different perturbations require different compensatory interventions.

(2) The authors adopt posterior estimation to systematically rank the efficiency of different mechanisms in balancing hyperexcitability.

(3) Code and data are available.

Comments on revised version:

I appreciate the authors' extensive efforts in revising the manuscript and responding to the previous review. The revised version is substantially improved in clarity, organization, and presentation. In particular, the addition of schematic figures, the reorganization of the Methods section, the improved explanation of the model, and the inclusion of replication analyses all strengthen the manuscript.

The manuscript remains entirely computational, and therefore its conclusions should be interpreted as predictions generated by a specific model rather than validated biological mechanisms. I believe the work has the potential to make a useful methodological contribution. However, several concerns remain regarding validation, interpretation of inferred posteriors, organization of the manuscript, and presentation.

Major comments:

(1) The manuscript states that simulation-based calibration showed the amortized posterior estimator was unreliable (85-88), but these results are not shown. The manuscript explicitly states that simulation-based calibration demonstrated substantial failures of the amortized posterior estimator, yet the corresponding analyses are not presented. Since these results motivate the transition to sequential NPE and are central to assessing inference reliability, they should be reported quantitatively, either in the main text or supplementary material.

(2) The authors present two independently trained estimators and show strong agreement between them. This is a useful robustness analysis. However, the rebuttal occasionally presents this as addressing concerns regarding cross-validation and generalization. The new analysis does not constitute cross-validation in the usual sense and does not directly assess generalization to held-out targets or posterior accuracy.<br /> I recommend that the authors explicitly describe Figure 4 as a reproducibility analysis and avoid presenting it as a substitute for validation.

(3) Posterior correlations are useful for generating hypotheses about compensatory mechanisms, but they should not be interpreted as direct evidence of compensation. The compensatory interpretation should instead be supported by the perturbation analyses (e.g., Figure 6), which provide mechanistic validation.

The manuscript consistently treats posterior correlations and conditional posterior shifts as direct evidence of compensatory mechanisms. These are consistent with compensatory mechanisms, but they do not by themselves establish that the corresponding biological parameters causally compensate for the perturbation. I recommend clarifying this distinction and emphasizing that the conditional posterior analyses generate hypotheses regarding compensation, which are then partially supported by the perturbation experiments shown later in the manuscript.

The language throughout the manuscript should therefore be softened.

(4) The manuscript repeatedly suggests that the inferred conditional distributions may be useful for identifying precise interventions or guiding personalized treatments (examples include lines 24-29, lines 217-223, lines 242-246, lines 277-282, lines 283-286). These claims go beyond what is directly demonstrated.

The study does not evaluate treatment outcomes, patient-specific inference, intervention efficacy, or clinical decision-making. Rather, it demonstrates differences in inferred parameter distributions within a computational model. While these results are valuable and may generate clinically relevant hypotheses, they do not yet establish predictive utility for treatment selection or precision medicine. I therefore recommend substantially softening these translational claims and emphasizing that the current findings generate hypotheses that could be tested experimentally in future work.

(5) The revised manuscript still mixes presentation of findings with interpretation.

For example, lines 217-226 largely continue to describe findings from Figure 6 and would fit better in the Results section. The Discussion would be strengthened by focusing more exclusively on biological implications, limitations, and future directions.

A similar issue appears later in the discussion comparing posterior correlations and conditional distributions. Much of this section effectively reinterprets Figures 2 and 3 rather than discussing broader implications.

(6) The discussion around lines 271-282 overstates what can be concluded from the inferred posteriors.<br /> The statement that correlations "discover broadly applicable mechanisms" whereas conditionals "identify specific mechanisms" is stronger than the presented evidence supports. Likewise, the conclusion that conditional distributions are more useful for precision treatments is speculative and not directly demonstrated.

I recommend reformulating these statements as interpretations or hypotheses rather than conclusions.

(7) Around line 84, the manuscript introduces q(theta|x) without clearly defining θ, x, or q. Readers unfamiliar with SBI may struggle to follow the notation. All quantities should be defined when first introduced.

(8) The manuscript equates larger KS distances between conditional posteriors with greater compensatory potential. While KS distance provides a useful measure of posterior redistribution, it is not obvious that it should be interpreted as a measure of biological efficacy.

(9) The manuscript would benefit from a discussion of parameter identifiability. The inference problem maps 32 model parameters to 7 summary statistics, implying substantial non-identifiability. While complete identifiability analysis is likely beyond the scope of the current work, this limitation should be discussed explicitly.

All in all, the revised manuscript is significantly improved and addresses several concerns raised in the previous review. However, important issues remain as discussed above.

Reviewer #1 (Public review):

Summary:

In this study, Qiu et al. examine the effects of the estrogen mimic STX on mitochondrial function and its interaction with VDAC2 in PMOC neurons.

Strengths:

The authors employ a broad range of molecular, cellular, and chemoproteomic approaches with generally sound methodology.

Weaknesses:

The work suffers from major conceptual and experimental issues that substantially limit its scientific impact.

Major Concerns

(1) Lack of Rationale.<br /> The study provides no justification for investigating sex specific aspects of Alzheimer's disease by focusing on VDAC-mediated mitochondrial dysfunction in PMOC neurons. These hypothalamic neurons are not recognized as early or primary sites of AD vulnerability, making the biological premise unclear.

(2) Weak Link to AD Pathogenesis.<br /> Although mitochondrial dysfunction is well established in AD, the authors do not convincingly demonstrate a mechanistic or pathological connection between VDAC2 and AD. VDACs are not established contributors to AD etiology, and the manuscript does not strengthen this association.

(3) Unclear Relevance to AD Contexts.<br /> While the data support an interaction between STX and VDAC2 affecting mitochondrial parameters (ATP production, membrane potential, glycolysis, respiration) in PMOC neurons, the study does not show whether this mechanism is relevant to mitochondrial dysfunction in AD. No validation is provided in AD-related models or in contexts related to sex specific AD phenotypes.

(4) Interpretation of Competitive Binding Data.<br /> The competitive binding results in Figure S4B are not adequately interpreted. The dose-dependent competition observed for VDAC3 suggests it may be a stronger candidate than VDAC2, yet this possibility is not addressed.

Reviewer #1 (Public review):

This work evaluates the impact of reproductive history on growth, body weight and body composition in mammals. In mice, somatic growth is stimulated by the first pregnancy while the second pregnancy increases body weight mainly by increasing adiposity. To probe the role of pituitary growth hormone (GH), the key regulator of somatic growth in these processes, was addressed by comparing the impact of reproduction on growth in normal ("wild type") and genetically GH-deficient females and by detailed characterization of the profile of fluctuations in circulating GH levels in both types of animals. Additional studies addressed the possible role of other endocrine pathways (ghrelin and estrogen) in the pregnancy-related growth. Surprisingly, reproduction-related growth was independent of GH, ghrelin and estrogen. To determine whether these results may apply ("translate") to human physiology, data on various parameters of somatic growth were collected from women with hereditary GH deficiency. The findings indicate that GH-independent stimulation of growth by reproductive events also occurs in women.

Use of multiple animal models, rigorous characterization of GH levels in normal and GH-deficient females, and inclusion of data derived from a unique and well-characterised population of people with hereditary isolated GH deficiency and no GH replacement therapy are important strengths of these elegant and innovative studies. The results address a broader and clinically significant issue of permanent changes in body size, composition and function that result from pregnancy and lactation. This work also provides important background for further studies aimed at the identification of the mechanism involved and the role of specific reproductive events in the regulation of growth.

Reviewer #1 (Public review):

Summary:

Zhang et al. investigated EEG neurofeedback as a method to modulate brain activity prior to painful stimulation and its effect on pain perception. Neurofeedback was designed to train participants to upregulate alpha power contralateral to the site of painful stimulation. Real or sham neurofeedback was administered to two independent groups. Each group performed two tasks: one in which participants were asked to modulate their brain signals (training task) and another in which they were asked to passively watch the feedback (non-training task). The authors reported an increase in alpha power during real neurofeedback training compared with sham training and non-training conditions. The authors also reported a decrease in pain perception during the training task, both in the real and sham neurofeedback groups. Additionally, in an offline analysis, the authors investigated brain dynamics with microstate analysis during the neurofeedback training. Also, they implemented a mediation analysis to infer which brain responses to neurofeedback training mediated changes in pain perception.

Strengths:

(1) The research question is licit and sound. EEG neurofeedback is a promising non-invasive technique with the potential to alleviate at least the sensory component of pain. The rationale for applying neurofeedback at the alpha band in the somatosensory cortex is well justified by the alpha-gating theory in pain modulation.

(2) The sample size is adequate to capture neurofeedback effects. The effort to conduct a double-blind study with a complex design paradigm and an adequate sample size is valuable and appreciated.

Weaknesses:

(1) Reported behavioral effects on pain reduction might be due to the placebo effect rather than neurofeedback, as pain ratings were reduced both in the real and sham neurofeedback groups during training. It is important that authors report this effect appropriately and disclose which information was given to the participants when they enrolled in the study, i.e., whether the paradigm was designed to reduce pain perception.

(2) The utility of training effects, especially in the sham group, is unclear. I understand that including the non-training condition allows the distinction between neurofeedback effects and arousal effects. However, interpreting training effects should not be the point of this study. What does it tell us that participants who received sham stimulation increased or decreased alpha power in the training session vs the non-training session?

(3) There might be hidden time effects (habituation/sensitization) on pain responses and/or on brain responses to neurofeedback. A within-session analysis comparing the first half of the training with the second half should be conducted to discard them.

(4) Connectivity analysis reflects spurious effects. In EEG, deriving phase-based functional connectivity at the sensor level is problematic due to volume conduction effects. EEG functional connectivity should be performed after source reconstruction, and measures discarding instantaneous phase lags should be preferred, which is not the case with magnitude-squared coherence. See (Bastos and Schoffelen, 2015).

Although neurofeedback is a promising technique for modulating pain perception, the current study adds limited novelty to the field, as its design could not disentangle whether behavioral effects (reductions in pain intensity and unpleasantness) were specific to neurofeedback training or due to non-specific effects (e.g., placebo). Nevertheless, the authors corroborated that brain states before painful stimuli could be modulated with neurofeedback (enhancement of alpha power).

Reviewer #1 (Public review):

Summary:

This manuscript investigates whether the human brain contains a shared category-general representation of gender across faces, bodies, and gender-associated objects. The authors acquired fMRI data while participants viewed male and female stimuli from three categories in a one-back task. They then used searchlight MVPA, cross-category decoding, regression-based RSA, CNN vs. brain representational comparisons, and PPI analyses. Their main finding is that gender information could be decoded from distributed occipitotemporal regions within each category, whereas a cluster in the rMTG showed convergence across cross-category decoding and RSA. The authors concluded that this rMTG representation resembles intermediate layers of fine-tuned CNNs and that face and body gender processing share similar functional connectivity patterns.

Strengths:

The question is potentially important, particularly for social cognition, object recognition, and the use of neural network models to interpret high-level visual representations. Previous behavioral studies have shown cross-category adaptation between bodies and faces, and even between gender-associated objects and faces, so the attempt to test for a neural counterpart using fMRI is well motivated. The use of multiple complementary analyses including within-category decoding, cross-category decoding, regression RSA, CNN comparisons, and effective connectivity analyses is also a strength. The convergence of cross-category MVPA and RSA in a right MTG cluster is potentially interesting and deserves attention.

Weaknesses:

The largest problem is conceptual. The term gender is used as if it refers to the same construct across faces, bodies, and objects. This is not self-evident. In faces and bodies, the stimuli seem to contain visual cues from which observers infer binary gender categories. In objects, however, the relevant information is almost gender stereotype, cultural association, or learned semantic association. These are not equivalent constructs. The manuscript therefore needs to distinguish much more carefully between perceived gender, biological sex cues, gender-associated visual features, and gender stereotypes. Without this distinction, the title and main conclusion are too broad. The object condition is particularly problematic. Javadi & Wee (2012) showed that gender-associated objects can bias subsequent judgments of ambiguous face gender, and they discussed two possible mechanisms, including shared neural substrates or top-down modulation induced by the gender concept. However, their behavioral adaptation study does not directly demonstrate that objects, faces, and bodies are encoded in the same neural representational format. The present manuscript treats these object stimuli as if they provide evidence about the same kind of gender representation as faces and bodies, but that step requires additional empirical support. Independent ratings of object gender association, cultural familiarity, visual similarity, and semantic category are essential here.

A second major concern is stimulus control. The face images were taken from Chinese male and female actors, the body images were headless bodies in underwear, and the object images were selected because of prior gender associations. This design introduces many possible confounds: hairstyle, makeup, skin texture, body shape, clothing, color, luminance, object category, object function, curvature, spatial frequency, and cultural familiarity. Cross-category decoding can be significant even when a classifier relies on shared visual statistics rather than an abstract gender code. For example, female-associated stimuli may differ from male-associated stimuli in color, shape, brightness, texture, or semantic category in ways that are consistent across faces, bodies, and objects. The present analyses do not adequately rule out these alternatives. Foster et al. (2019) are especially relevant in this respect. They reported that body sex could be decoded from both body- and face-responsive regions. However, the sex of well-controlled faces, for example faces excluding hairstyle cues, could not be decoded from face- or body-responsive regions. This finding should make the authors more cautious. The fact that the present study used more ecological face stimuli may increase sensitivity to gender-related cues, but it also increases the possibilities that decoding is driven by uncontrolled external features rather than by an abstract gender representation. Accordingly, because no additional visual, semantic, or stereotype-based model RDMs were included in the RSA analysis, this result alone cannot establish an abstract, category-independent gender representation. Any systematic difference between male- and female-associated images will load onto the gender RDM. At least, the authors should include additional model RDMs for low-level visual features. In addition, the current RSA analysis has another limitation. The neural RDMs are based on only six condition-level patterns, producing a 6 × 6 matrix. The theoretical model includes only binary gender and category RDMs. This is too coarse to support the claim of category-independent gender representation. Ideally, all the RSA analysis should be performed at the item level rather than at the condition level.

The cross-category decoding result in rMTG is promising but not yet conclusive. The authors identify a right MTG cluster by overlapping thresholded maps from three cross-category decoding analyses. This is useful descriptively, but it does not by itself establish a common representational code. The overlap of thresholded maps depends on the chosen threshold. If the authors want to make a formal conjunction claim, they should use a valid conjunction-null approach such as a minimum-statistic conjunction evaluated under the appropriate conjunction null, rather than simply displaying the intersection of thresholded maps. Even if this approach cannot be adopted in this study, the issue should be included as a limitation.

In the PPI analysis, the reported similarity between face and body connectivity matrices is a little bit small (r = 0.08). The claim of a shared functional network should therefore be softened unless the authors test whether this correlation is significantly larger than the face-object and body-object correlations, correct for multiple comparisons, account for the non-independence of matrix elements, and report participant-level distributions and confidence intervals.

Reviewer #1 (Public review):

In this paper, Pal and colleagues propose a mechanistic unification of two influential accounts of inter-areal communication: communication through coherence and communication subspaces. A major strength of the paper is that it does not treat coherence and communication subspaces as independent phenomena, as typically done, but instead derives both from the same circuit with divisive normalization. In this framework, noise-driven fluctuations around the normalized fixed point determine covariance and cross-power structure (which, in retrospect, makes so much sense to be related). Then, they show how these determine linear prediction performance and the effective dimensionality of the communication subspace. They also show (however not very visually, see recommendation below for a figure) how divisive normalization is crucial to shape inter-areal coherence and the dimensionality of communication.

I found this conceptual contribution potentially very influential, but somewhat obscured by the technical complexity of the model. The central intuition (I think) is that recurrent normalization can organize cross-area fluctuations, both frequency-specific correlations and cross-covariances. Took me a while to grasp this insight, mostly because I was stuck with the model details. Note that I have some experience with network dynamics, but not with this particular model.

Reviewer #1 (Public review):

Summary:

Pang et al. investigated the expression pattern of the transcription factor foxQ2II in an adult beetle brain. They find nine distinct clusters, with many neurons expressing Glut/ChaT and dopamine. Some of the dopamine neurons resemble cell types described in Drosophila. Several neurons seem to project to prominent higher brain regions such as the MB and CX, and might even connect to both.

Strengths:

The authors use state-of-the-art labeling techniques for the analysis of individual cell types, such as beetle brainbow, to investigate the until now unknown expression of the transcription factor in the adult beetle brain.

Rigorous cell reconstruction and image analysis revealed a better understanding of the anatomy of the labeled cells.

Weaknesses:

The brainbow labeling seems to include all cells labeled by the enhancer trap line, as well as the ones not expressing foxQ2II. Thus, it is unclear how useful this data is to compare individual cells to other insects.

The functional relevance of this transcription factor in the adult brain cell is still unknown. It is therefore unclear if the described neurons have any specific function and if they require this transcription factor for normal function.

Overall, the neural reconstructions are missing single-neuron details; it is difficult to compare the shown cell types to specific cell types in Drosophila based on the presented data, and this finding remains speculative.

Reviewer #1 (Public review):

Summary:

Esfahany et al. describe a new platform (Toothy) to identify and analyze dentate spikes and sharp wave ripples from silicon probe electrophysiology data. The goal is to facilitate and standardize the extraction of DS1 and DS2 events, which have highly variable properties across recordings from different labs. The manuscript describes the basic workflow of the Toothy pipeline, including loading data, assigning channels along a linear probe, customizing parameters, selecting ideal channels for analysis, and classifying DS1 and DS2 events.

Strengths:

The manuscript is clear and easy to follow and does a good job of describing the platform. Overall, this will be a useful analysis pipeline that can help to standardize DS analysis across labs and datasets.

Weaknesses:

The current version has several bugs that prevent analysis, and the documentation of analysis parameters needs to be improved.

(1) In limited testing, the pipeline had several bugs, and I was not able to complete the full analysis of a dataset. Loading data from .mat or .npy files gave errors (it seemed that the metadata was not loaded correctly from the pop-up window). I was able to load a .nwb file, which worked well. The probe configuration tool was a bit difficult to understand, and there was not much documentation to help, although it worked when simply entering the x-y coordinates of the channels. It also crashed several times while trying to make a probe configuration due to it trying to save when a small typo was briefly entered. The initial analysis worked well, and the auto-selected channels matched our recording notes and seemed appropriate. DSs and ripples were extracted. An error came when trying to classify DSs, and the program repeatedly crashed across a variety of parameters. Overall, parts of the pipeline worked well, but others had significant bugs that need to be addressed.

(2) The authors should provide test data that can be run through the pipeline. Ideally, this could use a variety of data types, probes, and conditions so that it is clear how they differ.

(3) There are a lot of parameters that can be adjusted, but very little information about how they are chosen and what goes into parameter selection for a dataset. Additional documentation with more information on adjustable parameters, channel selection, and best practices would help improve the utility of the tool. Ideally, this could also integrate citations (either in the manuscript or documentation) to support some of the choices made during parameter selection.

(4) There is no validation presented against other analysis methods or datasets. While there is no ground truth of when DSs occur, this may limit the ability of this tool to become the standard for DS analysis. A section comparing the analysis used in the pipeline to other published analyses would be helpful.

(5) In the manuscript, it would be helpful to further describe the rationale for initially detecting DSs and SPW-Rs on all channels, when they are network events that occur across channels.

(6) A section on what hardware and software are necessary to run the pipeline should be added.

Reviewer #2 (Public review):

Summary:

In natural visual behavior, such as when one is looking for a face in the crowd, the eyes are moved from site to site, seeking possible matching targets. This involves attention both to the current view at center of vision (the foveal location) as well as to upcoming views via attention to targets in the periphery. While it has been established that attention generally enhances neuronal response (compared to simple visual activation) at the attended spatial location, this study provides solid evidence that attention during active visual search leads to neuronal response enhancement only when the eye moves towards targets that exhibit the desired feature and category. This study thus moves the field towards understanding the neural encoding of active vision.

This study examines the neuronal basis of feature selective attention during active, freely behaving visual search. Traditional electrophysiological studies on visual attention in monkeys commonly used an eye fixation with covert attention paradigm, but have not sufficiently addressed the roles of both foveal and peripheral attention in play during natural looking behavior. Here, the authors present a novel paradigm in which, during eye movement mediated search neuronal receptive fields are recorded in multiple cortical areas (sensory V4, temporal and prefrontal areas). In this manner, as the eye foveates, items in the array fall into foveal or non-foveal recorded sites. Thus, the experimental paradigm is elegant, offering the opportunity to make multiple types of comparisons: target/distractor, towards/away from fovea, areal. Specifically, following a category cue (face, house, hand, flower), freely initiated saccades are made to locate a categorically matching 'target' in an array of distractors. Feature attention is assessed by comparing eye saccades made to targets vs to distractors. Spatial attention is assessed by comparing saccades made 'towards' vs 'away' from targets. Statistics are rigorous and nicely designed. Detailed association of simultaneously obtained eye movement sequences and neural parameters are well done. These are valuable data which will contribute to our understanding of attentional modulation in visual search.

The significance of these findings is fundamental. Decades of attention research in vision have been based on the paradigm of visual fixation and covert peripheral attention. However, increasingly the field has moved towards understanding how the visual system works during active vision. Here, the authors use an active visual search paradigm and record from key mid-tier (V4) and higher order (IT, PFC) areas. They find enhancement of attention both in the foveal and peripheral locations, and, furthermore, marked by a high degree of feature and categorical specificity. That is, while attention generally enhances neuronal response (compared to simple visual activation) at the attended spatial location, this study provides solid evidence that attention during active visual search leads to neuronal response enhancement only when the eye moves towards targets that exhibit the desired feature and category. This provides valuable data for the concept of a foveal-peripheral spatiotemporal attentional window in natural vision. The controls (comparisons of neuronal response during looks to targets vs distractors and looks towards and away from the target) and statistical rigor make these findings compelling. There will likely be additional future impacts of this study. For example, the eye movement patterns collected in this study may also provide a valuable dataset for future study of understanding search strategies. Goal-directed vs non-goal-directed task comparisons could be designed to test possible circuit models. Although much remains unknown regarding how and where frontal and temporal signals are integrated during active search, these data contribute important guideposts for future models of active visual search.

Reviewer #1 (Public review):

Summary:

Based on previous work showing that viral evolution follows reproducible patterns in diverse animals, the authors sought to examine whether the antibody response operates under similar constraints. By analyzing over 17,000 B cells isolated from 6 monkeys at 3 different time points, the authors convincingly show that the immune response does follow specific patterns of responses to different classes of epitopes based on the infecting virus. Moreover, each of these clusters has characteristic (cross-) binding and neutralization properties. Importantly, these classes are independent of the underlying immunogenetics, which (as expected) vary significantly between monkeys. This last point is particularly relevant for vaccine design, as it means that immunogens may not need to be as narrowly focused on specific germline genes as previously thought.

Strengths:

The large number of B cells cultured for this study is a particular strength, as is the fact that they were isolated in an antigen-unbiased fashion. The experiments are well-designed and comprehensive.

Weaknesses:

The genetic element is a relatively minor component overall and more qualitative than quantitative. It would be nice to investigate other properties of the repertoire like CDRH3 length and possible public clones, as well.

Reviewer #1 (Public review):

Summary:

Foik et al. report that hypochlorous acid, a reactive chlorine species generated during host defense, activates the transcription of the froABCD in P. aeruginosa. This gene cluster had previously been associated with a potential role during flow of fluids and appears to be regulated by the sigma factor FroR and its anti-sigma factor FroI. In the present study, the authors show that froABCD is expressed both in neutrophils and macrophages, which they claim is likely a result of HOCl but not H2O2 production. Fro expression is also induced in a murine model of corneal infection, which is characterized by immune cells invasion. Expression of the fro system can be quenched by several antioxidants, such as methionine, cysteine, and others. FroR-deficient cells that lack froABCD expression during HOCl stress, appear more sensitive to the oxidant.

Strengths:

The authors provide a number of data supporting their claim that transcription of the froABCD system is induced by reactive chlorine species. This was shown by RNAseq, qRT-PCR, and through microscopy using a transcriptional reporter fusion. Likewise, elevated expression of froABCD was shown in vitro and in vivo, excluding potential in vitro artifacts. The manuscript, while mostly descriptive, is easy to follow and the data were presented clearly and convincingly. The authors have also been responsive to concerns from the previous review.

Weaknesses:

(1) Line 10: "HOCl preferentially oxidizes....". Please consider modifying the language to: "the second-order rate constant of HOCl is significantly higher with Met/Cys compared to other aa."

(2) I am not sure I completely understand Fig 1B. Is the promoter right upstream of yfp or is yfp located downstream of froA? If the latter is the case, wouldn't this be a translational fusion?

(3) My previous comment regarding why fro expression is higher during phagocytosis in macrophages compared to neutrophils has been somewhat (albeit not convincingly), addressed by the authors in the response to the reviewer, but this discussion should be part of the manuscript as the macrophage data were shown.

(4) Line 122: The statement "The degree of fro inhibition by 4-ABAH...." is incorrect unless the authors can provide experimental evidence. Fro expression is not upregulated because MPO is inhibited by 4-ABAH, which results in less hOCL production.

(5) Can Supp Fig. 1 be quantified in a similar way it was done for HOCl to allow for a better comparison if HOCl or flow is the more potent inducer?

(6) Overall, the fro expression (YFP/mCherry) seems highly variable for treatment with HOCl (Fig. 2C: ~65; 2D: ~20; why is fro expression 3x lower?

(7) The authors should provide evidence that N-chlorotaurine can activate fro expression also. They said they weren't able to obtain chlorinated taurine, but this is quite simple to produce: PMCID: PMC1219228

(8) Fig. 4 supplement 1: Please provide concentrations for the oxidants used in these experiments.

(9) Lines 251/252: change to: upregulation of instead of in

(10) Chaperones and other heat-shock genes are more upregulated in ∆froR, indicating elevated HOCl-mediated oxidative damage, which supports their findings.

(11) Complementation of ∆froR is missing

(12) Line 198: The growth experiment at 4 uM shows differences between WT and mutant, but at 2 uM cells showed already low fro expression due to cell death (which has not been proven by CFU counts). This discrepancy should at least be discussed.

(13) The critical in vitro experiment is missing: does purified FroI get oxidized by HOCl and dissociated from FroR?

(14) Lines: 350-355: The claim that the fro system is the first-line defense is unproven.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have considered and discussed the comments raised in the previous round of review.]

Summary:

The authors aim to use state-of-the art behaviour, imaging and connectome techniques to identify the neural interaction between sleep and long-term memory consolidation in the PAM-DPM circuits, a well-known dopaminergic pathway within Drosophila Mushroom Body.

