Reviewer #1 (Public review):

Summary:

This study identifies HSD17B7 as a cholesterol biosynthesis gene enriched in sensory hair cells, with demonstrated importance for auditory behavior and potential involvement in mechanotransduction. Using zebrafish knockdown and rescue experiments, the authors show that loss of hsd17b7 reduces cholesterol levels and impairs hearing behavior. They also report a heterozygous nonsense variant in a patient with hearing loss. The gene mutation has a complex and somewhat inconsistent phenotype, appearing to mislocalize, reduce mRNA and protein levels, and alter cholesterol distribution, supporting HSD17B7 as a potential deafness gene.

The study presents an interesting deafness candidate with a complex mechanism, and highlights an underexplored role for cholesterol (and lipids) in hair cell function.

The authors were very responsive to the initial reviews, and the manuscript is now significantly stronger.

Strengths:

- HSD17B7 is a new candidate deafness gene with plausible biological relevance.

- Cross-species RNAseq convincingly shows hair-cell enrichment.

- Lipid metabolism, particularly cholesterol homeostasis, is an emerging area of interest in auditory function.

- The connection between cholesterol levels and MET is potentially impactful and, if substantiated, would represent a significant advance.

- The localization of HSD17B7 is reasonably convincing, despite the lack of a KO control: In HEI-OC1 cells, HSD17B7 localizes to the ER, as expected. In mouse hair cells, the staining pattern is cytosolic. The developmental increase between P1 and P4, and the higher expression in OHCs aligns nicely with RNAseq data.

Weaknesses:

- The pathogenic mechanism of the E182STOP variant is unclear: The mutant protein presumably does not affect WT protein localization, arguing against a dominant-negative effect. Yet, overexpression of HSD17B7-E182* alone causes toxicity in zebrafish and it binds and mislocalizes cholesterol in HEI-OC1 cells, suggesting some gain-of-function or toxic effect. In addition, the mRNA of the variant has low expression level, suggesting nonsense-mediated decay. The mechanistic conclusions of the study are therefore not as clear cut as one would had hoped, but it might just be a reflection of real biological complexity.

- The link to human deafness is based on a single heterozygous patient with no syndromic features. Given that nearly all known cholesterol metabolism disorders are syndromic, this raises concerns about causality or specificity. HSD17B7 is therefore, at this point, a candidate deafness gene, and not a fully established "novel deafness gene". This is acknowledged by the authors.

- This study does not directly investigate how reduced cholesterol levels affect MET. However, this is not a significant limitation given the study's scope, and it is reasonable that such detailed functional analyses are left to specialists in hair cell physiology.

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors describe the use of BindCraft computational protein design to create a series of binders to the chemokine CCL25. This chemokine normally mediates CCR9-dependent trafficking of immune cells to the gut, making it a potential target for the treatment of inflammatory bowel disease and related conditions. Importantly, CCL25 also binds a scavenging receptor, ACKR4. The computational protein design approach used does not involve defining particular epitopes to be targeted, allowing a free search for any potential interaction surface.

Among four designs tested, three were predicted to interact at a similar site on the chemokine, while a fourth clone, VUP25111, was predicted to bind to a different site. All four designs showed binding to CCL25, with similar high-nM KD values in all cases. The first three clones showed evidence of direct competition with the receptor for CCL25 binding, while VUP25111 showed incomplete inhibition of binding. In functional assays, all clones acted as antagonists except for VUP25111, which inhibited arrestin recruitment by CCR9, but did not affect G protein activation by CCR9 or arrestin recruitment by ACKR4 (which signals exclusively through arrestin and not G protein).

Strengths:

The work is completed to a high technical standard, and the functional diversity of the clones is intriguing. It is exciting to see pathway-selective modulation of signaling, and this basic paradigm is likely to generalize to other chemokine/receptor systems. The exceptional complexity of chemokine signaling makes this an excellent area to explore the development of modulators that can restrict signaling to a specific subset of receptors.

Weaknesses:

No major weaknesses were noted by this reviewer.

Reviewer #1 (Public review):

This manuscript makes a significant contribution to the field by exploring the dichotomy between chemical synaptic and gap junctional contributions to extracellular potentials. While the study is comprehensive in its computational approach, adding experimental validation, network-level simulations, and expanded discussion on implications would elevate its impact further.

Strengths:

Novelty and Scope:<br /> The manuscript provides a detailed investigation into the contrasting extracellular field potential (EFP) signatures arising from chemical synapses and gap junctions, an underexplored area in neuroscience.<br /> It highlights the critical role of active dendritic processes in shaping EFPs, pushing forward our understanding of how electrical and chemical synapses contribute differently to extracellular signals.

Methodological Rigor:<br /> The use of morphologically and biophysically realistic computational models for CA1 pyramidal neurons ensures that the findings are grounded in physiological relevance.<br /> Systematic analysis of various factors, including the presence of sodium, leak, and HCN channels, offers a clear dissection of how transmembrane currents shape EFPs.

Biological Relevance:<br /> The findings emphasize the importance of incorporating gap junctional inputs in analyses of extracellular signals, which have traditionally focused on chemical synapses.<br /> The observed polarity differences and spectral characteristics provide novel insights into how neural computations may differ based on the mode of synaptic input.

Clarity and Depth:<br /> The manuscript is well-structured, with a logical progression from synchronous input analyses to asynchronous and rhythmic inputs, ensuring comprehensive coverage of the topic.

Weaknesses and Areas for Improvement:

Generality and Validation:<br /> The study focuses exclusively on CA1 pyramidal neurons. Expanding the analysis to other cell types, such as interneurons or glial cells, would enhance the generalizability of the findings.<br /> Experimental validation of the computational predictions is entirely absent. Empirical data correlating the modeled EFPs with actual recordings would strengthen the claims.

Role of Active Dendritic Currents:<br /> The paper emphasizes active dendritic currents, particularly the role of HCN channels in generating outward currents under certain conditions. However, further discussion of how this mechanism integrates into broader network dynamics is warranted.

Analysis of Plasticity:<br /> While the manuscript mentions plasticity in the discussion, there are no simulations that account for activity-dependent changes in synaptic or gap junctional properties. Including such analyses could significantly enhance the relevance of the findings.

Frequency-Dependent Effects:<br /> The study demonstrates that gap junctional inputs suppress high-frequency EFP power due to membrane filtering. However, it could delve deeper into the implications of this for different brain rhythms, such as gamma or ripple oscillations.

Visualization:<br /> Figures are dense and could benefit from more intuitive labeling and focused presentations. For example, isolating key differences between chemical and gap junctional inputs in distinct panels would improve clarity.

Contextual Relevance:<br /> The manuscript touches on how these findings relate to known physiological roles of gap junctions (e.g., in gamma rhythms) but does not explore this in depth. Stronger integration of the results into known neural network dynamics would enhance its impact.

Suggestions for Improvement:

Broader Application:<br /> Simulate EFPs in multi-neuron networks to assess how the findings extend to network-level interactions, particularly in regions with mixed synaptic connectivity.

Experimental Correlation:<br /> Collaborate with experimental groups to validate the computational predictions using in vivo or in vitro recordings.

Mechanistic Insights:<br /> Provide a more detailed mechanistic explanation of how specific ionic currents (e.g., HCN, sodium, leak) interact during gap junctional vs. chemical synaptic inputs.

Implications for Neural Coding:<br /> Discuss how the observed differences in EFP signatures might influence neural coding, especially in circuits with heavy gap junctional connectivity.

Reviewer #1 (Public review):

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

Summary:

This study resolves a cryo-EM structure of the GPCR, GPR30, in the presence of bicarbonate, which the author's lab recently identified as the physiological ligand. Understanding the ligand and the mechanism of activation is of fundamental importance to the field of receptor signaling. This solid study provides important insight into the overall structure and suggests a possible bicarbonate binding site.

Strengths:

The overall structure, and proposed mechanism of G-protein coupling are solid. Based on the structure, the authors identify a binding pocket that might accommodate bicarbonate. Although assignment of the binding pocket is speculative, extensive mutagenesis of residues in this pocket identifies several that are important to G-protein signaling. The structure shows some conformational differences with a previous structure of this protein determined in the absence of bicarbonate (PMC11217264). To my knowledge, bicarbonate is the only physiological ligand that has been identified for GPR30, making this study an important contribution to the field. However, the current study provides novel and important circumstantial evidence for the bicarbonate binding site based on mutagenesis and functional assays.

Weaknesses:

Bicarbonate is a challenging ligand for structural and biochemical studies, and because of experimental limitations, this study does not elucidate the exact binding site. Higher resolution structures would be required for structural identification of bicarbonate. The functional assay monitors activation of GPR30, and thus reports on not only bicarbonate binding, but also the integrity of the allosteric network that transduces the binding signal across the membrane. However, biochemical binding assays are challenging because the binding constant is weak, in the mM range.

The authors appropriately acknowledge the limitations of these experimental approaches, and they build a solid circumstantial case for the bicarbonate binding pocket based on extensive mutagenesis and functional analysis. However, the study does fall short of establishing the bicarbonate binding site.

Reviewer #1 (Public review):

Summary:

This study identifies NK2R as an intestinal GPCR that tunes enterocyte lipid uptake, lipid droplet storage, and chylomicron output, with loss or antagonism enhancing post‑prandial triglyceridemia and epithelial lipid stores, and agonism reducing adiposity and improving glycemia in DIO mice. Through bulk RNA‑seq, deconvolution, DSS colitis, and 16S profiling, the authors link Tacr2 deletion to coordinated induction of epithelial lipid‑metabolic programs, dampened immune gene expression, sex‑specific remodeling of secretory lineages, and male‑biased protection from experimental colitis despite dysbiotic microbiota. This is an overall important and thorough paper on an emerging obesity drug target, but it should temper some interpretations, and the following points would be needed to strengthen the claims in the manuscript.

Strengths:


The study uses an impressive combination of genetic loss‑of‑function, pharmacological agonism/antagonism, transcriptomics, and in vivo physiology to establish NK2R as a bidirectional regulator of epithelial lipid handling. The integration of RNA‑seq, epithelial cell‑type deconvolution, DSS colitis, and microbiome profiling provides a rich, systems‑level view of how Tacr2 deletion reshapes epithelial metabolism, lineage allocation, and inflammatory responsiveness in a sex‑specific manner. The gain- and loss‑of‑function data particularly support a model in which NK2R acts as an epithelial metabolic rheostat that restrains lipid absorption and chylomicron export, with downstream consequences for barrier fitness and immune tone.

Weaknesses:

Major points

While the data convincingly establish NK2R's role in epithelial lipid handling, the manuscript arguably overstates a primary "pro‑inflammatory" function for NK2R, given that Tacr2‑/‑ mice show enhanced enterocyte lipid uptake and storage, higher post‑prandial triglycerides, and a dysbiotic microbiota yet reduced mucosal immune gene expression and, in males, protection from DSS colitis. It remains equally plausible that the apparent "protection" reflects a mucosa that is less reactive to unfavorable microbiota rather than genuinely protected, and that NK2R's main function is metabolic, with immune changes emerging secondarily. Such a model would actually help reconcile the long-standing question as to why NK2R antagonism has not translated into clear benefit in clinical trials for GI inflammation over the past several decades.

Without temporal resolution, it is equally plausible that antagonists primarily perturb epithelial lipid homeostasis rather than directly and beneficially modulating immune tone. To discriminate between these possibilities and strengthen the potential direct inflammatory claims, the authors should:

(1) generate epithelial‑specific, immune‑cell-specific, and nociceptor‑specific Tacr2 deletions in the DSS model

(2) test gut‑restricted NK2R agonism versus antagonism under controlled dietary fat conditions for effects on LD load, barrier integrity, and colitis severity

(3) perform ex vivo tachykinin/NK2R stimulation of isolated epithelial versus immune compartments with functional readouts

(4) assess whether microbiota transfer from Tacr2‑/‑ versus WT donors into germ‑free or antibiotic‑treated recipients can recapitulate protection or susceptibility independently of epithelial NK2R status.

Minor points

Additional clarifications on Tac1 and tachykinin receptor expression in male/female colitis models, and validation of the NK2R antibody in KO tissue (or in situ hybridization), would also be needed to strengthen key mechanistic and localization claims.

Reviewer #1 (Public review):

Summary:

The authors have attempted to establish a role for XAP5, a transcriptional regulator they have previously identified for flagellar biogenesis in Chlamydomonas and mice, in primary cilia differentiation.

Strengths:

Genetic and biochemical analysis using a cultured mouse cell line, NIH3T3.

Weaknesses:

(1) The authors have ignored established data that, like in C. elegans and Drosophila, there is in vivo genetic evidence that primary cilia formation is regulated by the RFX transcriptional module (for example, PMID 19887680, PMID 29510665).

(2) The analysis with one mammalian cell line, NIH3T3, while done quite rigorously, is not sufficient. Also, the effect on cilia differentiation is very modest - a shortening of cilia length on XAP5, NONO and SOX5 knockout - which can happen for a variety of reasons, especially in culture conditions. In my view, this relatively mild phenotype does not establish that the XAP5/NONO and SOX5 axis is an important regulator of primary cilia differentiation.

(3) The lack of any data that validates the findings in the model vertebrate is a major weakness of this paper. Validation using clean genetics (whole body knockouts or tissue-specific conditional knockouts) is absolutely essential for these data to be acceptable.

Reviewer #1 (Public review):

Summary:

Heller et al use a murine model of AIRE deficiency, a disease that leads to systemic autoimmune disease, to demonstrate differential effects of selective JAK inhibitors. This group and others have previously demonstrated the efficacy of the JAK1/2 inhibitor ruxolitinib in patients with AIRE deficiency. Here, they focus on the ability of ruxolitinib versus drugs inhibiting either JAK1, JAK2, or JAK3 to alter organ pathology and accumulation of interferon-gamma producing immune cells in the lungs, which are important mediators of inflammation in patients with this disease. The current study provides evidence that selective JAK2 or JAK1 both reduce disease in this mouse model. There is potentially a more beneficial effect of selective JAK2 inhibition, although these differences are minor, and it is uncertain whether this is clinically relevant for patients. They demonstrate that inhibition of JAK3 alone in the mouse was clearly not beneficial for disease. Overall, this study provides evidence for consideration of more selective JAK inhibition in patients with AIRE deficiency.

Strengths:

(1) Robust model for investigating AIRE deficiency.

(2) They combine cellular studies (immune cell production of IFN-g) and robust organ pathology scoring to evaluate the effects of the drugs tested here.

(3) Data clearly demonstrates that JAK3 inhibition, at least as used here, may increase IFN-g production and does not reduce organ pathology.

Weaknesses:

(1) There is no direct comparison of the effects of JAK2 vs. JAK1 inhibition to support that JAK2 inhibition is clearly superior.

(2) They were not able to perform pharmacokinetic studies or measure the efficacy of JAK inhibition in their model, and it is uncertain how the doses of drug used here will translate to the treatment of patients.

(3) It is uncertain whether this study, performed in a murine model, will correspond to tissue/cell specificity of JAK inhibition in patients.

Reviewer #1 (Public review):

Summary:

This study examines the role of the long non-coding RNA Dreg1 in regulating Gata3 expression and ILC2 development. Using Dreg1 deficient mice, the authors show a selective loss of ILC2s but not T or NK cells, suggesting a lineage-specific requirement for Dreg1. By integrating public chromatin and TF-binding datasets, they propose a Tcf1-Dreg1-Gata3 regulatory axis. The topic is relevant for understanding epigenetic regulation of ILC differentiation.

Strengths:

(1) Clear in vivo evidence for a lineage-specific role of Dreg1.

(2) Comprehensive integration of genomic datasets.

(3) Cross-species comparison linking mouse and human regulatory regions.

Weaknesses:

(1) Mechanistic conclusions remain correlative, relying on public data.

(2) Lack of direct chromatin or transcriptional validation of Tcf1-mediated regulation.

(3) Human enhancer function is not experimentally confirmed.

(4) Insufficient methodological detail and limited mechanistic discussion.

Comments on revisions:

The authors have provided clear evidence that Dreg1 is necessary for ILC2 development, but their refusal to perform any mechanistic experiment remains a significant weakness. While their appeal to the 3Rs and the use of public datasets is noted, re-analyzing external data from heterogeneous sources cannot substitute for direct, internal validation of the Tcf1-Dreg1-Gata3 axis in their specific knockout model. This is particularly problematic because ILC2 progenitors, though rare, can be isolated from bone marrow, especially since assays like CUT&Tag and others are specifically designed for low cell numbers. By relying on public T-cell CRISPR screens to justify human ILC2 functions, the authors are substituting cross-cell-type correlation for definitive functional proof. Consequently, the manuscript currently describes a discovery of necessity without providing a verified molecular mechanism, which should be more explicitly reflected in the title and conclusions.

Reviewer #1 (Public review):

In this manuscript, the authors combine single-nucleus RNA sequencing with spatial transcriptomics to generate a spatiotemporal atlas of mouse placental development and explore the role of glycogen trophoblast cells in fetal viability. The study integrates several computational approaches, including trajectory analysis, regulatory network inference, and spatial mapping, together with histology and glycogen measurements. Based on these analyses, the authors propose that glycogen trophoblast cells provide metabolic support that is important for maintaining placental function and fetal survival.

One of the main strengths of the study is the quality and scope of the dataset. The integration of snRNA-seq with Stereo-seq spatial transcriptomics provides a detailed view of placental organization across regions and developmental stages. This type of combined spatial and transcriptional analysis is still relatively rare in placental biology and represents an important contribution to the field. The atlas itself will likely be a valuable resource for future studies.

Another strength is the effort to connect transcriptional findings with tissue-level validation. The glycogen staining and biochemical measurements support the interpretation that glycogen trophoblast cells contribute to placental metabolic function. The spatial analyses identifying macrophage accumulation in the labyrinth region of mutant placentas are also interesting and illustrate how spatial approaches can reveal microenvironmental changes that are difficult to detect otherwise.

The main limitation of the study is that the conclusion that glycogen cells are essential mediators of metabolic support for fetal viability remains partly indirect. The transcriptomic and spatial data strongly suggest a role for these cells, but it is still difficult to determine whether glycogen cell dysfunction is the primary cause of fetal lethality or a consequence of broader placental abnormalities. Clarifying this point would strengthen the central message of the paper.

Similarly, the macrophage accumulation observed in the labyrinth appears consistent with a response to tissue stress or injury, but its relationship to glycogen cell function is not fully explained. A clearer discussion of whether this represents a primary mechanism or a secondary effect would improve the interpretation.

Overall, this is a strong dataset and a useful spatial atlas of placental development. The study provides convincing descriptive insight into glycogen trophoblast biology, and with some clarification of the mechanistic conclusions, the manuscript will be even stronger.

Annotation #1 (5/3/2026): In this segment of the text, we learn that William Henry Singleton and other black men petitioning to enlist in the Union Army to assist in gaining their freedom. I feel that this segment is quite significant because we see how despite their willingness to put themselves at risk for the ideals they stood for, they were not accepted right away nor treated equally as citizens. This should remind all of us how the African Americans had to fight not only against their slavery but also for recognition and basic rights.

Reviewer #1 (Public review):

Summary:

The authors present a new autofocusing method, LUNA (Locking Under Nanoscale Accuracy), designed to overcome severe focus drift, a major challenge in long-term time-lapse microscopy. Using this method, they address a fundamental question in bacterial cold shock response: whether cells halt growth and division following an abrupt temperature downshift. Through single-cell analysis, the authors uncover a multi-phase adaptation process with distinct growth deceleration dynamics, and show that bacterial cells adapt to cold shock in a largely uniform manner across the population. Overall, this work provides new insights into the bacterial cold shock response at the single-cell level, extending beyond what can be inferred from population-level measurements.

Strengths:

(1) The LUNA method shows improved performance compared to existing autofocusing systems, achieving nanoscale precision over a large focusing range. Its focusing speed is sufficient for the experiments presented, with potential for further improvement through faster motors and optimized control algorithms, suggesting broad applicability. Theoretical simulations and experimental validation together provide strong support for the method's robustness.

(2) Using LUNA, the authors address a long-standing question in bacterial physiology: whether cells arrest growth and division during the acclimation phase following cold shock. Single-cell analyses across the full course of cold adaptation reveal features that are obscured in bulk-culture studies. Cells continue to grow and divide at reduced rates while maintaining cell size regulation, and exhibit a three-phase adaptation program with distinct growth dynamics. This response appears uniform across the population, with no evidence for bet-hedging. Overall, the experiments are well designed, and the analyses are solid and support the authors' conclusions.

(3) The authors further propose a model describing how population-level optical density (OD) depends on cell dry mass density, volume, and concentration. Following cold shock, cells grow more slowly and exhibit smaller sizes, explaining the apparently unchanged OD. This model provides a valuable conceptual framework for interpreting OD-based growth measurements, a widely used method in microbiology, and will be of broad interest to the field.

Weaknesses:

No major weaknesses identified.

Comments on revisions:

The authors have thoroughly addressed all of my questions. I thank them for their clear clarifications and thoughtful revisions, and I greatly appreciate their efforts in improving the manuscript.

Reviewer #1 (Public review):

Summary:

The manuscript, titled Hippocampal Single-Cell RNA Atlas of Chronic Methamphetamine Abuse-Induced Cognitive Decline in Mice, focuses on single-cell RNA sequencing (scRNA-seq) analysis following chronic methamphetamine (METH) treatment in mice. The authors propose two hypotheses: (1) METH induces neuroinflammation involving T and NKT cells, and (2) METH alters neuronal stem cell differentiation.

Strengths:

The authors provide a substantial dataset with numerous replicates, offering valuable resources to the research community.

Weaknesses:

Concerns remain regarding the interpretation of the data and the appropriateness of the statistical analyses.

Although the authors provided detailed responses to the reviewer's concerns, I am still concerned that several key issues have not yet been fully addressed in the revised manuscript.

First, in Figure 5, the authors state that neural stem cells (NSCs) preferentially differentiate into astrocytes rather than neuroblasts following METH treatment. However, based on the presented trajectories, it is difficult to visually confirm differences in the relative proportions of astrocyte versus neuroblast differentiation between the control and METH-treated conditions. The current figures do not provide a quantitative or clearly interpretable comparison of lineage allocation that would support this conclusion.

Moreover, in Figures 5C and 5F, the inferred pseudotime trajectories differ both the starting cell populations and the intermediate and terminal cell identities. As a result, the trajectories are not directly comparable between the control and METH conditions. Under these circumstances, it is inappropriate to interpret gene expression changes as occurring along equivalent differentiation paths, and the current analysis does not convincingly support the stated conclusions regarding altered NSC differentiation.

If the authors intend to claim differential gene expression associated with altered differentiation trajectories, the analysis should at minimum present the expression of the same set of genes (e.g., Bsg, Ccl4, Fos, Sox11, Flt1, Hspb1, Igfbp7, and Tmsb10) plotted along a matched trajectory (for example, NSC-to-astrocyte or NSC-to-neuroblast lineages) in both control and METH-treated samples, so that readers can directly compare expression dynamics across conditions.

In addition, several statements throughout the manuscript describing changes in cell-type proportions are not supported by corresponding statistical analyses. For example, in Figure 2C (around line 430), the authors report changes in cell proportions of ~0.1% or 2-3%. Without appropriate statistical testing, it is unclear whether such marginal differences are biologically meaningful or reproducible. The authors should either provide statistical testing (e.g., sample-level proportion analysis with p-values or confidence intervals) or revise the text to describe these findings as descriptive rather than significant changes.

Finally, the reported decrease in astrocyte proportion following METH exposure (from 6.6% to 5.5%), together with the lack of reported changes in neuroblast proportions, appears inconsistent with the trajectory-based conclusion that NSCs preferentially differentiate into astrocytes in METH-treated mice. This apparent discrepancy should be clarified or the conclusions appropriately tempered.

Reviewer #1 (Public review):

Summary:

This study combined high-field fMRI with computational modelling (including a Bayesian population receptive field [pRF] model and functional gradient analysis) in humans to demonstrate that the architecture of the corpus callosum (CC) and its interhemispheric connections is organized into parallel ipsilateral and contralateral streams, rather than functioning as a mixed integration of inputs from both hemispheres. The human findings were validated through preclinical experiments in mice using viral axonal tracing, which revealed a non-overlapping laminar arrangement of axons carrying left and right visual field information.

These results suggest that the CC operates as a set of parallel, segregated pathways, with each stream independently conveying information from one side of the visual field. This organization preserves the spatial origin of visual signals within the white matter. Although the overall concept of interhemispheric parallel pathways is not entirely unexpected, this refined understanding of callosal organization provides important scientific and clinical insights in relation to pathway-specific perturbations and in neurological disorders.

Strengths:

The manuscript is well written, the methodology is sound, and the analyses are carefully conducted. I particularly appreciate the effort to integrate functional and structural approaches and to validate the human neuroimaging findings with more sensitive preclinical techniques, such as viral tracing.

Weaknesses:

Several points require clarification to allow a more complete interpretation of the results. In addition, some further analyses are necessary to fully substantiate the claims made in the manuscript. These are detailed below

Comment 1:

BOLD signals in white matter remain a matter of debate, although this is not the central focus of the present study. Nevertheless, it is important to establish whether the underlying data have sufficient tSNR to support robust pRF estimation in white matter. In Figure 1, the EV appears relatively robust; however, it seems that only the best-fitting examples are shown. In contrast, the group-average EV reported in Figure 2, and the individual maps in the Supplementary Information indicate very low EV values, typically below 5%. In conventional fMRI analyses, thresholds of approximately 15-20% EV are often applied to exclude poor fits that may bias pRF parameter estimates. It appears that no such threshold was applied here. Interestingly, in Figure S6, the average EV for dual pRF models appears to be approximately 17%. Do dual and triple pRF models systematically produce higher EV compared to single pRF models? Additionally, Figure 2 suggests the presence of baseline activation that is captured by the model. Could this be related to a delayed or altered hemodynamic response function (HRF) in white matter? Clarification would be helpful. To better assess the robustness of the reported findings, the authors should provide quantitative measures of tSNR within the white matter tracts where the pRF model was fitted. Furthermore, a plot showing the average BOLD signal during visual stimulation versus baseline in those tracts would greatly strengthen confidence in the signal quality.

Although the reported linear relationship between pRF size and eccentricity, as well as the test-retest reliability analyses, suggest the presence of consistent receptive field estimates, these analyses are based on distributions and may lack the sensitivity required to differentiate single, dual, and triple pRF models. Moreover, the pRF estimates within the FMA appear noisy, particularly at the individual level (Figure S4), making it difficult to clearly dissociate information originating from the left and right hemifields.

Comment 2.1:

The Bayesian modelling approach is interesting and robust. However, as I understand it, the authors must specify a priori the number of pRFs to be estimated. This introduces a strong assumption about the expected underlying receptive field structure. An alternative Bayesian approach, such as micro-probing (Carvalho et al., 2020), does not require prior assumptions regarding the number or shape of pRFs. Instead, it estimates receptive field profiles in a more data-driven manner and provides a direct visualization of the pRF structure. Implementing such an approach, or at least comparing it with the current modelling strategy, could yield more reliable and potentially less biased estimates of multiple pRFs, particularly in white matter where signal quality is limited.

Comment 2.2:

Some clarifications regarding the pRF model are needed: in the Methods section, the authors mention the use of a Difference-of-Gaussians (DoG) model. However, it appears from the Results that the analyses were performed using a single-Gaussian model. Additionally, in Section 5.6, the authors state that six different pRF models were tested. Which specific models were included in this comparison? A clear description of each model, along with justification for the final model selection criteria, would help better understand the study

Comment 3:

Throughout the manuscript, the authors repeatedly refer to laminar-specific findings. However, the reported functional resolution of 1.6 mm isotropic is insufficient to reliably resolve cortical layers. Given this limitation, the laminar interpretations appear overstated. For example, in the Discussion section titled "Integrating White Matter with Laminar-Resolved Function", the authors state: "The combination of anatomically segregated white matter pathways with functionally specific cortical laminae presents a powerful synergy for human brain circuit research." Given the spatial resolution of the functional data, how are laminar-specific functional claims justified?

Similarly, the authors suggest that: "It becomes possible to assess not just if the CC is damaged, but precisely which directional pathways are compromised-either the pathways projecting from the lesioned hemisphere, or those projecting to the other, or both." It is unclear to me how the current methodology uniquely enables this level of directional specificity, and whether this was not already feasible using existing structural and diffusion-based approaches. The authors should clarify what is genuinely novel in this study.