Strengths:

The investigation follows a logical strategy to collect huge dataset of sleep, appetitive memory and live imaging. The authors identified and showed that activation of a PAM subset: alpha-1 reduces sleep quality and memory consolidation in a starvation dependant manner. The author also convincingly demonstrated the corresponding neuronal responses of DPM neurons following PAM alpha-1 activation, and the positive role of DPM neural activity in sleep and memory consolidation. Moreover, the new data provide TRIC-LUC provided better temporal resolution of neural activity correlates for PAMalpha1-DPM inhibition. Importantly, the author demonstrated that memory loss derived from PAM alpha 1 activation can be partly restored by ectopic sleep enhancement via feeding THIP at the memory consolidation period after training.

Weaknesses:

Although the revised version carries arguments to satisfy the reviewers' concern, the writing is now less cohesive. Crucially an explanation however remains required for the following experimental contradiction: the central observation of the study indicates that PAM alpha1 activation cause DPM inhibition which disrupt sleep and memory consolidation. Therefore, one would expect a reduced PAMalpha1 and increased DPM activities after memory training, but the authors found the opposite is true from now enhanced TRIC-LUC dataset. The authors indicate this data reinforce the inhibitory nature of PAM-alph1-DPM, but it does not explain why such a reduced DPM activity is observed after training.

Reviewer #2 (Public review):

Summary:

This manuscript presents the "NoSeMaze", a novel automated platform for studying social behavior and cognitive performance in group-housed male mice. The authors report that mice form robust, transitive dominance hierarchies in this environment and that individual social rank remains largely stable across multiple group compositions. They further demonstrate that social dominance and aggressive behaviors, like chasing, are partially dissociable and that dominance traits are independent of non-social cognitive performance. The study includes a genetic manipulation of oxytocin receptor expression in the anterior olfactory nucleus, which showed only transient effects on social rank.

Strengths:

(1) Innovative Methodology:<br /> The NoSeMaze platform is a technically elegant and conceptually well-integrated system that enables fully automated, long-term monitoring of both social and cognitive behaviors in large groups of group-housed mice. It combines tube-test-like dominance contests, voluntary chase-escape interactions, and an embedded operant olfactory discrimination task within a single, ethologically relevant environment. This modular design allows for high-throughput, minimally invasive behavioral assessment without the need for repeated handling or artificial isolation.

(2) Experimental Scale and Rigor:<br /> The study includes 79 male mice and over 4,000 mouse-days of observation across multiple group reshufflings. The use of RFID-based identification, automated data logging, and longitudinal design enables robust quantification of individual trait stability and group-level social structure.

(3) Multidimensional Behavioral Profiling:<br /> The integration of social (tube dominance, proactive chasing), physical (body weight), and cognitive (olfactory learning task) measures offers a rich, multi-dimensional profile of each individual mouse. The authors' finding that social dominance traits and non-social cognitive performance are largely uncorrelated reinforces emerging models of orthogonal behavioral trait axes or "animal personalities".

(4) Clarity and Data Analysis:<br /> The analytical framework is well-suited to the study's complexity, with appropriate use of dominance metrics, mixed-effects models, and permutation tests. The analyses are clearly explained, statistically rigorous, and supported by transparent supplementary materials.

Weaknesses:

(1) Scope Limitations (Sex):<br /> The study is limited to male mice, which represents a common but problematic bias.

(2) Ambiguity of Dominance as a Construct:<br /> While the study robustly quantifies social rank and hierarchy structure, the broader functional meaning of "dominance" remains unclear.

Reviewer #2 (Public review):

Summary:

This convincing study builds on previously published findings in both mice and humans to advance quantitative insights into the coupling between noradrenergic activity fluctuations during mouse NREM sleep and heart rate fluctuations. The work reaffirms the presence of coordinated infraslow fluctuations in sigma power and heart rate during NREM sleep and that this coordination is enabled by noradrenaline-releasing neurons in the locus coeruleus. Also supporting previously published work in mice and humans, the authors describe a link between the strength of these infraslow fluctuations and memory consolidation in mice and humans.

Strengths:

A major finding of this study is the mechanistic insight it provides into the regulation of the previously understudied very-low-frequency (0-0.15 Hz) component of heart rate variability, and the demonstration, through elegant optogenetic bidirectional interference, that infraslow noradrenergic fluctuations are an underlying driving force. This finding will promote recognition of heart rate variability in sleeping mice as a read-out of neuronal activity patterns that control autonomic balance.

Another strength of the study is its translational part, whereby the sigma power-heart rate coupling in mouse is used to identify a previously unrecognized correlation between such coupling and memory consolidation in humans. This widens the applicability of heart rate variability measures, highlighting their use as biomarkers for noradrenergic fluctuations and associated sleep-dependent memory consolidation.

Weaknesses:

The study impresses by the thorough parallel analysis of both mouse and human correlational data between electrophysiological and fluorescent activity measures of the sleeping brain. Further work will be needed to disentangle the mechanisms by which heart rate is regulated, notably the contribution of parasympathetic and sympathetic nervous systems, to establish the very low frequency heart rate variability in mice as a novel biomarker for noradrenergic dynamics in the sleeping brain.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

Summary:

In this manuscript, Scheib et al. identify distinct calcium dynamics in the somata and tuft dendrites of layer 5 pyramidal cells in mice performing a licking task. Animals are trained to lick water ports on the left or right following an acoustic cue, and can adjust their targeting when the ports are displaced. For tongue premotor cortical neurons projecting to the ventromedial thalamus, calcium transients in tuft dendrites are tightly locked to the direction-instructive cue, while somatic calcium signals are more broadly dispersed and more frequently synchronized with tongue motion and port contact. Finally, when the targets are shifted, tufts exhibit a sparse but large corrective signal on an improperly-targeted first lick, and the changes in population activity in the tufts and somata differ after adaptation to the new port locations.

Strengths:

In my opinion, this is a very strong manuscript which reports several novel and significant observations, contains high-quality data and (for the most part) reasonable analyses, and is clear and well-written. Most prior studies of cortical sensorimotor processing have measured the output of neurons using extracellular recording - an approach which obscures potentially important signaling differences between neuronal compartments. This study leverages cutting-edge imaging techniques in mice to document large, time-dependent differences between calcium signals at cortical somata and tuft dendrites. This phenomenon could have major implications at the cellular level for synaptic plasticity, and at the systems and behavioral levels for motor adaptation.

Weaknesses:

At a conceptual level, the authors may wish to elaborate a bit on what sensorimotor computation they think the circuit is implementing, and how their results help explain this implementation. Several possibilities are raised: tuft activation could "prime" the pyramidal cells in advance of movement initiation (line 319ff), or could track errors to engage plasticity (line 351ff) and solve the credit assignment problem (line 362ff). It might be helpful to make one of these proposals more concrete with a computational model, but this is not strictly necessary. [The authors explain that they will address this with modeling work in subsequent research.]

Reviewer #1 (Public review):

Summary:

The paper investigates how AVP modulates pancreatic alpha and beta cell activity using acute mouse pancreatic tissue slices, calcium imaging, hormone secretion assays, RNAscope, and newly synthesized receptor-selective ligands. The Authors report that AVP regulates islet cell activity in a glucose- and state-dependent manner, with maximal effects occurring within physiological AVP concentrations and a bell-shaped concentration-response profile. They conclude that V1b receptors are the principal mediators of these effects and propose that IP3 receptor-dependent signaling underlies the observed nonlinear responses.

Strengths:

The use of fresh pancreatic tissue slices preserves islet architecture and cell-cell interactions, providing a physiologically relevant experimental model compared with isolated islets or immortalized cell lines.

The combination of live calcium imaging, hormone secretion measurements, RNAscope, and pharmacological characterization of newly synthesized receptor-selective ligands represents a technically comprehensive experimental approach that addresses AVP signaling from multiple complementary perspectives.

Weaknesses:

(1) The central mechanistic model of the manuscript is not supported by the experimental data. Although the Authors repeatedly attribute the observed bell-shaped responses to IP3 Receptor activation and inactivation, no direct mechanistic evidence is provided to implicate IP3 receptors. Experiments assessing IP3 receptor function using genetic manipulation and direct measurements of IP3 signaling are necessary before such mechanistic conclusions can be drawn.

(2) The Authors should directly demonstrate V1b receptor expression in β cells using complementary approaches, since the RNAscope data indicate broader expression but do not convincingly establish receptor localization within specific endocrine populations.

(3) In my opinion, the central conclusion that V1b receptors are the predominant mediators of the observed effects is insufficiently supported because definitive loss-of-function experiments are lacking. Genetic deletion or selective silencing of V1b receptors should be provided to validate the proposed mechanism.

(4) The heterogeneous responses observed among islets substantially weaken the proposed mechanistic model. Data should be provided to identify the determinants responsible for activation, absence of response, or inhibition in individual islets.

(5) Please explain why the marked changes in alpha-cell calcium activity were not accompanied by corresponding alterations in glucagon secretion. This apparent discrepancy requires additional experimental evidence.

(6) The Authors need to provide stronger evidence linking the observed calcium dynamics with insulin secretion, since calcium measurements alone cannot establish the proposed functional consequences.

(7) Proper assays should be provided to assess whether the newly synthesized ligands exhibit comparable selectivity and efficacy at murine receptors rather than relying primarily on pharmacological characterization performed using human receptor-expressing cell lines.

(8) The proposed absence of V1a receptor involvement is based primarily on pharmacological inhibition. Independent experimental approaches should be provided to exclude a contribution of this receptor subtype.

(9) They must provide additional quantitative analyses demonstrating that the reported bell-shaped concentration-response relationship is robust across individual experiments rather than reflecting substantial biological variability.

(10) The Authors should include experiments evaluating endogenous AVP signaling under more physiological conditions instead of relying predominantly on exogenous agonist administration.

(11) I believe the role of forskolin deserves further clarification because many conclusions were obtained under cAMP-permissive conditions that may substantially influence AVP responses. Additional experiments without pharmacological cAMP stimulation should be presented.

(12) Please clarify how beta cells and alpha cells were identified exclusively from functional activity patterns during calcium imaging and provide independent validation of cell identity within the analyzed recordings.

(13) In my opinion, the manuscript relies heavily on changes in intracellular calcium activity as a surrogate for endocrine function, whereas the secretion data do not consistently support the proposed functional conclusions. Additional evidence is needed to establish a direct relationship between the observed calcium dynamics and hormone release.

(14) The Authors should better reconcile their findings with previous reports showing minimal or absent AVP receptor expression in β cells and explain how the current data resolve these discrepancies rather than adding another possible interpretation.

Reviewer #1 (Public review):

Summary:

This paper develops a formalism for quantifying epidemic dynamics in terms of relative fitnesses of circulating variants, uses the formalism to elucidate fundamental tradeoffs of epidemics driven by variants with increased transmissibility versus immune escape capability, shows the formalism implies a natural quantity measuring the impact of selection on epidemic growth, and demonstrates that the formalism enables a decomposition of epidemic dynamics into circulation among different immunity groups. The relative fitness formalism enables these analyses to be performed with genetic sequence data only, a major benefit of the model given the relatively high availability of sequence data compared to other data streams such as case counts and titers.

Strengths:

Linking epidemic dynamics to pathogen evolution is a fundamental problem in studies of antigenically variable pathogens, with models of epidemic dynamics and immune-driven evolution going back decades in applications to respiratory pathogens such as influenza. The COVID-19 pandemic heightened the urgency for developing methods for quantifying epidemic growth in contexts where novel variants emerge, leading to differential susceptibility among individuals with diverse exposure histories with implications for vaccination strategies. Real-world data streams such as case counts and immunological measurements have a variety of shortcomings that pose major challenges for quantitative models aiming to inform policy. In recent years, genetic sequencing data has become widely available for pathogens including SARS-CoV-2 and influenza, allowing tracking of pathogen evolution at unprecedented detail in real time, yet biases in the collection of sequence data across different populations make connections between absolute epidemic size and variant frequencies from sequence data not immediately transparent.

This paper's contributions are exciting because they demonstrate new ways to link pathogen evolution and epidemic dynamics using very accessible data. From a theoretical perspective, the model is appealing because of its simple derivation in terms of compartmental models of epidemics, which are standard in the literature, and its clear extension to populations with heterogeneous immune histories. The latter extension leads directly to new methods for inferring immune groups with differential susceptibility to antigenically distinct variants in populations with heterogeneous immune histories without access to immunological data such as titers, an important advance given the wide applicability of quantification of antigenic relationships among variants in real populations.

Weaknesses:

While the demonstrated methods for forecasting short-term epidemic growth and for quantifying population immunity using sequence data are exciting as proofs of principle, the validation and statistical support provided in the analyses have drawbacks that are not fully addressed in the manuscript, weakening the evidence for the usefulness of the methods in their current form.

The analyses forecasting epidemic growth using Gaussian process models are justified using Pearson correlation coefficients whose values are extremely low for the test data period. The explanation given for this is that the case data used to validate the predictions has worse ascertainment over time, but it is not shown directly that the model may be working well despite the low correlations. Whereas, by eye, the predicted epidemic growth curves appear to capture features of the observed epidemic growth curves, the computed metrics don't support the claim of success of the predictions. Additionally, nearly all the model fits lack estimates of uncertainty, so it is not possible to discern the significance of departures between the model and data, or subtle differences in relative fitness calculations across geographies.

The analysis of latent pseudo-immune components also suffers drawbacks that render it more of an interesting proof of principle than a convincing tool for prediction at this point. In particular, in figures S18 and S19, metrics meant to quantify the statistical significance of the results show no difference from null models computed by permuting variants and their escape vectors, yet no interpretation is given for the lack of significance. Moreover, the model fits relating titer distance to pseudo escape distance seem unsuccessful for JN.1 infection and XBB infection histories, which is not adequately accounted for in the text, which cites just "weaker correlations" in these cohorts.

In several instances, the evidence for the new data analyses is weakened by a lack of clarity in the presentation of the technical details of the methods. For example, in the discussion of the Gaussian process models, it was not clear what features of the problem inform the choice of kernel (Matern 5/2), which hyperparameters were used, and how novel this use of Gaussian processes is. In the section describing methods for predicting epidemic growth rate from selective pressure, the discussion of the gradient boosting regressor model provided no intuition as to why this method performed better than the others tested or whether this was particularly important to the conclusions, and the lack of discussion of uncertainty or variability in the model predictions makes it difficult to assess the significance of the time series estimates alone. In the discussion of the latent immune factor model, the mismatch between the notation used in Equation 5 compared to that in Equation 18 made the derivations more difficult to follow. Subsequently, the explanation of the fitting of the pseudo-immune model left out details, such as an explicit definition of distance in pseudo-escape space, to what extent the group-level mean aggregated titer measurement captured features of the titer data (despite ignoring interindividual variability), and a thorough discussion of the successes and shortcomings of the fits in different scenarios. More explicit presentation of the mathematical choices going into the methods, sources and quantification of uncertainty, and cases where the model performs well or poorly could significantly bolster the case for the usefulness of sequence data in quantitatively predicting epidemic growth and antigenic relationships among variants in practice, in more general settings than those carried out here.

Reviewer #1 (Public review):<br /> <br /> Summary:

This tumour type is missing from the big pan-cancer databases, so none of the popular online analysis tools works for it. That's a real gap, and it's the right one to go after. The authors build an online resource that gathers the scattered public molecular datasets for this disease, adds three of their own patient cohorts, ties everything to clinical data, and exposes interactive tools, downloads, and programmatic access so other people can build on it. To show what it does, they take one gene through the whole platform - clinical, gene-expression, protein, single-cell, immune, and drug-response and then test that gene in cell lines. So there are really two things on offer here: a resource and a practical example of using it. They land very differently.

Strengths:

The resource is the real contribution, and it's done with care. It covers 37 centres and nearly 2,000 samples across five kinds of molecular data, and the authors are honest about provenance: how they screened datasets in or out, where they recorded the diagnostic codes, and why they dropped ambiguous mixed-tumour collections. The key methodological decision is the right one; every analysis runs inside its own cohort, and the cross-cohort views are explicitly "for looking, not for combining." That's exactly how you should treat heterogeneous public data, and they say so plainly instead of quietly pooling everything. Their three pathologist-confirmed cohorts add genuine independent material, so this isn't a re-skin of data that already existed. And because the code and a public access point are actually available, the reuse claim holds.

The example is internally consistent, which is what makes it persuasive. The gene reads higher in higher-risk patients across several independent cohorts and in their own protein data, tracks with the disease spreading and recurring, and lines up with worse survival. The single-cell data put it in the dividing cells; the pathway analysis points to proliferation. Three independent data types landing on the same proliferation story are the strongest part of the biology.

Weaknesses:

The honest problem is that the entire biological story rests on one gene, tested one way. The lab work is two cell lines with the gene knocked down, showing less growth and migration: there is no rescue to confirm the effect is real, no second gene to show the approach generalises, nothing in a living animal. That earns the modest claim: the resource can point you at a candidate worth testing. It does not earn the headline claim that the platform reliably generates good target hypotheses, because we only ever watch it succeed once. One example illustrates a workflow; it doesn't establish a method.

Some of the statistics won't survive scrutiny. The clearest case is a perfect separation between treatment-resistant and treatment-sensitive cases from a single immune cell population, reported with no error bars, no check for information leakage, and apparently from very few samples. A perfect result in that setting is almost always overfitting or a small-sample artefact, not a strong classifier. The same pattern shows up elsewhere: small groups, p-values with no effect sizes or error bars, and no correction for the enormous number of features and cohorts being tested across the whole platform. Separately, one drug result is a correlation against a predicted sensitivity score from a model.

The AI assistant gets far more weight than the evidence supports. Credit where due: the authors are clear and consistent that it only helps interpret and navigate, and never touches the data, the statistics, or the results. That's the correct line to draw, and they hold it. But the assistant itself is never tested, no accuracy numbers, no benchmark, no error analysis, no described way for a human to check what it produces. Calling it something that "fundamentally transforms the user experience" is an assertion, not a finding. And since even the literature feature is admitted not to be a proper systematic review, the prominence of the artificial-intelligence framing runs ahead of what's been shown.

Reviewer #1 (Public review):

Summary:

The manuscript by Waterman et al. describes the development of a mathematical model that quantifies plant volatile emissions dynamics in response to mechanical/biotic stress. Model outputs were based on volatile emission measurements from maize plants using PTR-MS. Modeling revealed differences in emission patterns dependent on the intensity of wounding damage, application of herbivore oral secretions, age of leaf, circadian clock, and genotype. Differences were also observed between different types of volatiles, and the response curves somewhat correlated with expression patterns of biosynthetic genes. Moreover, the model showed priming effects from overlapping response curves upon multiple wounding events.

Strengths:

As a non-expert in modeling, this reviewer assesses the work from a broader point of view. Overall, I consider this model to be useful for other researchers to quantify volatile emission dynamics for their plant system. Generating the models does not seem to be overly complicated as long as emissions can be measured with a real-time system such as PTR-MS, which is costly and not available to every lab. The advantage of this approach is that it does not rely on parameters of underlying enzymatic pathways or transport processes. The authors claim that it can be easily applied to other biological responses.

Weaknesses:

The manuscript lacks a deeper discussion of how the model can help make predictions of volatile emission dynamics from plants in the greenhouse or field. Can the model be trained and validated with volatile measurements from plants under different environmental conditions? How realistic is this approach given the complexity of a field environment? It would be helpful to provide a better outlook of the application of the model for scientists in the field of plant volatile biology and beyond.

The authors state that "emissions can be regulated independently of each other" (Line 359). I would assume that regulatory mechanisms in different genotypes are similar but show genotype-specific variation.

Case#: Patient 11, male, age 53

DiseaseAssertion: STGD

FamilyInfo: diagnosis of autosomal recessive STGD based on the pedigree and clinical phenotype of fleck deposits with or without genetic testing

CasePresentingHPOs: HP:0000608, HP:0000007, HP:0030610, HP:0030500

CaseHPOFreeText: Macular degeneration. autosomal recessive, Photoreceptor outer segment loss on macular OCT, Yellow/white lesions of the macula

CaseNotHPOs: n/a

CaseNotHPOFreeText: n/a

Genotyping Method: n/a

PreviouslyPublished: n/a

Variant: NM_000350.3:c.5461-10T>C

ClinVar: NM_000350.3(ABCA4):c.5461-10T>C

CAID: CA220687

SupplementalData: composite mask analysis shown in figure 3 for patient 11, show large areas of matched degeneration and isolated IS/OS loss

Case#: Patient 14, female, age 42

DiseaseAssertion: STGD

FamilyInfo: diagnosis of autosomal recessive STGD based on the pedigree and clinical phenotype of fleck deposits with or without genetic testing

CasePresentingHPOs: HP:0000608, HP:0000007, HP:0030610, HP:0030500

CaseHPOFreeText: Macular degeneration. autosomal recessive, Photoreceptor outer segment loss on macular OCT, Yellow/white lesions of the macula

CaseNotHPOs: n/a

CaseNotHPOFreeText: n/a

Genotyping Method: n/a

PreviouslyPublished: n/a

Variant: Allele 1: NM_000350.3:c.4222T>C Allele 2: NM_000350.3:c.4918C>T

ClinVar:Allele 1: NM_000350.3(ABCA4):c.4222T>C (p.Trp1408Arg) Allele 2: NM_000350.3(ABCA4):c.4918C>T (p.Arg1640Trp)

CAID:Allele 1: CA227166 Allele 2: CA227253

SupplementalData: composite mask analysis shown in figure 3 for patient 14, show diffusely intact IS/OS and RPE with central area of mixed types of degeneration. Both patient 2 and 14 show foveal preservation of IS/OS and RPE

Reviewer #1 (Public review):

Summary:

This manuscript describes a chemical screen for activators of the eIF2 kinase GCN2 (EIF2AK4) in the integrated stress response (ISR). Recently, reported inhibitors of GCN2 and other protein kinases have been shown at certain concentrations to paradoxically activate GCN2. The study uses CHO cells and ISR reporter screens to identify a number of GCN2 activator compounds, including a potent "compound 20." These activators have implications for the development of new therapies for ISR-related diseases. For example, although not directly pursued in this study, these GCN2 activators could be helpful for the treatment of PVOD, which is reported for patients with certain GCN2 loss-of-function mutations. The identified activators are also suggested to engage with the GCN2 directly and can function devoid of GCN1, a co-activator of GCN2.

Strengths:

The manuscript appears to be a largely rigorous study that flows in a logical manner. The topic is interesting and significant.

Weaknesses:

Portions of the manuscript are not fully clear. There are some experimental presentation and design concerns that should be addressed to support the stated conclusions.

Reviewer #1 (Public review):

The authors show that during prophase I of male meiosis, nucleoli disassemble and nucleolar components relocalize to the sex chromosome (XY) body. They further demonstrate that this process is regulated by the ATR-dependent signaling pathway that mediates meiotic sex chromosome inactivation (MSCI). Pharmacological disruption of pre-rRNA synthesis using the RNA polymerase I inhibitor BMH-21 leads to the recruitment of RNA polymerase II to the sex chromosomes and ectopic expression of sex chromosome-linked genes. These findings uncover a previously unrecognized role for pre-rRNAs in maintaining transcriptional silencing during meiosis. The study employs a combination of cell biology, genetics, and genomics approaches, and the conclusions are supported by compelling, well-organized data.

Comments:

(1) The current study focuses on transcriptional regulation of the sex chromosomes. It would be interesting to know whether perturbation of pre-rRNA synthesis also affects transcription of autosomal genes.

(2) Is ribosome biogenesis still active during prophase I of male meiosis? Additional discussion of the timing and extent of rRNA synthesis at this stage would help place the findings in a broader biological context.

(3) A recent preprint reports active RNA polymerase II-mediated transcription of Y chromosome genes within nucleolus-like bodies (NLBs) during prophase I of meiosis in Drosophila male germ cells (https://doi.org/10.64898/2026.05.20.726666). These findings suggest that the meiotic nucleolus may have species-specific roles in regulating sex chromosome gene expression. It would be valuable for the authors to discuss how their findings compare with these observations and the potential evolutionary implications.

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors investigate the functional requirements for glutamine and glutaminolysis in antibody responses. The authors first demonstrate that the concentrations of glutamine in lymph nodes are substantially lower than in plasma, and that at these levels, glutamine is limiting for plasma cell differentiation in vitro. The authors go on to use genetic mouse models in which B cells are deficient in glutaminase 1 (Gls), the glucose transporter Slc2a1, and/or mitochondrial pyruvate carrier 2 (Mpc2) to test the importance of these pathways in vivo. Interestingly, deficiency of Gls alone showed clear antibody defects when ovalbumin was used as the immunogen, but not the hapten NP. For the latter response, defects in antibody titers and affinity were observed only when both Gls and either Mpc2 or Slc2a1 were deleted. These latter findings form the basis of the synthetic auxotrophy conclusion. The authors go on to test these conclusions further using in vitro differentiations, Seahorse assays, pharmacological inhibitors, and targeted quantification of specific metabolites and amino acids. Finally, the authors document reduced STAT3 and STAT1 phosphorylation in response to IL-21 and interferon (both type 1 and 2), respectively, when both glutaminolysis and mitochondrial pyruvate metabolism are prevented.

Strengths:

(1) The main strength of the manuscript is the overall breadth of experiments performed. Orthogonal experiments are performed using genetic models, pharmacological inhibitors, in vitro assays, and in vivo experiments to support the claims. Multiple antigens are used as test immunogens--this is particularly important given the differing results.

(2) B cell metabolism is an area of interest but understudied relative to other cell types in the immune system.

(3) The importance of metabolic flexibility and caution when interpreting negative results is made clear from this study.

Weaknesses:

(1) All of the in vivo studies were done in the context of boosters at 3 weeks and recall responses 1 week later. Primary responses, including germinal centers, may still be ongoing at 3 weeks after the initial immunization and defects in GCs may contribute to the findings. Nonetheless, the authors do check antibody levels prior to the boost, and it is likely that most of the observed defects in Gls/Mpc2-deficiency are driven by faulty recall responses.

Reviewer #1 (Public review):

Summary:

Redchuk et al. explore the dynamic properties of chromatin upon serum starvation using machine learning approaches. They use CRISPR-tagging to visualize a region on chromosome 1 in human cells and show that in their system, chromosome 1, but not the previously reported chromosomes 10, 13, and X, undergo a change in radial position upon serum starvation. Live cell imaging showed a position change towards the periphery after serum starvation. They then apply a machine learning algorithm for the analysis of the imaging data, which reveals changes in nuclear area during serum starvation and longer displacements of the chromosome 1 locus near the nuclear periphery. Differential behavior of homologues is also reported.

Strengths:

(1) The study of chromatin dynamics is an interesting and important area of research.

(2) The use of machine learning approaches to analyze live cell imaging data is timely.

(3) With serum starvation, the authors use a simple, well-controllable model system.

Weaknesses:

(1) This study provides limited new insight into chromatin dynamics.

(2) It was not immediately evident what the use of machine learning approaches added to this study. It appears that the main conclusions could have been reached by conventional analysis.

Comments on revised version:

The authors have added some technical information, but have not made any major efforts to clarify some of the major points or to strengthen the paper. The degree of advance remains limited and several conclusions are not convincingly supported by the presented data.

Reviewer #1 (Public review):

The authors clearly demonstrate that overexpressed Dcp-1, but not Drice, is activated without canonical apoptosome components.

Using TurboID-based proximity labeling they revealed distinct proximal proteomes, among which Sirtuin 1, an Atg8a deacetylase, which promotes autophagy, was specifically required for Dcp-1 activation. Additionally, the show that autophagy-related genes, including Bcl-2 family members Debcl and Buffy, are required for Dcp-1 activation. Using structure-based prediction using AlphaFold3 they identified that Bruce, an autophagy-regulated inhibitor of apoptosis, as a Dcp-1-specific regulator acting outside the apoptosome-mediated pathway. Finally, they show that Bruce suppresses wing tissue growth. These findings indicate that non-lethal Dcp-1 activity is governed by the autophagy- Bruce axis, enabling distinct non-lethal functions independent of cell death.

Comments on revised version.

No further comments.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

Summary:

In this study, the authors describe an early diverging vertebrate KCNE gene present in jawless lampreys that they denote KCNE0.

Three forms of the protein are isolated from different lampreys, which have 95% homology to each other, but only moderate homology to KCNE1-6.

Co-expression with lamprey KCNQ1 produced a non-inactivating current, whereas co-expression with mammalian KCNQ1 resulted in less modulation. Introduction of a tetra-leucine motif from KCNE4 into KCNE0 reduced current on co-expression with KCNQ1, conferring an inhibitory effect.

Strengths:

This is an interesting and uncontroversial report of a new KCNE isoform from lower vertebrates that gives insight into the evolutionary progression of the sequence and functional properties of the accessory protein.

Reviewer #1 (Public review):

Summary:

This paper investigates whether semantic prioritization in visual working memory reflects pre-decisional access, evidence accumulation, or both, using drift diffusion modeling across a reanalysis of prior data and two new experiments. The core finding - that semantic information receives a robust pre-decisional access advantage that is amplified by attentional disruption rather than temporal delay alone - is novel and contributes meaningfully to ongoing debates about the format and accessibility of working memory representations.

Strengths:

The experimental approach is well-motivated, and the use of drift-diffusion modeling to decompose decision components adds analytical value beyond standard RT and accuracy measures. The two new experiments are pre-registered and address important questions. The broader theoretical conclusion - that working memory limits are shaped not only by storage capacity but by which representational formats remain accessible under attentional uncertainty - is an important and timely contribution to the field.

Weaknesses:

The central interpretive claims rely heavily on differences in non-decision time, a parameter that aggregates many processes unrelated to memory retrieval, making it rather difficult to uniquely attribute the observed effects to access or retrieval mechanisms specifically. Additionally, the characterization of the two memory conditions as genuinely perceptual versus semantic warrants further justification, as both may primarily require categorical rather than format-specific knowledge.

Reviewer #1 (Public review):

Summary:

This manuscript describes three conformers derived from a complex between ERK2-T185V, a variant of MEK1-DD with the KIM sequence replaced by the KIM from the p38 activator, GRA24, ADP, and AlF4-. The goal was to try to capture the complex in its active state. The results show contacts between the kinases between their N-lobe and their C-lobes that resemble MKK6-p38 complexes previously reported by the authors. Two MEK1-ERK2 conformers (States 1,3) are deemed inactive based on the lack of access of ERK-Y187 to the MEK1 active site, and the absence of ADP bound to MEK1 in State 3, while one conformer (State 2) is deemed active, but not fully active due to disorder in MEK1 activation loop (A-loop) and an essential salt bridge between strand beta3 and helix aC. HDX-MS and SAXS solution measurements and all-atom MD simulations are used to model the mutant complex and variants with WT ERK2. The study concludes that substrate recognition involves low-energy contacts with MEK, allowing substantial protein flexibility within the complex in a manner that may accommodate processive phosphorylation of ERK2.

Strengths:

The strengths of the work are that the findings provide important structural insights for MEK-ERK signaling and protein phosphorylation in general. These are valuable given that atomic resolution structures of kinase-substrate complexes are still limited in number. The authors succeeded in showing key contacts between subunits and conformational variations within the complex.

Weaknesses:

Weaknesses were that some of the conclusions about activity state, dynamics, and effects of ligand binding were less convincing. For example, that State 2 truly represents an active configuration seemed ambiguous, given the absence of Mg2+ and AlF4- in the cryoEM structure and disorder in the activation loop and the K97-E114 salt bridge. Conclusions by SAXS that ADP-AlF4 binding increases active site compaction while increasing local flexibility were not rigorously supported by HDX data, given that the latter were performed without ligand. Sections of the narrative and figures throughout were often confusing, and many assertions were made without clear explanation. Data shown in the supplementary materials were not always described in the Results, even those important for the conclusions. Figure legends and text lacked clear descriptions of specific complexes analyzed. Substantial changes are recommended to improve the readability and clarity of the work.

Reviewer #1 (Public review):<br /> <br /> Summary:

In this study, Shuler and colleagues record neurons from the DMS in mice performing a patch foraging task. In this task, mice had the choice between harvesting rewards from 2 ports - one the time-investment port where the rate of reward declined over time and the other a context port where the rate of reward was either high or low. Mice performed the task appropriately, switching between ports as the rate of reward declined in the time-investment port and switching more rapidly when the context port delivered high versus low rewards. The behavior of the mice was also strongly driven by time since the most recent reward receipt, in conflict with normative accounts of patch foraging. Individual DMS neurons showed bistable firing patterns, transitioning to high rates of activity at various times from reward. Overall, the population tiled the temporal space, and the accumulation of the number of neurons in the high firing state was predictive of patch exit. The rate of accumulation varied with things that also affected behavior.

Strengths:

Overall, the aims of the study were clear and important, the experiment directly addresses them, and the results are clear and provide compelling support for the authors' conclusions.

Weaknesses:

I have only a few comments and questions to consider, none of which are criticisms of what was done, really.

(1) Probably my chief question, alluded to in the discussion, is what the evidence is that DMS plays a causal role in generating these correlates and the resulting behavior, in light of the lack of causal evidence here. What are other options? Could such information depend on upstream areas such as OFC or mPFC, with DMS just a pass-through? And while I would not ask for causal data, is there a specific prediction? That is, if the area were inactivated, would mice stay longer or shorter? Not do the task? If I wanted to do a causal test of the authors' idea regarding the contribution of DMS to this behavior, what would be predicted, and what result would invalidate the hypothesis? Speculating on this a bit, beyond just saying DMS is involved, would be useful.

(2) Not much is said about the suboptimal strategy. Would DMS continue to play the same role if the mice showed no effect of recent reward and instead performed appropriately? Or is some other area doing that job? Or is this not important? I thought it was interesting that the mice basically did not treat the game quite like they were supposed to. Is it important to go back and look at what is happening in DMS under normative conditions to really know how this area contributes to proper foraging?

(3) Do these neurons also track time in the context port? Or do they only exhibit this behavior in the port where rewards are depleting? This seems like an interesting question. Do they show the same profile in different ports, if so?

Case#: Female, age 44 years old

DiseaseAssertion: Discordant STGD phenotype

FamilyInfo: Information revolving the sister of this patient is given as well as they both have a discordant STGD phenotype. Additionally, it mentions that the parents each harboured a mutation but were asymptomatic/had normal examination results.

CasePresentingHPOs: HP:0001141, HP:0007401, HP:0030602

CaseHPOFreeText: At 23 central vision was deteriorating and patient had a VA of 6/36 in the right eye and 6/60 in the left. Through fundus photography, bilateral yellow/white flecks were found at the posterior pole. 12 years later, her VA was retested and it was 6/60 in both eyes. After autofluorescnece (AF) imaging was done, there was localized low signal at the macula found with abnromal foci. In 2008 her macular atrophy had enlarged and the flecks were less apparent.

CaseNotHPOs: N/a

CaseNotHPOFreeText: In this article there was not a phenotype presented that was normal.

CasePreviousTesting: It mentioned that there were two previously reported variants on the same allele detected in the siblings and one unique novel variant on the second allele for this patient. However, the testing they used was not listed, it just stated that the variants were found through sequencing. For this patient the variants were p.L541P/p.A1038V and p.R881C.

GenotypingMethod: Just mentioned sequencing and ABCA4 screening to look for two variants p.L541V and p.A1038V and a third novel variant p.R881C.

PreviouslyPublished: N/a

Variant: 1) NM_000350.3(ABCA4):c.1622T>C (p.Leu541Pro) 2) NM_000350.3(ABCA4):c.3113C>T (p.Ala1038Val) 3) N/a

ClinVar ID: 1) 99067 2) 7894 3) N/a

**CAID: ** 3) Because there was not a reference or alternate allele provided in this article I was unable to find a CAID for p.R881C.

gnomAD: 1) Highest minor allele frequency was 0.00017 (https://www.ncbi.nlm.nih.gov/clinvar/variation/99067/) 2) Highest minor allele frequency was 0.00188 (https://www.ncbi.nlm.nih.gov/clinvar/variation/7894/) 3) N/a

SupplementalData: Figure 1 had information regarding imaging and other testing done on the patient that is vital for phenotypic characterization. Also, it mentions a variant known as p.R881C, but was unable to find anything on ClinVar or gnomAD.

Reviewer #1 (Public review):

Lohse et al. describe an open-source system for laser scanning photostimulation (LSPS) in head-fixed animals. Although similar systems have been developed and used by different groups, Zapit provides an open-source solution requiring few custom parts and minimal coding. This tool can clearly facilitate and speed the adoption of LSPS, particularly for the increasingly used purpose of mapping the effects of focal cortical silencing during behavior. Other potential uses include mapping optogenetically evoked movements and selectively activating genetically labeled neuronal subtypes of interest in the cortex. The design is well thought through, and the presentation is mostly clear and well written.

In general, the more modular such a system is, the better, in terms of compatibility with existing hardware and software that potential users may already have purchased - laser, galvo, and camera in particular. The system has struck a reasonable balance between allowing modularity and providing an integrated complete package, but even more flexibility would be welcome for potential users looking to cut costs, as would clearer presentation of such flexibility as already exists.

Comments and suggestions are mostly minor, as follows.

(1) Command signals:

How is the relationship between analog voltage commands and laser power determined? Is this assumed (or required) to be linear (as Figure 7F implies)? Usability and modularity would be improved by an option to measure or provide a calibration curve for systems with a nonlinear mapping between command voltage and laser power.

For the grid calibration step, how is the initial mapping from galvo voltage commands to image position determined? Presumably, some sort of initial guess or calculation based on the hardware specifications is needed for the grid calibration to be feasible. Also, how are the number of grid lines and the distance between them determined?

Why is the mapping between analog outputs and hardware (galvos, laser, masking light) fixed? This would be trivial to make configurable and allow labs with existing setups to adopt Zapit without rewiring existing hardware.

(2) Laser and optics:

In Figure 1, the authors should consider explaining the scanning principle schematically, i.e., depicting how tilting of the scan mirrors translates via the scan lens into beam displacement in the specimen plane. Perhaps Zemax can be used for accurate rendering.

Since the unexpanded beam greatly under-fills the back aperture of the lens, the z resolution is presumably terrible - which is good! That is, for the purposes of LSPS, this advantageously avoids focus-dependent effects, which might otherwise arise due to (e.g.) skull curvature. The authors should consider pointing this out, as well as providing an estimate of the z resolution.

What is the working distance?

Reviewer #1 (Public review):

Summary:

This manuscript reports a prospective longitudinal study examining whether infants with high likelihood (HL) for autism differ from low-likelihood (LL) infants in two levels of word learning: brain-to-speech cortical entrainment and implicit word segmentation. The authors report reduced syllable tracking and post-learning word recognition in the HL group relative to the LL group. Importantly, both the syllable-tracking entrainment measure and the word recognition ERP measure are positively associated with verbal outcomes at 18-20 months, as indexed by the Mullen Verbal Developmental Quotient. Overall, I found this to be a thoughtfully designed and carefully executed study that tackles a difficult and important set of questions. With some clarifications and modest additional analyses or discussion on the points below, the manuscript has strong potential to make a substantial contribution to the literature on early language development and autism.

Strengths:

This is an important study that addresses a central question in developmental cognitive neuroscience: what mechanisms underlie variability in language learning, and what are the early neural correlates of these individual differences? While language development has a relatively well-defined sensitive period in typical development, the mechanisms of variability-particularly in the context of neurodevelopmental conditions-remain poorly understood, in part because longitudinal work in very young infants and toddlers is rare. The present study makes a valuable contribution by directly targeting this gap and by grounding the work in a strong theoretical tradition on statistical learning as a foundational mechanism for early language acquisition.

I especially appreciate the authors' meticulous approach to data quality and their clear, transparent description of the methods. The choice of partial least squares correlation (PLS-c) is well motivated, given the multidimensional nature of the data and collinearity among variables and the manuscript does a commendable job explaining this technique to readers who may be less familiar with it.

The results reveal interesting developmental changes in syllable tracking and word segmentation from birth to 2 years in both HL and LL infants. Simply mapping these trajectories in both groups is highly valuable. Moreover, the associations between neural indices of brain-to-speech entrainment and word segmentation with later verbal outcomes in the LL group support a critical role for speech perception and statistical learning in early language development, with clear implications for understanding autism. Overall, this is a rich dataset with substantial potential to inform theory.

Comment on revised version.

The revised manuscript has provided additional analyses that lead to critical clarification of the main findings, including the longitudinal nature of the relationship between neural tracking of speech and language, the role of sleep, and the potential modulation effect of stream structure on syllable-level neural tracking. The overall results highlight the robustness of the findings as well as the specific relevance of the structured speech tracking to verbal outcomes of infants with high likelihood (HL) of autism.

Reviewer #1 (Public review):

Summary:

GCPs, which drive postnatal cerebellar growth and can give rise to SHH-MB, are not uniform. The authors show that GCPs include a rare Nestin-expressing subpopulation with distinct molecular features. This subpopulation is spatially restricted, enriched for stem cell-like properties, and shows a high competency for tumor formation comparable to larger GCP pools, with tumors preferentially arising in the posterior-lateral cerebellum. Overall, the findings indicate that SHH-MB might originate preferentially from this small, tumor-competent Nestin-expressing GCP subset.

Strengths:

(1) The authors use a breadth of approaches from histology, mouse genetics, and single-cell RNA sequencing.

(2) Throughout, this paper uses very elegant genetic approaches, such as the double Nes-FlpoER; Atoh1-FSF-Cre; LSL-Smo-M2, to generate tumors only from Atoh1+; Nes+ double-positive cells. This intersectional genetic experiment makes for a very clear answer.

(3) The findings reported in this manuscript are valuable since they reveal a novel GCP subpopulation defined by spatial and molecular identity. Some of their experiments suggest that these cells could represent the main cell-of-origin of SHH MB. The experiments are carefully performed, and the evidence is convincing.

Weaknesses or elements that could be improved:

(1) A transgenic Nestin-CFP mouse is used in this study. However, it is not clear whether CFP accurately reflects the Nestin protein. Figure 1: After the promoter is turned off, these cells might remain positive for CFP for longer than they are positive for Nestin, due to CFP protein stability. Is the Nestin protein present in these cells? Nestin double immunofluorescence with CFP and Sox2 and Barhl1 could be performed to address this. Related to this comment, it is also important to note that this is a rat promoter transgene. So the transgene might not reflect exactly the endogenous Nestin expression.

(2) Could the posterior restriction of Nestin-CFP be due to the timing (P1) at which the authors looked? In other words, if they look earlier, would the authors see Nestin-CFP cells more anterior?

(3) Since only one medulloblastoma mouse model (Smo-M2) is used to conclude that "the Nes-expressing GCP population in the normal cerebellum is transcriptionally closer to SHH MB tumor cells than the remainder of the GCPs", the findings might not apply to other SHH-MB models. This should be mentioned.

Reviewer #1 (Public review):

Summary:

In this manuscript from the Levy lab, the authors investigate whether SETD6 regulates hepatic lipid accumulation through direct methylation of PPARγ. They show that SETD6 binds and mono-methylates PPARγ at K170 and provide evidence that this modification enhances PPARγ occupancy at target promoters, promotes expression of lipid metabolism genes, as well as facilitates lipid droplet accumulation in HepG2 cells. The authors also find a positive feedback loop or circuit in which PPARγ activates SETD6 transcription in a methylation-dependent manner, thereby reinforcing this lipogenic program. Overall, the work presents a novel SETD6-PPARγ regulatory axis linking lysine methylation to transcriptional control of lipid storage genes, with possible relevance to NAFLD-associated biology.

In all, I find this to be an important paper that describes and advances a new regulatory pathway that has significance to human health and disease. It would also be of interest to a broad audience. That said, there are also some concerns that the authors should address, as outlined below.

Major concerns (pertains to rigor - highest priority)

(1) Overall, the work presented is of high quality and the data nicely support the conclusions; however, a few panels should be strengthened that have missing controls or information:<br /> a. The co-IP panel in Fig. 1B lacks a lane where HA SETD6 is expressed without PPARγ. This control is needed to verify that the SEDT6-HA signal depends on PPARγ.<br /> b. In Fig. 1C, the authors should show that the co-IP works in both directions (include IP for PPARγ/blot for SETD6). I am a bit confused also over the labeling with IP on the left and on top of the panel next to the beads label. More importantly, the data would be stronger if the authors take advantage of a deletion line to validate the co-IP is specific to the presence of both.<br /> c. The same IP labeling issue exists for Fig 3B (label is on the same and on top).<br /> d. Antibody information (e.g., where the pan-methyl Ab comes from and at what dilutions they are used at) is missing.

Nice to have experiments (medium priority - strongly consider)

(2) A missing gap is how K170me1 contributes to DNA binding and gene transcription. One possibility is that methylation enhances the DNA binding activity of PPARγ. Given the authors have all of the reagents, it would be possible to perform a gel shift assay (or other approach) with and without SETD6-mediaetd methylation. Is DNA binding affected/enhanced?

(3) Along these lines, I wonder if there is another possibility: could SETD6-mediated methylation of PPARγ drive SETD6-PPARγ interaction? In other words, in the K170R, is SETD6 still even associated with PPARγ, and this interaction is required for promoter recruitment? Alternatively, would a catalytic dead version of SETD6 fail to associate with PPARγ? Currently, no experiments test the impact of an unmethylatable version of PPARγ or catalytic dead version of SETD6 on SETD6-PPARγ interaction or SETD6 recruitment to promoters.

Minor concerns (text and figure display)

(4) The text has multiple typos and grammatical errors.

Comments on revised version.

Great job on addressing the comments. It is a nice study.

Reviewer #1 (Public review):

Summary:

Alveolar macrophages (AMs) are key sentinel cells in the lungs, representing the first line of defense against infections. There is growing interest within the scientific community in the metabolic and epigenetic reprogramming of innate immune cells following an initial stress, which alters their response upon exposure to a heterologous challenge. In this study, the authors show that exposure to extracellular ATP can shape AM functions by activating the P2X7 receptor. This activation triggers the relocation of the potassium channel TWIK2 to the cell surface, placing macrophages in a heightened state of responsiveness. This leads to the activation of the NLRP3 inflammasome and, upon bacterial internalization, to the translocation of TWIK2 to the phagosomal membrane, enhancing bacterial killing through pH modulation. Through these findings, the authors propose a mechanism by which ATP acts as a danger signal to boost the antimicrobial capacity of AMs.

Strengths:

This is a fundamental study in a field of great interest to the scientific community. A growing body of evidence has highlighted the importance of metabolic and epigenetic reprogramming in innate immune cells, which can have long-term effects on their responses to various inflammatory contexts. Exploring the role of ATP in this process represents an important and timely question in basic research. The study combines both in vitro and in vivo investigations and proposes a mechanistic hypothesis to explain the observed phenotype.

Weaknesses:

Although these findings are convincing and intrinsically interesting, they do not support the conclusion that ATP induces trained immunity. By definition, trained immunity refers to long-lasting metabolic and epigenetic reprogramming initiated by a primary stimulus. Importantly, some of these changes persist after the cells have returned to a basal activation state, thereby generating an altered response upon secondary stimulation (https://doi.org/10.1038/s41590-020-00845-6). In the present study, the data demonstrate a sustained increase in inflammasome activation and enhanced microbicidal activity for up to seven days following ATP exposure. While this sustained activation is noteworthy as well as metabolic shift, it does not demonstrate the existence of trained immunity. The terms priming or sustained activation would therefore be more appropriate than trained immunity.

Similarly, the observation of increased chromatin accessibility at inflammasome-related genes is expected given the robust activation of this pathway. The presence of open chromatin at these loci does not, by itself, constitute evidence for long-term trained immunity. The authors should therefore be cautious with their terminology and avoid overinterpreting their findings.

The authors have revised the manuscript to address the comments raised during the first rounds of review. However, several figures, figure legends, and methodological sections still require additional adjustments and clarification.

The Methods section remains incomplete and requires substantial revision. For instance, the methodology used to quantify immune cell populations presented in Figure 2 is still not described. It is not stated how immune cells were isolated and identified (e.g. flow cytometry from lung tissue). No information is provided regarding tissue digestion, cell isolation procedures, or gating strategy (presumably by flow cytometry). These details are essential and should be included, together with the corresponding gating strategy and absolute cell numbers.

There are inconsistencies throughout the manuscript. For example, the authors report n = 3 in the figure legend 2 and 3 independent experiments, whereas 3 or 4 points are represented in the graphs. This discrepancy is unclear and should be clarified.

Overall, while the study addresses an interesting biological question, the manuscript would benefit from substantial revision prior to publication. In particular, clarifications and improvements regarding the methodology, data presentation, and interpretation are required to strengthen the rigor and reproducibility of the conclusions. Several of the conclusions extend beyond what is directly supported by the data. In particular, the interpretation that these findings demonstrate trained immunity should be revised, and additional methodological clarifications and corrections are required.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The revised version of the manuscript addresses the previous concerns. Importantly, a major role of the RAP80-ABRAXAS pathway is now demonstrated in the recruitment of BRCA1, PALB2, and RAD51 to nucleolar DSBs.]

This study elucidates the molecular linkage between the mobilization of damaged rDNA from the nucleolus to its periphery and the subsequent repair process by HDR. The authors demonstrate that the nucleolar adaptor protein Treacle mediates rDNA mobilization, and the MDC1-RNF8-RNF168 pathway coordinates the recruitment of the BRCA1-PALB2-BRCA2 complex and RAD51 loading. This stepwise regulation appears to prevent aberrant recombination events between rDNA repeats. This work provides compelling evidence for the recruitment of the Treacle-TOPBP1-NBS1 complex to rDNA DSBs and demonstrates the critical role of MDC1 in the rDNA damage response. There are some issues with the over-interpretation of results as described subsequently. Some aspects could be strengthened, for example, a potential role of the RAP80-Abraxas axis, the origin of the repair synthesis (HDR vs. NHEJ), and a direct comparison of the RNF8 and RNF168 recruitment in the absence or presence of MDC1.

Reviewer #2 (Public review):

Summary:

The manuscript titled "p66Shc Mediates SUMO2-induced Endothelial Dysfunction" by Kumar et al. builds upon established literature demonstrating that both p66Shc and SUMOylation are essential players in nitric oxide (NO)-mediated endothelial vascular homeostasis and development (PMID: 10580504, 28760777, and 35187108).

In this study, the authors uncover a novel mechanism showing how the SUMO2ylation of p66Shc drives reactive oxygen species (ROS) production in endothelial cells. Specifically, they identify Lysine 81 (K81) as the critical residue on p66Shc conjugated to SUMO2, proving it is essential for the protein's mitochondrial localization.

The authors convincingly demonstrate that:

p66Shc is actively SUMO2ylated at the K81 site in cellular models.

Phosphorylation at Serine 36 (S36) is significantly reduced upon the loss of this critical SUMOylation site.

Conclusion:

Overall, this study provides strong evidence for a novel regulatory axis in endothelial cells. It successfully opens the door for further dissection of the complex mechanistic crosstalk between three key post-translational modifications on p66Shc: S36 phosphorylation, K81 SUMO2ylation, and acetylation.

Reviewer #1 (Public review):

Summary:

This manuscript investigates the degradation dynamics of extracellular DNA in soils and its impact on estimates of microbial abundance and diversity. By combining a broad geographic sampling design with a primer-labeling strategy, qPCR quantification, amplicon sequencing, and PMA treatment, the authors aim to disentangle total versus intracellular DNA signals and explore sequence-specific degradation patterns. The topic is relevant, particularly given the increasing awareness of relic DNA as a confounding factor in microbial ecology. The experimental design is ambitious and potentially impactful. However, several conceptual inconsistencies, methodological ambiguities, and statistical limitations currently weaken the robustness of the conclusions. These issues need to be addressed.

Strengths:

The manuscript addresses a timely and important question in microbial ecology, particularly given the growing recognition that relic DNA can bias interpretations of community composition derived from amplicon sequencing. The study is ambitious in scope, incorporating a broad geographic sampling design across multiple soil types, which enhances the generalizability of the findings. The use of a controlled microcosm experiment combined with a primer-labeling strategy to track extracellular DNA dynamics is conceptually innovative and provides a structured framework to investigate degradation processes.

In addition, the integration of multiple approaches, including qPCR for absolute quantification, high-throughput sequencing for community profiling, and PMA treatment to differentiate extracellular from intracellular DNA, represents a comprehensive attempt to disentangle complex sources of bias in soil microbiome analyses. The effort to link degradation dynamics with environmental variables and to explore sequence-level patterns further demonstrates the authors' intent to move beyond descriptive analyses toward a mechanistic understanding.

Weaknesses:

Several conceptual and methodological issues currently limit confidence in the study's conclusions. Key terms such as "sequence-specific degradation" are not clearly defined or supported by a mechanistic or structural hypothesis, making it difficult to interpret the biological meaning of the results. In addition, the bioinformatic workflow presents inconsistencies, particularly the use of ASVs followed by clustering at 97% similarity, which undermines the resolution required to support sequence-level inferences. Statistical analyses are also insufficiently described, including unclear definitions of "T values," a lack of detail on pairing structure, and no indication of multiple testing correction.