Comment 4:

In the Discussion, the authors state: "These findings fundamentally reframe our understanding of interhemispheric communication, moving beyond static connectivity to reveal a dynamic, directionally specific highway where spatial location encodes the origin of information. This framework provides a novel blueprint for decoding directional information flow in the living human brain." Based on the analyses presented, it is unclear how the findings of this study demonstrate dynamic connectivity or true directional specificity. The reported results appear to characterize spatial organization and segregation of callosal pathways, but they do not measure the directionality of information flow, temporal dynamics, or causal directionality between hemispheres. To substantiate claims regarding dynamic or directional communication, additional analyses, such as connective field model (Haak et al.2013), effective connectivity modelling, time-resolved approaches, or perturbation-based methods (neuromodulation) would be required. As currently presented, the findings seem to support structural and functional segregation rather than dynamic or directionally resolved interhemispheric information transfer. The authors should either provide stronger evidence for these claims or moderate them.

Comment 5:

I agree with the authors that pooling of information across hemispheres represents a plausible explanation for the presence of dual pRFs. As discussed in the manuscript, such an effect would be expected to predominantly affect pRFs located near the vertical meridian. However, Figures S6C and S6D do not appear to demonstrate that bilateral pRFs are preferentially located along the vertical meridian.

Reviewer #1 (Public review):

Summary:

The manuscript entitled "Autonomic reflex plasticity associates with time-dependent SUDEP susceptibility in a murine model with hyperactive stress circuits" by Dr. Saunders and colleagues combined a traditional mouse model of SUDEP, ventral intrahippocampal kainite (vIHKA) injection, with a genetic model of chronic hyperactivity of central corticotropin-releasing hormone (CRH) neurons (Kcc2/Crh) that further increases the risk of SUDEP in the weeks following seizure.

Strengths:

Their results show during spontaneous seizures Kcc2/Crh mice had more pronounced reflex-like ictal bradycardias compared to WT controls that notably occurred prior (~10 sec) to seizure termination and had greater autonomic disturbances compared to WT controls, including a pronounced serotonin-mediated Bezold Jarisch reflex. These results show chronic hyperactivity of central corticotropin-releasing hormone (CRH) neurons (Kcc2/Crh) increased autonomic disturbances and risk of SUDEP in a kainic acid model of epilepsy.

Weaknesses:

This study could be improved with a more thorough assessment of heart rate, blood pressure and breathing during and following the seizures, and in particular the fatal event. It is unclear if the bradycardias were spontaneous or a result of preceding central or obstructive apneas, oxygen desaturations, hypercapnia, arrhythmias, or other possible triggers.

Considerable prior work in the literature suggests SUDEP could be mediated, in some patients, by a burst of parasympathetic activity to the heart. Were the heart rate changes in these animals during seizures inhibited or blocked by atropine or atenolol?<br /> The injection of the 5HT agonist phenylbiguanide into the right jugular is not a selective approach for activating the Bezold Jarisch Reflex (BJR), which is caused by increased activity of intracardiac sensory neurons (generally activated with ischemia or a combination of low preload with high contractility). The results should be interpreted more cautiously, as a response to systemic administration of phenylbiguanide only.

Reviewer #1 (Public review):

In this study, Szinte et al. measured the spatial selectivity of fMRI BOLD responses while subjects viewed dynamic noise stimuli vignetted by a moving bar aperture. Subjects viewed these moving bar stimuli as they fixated at one of three screen locations. This design enabled the authors to test whether fMRI responses are better explained by a model in which stimulus location is encoded relative to the retina or relative to the screen (in other words, 'retintopic' vs. 'spatiotopic' encoding). In retinotopic encoding, the pRFs should move with the eyes. In spatiotopic encoding, the pRFs should be locked to particular screen locations, regardless of eye position. The results are unambiguous: the retinotopic model wins.

A number of prior human fMRI studies have addressed this issue, and there is an overwhelming consensus in the field that spatial encoding throughout human visual cortex (and high-level cortex) is retinotopic (during fixation). All of the results shown in the present manuscript are consistent with these earlier observations. Szinte et al. also find that the degree of retinotopic selectivity is not affected by the task or locus of spatial attention. This too has been observed in multiple prior studies.

So, while this manuscript is primarily confirmatory, the study does nonetheless provide valuable measurements at 7T with a higher signal-to-noise ratio and high spatial resolution than previous studies. The authors also apply an innovative Bayesian decoding analysis (which is beautifully documented on their webpage, with a step-by-step tutorial and ample examples). So, a major strength of this paper is the methods; this study does set a high standard and is an ideal example for a rigorous, replicable analysis pipeline and cutting-edge statistical inference.

The results focus on the spatial profile of pRFs with different eye positions. However, the main idea behind eye-position gain fields is that the amplitude of the visual responses changes with eye position. I could not find any analysis testing response amplitude as a function of eye position. In the Discussion, the authors assert: "We did not find an influence of gaze position at the level of individual voxels nor at the level of visual areas." The authors speculate that this might be because gain fields have a salt-and-pepper organization in the cortex that cancel out when pooled across a voxel. While the salt-and-pepper explanation seems like perfectly fine speculation, here they are discussing a result that isn't shown in the Results!

Several prior human fMRI studies have reported eye position gain fields in humans, suggesting that the salt-and-pepper explanation is not correct. Rather, it is likely the case that the authors did not test a sufficiently wide range of eye positions to detect a gain modulation. For example, a study from Merriam et al. (J. Neurosci, 2013), which is mysteriously not cited here, measured both the spatial selectivity of visual receptive fields AND the response amplitude at 8 different eye positions that were spaced by as much as 24 degrees of visual angle (including both vertical and horizontal changes in eye position). Under these conditions, Merriam et al. did find reliable modulation in response amplitude with changes in eye position, even though the spatial selectivity of the responses did not change. Importantly, Merriam et al. found that visual response selectivity was consistent with a retinotopic reference frame (not a spatiotopic reference frame) and that this selectivity was invariant to the attention task. Consideration of these issues suggests that the experimental design used in the current experiment may have precluded the detection of eye position gain fields. The current manuscript would be much improved by a careful consideration of this prior literature, which is so closely related to what the authors report here.

Reviewer #1 (Public review):

Summary:

This manuscript investigates whether newborns can use speaker identity to separate verbal memories, aiming to shed light on the earliest mechanisms of language learning and memory formation. The authors employ a well-designed experimental paradigm using functional near-infrared spectroscopy (fNIRS) to measure neural responses in newborns exposed to familiar and novel words, with careful counterbalancing and acoustic controls. Their main finding is that newborns show differential neural activation to novel versus familiar words, particularly when speaker identity changes, suggesting that even at birth, infants can use indexical cues to support memory.

Strengths:

Major strengths of the work include its innovative approach to a longstanding question in developmental science, the use of appropriate and state-of-the-art neuroimaging methods for this age group, and a thoughtful experimental design that attempts to control for order and acoustic confounds. The study addresses a significant gap in our understanding of how infants process and remember speech, and the data are presented transparently, with clear reporting of both significant and non-significant results.

A previous concern was that the recognition effect appeared restricted to a subgroup of participants. The authors clarify that the bilateral STG and left IFG effects were present in both groups - it was only the right IFG modulation that was group-dependent. This is an important distinction and is now clearer in the revised manuscript. The timing of the effect emerging in a specific testing window also appears less arbitrary given the authors' explanation that prior work guided the analytical approach, and that task difficulty was expected to determine whether recognition would appear in earlier or later test blocks.

The sample size question is handled honestly. A power analysis based on a related ANOVA study produced an implausibly small estimate of N=5-7, which the authors rightly set aside. Aligning with fNIRS neonate studies - where mean sample sizes around N=24 are standard - is defensible, and the within-subject design with mixed-model analysis does improve sensitivity relative to simpler approaches. This is now explained in the manuscript.

The episodic memory framing has been scaled back appropriately. The revised discussion is clear that the study demonstrates what-who binding - an early component of episodic-like processing - rather than mature episodic memory in the Tulvingian sense. This is a more honest characterization of what the paradigm can show, and it opens a reasonable developmental question about how the remaining components (where, when) come online over the first months and years of life.

Weaknesses

The weaknesses are largely interpretive rather than fatal to the core findings. The absence of a same-speaker interference control within the current paradigm means the causal role of speaker change cannot be established entirely from internal evidence alone - the inference relies partly on comparison with Benavides-Varela et al. (2011), which used a somewhat different design. This is a reasonable approach given the ethical and practical constraints of testing newborns, and the authors are transparent about it, but readers should keep in mind that the conclusion about speaker change as the critical variable is supported by converging evidence across studies rather than a direct within-study manipulation.

Overall, the study contributes new and meaningful data on an underexplored aspect of early speech processing: the role of the speaker as a contextual dimension in word memory. The findings, taken together with the prior literature, tell a coherent story and have real implications for theories of early language acquisition and the developmental origins of episodic-like memory. The paradigm is sound and the results are worth pursuing in larger and more controlled follow-up studies.

Reviewer #1 (Public review):

Summary

The manuscript by K.H. Lee et al. presents Spyglass, a new open-source framework for building reproducible pipelines in systems neuroscience. The framework integrates the NWB (Neurodata Without Borders) data standard with the DataJoint relational database system to organize and manage analysis workflows. It enables the construction of complete pipelines, from raw data acquisition to final figures. The authors demonstrate their capabilities through examples, including spike sorting, LFP filtering, and sharp-wave ripple (SWR) detection. Additionally, the framework supports interactive visualizations via integration with Figurl, a platform for sharing neuroscience figures online.

Strengths:

Reproducibility in data analysis remains a significant challenge within the neuroscience community, posing a barrier to scientific progress. While many journals now require authors to share their data and code upon publication, this alone does not ensure that the code will execute properly or reproduce the original results. Recognizing this gap, the authors aim to address the community's need for a robust tool to build reproducible pipelines in systems neuroscience.

Comments on revisions:

In this revised version, the authors have addressed the majority of the concerns raised in the initial review. The manuscript is clearer, the documentation and explanations have been strengthened, and several important practical issues-particularly regarding usability, terminology, and deployment-have been meaningfully improved. While the framework continues to position itself both as a flexible analysis environment and as a mechanism for freezing and preserving reproducible pipelines, the authors have clarified their rationale for maintaining this dual role. I have no additional comments at this stage.

Reviewer #1 (Public review):

Summary:

The authors wanted to determine whether the set-19 gene, one of 38 SET-domain containing genes in C elegans, has a clear function in vivo with respect to lysine methylation. The question is not only whether it can modify this histone tail residue, but also what the impact of a loss of this locus is on the inheritance of repressive chromatin states.

Strengths:

The authors clearly achieved their goal, and it is convincingly shown that SET_19 is indeed a somatic cell histone methyltransferase with a striking specificity for H3K23. There is both recombinant protein work, quantitative mapping in vivo, of histone marks and transcriptional changes, and the authors rule out some other hypotheses that have been in the literature. Overall, this provides a compelling argument that SET-19 is indeed the major somatic cell HMT for this residue. Interestingly, the phenotypes are rather minimal, consistent with redundancy in the physiological roles of histone methylation, and redundancy as well in HMT function. For the most part, the data are not over-interpreted. The genetic alleles used, assuming they are confirmed, were revealing and well-documented.

Weaknesses:

The major weaknesses are easily fixed. The major weaknesses mainly reflect a slight overstatement of certain data (claiming insignificance, when it is not clear how that was determined) and claiming a bit too much about SET-32, which was independently claimed to be an H3K23 HMT. Clearly, the two SET domain enzymes are not redundant, nor is the claim that SET-32 has no role in H3K23 methylation completely convincing. Especially in germline or embryonic conditions. Finally, the imaging is not of very high quality, nor are the images fully quantitated. These points can be easily remedied.

Reviewer #2 (Public review):

Summary:

This study aims to examine the effects of the subcellular localization of the mammalian clock protein PER2 and its dedicated binding partners CRY1 and the kinase CK1. Using a combination of transient transfection and a Dox-inducible expression system, they show that CRY1 promotes nuclear retention of PER2, and that phosphorylation of PER2 by CK1 promotes cytoplasmic localization and release of CRY1. Changes in complex assembly and subcellular localization could impact the transcriptional repressive function of the CK1-PER2-CRY1 complex in the molecular clock.

Strengths:

The study establishes a system of transient transfection and Dox-inducible expression that allows for strict temporal control of the presence of fluorescently-tagged clock proteins. This is essential to conduct time-lapse microscopy studies that determine changes in the apparent subcellular localization and stability of associated clock proteins. With the potential caveats of overexpression set aside, the authors make use of good controls and supplement cell-based work with in vitro experiments where possible. The discovery that phosphorylation of PER2 by CK1 in the nucleus leads to cytoplasmic localization of PER2 and PER2-CRY1 complexes is a new finding. Moreover, the apparent dissociation of CRY1 from PER2 after CK1 phosphorylation provides a potentially new mechanism by which the repressive activity of this complex could be regulated.

Weaknesses:

Overexpression of circadian clock components, normally expressed at low levels, could disrupt the stoichiometry of native interactions, Although the authors provide a reasonable rationale for the Dox-inducible approach and use appropriate controls throughout the experiments, there is still concern that overexpression of the components of this transcriptional repressive complex far exceed the concentration of the transcription factor they regulate, and this has not been taken into consideration here. In addition, the interesting discovery that CK1 phosphorylation of PER2 leads to dissociation of CRY1 has not identified the phosphorylation site(s) responsible for this, so the mechanism by which this occurs is still unknown. Still, this study provides some interesting hypotheses regarding CK1 regulation of PER2 and CRY1 that could drive future work in the field.

Comments on latest version:

This manuscript has already undergone two rounds of review at a reputable journal, and we have been provided with the previous reviewers' comments and the authors' responses. I am satisfied with the responses and changes to the manuscript made in these previous rounds of review and don't have any further experiments to suggest that wouldn't represent significant additional work.

Reviewer #2 (Public review):

In this study, the authors investigate how increasing cognitive demand shapes activity patterns in the dorsal dentate gyrus (DG). Using a touchscreen-based TUNL task combined with TRAP/c-Fos tagging, birth-dating of adult-born granule cells (abDGCs), and chemogenetic inhibition, they show that higher task demand increases mature granule cell (mGC) recruitment and enhances suprapyramidal (SB) versus infrapyramidal (IB) blade bias. Functionally, mGC inhibition reduces overall activity and impairs performance without disrupting blade bias, whereas inhibition of {less than or equal to}7-week-old abDGCs increases mGC activity, abolishes blade bias, and impairs discrimination under high-demand conditions. These findings suggest that effective pattern separation depends not only on overall DG activity levels but also on the spatial organization of recruited ensembles.

The integration of touchscreen TUNL with temporally controlled activity tagging and birth-dated cohorts is technically strong. Quantification of SB-IB bias and radial/apical distributions adds anatomical precision beyond bulk activity measures. The comparison between mGC and abDGC inhibition is conceptually compelling and supports dissociable functional roles. Overall, the data convincingly demonstrate that increasing cognitive demand amplifies blade-biased DG recruitment and that mGCs and abDGCs differentially contribute to both behavioral performance and network organization.

However, how abDGCs are integrated into the mGC network under high cognitive demand remains unresolved. Additional experiments are needed to clarify how abDGCs shape spatial recruitment patterns and whether they directly inhibit or indirectly regulate mGC activity to maintain high performance.

Furthermore, the authors frame "high cognitive demand" as a multidimensional construct encompassing broad behavioral challenge. It would strengthen the work to delineate how local abDGC-mGC circuit interactions regulate specific task components in real time. This will require higher temporal resolution approaches, as TRAP and c-Fos labeling integrate activity over prolonged windows and primarily reflect sustained engagement rather than moment-to-moment computations.<br /> The central conclusion that dentate function depends on coordinated spatial recruitment rather than total activity magnitude is supported by the data, although mechanistic interpretations are tempered given methodological limitations.<br /> Overall, this work advances models of adult neurogenesis by emphasizing a critical-period modulatory role of abDGCs in organizing DG network activity during high-demand discrimination. The combined behavioral and circuit-level framework is likely to be influential in the field.

Comments on revisions:

None remaining.

Reviewer #1 (Public review):

The manuscript presents a compelling new in vitro system based on isogenic co-cultures of human iPSC-derived hepatocytes and macrophages, enabling the modelling of hepatic immune responses with unprecedented physiological relevance. The authors show that co-culture leads to enhanced maturation of hepatocytes and tissue-resident macrophage identity, which cannot be achieved through conditioned media alone. Using this system, they functionally validate immune-driven hepatotoxic responses to a panel of drugs and compare the system's predictive power to that of monocyte-derived macrophages. The results underscore the necessity of macrophage-hepatocyte crosstalk for accurate modelling of liver inflammation and drug toxicity in vitro. The manuscript is clearly written and addresses a key limitation in liver organoid systems: the lack of immune complexity and tissue-specific macrophage imprinting.

Strengths:

• Novelty and Relevance: The study presents a highly innovative co-culture system based on isogenic human iPSCs, addressing an unmet need in modelling immune-mediated hepatotoxicity.

• Mechanistic Insight: The reciprocal reprogramming between iHeps and iMacs, including induction of KC-specific pathways and hepatocyte maturation markers, is convincingly demonstrated.

• Functional Readouts: The application of the model to detect IL-6 responses to hepatotoxic compounds enhances its translational relevance.

Weaknesses:

The co-culture model with monocyte-derived macrophages is not fully characterised, making comparisons less informative.

Reviewer #1 (Public review):

The authors previously reported that Heliconius, one genus of the Heliconiini butterflies, evolved to be efficient foragers to feed pollen of specific plants and have massively expanded mushroom bodies. Using the same image dataset, the authors segmented the central complex and associated brain regions and found that the volume of the central complex relative to the rest of brain are largely conserved across the Heliconiini butterflies. By performing immunostaining to label specific subset of neurons, the authors found several potential sites of evolutional divergence in the central complex neural circuits, including the numbers of GABAergic ellipsoid body ring neurons and the innervation patterns of Allatostatin A expressing neurons in the noduli. These neuroanatomical data will be helpful to guide the future studies to understand the evolution of the neural circuits for vector-based navigations.

Strength

The authors used sufficiently large scale of dataset from 307 individuals of 41 specifies of Heliconiini butterflies to solidify the quantitative conclusions, and present new microscopy data for fine neuroanatomical comparison of the central complex.

Weakness

(1) Although the figures display a concise summary of anatomical findings, it would be difficult for non-experts to learn from this manuscript to identify the same neuronal processes in the raw confocal stacks. It would be helpful to have instructive movies to show step by step guide for identifications of neurons of interests, segmentations and 3D visualizations (rotation) for several examples including ER neurons (to supplement texts in line 347-353) and Allatostatin A neurons.

(2) Related to (1), it was difficult for me to access if the data in Fig 7 support the author's conclusions that ER neuron number increased in Heliconius Melpomene. By my understanding, the resolution of this dataset isn't high enough to trace individual axons and therefore authors do not rule out that the portion of "ER ring neurons" in Heliconius may not innervate the ER, as stated in Line 635 "Importantly, we also found that some ER neurons bypass the ellipsoid body and give rise to dense branches within distinct layers in the fan-shaped body (ER-FB)". If they don't innervate the ellipsoid body, why are they named as "ER neurons"?

(3) Discussions around the line 577-584 requires the assumption that each ellipsoid body (EB) ring neuron typically arborise in a single microglomerulus to form largely one-to-one connection with TuBu neurons within the bulb (BU), and therefore the number of BU microglomeruli should provide an estimation of the number of ER neurons. Explain this key assumption or provide an alternative explanation.

(4) The details of antibody information are missing in the Key resource table. Instead of citing papers, list the catalogue numbers and identifier for commercially available antibodies, and describe the antigen and if they are monoclonal or polyclonal. Are antigens conserved across species?

(5) I did not understand why authors assume that foraging to feed on pollens is more difficult cognitive task than foraging to feed on nectars. Would it be possible that they are equality demanding tasks but pollen feeding allows Heliconius to pass more proteins and nucleic acids to their offsprings and therefore they can develop larger mushroom bodies?

Comments on revisions:

The authors fully addressed my concerns and significantly improved the accessibility of the manuscript.

Reviewer #1 (Public review):

The author presents a new method for microRNA target prediction based on (1) a publicly available pretrained Sentence-BERT language model that the author fine-tunes using MeSH information and (2) downstream classification analysis for microRNA target prediction. In particular, the author's approach, named "miRTarDS", attempts to solve the microRNA target prediction problem by utilizing disease information (i.e., semantic similarity scores) from their language model. The author then compares the prediction performance with other sequence- and disease-based methods and attempts to show that miRTarDS is superior or at least comparable to existing methods. The author's general approach to this microRNA target prediction problem seems promising, but fails to demonstrate concrete computational evidence that miRTarDS outperforms other existing methods. The author's claim that disease information-based language models are sufficient is unfounded. The manuscript requires substantial rewriting and reorganization for readers with a strong background in biomedical research.

A major issue related to the author's claim of computational advance of miRTarDS: The author does not introduce existing biomedical-specific language models, and does not compare them against miRTarDS's fine-tuned model. The performance of miRTarDS is largely dependent on the semantic embedding of disease terms. The author shows in Figure 5 that MeSH-based fine-tuning leads to a substantial improvement in MeSH-based correlation compared to the publicly available pretrained SBERT model "multi-qa-MiniLM-L6-cos-v1" without sacrificing a large amount of BIOSSES-based correlation. However, the author does not compare the performance of MeSH- and BIOSSES-based correlation with existing language models such as ChatGPT, BioBERT, PubMedBERT, and more. Also, the substantial improvement in MeSH-based correlation is a mere indication that the MeSH-based fine-tuning strategy was reasonable and not that it's superior to the publicly available pretrained SBERT model "multi-qa-MiniLM-L6-cos-v1".

Another major issue is in the author's claim that disease-information from miRTarDS's language model is "sufficient" for accurate microRNA target prediction. Available microRNA targets with experimental evidence are largely biased for those with disease implications that have been reported in the biomedical literature. It's possible that their language model is biased by existing literature that has also been used to build microRNA target databases. Therefore, it is important that the author provides strong evidence that excludes the possibility of data leakage circularity. Similar concerns are prevalent across the manuscript, and so I highly recommend that the author reassess the evaluation frameworks and account for inflated performance, biased conclusions, and self-confirming results.

Last but not least, the manuscript requires a deeper and careful description and computational encoding of microRNA biology. I'd advise the author to include an expert in microRNA biology to improve the quality of this manuscript. For example, the author uses the pre-miRNA notation and replaces the mature miRNA notation to maintain computational encoding consistency across databases. However, the mature microRNA notation "the '-3p' or '-5p' is critical as the 3p and 5p mature microRNAs have different seed sequences and thus different mRNA targets. The 3p mature microRNA would most likely not target an mRNA targeted by the 5p mature microRNA.

Reviewer #1 (Public review):

Summary:

This study examines how traumatic brain injury (TBI) alters hippocampal network dynamics and single-unit activity in awake, behaving rats. Using laminar recordings, the authors report reductions in theta power, theta-gamma phase-amplitude coupling, and spike-field entrainment, alongside impairments in spatial memory performance.

Strengths of the study include the use of high-density laminar electrodes to localize activity across hippocampal layers and the integration of electrophysiological and behavioral measures. Analyses that consider behavioral state and account for broadband power changes improve confidence in the interpretation of oscillatory effects. Additional controls suggest that the observed differences are unlikely to be explained by gross motor or motivational deficits. The reported relationships between theta amplitude, phase-amplitude coupling, and spike entrainment provide useful insight into how network coordination is disrupted following injury.

There are a few minor weaknesses. The analyses of single-unit activity across environments are relatively limited, and more comprehensive approaches to characterizing spatial coding would strengthen conclusions about how TBI impacts hippocampal representations. The behavioral assessment relies primarily on a single task, which constrains the interpretation of the cognitive deficits. In addition, the relatively small number of animals is a limitation, although this is partially mitigated by the number of recorded units and the consistency of effects across measures.

Overall, this work provides a careful characterization of hippocampal circuit dysfunction following TBI and contributes to understanding how disruptions in oscillatory coordination and spike timing may relate to cognitive impairment.

Comments on revisions:

The authors have adequately addressed all of my concerns.

Reviewer #1 (Public review):

Summary:

This study utilizes fNIRS to investigate the effects of undernutrition on functional connectivity patterns in infants from a rural population in Gambia. fNIRS resting-state data recording spanned ages 5 to 24 months, while growth measures were collected from birth to 24 months. Additionally, executive functioning tasks were administered at 3 or 5 years of age. The results show an increase in left and right frontal-middle and right frontal-posterior connections with age and, contrary to previous findings in high-income countries, a decrease in frontal interhemispheric connectivity. Restricted growth during the first months of life was associated with stronger frontal interhemispheric connectivity and weaker right frontal-posterior connectivity at 24 months of age. Additionally, the study describes some connectivity patterns, including stronger frontal interhemispheric connectivity, which is associated with better cognitive flexibility at preschool age.

Strengths:

- The study analyses longitudinal data from a large cohort (n = 204) of infants living in a rural area of Gambia. This already represents a large sample for most infant studies, and it is impressive, considering it was collected outside the lab in a population that is underrepresented in the literature. The research question regarding the effect of early nutritional deficiency on brain development is highly relevant and may highlight the importance of early interventions. The study may also encourage further research on different underrepresented infant populations (i.e., infants not residing in Western high-income countries) or in settings where fMRI is not feasible.

- The preprocessing and analysis steps are carefully described, which is very welcome in the fNIRS field, where well-defined standards for preprocessing and analysis are still lacking.

Weaknesses:

- The study provides a solid description of the functional connectivity changes in the first two years of life at the group level and investigates how restricted growth influences connectivity patterns at 24 months. However, it does not explore the links between adverse situations and developmental trajectories for functional connectivity. Given the longitudinal nature of the dataset, future work should expand the analysis using more sophisticated tools to link undernutrition to specific developmental trajectories in functional connectivity, and eventually incorporate additional data to increase statistical power.

- Connectivity was assessed in 6 big ROIs to reduce variability due to head size and optode placement. Nevertheless, this also implies a significant reduction in spatial resolution. Individual digitalisation and co-registration of the optodes to a head model, followed by image reconstruction, could provide better spatial resolution. This is not a weakness specific to this study but rather a limitation common to most fNIRS studies, which typically analyse data at the channel level since digitalisation and co-registration can be challenging, especially in complex setups like this. The authors made an important effort to identify subjects with major optode displacement; however, future work might use tools to digitally record the positions of optodes and head markers.

Reviewer #1 (Public review):

Summary:

Hoverflies are renowned for their striking sexual dimorphism in eye morphology and early visual system physiology, as well as in sexually dimorphic behaviors. Surprisingly, male and female flight behaviors in response to optic flow exhibit only subtle differences. Nicholas et al. investigate the sensorimotor transformation of sexually dimorphic visual information into flight steering commands via descending neurons. Using a combination of intracellular and extracellular recordings, neuroanatomical analysis, and behavioral assays, the authors convincingly demonstrate that descending neurons-particularly at high optic flow velocities-exhibit pronounced sexual dimorphisms, while wing steering responses remain largely monomorphic. The study highlights a very interesting discrepancy between neuronal and behavioral response properties.

More specifically, the authors focused on two types of descending neurons that receive inputs from well-characterized wide-field sensitive tangential cells: OFS DN1 and OFS DN2. Their likely counterparts in Drosophila connect to neck, wing and haltere neuropils. The authors characterized the visual response properties of these two neuronal classes in both male and female hoverflies and identified several interesting differences. They then presented the same set of stimuli, tracked wing beat amplitude and analyzed the sum and the difference of right and left wing beat amplitude as a readout of lift or thrust, and yaw turning, respectively. Behavioral responses showed little to no sexual dimorphism, despite the observed neuronal differences.

Strengths:

I find the question very interesting and the results both convincing and intriguing. A fundamental goal in neuroscience is to link neuronal responses and behavior. The current study highlights that the transformations - even at the level of descending neurons to motoneurons - is complex and less straightforward than one might expect.

Weaknesses:

The authors investigated two types of descending neurons, but it was not clear to me how many other descending neurons are thought to be involved in wing steering responses to wide-field motion. I would suggest providing a more in-depth overview of what is known in hoverflies and Drosophila, since the conclusions drawn from the study would be different if these two types were the only descending neurons involved, as opposed to representing a subset of the neurons conveying visual information to the wing neuropil.

Both neuronal classes have counterparts in Drosophila that also innervate neck motor regions. The authors filled hoverfly DNs in intracellular recordings to characterize their arborization in the ventral nerve cord. In my opinion, these anatomical data could be further exploited and discussed a bit more: is the innervation in hoverflies also consistent with connecting to the neck and haltere motor regions? Are there any obvious differences and similarities to the Drosophila neurons mentioned by the authors? If the arborization also supports a role in neck movements, the authors could discuss whether they would expect any sexual dimorphism in head movements.

Revision comment:

I thank the authors for their detailed replies to my questions and the additional clarifications and analysis included in the paper. All my concerns have been addressed.

Reviewer #1 (Public review):

[Editors' note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The editors have determined that the authors adequately addressed the prior reviewer comments.]

Summary:

The author's goal was to arrest PsV capsids on the extracellular matrix using cytochalasin D. The cohort was then released and interaction with the cell surface, specifically with CD151 was assessed.

Note on previous revisions:

The authors did an excellent job in their revision to include data from the effect of proteolytic priming on their observed virion transfer to the cell body. All other minor issues were addressed adequately.

The work could be especially critical to understanding the process of in vivo infection.

Reviewer #1 (Public review):

Summary:

Using comprehensive profiling of normal and cancerous tissue via bulk and single-cell RNA sequencing, the authors identified that high-grade serous ovarian cancer is likely to originate from the epithelial progenitor cells from the distal fimbrial region of the fallopian tube, where it has been previously shown to be most prone to ovulatory stress and other microenvironmental influences. The authors also included a CNV analysis to identify hotspots in HGSOCs.

The findings are preliminary, but the resource on its own has great potential and can be used for developing methods for early detection, stratification and treatment.

The main limitation of this study is that the lineage is purely inferred from bioinformatics analysis. More validation work is required, perhaps using cell models / other model organisms.

Strengths and weaknesses:

The authors investigated the origin of high-grade serous ovarian cancer, which is one of the deadliest. They performed comparative analysis using both bulk and single-nucleus RNA sequencing between cancerous and normal tissues (fallopian tube and ovaries) and identified a population of epithelial progenitor cells from the distal fimbrial region that are exposed to ovulatory stress, as the most plausible cells of origin. The extensive profiling of the molecular signatures can also be used for early detection and stratification for treating the disease.

Previous studies have shown that HGSOCs likely originated from the epithelial lining of the fallopian tubes (PMID 32349388). The bulk RNAseq data is confusing in that neither the overall correlation of the transcriptome nor the sample clustering (Figure 1) supports the idea that the HGSOCs are close to the fallopian tube. The authors could perform a more comprehensive marker gene-based analysis to demonstrate their relationship.

The authors also performed a comprehensive analysis of single-cell datasets on both normal and cancerous tissue in humans. From there, they performed a combination of RNA velocity, PAGA and pseudotime, etc, to try and delineate the relationship amongst related cell populations. It would be helpful if the authors could clarify why they applied this particular suite of tools (explaining the differences between tools and bioinformatic approaches) to assist the broader readership who may not be familiar with this type of single-cell bioinformatic analysis.

It also seems to me that the authors did not account for patient effect when they performed the data integration (this point is discussed in the text). This may explain at least partially why the clusters are segregated by patient samples. Another explanation is that it could be due to uneven sampling, as only very few cells (1000s) were captured from each of the tumour samples, and this is clear when a dramatic difference can be seen in their cellular composition.

The trajectory analysis of normal and cancer single-cell data should also include other cells to prevent confirmation bias, as these analyses would only consider relationships amongst the cells available in the model.

As the authors indicated in the limitations, the cell lineage in the studies is largely inferred from the bioinformatics analysis. Experimental lineage tracing via other experimental models (organoids/animal models) would be required.

Despite these limitations, this study will serve as an important resource for the scientific community. I would also suggest that the authors should share this resource via additional portals in addition to the GEO data deposit (e.g. the HCA, or single-cell portals such as at the Broad Institute or CellXGene Discover).

Reviewer #1 (Public review):

This work demonstrates that MORC2 undergoes phase separation (PS) in cells to form nuclear condensates, and the authors demonstrate convincingly the interactions responsible for this phase separation. Specifically, the authors make good use of crystallography and NMR to identify multiple protein:protein interactions and use EMSA to confirm protein:DNA interactions. These interactions work together to promote in vitro and in cell phase separation and boosted ATPase activity by the catalytic domain of MORC2.

Moreover, the authors show solid evidence supporting their important claim that MORC2 PS is important for MORC2-mediated gene regulation. Exploring causal links between PS and function is an important need in the phase separation field, particularly as regards the role of condensates in gene regulation, and is a non-trivial matter. It is crucial and challenging to properly explore the alternative possibility that soluble complexes, existing in the same conditions as phase-separated condensates, are the functional species. The authors have attempted to address this concern by manipulating the physical nature of the MORC2 condensates using a killswitch (KS) peptide (MORC2 +KS), finding that reducing condensates dynamics results in a cellular phenotype very similar to that of the phase separation-deficient MORC2 condensates. While not fully ruling out the alternative, soluble-complex hypothesis, this experiment suggests that function is indeed localized inside the MORC2 condensates, and that perturbing the condensate can be functionally equivalent to removing condensate formation.

The authors also look at several disease related mutants of MORC2. While most of these do not seem to have an obvious connection to the phase separation data, it is quite interesting that one mutant, E236G, displays similar intra-condensate dynamics compared to MORC2 +KS, strengthening the claim that MORC2 phase separation is important for function and suggesting that the observations in this paper may indeed have some disease relevance.

Strengths

Static light scattering and crystallography are nicely used to demonstrate the dimerization of MORC2FL and to discover the structure of the CC3 domain dimer, presumably responsible for the dimerization of MORC2FL (Figure 1).

Extensive use of deletion mutants in multiple cell lines is used to identify regions of MORC2 that are important for forming condensates in the nucleus: the IBD, IDR, and CC3 domains are found to both be essential for condensate formation, while the CW domain plays an unknown role in condensate morphology (Figure 3). The authors use NMR to further identify that the IBD domain seems to interact with the first third of the centrally located IDR, termed IDRa, but not with the latter two thirds of the IDR domain (Figure 4). This leads them to propose that phase separation is the product of IDB:IDRa interaction, CC3 dimerization, and an unknown but important role for the CW domain.

Based on the observation that removal of the NLS resulted in diffuse cytoplasmic localization, they hypothesized that DNA may play an important role in MORC2 PS. EMSA was used to demonstrate interaction between DNA and several MORC2 domains: CC1, CC2, IDR, and TCD-CC3-IBD. Further in vitro microscopy with purified MORC2 showed that DNA addition significantly reduces MORC2 saturation concentration (Figure 5).

These assays convincingly demonstrate that MORC2 phase separates in cells and identifies the protein domains and interactions responsible for this phenomenon.

Weaknesses

The connection between condensates and function, while improved from the original manuscript, still has some weak points.

The central experiment demonstrating that MORC2 condensates mediate function takes the form of RNA-Seq in MORC2 KO HeLa cells (Figure 6), rescued with WT, condensate-deficient mutants, and a KS peptide mutant that reduces dynamics by increasing homotypic protein interactions. The observation that rescuing with MORC2 +KS is ineffective, in a manner similar to rescue with condensate-deficient MORC2 mutants, suggests that unperturbed condensates are important for function. An alternative possibility, however, is that condensates are non-functional bystanders, and that the increased homotypic interactions present in MORC2 +KS result in stronger MORC2 +KS recruitment to condensates, reducing the pool of functional, dilute phase MORC2 +KS and squashing function via sequestration. Similar ideas have been explored by others for transcription factors (e.g. Chong et al, Mol Cell, 2022). This possibility is neither discussed nor ruled out. The absence of microscopy data showing similar localization of MORC2 and MORC2 +KS (particularly the amount of diffuse MORC2 outside condensates) amplifies this concern.

The RNA-Seq data presented in Figure 6h also has some concerning qualities. Inter-replicate variability is higher than ideal, particularly for MORC2 deltaCC3. This may be a product of the transient transfection system used for these experiments, which inherently results in stochasticity. Specific sets of genes regulated by MORC2 are consistent with the main conclusion (Figure 6i, individual genes in 6h, showing that all mutants are more similar to one another than to WT MORC2), but global transcription shifts seem quite different between MORC2 condensate-deficient mutants and MORC2 +KS (Figure 6h heatmap), suggesting much more than simple condensate disruption is taking place. Together, this weakens the conclusion that MORC2 condensates are the functional form of MORC2.

Reviewer #1 (Public review):

Summary:

This study investigates how ingestive behaviors are reflected in muscle activity and how these behaviors relate to neural dynamics in the brain. By combining muscle recordings with computational analysis, the authors identify patterns of mouth movements and show that these change over time and align with changes in brain activity. The work suggests that ingestion is not defined by a single action but by coordinated changes across multiple behaviors.

Strengths:

(1) Addresses an important and underexplored question about how ingestive behavior is organized.

(2) Combines behavioral, physiological, and computational approaches creatively.

(3) Provides a novel framework for quantifying complex ingestive movements.

(4) Demonstrates a clear temporal relationship between behavior and brain activity.

Weaknesses

(1) Behavioral labels rely on video-based scoring, which may not fully capture subtle or hidden movements.

(2) The relationship between brain activity and behavior is correlational, but sometimes interpreted more strongly.

(3) The manuscript could be clearer and more accessible to readers outside the field.

Reviewer #1 (Public review):

Summary:

The authors have used a macaque (two animals only) to follow the migration of 'seeded' TDP43 protein in neuronal pathways - thus mimicking the spread of ALS in the human CNS. Previous experiments in rodents failed to demonstrate this, posing interesting and important biological differences, possibly related to the UMN-LMN system in higher order apes and humans.

Strengths:

An important step forward.

Weaknesses:

No weaknesses were identified by this reviewer. Only 2 animals were used, but that is appropriate given the sensate status of the macaque. In the opinion of this reviewer, the results are entirely convincing.

Reviewer #1 (Public review):

Summary:

The question of how or whether "extensive memory training affects neocortical memory engrams" (to use the words of the authors) is an interesting question and an area where I think there is room for advancing current knowledge. That said, I do not think the current paper succeeds in meaningfully addressing this question. At a conceptual level, I really struggled with the predictions and interpretations of the findings. There are also several elements of the experimental paradigm and analysis decisions that feel incompatible with the claims that are made. While the manuscript does demonstrate that several measures of neural pattern similarity differ between the various groups of individuals, the issue is that it is difficult to draw clear conclusions from these findings.

Strengths:

(1) This is a very unique dataset. Being able to recruit and enroll high-level memory athletes is impressive.

(2) In principle, comparing memory athletes to control subjects, active control subjects (who received working memory training), and trained subjects (who received method of loci training) is very appealing.

(3) In several ways, the authors were rigorous in their analyses.

(4) In principle, the question of how memory training influences neural similarity vs. dissimilarity is of potential interest.

Weaknesses:

(1) As far as I can tell, the training manipulation is fully confounded with instructions. That is, subjects were only instructed to use the method of loci if they had completed method of loci training (or if they were the memory athletes). For the training group, in the pre-training session, there was no strategy instruction (subjects could do whatever they wanted), but post-training, they were told to use the method of loci. I understand the argument, of course, that naïve subjects might not be very good at using the method of loci if they had no experience with it. But, it does seem entirely possible that some (or even many) of the observed fMRI results that are attributed to "extensive training" are better explained by strategy use. That is, maybe the effects can be explained by TRYING to use the method of loci as opposed to actual proficiency with the method of loci. It seems impossible to address this, given the design of the experiments. As such, any claims about the effects of memory training, per se, feel inappropriate. It feels equally plausible that the effects are due to the strategy instruction. If the same results could be obtained through a simple strategy manipulation without ANY training at all, that would radically alter the interpretation of the effects. I think the strategy use account is, in fact, quite viable because it is very easy to improve subjects' memories with a method of loci instruction (relative to no strategy instruction) without ANY practice at all. Obviously, practice does improve memory performance with the method of loci, but my point is that even without any meaningful practice, there is likely to be SOME immediate benefit to adopting the method of loci as a strategy. There is also the question of why the effects for the memory athletes weren't obviously stronger than for the trained group, given that the memory athletes have much more experience with the method of loci. Ultimately, the problem with the current design is that I don't see how one can tease apart the role of training, per se, vs. strategy use.

(2) There is no clear theoretical framework for the predictions or interpretations. The Results section is mostly a list of lots of different permutations of analyses (similarity within a group, between groups, between trials, across trials between subjects, during encoding vs. retrieval, frontal vs. hippocampal vs. parietal ROIs, etc). For each analysis, I did not have an intuition for what the prediction should be (e.g., should athletes have higher or lower pattern similarity?), and even after seeing all the results, I still do not have an intuition for how to interpret them. For the main results related to dissimilarity in prefrontal cortex, I would have, if anything, predicted the opposite: that when individuals are trained to use a common strategy, there would be MORE similarity between them. The Discussion acknowledges a very wide range of possible factors that might contribute to measures of similarity/dissimilarity, but I am ultimately left feeling that I have no idea how to interpret the results because the design and analyses were not structured such that any of these interpretations could be teased apart.

(3) Same theme: the analyses shift from frontal regions (when looking at encoding) to hippocampus and precuneus (when looking at temporal recency). This shift in ROIs is confusing. The analyses (encoding vs. recognition) are essentially confounded with the ROIs (frontal vs. hippocampal/precuneus), so it's hard to know whether different analyses yielded different patterns or different ROIs yielded different patterns. Why were the frontal regions that were important for encoding ignored for the temporal recency judgments? And the fact that medial temporal lobe regions showed opposite effects to the frontal regions during encoding did not get much attention. Given that there were opposing patterns (dissimilarity vs. similarity) across different brain regions, the framing of the paper (that "the method of loci may bolster uniqueness") feels like a very selective representation of the data.

(4) One of the more surprising aspects of the analyses (or at least one of the analyses) is that representational similarity analyses (RSA) are used to compare the average activity pattern (averaged across all trials) between different individuals. At a conceptual level, this really just reduces to a univariate analysis. It is not standard (or intuitive) to think about RSA that is essentially blind to the actual representational content. In other words, averaging across trials obviously washes out the content, and what is left are process-level effects. For process-level analyses, univariate analyses are far more common and seem more straightforward. However, these 'RSA' analyses are described as reflecting the "uniqueness of each word-location association" (an account which strongly implies content-level effects). This feels like an inappropriate description of what the analyses actually reflect.

(5) I think the analysis looking at trial-by-trial similarity during word encoding (showing greater dissimilarity among the experienced individuals) is a somewhat interesting result, but again, I think the interpretation is very difficult. It is hard (or, impossible, I think) to get a clear sense of what is driving those differences. Is it the association of a unique spatial context? Is it somehow a product of better encoding, per se (as opposed to distinct spatial contexts)? These things could be tested by actually manipulating the spatial contexts in a more controlled way. For example, the paper by Liu et al. that is cited several times - and also a just-published paper by Christopher Baldassano (Nature Human Behaviour) - each used a very controlled paradigm where the (imagined) spatial location associated with each item was known/manipulated. However, the design of the current study does not allow for these things to be teased apart.

(6) Relatedly, the training group seemed to receive instruction on a common spatial route, but, surprisingly, "Participants were free to choose which route and how many they would use to anchor the 72 items." Thus, if I understand correctly, we don't know whether the trained individuals were using common or distinct locations. And the fact that they learned a 50-location route but then studied a 72-word list is also a bit strange. Not having control or knowledge of the location that was associated with each word (sequence position) is a major limitation and also a major difference between the current study and other recent studies. For that matter, the word order was also randomized, so there was no control over whether the words and/or locations matched. These issues really complicate interpretation.

(7) Again, same theme: for the result showing lower trial-by-trial similarity (within-subject similarity), the question is why, exactly, training/experience is associated with lower trial-by-trial similarity. Does training specifically or preferentially lead to greater differentiation between temporally-adjacent trials (as in Liu et al)? Does it lead to greater differentiation IF subjects associate each word with a unique location? Or maybe there is a more abstract effect of sequence/position that is independent of spatial location? Importantly, each of these three possibilities that I mention here has a precedent in prior studies that were more tightly controlled. But here, there is no way to tease these apart because of the experimental design, limiting the conclusions.

(8) The ISC analysis described on p. 9 (line 328) is confusing. If I understand correctly, correlations between different trials were not computed (e.g., subject 1 trial 1 was not correlated with subject 2 trial 2). Rather, trial 1 was always correlated with trial 1 (in other subjects). Thus, it is not clear whether trial-level alignment matters at all. Maybe the same results would be obtained if there were no correspondence across subjects in trial number. Or if the trial order was shuffled within the subject. Given this, I simply don't know how to think about the data. And why did memory athletes show higher pattern similarity in this analysis as opposed to lower pattern similarity (as in some other analyses)? And why was this analysis performed by comparing memory athletes to each other as opposed to memory athletes to non-athletes? And, conceptually, why was this selective to the memory athletes or to the precuneus? And why was it selective to the temporal order test and not encoding? I am not asking the authors to answer each of these questions; rather, the point I am trying to make is that this analysis, and many of the analyses, seem to raise more questions than they answer.

(9) The ISC analyses are interpreted in terms of scene construction and context reinstatement, but these conclusions go (very) far beyond what the data actually shows. Again, I don't see how this analysis lends itself to a meaningful conclusion. And this general critique applies to many of the analyses reported in this paper.

(10) The fact that words were in random order per subject also makes the ISC analysis even more confusing to think about. The memory athletes had unique spatial routes (that they used for the method of loci) and unique word lists. So, why would it make sense to look at trial-level ISC? At a conceptual level, I simply don't understand what this is intended to capture.

(11) Differences in the pattern of results between the encoding and temporal memory recognition task are hard to make sense of and are not addressed in much detail. Why would it make more sense to have across-trial similarity during recognition than during encoding? I think any account of this is very speculative.

Reviewer #2 (Public review):

In this revised version of the manuscript, the authors have addressed many of my concerns. The representative confocal images now provided, allow for a much better assessment of the claims being made and hence the data to be understood, for example the level of protein expression of Chi3l1 in the macrophages.

There is just 1 concern remaining, which is a main claim of the manuscript, that loss of Chi3l1 drives KC death in MASLD. This claim is made based on gene expression profiles and the presence of Tunel staining in liver sections. However the KC numbers are not altered compared with WT when assessed by flow cytometry. This discrepancy is not really addressed. If the cells are not actually dying this would explain the lack of moKCs (a concern raised by reviewer 1) and would indeed suggest that the loss of these cells is, as suggested by that reviewer, trivial in this timeframe. The authors propose in their rebuttal that the KCs are in a prolonged state of stress, explaining the Tunel staining, but to make the claim that they die, the authors need to show their eventual loss from the liver. Otherwise the claims of death should be revised.

Reviewer #1 (Public review):

Ma et al. use human-chimpanzee tetraploid cells across different cell types to identify the genetic causes and then transcriptomic consequences of divergence in DNA methylation. They conclude that the evolution of DNA methylation is driven primarily by cis-regulatory changes, and that the evolution of CpG sites contributes to cis-regulation, while transcription factor expression underlies some trans changes. They then argue that divergence in DNA methylation is associated with changes in gene expression and may contribute to human phenotypes.

The tetraploid model is able to provide compelling evidence that most regulatory evolution occurs due to cis-regulatory changes. My only concern is that the extent of trans-changes may be overstated, as almost all are eliminated by changing from a nominal p-value criterion to even a 25% false discovery rate. The follow-up analyses are incomplete with major gaps. The authors focus on single potential mechanisms for cis- and trans-changes, but it is not clear to what degree these mechanisms explain the extent of cis and trans changes. There are also other mechanisms which are not investigated, such as the importance of TF binding sites for cis-regulatory evolution. While likely beyond the scope of this work, communicating these areas for future work would have helped define the niche for this manuscript.

Next, the authors seek to show that differences in DNA methylation are functionally relevant. Consistent with previous results, they show that differences in DNA methylation are (weakly) associated with changes in gene expression. They hypothesize that genes with concordant regulatory elements should exhibit greater methylation-expression coupling than other genes and show that cis-expression/cis-methylation pairs are more strongly correlated than trans/trans pairs. However, I worry that this result could be confounded by larger effect sizes for cis-changes than trans effects. I also think that looking at cis/trans or trans/cis changes would have been useful to directly test the driving hypothesis. Another limitation is that this analysis is limited to promoter regions. It is not clear how many divergent DMRs are included and how many of those genes have differences in expression. The key question is whether differences in DNA methylation are functionally important, and the answer provided by these analyses is "sometimes".

Finally, the authors make a case for lineage-specific selection on DNA methylation that is connected to human traits. This evidence was not convincing. In fact, it is even said that these tests cannot be interpreted as evidence of lineage-specific selection (lines 399-401), so I am confused why these results are framed as testing for selection. The evidence better supports an argument connecting DNA methylation to human phenotypes.

In conclusion, I think this study provides a valuable resource for differences in DNA methylation between humans and chimpanzees across tissues, and provides important insight into the relative abundance of cis and trans regulatory evolution. Additional research is necessary to investigate the underlying regulatory mechanisms, and more care needs to be taken in exploring the functional consequences.

Reviewer #1 (Public review):

[Editor's note: this version has been assessed by the Reviewing Editor without further input from the original reviewers. The authors have satisfactorily addressed the previous concerns raised by the reviewers.]

Summary:

This study presents convincing findings that oligodendrocytes play a regulatory role in spontaneous neural activity synchronization during early postnatal development, with implications for adult brain function. Utilizing targeted genetic approaches, the authors demonstrate how oligodendrocyte depletion impacts Purkinje cell activity and behaviors dependent on cerebellar function. Delayed myelination during critical developmental windows is linked to persistent alterations in neural circuit function, underscoring the lasting impact of oligodendrocyte activity.

Strengths:

(1) The research leverages the anatomically distinct olivocerebellar circuit, a well-characterized system with known developmental timelines and inputs, strengthening the link between oligodendrocyte function and neural synchronization.

(2) Functional assessments, supported by behavioral tests, validate the findings of in vivo calcium imaging, enhancing the study's credibility.

(3) Extending the study to assess long-term effects of early life myelination disruptions adds depth to the implications for both circuit function and behavior.

Weaknesses:

(1) The study would benefit from a closer analysis of myelination during the periods when synchrony is recorded. Direct correlations between myelination and synchronized activity would substantiate the mechanistic link and clarify if observed behavioral deficits stem from altered myelination timing.

(2) Although the study focuses on Purkinje cells in the cerebellum, neural synchrony typically involves cross-regional interactions. Expanding the discussion on how localized Purkinje synchrony affects broader behaviors-such as anxiety, motor function, and sociality - would enhance the findings' functional significance.

(3) The authors discuss the possibility of oligodendrocyte-mediated synapse elimination as a possible mechanism behind their findings, drawing from relevant recent literature on oligodendrocyte precursor cells. However, there are no data presented supporting these assumptions. The authors should explain why they think the mechanism behind their observation extends beyond the contribution of myelination or remove this point from the discussion entirely.

Comment for resubmission: Although the argument on synaptic elimination has been removed, it has been replaced with similarly unclear speculation about roles for oligodendrocytes outside of conventional myelination or metabolic support, again without clear evidence. The authors measured MBP area but have not performed detailed analysis of oligodendrocyte biology to support the claims made in the discussion. Please consider removing this section or rephrasing it to align with the data presented.

(4) It would be valuable to investigate secondary effects of oligodendrocyte depletion on other glial cells, particularly astrocytes or microglia, which could influence long-term behavioral outcomes. Identifying whether the lasting effects stem from developmental oligodendrocyte function alone or also involve myelination could deepen the study's insights.

(5) The authors should explore the use of different methods to disturb myelin production for a longer time, in order to further determine if the observed effects are transient or if they could have longer-lasting effects.

(6) Throughout the paper, there are concerns about statistical analyses, particularly on the use of the Mann-Whitney test or using fields of view as biological replicates.

Reviewer #1 (Public review):

Summary:

The manuscript by Seraj et al. introduces a transformative structural biology methodology termed "in extracto cryo-EM." This approach circumvents the traditional, often destructive, purification processes by performing single-particle cryo-EM directly on crude cellular lysates. By utilizing high-resolution 2D template matching (2DTM), the authors localize ribosomal particles within a complex molecular "crowd," achieving near-atomic resolution (~2.2 Å). The biological centerpiece of the study is the characterization of the mammalian translational apparatus under varying physiological states. The authors identify elongation factor 2 (eEF2) as a nearly universal hibernation factor, remarkably present not only on non-translating 80S ribosomes but also on 60S subunits. The study provides a detailed structural atlas of how eEF2, alongside factors like SERBP1, LARP1, and IFRD2, protects the ribosome's most sensitive functional centers (the PTC, DC, and SRL) during cellular stress.

Strengths:

The "in extracto" approach is a significant leap forward. It offers the high resolution typically reserved for purified samples while maintaining the "molecular context" found in in situ studies. This addresses a major bottleneck in structural biology: the loss of transiently bound or labile factors during biochemical purification.

The finding that eEF2 binds and sequesters 60S subunits is a major biological insight. This suggests a "pre-assembly" hibernation state that allows for rapid mobilization of the translation machinery once stress is relieved, which was previously uncharacterized in mammalian cells.

The authors successfully captured eIF5A and various hibernation factors in states that are traditionally disrupted. The identification of eIF5A across nearly all translating and non-translating states highlights the power of this method to detect ubiquitous but weakly bound regulators.

The manuscript beautifully illustrates the "shielding" mechanism of the ribosome. By mapping the binding sites of eEF2 and its co-factors, the authors provide a clear chemical basis for how the cell prevents nucleolytic cleavage of ribosomal RNA during nutrient deprivation.

Weaknesses:

While 2DTM is a powerful search tool, it inherently relies on a known structural "template." There is a risk that this methodology may be "blind" to highly divergent or novel macromolecular complexes that do not share sufficient structural similarity with the search model. The authors should discuss the limitations of using a vacant 60S/80S template in identifying highly remodeled stress-induced complexes. For instance, what happens if an empty 40S subunit is used as template? In the current work, while 60S and 80S particles are picked, none are 40S. The authors should comment on this.

In the GTPase center, the authors identify density for "DRG-like" proteins. However, due to limited local resolution in that specific region, they are unable to definitively distinguish between DRG1 and DRG2. While the structural similarity is high, the functional implications differ, and the identification remains somewhat speculative. The authors should acknowledge this in the text.

While "in extracto" is superior to purified SPA, the act of cell lysis (even rapid permeabilization) still involves a change in the chemical environment (pH, ion concentration, and dilution of metabolites). The authors could strengthen the manuscript by discussing how post-lysis changes might affect the occupancy of factors like GTP vs. GDP states.

The study provides excellent snapshots of stationary states (translating vs. hibernating), but the kinetic transition-specifically how the 60S-eEF2 complex is recruited back into active translation-is not well discussed. On page 13, the authors present eEF2 bound to 60S but do not mention anything regarding which nucleotide is bound to the factor. It only becomes clear that it is GDP after looking at Figure S9. This should be clarified in the text. Similarly, the observations that eEF2 is bound to GDP in the 60S and 80S raises the questions as to how the factor dissociates from the ribosome. This could also be discussed.

Overall Assessment:

This work reported in this manuscript likely represents the future of structural proteomics. The combination of high-resolution structural biology with minimal sample perturbation provides a new standard for investigating the cellular machines that govern life. After addressing minor points regarding template bias, protein identification, and transition dynamics, this work may become a landmark in the field of translation.

Comments on revisions:

In the revised version of the manuscript, the authors have addressed my prior concerns.

Reviewer #1 (Public review):

Summary:

Rolland and colleagues investigated the interaction between Vibrio bacteria and Alexandrium algae. The authors found a correlation between the abundance of the two in the Thau Lagoon and observed in the laboratory that Vibrio grows to higher numbers in the presence of the algae than in monoculture. Timelapse imaging of Alexandrium in coculture with Vibrio enabled the authors to observe Vibrio bacteria in proximity to the algae and subsequent algae death. The authors further determine the mechanism of the interaction between the two and point out similarities between the observed phenotypes and predator prey behaviours across organisms.

Strengths:

The study combines field work with mechanistic studies in the laboratory and uses a wide array of techniques ranging from co-cultivation experiments to genetic engineering, microscopy and proteomics. Further, the authors test multiple Vibrio and Alexandria species and claim a wide spread of the observed phenotypes.

Comments on revisions:

I thank the authors for their additional work on the manuscript. My comments were addressed to my satisfaction.

Reviewer #1 (Public review):

Summary:

The main goal of this manuscript is to develop a mathematical model of the regulation of cortical dynamics by Cdk1 activity to explain why, in some embryos (e.g., Xenopus), surface contraction waves are believed to move in the same direction as Cdk1, while in other embryos (e.g., starfish) they are believed to move in the opposite direction.

Strengths:

(1) The paper addresses a very important question.

(2) The mathematical model is sensible and suggests that the different relationship between Cdk1 and surface contraction waves might arise from the different behavior of the mitotic entry wave and the mitotic exit wave.

(3) The authors propose a mechanism by which the wave observed at mitotic exit might not passively follow the trigger wave observed at mitotic entry'

(4) The proposed mechanism is a potential explanation of the observed differences.

(5) The proposed mechanism is centered on different dynamics between the nucleus and the cytoplasm, highlighting the potential importance of the nucleus (and nuclear size) in organizing cortical dynamics.