Furthermore, important methodological details are missing or unclear, including primer design (e.g., GAPDH tag vs ACTF), Illumina library preparation (e.g., adapter and indexing strategy), and validation of PMA treatment efficiency. The interpretation of PMA-treated samples as representing "living communities" is likely overstated, given the known limitations of the method in soil systems. Finally, typographical errors, inconsistent terminology, and unclear phrasing throughout the manuscript reduce readability and further complicate interpretation.

Reviewer #1 (Public review):

Summary:

This study focuses on characterizing the EEG correlates of item-specific proportion congruency effects. In particular, two types of learned associations are studied. One association involves associations between stimulus features and control states (SC), and the other involves stimulus features and responses (SR). Decoding methods are used to identify time-resolved SC and SR correlates.

The authors conclude that SC and SR associations can independently and simultaneously guide behavior. This conclusion is based on results showing that SC and SR correlates are (1) not entirely overlapping in cross-decoding, (2) simultaneously observed on average over trials, (3) independently correlate with RT, and (4) have a positive within-trial correlation.

Strengths:

Fearless, creative use of EEG decoding to test tricky hypotheses regarding latent associations.

Nice idea to orthogonalize ISPC condition (MC/MI) from stimulus features.

Response:

In their last response to the reviewers, the authors write:

"... constructing a theoretically unbiased decoder requires perfectly counter-balanced training data (i.e., for every training trial of class A that is X trials away from the test data, there must be a training trial of all other classes that is exactly X trials away from the test data). As we were unable to achieve such a perfect design, we chose not to run an additional experiment."

This isn't really an issue about whether this design is "perfectly" orthogonal. It's an issue regarding a clear confound among the decoded classes for SC/SR decoders. To be clear: of the 8 classes in the SC decoder, 4 are overwhelmingly presented in the first half (PHASE 2) of the session, whereas the other 4 are overwhelmingly presented in the second half (PHASE 3). The same is true for the SR decoder. So, session-half correlated noise could readily contribute to distinguishing among these classes. And counterbalancing this across subjects won't help because decoders lose sign.

To me, the conducted control analyses don't really make strong contact with this issue. The split-half cross-validation is a nice idea but, as the authors acknowledge, it's also subject to slower cross-session noise, as is the original analysis. This sort of noise is not exactly exotic in EEG. Caps/hair/electrodes shift, gel dries and impedance changes, posture / muscle tension / skin conductance changes, fatigue may wax and wane (e.g., linked to increasing alpha), etc. And the newest analysis didn't really seem to engage with this issue either, as it only assessed minimum distances between classes, on the order of 5 +- 2 SD trials. This seems to assume that the dominant potential sources of noise will be scale-free, such that the strength of the relation at short time scales would generalize to longer ones. I'm not sure why that's expected here.

Here are some suggestions for alternative control analyses that I think would be more targeted to this issue:

(1) Explicitly train a decoder to separate the three levels of PHASE from each other. Successful decoding would provide positive evidence for the presence of structured noise at this timescale.

(2) Specify an RDM for the PHASE variable and regress this component separately from each time-point/trial of the SC and SR decoders. This is a post-hoc band-aid, but it is in the spirit of correcting for a known confound.

(3) In the spirit of the authors' distance analysis, but without assuming that the noise is scale-free: perform a time-series RSA like that in Alink et al. (2015; https://doi.org/10.1101/032391), Fig. 1 and 3. This would allow one, e.g., to estimate the structure & timescales of the noise processes across the session.

Other readers may, like me, be puzzled by the selection of this particular experimental design to test this question of SC and SR coding, given the temporal confound among SC/SR classes, and given that there would seem to be many possible designs that are less confounded. For example, why not use a design where ISPC was swapped/shuffled several more times within each subject, so that PHASE is more orthogonal to long-timescale noise? Isn't ISPC learning fast enough to support learning phases shorter than 700 trials? Such readers would likely appreciate a frank discussion of this dilemma, and a motivation for the choice of the present design, within the manuscript.

Pre-stimulus coding:

To explain the apparent pre-stimulus coding of several task variables, the newest version of the manuscript proposes that subjects were proactively coding these variables via predictive mechanisms. This is an interesting account of item-specific control. It is also surprising, given that item-specific control mechanisms are typically conceptualized as reactive or stimulus-driven phenomena. But I think support for a proactive control account was incomplete. The mechanistic logic was not presented, and no hypotheses under this account were developed or tested. So I would suggest pinning down some hypotheses here and actually putting this account to the test.

Outliers & t-values: thank you for checking this!

Random slopes were omitted due to convergence failure, but this can inflate false positive inferences (e.g., Barr et al. 2013), and doesn't really motivate a minimal model. I'd suggest trying a slightly reduced model (e.g., drop correlations via `slope || subject`) using buildMer automated selection, or switching to brms.

Reviewer #1 (Public review):

Summary:

Duan, Li, Kulkarni et al. apply a multiplexed single-cell overexpression screen (Reprogram-Seq) to combinatorially perturb 105 transcription factors across 7 target cell types in mouse embryonic fibroblasts, generating a resource of ~200,000 single-cell transcriptomes spanning over 1,300 TF combinations. They develop a framework for classifying pairwise TF-TF interactions, identify a modular, shared architecture of gene regulatory programs across diverse TF combinations, and use these tools to nominate and partially validate new reprogramming cocktails.

Strengths:

The scale of the combinatorial screen is substantial, and the resulting dataset is a genuine resource for the field. The TF-TF interaction typing framework is a useful conceptual extension of prior genetic-interaction approaches to an overexpression/reprogramming context, and the modularity finding that diverse TF combinations converge on shared gene programs is a compelling organizing principle. The authors are, for the most part, careful and appropriately hedged in their claims; the overclaiming we flag below is the exception, not the rule. We also note that the core Reprogram-Seq assay itself builds directly on the authors' own prior work; the novelty here rests on scale, the interaction framework, and the modularity analysis.

Weaknesses:

Most of the concerns raised below relate to how existing data are quantified, cited, and reconciled with the text, rather than to the underlying experiments themselves. Several quantitative and comparative claims in the Results are not fully supported by the figures cited, and some conclusions are in tension with the authors' own data. Key methodological details relevant to interpreting the central TF-TF interaction framework, including TF expression dosage and within-combination transcriptional variability, are not reported or controlled for, which limits confidence in the resulting interaction classifications. The relationship between TF number and reprogramming efficiency is not clearly distinguished from a simple combinatorial coverage effect and does not consistently generalize across batches. Experimental validation of predicted cocktails is limited to a single target cell type. The manuscript would also benefit from addressing whether TF overexpression in fibroblasts can fully capture a factor's endogenous regulatory network, given that pioneer activity and chromatin accessibility are not addressed.

Reviewer #1 (Public review):

Summary:

The study of Drosophila mating behaviors has offered a powerful entry point for understanding how complex innate behaviors are instantiated in the brain. The effectiveness of this behavioral model stems from how readily quantifiable many components of the courtship ritual are, facilitating the fine-scale correlations between the behaviors and the circuits that underpin their implementation. Detailed quantification, however, can be both time consuming and error prone, particularly when scored manually. Song et al. have sought to address this challenge by developing DrosoMating, software that facilitates the automated and high-throughput quantification of 6 common metrics of courtship and mating behaviors. Compared to a human observer, DrosoMating matches courtship scoring with high fidelity. Further, the authors demonstrate that the software effectively detects previously described variations in courtship resulting from genetic background or social conditioning. Finally, they validate its utility in assaying the consequences of neural manipulations by silencing Kenyon cells involved in memory formation in the context of courtship conditioning.

Strengths:

(1) The authors demonstrate that for three key courtship/mating metrics, DrosoMating performs virtually indistinguishably from a human observer, with differences consistently within 10 seconds and no statistically significant differences detected. This demonstrates the software's usefulness as a tool for reducing bias and scoring time for analyses involving these metrics.

(2) The authors validate the tool across multiple genetic backgrounds and experimental manipulations to confirm its ability to detect known influences on male mating behavior.

(3) The authors present a simple, modular chamber design that is integrated with DrosoMating and allows for high throughput experimentation, capable of simultaneously analyzing up to 144 fly pairs across all chambers.

Weaknesses:

(1) DrosoMating appears to be an effective tool for the quantification of key courtship and mating metrics, but similar tools for automated analysis already exist. The authors present a compelling use case for DrosoMating, where it has particular advantages over tools like FlyTracker and Ctrax for high-throughput analysis. This comparative analysis, however, leaves out modern pose-estimation methods (SLEAP, DeepLabCut), better able to tolerate low contrast and occlusion. It therefore remains unclear what specific advantages it might offer over current machine learning approaches.

(2) The courtship behaviors of Drosophila males represent a series of complex behaviors that unfold dynamically in response to female signals. While metrics like courtship latency, courtship index, and mating duration are useful, they compress the complexity of actions that occur throughout the mating ritual. The authors suggest DrosoMating's modular architecture facilitates integration with behavioral classifiers like JAABA. Such integration could substantially expand the utility of this tool for the broader Drosophila neuroscience community, but in its current form its applications are confined to summary timing metrics.

(3) Validation is limited to multiple D. melanogaster strains. Cross-species studies of mating behavior diversity are increasingly common, so demonstrating the tool's accuracy across species would strengthen claims about its broader applicability.

Reviewer #1 (Public review):

Summary:

This work investigates the membrane insertion of aromatic-centered sequences in IDPs. Using a combination of all-atom MD simulations, the PPM method, and development of the sequence-based predictor AroMIP, the authors aim to establish a quantitative membrane insertion role for aromatic-centered motifs. The study demonstrates that flanking aliphatic and basic residues promote membrane insertion, whereas acidic and polar residues suppress insertion, and further reveals a difference between F/W-centered motifs and Y-centered motifs. The resulting AroMIP model achieves high predictive accuracy on human IDPs and is implemented as a publicly accessible web server.

Strengths:

This work addresses an important biological problem, as aromatic-driven membrane insertion remains poorly characterized despite mediating diverse functions like membrane remodeling and signaling. A key strength is the combination of complementary approaches, e.g., MD simulations provide mechanistic insight into insertion pathways, while PPM enables exhaustive sequence space exploration. The large-scale analysis clearly establishes L and R as promoters and E, N, and G as suppressors. The work also provides valuable mechanistic insight into how aromatic, aliphatic, and basic residues cooperate to stabilize membrane insertion states. Another important strength is the development of AroMIP as a practical prediction tool with a user-friendly online server that appears computationally efficient and broadly accessible to the community. The work is also well connected to prior experimental and computational literature, and the authors carefully position their findings within existing knowledge of membrane-associated IDPs.

Comments on revised version:

I think the authors have addressed all my concerns. I do not have further comments or requests for additional revisions. Thank you for all the hard work!

Reviewer #1 (Public review):

Naim et al., use genetically engineered mouse models and tissue culture cell lines to investigate the role of the SLAP adaptor protein in colonic epithelium and colon tumour formation. The SLAP adaptor protein is known to be a negative regulator of tyrosine kinase signaling in hematopoietic cells but its role outside the immune system is less well defined. Here the authors use genetically engineered SLAP deficient mice, tissue specific SLAP KO, and colonic organoids to demonstrate that SLAP is expressed in cells of the colonic epithelium where it acts as a cell autonomous regulator of proliferation and differentiation. In addition, they provide biochemical evidence that loss of SLAP expression in cultured colonic organoids results in increased Src family kinase activity and global tyrosine phosphorylation, consistent with its known role as a suppressor of tyrosine kinase activity in immune cells. Consistently, treatment with a SRC kinase inhibitor inhibited growth of SLAP deficient organoids. These data provide solid evidence of a cell autonomous role of SLAP in the colonic epithelium.

Using a chemically induced model of colitis-associated cancer the authors demonstrate that inactivation of SLAP shows a trend toward increased tumor formation as well as significantly increased Src family kinase activity within tumors. Tumor spheres from SLAP deficient animals showed enhanced growth that was suppressed by treatment with a Src family kinase inhibitor. Of note, the latter effect was specific to SLAP deficient tumor spheres. These observations are convincing and support the authors conclusion that SLAP has a tumor suppressor role in CRC through inhibition of SFK signaling.

Mechanistically, elevated expression of the EPHB2 receptor tyrosine kinase was detected in immunoblots and by IHC of SLAP KO colonic crypts. In addition, in SLAP deficient crypts, levels of phosphorylated EPHB2 are increased and associated with activated SRC family kinases. Using an EPHB2 inhibitor, the role of EPHB2 in the growth of SLAP deficient colonic organoids, and downstream SRC phosphorylation was demonstrated. The authors also show that low expression of SLAP in human CRC cell line organoids sensitizes to the growth inhibitory effects EPH inhibition which can be reversed by SLAP over expression but not expression of a SH2/SH3 mutant form of SLAP.

Overall, this work provides evidence of SLAP adaptor function in restricting EPH tyrosine kinase signaling the colonic epithelium and suggests that loss of SLAP expression promotes tumorigenesis in this context.

Reviewer #1 (Public review):

Summary:

The manuscript by Kostanjevec et al. investigates the mechanism behind spiral pattern formation in the cornea. The authors demonstrate that the spiral motion pattern on the mammalian corneal surface emerges from the interaction between the limbus position, cell division, extrusion, and collective cell migration. Using LacZ mosaic murine corneas, they reveal a tightening spiral flow pattern and show that their cell-based, in silico model accurately reproduces these patterns without global guidance cues. Additionally, they present a continuum model that extends the XYZ hypothesis to describe cell flux on the cornea, offering a quantitative explanation for tissue-scale processes on curved surfaces.

Strengths:

The manuscript is well-written, with a systematic approach that clearly explains experimental setups, model construction, assumptions, parameter selection, and predictions. The discussion also provides insightful perspectives on the broader implications of the results for both physics and biology.

Weaknesses:

The authors emphasize polar alignment as a key feature of the spiral pattern based on simulation results. However, they do not provide experimental evidence for this polar alignment.

Reviewer #1 (Public review):

Summary:

Wang, Po-Kai et al., utilized the de novo polarization of MDCK cells cultured in Matrigel to assess the interdependence between polarity protein localization, centrosome positioning and apical membrane formation. They show that the inhibition of Plk4 with Centrinone does not prevent apical membrane formation, but does result in its delay, a phenotype the authors attribute to the loss of centrosomes due to the inhibition of centriole duplication. However, the targeted mutagenesis of specific centrosome proteins implicated in the positioning of centrosomes in other cell types (CEP164, ODF2, PCNT and CEP120), as well as the use of dominant negative constructs to inhibit centrosomal microtubule nucleation did not affect centrosome positioning in 3D cultured MDCK cells. A screen of proteins previously implicated in MDCK polarization revealed that the polarity protein Par-3 was upstream of centrosome positioning, similar to other cell types.

Strengths:

The investigation into the temporal requirement and interdependence of previously proposed regulators of cell polarization and lumen formation is valuable. The authors have provided a detailed analysis of many of these components at defined stages of polarity establishment and well demonstrate that centrosomes are not necessary for apical polarity formation, but are involved in the efficient establishment of the apical membrane.

Weaknesses:

Key questions remain regarding the structure of the intracellular cytoskeleton following depletion of centrosomes, centrosome proteins, or abrogation of centrosome microtubule nucleation. The authors strengthen their model that centrosomes are positioned independently of microtubule nucleation using dominant negative Cdk5RAP2 and NEDD-1 constructs, however, the structure of the intracellular microtubule network remains unresolved and will be an important avenue for future investigation.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments in their discussion of the limitations that were raised in the previous round of review.]

This study by Li and colleagues examines how defensive responses to visual threats during foraging are modulated by both reward level and social hierarchy. Using a semi-naturalistic paradigm, the authors test how the availability of water or sucrose, with sucrose being more rewarding than water, shapes escape behavior in mice exposed to looming stimuli of different intensities, which are used to probe perceived threat level and defensive responses. In parallel, the study compares dominant and subordinate animals to assess how social rank biases the trade-off between reward seeking and threat avoidance. By combining behavioral analyses with computational modeling, the work addresses how reward level and social context jointly influence escape decisions in an ethological setting.

Across the different experimental conditions, perceived threat level is the main determinant of behavior. The authors show that looming stimuli associated with higher threat (contrast) consistently elicit faster and more robust escape responses than lower threat stimuli. This effect is particularly evident during early exposures, when animals are highly vigilant and have not yet habituated to the looming stimulus (learned that it is not dangerous). Later they described that as animals gain experience and habituate, behavior becomes more flexible, and reward level begins to exert a graded modulation of the escape response. Importantly, the authors show that under high threat conditions increasing reward value leads to more frequent and faster escape rather than greater reward pursuit, specifically in dominant mice. This finding is particularly relevant, as it suggests that highly valued rewards can heighten vigilance and thereby enhance responsiveness to threat, highlighting that reward does not simply compete with defensive behavior but can also reshape it depending on the perceived level of danger, in contrast to low threat conditions, where threat can be more easily outweighed by reward. However, it is worth noting that the authors use an extremely low contrast for the low threat condition (20%), which may to some extent be insufficient to reliably trigger escape responses. Thus, an important conceptual contribution of the study is the introduction of vigilance as a useful framework to interpret these effects. Vigilance is treated as a behavioral state reflecting heightened attention to potential danger. In line with what is known from natural foraging, mice initially maintain high vigilance when confronted with an innate threat. This perspective helps clarify a finding that might otherwise appear counterintuitive. One might expect higher rewards to motivate animals to tolerate risk, explore more, and habituate faster in any scenario. Instead, the data suggest that highly rewarding outcomes can elevate vigilance, making animals more responsive to threat and leading to faster or more frequent escape under high threat conditions. In this sense, reward does not simply compete with threat but can also amplify sensitivity to it, depending on the internal state of the animal.

The social results are particularly interesting in this context as well. Dominant mice consistently prioritize avoidance over reward, showing stronger escape responses and slower habituation than subordinates. This behavior is well captured by the vigilance framework proposed by the authors: dominant animals appear to maintain higher vigilance, which biases decisions toward threat avoidance. The authors further suggest that stable social relationships sustain high vigilance and slow habituation, framing this as an evolutionarily conserved strategy that may enhance survival. This interpretation provides a valuable perspective on how social structure shapes defensive behavior beyond immediate physical interactions. At the same time, there are important limitations to this interpretation. All experiments were conducted in male mice, and it is possible that the relationship between social hierarchy, vigilance, and defensive behavior would differ substantially in females. In addition, the idea that stable social relationships sustain elevated vigilance should be interpreted carefully, as it does not fully align with broader views of social stability as protective against anxiety and stress and generally beneficial for mental health and resilience. These points do not undermine the findings but suggest that the social effects described here should be interpreted with caution and within the specific context of the task and sex studied.

Another important limitation is that the neural mechanisms underlying these effects remain highly speculative. Although the manuscript includes an extensive discussion of candidate circuits, particularly involving the superior colliculus and downstream structures, these interpretations go far beyond the data presented in the study and are not directly supported by experimental evidence within the paper itself. The discussion gives substantial weight to potential circuit mechanisms based primarily on previous literature rather than on findings from the current study. Given the complexity and distributed nature of the circuits likely involved in integrating vigilance, reward, social context, and defensive behavior, the present work is better viewed as providing a strong behavioral framework rather than direct mechanistic insight into the underlying neural substrates. In this context, some references discussing how animals learn to suppress defensive responses to repeated looming threats and the neural mechanisms supporting this process could further strengthen the discussion (Salay et al 2021; Fratzl et al. 2021; Conway et al. 2025; Mederos et al. 2025).

Methodologically, the behavioral paradigm is well suited for studying escape decisions in socially housed animals, and the machine learning based classification of defensive responses is a strength. The computational model provides a useful formalization of how threat level, reward level, and vigilance interact and may be valuable for other laboratories studying escape, approach avoidance, or conflict situations, particularly as a way to classify behavioral outcomes after pose estimation. More generally, the work will be of interest to the neuroethology community for its detailed characterization of escape behavior under naturalistic conditions. At the same time, some statements in the discussion slightly overstate the novelty of the methodological approach. For example, the claim that the study differs from earlier work by using machine learning rather than manual annotation overlooks that several previous studies have already implemented automated or semi-automated strategies to classify looming evoked defensive behaviors beyond manual scoring alone.

Given the ethological nature of the study and the high inter individual variability reported by the authors, clarity and precision in the methods are especially important for reproducibility. While the revised manuscript addresses many earlier concerns, some aspects remain slightly difficult to follow. For example, the main text states that animals were not water deprived to minimize differences in internal state across conditions, whereas parts of the methods describe experiments in which animals were water deprived. This distinction is not always clearly explained across the different experimental sections, despite internal state being central to the interpretation of the behavioral findings. A clearer separation and description of these conditions would further strengthen confidence in the work. In addition, it was somewhat surprising that the low contrast (20%) looming condition was still sufficient to trigger robust escape responses, and additional clarification or discussion regarding stimulus saliency at this contrast level could help readers better contextualize these findings.

Overall, this study provides a rich analysis of how reward level and social hierarchy modulate defensive behavior through changes in vigilance. It offers a useful conceptual advance for thinking about escape behavior in semi-naturalistic settings and lays a solid foundation for future work aimed at linking these behavioral states to underlying neural circuits.

Reviewer #1 (Public review):

Summary:

Garcia-Alcala, Kratz and Cluzel investigate to what extent our understanding of bacterial physiology in bulk experiments can be applied to single-cell observations. They find that intrinsic noise may be powerful enough to even inverse the trends found in the bulk. The authors hypothesize that asymmetric distribution of ribosomes to daughter cells during the cell division plays the dominant role in the intrinsic noise and is able to generate the observed phenomenon. They do not show it directly, but the data and its agreement with the model suffice to support this claim.

Strengths:

The experimental part is convincing: the positive correlation between the elongation rate and promoter activity of unnecessary protein is clear, as well as the negative correlation between the mean values while changing the promoter strength. This was demonstrated in both rich and poor media. The causality between the growth rate and the promoter activity was shown using the negative lag time of the cross-correlation function. A simple, reasonable model accounts well for the data. This paper demonstrates an interesting phenomenon and provides a plausible theory for it, advancing our understanding of bacterial physiology on the single-cell level.

Weaknesses:

(1) Mean-reversion timescales were assumed to be longer than the simulation time and much longer than the cell cycle time. It is not clear whether the results robust in case mean-reversion timescales become of the order of cell cycle or smaller. If not, is there an argument for such practically infinite reversion timescales?

(2) It is not easy to understand the simulation part unless one reads Ref. [14]. Is k(t) assumed to follow Eq. (1) from ref. [14]? Is this crucial that the ribosome noise appears only at the division? The ribosome noise strength \sigma_R=0.06 - is it lower or higher than the naively expected binomial division?<br /> Also, more intuitive explanation of the Simpson paradox would help the reader.

(3) It would be useful for the reader to see the raw data and not only the filtered one to appreciate the measurement noise level.

(4) Negative lag time of the cross-correlation function is visible, but consider adding statistical test for it.

(5) Can you make similar cross-correlation plots using the model? Can you infer using it whether the data agrees better with the assumption that ribosomes noise appear only at division or continuous fluctuations during the cell cycle?

Comments on revised version:

The authors addressed the five comments listed above.

Reviewer #2 (Public review):

Summary:

In striated muscle, myosin motors can dynamically switch between an energy-conserving OFF state and an activated-ON state. This switching is important for meeting the body's needs under different physiological conditions, and previous studies have shown that disease causing mutations associated with cardiomyopathies can affect the population of these states, leading to aberrant contractility. Studying these structural states in muscle has previously only been possible via X-ray diffraction which requires access to a beam line. Here, Arecchi et al. demonstrate that polarized second-harmonic generation microscopy (pSGH), a technique that is more accessible, can be used to probe the ON/OFF states of myosin in both permeabilized and intact muscle.

Comments on revised version:

The manuscript has been significantly strengthened in the revision. The authors have addressed my concerns.

Reviewer #2 (Public review):

Summary:

In this EEG study, Huang et al. investigated the relative contribution of two accounts to the process of conflict control, namely the stimulus-control association (SC), which refers to the phenomenon that the ratio of congruent vs. incongruent trials affects the overall control demands, and the stimulus-response association (SR), stating that the frequency of stimulus-response pairings can also impact the level of control. The authors extended the Stroop task with novel manipulation of item congruencies across blocks in order to test whether both types of information are encoded and related to behaviour. Using decoding and RSA they showed that the SC and SR representations were concurrently present in voltage signals and they also positively co-varied. In addition, the variability in both of their strengths was predictive of reaction time. In general, the experiment has an innovative design and the analytical choices are appropriate and the evidence supporting the conclusions are overall solid after taking consideration the control analyses the authors have provided, although within limits of their study design.

Strength:

(1) The authors used an interesting task design that extended the classic Stroop paradigm and is effective in teasing apart the relative contribution of the two different accounts regarding item-specific proportion congruency effect.

(2) Linking strength of RSA scores with behavioural measure is critical to demonstrating the functional significance of the task representations in question.

Comments on revised version.

I appreciate the tireless effort the authors have presented to provide extra control analyses, however, on the other hand I do wish to remind them that sometimes limitations of one study is better addressed by a new study with improved design. There is very good reason why orthogonalization is critical to separating confounding influencing factors which the current study did not completely achieve. Reviewer 1's suggestion on alternative designs is certainly worth considering, and I encourage the authors to continue working on perfecting the experimental design in future work.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

The manuscript examines whether insects can use bat odor as a cue of predation risk. The authors focus on the insectivorous bat Scotophilus kuhlii and the cricket Loxoblemmus equestris. They first use fecal DNA metabarcoding to show that crickets are part of the bat's diet, and field surveys to show that L. equestris is abundant at local foraging sites. In laboratory Y-tube assays, the authors show that crickets strongly avoid air carrying bat body odor. Gas chromatography coupled with electroantennographic detection showed that cricket antennae respond to components of bat odor. Chemical analyses identified several volatile compounds, with 2,2-dimethylheptane and (−)-limonene associated with antennal responses. Further analyses suggested that snout secretions are likely to contribute to the bat's body odor. The authors then tested individual compounds. Among the commercially available candidates, (−)-limonene elicited a strong antennal response and was sufficient to cause avoidance in the olfactometer. In field plots, spraying (−)-limonene reduced cricket calling activity relative to pre-exposure levels, whereas calling increased in control plots treated with hexane. Overall, the study argues that crickets can detect a vertebrate predator through olfactory cues and that a single bat-associated volatile can trigger antipredator behavior.

This is an interesting and enjoyable study that addresses an understudied aspect of predator-prey interactions. The manuscript is clearly written, the experiments are presented in a logical sequence, and the figures are crisp and easy to follow. I really appreciated the combination of behavioral assays, electrophysiology, chemical analysis, and field observations.