Weaknesses:

(1) The proposed mechanism works if the activity in the nucleus is much higher than the high activity (high state of the bistable system) of the cytoplasm. So, as the wave propagates across the cytoplasm, the activity around the nucleus remains higher, which potentially causes a delay in the onset of Cyclin B-Cdk1 degradation in the region around the nucleus compared to the surrounding cytoplasm. This effect happens over a typical length scale, and if such a length scale is comparable to embryo size, this becomes the predominant mechanism. However, such a mechanism should exist near the nucleus independently of embryo size. So, it seems that for embryos where the wave back and wave front should travel together, nuclear activity must be adjusted not to be much higher than cytoplasmic activity. A better discussion of the discovered process and its implications would strengthen the paper. It requires careful reading to understand what, in hindsight, is a rather simple explanation. Is there any experimental evidence that the overall activity of Cdk1 is higher in the nucleus than in the cytoplasm?

(2) While the fact that Cdk1 can enslave cortical dynamics is clearly shown in the model, this is expected from the literature. There are systems where the enslavement of cortical and bulk actomyosin contractility to Cdk1 activity has been more clearly demonstrated (Drosophila and zebrafish embryos), as well as shown to have clear functions (nuclear positioning and ooplasmic segregation).

(3) The writing could be improved. The authors make some claims of originality that seem a stretch, e.g., in the abstract, they say: "we develop a reaction-diffusion model of Cyclin B-Cdk1 signaling in spherical cells with localized nuclear activation", but they essentially use a previous model with a few numerical tweaks. The figures are sometimes mislabelled or not explained, and some of the units seem wrong.

(4) The authors give the existence of trigger waves as a fact. While the predominant view is that such waves exist in the first cycle of the Xenopus embryos (however, this is from measurement of the cortical contractions, so a bit circular for this paper), it is unclear if waves exist in the starfish embryo, so the potential explanation that the starfish embryo simply has different Cdk1 dynamics cannot be ruled out.

Reviewer #1 (Public review):

Summary:

This study examines Müller glia (MG) reprogramming in the uninjured mouse retina through a combination of Notch signaling inhibition and AAV-induced proliferation. Building on their prior work showing that Cyclin D1 overexpression and p27^Kip1^ knockdown (CCA) promotes MG proliferation with very limited neurogenesis, the authors now demonstrate that Rbpj deletion alone induces a modest degree of MG-to-neuron conversion without proliferation, in agreement with recent work in the field. However, combining Rbpj deletion with CCA-mediated proliferation substantially enhances MG dedifferentiation and the generation of retinal neuron-like cells. Through genetic lineage tracing, histological analyses, and single-cell transcriptomics, the authors provide evidence that MG-derived cells acquire molecular features of bipolar (ON, OFF, and rod bipolar) and amacrine neurons. Most MG-derived cells appear to survive long-term (up to 9 months).

Strengths:

Overall, the study is carefully designed and executed, and the manuscript is clearly written with well-presented figures. While the work does not significantly expand the repertoire of neuronal types generated from mammalian MG beyond what has been previously reported in the field, it provides a valuable and improved strategy for inducing robust MG proliferation and neurogenesis in the mammalian retina.

Weaknesses:

(1) It would be better to include a negative control AAV when evaluating the effect of CCA AAV in the Rbpj KO background. This could help distinguish the specific contribution of the CCA construct from potential effects of intravitreal AAV injection itself, which can induce mild inflammation, known to influence MG reprogramming.

(2) The extent of MG transduction by the CCA AAV is not clear. As quantifications are normalized to total MG (GFP^+^ or TdTomato^+^) or retinal length, it would be useful to clarify whether near-complete transduction is assumed, or if additional information on transduction efficiency can be provided.

(3) In Figure S10, the reduced MG proliferation observed in the CCA + Rbpj deletion group could also potentially reflect decreased GFAP promoter activity in dedifferentiated MG following Rbpj deletion. Alternatively, MG-derived cells may be more fragile under these conditions.

(4) In the CCA + Rbpj deletion condition, do MG undergo single or multiple rounds of cell division?

(5) What fraction of neuron-like cells (bipolar- and amacrine-like) arises from proliferation versus direct transdifferentiation? Quantification of MG-derived cells expressing neuronal markers (e.g., Otx2, HuC/D), with and without EdU labeling, would help distinguish these mechanisms.

(6) In Figure S18a, the authors state that "while the neuron-like clusters were best classified as BC-like and AC-like based on their distinct marker gene expression, they also exhibited mixed expression of genes associated with other retinal neuronal types, including RGC markers (e.g., Tubb3, Myt1l, Grin1) and photoreceptor markers (e.g., Crx, Prom1, Epha10, Gucy2e, Scg3) (Fig. S18a), suggesting that the regenerated cells exist in a hybrid state" and "MG derived neuron like cells also expressed genes characteristic of RGCs and photoreceptors, indicating enhanced lineage". However, many of these genes are not specific to RGCs or photoreceptors and are instead broadly expressed in retinal neurons or enriched in bipolar/amacrine populations. Therefore, it is unclear whether these cells exhibit hybrid RGC or photoreceptor identity.

(7) The authors provide a thorough molecular characterization of MG-derived cells through immunostaining and single-cell sequencing. However, their morphological features, synaptic connectivity (e.g., synaptic marker expression), and electrophysiological properties remain largely uncharacterized. While these experiments may be technically challenging, this limitation should be discussed.

(8) The conclusion that CCA + Rbpj deletion induces neurogenesis without compromising MG supportive functions or retinal homeostasis appears somewhat oversold. This claim is primarily based on gross retinal morphology and ZO-1 staining. Given the extent of MG dedifferentiation and ectopic cell generation in the ONL and INL, it is likely that retinal function is affected. Functional assessments (e.g., ERG) would be required to support this conclusion. The authors should consider tempering this statement.

(9) Regarding the mechanism by which CCA-induced proliferation enhances MG reprogramming in the Rbpj knockout background, one plausible explanation is that chromatin states (e.g., histone modifications and DNA methylation) are transiently reset during DNA replication and cell division. While this alone may be insufficient to activate neurogenic programs, it could synergize with Rbpj deletion to allow neurogenic transcription factors (such as Ascl1, Otx2, NeuroD1, and NeuroD2) to access previously inaccessible chromatin regions, thereby promoting MG reprogramming.

Reviewer #1 (Public review):

Summary:

This interesting paper probes the problematic relationships between the classical "spiralian" taxa, i.e., annelids, molluscs, brachiopods, platyhelminths and nemerteans, and shows that the branches leading to them are so short as to be unreliable guides to their relationships. This, in turn, has important implications for how we view the origin of the animal phyla.

Strengths:

A very careful analysis of a famous old problem with quite significant results. The results seem to be robust and support their conclusions.

It often passes uncommented that many different trees are published about animal relationships, yet some parts of the tree seem extremely difficult to resolve; the spiralians are perhaps the most difficult case. More recently, problems about sponges or ctenophores as sister groups to the rest of the animals have alerted us to major areas of uncertainty in large-scale phylogenetic reconstruction; this paper is a welcome reminder that other, perhaps even harder, problems exist which may be difficult to ever resolve with the (molecular) data we have.

Weaknesses:

The paper could have perhaps drawn out some of the implications of its results in a clearer manner.

Reviewer #1 (Public review):

This study integrates Xenium spatial transcriptomics of paired inflamed and uninvolved Crohn's disease tissues with functional analyses in a csf2rb-/- larval zebrafish DSS intestinal injury model to investigate the spatial and cell-type-specific roles of GM-CSF. The work is limited mechanistically and adds little to an already disputed field: GM-CSF's role in intestinal inflammation is context-dependent and extensively studied in mice and humans, and this study does not resolve these controversies. The zebrafish appears to be a poor model for these questions: it lacks mammalian intestinal architecture, complex microbiota, and clearly validated functional ILC populations. Putative ILC1s are poorly defined based on stress-response gene modules, while ILC3s are somewhat better characterized, but overall, the system does not allow mechanistic insights into GM-CSF regulation of ILCs. The DSS experiments largely recapitulate the known protective effects of GM-CSF in epithelial injury without clarifying underlying mechanisms.

Figure 1

GM-CSF expression is extremely sparse, rarely exceeding 0.005 frequency even in inflamed regions. The authors should acknowledge this and discuss why. Xenium could be used to characterize the niche around GM-CSF-producing cells, but no new cellular circuit is revealed beyond known myeloid-lymphoid interactions.

Figure 2

Colon length in DSS colitis is not decreased in Csf2rb⁻/⁻ versus wild-type zebrafish under untreated conditions, suggesting endogenous GM-CSF has minimal impact. In Figure 2E, Tg(mpeg1:mCherry) larvae show staining in vessel- or epithelial-like structures expressing Csf2rb, which does not resemble macrophages and requires clarification. pSTAT5 is upregulated with GM-CSF treatment, but the responding cell types are unclear.

Figure 3

Putative ILC1s are defined by stress-response gene modules rather than canonical markers. Overlapping genes with human (HSP90AA1, UBB, MCL1, DOK2) do not indicate ILC1 identity, which is described by IL7R, KLRB1, or TBX21 expression in the human Xenium dataset. ILC2s were not detected, and Ifng expression is broadly distributed, making attribution to ILC1s uncertain. ILC3s are somewhat better defined, but overall, the data do not support mechanistic conclusions about GM-CSF regulation of ILC populations.

Reviewer #1 (Public review):

Summary:

The authors sought to define the molecular mechanism by which the adaptor protein Egalitarian (Egl) recognizes and binds specific mRNA localization signals -- in particular, the K10 transport and localization signal (TLS) -- to initiate dynein-based transport in Drosophila. In doing so, they identified the minimal Egl domains required for RNA binding, determined the atomistic structure of the Egl-RNA complex, and explored the recognition mechanism (shape vs. structure). They furthermore performed in vivo functional validation using CRISPR-mediated genome editing in Drosophila that showed that the identified binding residues are biologically essential.

Strengths:

The authors provided a detailed crystal structure of the Egl-RNA complex at high resolution. In particular, they used a MBP-fusion crystallization driver to be able to resolve the flexible C-terminal domain of Egl (EHD). The authors' use of an integrative approach combining X-ray crystallography with binding assays and in vivo functional validation provides compelling evidence for their claims.

The work provides a detailed interaction mapping that identifies the protein residues responsible for the electrostatic interaction with the RNA. In doing so, the work explains how Egl can recognize diverse RNA sequences by demonstrating that Egl binds primarily to the phosphate backbone and specific structural bulges, providing a plausible model for how one protein can recognize many different localization signals that share little sequence similarity.

Weaknesses:

Discrepancy in the stoichiometric Egl-to-RNA ratio (the structural data in the paper indicate a 1:1 ratio, whereas previous single-molecule transport studies suggest a 2:1 ratio) remains unanswered, with the likely explanation that the truncated version of the protein might not capture the full (native) assembly. While the authors acknowledge this in the Discussion, the paper would benefit from this issue being raised earlier, already in the Results section. Moreover, there is a notable omission of a recent preprint on a very similar topic [https://www.biorxiv.org/content/10.1101/2025.08.02.668268v1.full].

In vitro, Egl shows a relatively high affinity for non-target RNAs such as the MS2 loop, whereas it is highly selective in vivo. Is it possible that other cofactors are required for the high-fidelity sorting not present in the study? Testing binding in the presence of co-factors (BicD or Dlc) could indicate whether they increase the specificity for target RNAs over non-target ones.

Including a more diverse set of size-matched RNA controls would have significantly strengthened the paper's claims regarding specificity. Using RNAs that mimic K10 TLS would have provided a more rigorous test of the shape-recognition by Egl - using, for instance, decoy RNAs of the same length but with differently positioned bulges (or no bulges at all) or testing other known localization signals (like bicoid or hairy) of similar length.

Appraisal of aims:

The authors successfully determined the crystal structure of the Egl-RNA complex, identifying a modular binding surface composed of the EXO domain, a helical linker, and the EHD. They effectively demonstrated that Egl uses a combination of shape-specific recognition (targeting RNA bulges) and sequence-specific interactions (bonding with specific bases), and confirmed the biological necessity of these findings by showing that mutating the identified residues in living flies leads to infertility and oocyte differentiation defects. These results provide robust evidence for the authors' claims that they have defined a minimal RNA localization signal. In particular, the correlation between the L-Triple mutation's binding defect and its total sterility in flies provides proof that the identified binding surface is the functional one. While the 1:1 stoichiometry remains a point for further investigation, the authors transparently address that full-length transport may require a 2:1 assembly, suggesting their structure represents the fundamental building block of that larger complex.

Impact of the work on the field:

This study provides a high-resolution picture of how a dynein adaptor recognizes its cargo. It moves the field from predictive models to atomic-level certainty, setting a benchmark for studying other similar transport complexes. By proving that Egl recognizes RNA shape (bulges) as much as sequence, the work changes the outlook on the search for localization signals in other genomes, moving beyond simple sequence motifs to 3D structural signatures. The coordinates deposited in the EBI (IDs: 9UJU, 9UJY, 9UUG) provide a resource for the modelling of higher-order transport complexes. The identification of specific residues (e.g., the L-Triple) provides the community with tools to disrupt RNA transport in Drosophila without destroying the entire protein, allowing for more nuanced studies of development.

Reviewer #1 (Public review):

Summary:

Zare‑Eelanjegh et al. investigate how the endoplasmic reticulum, the nucleus, and the cell periphery are mechanically linked by indenting intact cells with specially shaped atomic‑force probes that double as drug injection devices. Fluorescence‑lifetime imaging of the membrane tension reporter Flipper‑TR reveals that these three compartments are mechanically linked and that the actin cytoskeleton, microtubules, and lamins modulate this coupling in complex ways.

Strengths:

* The study makes an important advance by applying FluidFM to probe organelle mechanics in living cells, a technically demanding but powerful approach.

* Experimental design is quantitative, the data are clearly presented, and the conclusions are broadly consistent with the measurements.

Weaknesses:

* Calcium‑dependent effects: Indentation can evoke cytoplasmic Ca²⁺ elevations that drive myosin contraction and reshape the internal membrane network (e.g., vesiculation: PMID : 9200614, 32179693) possibly confounding the Flipper-TR responses; without simultaneous/matching Ca²⁺ imaging, cell viability assays (e.g., Sytox), and intracellular Ca²⁺ sequestration or myosin inhibition experiments, a more complex mechanochemical coupling cannot be excluded, weakening conclusions.

* Baseline measurements: Flipper‑TR lifetime images acquired without indentation do not exclude potential light‑induced or time‑dependent changes, which weakens the conclusions.

* Indentation depth versus nuclear stiffness/tension: Because lamin‑A/C depletion softens nuclei, a given force may produce a deeper pit and thus greater membrane stretch. It is unclear how the cytoskeletal perturbations affect indentation depth, which weakens the conclusions.

Comments on revisions:

With their responses, the authors have relieved my initial concerns.

Reviewer #1 (Public review):

Summary:

In the manuscript entitled 'The Role of ATP Synthase Subunit e (ATP5I) in 1 Mediating the Metabolic and Antiproliferative 2 Effects of Biguanides', Lefrancois G et al. identifies ATP5I, a subunit of F1Fo-ATP synthase, as a key target of medicinal biguanides. ATP5I stabilizes F1Fo-ATP synthase dimers, essential for cristae morphology, but its role in cancer metabolism is understudied. The research shows ATP5I interacts with a biguanide analogue, and its knockout in pancreatic cancer cells mimics biguanide treatment effects, including altered mitochondria, reduced OXPHOS, and increased glycolysis. ATP5I knockout cells resist biguanide-induced antiproliferative effects, but reintroducing ATP5I restores the effects of metformin and phenformin. These findings highlight ATP5I as a promising mitochondrial target for cancer therapies. The manuscript is well written.

Strengths:

Demonstrated the experiments in a systematic and well accepted methods

Weaknesses:

Significance of the target molecule and mechanisms may help in understanding the molecular mechanisms of metformin.

Comments on revisions:

In the revised manuscript, the authors addressed all the queries.

Joint Public Review:

Summary

Riva et al. introduce a semi-automatic setup for measuring Drosophila melanogaster oviposition rhythms and use it to map the timekeeping function underlying egg laying rhythms to a subset of clock cells. Using a combination of neurogenetic manipulations and referencing the publicly available female hemi-brain connectome dataset, they narrow the critical circuit down to two of the three CRYPTOCHROME expressing lateral-dorsal neurons (CRY[+] LNds). Their findings suggest that different overlapping sets of clock neurons may control different behavioral rhythms in D. melanogaster.

This work will be of interest to researchers interested in the circadian regulation of oviposition in D. melanogaster (and possibly other insects), a phenomenon which has been left relatively under-explored. The construction of a semi-automated setup which can be made relatively cheaply using available motors and 3D printed molds provides a useful model for obtaining longer records of oviposition activity.

Strengths

The authors use a semi-automated monitoring system to detect circadian egg laying rhythms in spite of inherently noisy data. Using this approach they use a variety of different genetic tools to show that CRY+ LNds play a role in generating the circadian rhythm of oviposition, that PDF-expressing neurons seem to be important for maintaining the circadian period of egg laying, and that period locus function is required for the circadian rhythmicity of oviposition. The authors also point to some potentially interesting connectome data that suggest hypotheses regarding the neuronal circuit linking daily timekeeping to oviposition, which will require further validation in future studies.

Weaknesses:

The major weaknesses of this work result from the noisy nature of the data, and the need to average the individual records of many animals in order to extract significant rhythmicity values. The predicted neural output pathways will require validation in future studies.

Reviewer #2 (Public review):

Summary:

Shahbazi et al. trained recurrent neural networks (RNNs) to simulate human upper limb movement during adaptation to a force field perturbation. They demonstrated that throughout adaptation, the pattern of motor commands to the muscles of the simulated arm changed, allowing the perturbed movements to regain their typical, perturbation-free straight-line paths. After this initial learning block (FF1), the network encountered null-fields to wash out the adaptation, before re-experiencing the force in a second learning block (FF2). Upon re-exposure, the network learned faster than during initial learning, consistent with the savings observed in behavioral studies of adaptation. They also found that as the number of hidden units in the RNN increased, so did the probability of exhibiting savings. The authors concluded that these results propose a neural basis for savings that is independent of context and strategic processes.

Strengths:

The paper addresses an important and controversial topic in motor adaptation: the mechanism underlying motor memory. The RNN simulation reproduces behavioral hallmarks of adaptation, and it provides a useful illustration of the pattern of muscle activity underlying human-like movements under both normal and perturbing conditions. While the savings effect produced by the network, though significant, appears somewhat small, the simulation demonstrating an increase in savings with a greater number of hidden units is particularly intriguing.

Main weakness:

The introduction details the ongoing debate in the literature regarding the mechanisms underlying savings, particularly whether it stems from explicit or implicit learning processes. However, it remains unclear how the current work addresses this debate. There is already a considerable body of research, particularly in visuomotor adaptation, demonstrating that savings is predominantly driven by explicit strategies (e.g., Morehead et al. 2015, Haith et al., 2015; Huberdeau et al., 2019; Avraham et al., 2021). Furthermore, there have been multiple reports that implicit adaptation exhibits attenuation upon relearning (Avraham et al., 2021, Leow et al., 2020; Yin and Wei, 2020; Hamel et al., 2021; Hamel et al., 2022; Wang and Ivry, 2023; Hadjiosif et al., 2023). In the discussion, the authors acknowledge that their goal was not to model a complete explicit-implicit system, but rather to probe how savings may emerge from a purely implicit mechanism. Given the central debate introduced by the authors, the manuscript would benefit from a more detailed discussion explaining how their findings elucidate the specific conditions under which savings emerge from purely implicit mechanisms versus when cognitive strategies predominate.

Reviewer #1 (Public review):

[Editors' note: The Reviewing Editor has assessed the revised manuscript without seeking further input from the original reviewers. The authors have addressed the main points raised during peer review, including clarifying methodological differences with prior work, providing additional analysis, and expanding the discussion of potential mechanisms. These revisions strengthen the interpretation and presentation of the findings, and the conclusions remain supported by the data.]

Summary:

Ritzau-Jost et al. investigate the potential contribution of AP broadening in homeostatic upregulation of neuronal network activity with a specific focus on dissociated neuronal cultures. In cultures obtained from a few brain regions from mice or rats using different culture conditions and examined by different laboratories, AP half-width remained stable despite chronic activity block with TTX. The finding suggests that AP width is not significantly modulated by changes in sodium channel activity.

Strengths:

The collaborative nature of the study amongst the neuronal culture experts and the rigorous electrophysiological assessments provides for a compelling support of the main conclusion.

Reviewer #1 (Public review):

Summary:

The authors investigated the role of an E3 ubiquitin ligase ITCH in regulating the viral life cycle of SARS-CoV-2. The authors showed that ITCH mediates ubiquitination of the membrane (M) and envelope (E) proteins of SARS-CoV-2. Ubiquitination of E and M result in enhanced interactions between the structural proteins and redistribution of the structural proteins into autophagosomes. The authors claim that the enhanced interactions between structural proteins and trafficking of the structural proteins into autophagosomes contribute to SARS-CoV-2 replication and egress, prompting ITCH as a potential antiviral target. ITCH also alters the cellular distribution of host proteases important for spike cleavage which protect and stabilize spike with cleavage. The authors also demonstrated that SARS-CoV-2 replication is augmented by ITCH in which virus replication is significantly impaired in cells lacking ITCH expression.

Strengths:

The authors provided high quality data with appropriate experimental controls to justify their claims and conclusions. The mechanistic analyses are excellent and presented in a logical manner. The investigation of the role of ubiquitination in coronavirus assembly and egress is novel as most previous studies focused on its role in mediating innate immune responses.

Comments on revisions:

The authors have addressed my previous concerns.

Reviewer #1 (Public review):

Summary:

In this remarkable study, the authors use some of their recently-developed oxytocin receptor knockout voles (Oxtr1-/- KOs) to re-examine how oxytocin might influence partner preference. They show that shorter cohabitation times leads to decreased huddling time and partner preference in the KO voles, but with longer periods preference is still established, i.e., the KO animals have a slower rate of forming preference, or are less sensitive to whatever cues or experiences lead to the formation of the pair bond as measured by this assay. This helps relate the authors recent study to the rest of the literature on oxytocin and partner preference in prairie voles. To better understand what might lead to slower partner preference, they quantified changes to the durations and frequency of huddling. In separate assays they also found that Oxtr1-/- KOs interacted more with stranger males than wild-type females. In a partner choice assay they found that wild-type males prefer wild-type females more than Oxtr1-/- KO females. They then performed bulk RNA-Seq profiling of nucleus accumbens of both wild-type and Oxtr1-/- KO males and females, either housed with animals of the same sex or paired with a wild-type of opposite sex. 13 differentially expressed genes were identified, mostly due to downregulation in wild-type females. These genes were also identified in a module lost in the Oxtr1-/- voles by correlated expression profiling. They also compared results of transcriptional profiling in female and male wild-type vs Oxtr1-/- voles (independently of bonding state), and found hundreds of differentially expressed genes in nucleus accumbens, mostly in females and often with some relation to neural development and/or autism. Some of the reduction in transcript was confirmed with in situs, as well as compared to changes in transcription in the lateral septum and paraventricular nucleus (PVN) of the hypothalamus. Finally they find fewer oxytocin+ and AVP+ neurons in the anterior PVN.

Strengths:

This is an important study helping to reveal the effects of oxytocin receptor knockout on behavior and gene expression. The experiments are thorough and reveal a surprising number of genetic and anatomical differences, with some sexual dimorphism as well, and the authors have more carefully examined the behavioral changes after shorter and longer periods of partner preference formation.

Weaknesses:

It is surprising that given all the genetic changes identified by the authors, that the behavioral phenotypes are fairly mild. The extent of gene changes also might be under-reported given the variability in the behavior and relative low number of animals profiled.

Comments on revisions:

No further recommendations. I commend the authors for finding the typos in their first version and correcting the manuscript.

Reviewer #1 (Public review):

The authors investigated the response of worms to the odorant 1-octanol (1-oct) using a combination of microfluidics-based behavioral analysis and whole-network calcium imaging. They hypothesized that 1-oct may be encoded through two simultaneous, opposing afferent pathways: a repulsive pathway driven by ASH, and an attractive pathway driven by AWC. And the ultimate chemotactic outcome is likely determined by the balance between these two pathways.

It is not surprising that 1-octanol is encoded as attractive at low concentrations and repulsive at higher concentrations. However, the novel aspect of this study is the discovery of the combinatorial coding of 1-oct in the periphery, where it serves as both an attractant and a repellent. Furthermore, the study uses this dual encoding as a model to explore the neural basis of sensory-driven behaviors at a whole-network scale in this organism. The basic conclusions of this study are well supported by the behavioral and imaging experiments, though there are certain aspects of the manuscript that would benefit from further clarification.

A key issue is that several previous studies have demonstrated a combinatorial and concentration-dependent coding of odorant sensing in the nematode peripheral nervous system. Specifically, ASH and AWC are the primary receptors for repellent and attractive responses, respectively. However, other neurons such as AWB, AWA, and ADL are also involved in the coding process. These neurons likely communicate with different interneurons to contribute to 1-oct-induced outputs. The authors' conclusion that loss of tax-4 reduces attractive responses and that osm-9 mutants reduce repulsive responses is not entirely convincing. TAX-4 is required for both AWC (an attractive neuron) and AWB (a repulsive neuron), and osm-9 is essential for ASH, ADL, and AWA (attraction-associated). Therefore, the observed effects on the attractive and repulsive responses could be more complex. Additionally, the interpretation of results involving the use of IAA to reduce the contribution of AWC at lower concentrations lacks clarity.

The authors did not observe any increased correlation between motor command interneurons and sensory neurons, which is consistent with the absence of a consistent relationship between state transitions and 1-oct application. Furthermore, they did not observe significant entrainment of AIB activity with the 2.2 mM 1-oct application. This might be due to the animals being anesthetized with 1 mM tetramisole hydrochloride, which could affect neural activity and/or feedback from locomotion.

Comments on revisions:

The authors have addressed all my previously raised concerns.

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:

The Authors test the hypotheses, using and effort-exertion and an effort-based decision-making task, while recording brain dynamics with EEG, that the brain processes reward outcomes for effort differentially when they earned for themselves versus others.

Strengths:

The strengths of this experiment include what appears to be a novel finding of opposite signed effects of effort on the processing of reward outcomes when the recipient is self versus others. Also, the experiment is well-designed, the study seems sufficiently powered, and the data and code are publicly available.

Weaknesses:

There is some concern about the fact that participants report feeling less subjective effort, but also more disliking of tasks when they were earning rewards for others versus self. The concern is that participants worked with less vigor during self-versus-others trials and this may partly account for a key two-way Recipient x Effort interaction on the size of the Reward Positivity EEG component. Of note, participants took longer to complete tasks when working for others. While it is true that, in all cases, participants met the requisite task demands (they pressed the required number of buttons) they did so more sluggishly when earning rewards for others. The Authors argue that this reflects less motivation when working for others, which is a plausible explanation. The Authors also try to rule out this diminished vigor as a confounding explanation by showing that the two way interaction remains even when including reaction times (and also self-reported task liking) as a covariate. Nevertheless, it is possible that covariates do not fully account for the effects of differential motivation levels which would otherwise explain the two-way interaction. As such, I think a caveat is warranted regarding this particular result.

Reviewer #1 (Public review):

Summary:

This is a wonderful and landmark study in the field of human embryo modeling that uses patterned human gastruloids and conducts a functional screen on neural tube closure and identified positive and negative regulators and defines the epistasis among them.

Strengths:

This was achieved following optimization of micro-pattern based gastruloid protocol to achieve high efficiency, and then optimize was to conduct and deliver CRISPRi without disrupting the protocol. This is a technical tour de force as well as one of the first studies to reveal new knowledge on human development through embryo models which has not been done before.

Weaknesses:

A minor one. One can never find out if findings in human embryo models can be in vitro revalidated in humans in vivo for obvious and justified ethical reasons. However, the authors indicate that in the "limitations of study" section.

Comments on revisions:

The authors have adequately addressed all comments raised.

Reviewer #1 (Public review):

Summary:

This study investigates the role of the medial prefrontal cortex (mPFC) in generating goal-directed actions under threat, using a progressive behavioral paradigm, neural recordings, and optogenetic inhibition in mice. The authors demonstrate that while mPFC GABAergic neurons strongly encode cues, actions, and errors, particularly under high cognitive demand, this neural activity is not causally required for executing avoidance behaviors. By rigorously controlling for movement and arousal, the researchers found that much of the observed mPFC signaling actually reflects baseline behavioral states rather than the generation of the actions themselves. This dissociation between encoding and causality challenges traditional views of mPFC as an executive controller of action and provides a nuanced understanding of its role in evaluative and contextual processing.

Strengths:

The behavioral paradigm employed in this study is one of its greatest strengths, offering a rigorous, progressive, and well-controlled framework to dissect the neural mechanisms underlying avoidance under threat. This three-phase task design is particularly well-suited to tease apart the contributions of learning, discrimination, and cognitive load to both behavior and neural activity.

By tracking movement (speed, rotations) and including it as a covariate in statistical models, the authors also underscore the need to control for movement and baseline activity when interpreting cortical signals, which is relevant for all studies of brain-behavior relationships, ensuring that behavioral changes are not due to general arousal or motor activity.

Finally, the study combines multiple advanced techniques-fiber photometry, single-cell calcium imaging (miniscopes), and two distinct optogenetic inhibition methods-to provide a comprehensive look at both neural encoding and causal necessity.

Weaknesses:

The authors conclude that mPFC is not required for avoidance, based on the minimal behavioral effects of optogenetic inhibition. While this interpretation is supported by the data, the choice of viral constructs could lead to an underestimation of the mPFC's role for other reasons. First, the choice of viral constructs could lead to an underestimation of the mPFC's role for several reasons. Specifically, the efficacy of eArch3.0 inhibition was not verified beyond histology, and its non-cell-type-specific nature could lead to disinhibition or compensatory activity in downstream regions. Although the authors' use of visual cortex (VI) inhibition as a control suggests that broad cortical inhibition does not impair avoidance, subcortical compensation cannot be ruled out. Additionally, Vgat-ChR2 targets only GABAergic neurons, potentially missing glutamatergic contributions. Addressing these limitations in the Discussion section would strengthen the manuscript.

Reviewer #1 (Public review):

Summary:

Liao et al. present SCOPE (Spatial reConstruction via Oligonucleotide Proximity Encoding), a method for reconstructing spatial organization from diffusion-defined DNA barcode interactions without the use of optical imaging. In SCOPE, hydrogel beads bearing unique DNA barcodes contain both "sender" and "receiver" oligonucleotides. Upon enzymatic release, sender oligos diffuse locally and hybridize to receiver oligos on neighboring beads, forming chimeric molecules that encode spatial proximity. Sequencing these products yields an interaction matrix, which is then used to reconstruct a spatial coordinate map.<br /> The authors demonstrate reconstruction of synthetic two-dimensional shapes, a large multicolor Snellen eye chart, and the interior surface of three-dimensional molds. The work expands the conceptual and experimental landscape of optics-free spatial sequencing.

Strengths:

SCOPE employs bidirectional sender and receiver oligonucleotides on every bead, rather than using asymmetric transmitter-receiver architectures found in other diffusion-based methods. The symmetric design may improve detection sensitivity and reconstruction strategies, and represents a meaningful variation on optics-free spatial encoding.

A notable strength of this study is the physical scale achieved. The authors reconstruct a Snellen chart spanning approximately 704 mm² and demonstrate molded 3D structures on the order of 75-100 mm³. Although some larger-scale warping is evident, and is discussed as potentially due to non-uniform diffusion, the relative local positioning across these large areas appears impressively accurate.

The authors extend reconstruction beyond two-dimensional arrays to three-dimensional molded surfaces. This demonstrates that the assay and the computational methods for interpreting proximity graphs can support non-planar spatial relationships, expanding the scope of optics-free spatial inference.

Weaknesses:

Although the method is discussed in the context of spatial genomics and potential tissue applications, it is currently demonstrated only on engineered two-dimensional bead arrays and three-dimensional shapes fabricated in molds. It remains unclear how SCOPE would perform in heterogeneous biological environments, where diffusion may exhibit additional non-uniformities. A biological proof-of-concept, even limited in scope, would help define the method's strengths and limitations more clearly.

The reconstruction of three-dimensional structures lacks strong sampling from volume interiors. This is speculated to be due to several possible factors; however, this limitation constrains the method to reconstruction of volume surfaces rather than comprehensive three-dimensional profiling.

The reconstruction workflow involves multiple preprocessing steps and embedding choices. While these appear to work well for synthetic shapes with known geometry, it is less clear how parameter choices would be made in contexts where ground truth is unknown. Clarifying how reconstruction robustness is assessed without prior knowledge of spatial structure would help readers understand how the method could be practically deployed, particularly in more heterogeneous tissue contexts.

Reviewer #1 (Public review):

Summary:

This study demonstrates, through a series of EEG and MEG experiments, that the human brain automatically categorizes words from alphabetic and non-alphabetic languages, and it unpacks the neural mechanisms of this process from multiple angles. The work examines not only univariate repetition-suppression (RS) effects, but also how repeating or alternating languages influences the representational similarity of words within and across language categories.

Strengths:

The univariate RS effects across multiple experiments lend support to some of the main conclusions

Weaknesses:

I have reservations about the logic underlying the multivariate analyses, and I believe the implications of the control experiments merit fuller discussion.

(1) Question 1: Logic of the multivariate analyses

The original text states:

"The processing of intra-language similarity was quantified as correlation distances between neural responses to two words of the same language, which occurred more frequently and would be inhibited in the Rep-Cond (vs. Alt-Cond) due to habituation (Fig. 1c)...".

I argue that this passage conflates two levels. Building a representational dissimilarity matrix (RDM) is a data-analysis step; it cannot be equated with a cognitive computation. Hence, there is no sense in which this computation occurs "more frequently" in one condition. RDM construction rests on the pairwise similarity of activity patterns, so even if a task engaged no cognitive computation of representational similarity, we could still compute an RDM. Conversely, if a task factor alters the RDM, we must explain how that factor changes the underlying neural patterns, not claim that it triggers specific cognitive processing. Therefore, I neither understand what "more frequent processing" the authors refer to, nor accept their account of the multivariate results.

The multivariate result pattern, briefly, is that distances between words, both within and across languages, are larger under the repetition condition. One plausible interpretation is that a word representation comprises two parts: language-type (alphabetic vs. non-alphabetic) and fine-grained identity features (visual shape, orthography, semantics, phonology, etc.). Repetition of language type may, via RS, reduce the weight of the first component, thereby increasing the relative contribution of fine-grained features and amplifying inter-word differences. This could explain the multivariate findings.

(2) Question 2:

For unlearned languages, people cannot distinguish lexical from sub-lexical levels. What, then, determines (i) the RS-effect difference between letters and radicals in familiar languages and words in unlearned ones, and (ii) the similarity of repetition effects between words in unlearned and familiar languages? An explicit account is needed.

Reviewer #1 (Public review):

Summary:

The behaviour of cells expressing constitutively active HRas is examined in mosaic monolayers, both in MCF10a breast epithelial and Beas2b bronchial epithelial cell lines, mimicking the potential initial phase of development of carcinoma. Single HRas-positive cells are excluded from MCF10a but not Beas2b monolayers. Most interestingly, however, when in groups, these cells are not excluded, but rather sharply segregated within a MCF10a monolayer. In contrast, they freely mix with wt Beas2b cells. Biophysical analysis identifies high tension at heterotypic interfaces between HRas and wild-type cells as the likely reason for segregation of MCF10a cells. The hypothesis is supported experimentally, as myosin inhibition abolishes segregation. The probable reason for lack of segregation in the bronchial epithelium is to be found in the different intrinsic properties of these cells, which form a looser tissue with lower basal actomyosin activity. The behaviour of single cells and groups is recapitulated in a vortex model based on the principle of differential interfacial tension, under the condition of high heterotypic interfacial tension.

Strengths:

Despite being long recognized as a crucial event during cancer development, segregation of oncogenic cells has been a largely understudied question. This nice work addresses the mechanics of this phenomenon through a straightforward experimental design, applying the biophysical analytical approaches established in the field of morphogenesis. Comparison between two cell types provides some preliminary clues on the diversity of effects in various cancers.

Weaknesses:

Although not calling into question the main message of this study, there are a few issues that one may want to address:

(1) One may be careful in interpreting the comparison between MCF10a and Beas2b cells as used in this study. The conditions may not necessarily be representative of the actual properties of breast and bronchial epithelia. How much of the epithelial organization is reconstituted under these experimental conditions remains to be established. This is particularly obvious for bronchial cells, which would need quite specific culture conditions to build a proper bronchial layer. In this study, they seemed to be on the verge of a mesenchymal phenotype (large gaps, huge protrusions, cells growing on top of each other, as mentioned in the manuscript).

As an alternative to Beas2b, comparison of MCF10a with another cell line capable of more robust in vitro epithelial organization, but ideally with different adhesive and/or tensile properties, would be highly interesting, as it may narrow down the parameters involved in segregation of oncogenic cells.

(2) While the seminal description of tissue properties based on interfacial tensions (Brodland 2002) is clearly key to interpreting these data, the actual "Differential Interfacial Tension Hypothesis" poses that segregation results from global differences, i.e., juxtaposition of two tissues displaying different intrinsic tensions. On the contrary, the results of the present work support a different scenario, where what counts is the actual difference in tension ALONG the tissue boundary, in other words, that segregation is driven by high HETEROTYPIC interfacial tension. This is an important distinction that should be clarified.

(3) Related: The fact that actomyosin accumulates at the heterotypic interface is key here. It would be quite informative to better document the pattern of this accumulation, which is not clear enough from the images of the current manuscript: Are we talking about the actual interface between mutant and wt cells (membrane/cortex of heterotypic contacts)? Or is it more globally overactivated in the whole cell layer along the border? Some better images and some quantification would help.

(4) In the case of Beas2b cells, mutant cells show higher actin than wt cells, while actin is, on the contrary, lower in mutant MCF10a cells (Figure 2b). Has this been taken into account in the model? It may be in line with the idea that HRas may have a different action on the two cell types, a possibility that would certainly be worth considering and discussing.

Comments on revisions:

There is still one last point that should be made even clearer:

The system is being modelled based on the principle of INTERFACIAL TENSION, a description pioneered by the works of Steinberg and of Harris, and nicely conceptualized by Brodland (2002). Now the observed behaviour is a perfect case of sorting based on higher interfacial tension AT the boundary between cell types (with nice additional documentation of local actin and myosin enrichment in the revised manuscript). What needs to be made crystal clear it that this is NOT equivalent to the model of DITH ("DIFFERENTIAL INTERFACIAL TENSION HYPOTHESIS)" (Brodland 2002, Krieg et al 2008). It is important to stop using DITH in this context, as it leads to confusion and misinterpretations. Indeed, DITH predicts cell/tissue sorting based on differences in interfacial tension WITHIN the two cell types. While DITH accounts for relative POSITIONING (one tissue engulfing the other), it is now established that this is not the motor for cell sorting and tissue segregation, the key parameter is being heterotypic tension at the heterotypic interface. I thus invite the authors to avoid the terms "differential"/DITH, and rather use either "interfacial tension", or specifically to "HIGH HETEROTYPIC INTERFACIAL TENSION".

Related: the authors correctly cite Canty et al NatComm2017 when discussing this phenomenon. I suggest to add an additional key supporting reference "D.M. Sussman, J.M. Schwarz, M.C. Marchetti, M.L. Manning, Soft yet sharp interfaces in a vertex model of confluent tissue, Phys. Rev. Letters 120 (2018) 058001". One may also include another pioneer work in Drosophila is "M. Aliee, J.C. Roper, K.P. Landsberg, C. Pentzold, T.J. Widmann, F. Julicher, C. Dahmann, Physical mechanisms shaping the Drosophila dorsoventral compartment boundary, Curr. Biol. 22 (2012) 967-976."

Reviewer #1 (Public review):

Summary:

The aim of this work is to directly image collagen in tissue using a new MRI method with positive contrast. The work presents a new MRI method that allows very short, powerful radio frequency (RF) pulses and very short switching times between transmission and reception of radio frequency signals.

Strengths:

The experiments with and without removal of 1H hydrogen, which is not firmly bound to collagen, on tissue samples from tendons and bones are very well suited to prove the detection of direct hydrogen signals from collagen. The new method has great potential value in medicine, as it allows for better investigation of ageing processes and many degenerative diseases in which functional tissue is replaced by connective tissue (collagen).

Comments on revisions:

All points of criticism in the reviews were answered very well and led to further improvement of the article.

Reviewer #1 (Public review):

Summary:

In this study, Angla et al investigate the basis of observations made from previous studies where loss of Onecut (OC) transcription factors leads to changes in spinal interneuron populations that do not themselves normally express OC. The authors hypothesize that OC expression in spinal motor neurons has non-cell-autonomous effects on pre-motor interneuron (V1, V2a/b/c) population size and distribution. By knocking out OC in the motor neuron lineage (i.e., downstream of Olig2, a motor neuron progenitor marker gene), they indeed show that motor neuron-specific loss of OC expression decreases V2c interneuron number and alters the spatial distribution of V1, V2a, V2b, and V2c populations. Using bulk RNA-sequencing of WT and OC conditional knockout (cKO) motor neurons, the authors identify that the neurotrophic factor Ntf3 is downregulated by OC expression. They subsequently hypothesize that the non-cell-autonomous effects observed by loss of OC expression in motor neurons can be explained by de-repression of Ntf3. To test this, the authors conditionally knock out Ntf3 downstream of Olig2 and show that this leads to increased interneuron numbers and alters their spatial distribution, ultimately leading to dysregulation of spinal motor circuits and motor activity.

Strengths:

The authors use sophisticated genetic tools to precisely remove OC and Ntf3 expression in a lineage-specific manner and comprehensively assess the downstream effects across brachial, thoracic, lumbar levels of the spinal cord, as well as at two developmental timepoints, E12.5 and E14.5.

Weaknesses:

There are two main concerns that are not fully addressed:

(1) Based on the effects observed with OC vs. Ntf3 cKO, it is unclear whether OC is indeed exerting its non-cell-autonomous effects via Ntf3. Knocking out both Ntf3 and OC and comparing the effects to those seen with just OC cKO alone could provide more insight on this point. Also, a quantitative summary of the effects of Ntf3 overexpression in motor neurons in the chick is lacking.

(2) How the authors assess changes in the spatial distribution of interneurons is unclear. In Figures 2 and 4, the control distributions (despite reporting the same populations in the same regions) look different, suggesting large sample-to-sample variance in distribution. Although the authors report that several sections in each level were taken from at least three animals for each condition, it's unclear how variance within WT or cKO sections was accounted for in the final statistical evaluation. It seems at a glance that a comparison between control samples in Figure 2 and Figure 4 could report statistically significant differences, which would be problematic. A more rigorous report of sample-to-sample variance and a more in-depth explanation of the statistical methods are needed.

Reviewer #1 (Public review):

Summary:

In this manuscript, Zmojdzian et al. provide an analysis of ryanodine receptor (RyR) expression and function in Drosophila. They also use CRISPR to engineer into flies a RyR variant of unknown significance (VUS) found in a human myopathy patient and demonstrate that it is likely a pathogenic mutation. From studies of RyR expression in embryonic and larval stages, and effects of RyR knockdown or overexpression in various muscle groups, the authors show that, in addition to its known actions in calcium-dependent excitation-contraction coupling, RyR promotes myogenesis during development.

The key conclusions of the paper are convincing. I do not have suggestions for necessary additional experimental work, and my comments are minor. One conclusion, that RyR dysfunction may be involved in aging, is stated in multiple places, sometimes speculatively but once very forcefully. The latter is in the final paragraph of the Discussion, which states RyR "plays an instrumental anti-aging role in differentiated striated muscle". This conclusion must be tempered, as even if RyR knockdown phenotypes resemble some of those seen in aging flies, the study does not examine aged flies, and there is no mechanistic analysis that might link the two. I assume the authors would prefer to modify that sentence than initiate work with aging flies to prove the assertion. Finally, the use of CRISPR to test a VUS is excellent and suggests a good way for testing of additional RyR variants in the future.

Significance:

The paper is significant in that RyR is known to be a critical protein in calcium-dependent excitation-contraction coupling but its role in developmental myogenesis is poorly studied. This study demonstrates that it is expressed during, and is important for, embryonic and larval myogenesis in the fly. RyR is also understudied in this valuable model organism, even though a P element-based mutant has been available since 2000. The mechanistic basis for the functional observations is not explored here but the work is well performed and will be of interest to investigators studying muscle development (my own field) and diseases caused by RyR mutations.

Reviewer #1 (Public review):

This is a very interesting paper. The research question is intriguing, allowing the authors to address commonly observed comorbidities between depression and anxiety and their dissociable and opposite relationship to mood fluctuations and sensitivity to reward prediction errors. The computational analyses are very in-depth, including many state-of-the-art checks and validations. Another strength is the inclusion of several large or very large samples, including a patient sample in addition to the general population sample.

I have the following questions:

(1) Factor analysis I found the hierarchical organization of the factors interesting. While this is a very common procedure in, for example, the field of intelligence (producing sub-scores and a general g factor), it is not yet very commonly used in the field of computational psychiatry (though it has been validated before for anxiety/depression, so it is used here with good reason). I was also impressed by the methodological depth. In particular, it was of note how thoroughly done it was (for example, repeating the EFA on the second half of the data set). I have one question though: is the sample size too small for the exploratory analyses, given the number of items? Given the stability across the half-split, I imagine it is not. Perhaps the authors could spell out how many items, what would be the recommended standard for a subject-to-item ratio, and comment on this. A very technical point, the authors should specify how they extracted the factor scores from the other data sets (is it using the Thurstone or Bartlett method)? From experience (though not doing a hierarchical factor analysis), Bartlett can be somewhat better compared to the default (Thurstone) - better as in the resulting factors more closely recapitulating the factor correlations in the original sample (and independence of responses of other participants in a sample for computing a person's factor score). Could you also comment on similarities or divergences in this hierarchical factor analysis approach from another one recently used transdiagnostically in Wise et al. (2026, Translational Psychiatry)?

(2) Linking factors to task parameters As I understand it, the authors relate the orthogonalized depression/anxiety to task parameters (sensitivity to RPEs on mood and mood variations) using correlations. In order to have a better understanding of how this relates to other commonly used approaches, I would pose two questions:

(i) What are the correlations when the full (non-orthogonalized) factor scores for depression and anxiety are used? Are the signs the same? (ii) What are the results when, instead of the independent correlations, the authors perform b_RPE ~ anxiety + depression (again using the non-orthogonalized factors)?

I'm assuming all of these analyses should give the same results if the authors' hypothesis of opposing effects of anxiety and depression holds true.

Minor comments:

(1) The authors should write down when the data were collected for each study. This is because AI capabilities have massively increased since ~2020 in quite specific steps (with the public release of new AI models), meaning that AI is likely to have been able to do tasks and questionnaires without detection if data were collected recently.

(2) The authors should include a statement in the methods section that checks for AI were done. If none yet, could you do any? Recent papers (Westwood, PNAS 2025; van der Stigchel PNAS, 2026) point to the risk since at least the release of o4-mini (used in the cited paper to create very human-like behaviour).

(3) It would have been good to collect questionnaires of other, thought to be unrelated psychiatric traits, like compulsivity or schizophrenia symptoms, to check the specificity of the results, also under the assumption that higher scores on either of these skewed questionnaires can pick up individual differences in 'bad questionnaire completion'. The authors should comment on the absence of other questionnaires in the discussion in the limitations section.

(4) The authors could include a more explicit sentence in the abstract stating that the anxiety result did not hold up in the clinical population.

Reviewer #1 (Public review):

Summary:

This is a study utilizing several types of analyses (computational modeling, neuronal cultures, rodent epilepsy model, and human intracranial multi-scale recordings) to address a highly relevant conceptual question: Are fast ripples (FRs) distinct pathological entities or largely emergent products of stochastic spike clustering? The results can potentially reshape current approaches to incorporating fast ripples into the epilepsy surgery evaluation.

Strengths:

The conceptualization of fast ripples as potentially arising by chance is highly novel and builds effectively on questions raised in prior studies that have never been satisfactorily resolved.

The integration across biological scales and models is a major strength. The state dependency analysis provides additional, strong support. The methodology and statistical approaches used are thoughtfully presented and rigorously applied.

In particular, this paper provides a strong response to the findings from Gliske et al, Nat Commun 2018. This study utilized long-term data analysis to uncover low rates of FRs detected from most recording sites, suggesting spurious detections, although FRs were concentrated within seizure onset areas.

Weaknesses:

The authors clearly aimed to use a statistical rather than a mechanism-based approach in this work. However, the paper's framing of true fast ripples as oscillatory events with stochastic fast ripples considered as confounders does not take prior investigations into biological mechanisms, particularly prior studies that point to an important role for stochastic fast ripples in some contexts. Incorporating recognition of these mechanisms would strengthen the manuscript and provide a more complete and nuanced characterization.

Some examples from the literature:

Eissa et al, eNeuro 2016, a paper that closely parallels this manuscript but took a mechanistic rather than statistical approach, showed that fast ripples can arise from population paroxysmal depolarizations - a key feature of epileptiform discharges - as temporally clustered, jittered population firing, with FRs appearing in LFP or EEG due to summated postsynaptic potentials (which are slower than action potentials and can generate signals in the high gamma range).

Foffani et al., 2007, Neuron, and Ibarz et al., 2010, J Neurosci, argue that FRs are pseudo-oscillations created by jittered neuronal populations in the setting of altered spike timing.

Smith et al., 2020, Sci Rep, contrasts FR characteristics in different regimes, i.e., intact inhibition early in a seizure vs. implied collapse of inhibition after recruitment. Schlingloff et al., 2025, J Neurosci, reported analogous findings in an animal model.

The computational model and subtraction approach provide a strong case for the random emergence of clustered activity in the high gamma band, given its assumptions. However, any such modeling effort needs to account for inhibitory activity, including impaired inhibitory function that is expected in epileptic brain regions, which has a strong modulating effect on excitatory firing and is thought to play a significant role in FR generation.

The shuffling procedure aims to preserve the power spectrum but randomizes high frequency phase (>200 Hz). However, this procedure removes biologically meaningful spike timing correlations, as well as structured cross-frequency coupling. The subtraction method thus likely underestimates the incidence of structured "distinct" FRs, while perhaps overestimating "chance" FRs due to biologically infeasible activity, making the statement that most FRs are due to chance correlation too strong.

The kainate findings underscore this point: the increase in the number of FR detections could be, as the authors state, an increase in chance clustering due to increased network excitability generally. However, the likelihood of a parallel increase in pathological FRs cannot be ruled out, given likely pro-epileptic alterations in spike timing and circuit function.

Reviewer #1 (Public review):

Summary:

This study examines whether gaze direction actively shapes choice during food preference decisions or whether gaze and choice evolve largely independently until the moment of commitment. The established framework in this context, the aDDM, assumes that gaze causally biases the accumulation of evidence in favour of the fixated item. The authors show convincingly that this model fails to fit key behavioural patterns across several datasets, as do other published models that make the same assumption. The authors propose an alternative model (Post-Decision-Gaze or PDG) in which gaze and decision formation are decoupled: gaze does not influence the decision process, nor is it drawn toward the ultimately chosen item, until after the decision threshold is reached. Only during the motor execution period (after commitment) is gaze directed to the chosen option. They demonstrate that this model fits several observed patterns better than the aDDM and related variants.

Strengths:

The work thoroughly considers multiple models and datasets. It advances an interesting alternative perspective on gaze-decision interactions and highlights meaningful shortcomings in existing models. The authors take the time to explain how modelling assumptions produce specific patterns in the data, which is certainly insightful to readers interested in the modelling of value-based decision making.

Weaknesses:

It is unclear to what extent the model's success relies on the way non-decision time is formalised in the model. In the proposed PDG model, non-decision time is decomposed into separate visual encoding, saccadic execution, and manual execution components. Several values (assumed or recovered) do not match known physiological or behavioural ranges. This is a common issue in the literature, and the authors may want to address it in light of broader work discussing what non-decision time consists of in both manual and saccadic actions (e.g., Bompas et al., 2024, Non decision time: the Higgs boson of decision, Psychological Review).

In particular, the "saccadic execution" parameter appears far too long and too variable to reflect merely execution; instead, it likely includes decisional components. This would make more sense since manual and saccadic planning essentially rely on distinct brain areas, hence it seems unrealistic that crossing a single threshold would trigger both manual and saccadic execution. Similarly, recovered manual non-decision times are substantially longer (though not more variable) than expected motor execution durations for button presses. These patterns suggest that parts of what the model treats as non-decision time are likely decisional in nature, although perhaps related to "action decision" rather than the "value-based decision" of interest to the authors. To what extent these two processes neatly follow each other or overlap could be usefully considered.

Reviewer #1 (Public review):

Summary:

This study examines whether gaze direction actively shapes choice during food preference decisions or whether gaze and choice evolve largely independently until the moment of commitment. The established framework in this context, the aDDM, assumes that gaze causally biases the accumulation of evidence in favour of the fixated item. The authors show convincingly that this model fails to fit key behavioural patterns across several datasets, as do other published models that make the same assumption. The authors propose an alternative model (Post-Decision-Gaze or PDG) in which gaze and decision formation are decoupled: gaze does not influence the decision process, nor is it drawn toward the ultimately chosen item, until after the decision threshold is reached. Only during the motor execution period (after commitment) is gaze directed to the chosen option. They demonstrate that this model fits several observed patterns better than the aDDM and related variants.

Strengths:

The work thoroughly considers multiple models and datasets. It advances an interesting alternative perspective on gaze-decision interactions and highlights meaningful shortcomings in existing models. The authors take the time to explain how modelling assumptions produce specific patterns in the data, which is certainly insightful to readers interested in the modelling of value-based decision making.

Weaknesses:

It is unclear to what extent the model's success relies on the way non-decision time is formalised in the model. In the proposed PDG model, non-decision time is decomposed into separate visual encoding, saccadic execution, and manual execution components. Several values (assumed or recovered) do not match known physiological or behavioural ranges. This is a common issue in the literature, and the authors may want to address it in light of broader work discussing what non-decision time consists of in both manual and saccadic actions (e.g., Bompas et al., 2024, Non decision time: the Higgs boson of decision, Psychological Review).

In particular, the "saccadic execution" parameter appears far too long and too variable to reflect merely execution; instead, it likely includes decisional components. This would make more sense since manual and saccadic planning essentially rely on distinct brain areas, hence it seems unrealistic that crossing a single threshold would trigger both manual and saccadic execution. Similarly, recovered manual non-decision times are substantially longer (though not more variable) than expected motor execution durations for button presses. These patterns suggest that parts of what the model treats as non-decision time are likely decisional in nature, although perhaps related to "action decision" rather than the "value-based decision" of interest to the authors. To what extent these two processes neatly follow each other or overlap could be usefully considered.

Reviewer #1 (Public review):

This manuscript investigates how chemogenetic depolarization of medial entorhinal cortex layer II stellate cells reshapes spatial coding in downstream hippocampal CA1. Building on the authors' prior work (Kanter et al., Neuron 2017), the study examines changes in grid cell subfield firing rates and CA1 place cell firing patterns after CNO administration. A central advance of the present work is the use of the same manipulation on two consecutive days. The authors show that the induced grid subfield rate changes are highly similar across days and that CA1 place field reorganization is likewise reproducible across days. In addition, they report that CA1 remapping after CNO is not arbitrary. The new main place field often emerges at a location that can be anticipated from the baseline rate map of the same cell, typically corresponding to a weak secondary peak outside the primary field. Finally, the authors demonstrate that these experimental findings can be recapitulated in a feedforward grid to place cell model by selectively redistributing grid subfield firing rates, supporting the interpretation that grid subfield rate changes are sufficient to drive predictable and reproducible place field reorganization.

Overall, this study is positioned as a follow-up to the authors' previous report in which the main phenomenon (grid subfield rate remapping and accompanying CA1 place cell remapping following chemogenetic depolarization of MEC layer II neurons) was already established. While the conceptual novelty is therefore incremental, the present manuscript adds important and convincing evidence about two key properties of this phenomenon, including its reproducibility across days and the extent to which the direction of place field reorganization is predictable from baseline activity. The experimental approach and analyses appear generally appropriate and carefully executed, and the inclusion of modeling strengthens the mechanistic interpretation. These results provide useful new insight into stable input-output relationships within the entorhinal hippocampal system, and the work will be of interest to researchers studying remapping and the grid to place cell transformation.

DOI: 10.1016/j.cub.2026.03.059

Resource: RRID:ZFIN_ZDB-ALT-080528-1

Curator: @nmaralla

SciCrunch record: RRID:ZFIN_ZDB-ALT-080528-1

What is this?

Reviewer #1 (Public review):

This study by Riegman & George et al. investigates the roles of the chromatin remodeling factor CHD7 and the proneural transcription factor Atoh1 at enhancers in cerebellar granule cells (GCs). Enhancers were categorized based on epigenetic marks and cross-referenced with promoter capture-HiC, ATAC-seq, and expression datasets to identify their long-range target genes, which were found to be enriched for critical neurodevelopmental processes. Differential expression and chromatin accessibility analyses in CHD7 knockout (KO) conditions suggest that this factor regulates a significant number of enhancers. These same enhancers are enriched for proneural transcription factor motifs, with Atoh1 being the most frequently present and likely the most affected. Finally, the direct interaction between CHD7 and Atoh1 was assessed via co-immunoprecipitation in co-transfected cells.