Reviewer #1 (Public review):

Summary:

In this manuscript, Rupasinghe and co-authors introduce a new statistical model for spiking neurons. Building on earlier work, they propose to model spikes as arising from a Poisson process whereby the firing rate is the product of stimulus drive and a stimulus-independent gain signal. The critical innovation of this work is that the gain signal is modeled in continuous time. Earlier explorations of this statistical construction treated the gain-signal as constant within a trial. This innovation is elegant and important. It makes the model richer, more plausible, and more broadly applicable. The authors show that the model parameters are recoverable from realistic amounts of data and then apply the framework to previously studied datasets. They show that the new model outperforms earlier models and alternative candidates in capturing spiking data across four visual areas of the macaque monkey. Analysis of the model parameters replicates some earlier findings and uncovers several new insights. The model and fitting methods can be broadly applied to partition different types of signals and noise from spiking data and are likely to be widely adopted in the systems neuroscience community.

Strengths:

(1) Through clever use of advanced statistical techniques, the authors manage to infer critical information from single trial single cell data.

(2) The question of which aspect of a spike train is signal and which is noise is omnipresent in neuroscience. By improving our ability to characterize the distinct factors that shape spiking activity, this work makes a fundamental contribution to the literature.

Weaknesses:

(1) The work is entirely focused on single cell data. While this is a great starting point, expanding the approach to spiking activity in neural populations is an important future goal. The discussion lays out a roadmap towards this goal.

Comments on revised version.

I thank the authors for their sincere engagement with the reviews. They have addressed all issues I had raised. I found the first version of the manuscript already impressive. The revised version is a bit clearer about the exact relationship to some prior work and now documents additional new findings that validate the successful partitioning of signal and noise and directly connect stimulus-induced variability quenching to the stabilization of the latent gain signal. This makes it a really great paper.

Reviewer #1 (Public review):

Summary:

The authors investigate how depressive symptoms relate to metacognitive confidence across multiple levels of a metacognitive hierarchy. Participants completed twice-daily assessments of depressive symptoms and bi-daily assessments of a perceptual confidence task for eight weeks. The study replicates prior findings that depression is associated with lower confidence and extends these findings by suggesting that trait depression weakens the temporal persistence of local confidence signals, thereby impairing their accumulation into global confidence.

The study addresses an important question in computational psychiatry: how disturbances in confidence contribute to persistent negative self-beliefs in depression. The longitudinal design and repeated assessments represent advances over prior cross-sectional studies. The attempt to bridge momentary confidence fluctuations with broader self-beliefs through a hierarchical metacognitive framework is novel.

The principal contribution is the finding that trait depression moderates the lagged relationship between local and global confidence. The authors interpret this as evidence that positive fluctuations in local confidence decay more rapidly in individuals with higher depression, limiting their integration into higher-order confidence beliefs. This contribution is potentially important.

However, several aspects of the interpretation warrant caution. The moderated mediation analysis remains correlational and does not establish that impaired local confidence persistence causally produces global under-confidence. Alternative explanations, including stable individual differences in response styles, latent third variables, or measurement properties of the confidence scales, remain plausible. The manuscript occasionally adopts language suggesting mechanistic or causal conclusions that exceed the inferential scope of the analyses.

The temporal resolution of the study also complicates interpretation. The absence of cross-lagged effects between depression and confidence may reflect a mismatch between the timescales over which mood and metacognition interact. The authors acknowledge this possibility, but it substantially limits conclusions regarding temporal precedence.

Furthermore, the sample size is not justified, and the sample differs considerably from populations typically studied in depression research. Participants were older, predominantly female, and self-selected citizen scientists with low and relatively stable depression scores. Consequently, it remains unclear whether the observed dynamics generalise to clinically depressed populations, where symptom severity and variability may differ substantially.

Overall, this is a thoughtful and technically sophisticated study that provides valuable new data on the temporal organisation of confidence in relation to depression. The central findings are interesting and likely to stimulate future work, although the mechanistic interpretations would benefit from greater caution.

Strengths:

(1) Innovative use of dense longitudinal sampling to investigate metacognitive processes.

(2) Large number of repeated observations per participant and good adherence over eight weeks.

(3) Integration of EMA, multilevel vector autoregression, Bayesian modelling, and computational modelling.

(4) Novel proposal that depression weakens the persistence of local confidence signals and their integration into global confidence.

(5) Careful consideration of local versus global metacognitive processes.

Weaknesses:

(1) The causal and mechanistic claims may exceed what can be inferred from the data.

(2) No justification is provided for the sample size, and the sample is older, predominantly female, and largely non-clinical, limiting generalisability.

(3) The sampling intervals may not be optimally suited to detect temporal relationships between mood and confidence.

(4) Several modelling decisions require additional justification and sensitivity analyses.

(5) The moderated mediation framework assumes a temporal ordering that cannot be conclusively established.

Reviewer #1 (Public review):

Summary:

The authors investigate whether the brain uses the same neural representations for "absence" when it comes to seeing nothing versus thinking of zero. To do this, they recorded MEG while participants performed two types of tasks: (1) a perceptual detection task where subjects reported the presence or absence of a faint visual stimulus, and (2) numerical comparison tasks where subjects saw streams of numbers or dot patterns (both including "0" or empty sets) and decided which of two color-coded streams had a larger average. Multivariate decoders were trained to distinguish "present" vs "absent" in the detection task and "zero" vs "non-zero" in the numerical tasks. As a sanity check, the authors first replicate that symbolic (digit "0") and non-symbolic (empty dot sets) zeros share a common neural code (cross-format generalization). Crucially, they find that this numerical "zero" code does not overlap with the code for perceptual absence: cross-decoding between the detection task and number tasks yields Bayes factors strongly favoring distinct representations. In other words, the brain's pattern for "no grating was seen" cannot decode the pattern for "the number zero was shown," and vice versa. A small brief cross-decoding effect around 300 ms was observed, which the authors attribute to low-level visual confounds (and which they test with an additional control decoder for stimulus presence), but overall the evidence supports a dissociation.

Strengths:

The authors investigate whether the brain uses the same neural representations for "absence" when it comes to seeing nothing versus thinking of zero. To do this, they recorded MEG while participants performed two types of tasks: (1) a perceptual detection task where subjects reported the presence or absence of a faint visual stimulus, and (2) numerical comparison tasks where subjects saw streams of numbers or dot patterns (both including "0" or empty sets) and decided which of two color-coded streams had a larger average. Multivariate decoders were trained to distinguish "present" vs "absent" in the detection task and "zero" vs "non-zero" in the numerical tasks. As a sanity check, the authors first replicate that symbolic (digit "0") and non-symbolic (empty dot sets) zeros share a common neural code (cross-format generalization). Crucially, they find that this numerical "zero" code does not overlap with the code for perceptual absence: cross-decoding between the detection task and number tasks yields Bayes factors strongly favoring distinct representations. In other words, the brain's pattern for "no grating was seen" cannot decode the pattern for "the number zero was shown," and vice versa. A small brief cross-decoding effect around 300 ms was observed, which the authors attribute to low-level visual confounds (and which they test with an additional control decoder for stimulus presence), but overall the evidence supports a dissociation.

Weaknesses:

My main concern is whether the perceptual and numerical tasks are truly matched aside from their "absence" content. The perceptual task is a simple yes/no detection of a faint grating, whereas the numerical tasks involve holding two streams of 5 items in working memory and comparing their average. These tasks differ in many ways (stimulus complexity, decision rule, cognitive load), so it is possible that the lack of cross-decoding is due to general task differences rather than a fundamental "absence vs zero" dissociation. The authors do partially address this by showing that other shared aspects (like color) can cross-generalize, but one might still worry that an "absence" decision in a detection task engages different attentional or decisional mechanisms than a "zero" decision in a numerical context.

The numerical averaging task closely resembles that used by Spitzer et al. (2017), who reported that both behavioral weighting and neural representational geometry exhibit anti-compression, with disproportionately stronger representations for larger numerosities. In contrast, the present manuscript interprets its decoding results as reflecting an ordered numerical continuum. It is therefore unclear whether the current analyses are sensitive only to ordinal structure or whether they also preserve the nonlinear representational geometry reported previously. This distinction is important because the interpretation of zero as part of a numerical continuum depends on the geometry of that continuum. The authors should clarify whether their representational analyses are compatible with the anti-compressed neural number line described by Spitzer et al., or explain why the two studies yield different conclusions.

Spitzer, B., Waschke, L., & Summerfield, C. (2017). Selective overweighting of larger magnitudes during noisy numerical comparison. Nature Human Behaviour, 1(8), 145. https://doi.org/10.1038/s41562-017-0145

The authors attempt to account for non-numerical visual information by controlling for Total Dot Area and Density. While this is an important control, these two variables do not exhaust the visual dimensions that covary with numerosity in dot displays. A large body of work has demonstrated that multiple continuous features, including average item area, total surface area, convex hull (field area), and density, are inherently intercorrelated and cannot all be independently controlled simultaneously (e.g., Piazza et al., 2004; Gebuis & Reynvoet, 2004; Castaldi et al., 2019; Karami et al., 2025). Consequently, controlling only two features does not fully establish that the decoded signal specifically reflects numerosity. To better characterize the stimulus space, I encourage the authors to report the correlation matrix among the principal visual features of the dot arrays (average item area, total surface area, convex hull/field area, density, and numerosity). In addition, it would be informative to quantify the unique contribution of each feature to the neural data using a multiple-regression RSA or semi-partial correlations RSA, similar to the analyses employed by Castaldi et al. (2019) and more recently by Karami et al. (2025). Such analyses would provide a more rigorous assessment of whether the decoded representations uniquely reflect numerosity after accounting for correlated visual properties.

Piazza, M., Izard, V., Pinel, P., Bihan, D. L., & Dehaene, S. (2004). Tuning curves for approximate numerosity in the human intraparietal sulcus. Neuron, 44(3), 547-555. https://doi.org/10.1016/j.neuron.2004.10.014

Gebuis, T., & Reynvoet, B. (2011). The interplay between nonsymbolic number and its continuous visual properties. Journal of Experimental Psychology General, 141(4), 642-648. https://doi.org/10.1037/a0026218

Castaldi, E., Piazza, M., Dehaene, S., Vignaud, A., & Eger, E. (2019). Attentional amplification of neural codes for number independent of other quantities along the dorsal visual stream. eLife, 8. https://doi.org/10.7554/elife.45160

Karami, A., Castaldi, E., Eger, E., & Piazza, M. (2025). Distinct neural representational geometries of numerosity in early visual and association regions across visual streams. Communications Biology, 8(1), 1029. https://doi.org/10.1038/s42003-025-08395-z

The manuscript reports predominantly diagonal temporal generalization for non-symbolic numerosity, implying a rapidly evolving neural code. However, a recent study using time-resolved decoding of numerical representations (Karami et al., 2025) reported substantial off-diagonal temporal generalization, consistent with a temporally stable representational format. Although methodological differences between the studies may account for this discrepancy, the apparent contrast deserves discussion. In particular, it would be useful for the authors to clarify whether the differences arise from task demands, stimulus characteristics, preprocessing and decoding procedures, or from theoretical differences in what is being decoded. More generally, these findings raise the possibility that the temporal stability of numerical representations is task-dependent rather than fixed. If so, it would be interesting to discuss whether task demands might also influence the relationship between perceptual and conceptual representations of absence. Such a possibility could help explain why cross-decoding was not observed in the present study and suggests an interesting direction for future research.

Karami, A., Castaldi, E., Eger, E., Hebart, M., & Piazza, M. (2025). Numerosity Is Directly Sensed and Dynamically Transformed in the Human Brain: Evidence from MEG-MRI Fusion. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2025.11.15.687894

Throughout the manuscript, the authors appear to treat non-symbolic numerosity as a conceptual representation and contrast it with perceptual absence. I find this interpretation insufficiently justified. A substantial body of behavioral (Anobile et al., 2013; Cicchini et al., 2016) and neuroimaging (Piazza et al., 2004; Castaldi et al., 2019; Karami et al., 2025) research has argued that non-symbolic numerosity is represented as a perceptual attribute extracted relatively early in the visual processing hierarchy, even if its precise computational origin remains debated. Consequently, it is not immediately clear why non-symbolic numerosity should be regarded as a conceptual representation comparable to symbolic number or the concept of zero. This distinction is important because it directly affects the interpretation of the negative cross-decoding results. If both perceptual absence and non-symbolic numerosity are primarily perceptual representations, the absence of cross-decoding cannot be taken as evidence that perceptual and conceptual absence are represented differently. Rather, it may simply indicate that these two perceptual representations encode different visual attributes. I therefore encourage the authors to clarify their theoretical position regarding the representational status of non-symbolic numerosity and to discuss how their interpretation relates to influential theories of numerical cognition that conceptualize non-symbolic numerosity as an early perceptual representation rather than an abstract conceptual one.

Anobile, G., Cicchini, G. M., & Burr, D. C. (2013). Separate mechanisms for perception of numerosity and density. Psychological Science, 25(1), 265-270. https://doi.org/10.1177/0956797613501520

Cicchini, G. M., Anobile, G., & Burr, D. C. (2016). Spontaneous perception of numerosity in humans. Nature Communications, 7(1), 12536. https://doi.org/10.1038/ncomms12536

Piazza, M., Izard, V., Pinel, P., Bihan, D. L., & Dehaene, S. (2004). Tuning curves for approximate numerosity in the human intraparietal sulcus. Neuron, 44(3), 547-555. https://doi.org/10.1016/j.neuron.2004.10.014

Castaldi, E., Piazza, M., Dehaene, S., Vignaud, A., & Eger, E. (2019). Attentional amplification of neural codes for number independent of other quantities along the dorsal visual stream. eLife, 8. https://doi.org/10.7554/elife.45160

Karami, A., Castaldi, E., Eger, E., Hebart, M., & Piazza, M. (2025). Numerosity Is Directly Sensed and Dynamically Transformed in the Human Brain: Evidence from MEG-MRI Fusion. bioRxiv (Cold Spring Harbor Laboratory). https://doi.org/10.1101/2025.11.15.687894

I have two related concerns regarding the discussion of Paul et al. (2022). First, I think it would be helpful to describe more explicitly what was measured in that study. To my understanding, Paul et al. quantified the aggregate Fourier power (AFP) of the stimuli. Moreover, AFP has primarily been discussed in the context of dot arrays with constant dot size within each stimulus. In the current manuscript, it is not entirely clear from the Methods whether dot sizes vary within displays. I therefore encourage the authors to explicitly describe how dot sizes were generated and varied across stimuli. If AFP is correlated with numerosity in the present stimulus set, it would also be helpful to explain how the analyses dissociate neural representations of numerosity from those potentially driven by AFP. Second, I am not entirely convinced by the argument that training a classifier to distinguish Hits from Correct Rejections is sensitive to aggregate Fourier power. It would be helpful if the authors could explain more explicitly why this decoding contrast should be expected to be sensitive to AFP. As currently written, the logical connection between the AFP hypothesis and the proposed control analysis is not entirely clear.

Reviewer #1 (Public review):

Summary:

This is an interesting and well-written manuscript in which the authors set out to answer a simple, longstanding question with a modern comparative approach. Namely where in crab evolution did sideways walking arise, how often has it been lost or regained, and is its evolution plausibly associated with the ecological and taxonomic success of true crabs. To address these questions the authors recorded locomotion from 50 live species, quantified the predominant direction of locomotion, and mapped these behavioral states onto a recent crab phylogeny to reconstruct the likely evolutionary history of sideways walking. The revised manuscript also includes analyses of the underlying movement-angle distributions and tests whether the evolutionary conclusions depend on the original behavioral classification scheme.

Strengths:

The strongest part of the study remains the dataset itself. Comparable behavioral measurements across dozens of crab species are rare, and obtaining and recording live representatives from this range of taxa required substantial field, aquarium, and husbandry effort. The overall pattern that emerges, in which most true crabs are strongly biased toward sideways locomotion while several specialized lineages move predominantly forward, is interesting and likely to be useful to researchers studying animal locomotion, functional morphology, and behavioral evolution.

The revised analyses substantially strengthened the manuscript. In the original version, I was concerned that the main behavioral classification depended too strongly on first assigning individual movements to forward or sideways bins using a fixed angular boundary. The authors have now analyzed the underlying continuous movement-angle distributions and have shown that, although mixed directional tendencies are present in some species, most taxa have a dominant directional preference. They also derived a separate, data-informed boundary from the distribution of dominant movement directions. I favor this alternative approach and it produces the same classification of species as the original index-based method, providing useful evidence that the main evolutionary reconstruction is not simply an artifact of the original 60{degree sign} cutoff.

The authors also responded appropriately to the limitation that locomotion was measured from one individual per species. This sampling design cannot establish the full extent of within-species, ontogenetic, or size-dependent variation, but the revised manuscript now states this limitation clearly and restricts its conclusions to broad interspecific patterns in predominant locomotor direction. This is a more appropriate interpretation of the available sampling.

The phylogenetic analysis provides a reasonable framework for addressing the main evolutionary question. Taken together, the behavioral and phylogenetic results support the conclusion that sideways locomotion likely arose once within the lineage leading to true crabs and was followed by multiple reversions toward predominantly forward locomotion in specialized groups. The manuscript therefore makes a convincing case that sideways walking is not simply an inevitable consequence of possessing a crab-like body plan.

Weaknesses:

My main remaining reservation concerns the interpretation of forward and sideways locomotion as two discrete biological modes. The revised analyses convincingly show that species can be classified according to their predominant direction of locomotion and that this classification is robust to alternative analytical approaches. However, this does not necessarily demonstrate that forward and sideways locomotion represent two intrinsically discrete or mutually exclusive behavioral modes. Indeed, the new analyses show that many taxa are better described by two-component movement-angle distributions, even though most of these have one dominant component. This is consistent with strong directional preferences, but it also indicates that mixed movement strategies are common. The supplementary circular distributions similarly show considerable variation in the shape and breadth of directional preferences among taxa. Having said that, I do not think this substantially weakens the central evolutionary conclusion. The phylogenetic analysis requires a defensible classification of predominant locomotor direction, and the revised analyses now provide one. The evolutionary story remains interesting whether the underlying behavioral variation consists of two sharply discrete modes or a broader continuum of directional strategies with strong clustering toward forward and sideways movement.

A second limitation is that the proposed relationship between sideways locomotion and diversification remains necessarily correlational. The revised manuscript handles this more cautiously than the original version and now frames sideways locomotion as a plausible key innovation whose emergence is associated with the exceptional diversity of true crabs, rather than as a demonstrated causal driver of diversification. This distinction is important because differences in species richness among lineages can also reflect ecological opportunity, extinction history, and other lineage-specific factors. The revised framing is therefore better aligned with the strength of the evidence.

Final assessment: Overall, this is a valuable comparative study with an unusually broad behavioral dataset. The revisions have addressed the principal methodological concerns raised in the original review, particularly by analyzing continuous movement directions and demonstrating that the main phylogenetic classification is robust to an alternative, data-informed approach. The evidence now convincingly supports the central conclusion concerning the evolutionary origin and repeated reversal of predominant locomotor direction in crabs, although the stronger interpretation that forward and sideways locomotion represent two strictly discrete biological modes remains less certain.

Reviewer #1 (Public review):

Summary:

The authors perform an analysis of the relationship between the size of an LMM and the predictive performance of an ECoG encoding model made using the representations from that LMM. They find a logarithmic relationship between model size and prediction performance, consistent with previous findings in fMRI. They additionally observe that as the model size increases, the location of the "peak" encoding performance typically moves further back into the model in terms of percent layer depth, an interesting result worthy of further analysis into these representations.

Strengths:

The evidence is quite convincing, consistent across model families and complementary to other work in this field. This sort of analysis for ECoG is needed and supports the decade-long enduring trend of the "virtuous cycle" between neuroscience and AI research, where more powerful AI models have consistently yielded more effective predictions of responses in the brain. The lag analysis showing that optimal lags do not change with model size is a nice result using the higher temporal resolution of ECoG compared to other methods like fMRI.

Comments on revised version.

After the latest revision, I am pleased to remove my previous remarks about weaknesses of the paper, as I believe the additional data scaling analysis, discussion of layerwise trends, and other additional commentary makes the paper a compelling addition to the literature.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have adequately addressed how the percentage of fibroblasts was derived.]

Summary:

In this manuscript, Seegren and colleagues demonstrate that in a mouse model of neonatal E. coli meningitis, loss of toll-like receptor 4 (TLR4) in VE-cadherin+ endothelial cells and a subset of meningeal fibroblasts leads to a marked decrease in transcriptional dysregulation across multiple leptomeningeal cell types, a decrease in vascular permeability, and a decrease in macrophage abundance. In contrast, loss of macrophage TLR4 had less pronounced effects. Using cultured wildtype and TLR4-knockout endothelial cells, the authors further demonstrate that TLR4 signaling leads to reversible internalization of the tight junction protein claudin-5, establishing a potential mechanism of increased vascular permeability. Authors also show that claudin-5 internalization is independent of NF-κB. Finally, the authors use RNA-sequencing of wildtype and TLR4-knockout endothelial cells to define the TLR4-dependent cell-autonomous transcriptional response to E. coli.

Comments on previous version:

The authors have considerably improved and strengthened the work through the addition of new experimental data, new data analyses, and modifications to their interpretation. Notably, the authors used additional Cre-reporter mice to clarify that Cdh5-CreER is active in endothelial cells and some meningeal fibroblasts, and thus revised nomenclature and interpretation to acknowledge that the Tlr4fl/-;Cdh5-CreER cKO (Tlr4-VEKO) is not exclusively endothelial. The authors also demonstrated that Tlr4-VEKO does not affect peripheral E.coli burden, but acknowledge that changes to periphery-derived signals (e.g., cytokines) may contribute to observed leptomeningeal phenotypes.

The authors added PCA plots to show similarity in gene expression shifts across biological replicates (mice). This provides support for the claim that Tlr4-VEKO attenuates infection-associated transcriptional changes. With respect to differential expression analysis, I agree with authors that characteristics of individual cells (e.g. heterogeneity) are of interest. I remain concerned, however, that the formal differential analysis strategy appears to consider cells as independent experimental units, which they are not because a single cell cannot be randomly assigned to an experimental group (control or cKO, uninfected or infected). The mouse is the correct experimental unit for a comparison across these groups because it can be randomized. I appreciate that many of the gene expression changes appear consistent across mice (e.g. Figure 1 - Figure supplement 7) and that there are clear infection- and genotype-associated phenotypes in other assays. I would simply caution that the authors' analysis strategy likely leads to a larger number of type I errors (false positives) than is generally accepted; a mixed (hierarchical) model or pseudo-bulk approach would be more appropriate for future studies.

Reviewer #3 (Public review):

Gonzaga-Saavedra et al report an analysis on genomic binding of Polycomb group proteins, and of H2Aub1 and H3K27me3 domain formation in the early Drosophila embryo. Using carefully stage embryos during the nuclear cycles (NC) leading up to the cellular blastoderm stage, the authors provide compelling evidence that H3K27me3 domains at PcG target genes are only established during NC14 and do not exist in NC13. In contrast, H2Aub1 domains already start to appear during NC13. The authors show that E(z), the catalytic subunit of the H3K27 histone methyltransferase PRC2, is readily detected in interphase nuclei during the rapid nuclear divisions in pre-blastoderm embryos. In contrast, the DNA-binding proteins Pho, Cg and GAF that are known (Pho) or have been postulated (Cg, GAF) to anchor PRC2 and PRC1 to Polycomb Response Elements (PREs) in Polycomb target genes only start to show nuclear localization from NC10 onwards with gradually increasing nuclear concentrations, reaching a maximum during NC14. These data strongly corroborate the simple straightforward view that targeting of PRC2 and PRC1 to PREs by sequence-specific DNA-binding proteins is a pre-requisite for the formation of H3K27me3 and H2Aub1 domains at Polycomb target genes.

The authors then explore the potential role of GAF/Trl in this process. They find that in embryos depleted of GAF/Trl, H3K27me3 domain formation is largely unperturbed.

The authors also depleted the pioneer factor Zelda (Zld) and found that removal of Zld results in a more complex outcome. Zelda appears to counteract accumulation of H3K27me3 at the Polycomb targets eve and zen but also appears to be required for effective H3K27me3 domain formation at Polycomb targets such as amos or atonal.

This is a very thorough study that reports data of superior technical quality that are highly relevant for the field. The study by Gonzaga-Saavedra et al extends and strengthens previous work from the labs of Eisen (Li et al, eLife 2014) and Zeitlinger (Chen et al, eLife 2013) to convincingly demonstrate that Polycomb domain formation in the early embryo occurs during ZGA but that such domains do not exist prior to ZGA. This should now finally put to rest earlier claims by the Iovino lab (Zenk et al, Science 2017) that H3K27me3 domains present in the zygote nucleus would be propagated and partially maintained during the rapid nuclear cleavage cycles and serve as seeds for H3K27me3 domain formation during ZGA.

The experiments analyzing H3K27me3 domain formation in embryos depleted of GAF/Trl or Zelda will be of great interest to the field.

Comments on revised version.

In the revised version, the authors have addressed the comments and suggestions raised by this reviewer and added the missing references to earlier work.

Reviewer #1 (Public review):

Summary:

Reynolds and colleagues provide a deep phenotypic analysis of behavior in adgrl3.1 mutant zebrafish at larval stages using a closed-loop optomotor response (OMR) assay. The analyses conducted are interesting and extract new locomotor phenotypes with possible relevance to the role of adgrl3.1 in ADHD. Reduced interbout interval (both in the OMR assay and in dark rest periods) and increased distance moved provide greater resolution on hyperactivity phenotypes already described in these mutants. Reduced variation in interbout interval and reduced variation in swim speeds throughout the assay provide new insights into how behavior is altered; the authors suggest that these findings reflect more stereotyped, less flexible behavior in adgrl3.1 mutant animals. Analyses of task performance are interesting and could help understand how / whether animals maintain vigilance over time in the OMR assay and reveal trends in adgrl3.1 mutants relative to siblings, but ultimately do not identify significant phenotypes for adgrl3.1 mutants. While methods are extremely clear and analyses and phenotypic insights are solid, the authors do not provide sufficient support for assertions that their paradigm separates anxiety from locomotor activity or extracts phenotypes central to ADHD (impulsiveness, attention, etc). In some instances, interpretation of behavioral phenotypes in the context of disease is difficult to follow or not well supported with citations, etc.

Strengths:

(1) Deeper phenotypic analysis of adgrl3.1 locomotor phenotypes reveals changes to bout timing/initiation of locomotion as potentially causative for broader hyperactivity phenotypes previously reported.

(2) Interesting dissection of OMR performance over time and variability in locomotor parameters, assessment of OMR performance in high- and low-contrast.

(3) Methods are clearly described and considered to be rigorous.

Weaknesses:

(1) The introduction does not clearly spell out why the closed-loop OMR assay is expected to capture phenotypes central to ADHD (impulsiveness, attention, etc). Similarly, it's stated in the discussion that hyperactivity is driven by shorter inter-bout intervals and longer bout lengths...reflecting a reorganization of locomotor timing," and that "such fine-scale insights are not possible in standard light/dark paradigms." But in fact, each of these parameters was examined in the dark periods and could be assessed in a standard light/dark assay. As explained at the end of the discussion, this work provides a detailed analysis of locomotion and extracts new and interesting phenotypes, but the assertion that this is a function of the assay / that the assay is uniquely relevant to ADHD is not well-supported. The final statement of the introduction more accurately captures the advantages of the assay used: "this allowed us to assess whether loss of adgrl3.1 alters not only overall locomotor drive...but also specific visuomotor behavioral responses under different stimulus demands."

(2) The statement early in the results that "this approach extends beyond classical locomotor assays conducted in static light / dark environments, where locomotor activity may conflate with anxiety-related responses" and later that the closed-loop OMR assay "disentangles hyperactivity from anxiety-related responses" are not well-supported. Anxiety states could influence performance on OMR (Braun et al., 2024, Molec Psychiatry).

(3) Some interpretations of the phenotypes are not well supported by citations and may be overstated. For example, "adgrl3.1 elevates baseline arousal...producing a phenotype of heightened but less exploratory visuomotor activation." Since bout duration is increased alongside reduced interbout interval and increased total distance traveled, reduced exploration is not well-supported by the data. Later in the results, it's suggested that the increase in distance traveled reflects "over compensatory hyperactivity under ambiguous sensory conditions, consistent with attentional deficits." It's not clear what this means - references would be helpful to create links between hyperactivity and detection of ambiguous sensory conditions, and also between hyperactivity under these conditions and attentional deficits.

Reviewer #1 (Public review):

Summary:

The authors report the results of a tDCS brain stimulation study (verum vs sham stimulation of left DLPFC; between-subjects) in 46 participants, using an intense stimulation protocol over 2 weeks, combined with an experience-sampling approach, plus follow-up measures after 6 months.

Strengths:

The authors are studying a relevant and interesting research question using an intriguing design, following participants quite intensely over time and even at a follow-up time point. The use of an experience-sampling approach is another strength of the work.

Comments on revised version.

With the last round of revisions, the authors have now addressed my concerns.

Reviewer #1 (Public review):

Summary:

A growing body of evidence indicates that Alzheimer's disease is not simply a disease of neurons accumulating toxic protein aggregates, but one in which the immune system, both its resident brain component and its circulating peripheral arm, plays an active and sustained role. Understanding how these two immune compartments interact with one another and with diseased neural tissue has been hampered by the fact that the mouse immune system differs fundamentally from the human one in ways likely to matter for disease progression. The authors set out to address this gap by building a modular laboratory model that brings together three human cell types in a three-dimensional setting: brain organoids derived from human stem cells to provide a neural substrate, stem cell-derived brain immune cells (microglia) to represent the resident immune compartment, and circulating immune cells (CD8-positive T cells) harvested from human blood to represent the peripheral adaptive immune response. By exposing this tri-cellular system to a toxic form of amyloid protein, the hallmark aggregating molecule of Alzheimer's disease, the authors aimed to dissect, step by step, how microglia respond to amyloid stress, what inflammatory signals they release as a consequence, and whether those signals are sufficient to attract T cells into the neural environment. They further aimed to test whether blocking the molecular receptors that guide T cell movement could interrupt this process, with the broader goal of positioning the platform as a tool for human-relevant drug screening.

Strengths

The conceptual architecture of the platform is one of its clearest strengths. The decision to add immune components in a stepwise, modular fashion, first characterising the neural response to amyloid, then adding microglia, then adding T cells, makes it possible to attribute observed changes to specific cellular contributions in a way that a more complex all-at-once model would not allow. This staged design is well thought-through, and its logic is clearly communicated. The combination of single-cell transcriptional profiling, calcium imaging for real-time functional readouts, transwell migration assays, and protein secretion measurements gives the study a genuinely multi-modal character that goes beyond what purely transcriptomic or purely imaging-based approaches can offer. The observation that T cells failed to migrate toward amyloid-treated organoids in the absence of microglia is a clean and conceptually important result, clearly supporting the idea that the resident immune response acts as an intermediary between amyloid pathology and the recruitment of peripheral immune cells. The identification of specific chemokine receptor pathways mediating T cell movement and the demonstration that pharmacological blockade of those receptors reduces migration and provide a degree of mechanistic resolution useful for thinking about future therapeutic strategies.

Weaknesses

Despite these strengths, several aspects of the work as presented substantially limit the confidence one can place in its conclusions.

The most consequential issue concerns the origin of the cells used in the model. The three cellular components: the brain organoids, the microglia, and the T cells are derived from genetically unrelated individuals. The T cells, in particular, come from healthy blood donors unrelated to the stem cell lines used to generate the neural tissue. This means the immune cells and the tissue they are interacting with carry different molecular identity markers (the proteins that the immune system uses to distinguish self from non-self). In this setting, any T cell activation or directed movement could reflect a generic rejection-like response to foreign tissue rather than a disease-relevant, chemokine-directed recruitment process. This is not a subtle concern: it represents a fundamental ambiguity at the heart of the model's central finding, and it is not acknowledged anywhere in the manuscript. For the transwell migration data to be interpretable as a model of Alzheimer's disease rather than of immune incompatibility, the authors would need to demonstrate that migration is driven by the specific chemokine environment and not by the genetic mismatch between cells, for example, using cells from the same donor or from matched donors, or by showing that blocking identity-marker recognition does not alter migration.

A related concern is that the T cells used are from healthy individuals, whereas T cells from people with Alzheimer's disease are known to differ in their activation state, surface receptor expression, and functional behaviour. The platform cannot yet claim to model the specific T cell biology of Alzheimer's disease until disease-relevant T cells are incorporated.

Beyond this foundational issue, the study frequently describes findings in causal terms that the experimental design does not support. The resident immune cells are said to "drive" T cell recruitment and "establish" a feedback loop. These are strong mechanistic claims. The evidence presented indicates that when microglia are present, more T cells migrate, and that blocking T cells receptors reduces migration. What is missing is direct evidence that the specific molecules measured, particularly the chemokines CCL4 and CCL5, are the agents responsible, as opposed to other signals also present in the conditioned environment. No experiment directly neutralises these chemokines to test whether their removal is sufficient to abolish T cell recruitment. Without such an experiment, the receptor-blocking data show only that the receptors matter, not that the measured ligands are the ones activating those receptors.

The abstract describes one particular molecule, CXCL10, as a contributor to T cell recruitment, but the data in the paper itself show no significant change in CXCL10 levels between conditions. This discrepancy between the abstract and the results is misleading to readers who may not read the figures in detail.

The single-cell sequencing data, which form the basis for claims about changes in cell populations following amyloid treatment or microglia addition, are presented without validation of the cell type labels against established reference datasets from human brain tissue. The proportional shifts in cell populations between conditions (Figures 1H and 3E) are described as significant findings but are shown without any statistical test appropriate for this type of compositional data. Comparisons of cell-type proportions derived from single-cell sequencing require specialised statistical approaches that account for the interdependence of proportions and the variability between samples; standard tests are not appropriate here, and none are applied.

There is also an unresolved inconsistency in the age at which the organoids were analysed by single-cell sequencing: the text states day 90, while the figure legend states day 60, and the methods section contains a passage describing experimental conditions (including a cholesterol treatment and a drug called semaglutide) that are entirely unrelated to this study and appear to have been copied from a different manuscript. These issues raise concerns about the rigour of the manuscript preparation and should be corrected.

Finally, the sample sizes underpinning several key conclusions are small (typically three to four organoids per group), particularly for the protein-secretion measurements used to identify the inflammatory signals responsible for T cell recruitment. While organoid studies are inherently limited in scale, the strength of the mechanistic claims made here would benefit from larger sample size or independent experimental replication.

Conclusion:

The authors have built a platform that is conceptually well-conceived and generates data consistent with a role for microglia in bridging amyloid pathology and T cell recruitment. In that sense, they have made meaningful progress toward their stated aims. However, the platform, as described, cannot yet deliver the human-specific mechanistic insight it claims to provide, primarily because the non-autologous configuration of the model introduces an uncontrolled variable that confounds the interpretation of the immune interaction data. The claim to have provided "the first human-specific mechanistic demonstration" of microglial activation as a bridge between amyloid pathology and adaptive immune recruitment is not supported by the evidence presented. The data are consistent with this interpretation but do not establish it.

The general approach, building increasingly complex human neural-immune models by adding components in a controlled, stepwise manner, is a valuable direction for the field and one that other groups working on neuroinflammation will find useful to consider. The combination of live calcium imaging and transcriptional profiling in the same experimental system is a practical contribution that demonstrates the kind of multi-modal readout this class of model can support. If the autologous confound is resolved in future iterations and if the mechanistic claims are grounded in more direct experimental evidence, this type of platform could become a genuinely useful tool for investigating human neuroimmune biology and for screening candidate therapeutic compounds in a human-relevant context. As currently presented, however, readers and researchers considering adopting this approach should be aware that the immune interaction data may reflect genetic mismatches between cell sources rather than disease-specific biology, and that the causal conclusions drawn from the chemokine and migration data go beyond what the experiments can support.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers.]

This manuscript by Rudich ZD et al. systematically profiled the transcriptomic changes in nine long-lived C. elegans mutants and presented a careful and informative comparative analysis of these aging-related changes. In addition to these valuable datasets and bioinformatics analyses, the authors performed a large-scale RNAi screen to assess the role of the differentially expressed genes (DEGs) in these mutants and identify several potential targets to promote healthy aging. Moreover, the authors have provided a user-friendly website to examine genes of interest in those longevity mutants from their datasets.

Strengths:

Compared to previous transcriptomic analyses of these mutants in different reports, this study minimized the technical variations and benefitted from the advances in RNA-Seq technology and bioinformatics tools. Therefore, it should provide a more consistent and comprehensive view of the molecular mechanisms underlying the longevity of these mutants. The datasets in this manuscript are valuable to other researchers in the biology of aging.

Weaknesses:

Meanwhile, since these mutants have been extensively studied, the advance of this study in unknown ageing mechanisms remains limited.

Comments on revised version.

The authors addressed the concerns successfully.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the comments raised in the previous round of review.]

This is a very cool paper that casts light on a persistent problem in the psychology and philosophy of visual representation: is there high-level perception? Every vision scientist agrees that low-level features such as shape, color, texture, motion and spatial frequency are represented in visual perception, but there is a great deal of controversy about the representation of high-level properties such as causation, faces, agency and animacy. Animacy is especially problematic because there are large differences in line curvature between stimuli that represent animate and inanimate items.

This article uses a novel approach-visual "anagrams" that are exactly the same image, except one is rotated 90 degrees relative to the other. They found persistent differences in visual processing between animate and inanimate stimuli. (Of course, the stimuli aren't animate-they represent animate items.). For example, there were processing differences between changes between animate and inanimate items (rabbit to boot) that were not present in rabbit to dog. They also showed such differences in two kinds of visual search tasks.

Of course, there are feature differences that exploit orientation. A classic example is the difference between a square and a diamond that is produced from the square by rotating it 45 degrees.

They addressed an aspect of this challenge having to do with some features using silhouettes. There was no search advantage for silhouetted stimuli.

Reviewer #1 (Public review):

Summary:

The authors aim to use state-of-the art behaviour, imaging and connectome techniques to identify the neural interaction between sleep and long-term memory consolidation in the PAM-DPM circuits, a well-known dopaminergic pathway within Drosophila Mushroom Body.

Strengths:

The investigation follows a logical strategy to collect huge dataset of sleep, appetitive memory and live imaging. The authors identified and showed that activation of a PAM subset: alpha-1 reduces sleep quality and memory consolidation in a starvation dependant manner. The author also convincingly demonstrated the corresponding neuronal responses of DPM neurons following PAM alpha-1 activation, and the positive role of DPM neural activity in sleep and memory consolidation. Moreover, the new data provide TRIC-LUC provided better temporal resolution of neural activity correlates for PAMalpha1-DPM inhibition. Importantly, the author demonstrated that memory loss derived from PAM alpha 1 activation can be partly restored by ectopic sleep enhancement via feeding THIP at the memory consolidation period after training.

Weaknesses:

Although the revised version carries arguments to satisfy the reviewers' concern, the writing is now less cohesive. Crucially an explanation however remains required for the following experimental contradiction: the central observation of the study indicates that PAM alpha1 activation cause DPM inhibition which disrupt sleep and memory consolidation. Therefore, one would expect a reduced PAMalpha1 and increased DPM activities after memory training, but the authors found the opposite is true from now enhanced TRIC-LUC dataset. The authors indicate this data reinforce the inhibitory nature of PAM-alph1-DPM, but it does not explain why such a reduced DPM activity is observed after training.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have updated the labelling of Figure 7. As all of the reviewer comments have now been addressed, I believe that this version of the manuscript can now be put forward as the Version of Record.]

Summary:

This study builds upon a major theoretical account of value-based choice, the 'attentional drift diffusion model' (aDDM), and examines whether and how this might be implemented in the human brain using functional magnetic resonance imaging (fMRI). The aDDM states that the process of internal evidence accumulation across time should be weighted by the decision maker's gaze, with more weight being assigned to the currently fixated item. The present study aims to test whether there are (a) regions of the brain where signals related to the currently presented value are affected by the participant's gaze; (b) regions of the brain where previously accumulated information is weighted by gaze.

To examine this, the authors developed a novel paradigm that allowed them to dissociate currently and previously presented evidence, at a timescale amenable to measuring neural responses with fMRI. They asked participants to choose between bundles or 'lotteries' of food times, which they revealed sequentially and slowly to the participant across time. This allowed modelling of the haemodynamic response to each new observation in the lottery, separately for previously accumulated and currently presented evidence.

Using this approach, they find that regions of the brain supporting valuation (vmPFC and ventral striatum) have responses reflecting gaze-weighted valuation of the currently presented item, where as regions previously associated with evidence accumulation (preSMA and IPS) have responses reflected gaze-weighted modulation of previously accumulated evidence.

A major strength of the current paper is the design of the task, nicely allowing the researchers to examine evidence accumulation across time despite using a technique with poor temporal resolution. The dissociation between currently presented and previously accumulated evidence in different brain regions in GLM1 (before gaze-weighting), as presented in Figure 5, is already compelling. The result that regions such as preSMA response positively to |AV| (absolute difference in accumulated value) is particularly interesting, as it would seem that the 'decision conflict' account of this region's activity might predict the exact opposite result. Additionally, the behaviour has been well modelled at the end of the paper when examining temporal weighting functions across the multiple samples.

In response to reviewer comments, the authors have explicitly tested for the effects of gaze-weighting over and above any main effect of value, and convincingly shown that these effects are both present in the main regions of interest - namely |SV| and gaze-weighted |SV| in the vmPFC, alongside |AV| and |AV_gaze| in the pre-SMA. This provides clear evidence in support of the notion of gaze-weighting of value signals in these regions.

Reviewer #1 (Public review):

[Editors' note: the second round of revision has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have addressed the minor comments raised in the previous round of review.]

Summary:

This manuscript reports the discovery and characterization of the first bifunctional degrader of tankyrase. Notably, the tankyrase degrader exhibits stronger β-catenin inhibition and tumor growth suppression compared to conventional tankyrase inhibitors. Mechanistically, while tankyrase inhibitors stabilize tankyrase and promote Axin puncta formation-thereby impairing β-catenin degradation-the degrader avoids this effect, resulting in deeper suppression of β-catenin signaling. These findings suggest that targeted degradation of tankyrase offers a novel therapeutic strategy for β-catenin-driven cancers. Overall, this is a compelling study with significant translational potential.

Strengths:

(1) The manuscript presents a rigorous and well-executed study on a timely and impactful topic.

(2) The biochemical and cellular characterization of the tankyrase degrader is thorough, and the comparative analysis with tankyrase inhibitors is insightful.

(3) The finding that tankyrase stabilization by inhibitors may interfere with Axin function is novel and significant. It aligns with earlier observations (e.g., Huang 2009) that transient tankyrase overexpression can stabilize β-catenin independently of PAR domain activity.

(4) The use of TNKS1/2 knockout cells expressing catalytically inactive tankyrase to demonstrate β-catenin inhibitory activity of the tankyrase degrader is elegant.

(5) The finding that the tankyrase degrader has superior anti-proliferative effects in colorectal cancer models has important therapeutic implications.

Comments on previous version:

I had a favorable opinion of the manuscript in the first round of review. I don't have additional comments on the revised manuscript. The manuscript looks fine to me.

Reviewer #1 (Public review):

Summary:

This study identifies a mechanism responsible for the accumulation of the MET receptor in invadopodia, following stimulation of Triple-negative breast cancer (TNBC) cells with HGF. HGF-driven accumulation and activation of MET in invadopodia causes the degradation of the extracellular matrix promoting cancer cell invasion, a process here investigated using gelatine-degradation and spheroid invasion assays.

Mechanistically, HGF stimulates the recycling of MET from RAB14-positive endodomes to invadopodia, increasing their formation. At invadopodia, MET induces matrix degradation via direct binding with the metallo protease MT1-MMP.

The delivery of MET from the recycling compartment to invadopodia is mediated by RCP which facilitates the colocalization of MET to RAB14 endosomes. On this compartment, HGF induces the recruitment of the motor protein KIF16B promoting the tubulation of the RAB14-MET recycling endosomes to the cell surface.

This pathway is critical for the HGF-driven invasive properties of TNBC cells as it is impaired upon silencing of RAB14.

Strengths:

The study is well organized and executed using state of the art technology. The effects of MET recycling in the formation of functional invadopodia are carefully studied taking advantage of mutant forms of the receptor that are degradation-resistant or endocytosis-defective.

Data analyses are rigorous and appropriate controls are used in most of the assays to assess the specificity of the scored effects. Overall, the quality of the research is high.<br /> The conclusions are well supported by the results and the data and methodology are of interest for a wide audience of cell biologists.

Previous Weaknesses:

The role of the MET receptor in invadopodia formation and cancer cell dissemination has been intensively studied in many settings including Triple Negative breast cancer cells. The novelty of the present study mostly consists in the detailed molecular description of the underlying mechanism based on HGF-driven MET recycling. The question of whether the identified pathway is specific for TNBC cells or represents a general mechanism of HGF-mediated invasion detectable in other cancer cells is not addressed or at least discussed.

Comments on revised version:

The authors have partially replied to my previous concerns.

Reviewer #1 (Public review):

Summary:

Festa et al. provide a detailed analysis of the outcome of spike-timing-dependent plasticity acting on inhibitory synapses for distinct shapes of the kernel that governs how pre- and postsynaptic spike times induce synaptic changes. The authors investigate symmetric and asymmetric kernels, providing a theoretical description of the ingredients that give rise to rate- or covariance-dominated plasticity based on a simplified two-neuron circuit. These analyses are confirmed via simulations of large recurrent networks with random excitatory connectivity. For excitatory connections arranged in a one-dimensional ring, the authors show that two distinct classes of inhibitory neurons (distinguished by their plasticity rules) form an effective Mexican-hat weight profile. Furthermore, the authors show that external inhibition of one of the inhibitory neuron types gives rise to the phenomenon of surround modulation.

Strengths:

The analytical description of the two-neuron circuit is robust and accurately captures the qualitative evolution of inhibitory weights in the recurrent network with random excitatory connectivity. The emergence of the Mexican hat from the combination of distinct inhibitory synaptic plasticity rules acting on different neuron types is an important result that reveals how such connectivity can be learned in biologically plausible networks. All the analyses are well done, and the simulation results are convincing, which supports a robust interpretation of the findings.

Weaknesses:

The two-neuron circuit model is a good choice for the analytics, but it may have hidden a covariance effect of the "rate-dominated" symmetric spike-based kernel that would appear when several inhibitory neurons, each sharing a different spike correlation with the postsynaptic neuron, converge onto it. The rate homeostasis achieved by the rate-dominated model arises from adjusting inhibitory weights according to their initial correlation with the output neuron, so that after learning, the weights are distributed such that these correlations are cancelled out (Vogels et al., 2011). In other words, even the rate-dominated rule is covariance-driven under the hood: with a single inhibitory input, the two-neuron circuit cannot expose this, but with several differently correlated inputs, the covariance dependence should reappear.

It is unclear whether the distribution of inhibitory weights has stabilised after 25 minutes of simulation time (Figure 3C), given that a considerable proportion of (mutual) weights reach the maximum allowed weight while (unidirectional) weights appear to vanish. Without a maximum-weight bound, and given sufficiently long simulations, the weights might diverge to infinity or decay to zero, so the apparent stationarity may be imposed by the bound rather than reflecting a true steady state. This could also be a finite-size effect, given the small number of excitatory connections per neuron.

The connections from excitatory neurons to the two inhibitory populations are different in the ring model (exc to PV is wider than exc to SST according to Table 3), and it is not clear whether this width difference, rather than the plasticity rules themselves, is responsible for the emergence of the Mexican hat.

Reviewer #1 (Public review):

Summary:

This manuscript by Laura Korobkova and Brian Dias describes an interesting study of the role of GABAergic neurons in the zona incerta (ZI) in incentive motivation for reward.

The authors report that DREADD inhibition of ZI neurons reduced the effort breakpoint in a progressive ratio task, which measures the intensity of incentive motivation to obtain food rewards. In other tests, chemogenetic inhibition did not alter food consumption or memory.

Conversely, DREADD excitation of ZI neurons increased incentive motivation in the progressive ratio task, expressed as a higher breakpoint for food rewards.

Korobkova and Dias report that prior stress exposure to a series of stressors (e.g., forced swim & water submersion, restraint, mild footshock) by itself reduced the breakpoint for food reward under vehicle, though it did not impair the ability to learn an instrumental response. However, DREADD excitation of ZI neurons in previously stressed mice increased the breakpoint to normal levels equivalent to the never-stressed group. This important finding indicates the ability of ZI stimulation to rescue the incentive motivational deficit induced by prior stress.

In fiber photometry studies using vGAT-CRE mice to specifically identify GABA neurons, Korobkova and Dias report that ZI GABA neurons are excited by sensory signals, including neutral cues. However, after reward conditioning, ZI GABA neurons increase their activation to the CS+ cue that predicts reward, but not to the CS- cue that doesn't. ZI neurons also respond in an instrumental reward task during both lever press and reward delivery. The authors conclude that ZI neurons respond to sensory stimuli, but specifically code the motivational significance of reward-related stimuli.

In optogenetic studies, the authors find that ZI GABA neuron stimulation during a reward CS+ enhances motivated responding to obtain reward, particularly in females, but not stimulation outside the CS+. This suggests the ZI stimulation in females may specifically enhance the incentive salience of the CS+, namely the cue's ability to trigger an increase in 'wanting' for the reward. However, that effect was not found here in males.

Altogether, this is a fine contribution to the literature, and the authors deserve congratulations on their study and manuscript.

Strengths:

This is a powerful and creative set of studies that clarifies the roles of ZI neurons in sensory processing and especially in incentive motivation for rewards. The use of multiple methods and test situations to triangulate on reward motivation functions gives a well-rounded perspective on ZI function. The discovery of incentive motivation roles for ZI neurons is intriguing and improves understanding of ZI, which traditionally has been a relatively understudied brain structure. The finding that ZI stimulation may rescue stress-induced deficits in motivation is especially notable and may have therapeutic implications.

Weaknesses:

Minor: This version of the manuscript focuses the introduction and discussion specifically on ZI GABA neurons. The ZI may be primarily GABAergic, but also contains other neurons, and DREADD studies may have used the hSyn promoter, which would impact all types of ZI neurons. Other studies here did more specifically target GABA neurons using vGAT Cre mice and specific targeting. The manuscript might be slightly improved by distinguishing in the discussion a bit more clearly which effects implicate GABA neurons specifically, and which effects might include other neurons too, to more clearly parse out the relative roles of GABA vs broader neuronal populations in ZI.

Case#: Patient 9, female, Mexican, symptoms onset 6 yrs. ago, Mexico City

DiseaseAssertion: IRD

FamilyInfo: parents are non-sanguineous and asymptomatic, they also denied any history related to ocular diseases. Information disclosed that the mother had one stillbirth and three miscarriages, but denied any related diseases/health issues to this child.

CasePresentingHPOs: HP:00305, HP:00080, HP:0000493, HP:0025586, HP:0030329, HP:0012713

CaseHPOFreeText: Proband presented with light sensitivity as well as adaptation difficulties when going from dark-to-light. Right eye was 20/200 and left eye was 20/160 from the visual acuity test. Macular bull's eye appearance. Subnormal rod and cone responses. Peripapillary sparing retina.

CaseNotHPOs: HP:0007737, HP:0000750, HP:0000510

CaseNotHPOFreeText: No afferent pupillary defect. No anomalies in anterior segment.

Genotyping Method: QIAamp DNA Blood Kit was used to extract gDNA and quantification/purity of the sample was found using a NanoDrop 2000 spectrophotometer. 293 genes were sequenced. gDNA was sequenced via Illumina technology. Following, certain sequences were additionally analyzed against a reference genome in order to identify changes and interpret.

PreviouslyPublished: n/a

Variant: NM_000350.3(ABCA4):c.4926C>G (p.Ser1642Arg), NM_000350.3(ABCA4):c.5044_5058del (p.Val1682_Val1686del)

ClinVar: 99332, 99340

CAID: n/a

SupplementalData: Phenotype data in results section as well as figures 1, 2, and 3 showing phenotypic testing results.

Reviewer #1 (Public review):

Summary:

This paper uses three different datasets to study the relationship between the standard deviation of dynamic brain state time series (state engagement variability or SEV) and measures of cognition. Results show associations between SEV and cognitive measures, with stronger associations in patients than controls (at least for inhibition).

Strengths:

Strengths include the use of innovative dynamic approaches to study cognition and the validations across three independent datasets.

Weaknesses:

With a highly innovative approach, it can be challenging to provide enough context for the reader to understand and interpret the results. In particular, the paper would benefit from:

(1) More detail on the brain state calculation, multiple comparison control, and added benchmarking of the novel summary SEV measure.

(2) Guidance on the interpretation of relatively low prediction performance, negative t-statistics, and more broadly regarding the justification for the multi-step approach going from 4 brain states to 1 SEV to a network of edges.