While the paper presents an interesting aspect of enhancer regulation in neurodevelopment, several points warrant attention:

Major Strengths:

The use of chromatin marks increases the resolution of promoter-interacting enhancer regions when integrated with capture-HiC, refining the identification of distal enhancers. Additionally, performing promoter capture-HiC experiments for the first time in this cell type constitutes a valuable resource for the community working on 3D genome organization and neurodevelopment.

Major Weaknesses:

As noted by the authors, limited sequencing depth reduces confidence in the conclusions and may result in missed weaker long-range interactions. Furthermore, the absence of capture-HiC and Atoh1 ChIP-seq experiments in the KO condition prevents direct comparison, thereby limiting the strength of the conclusions.

Additional Consideration:

Caution should be exercised regarding the assumption that every enhancer must physically contact its target promoter. While true for many enhancers, some act in trans through eRNAs or lncRNAs without direct physical contact.

Reviewer #1 (Public review):

Summary:

Overall, this is an interesting paper. The authors identify several experimental knobs that can perturb mechanical wave behavior driven by pili feedback. They frame these effects in terms of nonreciprocal interactions. While nonreciprocity could indeed play a role, it raises the question of whether mechanical feedback might also contribute. Phenomenological models can be useful, but the model currently lack direct mechanistic insight. It would be more compelling to formulate the model around potential mechanochemical feedback, which could help clarify the underlying microscopic mechanisms.

Strengths:

Report of mechanical waves in bacterial collectives, mechanism has potential application in multicellular context such as morphogenesis.

Weaknesses:

A minor concern about the language of 'left-right asymmetry.' I believe the correct term is simply 'radial asymmetry' which is a distinct concept. Left-right is not well defined in the current context.

Reviewer #1 (Public review):

Summary:

Sullivan and colleagues examined the modulation of reflexive visuomotor responses during collaboration between pairs of participants performing a joint reaching movement to a target. In their experiments, the players jointly controlled a cursor that they had to move towards narrow or wide targets. In each experimental block, each participant had a different type of target they had to move the joint cursor to. During the experiment, the authors used lateral perturbation of the cursor to test participants' fast feedback responses to the different target types. The authors suggest participants integrate the target type and related cost of their partner into their own movements, which suggests that visuomotor gains are affected by the partner's task.

Strengths:

The topic of the manuscript is very interesting, and the authors are using well-established methodology to test their hypothesis. They combine experimental studies with optimal control models to further support their work. Overall, the manuscript is very timely and shows important findings - that the feedback responses reflect both our and our partners tasks.

Reviewer #1 (Public review):

Summary:

This study investigates how collective navigation improvements arise in homing pigeons. Building on the Sasaki & Biro (2017) experiment on homing pigeons, the authors use simulations to test seven candidate social learning strategies of varying cognitive complexity, ranging from simple route averaging to potentially cognitively demanding selective propagation of superior routes. They show that only the simplest strategy-equal route averaging-quantitatively matches the experimental data in both route efficiency and social weighting. More complex strategies, while potentially more effective, fail to align with the observed data. The authors also introduce the concept of "effective group size," showing that the chaining design leads to a strong dilution of earlier individuals' contributions. Overall, they conclude that cognitive simplicity rather than cumulative cultural evolution explains collective route improvements in pigeons.

Strengths:

The manuscript provides a compelling argument that a simpler hypothesis is necessary and sufficient to explain the findings of a recent study on improvements to pigeon routes, through a rigorous, systematic comparison of seven alternative hypotheses. The authors should be commended for their willingness to critically re-examine established interpretations. The introduction and discussion are broad and link pigeon navigation to general debates on social learning, wisdom of crowds, and CCE.

Weaknesses:

The authors' method focuses on trajectory-level average behaviour rather than the fine-scale decision-making processes of organisms. This is acknowledged in the manuscript by the authors.

Comments on revision:

The authors have addressed most of the comments by me as well as the other reviewer.

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.]

Original review:

Summary:

This manuscript reports a very interesting, novel and important research angle to add to the now enormous interest in how pesticides can be toxic to beneficial insects like the honey bee. Many studies have reported on how pesticides in standard use formulations show both lethality as well as sublethal negative effects on behavior and reproduction. The authors propose to use machine learning algorithms to identify new volatile compounds that can be tested for repellency. They use as input chemical structures that are derived from chemicals that have known repellent effects as identified in their initial behavioral assays.

Strengths:

The conclusion is that such chemicals specific to repelling bees and not pest insects (using the fruit fly as a model for the latter) can be identified using the ML approach. Have a list of such chemicals that can be rotated among in any field application would be a benefit because of the honey bees' ability to learn its way around any kind of stimulus designed to keep it from nectar and pollen, even when they may be tainted by pesticide.

Weaknesses:

The use of machine learning seems well-executed and legitimate. But this is beyond my expertise. So other reviewers can maybe comment more on that.

The behavioral data report on the use of a two-choice assay for bees in small Petrie plates. Bess can feed from two small wells place of filter paper impregnated with control or the control containing a chemical. The primary behavior, for ex in Fig 2C, is the first choice by one of the five bees in the plate of which well to feed from. For some chemical compound, there seems to be a 50:50 choice, indicating no repellent effects. In other cases the first bee making the choice chose the control, indicating possible repellent effects of the test chemical. Choices in this assay were validated in a free flying assay.

Concerns with the choice assay:

- 50-70 microliters amounts to what one hungry bee will drink. Did the first bee drink most of it, such that measures of bait consumed reflect a single bee or multiple bees?<br /> - How many bees were repelled to the control side? Was it just the one bee? Were other measures considered? E.g. time to first approach; the number of bees feeding at different time points; the total number of bees observed feeding per unit time.

Reviewer #1 (Public review):

Summary:

Goicoechea et al. conducted a timely and thorough meta-analysis on the potential for indirect hippocampal targeted transcranial magnetic stimulation (TMS) to improve episodic memory. The authors included additional factors of interest in their meta-analysis which can be used to inform the next generation of studies using this intervention. Their analysis revealed critical factors for consideration: TMS should be applied pre-encoding, individualized spatial targeting improves efficacy, and improvement of recollection was stronger than recognition.

Strengths:

As mentioned previously, the meta-analysis is timely and summarizes an emerging set of studies (over the past decade since Wang et al., Science 2014). Those outside of the field may not be aware of the robustness in improvements in episodic memory from hippocampal targeted TMS. The authors were quite thorough in including additional factors which are important for the interpretation of these findings. These factors also address the differences in approach across studies. The evidence that individualized spatial targeting improves TMS efficacy is consistent with recent advances in TMS for major depressive disorder. The specificity of the cognitive improvements to recollection of episodic memory and not for other cognitive domains is consistent with hippocampal targeting. The authors also plan to post the complete dataset on an open-source repository which enables additional analysis by other researchers.

Weaknesses:

The write-up is succinct and emphasizes the scientific decisions that underly key differences in the various experimental designs. While the manuscript is written for a scientific audience, the authors are likely aware that findings like this will be of broad appeal to the field of neurology where treatments for memory loss are desperately needed. For this reason, the authors could consider including a statement regarding an interpretation of this meta-analysis from a clinical standpoint. Statements such as 'safe and effective' imply a clinical indication and yet the manuscript does not engage with clinical trials terminology such as blinding, parallel arm versus crossover design, and trial phase. While the authors might prefer not to engage with this terminology, it can be confusing when studies delivering intervention-like five-days of consecutive TMS (e.g., Wang et al., 2014) are clustered with studies that delivered online rhythmic TMS which tests target engagement (e.g., Hermiller et al., 2020). While the 'sessions' variable somewhat addresses the basic-science versus intervention-like approach, adding an explicit statement regarding this in the discussion might help the reader to navigate the broad scope of approaches that are utilized in the meta-analysis.

Following revision: The authors have adequately addressed my concerns.

Reviewer #1 (Public review):

Summary:

This work provides structural and mechanistic insights into the disordered protein recognition process inside the endoplasmic reticulum by the inositol-requiring enzyme 1. Using state-of-the-art molecular dynamics simulation tools, the authors propose a mechanism of disordered protein recognition that reconciles contradictory findings of biochemical and structural biology experiments.

Strengths:

(1) All MD simulations have been carried out in triplicates, and several different folded conformations were generated using alphafold2. This provides adequate statistics to draw meaningful conclusions from the simulations.

(2) Potential limitations of the disordered protein force fields and water models have been taken into consideration. Particularly, performing the simulation in both TIP3P and TIP4PD water models ensures that the conclusions drawn are not influenced by the force field choice.

(3) The binding of a large number of disordered peptides was investigated, ensuring that the conclusions drawn about disordered peptide recognition are sufficiently general.

Weaknesses:

(1) The timescales of the peptide recognition and unbinding process are much longer than what can be sampled from unbiased simulations. Therefore, the proposed mechanism of recognition should only be considered a hypothesis based on the results presented here. For example, peptides that do not dissociate within one microsecond MD simulation are considered to be stable binders. However, they may not have a viable way to bind to the narrow protein cleft in the first place.

(2) Oftentimes, representative structures sampled from MD simulation are used to draw conclusions (e.g., Figure 4 about the role of R161 mutation in binding affinity). This is not appropriate as one unbinding event being observed or not observed in a microsecond-long trajectory does not provide sufficient information about the binding strength of free energy difference.

Comments on revisions:

The authors have adequately addressed my comments. I have no further comments.

Reviewer #1 (Public review):

Summary:

Thach et al. report on the structure and function of trimethylamine N-oxide demethylase (TDM). They identify a novel complex assembly composed of multiple TDM monomers and obtain high-resolution structural information for the catalytic site, including an analysis of its metal composition, which leads them to propose a mechanism for the catalytic reaction.

In addition, the authors describe a novel substrate channel within the TDM complex that connects the N-terminal ZnZn²⁺-dependent TMAO demethylation domain with the C-terminal tetrahydrofolate (THF)-binding domain. This continuous intramolecular tunnel appears highly optimized for shuttling formaldehyde (HCHO), based on its negative electrostatic properties and restricted width. The authors propose that this channel facilitates the safe transfer of HCHO, enabling its efficient conversion to methylenetetrahydrofolate (MTHF) at the C-terminal domain as a microbial detoxification strategy. Experimental data that shows an involvement of TDM in the reaction of HCHO with THF is less convincing.

Strengths:

The authors provide convincing high-resolution cryo-EM structural evidence (up to 2 Å) revealing an intriguing complex composed of two full monomers and two half-domains. They further present evidence for the metal ion bound at the active site and articulate a hypothesis for the catalytic cycle. Substantial effort is devoted to optimizing and characterizing enzyme activity, including detailed kinetic analyses across a range of pH values, temperatures, and substrate concentrations. Furthermore, the authors validate their structural insights through functional analysis of active-site point mutants.

In addition, the authors identify a continuous channel for formaldehyde (HCHO) passage within the structure and support this interpretation through molecular dynamics simulations. These analyses suggest an exciting mechanism of specific, dynamic, and gated channelling of HCHO. This finding is particularly appealing, as it implies the existence of a unique, completely enclosed conduit that may be of broad interest, including potential applications in bioengineering.

Weaknesses:

Although the idea of an enclosed channel for HCHO is compelling, the experimental evidence supporting enzymatic assistance in the reaction of HCHO with THF is less convincing. The linear regression analysis shown in Figure 1C demonstrates a THF concentration-dependent decrease in HCHO; however, it is well established that HCHO and THF can react spontaneously in a non-enzymatic manner, raising the possibility that the observed effect does not require enzymatic involvement. I appreciate the authors' clarification that the data in Figure 1 were not intended to demonstrate enzymatic channelling or catalytic involvement in the HCHO-THF reaction, and that the assay does not distinguish between changes in HCHO production and downstream consumption. However, the statement "these findings show that TDM carries out two linked reactions: TMAO demethylation at one active site, and the HCHO produced can condense with THF at the C-terminal domain, connecting TMAO breakdown to one-carbon metabolism" (page 2) still implies a mechanistic and functional coupling that is not supported by the presented data and appears inconsistent with the authors' clarification. In light of this, I recommend revising this statement to avoid implying mechanistic or functional coupling between the two reactions unless additional experimental evidence is provided.

Overall, the authors were successful in advancing our structural and functional understanding of the TDM complex. They suggest an interesting oligomeric complex composition which should be investigated with additional biophysical techniques.

Additionally, they provide an intriguing hypothesis for a new type of substrate channelling. Additional kinetic experiments focusing on HCHO and THF turnover by enzymatic proximity effects would strengthen this potentially fundamental finding. If this channelling mechanism can be supported by stronger experimental evidence, it would substantially advance our understanding and knowledge of biologic conduits and enable future efforts in the design of artificial cascade catalysis systems with high conversion rate and efficiency, as well as detoxification pathways.

Reviewer #2 (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:

Ito and Toyoizumi present a computational model of context-dependent action selection. They propose a "hippocampus" network that learns sequences based on which the agent chooses actions. The hippocampus network receives both stimulus and context information from an attractor network that learns new contexts based on experience. The model is consistent with a variety of experiments both from the rodent and the human literature such as splitter cells, lap cells, the dependence of sequence expression on behavioral statistics. Moreover, the authors suggest that psychiatric disorders can be interpreted in terms of over/under representation of context information.

My general assessment of the work is unchanged, and I still have some questions requesting methodological clarification

Strengths:

This ambitious work links diverse physiological and behavioral findings into a self-organizing neural network framework. All functional aspects of the network arise from plastic synaptic connections: Sequences, contexts, action selection. The model also nicely links ideas from reinforcement learning to a neuronally interpretable mechanisms, e.g. learning a value function from hippocampal activity.

Reviewer #1 (Public review):

Studies investigating global gene expression changes induced by a single morphine administration have previously been conducted in several rodent brain regions. In this work, the authors focused on the ventral tegmental area (VTA), a key structure of the reward system that has not been extensively characterized in this context. To examine genome-wide transcriptional responses, they employed single-nucleus RNA sequencing (snRNA seq), a method well-suited for profiling gene expression in VTA cells, which are otherwise difficult to isolate.

The effects of morphine on gene expression in VTA cells were assessed in naive animals, in rats exposed to chronic inflammatory pain induced by local CFA injection into the paw, and in animals subjected to both conditions. The study revealed widespread transcriptional changes following morphine administration, whereas inflammation alone produced only limited alterations-an outcome that may reflect the sensitivity or resolution of the sequencing approach used.

Further in vitro experiments conducted in multiple astrocyte models demonstrated that the increase in Fkbp5 expression observed in the VTA is unlikely to result from opioid receptor activation. Instead, the data indicate that this effect is mediated by glucocorticoid receptor stimulation. These findings suggest that the elevated Fkbp5 expression in the rat VTA represents a secondary response rather than a direct consequence of morphine exposure. Comparable transcriptional changes, as well as similar mechanistic interpretations, have been reported in previous studies examining the nucleus accumbens (NAc), reinforcing the view that glucocorticoid-dependent regulation of Fkbp5 may be a broader feature of opioid related neuroadaptations.

The present paper showed largely similar morphine-induced gene changes in both male and female VTA samples. On the other hand, several studies indicate that males and females exhibit differences in dopaminergic activation and distinct gene expression profiles in response to opioids in the reward system. Preclinical studies have found marked sex differences in Fkbp5 expression in the dorsal striatum. This issue should be better addressed both experimentally and theoretically.

DOI: 10.1016/j.isci.2026.115830

Resource: (ZFIN Cat# ZDB-GENO-060207-1,RRID:ZFIN_ZDB-GENO-060207-1)

Curator: @nmaralla

SciCrunch record: RRID:ZFIN_ZDB-GENO-060207-1

What is this?

Joint Public Review:

In this manuscript, the authors proposed an approach to systematically characterise how heterogeneity in a protein signalling network affects its emergent dynamics, with particular emphasis on drug-response signalling dynamics in cancer treatments. They named this approach Meta Dynamic Network (MDN) modelling, as it aims to consider the potential dynamic responses globally, varying both initial conditions (i.e., expression levels) and biophysical parameters (i.e., protein interaction parameters). By characterising the "meta" response of the network, the authors propose that the method can provide insights not only into the possible dynamic behaviours of the system of interest but also into the likelihood and frequency of observing these dynamic behaviours in the natural system.

The authors study the Early Cell Cycle (ECC) network as a proof of concept, focusing on pathways involving PI3K, EGFR, and CDK4/6 with the aim of identifying mechanisms that may underlie resistance to CDK4/6 inhibition in cancer. The biochemical reaction model comprises 50 state variables and 94 kinetic parameters, implemented in SBML and simulated in Matlab. A central component of the study is the generation of large ensembles of model instances, including 100,000 randomly sampled parameter sets intended to represent intra-tumour heterogeneity. On the basis of these simulations, the authors conclude that heterogeneity in kinetic rate parameters plays a stronger role in driving adaptive resistance than variation in baseline protein expression levels, and that resistance emerges as a network-level property rather than from individual components alone. The revised manuscript provides additional clarification regarding aspects of the simulation and filtering procedures and frames the comparison with experimental data as qualitative. Nonetheless, the study is best interpreted as a theoretical and exploratory analysis of the model's behaviour under heterogeneous conditions. Consequently, questions remain regarding the biological grounding of the sampled parameter regimes and the extent to which the reported frequencies of resistance-associated behaviours can be directly interpreted in physiological terms.

While the authors propose a potentially useful computational framework to explore how heterogeneity shapes dynamic responses to drug perturbation, a number of important conceptual and methodological concerns remain to be addressed:

(1) The sampling of kinetic parameters constitutes the backbone of the manuscript, yet important concerns remain regarding its biological grounding and transparency. Although the revised version provides additional clarification on the exploration of "model instances", it is still not sufficiently clear how parameter values and initial conditions are generated, nor how the chosen ranges relate to biological measurements. The kinetic rates are sampled over broad intervals without explicit justification in terms of experimentally measured bounds or inferred distributions. As a consequence, it remains uncertain whether the ensemble of simulated behaviours reflects physiologically plausible cellular regimes or primarily the properties of the assumed parameter space. In this context, the large-scale sampling (100,000 parameter sets) resembles a Monte Carlo exploration of the model rather than a biologically calibrated representation of tumour heterogeneity.

Furthermore, the adequacy of the sampling strategy in such a high-dimensional space (94 free parameters) remains open to question. In the absence of biologically informed constraints, the combinatorial space of possible parameter configurations is vast, and it is unclear to what extent the sampled ensembles can be considered representative. This issue is particularly relevant because the manuscript interprets the frequency of resistance-associated behaviours as indicative of their likelihood.

The validation presented in Figure 7 does not fully resolve these concerns. The comparison with experimental data is qualitative, and the simulations are performed in arbitrary time units, which complicates direct interpretation alongside time-resolved experimental measurements. Moreover, certain qualitative discrepancies between simulated and experimental trends (e.g., persistent versus decreasing CDK4/6 activity) are not thoroughly discussed. As this figure represents the primary empirical reference point in the manuscript, the extent to which the model captures experimentally observed dynamics remains uncertain.

Finally, aspects of presentation continue to limit transparency. Parameter ranges are described at different points in the manuscript but are not consolidated clearly in the Methods, and the definition of initial conditions remains ambiguous - particularly whether these correspond to conserved quantities or to the dynamic variables used to initialise simulations. In addition, the exact number of model instances underlying specific analyses and figures is not always explicit. Greater clarity on these issues is essential for assessing reproducibility and for interpreting the quantitative claims of the study.

(2) A central conclusion of the manuscript is that heterogeneity in protein-protein interaction kinetics is a stronger driver of adaptive resistance than heterogeneity in protein expression levels. To assess the latter, the authors fix a nominal set of kinetic parameters and generate 100,000 random initial concentrations for the 50 model species. However, according to the simulation protocol described in the manuscript, each trajectory includes three phases: (i) simulation under starvation conditions to equilibrium, (ii) mitogenic stimulation to a second ("fed") equilibrium, and (iii) application of drug treatment. The equilibrium concentrations reached in phases (i) and (ii) are determined by the kinetic parameters of the model and are independent of the initial concentrations, provided the system converges to a stable steady state. In dynamical systems terms, stable equilibria are defined by the parameter set and attract all initial conditions within their basin of attraction. Since the kinetic parameters are fixed in this experiment, the pre-treatment equilibrium that serves as the starting point for drug application should likewise be fixed. Under these conditions, it is therefore not unexpected that sampling a large number of initial concentrations has limited influence on the treated dynamics.

This raises conceptual questions about the interpretation of the comparison between kinetic and expression heterogeneity. If the system converges to a unique stable steady state prior to treatment, then variability in initial concentrations does not propagate into variability in drug response, and the observed dominance of kinetic heterogeneity may partly reflect this structural property of the model rather than a biological principle. Clarification is needed regarding whether multiple steady states exist under the nominal parameter set, and if so, how basins of attraction are explored.

More broadly, it remains unclear why initial protein concentrations can be sampled independently of the kinetic parameters. In biological systems, steady-state expression levels are typically determined by the underlying kinetic rates. A more consistent approach might require constraining initial concentrations to correspond to equilibrium states of the chosen parameter set, thereby introducing relationships between at least some of the 50 initial conditions and the 94 kinetic parameters. Finally, the manuscript employs a non-standard terminology regarding "initial conditions," which may further obscure interpretation of these results and would benefit from clarification.

(3) The technical implementation of the modelling and simulation framework remains difficult to evaluate due to insufficient methodological detail. Although the authors state that kinetic parameters are randomly sampled, the manuscript does not specify the distributions from which parameters are drawn, nor whether potential correlations between parameters are considered or explicitly ignored. Without this information, it is not possible to assess how implicit modelling assumptions shape the ensemble of simulated behaviours. Given that the conclusions rely on frequency-based interpretations across sampled parameter sets, greater transparency regarding the sampling procedure is essential.

A further concern relates to the parameter filtering step. The authors report that the "vast majority" of sampled parameter sets produced systems that were "too stiff," and that these were excluded on the grounds that stiff dynamics are not biologically plausible. However, the manuscript does not clearly define how stiffness is assessed, nor why stiffness is interpreted as biologically unrealistic rather than as a numerical property of the formulation. In standard practice, stiff systems are typically handled using appropriate implicit solvers rather than being discarded. Similarly, parameter sets that produce negative state values are excluded, yet such behaviour may arise from numerical artefacts rather than from intrinsic model inconsistency. The rationale for excluding these parameter sets, rather than adapting the numerical scheme, is not sufficiently justified.

The reported rejection rate - approximately 90% of sampled parameter sets - is substantial and raises questions regarding the interplay between model structure, parameter ranges, and numerical methods. As currently described, the filtering step appears to select parameter sets based primarily on computational tractability rather than on experimentally motivated biological criteria. The manuscript would be strengthened by clarifying whether the retained parameter sets are representative of biologically meaningful regimes, and by distinguishing clearly between exclusions based on biological plausibility and those arising from numerical considerations.

Finally, important aspects of the simulation protocol require clarification. The model is simulated under "fasted" and "fed" conditions until equilibrium is reached, yet the criterion used to determine convergence is not specified. It would be important to describe how equilibrium is assessed (e.g., based on the norm of the time derivatives). Additionally, it remains unclear whether the mitogenic stimulus applied in the "fed" phase is assumed to be constant over time and, if so, how this assumption relates to biological experimental conditions. Greater detail on these implementation choices is necessary to ensure interpretability and reproducibility.

(4) The manuscript states that the modelling conclusions are strongly supported by existing literature; however, the validation presented does not fully substantiate this claim. As noted above, the comparison with CDK2 and CDK4/6 experimental data remains qualitative, and the use of arbitrary simulation time units complicates interpretation of temporal agreement. The extent to which the model quantitatively or mechanistically recapitulates experimentally observed dynamics therefore remains uncertain.

The claim that the model reproduces known resistance mechanisms is also difficult to assess in light of Figure S10, where a large fraction of network nodes (~80%) appear implicated in resistance under some conditions. If most components of the network can, in at least some parameter regimes, be associated with resistance phenotypes, the resulting lack of selectivity weakens the strength of model-based validation. It becomes challenging to distinguish specific mechanistic insights from generic consequences of network connectivity.<br /> In addition, the Supplementary Information notes that certain components of the mitogenic and cell-cycle pathways were abstracted or excluded in order to maintain computational tractability. While such abstraction is understandable in a large ODE framework, it raises interpretative questions. Proteins identified as potential resistance drivers within the model may, in some cases, represent aggregated or simplified pathway effects. Clarifying in the main text how such abstractions may influence the attribution of resistance mechanisms would strengthen the biological interpretation of the results.

Drug inhibition is central to the manuscript's conclusions. The revised version clarifies that inhibition is implemented as a fixed fractional modification of specific kinetic rate laws. This abstraction is appropriate for exploring network-level responses, but it represents a stylised perturbation rather than a pharmacologically calibrated model of drug action. For full interpretability and reproducibility, the mathematical form of the modified rate laws, as well as the timing of inhibition relative to network equilibration, should be specified unambiguously. The biological implications of the findings depend critically on understanding this modelling choice.

The one-at-a-time perturbation analysis presented in Figure 5 provides an interpretable ranking of first-order control points across the ensemble and offers mechanistic insight into primary sensitivities of the network. However, many targeted therapies act on multiple components, and resistance frequently arises through combinatorial mechanisms. The reported rankings should therefore be interpreted as identifying primary influences under isolated perturbations, rather than as a comprehensive account of multi-target drug behaviour.

Overall, the manuscript succeeds in presenting a conceptual and exploratory framework for analysing how signalling network topology can shape the qualitative landscape of adaptive responses under heterogeneous kinetic conditions. Its principal contribution lies in establishing a systematic platform for large-scale in silico exploration. At the same time, the current limitations in biological calibration, parameter grounding, and validation constrain the extent to which the conclusions can be interpreted as predictive or quantitatively representative of specific tumour contexts. Addressing these issues would further strengthen the connection between the theoretical landscape described here and experimentally observed resistance dynamics.

Reviewer #1 (Public review):

[Editors' note: The Reviewing Editor has assessed the work without involving the previous reviewers, updating the eLife Assessment accordingly. The authors did an excellent job of addressing the reviewers' comments and suggestions. The manuscript is now in line with the minor suggestions from the original reviewers, who were already enthusiastic about the first version.]

Summary:

This manuscript by Xiong and colleagues presents a compelling validation of UniDesign, a fully computational protein design framework, by using it to engineer a novel, PAM-relaxed variant of Staphylococcus aureus Cas9 (SaCas9) named KRH. The core achievement is the successful de novo generation of a high-performance nuclease (E782K/N968R/R1015H) solely through in silico modeling, without any subsequent experimental optimization or directed evolution. The authors demonstrate that KRH expands the SaCas9 PAM specificity from NNGRRT to NNNRRT, achieving genome editing and base editing efficiencies across multiple human cell types that are comparable to, and sometimes exceed, the well-known evolution-derived KKH variant. The work positions UniDesign not merely as an analytical tool, but as a powerful engine for the generative design of complex molecular functions, offering a scalable and mechanistically insightful alternative to traditional experimental screening.

Strengths:

This is an outstanding manuscript that serves as a powerful proof-of-concept for the next generation of computational protein design. The primary selling point-the raw predictive and generative power of UniDesign-is convincingly demonstrated throughout.

The manuscript shows that the tool can:

(1) successfully navigate a complex sequence landscape to identify a minimal set of three mutations (KRH) that remodel a critical protein-DNA interface;

(2) accurately model and balance the delicate interplay between specific base contacts and non-specific backbone interactions to achieve relaxed PAM specificity;

(3) deliver a final product whose performance is indistinguishable from, and in some cases superior to, a variant that required extensive wet-lab evolution.

The experimental validation is rigorous, thorough, and directly supports the computational predictions. This work will stand as a landmark study for the field, illustrating that computational design has matured to the point where it can reliably generate sophisticated tools for genome engineering.

(1) Demonstration of Generative Power:

The most significant finding is that UniDesign, without any experimental feedback, generated a variant (KRH) that matches the performance of the evolution-derived KKH. This is a remarkable achievement. The iterative design strategy-first reducing PAM bias (R1015H), then restoring binding through non-specific interactions (e.g., N968R, E782K)-is a textbook example of rational design, but it is executed entirely by the algorithm. This validates UniDesign's energy function and search algorithm as capable of capturing the subtle biophysical principles governing PAM recognition.

(2) Mechanistic Insight as a Built-in Feature:

A key advantage of UniDesign highlighted by this work is its inherent ability to provide mechanistic explanations. The computational models not only predicted which mutations would work (e.g., N968R over N968K in the KRH variant) but also why they work. The structural and energetic analyses showing the bidentate salt bridge formed by Arg968 versus the single bond formed by Lys968 (Figure 4A) is a perfect example of how the tool's output can rationalize functional differences, a level of insight that is rarely attainable from directed evolution campaigns alone.