(3) Removal of the moment-to-moment alignment results given the circularity of the edge time series extraction with overlapping contributions to SEV and cognitive control time series.

(4) Adjustment of text to avoid causal interpretations and to reduce the emphasis on transdiagnostics.

Major Points:

While the brain states were developed in prior work, SEV is a new metric and therefore warrants careful benchmarking in terms of test-retest reliability, sensitivity to scan length/quality, and associations with demographic variables like age and sex (which do not appear to be controlled for in analyses).

Although the external validation approach is appreciated, the prediction performance is pretty low (predicted-observed correlation 0.17-0.3). It would be good to also report other metrics of performance, such as balanced accuracy.

The steps in the paper are somewhat convoluted by going from 4 brain states to 1 SEV, back to specific FC networks. This makes the paper a bit complex and difficult to interpret. It would be helpful to provide a clear justification for these steps and/or a figure to orient the readers.

Many results are reported in the manuscript, and it is unclear whether/what multiple comparisons control was adopted where.

The moment-to-moment change section tries to test whether inter-individual variation in SEV maps onto cognitive control, which is very interesting. However, both measures were operationalized using edge-timeseries calculated from the same data with shared inputs (as shown in Figure 4B). As such, the 'alignment' (i.e., correlation) between resulting time series appears somewhat circular given that it is likely driven by the shared inputs. More broadly, edge timeseries were summed across edges (and subtracted between edges with positive and negative CPM associations), which further complicates their interpretability in the context of 'cognitive control'. I would recommend removing this section or using behavioral data to quantify cognitive control.

The descriptions of how brain states were derived are unclear. In line 466, what do 'these fMRI data' refer to? Was the least-squares regression performed across subjects (given that it results in one beta value per time point)? Was this performed as a multiple regression and - if so - what was the collinearity between brain state inputs?

Reviewer #1 (Public review):

Summary:

The paper submitted by Renard et al. seeks to capture the moment when learning occurs and to identify the associated changes in neuronal activity within cortical circuits. Specifically, the study aims to test whether sensory representations in the cortex reorganize on the same timescale over which behavioral changes first emerge.

To address this question, the authors developed a new behavioral paradigm in which mice were first trained on an auditory detection task and then introduced to whisker stimulation, which they learned to associate with reward. This design allowed mice to form a new whisker-reward association within a single behavioral session, enabling the authors to track learning-associated neuronal changes during the course of the experiment.

Using pharmacological and optogenetic interventions, the authors first show that learning depends on the whisker somatosensory cortex. They then combined the task with longitudinal two-photon calcium imaging to examine real-time changes in neuronal representations that accompany improvements in task performance over trials within a session and across days. By applying a range of analytical approaches, they show that learning induces a rapid reorganization of sensory cortical representations over tens of trials, on the timescale of minutes. They further propose that spontaneous reactivation of neurons during the task may contribute to these representational changes during learning.

Strengths:

(1) Overall, the experiments are thoughtfully designed, well controlled, and clearly presented. The conclusions are generally well supported by the data. The manuscript is clearly written, and the Discussion acknowledges potential caveats while outlining future directions.

(2) A major strength of the study is the design of a new learning paradigm in which head-fixed mice rapidly form a new sensory-motor association within a single session, on the timescale of minutes. This offers a unique opportunity to track real-time changes in neuronal dynamics associated with learning during a single recording experiment.

(3) Taking advantage of this behavioral design, the authors show that learning induces rapid reorganization of sensory cortical representations. They also report an increase in spontaneous reactivation of neurons that gained stimulus responsiveness during training, and propose that these reactivations may contribute to rapid representational reorganization. These findings provide important insights into the neural dynamics associated with learning.

Weaknesses:

(1) The authors propose that spontaneous reactivation mediates rapid reorganization of neuronal representations and thereby supports rapid task learning. However, as they also acknowledge in the Discussion, the present study does not directly test a causal role for these reactivations in facilitating representational changes or behavioral improvement.

(3) The authors show reorganization of neuronal representations even on the first day of training with the new whisker task. However, because there is no explicit control for natural representational drift, it remains unclear to what extent these changes reflect learning-related reorganization rather than spontaneous day-to-day drifts in neuronal responses.

Reviewer #1 (Public review):

The authors address a difficult and well-known problem in systems/computational neuroscience: how to estimate the magnitude of "information-limiting" noise. Existing approaches (direct Fisher-information estimation, decoding + Cramer-Rao, and large-N extrapolation) are data-hungry and unstable, which has left the field with conflicting empirical estimates across systems.

The central proposal - "split-trial analysis" - is simple and appealing. The recorded population is randomly partitioned into two non-overlapping halves; a decoder (continuous case) or classifier (binary case) is trained on each half using the same trials; and the covariance of the two halves' decoding errors is used to estimate the variance of the information-limiting noise. There is a clean mathematical derivation to support this conclusion (although there are a couple of mathematical errors in the methods section that should be fixed to avoid confusion on the part of the reader).

They benchmark the method in simulation against three prior methods (Moreno-Bote et al. 2014; Rumyantsev et al. 2020; Kafashan et al. 2021) and report substantially better sample efficiency, lower bias, and greater robustness. They then apply the method to three datasets: (1) mouse head-direction cells (Ajabi et al.), (2) mouse V1 (Stringer et al.), and (3) macaque PFC during a saccade task (Bartolo et al.).

This is a strong and timely contribution. The core idea is elegant, and the method appears to be more practical than existing alternatives in the finite-data regime that real experiments occupy. The three applications are well chosen, and each yields a non-trivial, biologically interpretable result. I am strongly supportive of the potential of this paper.

That said, the paper makes several strong empirical claims - most notably that prior V1 estimates were substantial overestimates, and that PFC information-limiting noise is temporally redundant - and the central estimator rests on an independence assumption whose finite-N validity is only partially characterized. Before these claims can be considered well supported, I would like the authors to address the following:

Major Points:

(1) The method relies on a key independence assumption that may not always be satisfied in the regime of finite neurons and trials. The author's main idea is to decompose the residuals of two decoders as follows:

X1 = delta + phi1<br /> X2 = delta + phi2

The covariance is equal to the scale of information limiting noise, Var[delta], plus three terms:

Cov[X1, X2] = Var[delta] + Cov[delta, phi1] + Cov[delta, phi2] + Cov[phi1, phi2].

We can define phi1 as the part of X1 that is orthogonal to delta and likewise define phi2 as the part of X2 that is orthogonal to delta; thus, the cross terms evaluate to zero, and we are left with:

Cov[X1, X2] = Var[delta] + Cov[phi1, phi2]

Now the authors introduce an assumption that Cov[phi1, phi2] = 0. This leaves us with Cov[X1, X2] = Var[delta], but the question is: when is it justified to assume that Cov[phi1, phi2] = 0? For example, it is possible that

phi1 = c(N) * z + e1<br /> phi2 = c(N) * z + e2

where z is another shared noise dimension that is not information limiting and e1 and e2 are truly independent. Here, c(N) is a constant that goes to zero as the number of neurons used to train the decoder, N, goes to infinity. Thus, in the limit of having very large neural populations at hand for the analysis, the author's assumption of Cov[phi1, phi2] = 0 can be justified. If the authors agree with this analysis, it would be nice to (a) flesh it out and include it in the methods / supplementary notes, and (b) to analyze in simulation how good this approximation is in finite N regimes. I suspect that the assumption works in finite N regimes if noise is low-dimensional, but that if there are many additional dimensions of correlation (i.e. many z's above), you will need a very large number of neurons before Cov[phi1, phi2] approaches zero.

Along these lines, another worthwhile analysis would be to report outcomes when the neural populations are sub-sampled further. Intuitively, it should fail once you subsample to only a handful of neurons, e.g. 3, but I'm curious where the breaking point is and whether the decline is graceful.

(2) In point 1, I raised the question of how the method behaves with a finite number of neurons. Another worry is that there is a finite number of trials. In particular, if you train two decoders on the same trials, I would worry that non-information-limiting fluctuations in those trials would induce correlations in the decoders that then would show up as correlations on the held-out test set. A more conservative approach would be to split trials into three disjoint subsets: a training set for decoder A, a training set for decoder B, and a common test set used to compute Cov[X1, X2].

As a concrete example, suppose that on the particular trials used for training, the animal happened to be more aroused when theta = 1 and less aroused when theta = 0, and that arousal added a fluctuation on top of the neural response. This arousal-related signal is not information-limiting - it would average away given enough trials - but because both decoders are fit to these same trials, each one adjusts its weights to partially discount the same spurious high-arousal/low-arousal trend. Their weights are now distorted in a correlated way, so when both are applied to the shared test set, their errors covary, and the method reads this shared-training artifact as information-limiting noise.

I think this dynamic should be acknowledged in the text and clarified in more detail. Ideally, simulations could be done to estimate how many trials are needed to average out this sort of confound, and similar to the suggestion in point 1 above, I would be interested in seeing what happens when the authors sub-sample trials before running their analysis. Together with point 1, the feedback is that I'd like to see more about "how many neurons and how many trials" are needed in order to trust your results. Similarly, are there diagnostics or resampling methods (e.g. bootstrapping) that could be helpful for a practitioner to know if they have enough neurons/trials?

(3) Unless I've fundamentally misunderstood something, there is an error on page 17 in the methods. There we find sigma2 = Var[delta] = ... = Cov[phi1, phi2], but I believe this is meant to be Cov[X1, X2]. Indeed, the method assumes that Cov[phi1, phi2] = 0, as discussed in point 1.

Additionally, on page 4, the authors introduce the main quantity as Cov[\hat{theta}_1, \hat{theta}_2] instead of Cov[X1, X2]. However, if theta is changing from trial to trial, then these two quantities are not technically equal to each other, so it would be more accurate to write down the conditioning on theta. That is, assuming conditionally unbiased decoders, Cov[X1, X2] = Cov[\hat{theta}_1, \hat{theta}_2 | theta] for a fixed theta.

More generally, I found it hard to wrap my head around the underlying math on my first read through the paper. The polarization identity, 1/4 * (Var(X1 + X2) - Var(X1 - X2)), seems like a very roundabout way to derive the method. This identity is very helpful for the deconvolution extension, but I would have thought that a simpler and more straightforward derivation would have just used the expansion, Cov[X1, X2] = Var[delta] + Cov[delta, phi1] + Cov[delta, phi2] + Cov[phi1, phi2], as I did in point 1. I suggest the authors revise the mathematical presentation for clarity.

Minor Points

(1) A very nice feature of the authors' method is that they make no parametric assumption on the distribution of noise. This is in contrast to Kanitscheider et al. [12]'s finite-sample bias correction using the inverse-Wishart distribution of $\hat\Sigma^{-1}$, which is derived under an assumption of multivariate Gaussianity. I think it is worth adding a sentence to highlight this feature of the model.

(2) Statistical inference claims (across sessions and population sizes) are supported by reported s.d.'s but no formal tests or confidence-interval-based comparisons. Given that several claims are comparative (split-trial < naive; V1 < prior reports; PFC stable over windows), please add appropriate uncertainty quantification (e.g., bootstrap CIs over sessions) and, where a difference is claimed, a test or effect size.

DOI: 10.7554/eLife.104182

Resource: RRID:ZFIN_ZDB-ALT-110124-1

Curator: @scibot

SciCrunch record: RRID:ZFIN_ZDB-ALT-110124-1

What is this?

https://sharktastica.co.uk/articles/selectric-golfball-70

Reviewer #1 (Public review):

Summary:

The manuscript by Nagvekar et al. studies engulfing macrophages in the killifish brain upon aging. It first describes the development of a transgenic knock-in killifish line overexpressing a secreted fluorescent protein in neurons. This becomes a tool for isolating myeloid cells that are capable of endocytosis or phagocytosis of the fluorescent protein, which seem to comprise the majority of the myeloid cells within the young adult brain. The paper then demonstrates the similarities of what they call "engulfing macrophages" to brain myeloid cell types of other species and investigates changes to this population upon aging. Overall, the study combines multiple complementary technologies to support their data, that are nicely presented and well described in the legends, while the textual description remains very concise. The findings are of interest to scientists studying brain aging, and microglia/macrophages.

Major comments:

(1) Although the authors describe and analyze their data from the viewpoint of engulfing macrophages, the paper would benefit from a broader perspective and a comparison to other studies on microglia in different species. Along this line, the title does not really seem to cover the data presented here very well, and the introduction lacks a proper explanation of terminology on microglia/brain macrophages and their known roles, cell types versus cell states and the current state of the art in fish versus other model species in the context of aging.

(2) The result that nearly all myeloid cells in the killifish brain are of the engulfing macrophage type is somewhat surprising. This appears to differ from other studies in for instance zebrafish (e.g. ref 80, that describes the heterogeneity of the myeloid cells in detail). There are two questions we like to raise: (Q1) What is the evidence towards this homogeneity? and (Q2) Could there be a technical bias?

Regarding (Q1): What is the evidence towards this homogeneity? The markers used are overlapping with markers for microglia. It would be helpful to clarify how canonical microglia populations are represented in the dataset. What is the heterogeneity of the oScarletHIGH cells? On several plots (Fig1f, Fig2d, Fig4a) this population of cells seems more heterogeneous than described. Are there different cell states or types? What is the percentage of myeloid cells that is oScarletLOW? To what extent do these cells compare transcriptionally to the oScarletHIGH cells?<br /> a. Fig1f-i depict an enriched oScarletHIGH group alongside oScarletLOW cells. This representation is a bit misleading since it seems to indicate that really all myeloid cells are of the engulfing macrophage type whereas it is the majority, but not all.<br /> b. Line 52: The authors describe that the oScarletHIGH cell group is "enriched for signatures characteristic of macrophage functions". This finding is logical, as the isolation procedure of this population of cells was based on the endocytic and phagocytic properties of the cells. This result appears more consistent with a validation of the isolation strategy than with definitive evidence for myeloid cell identity.

Regarding (Q2): Could there be a technical bias? An alternative explanation that may warrant discussion is whether aspects of the experimental pipeline (cell dissociation, FACS, scRNA-seq) could influence myeloid cell states. For instance, it is conceivable that dissociation induces a reactive program that enhances uptake of fluorescent protein, potentially enriching for oScarletHIGH cells. As the authors use a similar experimental setup to prove uptake of dextran and ovalbumin, such a technical artefact may merit consideration. As this would influence the major conclusions of the paper, the authors might want to address this comment with additional experimental controls, such as single-nuclei RNA-seq on control young and aged brains to profile the natural myeloid population when not submitted to a cell dissociation and FACS procedure.

(3) The authors compare the oScarletHIGH cell transcriptomes to mouse and killifish datasets. Both the mouse (Barr et al) and killifish (Nagvekar, this paper) dataset are from enriched immune cells (mouse= CD45+ cells, and the 3 cell types selected from that). Why did the authors not compare to the whole mouse CD45+ dataset? Including zebrafish (Rovira et al, 2025) here would strengthen the evolutionary comparison. I also feel that the additional comparison with young killifish (Ayana et al) might not be that solid since this dataset was initially not enriched and has a significantly lower number of myeloid cells, and thus much less power. The old age time point in that study contained more myeloid cells and might be interesting to include for cell type comparison. There are other, perhaps more unbiased ways of comparing cell types across species, for instance SAMap, developed by co-author Bo Wang. Did the authors consider using this or other methods?

(4) Regarding the comparison with the aged brain:<br /> a. Figure 4: It would be nice to include the same comparisons as for young fish (cfr Fig.1 panels F-I).<br /> The percentage of oScarletHIGH cells in the aged condition is 8% (Fig1-suppl1) compared to 4% at young age. On the other hand, a lower number of cells was isolated at old age compared to young age (Figure4a). Can the authors elaborate on this difference? Later on, it is stated that the engulfing capacity declines with aging, but could this be linked to the lower or potentially biased recovery of cells?

b. Figure4a: Transcriptional differences are stated between young and old (line 226), can a relevant selection be shown in e.g. a dot plot or heatmap?<br /> The UMAP clustering does seem to indicate batch effects on panels a and g. Can the authors provide sub clustering and show that young and old/ FACS sorted high and low cover similar cell types/states? The PCA plot (panel f) and marker analysis is not fully convincing, as PC1 and 2 alone do not suffice to explain all the variance in these cells, and the markers are common ones for many microglia/macrophage cell types (and thus likely to be expressed similarly).

c. Figure 5: It would be informative to include the corresponding aged condition for panels c and e.

Significance:

General assessment

Strengths: This manuscript introduces a valuable new transgenic tool to isolate and characterize myeloid cells in the brain of the fast-aging killifish (Nothobranchius furzeri), an emerging model organism in aging research. The study combines multiple complementary approaches, including transgenesis, FACS, histology, and single-cell transcriptomics, to investigate brain immune populations and their changes upon aging. The cross-species comparison and aging analyses provide useful datasets and candidate markers for the field of neuroimmunology and comparative brain aging. Overall, the data are clearly presented, the experiments are logically structured, and the manuscript provides a useful resource for future studies on brain immune cells in teleosts.

Limitations/points for improvement: The major limitation of the study concerns a potential technical bias introduced by the experimental pipeline (cell dissociation, FACS isolation, and transcriptomic profiling), which may have influenced the observed predominance and transcriptional state of the oScarletHIGH/engulfing macrophage population. At present, it remains difficult to fully exclude whether the protocol itself contributes to the apparent homogeneity of the myeloid compartment or induces a shared reactive state. Because this issue affects some of the central conclusions, the manuscript would benefit either from additional controls (e.g., dissociation-independent approaches such as single-nuclei RNA-seq) or from a more cautious interpretation and discussion of this possibility in the text.

Advance: The fast-aging killifish is becoming an important vertebrate model for studying aging, yet the brain immune compartment in this species remains relatively underexplored. This manuscript provides both a novel experimental tool and a transcriptomic resource for studying myeloid cells in the killifish brain. To my knowledge, the study is among the first to profile engulfing/endocytic myeloid populations in the context of brain aging in this model organism and to compare these cells across species. The advance is primarily technical and descriptive/resource-generating, while also offering conceptual insight into how brain myeloid populations may change during aging and how they compare evolutionarily across vertebrates. Although the mechanistic interpretation would benefit from additional validation, the study clearly extends current knowledge and provides a framework for future work on neuroimmune aging in fish.

Audience: The manuscript will primarily be of interest to a specialized basic research audience, including researchers in neuroimmunology, brain aging, microglia/macrophage biology, and comparative neuroscience. It will also be relevant to scientists using killifish or other emerging vertebrate models for aging research. Beyond the immediate field, the study may be of broader interest to researchers investigating immune-brain interactions and the evolutionary conservation of myeloid cell states across species. The transgenic line and transcriptomic datasets are likely to serve as a useful resource for future comparative and functional studies.

Reviewer #3 (Public review):

Summary:

Due to the low SNR of cryo-EM micrographs necessitated by radiation damage, determining the structure of proteins smaller than 50 kDa is exceedingly challenging, such that only a handful have been solved to date. This work aims to improve the reconstruction of small proteins in single-particle cryo-EM by using high-resolution 2D template matching, an algorithm previously used to locate and align macromolecules in situ, to align and reconstruct small proteins. This approach uses an existing macromolecular structure, either experimentally determined or predicted by AlphaFold, to simulate a noise-free 3D reference and generates whitened projections, crucially including high-spatial-frequency information, to align particles by the orientation with maximal cross-correlation. They demonstrate the success of this approach by generating a 3D reconstruction from an existing dataset of a 41.3 kDa protein kinase that had previously evaded attempts at high-resolution structure determination. To alleviate concerns that this is purely from template bias, they demonstrate clear density at two regions that were not present in the template: 6 residues in an alpha helix and an ATP in the ligand binding pocket. The latter is particularly important for its implications in determining structures of ligand-bound proteins for drug discovery. They also produce a composite omit map from 36 partial-deletion reconstructions spanning the entire protein, demonstrating a reconstruction can be obtained without template bias. Additionally, the authors provide an update to the classic calculation in Henderson 1995 to predict the minimum molecular mass of a protein that can be solved by single-particle cryo-EM.

Strengths:

I am in no doubt that this technique can be used to gain valuable insights into the structures of small proteins, and this is an important advancement for the field. It is complementary to single-particle cryo-EM and provides an extra tool for the experimentalist that may work better in certain cases. For cases where only a small region of the structure is of interest, such as in drug screening, this method provides a simple workflow to screen many structures.

The claim that using high-spatial frequency information is essential for aligning small proteins is a valuable insight. A recent pre-print published at a similar time to this manuscript used high-resolution information in standard ab-initio reconstruction to generate a high-resolution reconstruction from the same dataset, supporting the claims made in the manuscript.

The theoretical section outlined in the appendix is also theoretically sound. It uses the same logic as Henderson, but applies more up-to-date knowledge, such as incorporating dose-weighting and altering the cross-correlation based noise estimation. This update is valuable for understanding factors preventing us from reaching the theoretical limit.

Weaknesses:

This method is a complementary technique to determine the structure of small macromolecules to existing methods such as Blush regularization and HR-HAIR. Although the authors have demonstrated convincingly that their method selects a stack of high-quality particles, it is less clear whether it performs better than RELION when using the same stack of particles, particularly in the ATP binding pocket. As the authors discuss, systematic benchmarks comparing these methods over more targets than the one presented here, will be important for determining the utility of this method.

The method presented here also introduces template bias. Omit maps are used to reduce template bias by removing the region of interest from the template. Producing a full reconstruction through a composite omit map is computationally expensive and can introduce artifacts at boundaries. Therefore, unless this method outperforms modern SPA methods, its major use case will likely be restricted to ligand binding studies rather than full 3D reconstructions.

Reviewer #1 (Public review):

Hanako and colleagues demonstrated that glycolipid MPIase is essential for the TAT system, and they successfully reconstituted the TAT system in vitro for the first time. This will facilitate the understanding of the mechanism of the TAT system.

My major points are listed below for the authors to consider:

(1) The authors successfully reconstituted the TAT system using the purified TatA/B/C, but the translocation efficiency was much lower than that of native INV. The authors partly attributed this to the reason that "MPIase recovery would be too low to detect the TAT activity" in the Discussion part. So, what would happen to the translocation efficiency if you added more MPIase to the reconstituted system? How about the abundance of MPIase from the INV and reconstituted proteoliposomes?

(2) Why were only TatC levels measured in Figure 2C, whereas the expression levels of TatA were not detected? Also, from my observation, the amount of TatC in the third lane is lower than that in the previous two lanes.

(3) The authors should explain why the TatA/B/C ratios in Figure 3C (1:1:1) and Figure 3D (10:1:1) are inconsistent.

(4) ~30% of the fluorescence was recovered in the membrane fraction (Figure 4A) both in the functional TAT signal sequence (RR) and in the inactivating mutant signal sequence (KK), which suggests that MPIase acts as a relatively broad recognition factor. Given that MPIase does not discriminate between RR and KK, why do un-translocated substrates remain in the cytoplasm rather than non-specifically adhering to the membrane when MPIase is depleted in vivo?

Reviewer #1 (Public review):

Summary:

The authors study how the migration of distal visceral endoderm (DVE) cells in early mouse embryos becomes channeled towards one direction and the corresponding movement of the epiblast on which the DVE cells migrate. To this end, they develop an analysis pipeline of an in toto live data set previously obtained by the authors, which includes superpixel motion tracking of the visceral endoderm surface and subregions thereof. They find that a morphological asymmetry of the ectoplacental cone is indicative of anterior-posterior axis orientation. Even during the phases prior to and after collective migration, DVE cell speed was larger than in the surrounding tissue. The crossover from the pre-migratory to the migratory phase relies on the alignment of DVE cell motion. During the migration phase, counter-rotating vortices appeared in the emVE as expected when a rigid body moves through an incompressible fluid. Furthermore, DVE migration exhibits what the authors term a ratchet-like behavior, where the cells alternate between bursts of collective migration and periods of essentially no net motion. This behavior could be reproduced in vertex-model simulations, where DVE cells were subjected to a constant external force in an otherwise passive environment of cells. The observed intermittent behavior results from building up stress in the surrounding tissue that is released through cell rearrangements involving T1 transitions. These findings are in line with experimental results, although in embryos, T1 transitions are not as abundant as in the simulations and are largely confined to the region ahead of the DVE. Finally, the authors report a distally directed planar motion in the anterior epiblast underlying the visceral endoderm and thus opposite to the motion of the DVE. Cell migration in the posterior epiblast was slower and more random than in the anterior.

Strengths:

The authors provide a detailed analysis of the cell migration patterns in the embryo and show through vertex-model simulations that some of the observed features are really consequences of the properties of incompressible fluids.

Weaknesses:

Naming the intermittent dynamics of DVE cells as ratchet-like seems inappropriate, as it is rather reminiscent of stick-slip dynamics.. Quantitatively, the simulations do not provide much more insight beyond providing the flow profile of the (complex) fluid behavior of the tissue surrounding the DVE. It would be interesting to identify mechanisms that underlie migration alignment of DVE cells and to study in detail the T1 transitions - why are they confined to certain regions of the tissue? Furthermore, the theoretical analysis should be extended so that it also considers the dynamics of epiblast cells.

Reviewer #1 (Public review):

Summary:

Shpektor et al. propose a link between how humans learn abstract and hierarchical structures to support memory (for example, remembering the event of the first landing on the moon) and the medial temporal lobe (MTL) and grid cells in particular. Given that there is solid work on how grid cells in different modules jointly encode position in rodents, providing evidence for the existence of a similar code in humans in the non-spatial domain and in relation to memory formation, would constitute a valuable finding.

The authors first examine a small human intracranial dataset to demonstrate that sequence position is decodable in MTL population codes. They then examine behavioral data from two larger groups of participants who passively viewed content presented in a hierarchical sequence and show that errors in recall of positions within that sequence qualitatively match hierarchical predictions. The task design enabled distinct signatures of memory representations at different levels of hierarchy. While there were no multivariate patterns in MTL or any brain region that matched these patterns reliably, a follow-up analysis in MTL revealed a gradient along the anterior-posterior axis, such that lower levels of the hierarchy tended to have representational peaks in more anterior regions of the MTL, which was consistent across the two fMRI datasets.

Major strengths of the study include the novelty of the experimental paradigm and data.

In particular, single cell recording in MTL from a small number of human participants during sequence learning and testing a larger group of human participants on a sequence amenable to hierarchical structure learning, and collecting fMRI data during retrieval.

Furthermore, the paper tackles an important question and does so from both directions, using inspirations from both biology and computational science to navigate it.

The primary weaknesses of the paper are a lack of compelling support for the overarching claim about hierarchical representation and a lack of clarity and consistency about exactly what those hierarchical representations should and do look like. My concerns regarding these weaknesses are described below, and I believe that most, if not all, of them could be addressed through additional analysis and paper revisions.

In the first part of the paper, the authors provide single-cell recordings in MTL, and they report the existence of cells that are sensitive to position (more so than to picture). However, they don't elaborate on this result with a model for an abstract sequence code. This is an issue because one possible explanation for the sequential position decoding is that neurons just fire at the presentation of the first image and decay at different rates, or ramp up toward action or feedback. One might be able to decode the position in sequence from these cells' activity, but can hardly call this an abstract code of position in a sequence. However, the authors don't provide further investigation into what the single-cell result might suggest and move on to a completely different fMRI experiment in the second part of the paper. Being able to decode sequence position does not, in my view, necessarily imply an abstract positional code - and I felt that further analysis of the single unit data would be required to identify what representations gave rise to that decoding ability.

The most compelling evidence that participants were encoding temporal order hierarchically came from behavioral data in the second part of the paper. However, these results were not presented clearly enough to evaluate their reliability and specificity. Figure 2i shows histograms of errors across participants with arrows pointing to bars that apparently correspond to errors of different levels of hierarchy. There are three colored bars, corresponding to errors of one unit at the first, second, or third levels of hierarchy. The first level is not diagnostic of hierarchy, but the other two colored bars appear higher than the colors nearby them. However, my understanding is that these bars correspond to situations with the same tone - which seems like an obvious reason that two positions might be confused, which in my view would weaken the argument for hierarchical encoding. Furthermore, there is no display of variability in the plot or indication of individual differences, so it is hard to tell whether the histogram is dominated by a few participants who made a lot of errors or is reflective of a general tendency across participants.

The fMRI analyses, while creative, raise questions regarding interpretability. The authors report no representations of hierarchical position at any level, either in MTL or across the whole brain, which would typically be taken as a lack of evidence for the representations existing. Follow-up analyses revealed that what shadows of representations do exist seem to line up along the anterior-posterior gradient. But what does that mean if we can't be sure that the representations are really there? Typically, we tally up evidence supporting an overarching claim by testing multiple predictions that are all consistent with the same story - but in this case, it seems that not all such test results are consistent.

In many cases, it was difficult to judge the strength of evidence due to somewhat minimal reporting on the exact hypotheses tested and test statistics.

On a high level, I found the overarching story linking the two datasets together to be somewhat tenuous. While I understand that science rarely rolls out as a coherent story, presenting the authors' valuable experiments in this fashion makes it harder for the reader to digest the information and reach a conclusion. The relevance of the first section of the paper to the second is not immediately apparent. Each section provides somewhat incomplete evidence for a set of claims on its own - but my view was that combining the two studies led to more questions than answers - since the paradigms and measurements are so different.

In conclusion, the authors propose an interesting account of how memories are formed in the human brain, by building an abstract and hierarchical code. The paper identifies a few separate findings that are suggestive of hierarchical abstract memory encoding in the MTL - yet I believe that more work would need to be done to irrefutably support that claim.

Reviewer #1 (Public review):

Summary:

In this study, the authors investigate whether glycogen phosphorylase represents a molecular target of benzoylphenylurea insecticides and evaluate the physiological consequences of suppressing glycogen phosphorylase activity in the diamondback moth Plutella xylostella. The authors combine recombinant protein biochemistry, enzyme inhibition assays, RNA interference, structural modelling, metabolite profiling, gene expression analyses, and physiological measurements to determine whether diflubenzuron directly inhibits glycogen phosphorylase and whether suppression of this enzyme is sufficient to impair insect development. Based on these experiments, the authors conclude that diflubenzuron does not directly inhibit glycogen phosphorylase and that insects tolerate substantial suppression of this enzyme through compensatory metabolic responses.

Strengths:

This study addresses an important question in insect toxicology by systematically evaluating glycogen phosphorylase as a potential insecticidal target. The authors combine complementary biochemical, molecular, physiological, and structural approaches, including recombinant enzyme characterization, inhibitor assays, RNA interference, metabolite profiling, structural modelling, and measurements of fitness-related traits. This integrative approach provides a comprehensive evaluation of the biological consequences of glycogen phosphorylase suppression. In particular, the biochemical evidence that diflubenzuron does not inhibit glycogen phosphorylase, together with the observation that strong suppression of glycogen phosphorylase produces only transient physiological effects without measurable impacts on development or reproduction, provides strong support for the conclusion that glycogen phosphorylase is unlikely to represent an effective standalone insecticidal target.

Weaknesses:

The main limitation concerns the proposed mechanism underlying metabolic compensation. The observed increases in gluconeogenic gene expression, changes in metabolite abundance, and reductions in total protein are consistent with activation of compensatory metabolism, but are insufficient to directly demonstrate increased gluconeogenic flux or establish that amino acid-derived carbon is incorporated into newly synthesized glucose. Similarly, although the analyses of glycogen-associated enzymes strengthen the discussion of alternative metabolic pathways, changes in gene expression alone do not demonstrate that these pathways contribute to glycogen utilization in vivo.

Some mechanistic interpretations therefore extend beyond the data presented. For example, decreases in total protein are interpreted as evidence of protein catabolism fuelling gluconeogenesis, yet they do not directly demonstrate amino acid mobilization or incorporation into glucose. Likewise, increased expression of gluconeogenic genes is interpreted as evidence of increased pathway activity, although transcriptional changes do not necessarily reflect metabolic flux. Finally, the absence of major developmental defects following glycogen phosphorylase suppression is attributed primarily to metabolic compensation, but an alternative explanation is not fully considered. Such explanation could be that glycogen phosphorylase is not rate-limiting for glucose homeostasis under the nutrient-rich experimental conditions, where dietary carbohydrates are continuously available. Consequently, the proposed compensatory mechanism remains plausible and well supported by indirect evidence, but several aspects would benefit from more cautious interpretation.

Overall, the authors successfully achieve their primary objective of evaluating glycogen phosphorylase as a candidate insecticidal target. The study provides useful biochemical and physiological evidence that this enzyme is unlikely to represent an effective target for insecticide development in P. xylostella, while highlighting the importance of metabolic plasticity when assessing metabolic targets. The experimental approaches and datasets presented here should be valuable to researchers studying insect metabolism, insecticide mode of action, and target validation, although the precise mechanisms underlying the proposed metabolic compensation remain an important subject for future investigation.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The revision clarifies terminology, more carefully distinguishes element intactness from demonstrated transpositional activity, and better acknowledges the roles of lineage-specific loss and localized horizontal transfer alongside vertical inheritance.]

Summary:

This manuscript provides a comprehensive systematic analysis of envelope-containing Ty3/gypsy retrotransposons (errantiviruses) across metazoan genomes, including both invertebrates and ancient animal lineages. Using iterative tBLASTn mining of over 1,900 genomes, the authors catalog 1,512 intact retrotransposons with uninterrupted gag, pol, and env open reading frames. They show that these elements are widespread-present in most metazoan phyla, including cnidarians, ctenophores, and tunicates-with active proliferation indicated by their multicopy status. Phylogenetic analyses distinguish "ancient" and "insect" errantivirus clades, while structural characterization (including AlphaFold2 modeling) reveals two major env types: paramyxovirus F-like and herpesvirus gB-like proteins. Although bot envelope types were identified in previous analyses two decades ago, the evolutionary provenance of these envelope genes was almost rudimentary and anecdotal (I can say this because I authored one of these studies). The results in the present study support an ancient origin for env acquisition in metazoan Ty3/gypsy elements, with subsequent vertical inheritance and limited recombination between env and pol domains. The paper also proposes an expanded definition of 'errantivirus' for env-carrying Ty3/gypsy elements outside Drosophila.

Strengths:

(1) Comprehensive Genomic Survey:

The breadth of the genome search across non-model metazoan phyla yields an impressive dataset covering evolutionary breadth, with clear documentation of search iterations and validation criteria for intact elements.

(2) Robust Phylogenetic Inference:

The use of maximum likelihood trees on both pol and env domains, with thorough congruence analysis, convincingly separates ancient from lineage-specific elements and demonstrates co-evolution of env and pol within clades.

(3) Structural Insights:

AlphaFold2-based predictions provide high-confidence structural evidence that both env types have retained fusion-competent architectures, supporting the hypothesis of preserved functional potential.

(4) Novelty and Scope:

The study challenges previous assumptions of insect-centric or recent env acquisition and makes a compelling case for a Pre-Cambrian origin, significantly advancing our understanding of animal retroelement diversity and evolution. THIS IS A MAJOR ADVANCE.

(5) Data Transparency:

I appreciate that all data, code, and predicted structures are made openly available, facilitating reproducibility and future comparative analyses.

Original Major Weaknesses:

(1) Functional Evidence Gaps:

The work rests largely on sequence and structure prediction. No direct expression or experimental validation of envelope gene function or infectivity outside Drosophila is attempted, which would be valuable to corroborate the inferred roles of these glycoproteins in non-insect lineages. At least for some of these species, there are RNA-seq datasets that could be leveraged.

(2) Horizontal Transfer vs. Loss Hypotheses:

The discussion argues primarily for vertical inheritance, but the somewhat sporadic phylogenetic distributions and long-branch effects suggest that loss and possibly rare horizontal events may contribute more than acknowledged. Explicit quantitative tests for horizontal transfer, or reconciliation analyses, would strengthen this conclusion. It's also worth pointing out that, unlike retrotransposons that can be found in genomes, any potential related viral envelopes must, by definition, have a spottier distribution due to sampling. I don't think this challenges any of the conclusions, but it must be acknowledged as something that could affect the strength of this conclusion

(3) Limited Taxon Sampling for Certain Phyla:

Despite the impressive breadth, some ancient lineages (e.g., Porifera, Echinodermata) are negative, but the manuscript does not fully explore whether this reflects real biological absence, assembly quality, or insufficient sampling. A more systematic treatment of negative findings would clarify claims of ubiquity. However, I also believe this falls beyond the scope of this study.

(4) Mechanistic Ambiguity:

The proposed model that env-containing elements exploit ovarian somatic niches is plausible but extrapolated from Drosophila data; for most taxa, actual tissue specificity, lifecycle, or host interaction mechanisms remain speculative and, to me, a bit unreasonable.

Reviewer #1 (Public review):

[Editors' note: The authors addressed reviewer comments well, further strengthening the conclusions of the study.]

Summary:

A whole-organism drug screen was performed to identify molecules that decrease Apolipoprotein B (ApoB) as a target for agents to reduce atherosclerosis. Kelpsch et al. used a zebrafish reporter line, LipoGlo, which is a fusion of the Nano-luciferase protein to the ApoB protein as a proxy for the presence of ApoB-containing lipoproteins (B-lps) in larval stages. The LipoGlo line was screened against a well-characterized drug library and identified 49 hits from their primary screen. Follow-up studies further refined this list to 19 molecules that reproducibly reduced B-lps significantly. The authors focused their studies on enoxolone, a licorice root extract, and showed that larvae treated with this agent can reduce the production of B-lps. As enoxolone has been reported to suppress Hepatocyte Nuclear factor 4a (HNF4a), the authors investigated whether loss-of-hnf4a or pharmacological inhibition of hnf4a in zebrafish also produced similar phenotypes as enoxolone treatment. Their studies showed that this was the case. Transcriptomic studies after enoxolone treatment resulted in altered expression of genes involved in cholesterol biosynthesis and in glucose/insulin signaling pathways. This study highlights the utility of a zebrafish whole-organism chemical screen for modifiers of B-lps production and/or its clearance. A significant finding is that enoxolone inhibits hnf4a in zebrafish to reduce B-lps production and supports targeting HNF4a as a therapeutic means to reduce the emergence of atherosclerosis.

Strengths:

The authors performed a whole-organism chemical screen with over 3000 agents. Such screens are challenging, and the authors used strict criteria for determining hits. The conclusions of this study are well supported by the presented data.

Comment on revised version:

The authors have addressed all my comments.

Reviewer #1 (Public review):

This is an interesting study on the nature of representations across the visual field. The question of how peripheral vision differs from foveal vision is a fascinating and important one. The majority of our visual field is extra-foveal, yet our sensory and perceptual capabilities decline in pronounced and well-documented ways away from the fovea. Part of the decline is thought to be due to spatial averaging ('pooling') of features. Here, the authors contrast two models of such feature pooling with human judgments of image content. They use much larger visual stimuli than in most previous studies, and some sophisticated image synthesis methods to tease apart the prediction of the distinct models.

More importantly, in so doing, the researchers thoroughly explore the general approach of probing visual representations through metamers-stimuli that are physically distinct but perceptually indistinguishable. The work is embedded within a rigorous and general mathematical framework for expressing equivalence classes of images and how visual representations influence these. They describe how image-computable models can be used to make predictions about metamers, which can then be compared to make inferences about the underlying sensory representations. The main merit of the work lies in providing a formal framework for reasoning about metamers and their implications, for comparing models of sensory processing in terms of the metamers that they predict, and for mapping such models onto physiology. Importantly, they also consider the limits of what can be inferred about sensory processing from metamers derived from different models.

Overall, the work is of a very high standard and represents a significant advance over our current understanding of perceptual representations of image structure at different locations across the visual field. The authors do a good job of capturing the limits of their approach I particularly appreciated the detailed and thoughtful Discussion section and the suggestion to extend the metamer-based approach described in the MS with observer models. The work will have an impact on researchers studying many different aspects of visual function including texture perception, crowding, natural image statistics and the physiology of low- and mid-level vision.

The main weaknesses of the original submission relate to the writing. A clearer motivation could have been provided for the specific models that they consider, and the text could have been written in a more didactic and easy to follow manner. The authors could also have been more explicit about the assumptions that they make.

Comments on revised version.

The authors have now fully addressed my concerns and I think the paper is a valuable contribution. In future studies within the same research program I would appreciate seeing further consideration of how metamerism at different stages of visual processing interact to determine behaviour in tasks. For example, there are presumably interesting impacts of feedback that may modify feature spaces, thereby rendering aspects of appearance that were previously metameric perceptually discriminable.

Reviewer #1 (Public review):

Leukemia-driving NUP98 oncofusion proteins form chromatin-associated biomolecular condensates in the nucleus, and these structures are important for oncogenic transformation. Most NUP98 fusions do not contain domains that mediate the recognition of specific DNA elements. Instead, they entail domains that are important for chromatin regulation. For instance, the NUP98::KDM5A fusion features a fusion of the NUP98 N-terminus with the third PHD domain of the histone demethylase KDM5A. As PHD domains are critical for the recognition of methylated histones without any sequence specificity, it is not clear what controls the condensation and chromatin binding of NUP98::KDM5A, leading to the induction of oncogenic transcriptional programs.

In this work, the authors use a combination of cellular and in vitro studies to show that biomolecular condensation of NUP98::KDM5A is dependent on H3K4me3 binding. Their model proposes that concentration-dependent chromatin-associated condensation of NUP98::KDM5A depends on local densities of H3K4me3 on chromatin and the levels of the fusion oncoprotein. In line with this, the analysis of gene expression data from NUP98::KDM5A-positive AML cells shows a positive correlation between differentially expressed genes and H3K4me3 levels.

This is an interesting manuscript that aims to dissect the molecular mechanisms underlying biomolecular condensation of the NUP98::KDM5A oncoprotein. The work is solid, and the results are well explained and presented in a logical order. However, the study suffers from several weaknesses that if addressed would improve the study.

Major points:

(1) All cellular experiments are performed in settings of transient transfection of NUP98::KDM5A in non-hematopoietic cell types. These conditions are not physiologically relevant, as these cells do not depend on the fusion oncogene. Therefore, any claims about concentration-dependent effects on condensation need to be validated in AML cells that are driven by NUP98::KDM5A. While this may not be possible in primary patient-derived cells, several groups have published AML models of NUP98::KDM5A-driven AML that could be used.

(2) The results presented in Figure 4 are not entirely supportive of the mechanism. It is known that active gene expression correlates with high H3K4me3 levels; therefore, the correlations shown by the authors are expected. Yet, the authors do not discuss the fact that many H3K4me3-positive genomic regions do not show NUP98::KDM5A binding. This should be elaborated on in the discussion section.

(3) While the focus of the manuscript is on NUP98::KDM5A, this oncofusion is part of a family of >30 fusions that join the NUP98 N-terminus to a variety of factors with roles in epigenetic control and transcription. While the repertoire of NUP98 fusion partners is diverse with regard to functional domains, they all induce a conserved set of target genes that is characteristic of this leukemia subtype. How can this be achieved in the context of NUP98 fusion proteins that do not contain a PHD domain, such as NUP98::NSD1 or NUP98::HOXA9? Please discuss this.

Reviewer #1 (Public review):

Summary:

The authors used a panel of cell models to determine whether CDK4/6 overexpression resulted in resistance to the EGFR inhibitor Osimertinib, and the mechanisms underlying the resistance.

(1) Major Concerns (highest priority):

There is a lack of detail about the methodology in the results section/figure legends, which makes it difficult to interpret the data. Sometimes, adequate information is also not included in the methods themselves. For example, Figure 1A: how many doses did each mouse receive? How long after dosing were animals sacrificed? Figures 1E and 2A: is this RNA-seq analysis?

Using a second EGFR inhibitor for some of the key experiments would increase the rigor of the studies shown.

(2) Nice to have experiments:

Using CRISPR KO of CDK4 in the CDK4-amplified HCC827 and testing response to Osi and presence of replication stress would also increase the rigor of the studies.

The authors show that in their patient data, some cell cycle regulators which are amplified in NSCLC at similar rates to CDK4/6, such as CCNE1, had no increase in FGA. Overexpressing CCNE1 and testing Osi response in their cell models would be a nice test of their proposed mechanism that it is the genomic instability and FGA that are driving resistance. This wouldn't need to be done in vivo, but could be done using cell culture-based methods.

Similarly, testing the overexpression of some of the proposed target genes, such as STEAP1 and AGR2, on the therapeutic response to Osi in cell culture would also be a nice test of the mechanism proposed.

Case#: Patient 19, female, age 16

DiseaseAssertion: STGD

FamilyInfo: diagnosis of autosomal recessive STGD based on the pedigree and clinical phenotype of fleck deposits with or without genetic testing

CasePresentingHPOs: HP:0000608, HP:0000007, HP:0030610, HP:0030500

CaseHPOFreeText: Macular degeneration. autosomal recessive, Photoreceptor outer segment loss on macular OCT, Yellow/white lesions of the macula

CaseNotHPOs: n/a

CaseNotHPOFreeText: n/a

Genotyping Method: n/a

PreviouslyPublished: n/a

Variant: Allele 1: NM_000350.3:c.5714+5G>A Allele 2: NM_000350.3:c.4469G>A

ClinVar: Allele 1: NM_000350.3(ABCA4):c.5714+5G>A Allele 2: NM_000350.3(ABCA4):c.4469G>A (p.Cys1490Tyr)

CAID: Allele 1: CA227338 Allele 2: CA227198

SupplementalData: composite mask analysis shown in figure 3 for patient 19, show large areas of matched degeneration and isolated IS/OS loss

Reviewer #1 (Public review):

Summary:

Polymyxins are the last line of drugs to treat gram-negative bacteria-induced multi-drug resistance; however, they cause nephrotoxicity in 60% of patients. In this work, the authors have studied the structure-interaction relationship (SIR) of polymyxins with hPepT2 using computational and experimental methods. Moreover, it is observed that the electrostatic interactions coordinate the hPepT2-Polymyxin interactions; hence, an alanine scanning strategy is used to understand the interactions and derive the polymyxin variants.

Computational methods such as molecular modeling, coarse-grained and all-atom MD simulations, and interaction studies are performed, while the results are validated in the mouse model, which is a great strategy to prove the hypothesis.

Strengths:

A clear understanding of the hPepT2-Polymyxin interactions and the role of electrostatic interactions is one of the very important strengths of the paper. In addition, this work proposes a great pipeline for using computational approaches and experimental validation methods to guide the development of newer antibiotics.

Overall, the study proposes novel polymyxin analogues with reduced or no nephrotoxicity, thereby providing a promising foundation for the rational development of safer lipopeptide antibiotics.

Weaknesses:

This work is very well executed and presented; however, addressing the following concerns might improve the presentation of the work:

(1) The introduction is well articulated; however, including a paragraph on the known inhibitors might be helpful in understanding the current status. In addition, it might also help to introduce Dabs, FADDI variants, Gly-sar and MIPS.

(2) The following details of modeling with AlphaFold2 should be included: how the final structure was selected, what the RMSD and structure alignment of the template are, and the final selected structure. A section on modeling with all the parameter details might be useful for reproducing the structure. In addition, specify how the alanine scanning was performed alongside the structure prediction of polymyxins.

(3) In the all-atom MD simulation method, detailing several parameters might help in reproducing the results: simulation time for each system, water model, system composition, protonation state, box type and dimensions, salt ions and concentration, membrane parameters and ligand parameterization methods. Also, the following details on energy minimization might be useful: minimization algorithm, number of steps for minimization and structure restraints in place.

(4) On page 6, line 210, the MIC is used for the first time; although MIC is given in the abbreviation list, the first occurrence should have a complete name. A one-line explanation of MIC in the introduction or wherever suitable might be better but is not mandatory.

(5) Similarly, Gly-sar is first mentioned on page 8, line 301, but its complete name is only mentioned later on page 10, line 368. This can be addressed if a short description is included in the introduction section.

(6) For coarse-grained MD simulation, why were 2 replicates performed? Most studies perform 3 replicates, which are also good in terms of statistics and error bar calculations. In addition, the authors should specify whether an independent minimization is done for each of the two replicates or whether the minimization step is common for both.

(7) For MD simulation results, giving simulation movies in supplementary results might be a better way to show how the trajectories behaved.

(8) The description of visualisation software such as VMD or PyMol is missing. The authors should specify if any visualization tool is used.

(9) For the mouse model study, the authors claim that FADDI-795 has no observable nephrotoxicity; however, the n=3 shows that a very small number of mouse models were used to make the assumption. In addition, the number of mice used in each experiment is not explicitly mentioned in the methods section.

(10) In Table 2, the column 8 header is not visible.

Reviewer #1 (Public review):

Summary:

A prevailing view is that translation of 5' capped mRNAs, i.e. mRNAs that are translated via ribosome scanning, is inhibited by highly structured 5' untranslated regions (5' UTRs). Despite having a common, structured 5' UTR, the mRNAs produced by the SARS-CoV-2 virus are efficiently translated. In this study, the authors identified a DRACH motif in stem-loop 3 (SL3), suggesting a potential site of m6A methylation of A74 by the enzyme METTL3. Given that such m6A modifications are known to disrupt RNA structure formation, the authors tested the hypothesis that this may be the basis underlying the efficient translation of these mRNAs. Mutational approaches complemented by METTL3 siRNA knockdown were employed to support this hypothesis. Additional experiments showed that this is required for efficient association of a reporter mRNA with polysomes (indicative of active translation), and suggest that the 5' UTR is more highly structured when methylation is abrogated.

Strengths:

The data clearly indicate that N6 methylation of A74 is required for efficient translation of SARS-CoV-2 mRNAs.

Weaknesses:

While the evidence supports the authors' central hypothesis, there are two issues that should be addressed. The first is that all of the approaches are indirect. All of the evidence for the presence of mRNA structural elements is based on computational and genetic analyses. We now know that there is something there, but we still do not know what it is. The authors need to use a biochemical approach to actually map the structural elements of the 5' UTR and determine how such structure(s) are changed by loss of methylation. The second hinges on the assumption that these mRNAs are translated via canonical ribosome scanning. RNA viruses are well-known to use a variety of other mechanisms, e.g. internal ribosome entry signals and ribosome tethering, to promote efficient translation. Alternatives to ribosome scanning should be considered.

Reviewer #1 (Public Review):

Summary:

In this manuscript, the authors investigated the effect of chronic activation of dopamine neurons using chemogenetics. Using Gq-DREADDs, the authors chronically activated midbrain dopamine neurons and observed that these neurons, particularly their axons, exhibit increased vulnerability and degeneration, resembling the pathological symptoms of Parkinson's disease. Baseline calcium levels in midbrain dopamine neurons were also significantly elevated following the chronic activation. Lastly, to identify cellular and circuit-level changes in response to dopaminergic neuronal degeneration caused by chronic activation, the authors employed spatial genomics (Visium) and revealed comprehensive changes in gene expression in the mouse model subjected to chronic activation. In conclusion, this study presents novel data on the consequences of chronic hyperactivation of midbrain dopamine neurons.

Strengths:

This study provides direct evidence that the chronic activation of dopamine neurons is toxic and gives rise to neurodegeneration. In addition, the authors achieved the chronic activation of dopamine neurons using water application of clozapine-N-oxide (CNO), a method not commonly employed by researchers. This approach may offer new insights into pathophysiological alterations of dopamine neurons in Parkinson's disease. The authors also utilized state-of-the-art spatial gene expression analysis, which can provide valuable information for other researchers studying dopamine neurons. Although the authors did not elucidate the mechanisms underlying dopaminergic neuronal and axonal death, they presented a substantial number of intriguing ideas in their discussion, which are worth further investigation.

Weaknesses:

Many claims raised in this paper are only partially supported by the experimental results. So, additional data are necessary to strengthen the claims. The effects of chronic activation of dopamine neurons are intriguing; however, this paper does not go beyond reporting phenomena. It lacks a comprehensive explanation for the degeneration of dopamine neurons and their axons. While the authors proposed possible mechanisms for the degeneration in their discussion, such as differentially expressed genes, these remain experimentally unexplored.

Reviewer #1 (Public review):

The authors conducted a comprehensive benchmarking and evaluation of co-folding platforms, including AlphaFold3, Boltz-2, Chai-1, and the docking algorithm Dock3.7, which employs a physics-based scoring function that incorporates van der Waals interactions, electrostatics, and ligand desolvation energies. The system of interest was the SARS-CoV-2 NSP3 macrodomain (Mac1), an increasingly popular antiviral target, and the ligand sets comprised 557 unseen ligand poses (keeping the training for these co-folding platforms in mind). Additionally, the authors investigated whether the co-folding models could distinguish true ligands from non-binding small molecules. The study is thorough, with extensive statistical support and consensus across multiple metrics (chemoinformatics for quantifying ligand similarity and efficacy). The questions that the authors aim to address are whether the co-folding models struggle with memorization, whether they can distinguish between a true and a false binder, whether they replicate experimental binding affinities and efficacy, and how they compare to the physics-based docking algorithm (Dock3.7).

Strengths:

Overall, this is a scientifically solid paper.

The work is highly detailed and well executed, featuring thorough data analysis and statistical assessment.

Comments on revised version:

The authors have adequately addressed my concerns.