(3) Scalability and Accessibility for Engineering:

The authors explicitly contrast UniDesign's efficiency (minutes to hours per design run) with the computational expense of methods like COMET and the experimental overhead of directed evolution. The improvements to UniDesign v1.2, specifically the mutation-count and sequence-uniqueness penalties, directly address a key challenge in computational design (generating diverse, low-energy point-mutant libraries). This positions the tool as a highly accessible and scalable platform for engineering other CRISPR systems, a point that will be of immense interest to the community.

Reviewer #1 (Public review):

Summary:

The authors build a network model of the olfactory bulb and the piriform cortex and use it to run simulations and test their hypotheses. Given the the model's settings, the authors observe drift across days in the responses to the same odors of both the mitral/tufted cells, as well as of piriform cortex neurons. When representing the M/T and PCx responses within a lower dimensional space, the apparent drift is more prominent in the PCx, while the M/T responses appear in comparison more stable. The authors further note that introducing spike-time dependent plasticity (STDP) at bulb synapses involving abGCs slows down the drift in the PCx representations, and further link this to the observation that repeated exposure to the same odorant slows down drift in the piriform cortex.

The model is clearly explained and relies on several assumptions and observations: 1) random projections of MTC from the olfactory bulb to the piriform cortex, random intra-piriform connectivity and random piriform to bulb connectivity; 3) higher dimensionality of piriform cortex representations compared to M/T responses which enables superior decoding of odor identity in the piriform cortex; 2) spike time dependent plasticity (STDP) at synapses involving the abGCs.

The authors address an open topical problem and model is elegant in its simplicity. The authors addressed many of my concerns by plotting new analyses and by adding clarifying statements and discussion points, as well as testable predictions to the revised manuscript. In the revised manuscript, a few points remain unclear and I am listing them below for further potential discussion.

(1) Given the large in response (variability) across trials reported by Shani-Narkiss, Kay & Laurent - the question remains open: what fraction of the variability in response across days can be really accounted by adult born neurogenesis (the main topic of this study) vs. other mechanisms. I think the answer to this question is key for interpreting the results presented by the authors on the impact of adult neurogenesis on changes of mitral cell responses. Unfortunately, I could not find the answer in the revised version of the manuscript.

(2) Yamada indeed reported a "drastic reorganization of ensemble odor representation" in their manuscript (Figure 3D), but my understanding is that this was observed in the context of passive exposure to the same odor across several days in a row. This does not appear to contradict the findings of Kato et al., 2012 that when an odor is presented seldom, across days the mitral cell responses are stable. Also, data from Yamada et al. appears to show some degree of overall sparsening of odor responses in mitral cells at least at the level of a decrease in response amplitude between day 1 to day 7 of repeated passive exposure (Figure 3A, Yamada et al., 2017).

(3) There was mistake on my part on one of the papers referenced with respect to random vs. structured projections from the olfactory bulb to the piriform cortex. The one I was referring to is Chen et al., Cell, 2022 (not Chae et al., Neuron, 2022). The authors discussed the implications from the latter, while I was commenting in fact on the findings from Chen et al., 2022. This study identified structured projections of individual mitral cells along the A-P axis of the piriform cortex in conjunction with collaterals to specific subsets of extra-piriform target regions.

Reviewer #1 (Public review):

This work develops a simple, rapid, low-cost methodology for assembling combinatorially complete microbial consortia using basic laboratory equipment. The motivation behind this work is to make the study of microbial community interactions more accessible to laboratories that lack specialized equipment such as robotic liquid handlers or microfluidic devices. The method was tested on a library of Pseudomonas aeruginosa strains to demonstrate its practicality and effectiveness. It provided a means to explore the complex functional interactions within microbial communities and identify optimal consortia for specific functions, such as biomass production.

The primary strength of this manuscript lies in its accessibility and practicality. The method proposed by the authors allows any laboratory with standard equipment, such as multichannel pipettes and 96-well plates, to readily construct all possible combinations of microbial consortia from a given set of species. This greatly enhances access to full factorial designs, which were previously limited to labs with advanced technology.

Another strength of the manuscript is the measurement and analysis of the biomass of all possible combinations of 8 strains of P. aeruginosa. This analysis provides a concrete example of how the authors' new methodology can be used to identify the best-performing communities and map pairwise and higher-order functional interactions.

Notably, the authors do exceptionally well in providing a thorough description of the methodology, including detailed protocols and an R script for customizing the method to different experimental needs. This enhances the reproducibility and adaptability of the methodology, making it a valuable resource for researchers wishing to adopt this methodology.

Comments on revisions:

I thank the authors for their response. The revisions have addressed all of the issues raised in my original review, and I believe they have improved the clarity of the manuscript.

Reviewer #1 (Public review):

The manuscript analyzes previously published MEG and ECoG datasets to examine pre-onset neural encoding effects during language processing, replicating effects that have been reported in earlier work and demonstrating that they persist even after controlling for correlations in the stimulus sequence. Replication of these effects across recording modalities and datasets is a valuable contribution, as it strengthens confidence in the robustness of anticipatory neural activity related to upcoming linguistic input. However, I have significant concerns regarding the interpretation of these findings, particularly the conclusion that the absence of temporal generalization between pre- and post-onset activity implies that pre-onset activity does not reflect predictive pre-activation of the upcoming word.

The central inferential step in this argument relies on an implicit assumption: that if the brain were predicting an upcoming word, the neural representation prior to word onset should resemble, or generalize to, the representation observed after word onset. This assumption is not theoretically necessary and is not supported by a substantial body of work on predictive processing. Many contemporary models posit that predictions are represented in abstract, compressed, or probabilistic formats that differ from sensory-evoked representations, particularly in hierarchical systems such as language (e.g., Rao & Ballard, 1999; Friston, 2005; Federmeier, 2007; Kuperberg & Jaeger, 2016; de Lange et al., 2018). Under such accounts, predictive representations may encode expectations over latent semantic features or probability distributions rather than reinstating the neural code associated with perceptual input.

In this context, the temporal generalization analyses presented here convincingly demonstrate that pre-onset and post-onset activity do not share a stable representational code. However, this result does not rule out predictive processing per se. Rather, it rules out a specific and relatively strong hypothesis: that prediction takes the form of early reinstatement of the same neural representation used during post-onset word processing. The data are equally consistent with the interpretation that pre-onset activity reflects predictive information expressed in a different representational format that is transformed upon stimulus onset.

I therefore recommend that the authors substantially soften and clarify their conclusions regarding prediction. Statements suggesting that pre-onset activity does not reflect prediction should be revised to more precisely reflect what is directly supported by the analyses, namely, the absence of representational identity or stable overlap between pre- and post-onset activity. Explicit acknowledgement of alternative interpretations grounded in established predictive processing frameworks would improve theoretical alignment and avoid overstating the implications of the temporal generalization results.

Overall, the empirical analyses are carefully executed, and the replication across datasets is a strength. However, the current framing risks over-interpreting what the data can rule out about prediction. A clearer distinction between representational equivalence and predictive processing would significantly strengthen the manuscript's theoretical contribution.

Reviewer #1 (Public review):

Summary:

This study investigates the potential for the immune mediator, lipoxin A4 (LXA4), to alleviate inflammation/damage caused by the healthcare-associated pathogen, Clostridioides difficile. Using both a novel in vitro "gut-on-a-chip" system and a murine model of disease, the authors demonstrate potential disease attenuation by LXA4. Specifically, LXA4 at select administration times during development of C. difficile infection (CDI) may upregulate markers associated with intestinal barrier integrity (ZO-1) and attenuate immune markers typically associated with inflammation (IL-8, IFN-γ, etc.). Overall strengths of the study include the establishment of a novel in vitro model that incorporates anaerobic and aerobic environmental conditions of the gut, as well as some results suggesting a potential role for LXA4 in modulating CDI. However, critical weaknesses of the manuscript, including incomplete methods and a lack of some critical controls or measurements, lead to only partial support for the authors' conclusions. Collectively, the data suggest alternate potential (and perhaps more likely) mechanisms by which LXA4 might modulate CDI. Specific strengths and weaknesses are listed below.

Strengths:

(1) A major strength of the study is the use and description of the gastight, gut-on-a-chip system that allows for co-culture of host cells (with aerobic needs) with anaerobic bacteria. While perhaps this (and other in vitro) system does not exactly "more accurately recapitulate specific host-microbe interactions (line 82)", integration of oxic and anoxic conditions that recapitulate the gut is indeed difficult to incorporate in vitro. Results surrounding C. difficile and Caco-2 cell viability in the described system seem substantiated.

(2) Assessing LXA4 in both an in vitro and in vivo (mouse) model is a complementary strategy. Results from both experiments seem to support the observation that LXA4 can possibly attenuate C. difficile.

(3) Overall, the manuscript is well-written and straightforward (albeit lacking in some details-see below).

Weaknesses:

(1) A major weakness of the manuscript in its current state is that the methods are incomplete or unclear. Details on how C. difficile was handled (strain info, preparation in experiments, quantification) are lacking. Mouse model information (inoculation, housing, number of animals) is missing, particularly for the second set of mouse experiments, which is not described at all in the methods. An IACUC or similar statement is not included.

a) For in vitro experiments, how exactly were C. difficile quantified using flow cytometry? This is not exactly clear in the methods or the results, where C. difficile counts are referred to as 'normalized' without specific units (Figure 1D). What are these counts normalized to? How much of the total effluent was measured? This might also explain the discrepancy in C. difficile counts, referred to below.

b) How exactly were C. difficile quantified for the mouse studies? The authors state that fecal samples were plated on CCFA agar, but the y-axis merely states "numbers of bacteria". Other bacteria grow on CCFA. How were C difficile specifically enumerated?

c) Figure 4. For the vancomycin / LXA4 experiments, were mice subjected to antibiotics to render them initially susceptible to C. difficile? If so, this should be included in experimental timelines. If not, how do the investigators know that mice were colonized with C. difficile in each instance (usually mice require abx perturbation for susceptibility)? How was vancomycin administered to mice? In any case, C. difficile loads should be quantified for all conditions in these experiments.

d) Related to the above (Figure 4 experiments), were all of these measurements taken only 24 hours post-infection? These experiments are not described well in the results and are not described at all in the methods.

e) How many total mice were included in the study groups, and how were they housed? Cage effects can influence any mouse study, but are especially important in CDI studies, given the importance of the microbiome in the development of CDI.

f) How were mice inoculated with C. difficile? Was this a spore or vegetative inoculum, and how? The state inoculum of 1x10^-9 is quite large.

g) What is the history/ribotype of the C. difficile strain (1482?) used in all the experiments? How does this compare to other commonly used strains of C. difficile? Different strains demonstrate overall virulence, disease dynamics, and disease severity in animal and in vitro models.

(2) Related to some methodological clarifications, there are some missing controls that would bolster support for final interpretations and some odd discrepancies in the study that are not explained.

a) Figure 1C: How does the mucin layer (i.e., Caco-2 cell differentiation) look under anoxic conditions? This measurement was only included in the oxic conditions.

b) In initial C. difficile quantification within the system (Figure 1D), C. difficile counts seem to range from 3 - 12 (undefined units). In the C. difficile / LXA4 experiments, these counts only reach ~1.8 (undefined units) in the CDI group. What explains this large discrepancy? Furthermore, the prophylactic LXA4 group seems to hover around < 0.5, similar to what is seen at 0 or 3 hours with C. difficile alone. This suggests that C. difficile might not proliferate at all in the presence of LXA4, perhaps explaining why epithelial barrier functions and immune attenuation are improved.

c) Figure 2B. What do untreated controls (no CDI, but with or without LX4A) look like compared to the experimental groups? These controls should be included with the main Figure 2 results.

d) If all metrics in Figure 4 were measured only 24 hours after infection, this is a VERY short timeline for CDI. Depending on the strain, damage might not even be quantifiable by this time point. For instance, C. difficile 630 disease signs only appear 2-4 days post-infection. C. difficile VPI kills mice within 36 hours, but Figure 3 results suggest that mice survive just fine. What is known about this strain's disease dynamics in mice? Alternatively, is it possible that LXA4 alone increases barrier integrity / attenuates inflammation? The inclusion of non-CDI controls (with or without abx; untreated; etc) might help distinguish this.

(3) Perhaps the largest weakness of the manuscript is the interpretation of how LXA4 might attenuate CDI, which is also misleading as a title. The authors purport that disease attenuation is via LXA4, increasing barrier integrity and attenuating inflammation. However, much of the evidence suggests that LXA4 might limit C. difficile colonization. If there is less C. difficile (thus less toxin) in any system, all aspects of the disease will be attenuated. Indeed, their data suggest that there are decreasing amounts of C. difficile in the presence of LXA4, which could be due to direct inhibition of C. difficile or its toxin, removing nutrients necessary for C. difficile growth, or indirect effects on microbes in the gut environment (in mice). Proper quantification of C. difficile, toxin measurements, and dose responses would better distinguish which mechanism is more likely.

a) The initial LXA4 experiments assessing potential therapeutic effects (mainly Figure 2) were conducted at 6 hours post-infection. What is the C. difficile load and/or toxin burden at this time? In some ways, LXA4 administration at this time point could also be thought of as 'prophylactic', given that damage (and maybe C. difficile virulence?) has not occurred yet.

b) Is it possible that LX4A administration prior to C. difficile inoculation influences C. difficile physiology (colonization; toxin production), rather than alleviating C. difficile damage? C. difficile colonization should be quantified in all the LX4A experiments (only a subset is shown in Figure 2).

c) Line 213 / Figure 2G. While it is possible that "LXA4 reprograms the intestinal epithelial transcriptome to bolster barrier function and temper immune signaling", the decreased C. difficile measurements in the presence of LXA4 suggest it impacts C. difficile colonization / function. This decreased level of C. difficile (and thus less toxin) could also explain immune response attenuation. Toxin measurements, as well as some C. difficile dose responses within the system, could help distinguish which possibility is more likely.

d) Both in vitro and in vivo experimental results suggest a prophylactic role for LXA4 in CDI. However, the current experiments cannot distinguish whether this prophylactic response is due to host-specific anti-inflammatory attenuation (which the authors suggest) or due to an impact on C. difficile colonization/function (which is not acknowledged). The effect of LXA4 on C. difficile could be via direct inhibition of C. difficile growth or host remodeling that modulates C. difficile colonization or metabolism.

e) Figure 4. While the data seem to support some preservation of barrier function and attenuation of inflammatory responses, this could once again be due to delaying, decreasing, or inhibiting C. difficile colonization itself, rather than attenuation by LXA4. Indeed, vancomycin-induced improvements within this short amount of time are likely due to inhibiting C. difficile, as it is an antibiotic used to directly kill C. difficile.

(4) Other comments:

a) Given that the current results cannot preclude alternate, if not more likely, explanations for how LXA4 might attenuate CDI, the manuscript should include a more comprehensive discussion. This could include study caveats, C. difficile-specific context about infection (i.e., infection dynamics, context with other experiments).

b) Dysbiosis: undefined definition, as this is context-dependent. For CDI, what does this mean?

c) Unclear if in vitro intestinal models "more accurately recapitulate specific host-microbe interactions", even considering caveats of animal models. Rather, each model has their own purpose; I would be careful about this phrasing (line 82).

d) Line 86: not just "thrives under strict anaerobic conditions", but is necessary for growth. C. difficile is an obligate anaerobe.

Reviewer #1 (Public review):

This paper presents a reanalysis of a large existing dataset to examine whether serial dependence effects-systematic influences of recent stimulus history on current perceptual judgments-are associated with generalization in perceptual learning. The central hypothesis is that extended, longer-range history effects (beyond the most recent trials) are beneficial for transfer across locations. The authors reanalyze data from a texture discrimination task in which observers discriminated peripheral target orientation against a line background, with performance quantified by stimulus-onset asynchrony thresholds. Three training conditions were compared: a fixed single-location condition, a two-location alternating condition, and a dummy-trial condition with frequent target-absent trials. Transfer was assessed after training at new locations. Serial dependence was quantified using history-sequence analyses and linear mixed-effects models estimating bias weights across stimulus lags, with summary measures distinguishing recent (1-3 trials back) and more distant (4-6 trials back) dependencies.

The authors report extended serial dependence effects, persisting up to 6-10 trials back, with substantial cumulative bias that remains stable across multiple days of training and is not correlated with overall performance thresholds. Recent history effects are stronger for faster responses, suggesting a contribution from decision- or response-related processes, whereas more distant effects decline within sessions, potentially reflecting adaptation dynamics. Critically, longer-range serial dependence is significantly stronger in training conditions that promote generalization than in the single-location condition. Individual differences in the strength and decay profile of distant history effects predict the magnitude of transfer across locations, whereas recent history effects do not. History effects are also correlated across trained locations, suggesting stable individual differences.

The authors interpret longer-range serial dependence as reflecting integrative processes that extract task-relevant structure over time, thereby supporting generalization, while shorter-range effects are attributed to more transient mechanisms such as priming or decision-level bias. The discussion connects these findings to Bayesian accounts of perceptual stability and to concepts of overfitting in machine learning.

The study offers a novel and thoughtful link between short-term serial dependence and long-term generalization in perceptual learning, helping bridge two literatures that are often treated separately. The large dataset enables robust estimation of individual differences, and the use of mixed-effects modeling appropriately accounts for variability across observers. The empirical distinction between recent and more distant history effects is well-supported and adds important nuance to interpretations of serial dependence. Converging evidence from both group-level comparisons and individual-level correlations strengthens the central conclusions.

Comments on revisions:

The authors have effectively addressed my concerns. The new robustness analyses (Supp. Fig. S3), supplementary toy model, clearer DDM-based mechanistic distinctions, and expanded discussion of limitations and generality fully resolve my original points.

Reviewer #1 (Public review):

Summary:

Here, Pinto and colleagues set out to investigate whether the cow udder is a potential mixing site for the influenza virus. The authors have demonstrated that bovine mammary epithelial cells can be infected with both avian and human influenza A viruses, supporting the idea that the cow udder may be a potential site for reassortment. Furthermore, they demonstrate that the bovine-adapted IAV replicates to similar titers in avian epithelial cells when compared to an AIV precursor virus. Thus, suggesting there is no fitness trade-off, and confirms the potential for spill-back of the cattle B3.13 into poultry, which has already been observed. Overall, I believe the authors achieved their aims. However, there are instances in which the results do not entirely support the conclusions (noted in weaknesses). Given the ongoing questions surrounding highly pathogenic avian influenza A virus in dairy cows, this work provides valuable evidence for the potential of the cow udder as a site of reassortment. These findings highlight the need for surveillance of influenza A virus incursions into livestock species, particularly cows. Some specific strengths and questions regarding weaknesses have been outlined below.

Strengths:

(1) The authors use a diverse range of cell types and influenza A virus strains, as well as a wide range of techniques to address the questions at hand.

(2) The use of cells from multiple bovine breeds for the MAC-T, bMEC and explants suggests the phenomenon is not unique to a single breed.

(3) The results suggesting there is no fitness trade-off for Cattle Texas in an avian host are interesting, and confirm the potential for spill-back of the cattle B3.13 into poultry, which has been observed.

Weaknesses:

I have listed my complete questions/concerns below. However, there are two main weaknesses of the article in its current state. Firstly, there is no apples-to-apples comparison in terms of determining a preference for IAV to infect the cow udder over other organs (Q4). The mammary gland and respiratory tract are represented by epithelial cells, but for other organs, fibroblasts were chosen. I think the fairer comparison would be to compare epithelial cells from different organs to demonstrate a preference for the mammary gland. Secondly, the main premise of the article relies on bMEC and MAC-T (primary and immortalised mammary epithelial cells), facilitating higher viral growth than the cells from other organs. Yet throughout the article, a 10x higher dose of IAV is used in the bMEC cells compared to everything else (Q6). This raises the question of how much of the results are due to a preference for the mammary epithelial cells, and how much is simply due to the increased dose.

Reviewer #1 (Public review):

Summary:

The authors employ state-of-the-art single-cell sequencing technologies to map the gene expression profiles of the developing digestive tract in the ascidian Styela clava, a member of the invertebrate sister group to vertebrates. This data has the potential to provide a new perspective on the relationships between the guts of an invertebrate like this ascidian relative to vertebrate systems. Key findings include the elaboration of our understanding that the Styela gut arises from two distinct cellular origins, with this being comparable to the dual embryogenic origin of vertebrate guts (at least, as exemplified by the mouse digestive tract arising from both definitive and visceral endoderm).

Strengths:

The resolution that can be achieved from the series of developmental stages analysed by the authors through the metamorphosis and early gut specification and development is vital to the strength of this new dataset. This new scRNAseq data is likely to provide a useful foundation for future work that delves into the functions of various genes within regions of the ascidian gut.

Weaknesses:

The main weakness of the manuscript as it currently stands is the lack of clarity about the genetic comparisons between ascidian and mouse, and what the precise genetic underpinnings are for any statements of similarity.

Reviewer #1 (Public review):

Summary:

In their manuscript, Andriani et al. show intracellular zinc is exported from sperm during capacitation and suppresses the alkalinization-induced hyperpolarization in sperm. Intracellular zinc inhibits Slo3 current, which is enhanced by the co-expression of gamma subunit Lrrc52. Computational studies reveal that the Zn binding site on mSlo3 is located near E169 and E205, which are involved in the sustained zinc inhibition of mSlo3 current. The authors propose that intracellular zinc play a key role of sperm capacitation by inhibiting the Slo3 channel.

Strengths:

Overall, the work appears well designed (e.g., oocyte patch-clamp experiments), and clearly presented. Three-dimensional structural modeling and flooding simulations are executed.

Weaknesses:

The simple mutagenesis analysis of E169 and E205 showed partial abolishment, but the molecular mechanism by which zinc inhibits Slo3 current is not yet fully shown. The authors should consider performing more extensive experiments, such as creating double mutants or combination mutants involving other residues. Additionally, could other mechanisms explain the role of zinc in regulating the Slo3 current?

While elucidating the mechanism of Slo3 is interesting, there is substantial literature indicating how zinc regulates channel functions at a molecular level. Given this, the manuscript should provide a deeper understanding by clearly elucidating the molecular mechanism of the regulation of Slo3 current by zinc.

The manuscript includes no experimental data on the mechanism of intracellular zinc export during sperm capacitation, despite being crucial for the regulation of sperm function.

Reviewer #1 (Public review):

Summary:

The authors presented a simplified E. coli cell-free protein synthesis (eCFPS) system reduces core reaction components from 35 to 7, improving protein expression levels. They also presented a "fast lysate" protocol that simplifies extract preparation, enhancing accessibility and robustness for diverse applications.

Strengths:

The authors present a valuable new protocol for eCFPS, which simplifies its application.

Weaknesses:

The authors provide data for optimization but offer insufficient explanation of the fundamental mechanisms underlying the phenomenon.

Comments on revisions:

The authors have adequately addressed the concerns raised by the reviewers. However, the data added by the authors on this revision raised new concerns.

On page 17, lines 358-363, and Figure 3G, the authors compared the nLuc production of mRNA-based and DNA-based reactions using initial and optimized lysates.

The authors concluded that the optimized system showed significant enhanced transcription, which compensated for the decrease in translational efficiency. If this interpretation is correct, the low yield of the initial system is simply due to the insufficient level of effective T7 RNA polymerase in the initial lysate. Supplementing the initial lysate with sufficient T7 RNA polymerase could potentially make it outperform the optimized system, and the optimized system is not so much superior to the initial system in the protein production performance. This could be easily verified by quantifying mRNA using the real-time PCR method in both the initial and optimized systems.

Reviewer #1 (Public review):

Summary:

The study by Bobola et al reports single nuclear expression analysis with some supporting spatial expression data of human embryonic and fetal cardiac outflow tracts compared to adult aortic valves. The transcription factor GATA6 is identified as a top regulator of one of the mesenchymal subpopulations and potential interacting factors and downstream target genes are identified bioinformatically. Additional bioinformatic tools are used to describe cell lineage relationships and trajectories for developmental and adult cardiac cell types.

Strengths:

The strengths of the study are studies of human tissue and extensive gene expression data that will be valuable to the field.

Weaknesses:

In the revised manuscript the data remain largely correlative since functional relationships in cell lineages and gene regulatory interactions are based on coexpression data and bioinformatic analyses that were not subjected to further validation.

Reviewer #1 (Public review):

This thoughtful and thorough mechanistic and functional study reports ARHGAP36 as a direct transcriptional target of FOXC1 which regulates Hedgehog signaling (SUFU, SMO, and GLI family transcription factors) through modulation of PKAC. Clinical outcome data from patients with neuroblastoma, one of the most common extracranial solid malignancies in children, demonstrate that ARHGAP36 expression is associated with improved survival. Although this study largely represents a robust and near-comprehensive set of focused investigations on a novel target of FOXC1 activity, several significant omissions undercut the generalizability of the findings reports.

(1) It is notable that the volcano plot in Fig. 1a does now show evidence of canonical Hedgehog gene regulation even though the subsequent studies in this paper clearly demonstrate that ARHGAP36 regulates Hedgehog signal transduction. Is this because canonical Hedgehog target genes (GLI1, PTCH1, SUFU) simply weren't labeled? Or is there a technical limitation that needs to be clarified? A note about Hedgehog target genes is made in conjunction with Table S1, but the justification or basis of defining these genes as Hedgehog targets is unclear. More broadly, it would be useful to see ontology analyses from these gene expression data to understand FOXC1 target genes more broadly. Ontology analyses are included in a supplementary table, but network visualizations would be much preferred.

(2) Likewise, the ChIP-seq data in Fig. 2 are under-analyzed, focusing only on the ARHGAP36 locus and not more broadly on the FOXC1 gene expression program. This is a missed opportunity that should be remedied with unbiased analyses intersecting differentially expressed FOXC1 peaks with differentially expressed genes from RNA-sequencing data displayed in Fig. 1.

(3) RNA-seq and ChIP-seq data strongly suggest that FOXC1 regulates ARHGAP36 expression, and the authors convincingly identify genomic segments at the ARHGAP36 locus where FOXC1 binds, but they do not test if FOXC1 specifically activates this locus through the creation of a luciferase or similar promoter reporter. Such a reagent and associated experiments would not only strengthen the primary argument of this investigation but could serve as a valuable resource for the community of scientists investigating FOXC1, ARHGAP36, the Hedgehog pathway, and related biological processes. CRISPRi targeting of the identified regions of the ARHGAP locus is a useful step in the right direction, but these experiments are not done in a way to demonstrate FOXC1 dependency.

(4) It would be useful to see individual fluorescence channels in association with images in Fig. 3b.

(5) Perhaps the most significant limitation of this study is the omission of in vivo data, a shortcoming the authors partly mitigate through the incorporation of clinical outcome data from pediatric neuroblastoma patients in the context of ARHGAP36 expression. The authors also mention that high levels of ARHGAP36 expression were also detected in "specific CNS, breast, lung, and neuroendocrine tumors," but do not provide clinical outcome data for these cohorts. Such analyses would be useful to understand the generalizability of their findings across different cancer types. More broadly, how were high, medium, and low levels of ARHGAP36 expression identified? "Terciles" are mentioned, but such an approach is not experimentally rigorous and RPA or related approaches (nested rank statistics, etc) are recommended to find optimal cutpoints for ARHGAP36 expression in the context of neuroblastoma, "specific CNS, breast, lung, and neuroendocrine" tumor outcomes.

Comments on revisions:

I am underwhelmed by this revision, for which I recommended more visualizations of already-generated bioinformatic data that the authors have not provided. Some attempts were made (e.g. network analysis), but other suggestions for improvement were not incorporated (e.g. more comprehensive ChIP-seq analysis). Beyond these relatively straightforward missed opportunities for improvement, there remains a lack of in vivo data and the clinical relevance of these findings are unclear due to potential sources of bias in the data sets analyzed.

Reviewer #1 (Public review):

Summary:

The authors' goal was to advance the understanding of metabolic flux in the bradyzoite cyst form of the parasite T. gondii, since this is a major form of transmission of this ubiquitous parasite, but very little is understood about cyst metabolism and growth. This is an important advance in understanding and targeting bradyzoite growth.

Strengths:

The study used a newly developed technique for growing T. gondii cystic parasites in a human muscle-cell myotube format, which enables culturing and analysis of cysts. This enabled screening of a set of anti-parasitic compounds to identify those that inhibit growth in both vegetative (tachyzoite) forms and bradyzoites (cysts). Three of these compounds were used for comparative Metabolomic profiling to demonstrate differences in metabolism between the two cellular forms.<br /> One of the compounds yielded a pattern consistent with targeting the mitochondrial bc1 complex, and suggest a role for this complex in metabolism in the bradyzoite form, an important advance in understanding this life stage.

Weaknesses:

Studies such as these provide important insights into the overall metabolic differences between different life stages, and they also underscore the challenge with interpreting individual patterns caused by metabolic inhibitors due to the systemic level of some of the targets. The authors have employed mock treatment and non-metabolic inhibitor controls to alleviate these challenges.

Reviewer #1 (Public Review):

By mapping H3K4me2 in mouse oocytes and pre-implantation embryos, the authors aim to elucidate how this histone modification is erased and re-established during the parental-to-zygotic transition, as well as how the reprogramming of H3K4me2 regulates gene expression and facilitates zygotic genome activation.

Employing an improved CUT&RUN approach, the authors successfully generated H3K4me2 profiling data from a limited number of embryos. While the profiling experiments are very well executed, several weaknesses, particularly in data analysis, are apparent:

(1) The study emphasizes H3K4me2, which often serves as a precursor to H3K4me3, a well-studied modification during early development. Analyzing the new H3K4me2 dataset alongside published H3K4me3 data is crucial for comprehensively understanding epigenetic reprogramming post-fertilization and the interplay between histone modifications. However, the current analysis is preliminary and lacks depth.

(2) Tranylcypromine (TCP) is known as an irreversible inhibitor of monoamine oxidase and LSD1. While the authors suggest TCP inhibits the expression of LSD2, this assertion is questionable. Given TCP's potential non-specific effects in cells, conclusions related to the experiments using TCP should be made with caution.

(3) Some batches of H3K4me2 antibody are known to cross-react with H3K4me3. Has the H3K4me2 antibody used in CUT&RUN been tested for such cross-reactivity? Heatmaps in the figures indeed show similar distribution for H3K4me2 and H3K4me3, further raising concerns about antibody specificity.

(4) Certain statements lack supporting references or figures (examples on page 9 can be found on line 245, line 254, and line 258).

(5) Extensive language editing is recommended to clarify ambiguous sentences. Additionally, caution should be taken to avoid overstatement - most analyses in this study only suggest correlation rather than causality.

Reviewer #1 (Public review):

In the manuscript by Li et al., the authors perform a comprehensive study on the template and cofactor determinants of the SARS-CoV-2 nsp13 protein. They find that, alongside the classical processive unwinding ability of helicases driven by ATP consumption, other chaperone-like and ATP-independent functions exist for this enzyme. By testing DNA and RNA oligos in several conformations, the authors show that these functions are highly dependent on template identity, but also on the ratio of ATP to divalent cations. Ultimately, it is suggested that these distinct mechanisms of action are employed by nsp13 to orchestrate viral replication.

Overall, this study provides some novel insights into the functionality of a central and conserved enzyme of a relevant human pathogenic virus. While the approach is important and adds to the field, particularly by characterizing the chaperoning activities and adding G-quadruplexes as templates, previous studies have already identified several determinants of nsp13 template binding and processing in vitro (Sommers et al., 2023, JBC; Park et al., 2025, JBC). In addition, some issues regarding experimental design need to be addressed to increase the cogency and biological relevance of the study.

(1) Generally, low concentrations of monovalent cations (20 mM), as used throughout this study, may influence helicase activity and artificially enhance protein binding/oligomerization, which could favor the observed chaperoning activity (Venus et al., 2022, Methods). In contrast, some helicases, such as HCV NS3, are inhibited by higher K+ concentrations (Gwack et al., 2004, FEBS). Thus, the influence of higher concentrations of monovalent cations should be tested in relevant assays, as intracellular K+ levels are usually >100 mM. Additionally, this could significantly affect template stability. For instance, in some G4 assays, the addition of the trap already leads to observable duplex formation (Figure 5), which may be due to low K+ conditions.

(2) As in most publications that focus strictly on helicase (or other enzymatic) functions, the activity of the isolated protein is examined. However, particularly in the case of nsp13, core functions rely on other factors, such as nsp7/8 and other components of the replication-transcription complex (RTC). The overall structure and oligomerization state of nsp13 are altered within the complex (Chen et al., 2022, NSMB). The inclusion of such factors in key experiments would greatly improve the biological relevance of the findings.

(3) In Figure 4, the authors claim that Mg2+ concentration inhibits RNA unwinding. While this is likely considering previous findings, it must be validated that duplex stabilization is not the primary cause for the observed lower dissociation rates. As the template is only 12 bp long with extensive overhangs, higher ion concentrations may significantly stabilize base pairing by reducing fraying effects. Similarly, in Figure 6, template-dependent effects of Mg2+/ATP should be ruled out.

(4) It is not entirely clear to me by which principle the templates were chosen. In my opinion, it would improve the overall comparability of the experimental results if, for instance, the blunt-ended duplex had the same sequence as the oligos with overhangs, since factors such as length, G/C content, Tm, etc., may play a significant role in binding and unwinding. Similarly, the oligos for binding and unwinding should be kept somewhat comparable, e.g., the G4 for the binding assay has 3 stacks, whereas RG1 has only 2. This discrepancy could make a significant difference. Thus, key experiments should be repeated using comparable sequence pairs.<br /> Moreover, in the initial characterization of the binding abilities (Figure 1), the authors should include blunt-ended controls (duplex/hairpin) and, importantly, a pseudoknot (PK), as these structures are crucial for multiple steps in the viral life cycle (frameshifting, replication). Specifically, the PK in the 3'UTR (Sola et al., 2011, RNA Biology) may be an interesting target structure for unwinding assays, as it recruits the RTC, and, to my knowledge, no studies are available regarding nsp13 function at a PK. This would be particularly interesting in combination with nsp7/8 (Ohyama et al., 2024, JACS Au).

Reviewer #1 (Public review):

Pichon, Rémi et al. describe an in vitro method for transforming Schistosoma cercariae into mature adult worms. The authors show that human serum (HS) supports parasite growth and differentiation more effectively than fetal bovine serum (FBS). They also observed differences in parasite growth and activity, with worms cultured in HS efficiently digesting human red blood cells (hRBC). Cultured worms were able to pair with ex vivo adult worms and produce eggs, indicating functional maturation suitable for downstream applications such as drug screening. While the experimental approach is comprehensive and supports the advantages of HS culture conditions, the pairing efficiency was low (≈7%) and required long culture periods (70-80 days), highlighting limitations that may affect reproducibility.

A major strength of the study, in particular, is that the authors clearly differentiate the effects of FBS versus HS on developmental progression. The conversion rate observed in HS cultures is significant and consistent with previously published data.

While the study has several strengths, some aspects of the work are not fully explored. In particular, the role of hRBC supplementation requires further clarification. Although HS-cultured worms were shown to digest hRBC more readily, the implications of this observation remain unclear. Specifically, it would be useful to understand whether hRBC supplementation influences (1) long-term culture stability, (2) molecular pathways associated with development and differentiation, or (3) the pairing capacity of the worms. While addressing these questions may not be the main objective of the study, further discussion of these points would strengthen the manuscript.

The manuscript is clearly written and represents a valuable contribution to the field. Overall, the experimental approach is sound, and the results support a useful methodological framework for the in vitro culture of Schistosoma worms and the attainment of sexual maturity, particularly for adult male worms.

Reviewer #1 (Public review):

Summary:

Blue light exposure has been shown to induce mitochondrial dysfunction, including reduced mitochondrial membrane potential (MMP). In the present study, the authors present a protein-based optogenetic system capable of inducing mito-contacts upon blue LED illumination, and show that this technical platform attenuated blue-light-induced mitochondrial dysfunction and cytotoxicity via restoring mitochondrial membrane potential.

Strengths:

The overall study design is well organized, and the data appear to support the conclusions. Additionally, demonstrating effects in human retinal cells and C. elegans enhances the perceived robustness and translational potential of the findings.

Weaknesses:

(1) Quantification of MMP at contact sites: The use of Rhodamine 123 (Rh123) for MMP measurement can be problematic, as it is not ratiometric; its signals depend on loading conditions, cell size, mitochondrial mass, and focal thickness, rather than solely on ΔΨm. If mitochondrial content changes (e.g., via biogenesis or mitophagy), Rh123 readings can be misleading. This is particularly relevant here, as the mito-contact-induced MMP changes appear to be localized events. The authors should include controls for at least one experiment using FCCP/CCCP (to collapse ΔΨm) and oligomycin (to induce hyperpolarization in many cell types) to confirm the dynamic range of the assay. Where possible, Rh123 fluorescence intensity should be normalized to mitochondrial mass (e.g., using a mass marker or mitochondrial protein). Moreover, MMP changes should be validated using an alternative indicator, such as JC-1 or a genetically encoded probe, as this is foundational to the study.

(2) Mechanisms of mito-contact-induced MMP hyperpolarization: Building on the above, what is the mechanism by which mito-contacts induce MMP hyperpolarization? Does this involve fusion of the outer or inner mitochondrial membranes? MMP hyperpolarization typically reflects an increase in protons in the intermembrane space relative to the matrix. Where do these protons originate? The kinetics of mito-contact-induced MMP changes should also be investigated in more detail.

(3) Building on the above, what is the ratio of contact area to the overall mitochondrial surface area? If MMP increases only at relatively small contact sites, how does this translate to an overall increase in MMP and energy production?

(4) Blue light causes mitochondrial damage via increased reactive oxygen species (ROS), and MMP hyperpolarization can itself lead to excessive oxidative stress. The authors should measure ROS levels and discuss their potential impact on the observed effects.

(5) Although the main focus is on blue LED-mediated injury, the protective effects of the optogenetic system against other stressors (e.g., ischemia-reperfusion, H₂O₂, or FCCP exposure) should be examined. This would help exclude confounds related to blue light, which is central to both the manipulation and the damage model in the current study, and increase the overall impact of the findings.

Reviewer #1 (Public review):

In this study, the authors set out to develop a human disease model using stem cell-derived systems and to use this platform to investigate disease biology and evaluate potential therapeutic approaches. Their goal is to provide a tractable experimental system that captures key features of the disease and enables testing of candidate interventions.

The work has several important strengths. The authors present a carefully constructed model with improved genetic replication and clearer reporting of biological replicates, which enhances confidence in the reproducibility of the findings. The longitudinal design, spanning early developmental stages to later disease-relevant phenotypes, provides a useful framework for distinguishing temporal aspects of the disease process. The study also includes a comparative evaluation of multiple therapeutic strategies adding practical value to the field. In addition, statistical reporting and transparency have been strengthened, and key limitations of the model-such as the absence of certain cell types-are now clearly acknowledged.

At the same time, notable weaknesses temper the strength of the conclusions. Several central biological claims, particularly those related to specific signaling pathways, are supported primarily by transcriptomic and protein-level observations without direct functional validation. Similarly, measures used to interpret cellular processes do not fully distinguish between alternative biological explanations, leaving some mechanistic interpretations unresolved. The therapeutic findings are supported by biochemical changes, but evidence for functional recovery at the cellular level is limited. These gaps mean that some of the broader conclusions should be interpreted with caution.

Overall, the authors have largely achieved their aim of establishing a useful experimental model and demonstrating its potential for studying disease-related changes and testing interventions. The evidence is convincing for the descriptive and comparative aspects of the work, but more limited for mechanistic and functional claims.

The study is likely to have a meaningful impact by providing a platform that others in the field can build upon. The methods and datasets will be useful to researchers interested in disease modeling and therapeutic development. At the same time, the work is best viewed as an important foundation, with key mechanistic and functional questions remaining to be addressed in future studies.

Reviewer #1 (Public review):

Summary:

Proteins' misfolding into amyloid fibrils is the hallmark of neurodegenerative disorders. Tau fibrils, in particular, exhibit subtle structural variations that distinguish different pathologies. Understanding the mechanism of amyloid formation requires structural characterization, usually done by NMR or cryo-EM, and insights into fibril packing order and homogeneity remain limited.

Here, the authors exploit DEER echo decays of singly spin-labeled proteins to quantify packing order. While DEER is most used to measure intramolecular distances between two spin labels within a single protein, it also provides access to intermolecular distance distributions through the so-called background decay. This background decay has been theoretically described and can be used to characterize the spatial distribution of spins in terms of local spin concentration and the dimensionality of their arrangement. In the case of singly labeled proteins, the DEER signal contains only this intermolecular information. The authors propose using the extracted dimensionality as a reporter of packing disorder along the fibril axis and demonstrate this approach on the tau protein.

The background decay follows an exponential form with a time constant proportional to alphaD, where D is the dimensionality of the spin distribution and ranges from 1 to 3. For a homogeneous frozen solution of singly spin-labeled proteins, D = 3, and alpha is proportional to pbCL, where pb is the probability of changing the orientation of the spins excited by the DEER pump pulse, and CL is the local spin concentration. In a homogeneous system, CL equals the spin bulk concentration. The parameter pb is instrument-dependent and can be experimentally determined. When 𝐷<3, alpha takes a more complex form (given by Eq. 3), but remains linear C with a pre-factor that depends on 𝑝𝑏 and a defined function of D. For known C and pb, a plot of alpha vs C yields a linear curve, the slope of which can be used to determine D.

This approach was applied to the tau fragment tau187, labeled with a nitroxide spin label at positions 272C, 313C, 322C, and 404C. DEER measurements were performed on mixtures of labeled and unlabeled proteins at different ratios, and D was determined. DEER measurements were performed on mixtures of labeled and unlabeled protein at varying ratios to determine D. Fibril formation was induced by heparin, and the resulting decrease in D was monitored over time, reaching a final value of ~1.5. The authors find that the final dimensionality (D) is reached within 12 minutes and is independent of concentration. Consistent values of D ≈ 1.5 are observed for residues 272C, 313C, and 322C located in the protein core, whereas residue 404C, positioned in the C-terminal "fuzzy" region, yields a higher value of D ≈ 2.

Comparisons across tau variants show that heparin-induced fibrils of longer constructs are mispacked, whereas shorter tau fragments form well-ordered, seeding-competent fibrils with lower conformational variability. Seeded aggregation further improves templating and packing, as indicated by reduced dimensionality. Finally, the authors demonstrate that the local spin density derived from the α parameter can be used to estimate the number of protofilaments.

With the method now established, its application to other amyloid systems may reveal correlations between fibril packing order and disease-related properties.

Strengths:

This study presents an original, conceptually clear method for quantifying fibril packing using a single parameter (dimensionality). The approach is experimentally accessible and straightforward to analyze, making it broadly applicable with standard pulse EPR instrumentation.

Weaknesses:

A discussion about the meaning of D<1 is missing. In addition, the treatment of multi-protofilament fibrils is limited. In particular, it remains unclear how increases in dimensionality arising from multiple protofilaments start to affect D and how it can be distinguished from packing disorder.

Reviewer #1 (Public review):

Summary:

To understand the process of mRNA imprinting, the authors develop a series of unbiased methods to identify and follow proteins that associate with transcripts co-transcriptionally. The methods rely on RNA polymerase II pull-downs or proximity biotinylation to do so, and from these experiments, the authors identify some interesting candidate proteins, including Rpg1 / eIF3a, Ssa1/2, and Spt6. The authors characterize some of these proteins in follow-up experiments and show that Spt6 recruitment depends on Rpb4.

Strengths:

(1) The methods described in this study will be useful for the community beyond their immediate application.

(2) The topic of mRNA imprinting remains an open area in the field, and this paper provides hypothesis-generating datasets that may be of use.

(3) If correct, the idea that eIF3a binds co-transcriptionally would be of interest to the transcription and translation fields.

(4) The data showing the importance of Rpb4 for Spt6 binding are some of the strongest.

Weaknesses:

(1) Two main methods (PROFIT and BioPROFIT) are introduced in this study, both of which make use of a combination of tags, especially on RNA polymerase II subunits, to identify and track proteins that are potentially recruited co-transcriptionally. However, a more thorough characterization is needed to gain a sense of the false negatives and false positives. For instance, there are no direct experiments testing the requirement for transcription for the hits. This is a key experiment.

(2) Alternatives are also not robustly considered. For example, what is the evidence that the proteins remain bound to an RNA through its life cycle, as opposed to rebinding in the cytoplasm? For proteins with known cytoplasmic functions, like Rpg1/eIF3a, this conclusion needs more supporting evidence. This caveat is especially important to consider given the typical or known off-rates of many of these proteins.

(3) Showing direct evidence that biotinylated "target" proteins (like eIF3a) accumulate in the nucleus during short labeling or if nuclear export is blocked is an important control, as is an experiment inhibiting transcription and demonstrating that the signal decreases.

Reviewer #1 (Public review):

Summary:

In this study, Zhu et al. address spider silk spidroin evolution using long-read transcriptomics across 12 spider species. The study provides a novel evolutionary framework for spidroin diversification, proposing the existence of two ancient ancestral templates, i.e., AS and GS, and tracing how these templates diversified into major spidroin classes observed in radiated spiders. The manuscript further focused on the evolutionary history of multiple known spidroin proteins, with some previous hypotheses being revised.

Strengths:

A major challenge in silk biology, the highly repetitive content, was well addressed in this study by full-length transcriptome sequencing. Also, the authors performed very detailed analyses on sequence features across a wide range of species. I therefore think the study is supported by sound levels of sampling, technology, and analysis.

Weaknesses:

The manuscript presents a lot of detail regarding various sequence features and derived claims, but these features are sometimes not friendly to an audience not working with spider silks. Also, the current figures are not very helpful for understanding those described patterns. I found many colorful, trivial elements in almost every figure, but how their organization supported the corresponding statement was often unclear to me. I recommend that the authors further improve the figure design, including presenting a schematic evolutionary history for those spider silk proteins.

Reviewer #1 (Public review):

Summary:

This foundational study builds on prior work from this group to reveal the complexities underlying ligand-dependent RXRγ-Nur77 heterodimer formation, offering a compelling re-evaluation of their earlier conclusions. The Authors examine how a library of RXR ligands influences the biophysical, structural, and functional properties of Nur77. They find that although the Nur77-RXRγ heterodimer shares notable functional similarities with the Nurr1-RXRα complex, it also exhibits unique features - notably, both dimer dissociation and classical agonist-driven activities. This work advances our understanding of the nuanced behaviors of nuclear receptor heterodimers, which have important implications for health and disease.

Strengths:

(1) Builds on previous work by providing a comprehensive analysis that examines whether Nur77-RXRγ heterodimer formation parallels that of the Nurr1-RXRα complex.

(2) Systematic evaluation of a library of RXR ligands provides a broad survey of functional outputs.

(3) Careful reanalysis of previous work sheds new light on how NR4A heterodimers function.

Weaknesses:

(1) Some conclusions appear overstated or are not well substantiated by the work presented. It's unclear how the data support a non-classical mode of agonism, for example, based on the data shown.

(2) Some assays have relatively few replicates, with only two in some cases.

Comments on revisions:

I'm satisfied with the revised version.

Reviewer #1 (Public review):

Summary:

In this study, the authors evaluated inter-areal interactions in different types of neuronal recordings, timescales, and species". The method consists of computing the variance explained by a linear decoder that attempts to predict individual neural responses (firing rates) in one area based on neural responses in another area.

The authors apply the method to previously published calcium imaging data from layer 4 and layers 2/3 of 4 mice, and simultaneously recorded Utah array spiking data from areas V1 and V4 of 3 monkeys. They report distributions over "variance explained" numbers for several combinations: from mouse V1 L4 to mouse V1 L2/3, from L2/3 to L4, from monkey V1 to monkey V4, and from V4 to V1. For their monkey data, they also report the corresponding results for different temporal shifts. Overall, they find the expected results: responses in each of the two neural populations are predictive of responses in the other, more so when the stimulus is not controlled than when it is, and with sometimes different results for different stimulus classes (e.g., gratings vs. natural images).

Strengths:

(1) use of existing data

(2) addresses an interesting question

Weaknesses:

The data and analysis results are presented in a way that invites direct comparison between mouse L4<->L2/3 variance explained numbers, and monkey V1<->V4 variance explained numbers. This comparison is highly problematic and can't be taken at face value as the authors themselves clearly acknowledge in the Discussion and reply to the reviews. The datasets simply differ in too many aspects. If the goal of the authors is not to compare, then the analyses should be presented separately, allowing for a more detailed analysis of each (also see below).

Understanding which patterns in the data are robust and which are idiosyncratic to individual animals/recordings is complicated by the fact that some figures appear to show a single mouse and some averages over all four mice with no indication over whether the results are consistent across mice. For the monkey results, all figures in the main text appear to only show a single monkey, with the other two monkey results in the SI. Again, it is not clearly presented and discussed which aspects of the results are robust, and which differ between monkeys.

Furthermore, there are literally dozens of statistical comparisons between various conditions and metrics in the main figures without them being sufficiently organized around robust new insights, that will likely replicate, and that can inform our understanding of the underlying processes, or constrain computational models.

Reviewer #1 (Public review):

Summary:

This study presents a map of neurons responding to aversive stimuli in zebrafish and suggests that the regions containing these neurons are homologous to mammalian brain areas involved in aversive processing. Specifically, this study found that neurons in a part of the pallium, the homolog of the amygdala, responded vigorously to strongly noxious and fully looming stimuli, but not to the milder cues. In contrast, neurons in another part of the pallium responded to all of these stimuli. The findings provide valuable insights into the neural mechanisms underlying negative-valence computation in zebrafish.

Strengths:

This study performed whole-brain functional imaging using two-photon light-sheet microscopy and identified the activity of individual neurons in awake zebrafish. This technique is highly valuable and will be broadly applicable to future studies aimed at elucidating the neural mechanisms underlying zebrafish behavior at single-neuron resolution.

Weaknesses:

Although this study reports neuronal responses to aversive stimuli, it did not directly assess how aversive these stimuli were for zebrafish. In general, studies of this kind quantify the aversiveness of test stimuli by measuring behavioral indices such as avoidance or escape responses. The present study states that "neurons responded vigorously to strongly noxious and fully looming stimuli, but not to milder cues." However, the authors did not provide behavioral evidence demonstrating that the stimuli were indeed aversive or that the so-called milder cues were perceived as less aversive by the animals. Without a behavioral measure of aversiveness, it is difficult to determine whether the reported neural responses reflect negative-valence processing, rather than general sensory salience or stimulus intensity.

Reviewer #1 (Public review):

Summary:

This study makes a significant and timely contribution to the field of attention research. By providing the first direct neuroimaging evidence for the integration-segregation theory of exogenous attention, it fills a critical gap in our understanding of the neural mechanisms underlying inhibition of return (IOR). The authors employ a carefully optimized cue-target paradigm combined with fMRI to elegantly dissociate the neural substrates of cue-target integration from those of segregation, thereby offering compelling support for the integration-segregation account. Beyond validating a key theoretical hypothesis, the study also uncovers an interaction between spatial orienting and cognitive conflict processing, suggesting that exogenous attention modulate conflict processing at both semantic and response levels. This finding shed new light on the neural mechanisms that connect exogenous attentional orienting with cognitive control.

Strengths:

The experimental design is rigorous, the analyses are thorough, and the interpretation is well grounded in the literature. The manuscript is clearly written, logically structured, and addresses a theoretically important question. Overall, this is an excellent, high-impact study that advances both theoretical and neural models of attention.

Comments on revisions:

I appreciate the authors' thorough and thoughtful revisions, which have successfully addressed all of my prior concerns.

Reviewer #1 (Public review):

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

Summary:

Spinal projection neurons in the anterolateral tract transmit diverse somatosensory signals to the brain, including touch, temperature, itch, and pain. This group of spinal projection neurons is heterogeneous in their molecular identities, projection targets in the brain, and response properties. While most anterolateral tract projection neurons are multimodal (responding to more than one somatosensory modality), it has been shown that cold-selective projection neurons exist in lamina I of the spinal cord dorsal horn. Using a combination of anatomical and physiological approaches, the authors discovered that the cold-selective lamina I projection neurons are heavily innervated by Trpm8+ sensory neuron axons, with calb1+ spinal projection neurons primarily capturing these cold-selective lamina I projection neurons. These neurons project to specific brain targets, including the PBNrel and cPAG. This study adds to the ongoing effort in the field to identify and characterize spinal projection neuron subtypes, their physiology, and functions.

Strengths:

(1) The combination of anatomical and physiological analyses is powerful and offers a comprehensive understanding of the cold-selective lamina I projection neurons in the spinal cord dorsal horn. For example, the authors used detailed anatomical methods, including EM imaging of Trpm8+ axon terminals contacting the Phox2a+ lamina I projection neurons. Additionally, they recorded stimulus-evoked activity in Trpm8-recipient neurons, carefully selected by visual confirmation of tdTomato and GFP juxtaposition, which is technically challenging.

(2) This study identifies, for the first time, a molecular marker (calb1) that labels cold-selective lamina I projection neurons. Although calb1+ projection neurons are not entirely specific to cold-selective neurons, using an intersectional strategy combined with other genes enriched in this ALS group or cold-induced FosTRAP may further enhance specificity in the future.

(3) This study shows that cold-selective lamina I projection neurons specifically innervate certain brain targets of the anterolateral tract, including the NTS, PBNrel, and cPAG. This connectivity provides insights into the role of these neurons in cold sensation, which will be an exciting area for future research.

Weaknesses:

(1) The sample size for the ex vivo electrophysiology conducted on the calb1+ lamina I projection neurons (Figure 5) is limited to a total of six recorded neurons. Given the difficulty and complexity of the preparation, this is understandable. Notably, since approximately 87% of lamina I projection neurons heavily innervated by Trpm8+ terminals are calb1+, these six recordings of such neurons in Figure 4E could also be calb1+.

Reviewer #1 (Public review):

In this manuscript, Hinojosa and colleagues analysed the changes in V1 visual responses induced by locomotion in head-fixed mice using two-photon calcium imaging. The authors observe that locomotion strongly increases the visual responses of V1 excitatory neurons that exhibit sensitizing responses to visual stimuli. Also, there is an increased response in VIP interneurons, and to a lesser extent, PV interneurons and SST interneurons (non-significant). The authors used a model fitted with data presented in the manuscript, as well as previous knowledge on cortical connectivity among different neuron types. The model suggests that the major component of the increased responses during locomotion is an increase in excitatory drive from external inputs (feedforward, feedback and modulatory), most importantly onto VIP interneurons and excitatory neurons. However, the excitatory drive of local excitatory neurons onto other surrounding excitatory and inhibitory cells is reduced.

The manuscript is well presented and represents a valuable analysis of how locomotion modulates the activity of different subtypes of cortical neurons. However, major issues should be addressed to strengthen the results.

Major issues:

(1) Speed and mismatch between locomotion and visual stimulation.

The authors do not clearly describe the definition of locomotion versus the resting state. The speed should, by itself, have an impact on neuronal responses, especially at the onset of locomotion. Several published studies show that the mismatch between a visual stimulus and the speed of the animal induces specific responses in V1, both in excitatory and subtypes of inhibitory neurons. The authors should address these points upfront in the manuscript, since it is likely a major variable explaining their results

(2) Use of deconvolution with MLSpike.

Some results (Figure 2) exclusively depend on the deconvolution of calcium signals into spikes (since the initial peak is not seen in calcium transients). The authors should validate this result either with electrophysiological recordings or with the use of another deconvolution method (e.g. CASCADE), emphasising the limitations of this approach and the limitations of the time resolution of calcium imaging.

(3) The manuscript is centred around a specific increase in visual responses in sensitizing neurons during locomotion, both in the fraction of responsive neurons and response magnitudes.

It is hard to tell whether this difference is due to a greater scaling effect of locomotion, a difference in responses during the resting state, or both. The manuscript should further explore and discuss the differences in responses between sensitizing and depressing neurons, both during the resting state and locomotion. Adding metrics and direct comparisons of the magnitudes of fast responses, slow responses, and time integrals between sensitizing and depressing neurons in resting and locomotion states would help to clarify this. Same for fractions of responsive neurons of each type in each condition. E.g., the slow phase is harder to judge from the plots, but the DeltaF/F integral shown in Figure 1G seems to suggest the difference in response magnitude between sensitizing and depressing neurons is largest in locomotion state, rather than resting state. How do these integrals look for inferred firing rates shown in Figure 2?

(4) There is something counterintuitive about how the changes in inhibition onto sensitizing and depressing neurons during locomotion explain the reported activity changes.

Sensitizers receive reduced SST input and increased PV input during locomotion. If SSTs depress and PVs sensitize (and this is the main reason why sensitizers, which receive dominant input from SSTs sensitize, and vice-versa), how is it possible that this switch does not alter the sensitizing or depressing nature of these neurons' responses in locomotion? Are these changes insufficient to flip the dominant SST-PV drive? Figure 6D-E seems to show there is a flip, at least for sensitizers. How do authors explain this? Do authors think this is related to the narrowing of the adaptive index distribution shown in Figure 1C?

(5) Presentation of the experimental data and the model.

The manuscript introduces the results of interneuron recordings during the description of the model. Similarly, the results of optogenetic manipulations are presented inside the model's description. It would be clearer to present all experimental data first and introduce the model later, fitting it to all experimental evidence previously presented.